已经修复了data_pipeline API的问题，支持truncate_vector_tables和backup_vector_tables两个参数。

data_pipeline/schema_workflow.py

@@ -403,7 +403,7 @@ class SchemaWorkflowOrchestrator:
             )
             
             # 执行vector表管理
-            vector_stats = await vector_manager.execute_vector_management(
+            vector_stats = vector_manager.execute_vector_management(
                 backup=self.backup_vector_tables,
                 truncate=self.truncate_vector_tables
             )

data_pipeline/trainer/run_training.py

@@ -386,7 +386,7 @@ def process_training_files(data_path, task_id=None, backup_vector_tables=False,
             log_message("🗂️ 开始执行Vector表管理...")
             
             vector_manager = VectorTableManager(data_path, task_id)
-            vector_stats = asyncio.run(vector_manager.execute_vector_management(backup_vector_tables, truncate_vector_tables))
+            vector_stats = vector_manager.execute_vector_management(backup_vector_tables, truncate_vector_tables)
             
             log_message("✅ Vector表管理完成")
             

data_pipeline/trainer/vector_table_manager.py

@@ -34,7 +34,7 @@ class VectorTableManager:
             import logging
             self.logger = logging.getLogger("VectorTableManager")
     
-    async def execute_vector_management(self, backup: bool, truncate: bool) -> Dict[str, Any]:
+    def execute_vector_management(self, backup: bool, truncate: bool) -> Dict[str, Any]:
         """执行vector表管理操作的主流程"""
         
         start_time = time.time()
@@ -70,7 +70,7 @@ class VectorTableManager:
             # 4. 执行备份操作
             if backup:
                 self.logger.info("🗂️ 开始备份vector表...")
-                backup_results = await self.backup_vector_tables()
+                backup_results = self.backup_vector_tables()
                 result["tables_backed_up"] = backup_results
                 
                 # 检查备份是否全部成功
@@ -85,7 +85,7 @@ class VectorTableManager:
             # 5. 执行清空操作（仅在备份成功时）
             if truncate:
                 self.logger.info("🗑️ 开始清空vector表...")
-                truncate_results = await self.truncate_vector_tables()
+                truncate_results = self.truncate_vector_tables()
                 result["truncate_results"] = truncate_results
                 
                 # 检查清空是否成功
@@ -114,7 +114,7 @@ class VectorTableManager:
             self.logger.error(f"❌ Vector表管理失败: {e}")
             raise
     
-    async def backup_vector_tables(self) -> Dict[str, Any]:
+    def backup_vector_tables(self) -> Dict[str, Any]:
         """备份vector表数据"""
         
         # 1. 创建备份目录
@@ -226,7 +226,7 @@ class VectorTableManager:
         
         return results
     
-    async def truncate_vector_tables(self) -> Dict[str, Any]:
+    def truncate_vector_tables(self) -> Dict[str, Any]:
         """清空vector表数据（只清空langchain_pg_embedding）"""
         
         results = {}

data_pipeline/training_data/manual_20250720_130541/bss_section_route_area_link_detail.md

@@ -1,7 +0,0 @@
-## bss_section_route_area_link（路线与服务区关联表）
-bss_section_route_area_link 表路线与服务区关联表，记录高速公路路线对应的服务区信息。
-字段列表：
-- section_route_id (varchar(32)) - 路段路线ID [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp]
-- service_area_id (varchar(32)) - 服务区ID [主键, 非空] [示例: 08e01d7402abd1d6a4d9fdd5df855ef8, 091662311d2c737029445442ff198c4c]
-字段补充说明：
-- 复合主键：section_route_id, service_area_id

data_pipeline/training_data/manual_20250720_130541/db_query_decision_prompt.txt

@@ -1,11 +0,0 @@
-=== 数据库业务范围 ===
-当前数据库存储的是**高速公路服务区运营管理**的相关数据，主要涉及**服务区营收、车流统计、公司管理及路线关联信息**，包含以下业务数据：
-核心业务实体：
-- 服务区：表示高速公路沿线的服务区域，主要字段：service_area_name、service_area_no、company_id、service_state
-- 档口：表示服务区内的具体经营单位，主要字段：branch_no、branch_name
-- 公司：表示管理服务区的分公司，主要字段：company_name、company_no
-- 车辆：表示通过服务区的车辆类型与数量，主要字段：car_type、customer_count
-- 路段路线：表示高速公路的路线信息，主要字段：section_name、route_name、code
-关键业务指标：
-- 支付金额与订单数：包括微信、支付宝、现金等支付方式的金额与订单数量，用于分析营收结构与支付趋势
-- 车流量统计：按天、按类型统计车辆数量，用于评估服务区通行量与运营情况

data_pipeline/training_data/manual_20250720_130541/metadata.txt

@@ -1,62 +0,0 @@
--- Schema Tools生成的主题元数据
--- 业务背景: 高速公路服务区管理系统
--- 生成时间: 2025-07-20 13:09:46
--- 数据库: highway_db
-
--- 创建表（如果不存在）
-CREATE TABLE IF NOT EXISTS metadata (
-    id SERIAL PRIMARY KEY,    -- 主键
-    topic_name VARCHAR(100) NOT NULL,  -- 业务主题名称
-    description TEXT,                  -- 业务主体说明
-    related_tables TEXT[],			  -- 相关表名
-    biz_entities TEXT[],               -- 主要业务实体名称
-    biz_metrics TEXT[],                -- 主要业务指标名称
-    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP    -- 插入时间
-);
-
--- 插入主题数据
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '日营业数据分析',
-  '分析每个服务区和档口的每日营业收入、订单数量及支付方式分布，评估经营表现',
-  'bss_business_day_data',
-  '服务区,档口,支付方式',
-  '收入趋势,服务区对比,支付方式分布'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '车流统计分析',
-  '按天和车辆类型统计各服务区的车流量，辅助运营决策和资源配置',
-  'bss_car_day_count,bss_service_area',
-  '服务区,车辆类型,统计日期',
-  '车流趋势,服务区车流排名,车型占比'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '公司管辖分析',
-  '统计各公司所管辖的服务区数量和分布，支持公司管理与资源调配',
-  'bss_company,bss_service_area',
-  '公司,服务区,路段',
-  '公司服务区数量,服务区分布,路段关联分析'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '服务区关联分析',
-  '分析路段与服务区的关联关系，明确各路段覆盖的服务区资源分布',
-  'bss_section_route,bss_section_route_area_link,bss_service_area',
-  '路段,路线,服务区',
-  '路段服务区数量,路线关联分布,服务区路段归属'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '服务区运营状态',
-  '分析各服务区的运营状态（开放/关闭）及其地理位置分布，支持运营管理决策',
-  'bss_service_area',
-  '服务区,运营状态,地理位置',
-  '开放服务区数量,关闭服务区分布,地理分布热力图'
-);
-

data_pipeline/training_data/manual_20250720_130541/qs_highway_db_20250720_130946_pair.json

@@ -1,186 +0,0 @@
-[
-  {
-    "question": "统计最近7天每个服务区的总营业收入和订单数量，并按营业收入降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入, SUM(order_sum) AS 总订单数量 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日当天微信支付金额最高的前5个档口。",
-    "sql": "SELECT branch_name AS 档口名称, wx AS 微信支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY wx DESC LIMIT 5;"
-  },
-  {
-    "question": "分析2023年4月1日各服务区现金支付金额占比，并按占比排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付占比 DESC;"
-  },
-  {
-    "question": "统计2023年4月1日每个服务区不同支付方式的订单数量总和。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx_order) AS 微信订单数量, SUM(zf_order) AS 支付宝订单数量, SUM(rmb_order) AS 现金订单数量 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "计算2023年4月1日至2023年4月7日每个服务区的平均每日营业收入。",
-    "sql": "SELECT service_name AS 服务区名称, AVG(pay_sum) AS 平均每日营业收入 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-07' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "找出2023年4月1日营业收入低于平均值的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, pay_sum AS 营业收入 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL AND pay_sum < (SELECT AVG(pay_sum) FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL);"
-  },
-  {
-    "question": "统计2023年4月1日各服务区支付宝支付金额的总和，并筛选出总和大于1000元的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(zfb) AS 支付宝支付总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name HAVING SUM(zfb) > 1000;"
-  },
-  {
-    "question": "列出2023年4月1日各服务区的档口名称及其对应的营业收入明细。",
-    "sql": "SELECT service_name AS 服务区名称, branch_name AS 档口名称, pay_sum AS 营业收入 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计2023年4月1日各服务区行吧支付金额占比，并按占比降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(xs) / SUM(pay_sum) * 100 AS 行吧支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 行吧支付占比 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日各服务区金豆支付金额为0的服务区名称。",
-    "sql": "SELECT DISTINCT service_name AS 服务区名称 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL AND jd = 0;"
-  },
-  {
-    "question": "统计最近一周每天各服务区的总车流量，并按日期排序。",
-    "sql": "SELECT count_date AS 统计日期, service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= CURRENT_DATE - 7 GROUP BY count_date, service_area_id ORDER BY count_date;"
-  },
-  {
-    "question": "按月汇总各车辆类型在所有服务区的平均车流量，并按车辆类型排序。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, car_type AS 车辆类型, AVG(customer_count) AS 平均车流量 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date), car_type ORDER BY car_type;"
-  },
-  {
-    "question": "找出2023年4月车流量最高的前5个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY service_area_id ORDER BY 总车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年每天车流量中危化品车辆的占比，并按日期排序。",
-    "sql": "SELECT count_date AS 统计日期, SUM(CASE WHEN car_type = '危化品' THEN customer_count ELSE 0 END) * 1.0 / SUM(customer_count) AS 危化品占比 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY count_date ORDER BY count_date;"
-  },
-  {
-    "question": "找出2023年6月各服务区城际车辆的总车流量，并按车流量降序排序。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 城际车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND car_type = '城际' AND count_date BETWEEN '2023-06-01' AND '2023-06-30' GROUP BY service_area_id ORDER BY 城际车流量 DESC;"
-  },
-  {
-    "question": "统计2023年每月各类型车辆的总车流量，并按月份和车辆类型排序。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, car_type AS 车辆类型, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY EXTRACT(MONTH FROM count_date), car_type ORDER BY 月份, 车辆类型;"
-  },
-  {
-    "question": "找出最近一天各服务区的过境车辆数量，并按数量降序排序。",
-    "sql": "SELECT service_area_id AS 服务区ID, customer_count AS 过境车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND car_type = '过境' AND count_date = (SELECT MAX(count_date) FROM bss_car_day_count WHERE delete_ts IS NULL) ORDER BY 过境车流量 DESC;"
-  },
-  {
-    "question": "计算2023年各季度不同车辆类型的平均车流量，并按季度和车辆类型排序。",
-    "sql": "SELECT EXTRACT(QUARTER FROM count_date) AS 季度, car_type AS 车辆类型, AVG(customer_count) AS 平均车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY EXTRACT(QUARTER FROM count_date), car_type ORDER BY 季度, 车辆类型;"
-  },
-  {
-    "question": "找出2023年车流量增长最快的前3个月份。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY EXTRACT(MONTH FROM count_date) ORDER BY 月份, 总车流量 DESC LIMIT 3;"
-  },
-  {
-    "question": "找出2023年车流量最低的后10个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY service_area_id ORDER BY 总车流量 ASC LIMIT 10;"
-  },
-  {
-    "question": "查询每个公司所管辖的开放状态的服务区数量。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(*) AS 开放服务区数量 FROM bss_service_area s JOIN bss_company c ON s.company_id = c.id WHERE s.service_state = '开放' AND s.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "列出所有公司及其对应的服务区数量，包括没有服务区的公司。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(s.id) AS 服务区数量 FROM bss_company c LEFT JOIN bss_service_area s ON c.id = s.company_id AND s.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "查询2023年4月1日各服务区的经营数据总支付金额，并按金额降序排列。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' GROUP BY service_name ORDER BY 总支付金额 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日支付总金额最高的前5个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, pay_sum AS 支付总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' ORDER BY pay_sum DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各公司下辖服务区在2023年4月1日的微信支付总额，并按公司分组。",
-    "sql": "SELECT s.company_id AS 公司ID, SUM(b.wx) AS 微信支付总额 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.oper_date = '2023-04-01' GROUP BY s.company_id;"
-  },
-  {
-    "question": "查询各公司服务区在2022年3月2日的车流量统计，并按车流量降序排列。",
-    "sql": "SELECT sa.company_id AS 公司ID, SUM(car.customer_count) AS 车流量 FROM bss_car_day_count car JOIN bss_service_area sa ON car.service_area_id = sa.id JOIN bss_company c ON sa.company_id = c.id WHERE car.count_date = '2022-03-02' GROUP BY sa.company_id ORDER BY 车流量 DESC;"
-  },
-  {
-    "question": "列出所有服务区及其所属公司名称，按服务区名称排序。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id ORDER BY sa.service_area_name;"
-  },
-  {
-    "question": "查询2023年4月1日各公司服务区的订单总数，并按公司分组。",
-    "sql": "SELECT sa.company_id AS 公司ID, SUM(b.order_sum) AS 订单总数 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date = '2023-04-01' GROUP BY sa.company_id;"
-  },
-  {
-    "question": "统计各公司服务区在2023年4月1日的现金支付金额总和，并筛选出总和大于10000的公司。",
-    "sql": "SELECT sa.company_id AS 公司ID, SUM(b.rmb) AS 现金支付总金额 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date = '2023-04-01' GROUP BY sa.company_id HAVING SUM(b.rmb) > 10000;"
-  },
-  {
-    "question": "统计每个路段关联的服务区数量，并按数量降序排列。",
-    "sql": "SELECT s.section_name AS 路段名称, COUNT(l.service_area_id) AS 服务区数量 FROM bss_section_route s JOIN bss_section_route_area_link l ON s.id = l.section_route_id WHERE s.delete_ts IS NULL GROUP BY s.section_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "查询某条路线关联的所有服务区的详细信息。",
-    "sql": "SELECT s.service_area_name AS 服务区名称, s.service_area_no AS 服务区编码, s.service_state AS 服务区状态 FROM bss_service_area s JOIN bss_section_route_area_link l ON s.id = l.service_area_id JOIN bss_section_route r ON l.section_route_id = r.id WHERE r.route_name = '昌栗' AND s.delete_ts IS NULL;"
-  },
-  {
-    "question": "列出没有关联任何服务区的路段信息。",
-    "sql": "SELECT s.section_name AS 路段名称 FROM bss_section_route s LEFT JOIN bss_section_route_area_link l ON s.id = l.section_route_id WHERE l.service_area_id IS NULL AND s.delete_ts IS NULL;"
-  },
-  {
-    "question": "查询每个公司管理的服务区数量，并按数量降序排列。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(s.id) AS 管理服务区数量 FROM bss_company c LEFT JOIN bss_service_area s ON c.id = s.company_id WHERE s.delete_ts IS NULL GROUP BY c.company_name ORDER BY 管理服务区数量 DESC;"
-  },
-  {
-    "question": "找出车流总量最高的前5个服务区及其所属路段。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sr.section_name AS 路段名称, SUM(car.customer_count) AS 总车流量 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_car_day_count car ON l.service_area_id = car.service_area_id JOIN bss_service_area sa ON l.service_area_id = sa.id WHERE car.delete_ts IS NULL GROUP BY sa.service_area_name, sr.section_name ORDER BY 总车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询某个服务区关联的所有路段信息。",
-    "sql": "SELECT sr.section_name AS 路段名称 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_service_area sa ON l.service_area_id = sa.id WHERE sa.service_area_name = '南昌南服务区' AND sr.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每个路段下不同状态的服务区数量（开放、关闭）。",
-    "sql": "SELECT sr.section_name AS 路段名称, sa.service_state AS 服务区状态, COUNT(sa.id) AS 数量 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_service_area sa ON l.service_area_id = sa.id WHERE sa.delete_ts IS NULL GROUP BY sr.section_name, sa.service_state ORDER BY 路段名称, 服务区状态;"
-  },
-  {
-    "question": "统计当前所有开放状态的服务区数量是多少？",
-    "sql": "SELECT COUNT(*) AS 开放服务区数量 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "列出所有关闭状态的服务区名称及其经纬度信息？",
-    "sql": "SELECT service_area_name AS 服务区名称, service_position AS 经纬度 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "按所属公司分组，统计每个公司管理的开放服务区数量，并按数量降序排列？",
-    "sql": "SELECT company_id AS 所属公司ID, COUNT(*) AS 开放服务区数量 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL GROUP BY company_id ORDER BY 开放服务区数量 DESC;"
-  },
-  {
-    "question": "列出所有开放的服务区名称、编码、经纬度，并限制结果前10条？",
-    "sql": "SELECT service_area_name AS 服务区名称, service_area_no AS 服务区编码, service_position AS 经纬度 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL LIMIT 10;"
-  },
-  {
-    "question": "统计最近一周内每天新增创建的服务区数量？",
-    "sql": "SELECT DATE(create_ts) AS 创建日期, COUNT(*) AS 新增服务区数量 FROM bss_service_area WHERE create_ts >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY DATE(create_ts) ORDER BY 创建日期;"
-  },
-  {
-    "question": "查找经纬度位于东经114到116度之间的所有服务区名称及其状态？",
-    "sql": "SELECT service_area_name AS 服务区名称, service_state AS 服务区状态 FROM bss_service_area WHERE service_position ~ '^11[4-5]' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "按服务区类型分组，统计每种类型的服务区数量？",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "列出所有关闭状态的服务区，按创建时间倒序排列？",
-    "sql": "SELECT service_area_name AS 服务区名称, create_ts AS 创建时间 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL ORDER BY create_ts DESC;"
-  },
-  {
-    "question": "统计每个公司管理的关闭服务区数量，仅显示数量大于5的公司？",
-    "sql": "SELECT company_id AS 所属公司ID, COUNT(*) AS 关闭服务区数量 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL GROUP BY company_id HAVING COUNT(*) > 5;"
-  },
-  {
-    "question": "查找最近更新时间在一个月内的开放状态的服务区信息？",
-    "sql": "SELECT service_area_name AS 服务区名称, update_ts AS 更新时间 FROM bss_service_area WHERE service_state = '开放' AND update_ts >= CURRENT_DATE - 30 AND delete_ts IS NULL;"
-  }
-]

data_pipeline/training_data/manual_20250720_130541/qs_highway_db_20250720_130946_pair.json.backup

@@ -1,202 +0,0 @@
-[
-  {
-    "question": "统计最近7天每个服务区的总营业收入和订单数量，并按营业收入降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入, SUM(order_sum) AS 总订单数量 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日当天微信支付金额最高的前5个档口。",
-    "sql": "SELECT branch_name AS 档口名称, wx AS 微信支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY wx DESC LIMIT 5;"
-  },
-  {
-    "question": "分析2023年4月1日各服务区现金支付金额占比，并按占比排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付占比 DESC;"
-  },
-  {
-    "question": "统计2023年4月1日每个服务区不同支付方式的订单数量总和。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx_order) AS 微信订单数量, SUM(zf_order) AS 支付宝订单数量, SUM(rmb_order) AS 现金订单数量 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "计算2023年4月1日至2023年4月7日每个服务区的平均每日营业收入。",
-    "sql": "SELECT service_name AS 服务区名称, AVG(pay_sum) AS 平均每日营业收入 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-07' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "找出2023年4月1日营业收入低于平均值的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, pay_sum AS 营业收入 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL AND pay_sum < (SELECT AVG(pay_sum) FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL);"
-  },
-  {
-    "question": "统计2023年4月1日各服务区支付宝支付金额的总和，并筛选出总和大于1000元的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(zfb) AS 支付宝支付总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name HAVING SUM(zfb) > 1000;"
-  },
-  {
-    "question": "列出2023年4月1日各服务区的档口名称及其对应的营业收入明细。",
-    "sql": "SELECT service_name AS 服务区名称, branch_name AS 档口名称, pay_sum AS 营业收入 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计2023年4月1日各服务区行吧支付金额占比，并按占比降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(xs) / SUM(pay_sum) * 100 AS 行吧支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 行吧支付占比 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日各服务区金豆支付金额为0的服务区名称。",
-    "sql": "SELECT DISTINCT service_name AS 服务区名称 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL AND jd = 0;"
-  },
-  {
-    "question": "统计最近一周每天各服务区的总车流量，并按日期排序。",
-    "sql": "SELECT count_date AS 统计日期, service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= CURRENT_DATE - 7 GROUP BY count_date, service_area_id ORDER BY count_date;"
-  },
-  {
-    "question": "按月汇总各车辆类型在所有服务区的平均车流量，并按车辆类型排序。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, car_type AS 车辆类型, AVG(customer_count) AS 平均车流量 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date), car_type ORDER BY car_type;"
-  },
-  {
-    "question": "找出2023年4月车流量最高的前5个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY service_area_id ORDER BY 总车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年每天车流量中危化品车辆的占比，并按日期排序。",
-    "sql": "SELECT count_date AS 统计日期, SUM(CASE WHEN car_type = '危化品' THEN customer_count ELSE 0 END) * 1.0 / SUM(customer_count) AS 危化品占比 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY count_date ORDER BY count_date;"
-  },
-  {
-    "question": "找出2023年6月各服务区城际车辆的总车流量，并按车流量降序排序。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 城际车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND car_type = '城际' AND count_date BETWEEN '2023-06-01' AND '2023-06-30' GROUP BY service_area_id ORDER BY 城际车流量 DESC;"
-  },
-  {
-    "question": "统计2023年每月各类型车辆的总车流量，并按月份和车辆类型排序。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, car_type AS 车辆类型, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY EXTRACT(MONTH FROM count_date), car_type ORDER BY 月份, 车辆类型;"
-  },
-  {
-    "question": "找出最近一天各服务区的过境车辆数量，并按数量降序排序。",
-    "sql": "SELECT service_area_id AS 服务区ID, customer_count AS 过境车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND car_type = '过境' AND count_date = (SELECT MAX(count_date) FROM bss_car_day_count WHERE delete_ts IS NULL) ORDER BY 过境车流量 DESC;"
-  },
-  {
-    "question": "计算2023年各季度不同车辆类型的平均车流量，并按季度和车辆类型排序。",
-    "sql": "SELECT EXTRACT(QUARTER FROM count_date) AS 季度, car_type AS 车辆类型, AVG(customer_count) AS 平均车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY EXTRACT(QUARTER FROM count_date), car_type ORDER BY 季度, 车辆类型;"
-  },
-  {
-    "question": "找出2023年车流量增长最快的前3个月份。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY EXTRACT(MONTH FROM count_date) ORDER BY 月份, 总车流量 DESC LIMIT 3;"
-  },
-  {
-    "question": "找出2023年车流量最低的后10个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date >= '2023-01-01' AND count_date <= '2023-12-31' GROUP BY service_area_id ORDER BY 总车流量 ASC LIMIT 10;"
-  },
-  {
-    "question": "统计各公司所管辖的服务区数量，并按数量降序排列。",
-    "sql": "SELECT company_name AS 公司名称, COUNT(*) AS 服务区数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY company_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "查询每个公司所管辖的开放状态的服务区数量。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(*) AS 开放服务区数量 FROM bss_service_area s JOIN bss_company c ON s.company_id = c.id WHERE s.service_state = '开放' AND s.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "列出所有公司及其对应的服务区数量，包括没有服务区的公司。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(s.id) AS 服务区数量 FROM bss_company c LEFT JOIN bss_service_area s ON c.id = s.company_id AND s.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "查询2023年4月1日各服务区的经营数据总支付金额，并按金额降序排列。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' GROUP BY service_name ORDER BY 总支付金额 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日支付总金额最高的前5个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, pay_sum AS 支付总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' ORDER BY pay_sum DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各公司下辖服务区在2023年4月1日的微信支付总额，并按公司分组。",
-    "sql": "SELECT s.company_id AS 公司ID, SUM(b.wx) AS 微信支付总额 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.oper_date = '2023-04-01' GROUP BY s.company_id;"
-  },
-  {
-    "question": "查询各公司服务区在2022年3月2日的车流量统计，并按车流量降序排列。",
-    "sql": "SELECT s.company_id AS 公司ID, SUM(car.customer_count) AS 车流量 FROM bss_car_day_count car JOIN bss_service_area sa ON car.service_area_id = sa.id JOIN bss_company s ON sa.company_id = s.id WHERE car.count_date = '2022-03-02' GROUP BY s.company_id ORDER BY 车流量 DESC;"
-  },
-  {
-    "question": "列出所有服务区及其所属公司名称，按服务区名称排序。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id ORDER BY sa.service_area_name;"
-  },
-  {
-    "question": "查询2023年4月1日各公司服务区的订单总数，并按公司分组。",
-    "sql": "SELECT sa.company_id AS 公司ID, SUM(b.order_sum) AS 订单总数 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date = '2023-04-01' GROUP BY sa.company_id;"
-  },
-  {
-    "question": "统计各公司服务区在2023年4月1日的现金支付金额总和，并筛选出总和大于10000的公司。",
-    "sql": "SELECT sa.company_id AS 公司ID, SUM(b.rmb) AS 现金支付总金额 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date = '2023-04-01' GROUP BY sa.company_id HAVING SUM(b.rmb) > 10000;"
-  },
-  {
-    "question": "统计每个路段关联的服务区数量，并按数量降序排列。",
-    "sql": "SELECT s.section_name AS 路段名称, COUNT(l.service_area_id) AS 服务区数量 FROM bss_section_route s JOIN bss_section_route_area_link l ON s.id = l.section_route_id WHERE s.delete_ts IS NULL GROUP BY s.section_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "查询某条路线关联的所有服务区的详细信息。",
-    "sql": "SELECT s.service_area_name AS 服务区名称, s.service_area_no AS 服务区编码, s.service_state AS 服务区状态 FROM bss_service_area s JOIN bss_section_route_area_link l ON s.id = l.service_area_id JOIN bss_section_route r ON l.section_route_id = r.id WHERE r.route_name = '昌栗' AND s.delete_ts IS NULL;"
-  },
-  {
-    "question": "列出没有关联任何服务区的路段信息。",
-    "sql": "SELECT s.section_name AS 路段名称 FROM bss_section_route s LEFT JOIN bss_section_route_area_link l ON s.id = l.section_route_id WHERE l.service_area_id IS NULL AND s.delete_ts IS NULL;"
-  },
-  {
-    "question": "查询每个公司管理的服务区数量，并按数量降序排列。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(s.id) AS 管理服务区数量 FROM bss_company c LEFT JOIN bss_service_area s ON c.id = s.company_id WHERE s.delete_ts IS NULL GROUP BY c.company_name ORDER BY 管理服务区数量 DESC;"
-  },
-  {
-    "question": "查询某时间段内，各路段服务区的车流总量。",
-    "sql": "SELECT s.section_name AS 路段名称, SUM(car.customer_count) AS 总车流量 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_car_day_count car ON l.service_area_id = car.service_area_id WHERE car.count_date BETWEEN '2023-01-01' AND '2023-01-31' AND sr.delete_ts IS NULL GROUP BY s.section_name;"
-  },
-  {
-    "question": "找出车流总量最高的前5个服务区及其所属路段。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sr.section_name AS 路段名称, SUM(car.customer_count) AS 总车流量 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_car_day_count car ON l.service_area_id = car.service_area_id JOIN bss_service_area sa ON l.service_area_id = sa.id WHERE car.delete_ts IS NULL GROUP BY sa.service_area_name, sr.section_name ORDER BY 总车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询某个服务区关联的所有路段信息。",
-    "sql": "SELECT sr.section_name AS 路段名称 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_service_area sa ON l.service_area_id = sa.id WHERE sa.service_area_name = '南昌南服务区' AND sr.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每个路段下不同状态的服务区数量（开放、关闭）。",
-    "sql": "SELECT sr.section_name AS 路段名称, sa.service_state AS 服务区状态, COUNT(sa.id) AS 数量 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_service_area sa ON l.service_area_id = sa.id WHERE sa.delete_ts IS NULL GROUP BY sr.section_name, sa.service_state ORDER BY 路段名称, 服务区状态;"
-  },
-  {
-    "question": "查询最近一个月内，各路段服务区的经营总金额。",
-    "sql": "SELECT sr.section_name AS 路段名称, SUM(b.pay_sum) AS 经营总金额 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_business_day_data b ON l.service_area_id = b.service_area_id WHERE b.oper_date BETWEEN '2023-01-01' AND '2023-01-31' AND sr.delete_ts IS NULL GROUP BY sr.section_name ORDER BY 经营总金额 DESC;"
-  },
-  {
-    "question": "找出经营总金额最低的3个服务区及其所属路段。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sr.section_name AS 路段名称, SUM(b.pay_sum) AS 经营总金额 FROM bss_section_route sr JOIN bss_section_route_area_link l ON sr.id = l.section_route_id JOIN bss_service_area sa ON l.service_area_id = sa.id JOIN bss_business_day_data b ON sa.id = b.service_area_id WHERE b.delete_ts IS NULL GROUP BY sa.service_area_name, sr.section_name ORDER BY 经营总金额 ASC LIMIT 3;"
-  },
-  {
-    "question": "统计当前所有开放状态的服务区数量是多少？",
-    "sql": "SELECT COUNT(*) AS 开放服务区数量 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "列出所有关闭状态的服务区名称及其经纬度信息？",
-    "sql": "SELECT service_area_name AS 服务区名称, service_position AS 经纬度 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "按所属公司分组，统计每个公司管理的开放服务区数量，并按数量降序排列？",
-    "sql": "SELECT company_id AS 所属公司ID, COUNT(*) AS 开放服务区数量 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL GROUP BY company_id ORDER BY 开放服务区数量 DESC;"
-  },
-  {
-    "question": "列出所有开放的服务区名称、编码、经纬度，并限制结果前10条？",
-    "sql": "SELECT service_area_name AS 服务区名称, service_area_no AS 服务区编码, service_position AS 经纬度 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL LIMIT 10;"
-  },
-  {
-    "question": "统计最近一周内每天新增创建的服务区数量？",
-    "sql": "SELECT DATE(create_ts) AS 创建日期, COUNT(*) AS 新增服务区数量 FROM bss_service_area WHERE create_ts >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY DATE(create_ts) ORDER BY 创建日期;"
-  },
-  {
-    "question": "查找经纬度位于东经114到116度之间的所有服务区名称及其状态？",
-    "sql": "SELECT service_area_name AS 服务区名称, service_state AS 服务区状态 FROM bss_service_area WHERE service_position ~ '^11[4-5]' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "按服务区类型分组，统计每种类型的服务区数量？",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "列出所有关闭状态的服务区，按创建时间倒序排列？",
-    "sql": "SELECT service_area_name AS 服务区名称, create_ts AS 创建时间 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL ORDER BY create_ts DESC;"
-  },
-  {
-    "question": "统计每个公司管理的关闭服务区数量，仅显示数量大于5的公司？",
-    "sql": "SELECT company_id AS 所属公司ID, COUNT(*) AS 关闭服务区数量 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL GROUP BY company_id HAVING COUNT(*) > 5;"
-  },
-  {
-    "question": "查找最近更新时间在一个月内的开放状态的服务区信息？",
-    "sql": "SELECT service_area_name AS 服务区名称, update_ts AS 更新时间 FROM bss_service_area WHERE service_state = '开放' AND update_ts >= CURRENT_DATE - 30 AND delete_ts IS NULL;"
-  }
-]

data_pipeline/training_data/task_20250702_174000/table_list.txt

@@ -1,11 +0,0 @@
-# 表清单文件
-# 生成时间: 2025-07-21 11:36:33
-# 表数量: 7
-
-bss_car_day_count
-bss_business_day_data
-bss_company
-bss_section_route
-bss_section_route_area_link
-bss_service_area
-bss_service_area_mapper

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_company.ddl

@@ -1,15 +0,0 @@
--- 中文名: `bss_company` 表用于存储高速公路服务区相关企业的基本信息
--- 描述: `bss_company` 表用于存储高速公路服务区相关企业的基本信息，包括公司名称、编码及操作记录，支撑服务区运营管理中的企业主体管理。
-create table public.bss_company (
-  id varchar(32) not null     -- 公司唯一标识，主键,
-  version integer not null    -- 版本号,
-  create_ts timestamp         -- 创建时间,
-  created_by varchar(50)      -- 创建人,
-  update_ts timestamp         -- 更新时间,
-  updated_by varchar(50)      -- 更新人,
-  delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除人,
-  company_name varchar(255)   -- 公司名称,
-  company_no varchar(255)     -- 公司编码,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_company_detail.md

@@ -1,17 +0,0 @@
-## bss_company（`bss_company` 表用于存储高速公路服务区相关企业的基本信息）
-bss_company 表`bss_company` 表用于存储高速公路服务区相关企业的基本信息，包括公司名称、编码及操作记录，支撑服务区运营管理中的企业主体管理。
-字段列表：
-- id (varchar(32)) - 公司唯一标识 [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
-- version (integer) - 版本号 [非空] [示例: 1, 2]
-- create_ts (timestamp) - 创建时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
-- created_by (varchar(50)) - 创建人 [示例: admin]
-- update_ts (timestamp) - 更新时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
-- updated_by (varchar(50)) - 更新人 [示例: admin]
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人
-- company_name (varchar(255)) - 公司名称 [示例: 上饶分公司, 宜春分公司, 景德镇分公司]
-- company_no (varchar(255)) - 公司编码 [示例: H03, H02, H07]
-字段补充说明：
-- id 为主键
-- company_name 为枚举字段，包含取值：抚州分公司、赣州分公司、吉安分公司、景德镇分公司、九江分公司、南昌分公司、其他公司管辖、上饶分公司、宜春分公司
-- company_no 为枚举字段，包含取值：H01、H02、H03、H04、H05、H06、H07、H08、Q01

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_section_route_area_link.ddl

@@ -1,7 +0,0 @@
--- 中文名: 路段路线与服务区关联表
--- 描述: 路段路线与服务区关联表，记录路线与服务区的绑定关系。
-create table public.bss_section_route_area_link (
-  section_route_id varchar(32) not null -- 路段路线ID，主键,
-  service_area_id varchar(32) not null -- 服务区ID，主键,
-  primary key (section_route_id, service_area_id)
-);

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_section_route_area_link_detail.md

@@ -1,7 +0,0 @@
-## bss_section_route_area_link（路段路线与服务区关联表）
-bss_section_route_area_link 表路段路线与服务区关联表，记录路线与服务区的绑定关系。
-字段列表：
-- section_route_id (varchar(32)) - 路段路线ID [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp]
-- service_area_id (varchar(32)) - 服务区ID [主键, 非空] [示例: 08e01d7402abd1d6a4d9fdd5df855ef8, 091662311d2c737029445442ff198c4c]
-字段补充说明：
-- 复合主键：section_route_id, service_area_id

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/db_query_decision_prompt.txt

@@ -1,10 +0,0 @@
-=== 数据库业务范围 ===
-当前数据库存储的是高速公路服务区运营管理的相关数据，主要涉及服务区营收、车辆统计、企业信息、路段路线关联及数据元信息，包含以下业务数据：
-核心业务实体：
-- 服务区：高速公路沿线提供停车、加油、餐饮等服务的场所，主要字段：service_area_name、service_area_no、service_state
-- 档口：服务区内的经营单位或商铺，主要字段：branch_name、branch_no
-- 企业：管理服务区的公司或分公司，主要字段：company_name、company_no
-- 车辆：在服务区内停留或通行的车辆，主要字段：car_type、customer_count
-关键业务指标：
-- 营收统计：通过微信、支付宝、现金等支付方式的金额与订单数，反映服务区档口的经营状况
-- 车流分析：按日期与车辆类型统计的车流量，用于评估服务区的使用频率与运营压力

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/metadata.txt

@@ -1,62 +0,0 @@
--- Schema Tools生成的主题元数据
--- 业务背景: 高速公路服务区管理系统
--- 生成时间: 2025-07-21 09:23:19
--- 数据库: highway_db
-
--- 创建表（如果不存在）
-CREATE TABLE IF NOT EXISTS metadata (
-    id SERIAL PRIMARY KEY,    -- 主键
-    topic_name VARCHAR(100) NOT NULL,  -- 业务主题名称
-    description TEXT,                  -- 业务主体说明
-    related_tables TEXT[],			  -- 相关表名
-    biz_entities TEXT[],               -- 主要业务实体名称
-    biz_metrics TEXT[],                -- 主要业务指标名称
-    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP    -- 插入时间
-);
-
--- 插入主题数据
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '日营收分析',
-  '分析各服务区每日营业收入、订单数量及支付方式分布，辅助经营决策。',
-  'bss_business_day_data',
-  '服务区,档口,支付方式',
-  '收入趋势,订单统计,支付分布'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '车流统计分析',
-  '基于 bss_car_day_count 表，分析各服务区每日车辆数量和类型分布，支撑交通流量管理。',
-  'bss_car_day_count',
-  '服务区,车辆类别',
-  '车流趋势,车辆占比,日均车流'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '公司运营对比',
-  '结合 bss_company 和 bss_service_area 表，对比不同公司管辖下服务区的业务表现。',
-  'bss_company,bss_service_area,bss_business_day_data',
-  '公司,服务区,营收',
-  '营收对比,服务区数量,平均营收'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '路段关联分析',
-  '通过 bss_section_route 和 bss_section_route_area_link 表，分析路段与服务区的绑定关系及分布。',
-  'bss_section_route,bss_section_route_area_link,bss_service_area',
-  '路段,路线,服务区',
-  '路段覆盖率,服务区关联数,路线分布'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '服务区状态监控',
-  '基于 bss_service_area 表，监控服务区状态（开放/关闭）及其地理分布，优化运营调度。',
-  'bss_service_area',
-  '服务区,所属公司,服务区类型,状态',
-  '开放率,区域分布,类型占比'
-);
-

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/qs_highway_db_20250721_092319_pair.json

@@ -1,202 +0,0 @@
-[
-  {
-    "question": "统计最近7天各服务区每日总营收金额，并按日期和服务区名称排序。",
-    "sql": "SELECT oper_date AS 统计日期, service_name AS 服务区名称, SUM(pay_sum) AS 总营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date >= CURRENT_DATE - 7 GROUP BY oper_date, service_name ORDER BY oper_date DESC, 总营收金额 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各档口的订单总数和总营收金额，并按总营收金额降序排序。",
-    "sql": "SELECT branch_name AS 档口名称, order_sum AS 订单总数, pay_sum AS 总营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' ORDER BY 总营收金额 DESC;"
-  },
-  {
-    "question": "分析2023年各月份各服务区的平均每日营收金额，并按月份和服务区名称排序。",
-    "sql": "SELECT DATE_TRUNC('month', oper_date) AS 月份, service_name AS 服务区名称, AVG(pay_sum) AS 平均每日营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date), service_name ORDER BY 月份, 平均每日营收金额 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日订单总数排名前5的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(order_sum) AS 总订单数 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' GROUP BY service_name ORDER BY 总订单数 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月1日各支付方式的总支付金额和总订单数。",
-    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 总支付金额, SUM(wx_order) AS 总订单数 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '支付宝', SUM(zfb), SUM(zf_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '现金', SUM(rmb), SUM(rmb_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '行吧', SUM(xs), SUM(xs_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '金豆', SUM(jd), SUM(jd_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01';"
-  },
-  {
-    "question": "查询2023年4月1日各服务区各档口的营收明细。",
-    "sql": "SELECT service_name AS 服务区名称, branch_name AS 档口名称, pay_sum AS 营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' ORDER BY 服务区名称, 营收金额 DESC;"
-  },
-  {
-    "question": "计算2023年各月各支付方式的总支付金额，按月份和支付方式排序。",
-    "sql": "SELECT DATE_TRUNC('month', oper_date) AS 月份, '微信' AS 支付方式, SUM(wx) AS 总支付金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '支付宝', SUM(zfb) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '现金', SUM(rmb) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '行吧', SUM(xs) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '金豆', SUM(jd) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) ORDER BY 月份, 支付方式;"
-  },
-  {
-    "question": "找出2023年4月1日微信支付订单数量超过100的档口信息。",
-    "sql": "SELECT branch_name AS 档口名称, wx_order AS 微信订单数 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' AND wx_order > 100 ORDER BY 微信订单数 DESC;"
-  },
-  {
-    "question": "统计2023年4月1日各服务区的现金支付金额占总营收金额的比例。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' GROUP BY service_name ORDER BY 现金支付占比 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日所有关闭状态的服务区营收数据。",
-    "sql": "SELECT bbd.service_name AS 服务区名称, bbd.oper_date AS 统计日期, bbd.pay_sum AS 总营收金额 FROM bss_business_day_data bbd JOIN bss_service_area sa ON bbd.service_no = sa.service_area_no WHERE sa.service_state = '关闭' AND bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计最近7天各服务区的总车流量，并按车流量降序排列。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_area_id ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各车辆类别在各服务区的数量分布。",
-    "sql": "SELECT service_area_id AS 服务区ID, car_type AS 车辆类别, customer_count AS 车辆数量 FROM bss_car_day_count WHERE count_date = '2022-03-02' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "找出2023年3月车流量最高的5个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2022-03-01' AND '2022-03-31' AND delete_ts IS NULL GROUP BY service_area_id ORDER BY 总车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "计算各车辆类别在所有服务区的占比。",
-    "sql": "SELECT car_type AS 车辆类别, SUM(customer_count) * 100.0 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE delete_ts IS NULL) AS 占比百分比 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY car_type;"
-  },
-  {
-    "question": "分析2023年各季度各服务区的平均每日车流量。",
-    "sql": "SELECT service_area_id AS 服务区ID, EXTRACT(QUARTER FROM count_date) AS 季度, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY service_area_id, 季度 ORDER BY 季度, 日均车流量 DESC;"
-  },
-  {
-    "question": "查询2023年1月1日至2023年1月7日每天的总车流量趋势。",
-    "sql": "SELECT count_date AS 统计日期, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2022-03-01' AND '2022-03-07' AND delete_ts IS NULL GROUP BY count_date ORDER BY count_date;"
-  },
-  {
-    "question": "列出2023年车流量最低的10个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY service_area_id ORDER BY 总车流量 ASC LIMIT 10;"
-  },
-  {
-    "question": "查找2023年3月15日车流量超过1000的车辆类别及其数量。",
-    "sql": "SELECT car_type AS 车辆类别, customer_count AS 车辆数量 FROM bss_car_day_count WHERE count_date = '2022-03-15' AND customer_count > 1000 AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计2023年各月各服务区的车流量并按月份排序。",
-    "sql": "SELECT service_area_id AS 服务区ID, EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY service_area_id, 月份 ORDER BY 月份;"
-  },
-  {
-    "question": "找出2023年3月车流量增长最快的三个服务区。",
-    "sql": "WITH daily_counts AS (SELECT service_area_id, count_date, SUM(customer_count) AS daily_count FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND EXTRACT(MONTH FROM count_date) IN (3, 2) AND delete_ts IS NULL GROUP BY service_area_id, count_date), monthly_avg AS (SELECT service_area_id, EXTRACT(MONTH FROM count_date) AS 月份, AVG(daily_count) AS avg_count FROM daily_counts GROUP BY service_area_id, 月份) SELECT m1.service_area_id AS 服务区ID, m2.avg_count - m1.avg_count AS 增长量 FROM monthly_avg m1 JOIN monthly_avg m2 ON m1.service_area_id = m2.service_area_id AND m1.月份 = 2 AND m2.月份 = 3 ORDER BY 增长量 DESC LIMIT 3;"
-  },
-  {
-    "question": "统计各公司管辖的服务区数量，并按数量降序排列。",
-    "sql": "SELECT b.company_name AS 公司名称, COUNT(a.id) AS 服务区数量 FROM bss_service_area a JOIN bss_company b ON a.company_id = b.id WHERE a.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY b.company_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "计算2023年4月1日各公司管辖服务区的总营收金额，并按营收降序排列。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.pay_sum) AS 总营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date = '2023-04-01' GROUP BY c.company_name ORDER BY 总营收 DESC;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的平均营收，并按平均营收从高到低排序。",
-    "sql": "SELECT c.company_name AS 公司名称, AVG(d.pay_sum) AS 平均营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 平均营收 DESC;"
-  },
-  {
-    "question": "找出2023年4月总营收排名前五的公司及其管辖的服务区数量。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(a.id) AS 服务区数量, SUM(d.pay_sum) AS 总营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 总营收 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的现金支付总金额，并按金额降序排列。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.rmb) AS 现金支付总额 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 现金支付总额 DESC;"
-  },
-  {
-    "question": "比较2023年4月各公司管辖服务区的微信支付与支付宝支付金额，并按总支付金额排序。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.wx) AS 微信支付总额, SUM(d.zfb) AS 支付宝支付总额, SUM(d.wx + d.zfb) AS 总支付金额 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 总支付金额 DESC;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的订单总数，并按订单数从高到低排序。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.order_sum) AS 订单总数 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 订单总数 DESC;"
-  },
-  {
-    "question": "找出2023年4月平均营收最低的三个公司及其管辖的服务区平均营收。",
-    "sql": "SELECT c.company_name AS 公司名称, AVG(d.pay_sum) AS 平均营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 平均营收 ASC LIMIT 3;"
-  },
-  {
-    "question": "统计2023年4月各公司管辖服务区的每日平均营收，并按公司名称排序。",
-    "sql": "SELECT c.company_name AS 公司名称, AVG(d.pay_sum) AS 日均营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 公司名称;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的营收总额及服务区数量，并按营收占比（营收总额除以服务区数量）排序。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.pay_sum) AS 总营收, COUNT(a.id) AS 服务区数量, SUM(d.pay_sum) / COUNT(a.id) AS 营收占比 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 营收占比 DESC;"
-  },
-  {
-    "question": "统计每个路段关联的服务区数量，并按数量降序排列。",
-    "sql": "SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "列出没有关联任何服务区的路段名称。",
-    "sql": "SELECT bsr.section_name AS 路段名称 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsral.section_route_id IS NULL AND bsr.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每个路线关联的服务区数量，并按路线名称分组。",
-    "sql": "SELECT bsr.route_name AS 路线名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.route_name;"
-  },
-  {
-    "question": "列出所有服务区及其所属路线名称，按服务区名称排序。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sr.route_name AS 路线名称 FROM bss_service_area sa LEFT JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id LEFT JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE sa.delete_ts IS NULL ORDER BY 服务区名称;"
-  },
-  {
-    "question": "统计每个路段的覆盖率，即关联服务区数量占总服务区数量的比例。",
-    "sql": "WITH total_areas AS (SELECT COUNT(*) AS 总服务区数 FROM bss_service_area WHERE delete_ts IS NULL), section_areas AS (SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name) SELECT 路段名称, 服务区数量 / (SELECT 总服务区数 FROM total_areas)::numeric AS 覆盖率 FROM section_areas;"
-  },
-  {
-    "question": "查找关联服务区数量最多的前5个路段。",
-    "sql": "SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name ORDER BY 服务区数量 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询特定路段（例如路段名称为'昌九'）关联的所有服务区名称。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称 FROM bss_service_area sa INNER JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id INNER JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE sr.section_name = '昌九' AND sa.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每条路线关联的路段数量，并按路线名称排序。",
-    "sql": "SELECT route_name AS 路线名称, COUNT(*) AS 路段数量 FROM bss_section_route WHERE delete_ts IS NULL GROUP BY route_name ORDER BY 路线名称;"
-  },
-  {
-    "question": "找出关联路段最多的路线名称及关联的路段数量。",
-    "sql": "SELECT route_name AS 路线名称, COUNT(*) AS 路段数量 FROM bss_section_route WHERE delete_ts IS NULL GROUP BY route_name ORDER BY 路段数量 DESC LIMIT 1;"
-  },
-  {
-    "question": "列出所有路段及其关联的服务区数量，筛选出关联数量大于等于2的路段。",
-    "sql": "SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name HAVING COUNT(bsral.service_area_id) >= 2;"
-  },
-  {
-    "question": "统计当前所有开放状态的服务区数量及占比。",
-    "sql": "SELECT COUNT(*) AS 开放服务区数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area WHERE delete_ts IS NULL), 2) AS 开放率百分比 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "列出所有关闭状态的服务区名称及其所属公司。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_state = '关闭' AND sa.delete_ts IS NULL;"
-  },
-  {
-    "question": "按公司统计各公司管理的服务区数量及开放率。",
-    "sql": "SELECT c.company_name AS 所属公司, COUNT(sa.id) AS 服务区总数, ROUND(SUM(CASE WHEN sa.service_state = '开放' THEN 1 ELSE 0 END) * 100.0 / COUNT(sa.id), 2) AS 开放率 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "按服务区类型统计信息化与智能化服务区的数量及占比。",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area WHERE delete_ts IS NULL), 2) AS 占比百分比 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "查询经纬度位于东经115度以东的服务区列表。",
-    "sql": "SELECT service_area_name AS 服务区名称, service_position AS 经纬度 FROM bss_service_area WHERE CAST(SPLIT_PART(service_position, ',', 1) AS NUMERIC) > 115 AND delete_ts IS NULL;"
-  },
-  {
-    "question": "列出最近一周内创建的服务区明细。",
-    "sql": "SELECT service_area_name AS 服务区名称, create_ts AS 创建时间 FROM bss_service_area WHERE create_ts >= NOW() - INTERVAL '7 days' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计各公司管理的服务区中，开放状态服务区数量排名前五的公司。",
-    "sql": "SELECT c.company_name AS 所属公司, COUNT(sa.id) AS 开放服务区数量 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_state = '开放' AND sa.delete_ts IS NULL GROUP BY c.company_name ORDER BY 开放服务区数量 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询没有关联任何路段路线的服务区列表。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称 FROM bss_service_area sa LEFT JOIN bss_section_route_area_link link ON sa.id = link.service_area_id WHERE link.section_route_id IS NULL AND sa.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每个服务区类型中处于关闭状态的服务区数量。",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 关闭数量 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "列出所有服务区及其所属公司信息，按公司名称排序。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL ORDER BY c.company_name;"
-  }
-]

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/qs_highway_db_20250721_092319_pair.json.backup

@@ -1,202 +0,0 @@
-[
-  {
-    "question": "统计最近7天各服务区每日总营收金额，并按日期和服务区名称排序。",
-    "sql": "SELECT oper_date AS 统计日期, service_name AS 服务区名称, SUM(pay_sum) AS 总营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date >= CURRENT_DATE - 7 GROUP BY oper_date, service_name ORDER BY oper_date DESC, 总营收金额 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各档口的订单总数和总营收金额，并按总营收金额降序排序。",
-    "sql": "SELECT branch_name AS 档口名称, order_sum AS 订单总数, pay_sum AS 总营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' ORDER BY 总营收金额 DESC;"
-  },
-  {
-    "question": "分析2023年各月份各服务区的平均每日营收金额，并按月份和服务区名称排序。",
-    "sql": "SELECT DATE_TRUNC('month', oper_date) AS 月份, service_name AS 服务区名称, AVG(pay_sum) AS 平均每日营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date), service_name ORDER BY 月份, 平均每日营收金额 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日订单总数排名前5的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(order_sum) AS 总订单数 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' GROUP BY service_name ORDER BY 总订单数 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月1日各支付方式的总支付金额和总订单数。",
-    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 总支付金额, SUM(wx_order) AS 总订单数 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '支付宝', SUM(zfb), SUM(zf_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '现金', SUM(rmb), SUM(rmb_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '行吧', SUM(xs), SUM(xs_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' UNION ALL SELECT '金豆', SUM(jd), SUM(jd_order) FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01';"
-  },
-  {
-    "question": "查询2023年4月1日各服务区各档口的营收明细。",
-    "sql": "SELECT service_name AS 服务区名称, branch_name AS 档口名称, pay_sum AS 营收金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' ORDER BY 服务区名称, 营收金额 DESC;"
-  },
-  {
-    "question": "计算2023年各月各支付方式的总支付金额，按月份和支付方式排序。",
-    "sql": "SELECT DATE_TRUNC('month', oper_date) AS 月份, '微信' AS 支付方式, SUM(wx) AS 总支付金额 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '支付宝', SUM(zfb) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '现金', SUM(rmb) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '行吧', SUM(xs) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) UNION ALL SELECT DATE_TRUNC('month', oper_date), '金豆', SUM(jd) FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 GROUP BY DATE_TRUNC('month', oper_date) ORDER BY 月份, 支付方式;"
-  },
-  {
-    "question": "找出2023年4月1日微信支付订单数量超过100的档口信息。",
-    "sql": "SELECT branch_name AS 档口名称, wx_order AS 微信订单数 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' AND wx_order > 100 ORDER BY 微信订单数 DESC;"
-  },
-  {
-    "question": "统计2023年4月1日各服务区的现金支付金额占总营收金额的比例。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE delete_ts IS NULL AND oper_date = '2023-04-01' GROUP BY service_name ORDER BY 现金支付占比 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日所有关闭状态的服务区营收数据。",
-    "sql": "SELECT bbd.service_name AS 服务区名称, bbd.oper_date AS 统计日期, bbd.pay_sum AS 总营收金额 FROM bss_business_day_data bbd JOIN bss_service_area sa ON bbd.service_no = sa.service_area_no WHERE sa.service_state = '关闭' AND bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计最近7天各服务区的总车流量，并按车流量降序排列。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_area_id ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各车辆类别在各服务区的数量分布。",
-    "sql": "SELECT service_area_id AS 服务区ID, car_type AS 车辆类别, customer_count AS 车辆数量 FROM bss_car_day_count WHERE count_date = '2022-03-02' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "找出2023年3月车流量最高的5个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2022-03-01' AND '2022-03-31' AND delete_ts IS NULL GROUP BY service_area_id ORDER BY 总车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "计算各车辆类别在所有服务区的占比。",
-    "sql": "SELECT car_type AS 车辆类别, SUM(customer_count) * 100.0 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE delete_ts IS NULL) AS 占比百分比 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY car_type;"
-  },
-  {
-    "question": "分析2023年各季度各服务区的平均每日车流量。",
-    "sql": "SELECT service_area_id AS 服务区ID, EXTRACT(QUARTER FROM count_date) AS 季度, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY service_area_id, 季度 ORDER BY 季度, 日均车流量 DESC;"
-  },
-  {
-    "question": "查询2023年1月1日至2023年1月7日每天的总车流量趋势。",
-    "sql": "SELECT count_date AS 统计日期, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2022-03-01' AND '2022-03-07' AND delete_ts IS NULL GROUP BY count_date ORDER BY count_date;"
-  },
-  {
-    "question": "列出2023年车流量最低的10个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY service_area_id ORDER BY 总车流量 ASC LIMIT 10;"
-  },
-  {
-    "question": "查找2023年3月15日车流量超过1000的车辆类别及其数量。",
-    "sql": "SELECT car_type AS 车辆类别, customer_count AS 车辆数量 FROM bss_car_day_count WHERE count_date = '2022-03-15' AND customer_count > 1000 AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计2023年各月各服务区的车流量并按月份排序。",
-    "sql": "SELECT service_area_id AS 服务区ID, EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY service_area_id, 月份 ORDER BY 月份;"
-  },
-  {
-    "question": "找出2023年3月车流量增长最快的三个服务区。",
-    "sql": "WITH daily_counts AS (SELECT service_area_id, count_date, SUM(customer_count) AS daily_count FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND EXTRACT(MONTH FROM count_date) IN (3, 2) AND delete_ts IS NULL GROUP BY service_area_id, count_date), monthly_avg AS (SELECT service_area_id, EXTRACT(MONTH FROM count_date) AS 月份, AVG(daily_count) AS avg_count FROM daily_counts GROUP BY service_area_id, 月份) SELECT m1.service_area_id AS 服务区ID, m2.avg_count - m1.avg_count AS 增长量 FROM monthly_avg m1 JOIN monthly_avg m2 ON m1.service_area_id = m2.service_area_id AND m1.月份 = 2 AND m2.月份 = 3 ORDER BY 增长量 DESC LIMIT 3;"
-  },
-  {
-    "question": "统计各公司管辖的服务区数量，并按数量降序排列。",
-    "sql": "SELECT b.company_name AS 公司名称, COUNT(a.id) AS 服务区数量 FROM bss_service_area a JOIN bss_company b ON a.company_id = b.id WHERE a.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY b.company_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "计算2023年4月1日各公司管辖服务区的总营收金额，并按营收降序排列。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.pay_sum) AS 总营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date = '2023-04-01' GROUP BY c.company_name ORDER BY 总营收 DESC;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的平均营收，并按平均营收从高到低排序。",
-    "sql": "SELECT c.company_name AS 公司名称, AVG(d.pay_sum) AS 平均营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 平均营收 DESC;"
-  },
-  {
-    "question": "找出2023年4月总营收排名前五的公司及其管辖的服务区数量。",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(a.id) AS 服务区数量, SUM(d.pay_sum) AS 总营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 总营收 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的现金支付总金额，并按金额降序排列。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.rmb) AS 现金支付总额 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 现金支付总额 DESC;"
-  },
-  {
-    "question": "比较2023年4月各公司管辖服务区的微信支付与支付宝支付金额，并按总支付金额排序。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.wx) AS 微信支付总额, SUM(d.zfb) AS 支付宝支付总额, SUM(d.wx + d.zfb) AS 总支付金额 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 总支付金额 DESC;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的订单总数，并按订单数从高到低排序。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.order_sum) AS 订单总数 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 订单总数 DESC;"
-  },
-  {
-    "question": "找出2023年4月平均营收最低的三个公司及其管辖的服务区平均营收。",
-    "sql": "SELECT c.company_name AS 公司名称, AVG(d.pay_sum) AS 平均营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 平均营收 ASC LIMIT 3;"
-  },
-  {
-    "question": "统计2023年4月各公司管辖服务区的每日平均营收，并按公司名称排序。",
-    "sql": "SELECT c.company_name AS 公司名称, AVG(d.pay_sum) AS 日均营收 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 公司名称;"
-  },
-  {
-    "question": "查询2023年4月各公司管辖服务区的营收总额及服务区数量，并按营收占比（营收总额除以服务区数量）排序。",
-    "sql": "SELECT c.company_name AS 公司名称, SUM(d.pay_sum) AS 总营收, COUNT(a.id) AS 服务区数量, SUM(d.pay_sum) / COUNT(a.id) AS 营收占比 FROM bss_service_area a JOIN bss_company c ON a.company_id = c.id JOIN bss_business_day_data d ON a.service_area_no = d.service_no WHERE d.oper_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY c.company_name ORDER BY 营收占比 DESC;"
-  },
-  {
-    "question": "统计每个路段关联的服务区数量，并按数量降序排列。",
-    "sql": "SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "列出没有关联任何服务区的路段名称。",
-    "sql": "SELECT bsr.section_name AS 路段名称 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsral.section_route_id IS NULL AND bsr.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每个路线关联的服务区数量，并按路线名称分组。",
-    "sql": "SELECT bsr.route_name AS 路线名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.route_name;"
-  },
-  {
-    "question": "列出所有服务区及其所属路线名称，按服务区名称排序。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sr.route_name AS 路线名称 FROM bss_service_area sa LEFT JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id LEFT JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE sa.delete_ts IS NULL ORDER BY 服务区名称;"
-  },
-  {
-    "question": "统计每个路段的覆盖率，即关联服务区数量占总服务区数量的比例。",
-    "sql": "WITH total_areas AS (SELECT COUNT(*) AS 总服务区数 FROM bss_service_area WHERE delete_ts IS NULL), section_areas AS (SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name) SELECT 路段名称, 服务区数量 / (SELECT 总服务区数 FROM total_areas)::numeric AS 覆盖率 FROM section_areas;"
-  },
-  {
-    "question": "查找关联服务区数量最多的前5个路段。",
-    "sql": "SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name ORDER BY 服务区数量 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询特定路段（例如路段名称为'昌九'）关联的所有服务区名称。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称 FROM bss_service_area sa INNER JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id INNER JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE sr.section_name = '昌九' AND sa.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每条路线关联的路段数量，并按路线名称排序。",
-    "sql": "SELECT route_name AS 路线名称, COUNT(*) AS 路段数量 FROM bss_section_route WHERE delete_ts IS NULL GROUP BY route_name ORDER BY 路线名称;"
-  },
-  {
-    "question": "找出关联路段最多的路线名称及关联的路段数量。",
-    "sql": "SELECT route_name AS 路线名称, COUNT(*) AS 路段数量 FROM bss_section_route WHERE delete_ts IS NULL GROUP BY route_name ORDER BY 路段数量 DESC LIMIT 1;"
-  },
-  {
-    "question": "列出所有路段及其关联的服务区数量，筛选出关联数量大于等于2的路段。",
-    "sql": "SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL GROUP BY bsr.section_name HAVING COUNT(bsral.service_area_id) >= 2;"
-  },
-  {
-    "question": "统计当前所有开放状态的服务区数量及占比。",
-    "sql": "SELECT COUNT(*) AS 开放服务区数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area WHERE delete_ts IS NULL), 2) AS 开放率百分比 FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "列出所有关闭状态的服务区名称及其所属公司。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_state = '关闭' AND sa.delete_ts IS NULL;"
-  },
-  {
-    "question": "按公司统计各公司管理的服务区数量及开放率。",
-    "sql": "SELECT c.company_name AS 所属公司, COUNT(sa.id) AS 服务区总数, ROUND(SUM(CASE WHEN sa.service_state = '开放' THEN 1 ELSE 0 END) * 100.0 / COUNT(sa.id), 2) AS 开放率 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "按服务区类型统计信息化与智能化服务区的数量及占比。",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area WHERE delete_ts IS NULL), 2) AS 占比百分比 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "查询经纬度位于东经115度以东的服务区列表。",
-    "sql": "SELECT service_area_name AS 服务区名称, service_position AS 经纬度 FROM bss_service_area WHERE CAST(SPLIT_PART(service_position, ',', 1) AS NUMERIC) > 115 AND delete_ts IS NULL;"
-  },
-  {
-    "question": "列出最近一周内创建的服务区明细。",
-    "sql": "SELECT service_area_name AS 服务区名称, create_ts AS 创建时间 FROM bss_service_area WHERE create_ts >= NOW() - INTERVAL '7 days' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计各公司管理的服务区中，开放状态服务区数量排名前五的公司。",
-    "sql": "SELECT c.company_name AS 所属公司, COUNT(sa.id) AS 开放服务区数量 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.service_state = '开放' AND sa.delete_ts IS NULL GROUP BY c.company_name ORDER BY 开放服务区数量 DESC LIMIT 5;"
-  },
-  {
-    "question": "查询没有关联任何路段路线的服务区列表。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称 FROM bss_service_area sa LEFT JOIN bss_section_route_area_link link ON sa.id = link.service_area_id WHERE link.section_route_id IS NULL AND sa.delete_ts IS NULL;"
-  },
-  {
-    "question": "统计每个服务区类型中处于关闭状态的服务区数量。",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 关闭数量 FROM bss_service_area WHERE service_state = '关闭' AND delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "列出所有服务区及其所属公司信息，按公司名称排序。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL ORDER BY c.company_name;"
-  }
-]

data_pipeline/training_data/task_20250721_083557/task_result.json

@@ -1,115 +0,0 @@
-{
-  "success": true,
-  "workflow_state": {
-    "start_time": null,
-    "end_time": null,
-    "current_step": "training_data_load",
-    "completed_steps": [
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-      "question_sql_generation",
-      "sql_validation",
-      "training_data_load"
-    ],
-    "failed_steps": [],
-    "artifacts": {
-      "ddl_md_generation": {
-        "total_tables": 7,
-        "processed_successfully": 7,
-        "failed": 0,
-        "files_generated": 14,
-        "duration": 96.3511290550232
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-      "question_sql_generation": {
-        "output_file": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_083557\\task_20250721_083557\\qs_highway_db_20250721_092319_pair.json",
-        "total_questions": 50,
-        "total_themes": 5,
-        "successful_themes": 5,
-        "failed_themes": [],
-        "duration": 167.25344610214233
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-      "sql_validation": {
-        "original_sql_count": 50,
-        "valid_sql_count": 50,
-        "invalid_sql_count": 0,
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-        "repair_stats": {
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-          "successful": 0,
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-        "file_modification_stats": {
-          "modified": 0,
-          "deleted": 0,
-          "failed_modifications": 0
-        },
-        "average_execution_time": 0.024091057777404785,
-        "total_retries": 0,
-        "duration": 2.209826707839966
-      },
-      "training_data_load": {
-        "training_data_dir": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_083557\\task_20250721_083557",
-        "load_successful": true,
-        "total_records": 128,
-        "data_type_counts": {
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-          "documentation": 16,
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-      "step3_duration": 2.209826707839966,
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-  "artifacts": {
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-      "processed_successfully": 7,
-      "failed": 0,
-      "files_generated": 14,
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-      "output_file": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_083557\\task_20250721_083557\\qs_highway_db_20250721_092319_pair.json",
-      "total_questions": 50,
-      "total_themes": 5,
-      "successful_themes": 5,
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-      "total_retries": 0,
-      "duration": 2.209826707839966
-    },
-    "training_data_load": {
-      "training_data_dir": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_083557\\task_20250721_083557",
-      "load_successful": true,
-      "total_records": 128,
-      "data_type_counts": {
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-      "duration": 66.15845227241516
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-}

data_pipeline/training_data/task_20250721_183935/bss_business_day_data.ddl

@@ -1,5 +1,5 @@
 -- 中文名: 高速公路服务区每日经营数据记录表
--- 描述: 高速公路服务区每日经营数据记录表，用于统计各服务区按日的运营情况。
+-- 描述: 高速公路服务区每日经营数据记录表，用于统计各服务区按日维度的运营情况。
 create table public.bss_business_day_data (
   id varchar(32) not null     -- 主键ID，主键,
   version integer not null    -- 数据版本号,

data_pipeline/training_data/task_20250721_183935/bss_business_day_data_detail.md

@@ -1,5 +1,5 @@
 ## bss_business_day_data（高速公路服务区每日经营数据记录表）
-bss_business_day_data 表高速公路服务区每日经营数据记录表，用于统计各服务区按日的运营情况。
+bss_business_day_data 表高速公路服务区每日经营数据记录表，用于统计各服务区按日维度的运营情况。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820]
 - version (integer) - 数据版本号 [非空] [示例: 1]

data_pipeline/training_data/task_20250721_183935/bss_car_day_count.ddl

@@ -1,5 +1,5 @@
--- 中文名: `bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量
--- 描述: `bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量，按车辆类型分类，支持车流分析与运营决策。
+-- 中文名: 高速公路服务区每日车辆分类统计表
+-- 描述: 高速公路服务区每日车辆分类统计表，记录各类型车辆流量数据。
 create table public.bss_car_day_count (
   id varchar(32) not null     -- 主键ID，主键,
   version integer not null    -- 数据版本号,

data_pipeline/training_data/task_20250721_183935/bss_car_day_count_detail.md

@@ -1,5 +1,5 @@
-## bss_car_day_count（`bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量）
-bss_car_day_count 表`bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量，按车辆类型分类，支持车流分析与运营决策。
+## bss_car_day_count（高速公路服务区每日车辆分类统计表）
+bss_car_day_count 表高速公路服务区每日车辆分类统计表，记录各类型车辆流量数据。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc]
 - version (integer) - 数据版本号 [非空] [示例: 1]

data_pipeline/training_data/task_20250721_183935/bss_company.ddl

@@ -1,14 +1,14 @@
 -- 中文名: 高速公路服务区管理系统中的公司信息表
--- 描述: 高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及审计字段。
+-- 描述: 高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及变更记录。
 create table public.bss_company (
-  id varchar(32) not null     -- 公司唯一标识，主键,
-  version integer not null    -- 版本号,
+  id varchar(32) not null     -- 公司唯一标识符，主键,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
-  updated_by varchar(50)      -- 更新人,
+  updated_by varchar(50)      -- 最后更新人,
   delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除人,
+  deleted_by varchar(50)      -- 删除操作人,
   company_name varchar(255)   -- 公司名称,
   company_no varchar(255)     -- 公司编码,
   primary key (id)

data_pipeline/training_data/task_20250721_183935/bss_company_detail.md

@@ -1,14 +1,14 @@
 ## bss_company（高速公路服务区管理系统中的公司信息表）
-bss_company 表高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及审计字段。
+bss_company 表高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及变更记录。
 字段列表：
-- id (varchar(32)) - 公司唯一标识 [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
-- version (integer) - 版本号 [非空] [示例: 1, 2]
+- id (varchar(32)) - 公司唯一标识符 [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
+- version (integer) - 数据版本号 [非空] [示例: 1, 2]
 - create_ts (timestamp) - 创建时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
 - created_by (varchar(50)) - 创建人 [示例: admin]
 - update_ts (timestamp) - 更新时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
-- updated_by (varchar(50)) - 更新人 [示例: admin]
+- updated_by (varchar(50)) - 最后更新人 [示例: admin]
 - delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人
+- deleted_by (varchar(50)) - 删除操作人
 - company_name (varchar(255)) - 公司名称 [示例: 上饶分公司, 宜春分公司]
 - company_no (varchar(255)) - 公司编码 [示例: H03, H02, H07]
 字段补充说明：

data_pipeline/training_data/task_20250721_183935/bss_section_route.ddl

@@ -1,8 +1,8 @@
--- 中文名: 路段路线信息表
--- 描述: 路段路线信息表，用于存储高速公路服务区关联的路段与路线名称等基础信息。
+-- 中文名: 高速公路路段与路线关联信息表
+-- 描述: 高速公路路段与路线关联信息表，用于管理服务区所属路段及路线关系。
 create table public.bss_section_route (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -11,6 +11,6 @@ create table public.bss_section_route (
   deleted_by varchar(50)      -- 删除人,
   section_name varchar(255)   -- 路段名称,
   route_name varchar(255)     -- 路线名称,
-  code varchar(255)           -- 编号,
+  code varchar(255)           -- 路段编号,
   primary key (id)
 );

data_pipeline/training_data/task_20250721_183935/bss_section_route_area_link.ddl

@@ -1,5 +1,5 @@
--- 中文名: 路线与服务区关联表
--- 描述: 路线与服务区关联表，记录高速公路路线对应的服务区信息。
+-- 中文名: 高速公路路段与服务区关联表
+-- 描述: 高速公路路段与服务区关联表，用于管理路线和服务区的对应关系。
 create table public.bss_section_route_area_link (
   section_route_id varchar(32) not null -- 路段路线唯一标识，主键,
   service_area_id varchar(32) not null -- 服务区唯一标识，主键,

data_pipeline/training_data/task_20250721_183935/bss_section_route_area_link_detail.md

@@ -1,5 +1,5 @@
-## bss_section_route_area_link（路线与服务区关联表）
-bss_section_route_area_link 表路线与服务区关联表，记录高速公路路线对应的服务区信息。
+## bss_section_route_area_link（高速公路路段与服务区关联表）
+bss_section_route_area_link 表高速公路路段与服务区关联表，用于管理路线和服务区的对应关系。
 字段列表：
 - section_route_id (varchar(32)) - 路段路线唯一标识 [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp]
 - service_area_id (varchar(32)) - 服务区唯一标识 [主键, 非空] [示例: 08e01d7402abd1d6a4d9fdd5df855ef8, 091662311d2c737029445442ff198c4c]

data_pipeline/training_data/task_20250721_183935/bss_section_route_detail.md

@@ -1,16 +1,17 @@
-## bss_section_route（路段路线信息表）
-bss_section_route 表路段路线信息表，用于存储高速公路服务区关联的路段与路线名称等基础信息。
+## bss_section_route（高速公路路段与路线关联信息表）
+bss_section_route 表高速公路路段与路线关联信息表，用于管理服务区所属路段及路线关系。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7]
-- version (integer) - 版本号 [非空] [示例: 1, 0]
+- version (integer) - 数据版本号 [非空] [示例: 1, 0]
 - create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16]
 - created_by (varchar(50)) - 创建人 [示例: admin]
 - update_ts (timestamp) - 更新时间
 - updated_by (varchar(50)) - 更新人
 - delete_ts (timestamp) - 删除时间
 - deleted_by (varchar(50)) - 删除人
-- section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁, 昌九]
-- route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶, /]
-- code (varchar(255)) - 编号 [示例: SR0001, SR0002, SR0147]
+- section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁]
+- route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶]
+- code (varchar(255)) - 路段编号 [示例: SR0001, SR0002]
 字段补充说明：
-- id 为主键
+- id 为主键
+- created_by 为枚举字段，包含取值：admin

data_pipeline/training_data/task_20250721_183935/bss_service_area.ddl

@@ -1,7 +1,7 @@
--- 中文名: `bss_service_area` 表用于存储高速公路服务区的基本信息
--- 描述: `bss_service_area` 表用于存储高速公路服务区的基本信息，包括服务区名称、编码、版本控制及操作记录，为核心业务系统提供基础数据支撑。
+-- 中文名: 高速公路服务区基础信息表
+-- 描述: 高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。
 create table public.bss_service_area (
-  id varchar(32) not null     -- 主键ID，主键,
+  id varchar(32) not null     -- 服务区唯一标识，主键,
   version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
@@ -12,7 +12,7 @@ create table public.bss_service_area (
   service_area_name varchar(255) -- 服务区名称,
   service_area_no varchar(255) -- 服务区编码,
   company_id varchar(32)      -- 所属公司ID,
-  service_position varchar(255) -- 服务区经纬度坐标,
+  service_position varchar(255) -- 经纬度坐标,
   service_area_type varchar(50) -- 服务区类型,
   service_state varchar(50)   -- 服务区状态,
   primary key (id)

data_pipeline/training_data/task_20250721_183935/bss_service_area_detail.md

@@ -1,7 +1,7 @@
-## bss_service_area（`bss_service_area` 表用于存储高速公路服务区的基本信息）
-bss_service_area 表`bss_service_area` 表用于存储高速公路服务区的基本信息，包括服务区名称、编码、版本控制及操作记录，为核心业务系统提供基础数据支撑。
+## bss_service_area（高速公路服务区基础信息表）
+bss_service_area 表高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。
 字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8]
+- id (varchar(32)) - 服务区唯一标识 [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8]
 - version (integer) - 数据版本号 [非空] [示例: 3, 6]
 - create_ts (timestamp) - 创建时间 [示例: 2021-05-21 13:26:40.589000, 2021-05-20 19:51:46.314000]
 - created_by (varchar(50)) - 创建人 [示例: admin]
@@ -12,7 +12,7 @@ bss_service_area 表`bss_service_area` 表用于存储高速公路服务区的
 - service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区, 南昌南服务区]
 - service_area_no (varchar(255)) - 服务区编码 [示例: H0814, H0105]
 - company_id (varchar(32)) - 所属公司ID [示例: b1629f07c8d9ac81494fbc1de61f1ea5, ee9bf1180a2b45003f96e597a4b7f15a]
-- service_position (varchar(255)) - 服务区经纬度坐标 [示例: 114.574721,26.825584, 115.910549,28.396355]
+- service_position (varchar(255)) - 经纬度坐标 [示例: 114.574721,26.825584, 115.910549,28.396355]
 - service_area_type (varchar(50)) - 服务区类型 [示例: 信息化服务区]
 - service_state (varchar(50)) - 服务区状态 [示例: 开放, 关闭]
 字段补充说明：

data_pipeline/training_data/task_20250721_183935/bss_service_area_mapper.ddl

@@ -1,8 +1,8 @@
 -- 中文名: 服务区信息映射表
--- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。
+-- 描述: 服务区信息映射表，用于管理高速公路服务区的基础数据与变更记录。
 create table public.bss_service_area_mapper (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -11,8 +11,8 @@ create table public.bss_service_area_mapper (
   deleted_by varchar(50)      -- 删除人,
   service_name varchar(255)   -- 服务区名称,
   service_no varchar(255)     -- 服务区编码,
-  service_area_id varchar(32) -- 服务区ID,
-  source_system_type varchar(50) -- 数据来源类别名称,
-  source_type integer         -- 数据来源类别ID,
+  service_area_id varchar(32) -- 服务区业务ID,
+  source_system_type varchar(50) -- 数据来源系统名称,
+  source_type integer         -- 数据来源系统编码,
   primary key (id)
 );

data_pipeline/training_data/task_20250721_183935/bss_service_area_mapper_detail.md

@@ -1,8 +1,8 @@
 ## bss_service_area_mapper（服务区信息映射表）
-bss_service_area_mapper 表服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。
+bss_service_area_mapper 表服务区信息映射表，用于管理高速公路服务区的基础数据与变更记录。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00e1e893909211ed8ee6fa163eaf653f, 013867f5962211ed8ee6fa163eaf653f]
-- version (integer) - 版本号 [非空] [示例: 1]
+- version (integer) - 数据版本号 [非空] [示例: 1]
 - create_ts (timestamp) - 创建时间 [示例: 2023-01-10 10:54:03, 2023-01-17 12:47:29]
 - created_by (varchar(50)) - 创建人 [示例: admin]
 - update_ts (timestamp) - 更新时间 [示例: 2023-01-10 10:54:07, 2023-01-17 12:47:32]
@@ -11,9 +11,9 @@ bss_service_area_mapper 表服务区信息映射表，用于管理高速公路
 - deleted_by (varchar(50)) - 删除人
 - service_name (varchar(255)) - 服务区名称 [示例: 信丰西服务区, 南康北服务区]
 - service_no (varchar(255)) - 服务区编码 [示例: 1067, 1062]
-- service_area_id (varchar(32)) - 服务区ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f]
-- source_system_type (varchar(50)) - 数据来源类别名称 [示例: 驿美, 驿购]
-- source_type (integer) - 数据来源类别ID [示例: 3, 1]
+- service_area_id (varchar(32)) - 服务区业务ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f]
+- source_system_type (varchar(50)) - 数据来源系统名称 [示例: 驿美, 驿购]
+- source_type (integer) - 数据来源系统编码 [示例: 3, 1]
 字段补充说明：
 - id 为主键
 - source_system_type 为枚举字段，包含取值：司乘管理、商业管理、驿购、驿美、手工录入

data_pipeline/training_data/task_20250721_183935/db_query_decision_prompt.txt

@@ -1,36 +1,10 @@
-{
-  "业务范围": "当前数据库存储的是高速公路服务区运营管理的相关数据，主要涉及服务区每日经营流水、车辆通行统计、公司组织架构及路段路线关联信息，包含以下业务数据：",
-  "数据范围": "交易类数据（微信/支付宝/现金等支付方式的金额与订单数）、车流类数据（按车型分类的车辆通行数量）、组织类数据（公司编码与名称）、空间类数据（服务区经纬度坐标与状态）、来源类数据（多系统数据映射关系）",
-  "核心业务实体": [
-    {
-      "实体类型": "服务区",
-      "描述": "高速公路沿线提供休息、餐饮、购物等服务的物理区域，用于关联经营数据与车流数据",
-      "主要字段": "service_area_name, service_area_no, service_state, service_position"
-    },
-    {
-      "实体类型": "档口",
-      "描述": "服务区内具体经营单位（如餐厅、便利店），用于细化营收来源",
-      "主要字段": "branch_no, branch_name"
-    },
-    {
-      "实体类型": "公司",
-      "描述": "管理多个服务区的运营主体，用于按组织维度统计业绩",
-      "主要_fields": "company_name, company_no"
-    },
-    {
-      "实体类型": "路段路线",
-      "描述": "服务区所属的高速公路段落，用于分析不同路线的服务区表现差异",
-      "主要字段": "section_name, route_name"
-    }
-  ],
-  "关键业务指标": [
-    {
-      "指标类型": "日均总营收",
-      "描述": "基于pay_sum字段计算各服务区或档口的日均收入，反映整体经营水平"
-    },
-    {
-      "指标类型": "支付方式占比",
-      "描述": "通过wx, zfb, rmb等支付金额字段分析用户支付习惯，辅助优化收款策略"
-    }
-  ]
-}
+=== 数据库业务范围 ===
+当前数据库存储的是高速公路服务区运营管理的相关数据，主要涉及服务区经营流水、车辆通行统计及基础信息管理，包含以下业务数据：
+核心业务实体：
+- 服务区：指高速公路上为驾乘人员提供休息、餐饮、购物等服务的场所，主要字段：service_no、service_name、service_area_type、service_state
+- 档口：指服务区内的具体经营单元或商户点位，主要字段：branch_no、branch_name、pay_sum、order_sum
+- 公司：指负责管理多个服务区的运营单位，主要字段：company_no、company_name
+- 车辆类型：指通过服务区的不同车辆分类，用于流量分析，主要字段：car_type、customer_count
+关键业务指标：
+- 支付方式分析指标：基于微信(wx)、支付宝(zfb)、现金(rmb)、行吧(xs)、金豆(jd)等多渠道支付金额和订单数计算的结构占比与效率指标
+- 日均车流量与消费关联指标：结合车辆通行数量(customer_count)和服务区每日总支付金额(pay_sum)进行交叉分析，衡量车流转化率和单客消费能力

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/backup_info.json

@@ -0,0 +1,31 @@
+{
+  "backup_time": "2025-07-21T20:09:34.513796",
+  "backup_directory": "file_bak_20250721_200934",
+  "moved_files": [
+    "bss_business_day_data.ddl",
+    "bss_business_day_data_detail.md",
+    "bss_car_day_count.ddl",
+    "bss_car_day_count_detail.md",
+    "bss_company.ddl",
+    "bss_company_detail.md",
+    "bss_section_route.ddl",
+    "bss_section_route_area_link.ddl",
+    "bss_section_route_area_link_detail.md",
+    "bss_section_route_detail.md",
+    "bss_service_area.ddl",
+    "bss_service_area_detail.md",
+    "bss_service_area_mapper.ddl",
+    "bss_service_area_mapper_detail.md",
+    "db_query_decision_prompt.txt",
+    "filename_mapping.txt",
+    "file_modifications_20250721_195201.log",
+    "metadata.txt",
+    "metadata_detail.md",
+    "qs_highway_db_20250721_195131_pair.json",
+    "qs_highway_db_20250721_195131_pair.json.backup",
+    "sql_validation_20250721_195201_summary.log",
+    "task_config.json"
+  ],
+  "failed_files": [],
+  "task_id": "task_20250721_183935"
+}

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_business_day_data.ddl → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_business_day_data.ddl

@@ -1,8 +1,8 @@
--- 中文名: 业务日统计表
--- 描述: 业务日统计表，记录高速公路服务区每日经营数据，支持业务分析与决策。
+-- 中文名: 高速公路服务区每日经营数据记录表
+-- 描述: 高速公路服务区每日经营数据记录表，用于统计各服务区按日的运营情况。
 create table public.bss_business_day_data (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -21,9 +21,9 @@ create table public.bss_business_day_data (
   rmb numeric(19,4)           -- 现金支付金额,
   rmb_order integer           -- 现金订单数量,
   xs numeric(19,4)            -- 行吧支付金额,
-  xs_order integer            -- 行吧订单数量,
+  xs_order integer            -- 行吧支付订单数,
   jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
+  jd_order integer            -- 金豆支付订单数,
   order_sum integer           -- 订单总数,
   pay_sum numeric(19,4)       -- 总支付金额,
   source_type integer         -- 数据来源类别,

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_business_day_data_detail.md → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_business_day_data_detail.md

@@ -1,8 +1,8 @@
-## bss_business_day_data（业务日统计表）
-bss_business_day_data 表业务日统计表，记录高速公路服务区每日经营数据，支持业务分析与决策。
+## bss_business_day_data（高速公路服务区每日经营数据记录表）
+bss_business_day_data 表高速公路服务区每日经营数据记录表，用于统计各服务区按日的运营情况。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820]
-- version (integer) - 版本号 [非空] [示例: 1]
+- version (integer) - 数据版本号 [非空] [示例: 1]
 - create_ts (timestamp) - 创建时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
 - created_by (varchar(50)) - 创建人 [示例: xingba]
 - update_ts (timestamp) - 更新时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
@@ -21,9 +21,9 @@ bss_business_day_data 表业务日统计表，记录高速公路服务区每日
 - rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000]
 - rmb_order (integer) - 现金订单数量 [示例: 56, 12]
 - xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000]
-- xs_order (integer) - 行吧订单数量 [示例: 0, 1]
+- xs_order (integer) - 行吧支付订单数 [示例: 0, 1]
 - jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000]
-- jd_order (integer) - 金豆订单数量 [示例: 0]
+- jd_order (integer) - 金豆支付订单数 [示例: 0]
 - order_sum (integer) - 订单总数 [示例: 324, 146]
 - pay_sum (numeric(19,4)) - 总支付金额 [示例: 6077.5000, 2687.0000]
 - source_type (integer) - 数据来源类别 [示例: 1, 0, 4]

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_car_day_count.ddl

@@ -0,0 +1,17 @@
+-- 中文名: `bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量
+-- 描述: `bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量，按车辆类型分类，支持车流分析与运营决策。
+create table public.bss_car_day_count (
+  id varchar(32) not null     -- 主键ID，主键,
+  version integer not null    -- 数据版本号,
+  create_ts timestamp         -- 创建时间,
+  created_by varchar(50)      -- 创建人,
+  update_ts timestamp         -- 更新时间,
+  updated_by varchar(50)      -- 更新人,
+  delete_ts timestamp         -- 删除时间,
+  deleted_by varchar(50)      -- 删除人,
+  customer_count bigint       -- 车辆通行数量,
+  car_type varchar(100)       -- 车辆类型,
+  count_date date             -- 统计日期,
+  service_area_id varchar(32) -- 服务区ID,
+  primary key (id)
+);

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_car_day_count_detail.md

@@ -0,0 +1,18 @@
+## bss_car_day_count（`bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量）
+bss_car_day_count 表`bss_car_day_count` 表用于按日统计进入高速公路服务区的车辆数量，按车辆类型分类，支持车流分析与运营决策。
+字段列表：
+- id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc]
+- version (integer) - 数据版本号 [非空] [示例: 1]
+- create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
+- created_by (varchar(50)) - 创建人
+- update_ts (timestamp) - 更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
+- updated_by (varchar(50)) - 更新人
+- delete_ts (timestamp) - 删除时间
+- deleted_by (varchar(50)) - 删除人
+- customer_count (bigint) - 车辆通行数量 [示例: 1114, 295]
+- car_type (varchar(100)) - 车辆类型 [示例: 其他]
+- count_date (date) - 统计日期 [示例: 2022-03-02, 2022-02-02]
+- service_area_id (varchar(32)) - 服务区ID [示例: 17461166e7fa3ecda03534a5795ce985, 81f4eb731fb0728aef17ae61f1f1daef]
+字段补充说明：
+- id 为主键
+- car_type 为枚举字段，包含取值：其他、危化品、城际、过境

data_pipeline/training_data/manual_20250720_130541/bss_company.ddl → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_company.ddl

@@ -1,5 +1,5 @@
--- 中文名: `bss_company` 表用于存储高速公路服务区相关公司的基本信息
--- 描述: `bss_company` 表用于存储高速公路服务区相关公司的基本信息，包括公司名称、编码及操作记录，为核心业务单位管理提供数据支持。
+-- 中文名: 高速公路服务区管理系统中的公司信息表
+-- 描述: 高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及审计字段。
 create table public.bss_company (
   id varchar(32) not null     -- 公司唯一标识，主键,
   version integer not null    -- 版本号,

data_pipeline/training_data/manual_20250720_130541/bss_company_detail.md → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_company_detail.md

@@ -1,5 +1,5 @@
-## bss_company（`bss_company` 表用于存储高速公路服务区相关公司的基本信息）
-bss_company 表`bss_company` 表用于存储高速公路服务区相关公司的基本信息，包括公司名称、编码及操作记录，为核心业务单位管理提供数据支持。
+## bss_company（高速公路服务区管理系统中的公司信息表）
+bss_company 表高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及审计字段。
 字段列表：
 - id (varchar(32)) - 公司唯一标识 [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
 - version (integer) - 版本号 [非空] [示例: 1, 2]
@@ -9,9 +9,8 @@ bss_company 表`bss_company` 表用于存储高速公路服务区相关公司的
 - updated_by (varchar(50)) - 更新人 [示例: admin]
 - delete_ts (timestamp) - 删除时间
 - deleted_by (varchar(50)) - 删除人
-- company_name (varchar(255)) - 公司名称 [示例: 上饶分公司, 宜春分公司, 景德镇分公司]
+- company_name (varchar(255)) - 公司名称 [示例: 上饶分公司, 宜春分公司]
 - company_no (varchar(255)) - 公司编码 [示例: H03, H02, H07]
 字段补充说明：
 - id 为主键
-- company_name 为枚举字段，包含取值：抚州分公司、赣州分公司、吉安分公司、景德镇分公司、九江分公司、南昌分公司、其他公司管辖、上饶分公司、宜春分公司
 - company_no 为枚举字段，包含取值：H01、H02、H03、H04、H05、H06、H07、H08、Q01

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_section_route.ddl → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_section_route.ddl

@@ -1,5 +1,5 @@
 -- 中文名: 路段路线信息表
--- 描述: 路段路线信息表，记录高速公路路段与路线关联关系及版本信息。
+-- 描述: 路段路线信息表，用于存储高速公路服务区关联的路段与路线名称等基础信息。
 create table public.bss_section_route (
   id varchar(32) not null     -- 主键ID，主键,
   version integer not null    -- 版本号,

data_pipeline/training_data/manual_20250720_130541/bss_section_route_area_link.ddl → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_section_route_area_link.ddl

@@ -1,7 +1,7 @@
 -- 中文名: 路线与服务区关联表
 -- 描述: 路线与服务区关联表，记录高速公路路线对应的服务区信息。
 create table public.bss_section_route_area_link (
-  section_route_id varchar(32) not null -- 路段路线ID，主键,
-  service_area_id varchar(32) not null -- 服务区ID，主键,
+  section_route_id varchar(32) not null -- 路段路线唯一标识，主键,
+  service_area_id varchar(32) not null -- 服务区唯一标识，主键,
   primary key (section_route_id, service_area_id)
 );

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_section_route_area_link_detail.md

@@ -0,0 +1,7 @@
+## bss_section_route_area_link（路线与服务区关联表）
+bss_section_route_area_link 表路线与服务区关联表，记录高速公路路线对应的服务区信息。
+字段列表：
+- section_route_id (varchar(32)) - 路段路线唯一标识 [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp]
+- service_area_id (varchar(32)) - 服务区唯一标识 [主键, 非空] [示例: 08e01d7402abd1d6a4d9fdd5df855ef8, 091662311d2c737029445442ff198c4c]
+字段补充说明：
+- 复合主键：section_route_id, service_area_id

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_section_route_detail.md → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_section_route_detail.md

@@ -1,5 +1,5 @@
 ## bss_section_route（路段路线信息表）
-bss_section_route 表路段路线信息表，记录高速公路路段与路线关联关系及版本信息。
+bss_section_route 表路段路线信息表，用于存储高速公路服务区关联的路段与路线名称等基础信息。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7]
 - version (integer) - 版本号 [非空] [示例: 1, 0]

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_service_area.ddl → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_service_area.ddl

@@ -1,8 +1,8 @@
 -- 中文名: `bss_service_area` 表用于存储高速公路服务区的基本信息
--- 描述: `bss_service_area` 表用于存储高速公路服务区的基本信息，包括名称、编码及操作记录，为核心业务提供数据支撑。
+-- 描述: `bss_service_area` 表用于存储高速公路服务区的基本信息，包括服务区名称、编码、版本控制及操作记录，为核心业务系统提供基础数据支撑。
 create table public.bss_service_area (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -12,7 +12,7 @@ create table public.bss_service_area (
   service_area_name varchar(255) -- 服务区名称,
   service_area_no varchar(255) -- 服务区编码,
   company_id varchar(32)      -- 所属公司ID,
-  service_position varchar(255) -- 服务区经纬度,
+  service_position varchar(255) -- 服务区经纬度坐标,
   service_area_type varchar(50) -- 服务区类型,
   service_state varchar(50)   -- 服务区状态,
   primary key (id)

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_service_area_detail.md → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_service_area_detail.md

@@ -1,8 +1,8 @@
 ## bss_service_area（`bss_service_area` 表用于存储高速公路服务区的基本信息）
-bss_service_area 表`bss_service_area` 表用于存储高速公路服务区的基本信息，包括名称、编码及操作记录，为核心业务提供数据支撑。
+bss_service_area 表`bss_service_area` 表用于存储高速公路服务区的基本信息，包括服务区名称、编码、版本控制及操作记录，为核心业务系统提供基础数据支撑。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8]
-- version (integer) - 版本号 [非空] [示例: 3, 6]
+- version (integer) - 数据版本号 [非空] [示例: 3, 6]
 - create_ts (timestamp) - 创建时间 [示例: 2021-05-21 13:26:40.589000, 2021-05-20 19:51:46.314000]
 - created_by (varchar(50)) - 创建人 [示例: admin]
 - update_ts (timestamp) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000]
@@ -12,7 +12,7 @@ bss_service_area 表`bss_service_area` 表用于存储高速公路服务区的
 - service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区, 南昌南服务区]
 - service_area_no (varchar(255)) - 服务区编码 [示例: H0814, H0105]
 - company_id (varchar(32)) - 所属公司ID [示例: b1629f07c8d9ac81494fbc1de61f1ea5, ee9bf1180a2b45003f96e597a4b7f15a]
-- service_position (varchar(255)) - 服务区经纬度 [示例: 114.574721,26.825584, 115.910549,28.396355]
+- service_position (varchar(255)) - 服务区经纬度坐标 [示例: 114.574721,26.825584, 115.910549,28.396355]
 - service_area_type (varchar(50)) - 服务区类型 [示例: 信息化服务区]
 - service_state (varchar(50)) - 服务区状态 [示例: 开放, 关闭]
 字段补充说明：

data_pipeline/training_data/manual_20250720_130541/bss_service_area_mapper.ddl → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_service_area_mapper.ddl

@@ -1,5 +1,5 @@
--- 中文名: 服务区基础信息映射表
--- 描述: 服务区基础信息映射表，用于统一管理全国高速公路服务区的编码与名称对应关系。
+-- 中文名: 服务区信息映射表
+-- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。
 create table public.bss_service_area_mapper (
   id varchar(32) not null     -- 主键ID，主键,
   version integer not null    -- 版本号,

data_pipeline/training_data/manual_20250720_130541/bss_service_area_mapper_detail.md → data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_service_area_mapper_detail.md

@@ -1,5 +1,5 @@
-## bss_service_area_mapper（服务区基础信息映射表）
-bss_service_area_mapper 表服务区基础信息映射表，用于统一管理全国高速公路服务区的编码与名称对应关系。
+## bss_service_area_mapper（服务区信息映射表）
+bss_service_area_mapper 表服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00e1e893909211ed8ee6fa163eaf653f, 013867f5962211ed8ee6fa163eaf653f]
 - version (integer) - 版本号 [非空] [示例: 1]

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/db_query_decision_prompt.txt

@@ -0,0 +1,36 @@
+{
+  "业务范围": "当前数据库存储的是高速公路服务区运营管理的相关数据，主要涉及服务区每日经营流水、车辆通行统计、公司组织架构及路段路线关联信息，包含以下业务数据：",
+  "数据范围": "交易类数据（微信/支付宝/现金等支付方式的金额与订单数）、车流类数据（按车型分类的车辆通行数量）、组织类数据（公司编码与名称）、空间类数据（服务区经纬度坐标与状态）、来源类数据（多系统数据映射关系）",
+  "核心业务实体": [
+    {
+      "实体类型": "服务区",
+      "描述": "高速公路沿线提供休息、餐饮、购物等服务的物理区域，用于关联经营数据与车流数据",
+      "主要字段": "service_area_name, service_area_no, service_state, service_position"
+    },
+    {
+      "实体类型": "档口",
+      "描述": "服务区内具体经营单位（如餐厅、便利店），用于细化营收来源",
+      "主要字段": "branch_no, branch_name"
+    },
+    {
+      "实体类型": "公司",
+      "描述": "管理多个服务区的运营主体，用于按组织维度统计业绩",
+      "主要_fields": "company_name, company_no"
+    },
+    {
+      "实体类型": "路段路线",
+      "描述": "服务区所属的高速公路段落，用于分析不同路线的服务区表现差异",
+      "主要字段": "section_name, route_name"
+    }
+  ],
+  "关键业务指标": [
+    {
+      "指标类型": "日均总营收",
+      "描述": "基于pay_sum字段计算各服务区或档口的日均收入，反映整体经营水平"
+    },
+    {
+      "指标类型": "支付方式占比",
+      "描述": "通过wx, zfb, rmb等支付金额字段分析用户支付习惯，辅助优化收款策略"
+    }
+  ]
+}

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/metadata.txt

@@ -0,0 +1,62 @@
+-- Schema Tools生成的主题元数据
+-- 业务背景: 高速公路服务区管理系统
+-- 生成时间: 2025-07-21 19:51:31
+-- 数据库: highway_db
+
+-- 创建表（如果不存在）
+CREATE TABLE IF NOT EXISTS metadata (
+    id SERIAL PRIMARY KEY,    -- 主键
+    topic_name VARCHAR(100) NOT NULL,  -- 业务主题名称
+    description TEXT,                  -- 业务主体说明
+    related_tables TEXT[],			  -- 相关表名
+    biz_entities TEXT[],               -- 主要业务实体名称
+    biz_metrics TEXT[],                -- 主要业务指标名称
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP    -- 插入时间
+);
+
+-- 插入主题数据
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '日营业数据分析',
+  '基于 bss_business_day_data 表，分析每个服务区和档口每天的营业收入、订单数量、支付方式等经营情况，帮助优化运营策略。',
+  'bss_business_day_data,bss_service_area',
+  '服务区,档口,支付方式,营收',
+  '收入趋势,服务区对比,支付方式分布'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '车流统计分析',
+  '基于 bss_car_day_count 表，按日期和车辆类型分析各服务区的车辆通行量，辅助车流高峰期的资源配置与营销决策。',
+  'bss_car_day_count,bss_service_area',
+  '服务区,车辆类型,日期,车流量',
+  '车流趋势,车型占比,服务区车流排名'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '公司营收对比',
+  '结合 bss_company 和 bss_service_area 表，分析不同公司下属服务区的营业收入情况，支持公司层级的业绩评估。',
+  'bss_company,bss_service_area,bss_business_day_data',
+  '公司,服务区,日期,营收',
+  '公司营收排名,月度对比,日均营收'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '路段车流与营收关联',
+  '结合 bss_section_route 和 bss_car_day_count、bss_business_day_data 表，分析不同路段对应服务区的车流与营收关系，支持路段运营优化。',
+  'bss_section_route,bss_section_route_area_link,bss_car_day_count,bss_business_day_data',
+  '路段,路线,车流量,营收',
+  '车流营收相关性,路段营收排名,车流营收趋势'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '服务区状态与营收',
+  '基于 bss_service_area 和 bss_business_day_data 表，分析不同状态（开放/关闭）的服务区对营收的影响，支持资源调度决策。',
+  'bss_service_area,bss_business_day_data',
+  '服务区,服务状态,日期,营收',
+  '状态营收对比,开放服务区排名,关闭影响分析'
+);
+

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/metadata_detail.md

@@ -0,0 +1,20 @@
+## metadata（存储分析主题元数据）
+
+`metadata` 主要描述了当前数据库包含了哪些数据内容，哪些分析主题，哪些指标等等。
+
+字段列表：
+
+- `id` (serial) - 主键ID [主键, 非空]
+- `topic_name` (varchar(100)) - 业务主题名称 [非空]
+- `description` (text) - 业务主题说明
+- `related_tables` (text[]) - 涉及的数据表 [示例: bss_business_day_data, bss_service_area]
+- `biz_entities` (text[]) - 主要业务实体名称 [示例: 服务状态, 车流量, 支付方式]
+- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 日均营收, 车流趋势, 关闭影响分析]
+- `created_at` (timestamp) - 插入时间 [默认值: `CURRENT_TIMESTAMP`]
+
+字段补充说明：
+
+- `id` 为主键，自增；
+- `related_tables` 用于建立主题与具体明细表的依赖关系；
+- `biz_entities` 表示主题关注的核心对象，例如服务区、车辆、公司；
+- `biz_metrics` 表示该主题关注的业务分析指标，例如营收对比、趋势变化、占比结构等。

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/task_config.json

@@ -0,0 +1,15 @@
+{
+  "task_id": "task_20250721_183935",
+  "created_at": "2025-07-21T18:39:37.171796",
+  "parameters": {
+    "db_connection": "postgresql://postgres:postgres@192.168.67.1:6432/highway_db",
+    "table_list_file": "{task_directory}/table_list.txt",
+    "business_context": "高速公路服务区管理系统",
+    "file_upload_mode": true,
+    "enable_llm_repair": true,
+    "modify_original_file": true,
+    "enable_sql_validation": true,
+    "enable_training_data_load": true
+  },
+  "output_directory": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_183935"
+}

data_pipeline/training_data/task_20250721_183935/metadata.txt

@@ -1,6 +1,6 @@
 -- Schema Tools生成的主题元数据
 -- 业务背景: 高速公路服务区管理系统
--- 生成时间: 2025-07-21 19:51:31
+-- 生成时间: 2025-07-21 20:13:55
 -- 数据库: highway_db
 
 -- 创建表（如果不存在）
@@ -17,46 +17,46 @@ CREATE TABLE IF NOT EXISTS metadata (
 -- 插入主题数据
 INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
 (
-  '日营业数据分析',
-  '基于 bss_business_day_data 表，分析每个服务区和档口每天的营业收入、订单数量、支付方式等经营情况，帮助优化运营策略。',
-  'bss_business_day_data,bss_service_area',
-  '服务区,档口,支付方式,营收',
-  '收入趋势,服务区对比,支付方式分布'
+  '日营业分析',
+  '基于每日经营数据，分析各服务区及档口的收入趋势、订单结构和支付方式分布，辅助运营决策优化。',
+  'bss_business_day_data',
+  '服务区,档口,支付方式',
+  '总收入,订单总数,支付方式占比,日环比增长'
 );
 
 INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
 (
-  '车流统计分析',
-  '基于 bss_car_day_count 表，按日期和车辆类型分析各服务区的车辆通行量，辅助车流高峰期的资源配置与营销决策。',
+  '车流特征分析',
+  '结合车辆类型与日期维度，识别不同服务区过境、城际等车流变化规律，支撑资源调度与服务配置。',
   'bss_car_day_count,bss_service_area',
-  '服务区,车辆类型,日期,车流量',
-  '车流趋势,车型占比,服务区车流排名'
+  '车辆类型,服务区,统计日期',
+  '车流量趋势,车型占比,服务区车流排名'
 );
 
 INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
 (
-  '公司营收对比',
-  '结合 bss_company 和 bss_service_area 表，分析不同公司下属服务区的营业收入情况，支持公司层级的业绩评估。',
-  'bss_company,bss_service_area,bss_business_day_data',
-  '公司,服务区,日期,营收',
-  '公司营收排名,月度对比,日均营收'
+  '公司业绩对比',
+  '按所属公司汇总营收和订单数据，横向比较各分公司经营能力，支持绩效考核与资源倾斜策略制定。',
+  'bss_business_day_data,bss_service_area,bss_company',
+  '公司,服务区,支付方式',
+  '总营收,订单量,人均消费,公司间对比'
 );
 
 INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
 (
-  '路段车流与营收关联',
-  '结合 bss_section_route 和 bss_car_day_count、bss_business_day_data 表，分析不同路段对应服务区的车流与营收关系，支持路段运营优化。',
+  '路段流量关联',
+  '通过路段-服务区映射关系，分析不同高速路段下辖服务区的车流与消费关联性，挖掘潜力区域。',
   'bss_section_route,bss_section_route_area_link,bss_car_day_count,bss_business_day_data',
-  '路段,路线,车流量,营收',
-  '车流营收相关性,路段营收排名,车流营收趋势'
+  '路段名称,路线名称,服务区',
+  '平均车流量,单位车流转化收入,路段综合活跃度'
 );
 
 INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
 (
-  '服务区状态与营收',
-  '基于 bss_service_area 和 bss_business_day_data 表，分析不同状态（开放/关闭）的服务区对营收的影响，支持资源调度决策。',
-  'bss_service_area,bss_business_day_data',
-  '服务区,服务状态,日期,营收',
-  '状态营收对比,开放服务区排名,关闭影响分析'
+  '数据来源质量',
+  '评估不同系统（如驿购、驿美）提供的经营数据在数量、完整性上的差异，提升数据治理水平。',
+  'bss_service_area_mapper,bss_business_day_data',
+  '数据来源系统,服务区,统计日期',
+  '记录数分布,数据更新频率,来源系统覆盖率'
 );
 

data_pipeline/training_data/task_20250721_183935/metadata_detail.md

@@ -7,9 +7,9 @@
 - `id` (serial) - 主键ID [主键, 非空]
 - `topic_name` (varchar(100)) - 业务主题名称 [非空]
 - `description` (text) - 业务主题说明
-- `related_tables` (text[]) - 涉及的数据表 [示例: bss_business_day_data, bss_service_area]
-- `biz_entities` (text[]) - 主要业务实体名称 [示例: 服务状态, 车流量, 支付方式]
-- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 日均营收, 车流趋势, 关闭影响分析]
+- `related_tables` (text[]) - 涉及的数据表 [示例: bss_business_day_data, bss_car_day_count]
+- `biz_entities` (text[]) - 主要业务实体名称 [示例: 服务区, 公司, 路线名称]
+- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 订单量, 平均车流量, 支付方式占比]
 - `created_at` (timestamp) - 插入时间 [默认值: `CURRENT_TIMESTAMP`]
 
 字段补充说明：

data_pipeline/training_data/task_20250721_183935/qs_highway_db_20250721_201355_pair.json

@@ -0,0 +1,190 @@
+[
+  {
+    "question": "最近7天各服务区的总收入和订单总数是多少？按收入降序排列。",
+    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总收入, SUM(order_sum) AS 订单总数 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总收入 DESC;"
+  },
+  {
+    "question": "昨日各档口的微信支付金额占比（占该档口总支付金额）是多少？仅显示占比超过50%的记录。",
+    "sql": "SELECT branch_name AS 档口名称, (wx / pay_sum * 100)::numeric(5,2) AS 微信支付占比 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND pay_sum > 0 AND delete_ts IS NULL GROUP BY branch_name, wx, pay_sum HAVING (wx / pay_sum * 100) > 50 ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "过去30天内日均订单数最高的前5个服务区是哪些？",
+    "sql": "SELECT service_name AS 服务区名称, AVG(order_sum) AS 日均订单数 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '30 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 日均订单数 DESC LIMIT 5;"
+  },
+  {
+    "question": "今日各支付方式（微信、支付宝、现金）的总金额和订单数量分布如何？",
+    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 总金额, SUM(wx_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE AND delete_ts IS NULL UNION ALL SELECT '支付宝' AS 支付方式, SUM(zfb) AS 总金额, SUM(zf_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE AND delete_ts IS NULL UNION ALL SELECT '现金' AS 支付方式, SUM(rmb) AS 总金额, SUM(rmb_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE AND delete_ts IS NULL;"
+  },
+  {
+    "question": "昨日哪个档口的现金支付订单占比最高？",
+    "sql": "SELECT branch_name AS 档口名称, (rmb_order::numeric / order_sum * 100)::numeric(5,2) AS 现金订单占比 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND order_sum > 0 AND delete_ts IS NULL ORDER BY 现金订单占比 DESC LIMIT 1;"
+  },
+  {
+    "question": "本月至今各服务区的总支付金额环比上月同期增长百分比是多少？",
+    "sql": "WITH this_month AS (SELECT service_name, SUM(pay_sum) AS tm_sum FROM bss_business_day_data WHERE oper_date >= DATE_TRUNC('month', CURRENT_DATE) AND delete_ts IS NULL GROUP BY service_name), last_month AS (SELECT service_name, SUM(pay_sum) AS lm_sum FROM bss_business_day_data WHERE oper_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') AND oper_date < DATE_TRUNC('month', CURRENT_DATE) AND delete_ts IS NULL GROUP BY service_name) SELECT t.service_name AS 服务区名称, ROUND(((t.tm_sum - l.lm_sum) / l.lm_sum * 100), 2) AS 环比增长率 FROM this_month t JOIN last_month l ON t.service_name = l.service_name WHERE l.lm_sum > 0;"
+  },
+  {
+    "question": "最近一周订单总数最低的服务区是哪个？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(order_sum) AS 总订单数 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总订单数 ASC LIMIT 1;"
+  },
+  {
+    "question": "昨日收入最高的服务区及其档口明细（包括各项支付金额和订单数）？",
+    "sql": "SELECT service_name AS 服务区名称, branch_name AS 档口名称, wx AS 微信金额, wx_order AS 微信订单数, zfb AS 支付宝金额, zf_order AS 支付宝订单数, rmb AS 现金金额, rmb_order AS 现金订单数, pay_sum AS 总支付金额 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND delete_ts IS NULL ORDER BY pay_sum DESC LIMIT 1;"
+  },
+  {
+    "question": "过去7天内每天的全网总收入趋势如何？",
+    "sql": "SELECT oper_date AS 统计日期, SUM(pay_sum) AS 当日总收入 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "昨日订单总数超过100的档口中，行吧支付占比最高的前3名是哪些？",
+    "sql": "SELECT branch_name AS 档口名称, (xs_order::numeric / order_sum * 100)::numeric(5,2) AS 行吧支付占比 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND order_sum > 100 AND delete_ts IS NULL ORDER BY 行吧支付占比 DESC LIMIT 3;"
+  },
+  {
+    "question": "各服务区在2023年过境车辆总量排名前5名是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cd.customer_count) AS 过境车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.car_type = '过境' AND cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 过境车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "2023年每月城际车辆总数趋势如何变化？",
+    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 城际车流量 FROM bss_car_day_count WHERE car_type = '城际' AND count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date) ORDER BY 月份;"
+  },
+  {
+    "question": "当前开放的服务区中，平均每日车流量最高的3个服务区是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cd.customer_count) AS 日均车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE sa.service_state = '开放' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 DESC LIMIT 3;"
+  },
+  {
+    "question": "2023年危化品车辆占比最高的服务区是哪个？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(CASE WHEN cd.car_type = '危化品' THEN cd.customer_count ELSE 0 END)::DECIMAL / SUM(cd.customer_count) AS 危化品占比 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 危化品占比 DESC LIMIT 1;"
+  },
+  {
+    "question": "近30天内各车型（过境、城际、危化品、其他）的总车流量对比情况如何？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - 30 AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
+  },
+  {
+    "question": "宜春分公司下属服务区2023年Q1车流量总和是多少？",
+    "sql": "SELECT SUM(cd.customer_count) AS 总车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '宜春分公司' AND cd.count_date BETWEEN '2023-01-01' AND '2023-03-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;"
+  },
+  {
+    "question": "最近一周每天的过境与城际车辆数量变化趋势是怎样的？",
+    "sql": "SELECT count_date AS 统计日期, SUM(CASE WHEN car_type = '过境' THEN customer_count ELSE 0 END) AS 过境车流量, SUM(CASE WHEN car_type = '城际' THEN customer_count ELSE 0 END) AS 城际车流量 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY count_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "哪些服务区在过去一个月中从未出现危化品车辆？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称 FROM bss_service_area sa WHERE sa.id NOT IN (SELECT DISTINCT service_area_id FROM bss_car_day_count WHERE car_type = '危化品' AND count_date >= CURRENT_DATE - 30 AND delete_ts IS NULL) AND sa.delete_ts IS NULL;"
+  },
+  {
+    "question": "2023年单日车流量最大值出现在哪一天？对应的服务区和车流量是多少？",
+    "sql": "SELECT cd.count_date AS 统计日期, sa.service_area_name AS 服务区名称, cd.customer_count AS 车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL ORDER BY cd.customer_count DESC LIMIT 1;"
+  },
+  {
+    "question": "每个服务区2023年各类车型的车流量分布明细（按服务区分组）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, cd.car_type AS 车辆类型, SUM(cd.customer_count) AS 车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name, cd.car_type ORDER BY 服务区名称, 车流量 DESC;"
+  },
+  {
+    "question": "各公司近30天总营收排名（万元）？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.pay_sum) / 10000 AS 总营收_万元 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND b.oper_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY c.id, c.company_name ORDER BY 总营收_万元 DESC;"
+  },
+  {
+    "question": "各公司最近一天订单量与人均消费对比（排除无订单公司）？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.order_sum) AS 订单总量, CASE WHEN SUM(b.order_sum) > 0 THEN ROUND(SUM(b.pay_sum) / SUM(b.order_sum), 2) ELSE 0 END AS 人均消费 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND b.oper_date = (SELECT MAX(oper_date) FROM bss_business_day_data WHERE delete_ts IS NULL) GROUP BY c.id, c.company_name HAVING SUM(b.order_sum) > 0 ORDER BY 订单总量 DESC;"
+  },
+  {
+    "question": "上月各公司现金支付占比最高的前三名？",
+    "sql": "SELECT company_name AS 公司名称, ROUND(SUM(rmb) / NULLIF(SUM(pay_sum), 0) * 100, 2) AS 现金支付占比 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND DATE_TRUNC('month', b.oper_date) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') GROUP BY c.id, c.company_name ORDER BY 现金支付占比 DESC LIMIT 3;"
+  },
+  {
+    "question": "当前开放的服务区中，各公司平均每日营收（万元）？",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(daily_revenue) AS 平均日营收_万元 FROM (SELECT s.company_id, b.oper_date, SUM(b.pay_sum) / 10000 AS daily_revenue FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND s.service_state = '开放' GROUP BY s.company_id, b.oper_date) sub JOIN bss_company c ON sub.company_id = c.id WHERE c.delete_ts IS NULL GROUP BY c.id, c.company_name ORDER BY 平均日营收_万元 DESC;"
+  },
+  {
+    "question": "本月各公司微信支付订单量环比上月变化率（%）？",
+    "sql": "WITH current_month AS (SELECT c.company_name, SUM(wx_order) AS wx_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND DATE_TRUNC('month', b.oper_date) = DATE_TRUNC('month', CURRENT_DATE) GROUP BY c.company_name), last_month AS (SELECT c.company_name, SUM(wx_order) AS wx_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND DATE_TRUNC('month', b.oper_date) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') GROUP BY c.company_name) SELECT cm.company_name AS 公司名称, ROUND((cm.wx_orders - COALESCE(lm.wx_orders, 0)) * 100.0 / NULLIF(lm.wx_orders, 0), 2) AS 微信订单环比变化率 FROM current_month cm LEFT JOIN last_month lm ON cm.company_name = lm.company_name ORDER BY 微信订单环比变化率 DESC;"
+  },
+  {
+    "question": "各公司订单总量前五的服务区明细（公司+服务区+订单量）？",
+    "sql": "SELECT c.company_name AS 公司名称, b.service_name AS 服务区名称, SUM(b.order_sum) AS 订单总量 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.id, c.company_name, b.service_name ORDER BY 订单总量 DESC LIMIT 5;"
+  },
+  {
+    "question": "各公司最近7天日均订单量与历史日均订单量对比（%）？",
+    "sql": "WITH recent AS (SELECT s.company_id, AVG(b.order_sum) AS avg_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND b.oper_date >= CURRENT_DATE - 7 GROUP BY s.company_id), historical AS (SELECT s.company_id, AVG(b.order_sum) AS avg_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.company_id) SELECT c.company_name AS 公司名称, ROUND((r.avg_orders - h.avg_orders) * 100.0 / NULLIF(h.avg_orders, 0), 2) AS 近七天相对历史变化率 FROM recent r JOIN historical h ON r.company_id = h.company_id JOIN bss_company c ON r.company_id = c.id WHERE c.delete_ts IS NULL ORDER BY 近七天相对历史变化率 DESC;"
+  },
+  {
+    "question": "各公司支付宝支付金额占总支付金额比例超过10%的公司列表？",
+    "sql": "SELECT c.company_name AS 公司名称, ROUND(SUM(b.zfb) / SUM(b.pay_sum) * 100, 2) AS 支付宝占比 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.id, c.company_name HAVING SUM(b.pay_sum) > 0 AND SUM(b.zfb) / SUM(b.pay_sum) > 0.1 ORDER BY 支付宝占比 DESC;"
+  },
+  {
+    "question": "各公司最近一个月无营收记录的服务区数量？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(DISTINCT s.service_area_no) AS 零营收服务区数 FROM bss_service_area s JOIN bss_company c ON s.company_id = c.id LEFT JOIN bss_business_day_data b ON s.service_area_no = b.service_no AND b.oper_date >= CURRENT_DATE - INTERVAL '1 month' AND b.delete_ts IS NULL WHERE s.delete_ts IS NULL AND c.delete_ts IS NULL AND b.id IS NULL GROUP BY c.id, c.company_name ORDER BY 零营收服务区数 DESC;"
+  },
+  {
+    "question": "各公司订单量最高的单日数据（公司+日期+订单量）？",
+    "sql": "SELECT company_name AS 公司名称, oper_date AS 日期, max_orders AS 最高单日订单量 FROM (SELECT c.company_name, b.oper_date, SUM(b.order_sum) AS max_orders, ROW_NUMBER() OVER (PARTITION BY c.id ORDER BY SUM(b.order_sum) DESC) AS rn FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.id, c.company_name, b.oper_date) sub WHERE rn = 1 ORDER BY 最高单日订单量 DESC;"
+  },
+  {
+    "question": "各路段下辖服务区的平均车流量是多少？按车流量从高到低排序。",
+    "sql": "SELECT sr.section_name AS 路段名称, AVG(cd.customer_count) AS 平均车流量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id WHERE sr.delete_ts IS NULL AND cd.delete_ts IS NULL GROUP BY sr.section_name ORDER BY 平均车流量 DESC;"
+  },
+  {
+    "question": "每条路线中单位车流转化收入最高的服务区是哪个？（仅统计开放状态的服务区）",
+    "sql": "SELECT t.route_name AS 路线名称, t.service_name AS 服务区名称, t.unit_income AS 单位车流转化收入 FROM (SELECT sr.route_name, sa.service_area_name AS service_name, SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) AS unit_income, ROW_NUMBER() OVER (PARTITION BY sr.route_name ORDER BY SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) DESC) AS rn FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_car_day_count cd ON sa.id = cd.service_area_id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY sr.route_name, sa.service_area_name) t WHERE t.rn = 1;"
+  },
+  {
+    "question": "近30天内哪些路段的综合活跃度（车流+消费）最高？取前5名。",
+    "sql": "SELECT sr.section_name AS 路段名称, SUM(cd.customer_count) AS 总车流量, SUM(bd.pay_sum) AS 总收入, (SUM(cd.customer_count) + SUM(bd.pay_sum)) AS 综合活跃度 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id JOIN bss_business_day_data bd ON cd.count_date = bd.oper_date JOIN bss_service_area sa ON link.service_area_id = sa.id WHERE sr.delete_ts IS NULL AND cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND cd.count_date >= CURRENT_DATE - 30 AND bd.oper_date >= CURRENT_DATE - 30 GROUP BY sr.section_name ORDER BY 综合活跃度 DESC LIMIT 5;"
+  },
+  {
+    "question": "昌栗路段下辖各服务区的日均车流量和日均收入对比如何？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cd.customer_count) AS 日均车流量, AVG(bd.pay_sum) AS 日均收入 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_car_day_count cd ON sa.id = cd.service_area_id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sr.section_name = '昌栗' GROUP BY sa.service_area_name;"
+  },
+  {
+    "question": "不同车辆类型的车流量分布情况如何？按路段分类统计。",
+    "sql": "SELECT sr.section_name AS 路段名称, cd.car_type AS 车辆类型, SUM(cd.customer_count) AS 总车流量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id WHERE sr.delete_ts IS NULL AND cd.delete_ts IS NULL AND sr.section_name IS NOT NULL GROUP BY sr.section_name, cd.car_type ORDER BY 路段名称, 总车流量 DESC;"
+  },
+  {
+    "question": "哪些服务区的单位车流转化收入低于整体平均水平？列出具体数值。",
+    "sql": "WITH overall_avg AS (SELECT AVG(unit_income) AS avg_unit_income FROM (SELECT SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) AS unit_income FROM bss_car_day_count cd JOIN bss_business_day_data bd ON cd.count_date = bd.oper_date JOIN bss_service_area sa ON cd.service_area_id = sa.id JOIN bss_service_area_mapper sm ON sa.id = sm.service_area_id WHERE cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND sm.delete_ts IS NULL GROUP BY sa.service_area_name) sub) SELECT sa.service_area_name AS 服务区名称, SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) AS 单位车流转化收入 FROM bss_car_day_count cd JOIN bss_business_day_data bd ON cd.count_date = bd.oper_date JOIN bss_service_area sa ON cd.service_area_id = sa.id JOIN bss_service_area_mapper sm ON sa.id = sm.service_area_id WHERE cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND sm.delete_ts IS NULL GROUP BY sa.service_area_name HAVING SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) < (SELECT avg_unit_income FROM overall_avg);"
+  },
+  {
+    "question": "昌宁路段下辖服务区中，订单总数排名前三的档口是哪些？",
+    "sql": "SELECT bd.branch_name AS 档口名称, SUM(bd.order_sum) AS 订单总数 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND bd.delete_ts IS NULL AND sr.section_name = '昌宁' GROUP BY bd.branch_name ORDER BY 订单总数 DESC LIMIT 3;"
+  },
+  {
+    "question": "最近一周内，各路段的日均车流量波动幅度（标准差）是多少？",
+    "sql": "SELECT sr.section_name AS 路段名称, STDDEV(cd.customer_count) AS 车流量标准差 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id WHERE sr.delete_ts IS NULL AND cd.delete_ts IS NULL AND cd.count_date >= CURRENT_DATE - 7 GROUP BY sr.section_name ORDER BY 车流量标准差 DESC;"
+  },
+  {
+    "question": "哪些路段存在服务区处于关闭状态？列出这些路段及其关闭的服务区数量。",
+    "sql": "SELECT sr.section_name AS 路段名称, COUNT(sa.id) AS 关闭服务区数量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id WHERE sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND sa.service_state = '关闭' GROUP BY sr.section_name HAVING COUNT(sa.id) > 0;"
+  },
+  {
+    "question": "不同数据来源系统（如驿购、驿美）每日记录数分布情况如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, oper_date AS 统计日期, COUNT(*) AS 记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type, oper_date ORDER BY 统计日期 DESC, 记录数 DESC;"
+  },
+  {
+    "question": "各数据来源系统的平均每日更新频率（记录数/天）是多少？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*)::float / COUNT(DISTINCT oper_date) AS 平均每日记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type ORDER BY 平均每日记录数 DESC;"
+  },
+  {
+    "question": "哪些服务区在所有数据来源系统中均有覆盖？即来源系统覆盖率100%的服务区",
+    "sql": "SELECT t.service_name AS 服务区名称 FROM (SELECT m.service_name, COUNT(DISTINCT m.source_system_type) AS 系统数量 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY m.service_name) t WHERE 系统数量 = (SELECT COUNT(DISTINCT source_system_type) FROM bss_service_area_mapper WHERE delete_ts IS NULL);"
+  },
+  {
+    "question": "最近7天内，哪个数据来源系统新增记录最多？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 新增记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL AND oper_date >= CURRENT_DATE - 7 GROUP BY source_system_type ORDER BY 新增记录数 DESC LIMIT 1;"
+  },
+  {
+    "question": "各数据来源系统的订单总数和总支付金额对比如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, SUM(order_sum) AS 总订单数, SUM(pay_sum) AS 总支付金额 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type ORDER BY 总支付金额 DESC;"
+  },
+  {
+    "question": "过去30天内，数据来源系统为'驿购'的日均记录数变化趋势是怎样的？",
+    "sql": "SELECT oper_date AS 统计日期, COUNT(*) AS 日记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL AND source_system_type = '驿购' AND oper_date >= CURRENT_DATE - 30 GROUP BY oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "数据来源系统中，哪个系统的记录缺失率最高（即未填充值字段比例）？",
+    "sql": "SELECT source_system_type AS 数据来源系统, AVG(CASE WHEN wx IS NULL THEN 1 ELSE 0 END) AS 微信缺失率, AVG(CASE WHEN zfb IS NULL THEN 1 ELSE 0 END) AS 支付宝缺失率 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type ORDER BY 微信缺失率 DESC LIMIT 1;"
+  },
+  {
+    "question": "不同数据来源系统的更新活跃度（最近30天有数据的天数占比）如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(DISTINCT oper_date)::float / 30 AS 活跃度 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL AND oper_date >= CURRENT_DATE - 30 GROUP BY source_system_type ORDER BY 活跃度 DESC;"
+  }
+]

data_pipeline/training_data/task_20250721_183935/qs_highway_db_20250721_201355_pair.json.backup

@@ -0,0 +1,202 @@
+[
+  {
+    "question": "最近7天各服务区的总收入和订单总数是多少？按收入降序排列。",
+    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总收入, SUM(order_sum) AS 订单总数 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总收入 DESC;"
+  },
+  {
+    "question": "昨日各档口的微信支付金额占比（占该档口总支付金额）是多少？仅显示占比超过50%的记录。",
+    "sql": "SELECT branch_name AS 档口名称, (wx / pay_sum * 100)::numeric(5,2) AS 微信支付占比 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND pay_sum > 0 AND delete_ts IS NULL HAVING (wx / pay_sum * 100) > 50 ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "过去30天内日均订单数最高的前5个服务区是哪些？",
+    "sql": "SELECT service_name AS 服务区名称, AVG(order_sum) AS 日均订单数 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '30 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 日均订单数 DESC LIMIT 5;"
+  },
+  {
+    "question": "今日各支付方式（微信、支付宝、现金）的总金额和订单数量分布如何？",
+    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 总金额, SUM(wx_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE AND delete_ts IS NULL UNION ALL SELECT '支付宝' AS 支付方式, SUM(zfb) AS 总金额, SUM(zf_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE AND delete_ts IS NULL UNION ALL SELECT '现金' AS 支付方式, SUM(rmb) AS 总金额, SUM(rmb_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE AND delete_ts IS NULL;"
+  },
+  {
+    "question": "昨日哪个档口的现金支付订单占比最高？",
+    "sql": "SELECT branch_name AS 档口名称, (rmb_order::numeric / order_sum * 100)::numeric(5,2) AS 现金订单占比 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND order_sum > 0 AND delete_ts IS NULL ORDER BY 现金订单占比 DESC LIMIT 1;"
+  },
+  {
+    "question": "本月至今各服务区的总支付金额环比上月同期增长百分比是多少？",
+    "sql": "WITH this_month AS (SELECT service_name, SUM(pay_sum) AS tm_sum FROM bss_business_day_data WHERE oper_date >= DATE_TRUNC('month', CURRENT_DATE) AND delete_ts IS NULL GROUP BY service_name), last_month AS (SELECT service_name, SUM(pay_sum) AS lm_sum FROM bss_business_day_data WHERE oper_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') AND oper_date < DATE_TRUNC('month', CURRENT_DATE) AND delete_ts IS NULL GROUP BY service_name) SELECT t.service_name AS 服务区名称, ROUND(((t.tm_sum - l.lm_sum) / l.lm_sum * 100), 2) AS 环比增长率 FROM this_month t JOIN last_month l ON t.service_name = l.service_name WHERE l.lm_sum > 0;"
+  },
+  {
+    "question": "最近一周订单总数最低的服务区是哪个？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(order_sum) AS 总订单数 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总订单数 ASC LIMIT 1;"
+  },
+  {
+    "question": "昨日收入最高的服务区及其档口明细（包括各项支付金额和订单数）？",
+    "sql": "SELECT service_name AS 服务区名称, branch_name AS 档口名称, wx AS 微信金额, wx_order AS 微信订单数, zfb AS 支付宝金额, zf_order AS 支付宝订单数, rmb AS 现金金额, rmb_order AS 现金订单数, pay_sum AS 总支付金额 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND delete_ts IS NULL ORDER BY pay_sum DESC LIMIT 1;"
+  },
+  {
+    "question": "过去7天内每天的全网总收入趋势如何？",
+    "sql": "SELECT oper_date AS 统计日期, SUM(pay_sum) AS 当日总收入 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "昨日订单总数超过100的档口中，行吧支付占比最高的前3名是哪些？",
+    "sql": "SELECT branch_name AS 档口名称, (xs_order::numeric / order_sum * 100)::numeric(5,2) AS 行吧支付占比 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - INTERVAL '1 day' AND order_sum > 100 AND delete_ts IS NULL ORDER BY 行吧支付占比 DESC LIMIT 3;"
+  },
+  {
+    "question": "各服务区在2023年过境车辆总量排名前5名是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cd.customer_count) AS 过境车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.car_type = '过境' AND cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 过境车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "2023年每月城际车辆总数趋势如何变化？",
+    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 城际车流量 FROM bss_car_day_count WHERE car_type = '城际' AND count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date) ORDER BY 月份;"
+  },
+  {
+    "question": "当前开放的服务区中，平均每日车流量最高的3个服务区是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cd.customer_count) AS 日均车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE sa.service_state = '开放' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 DESC LIMIT 3;"
+  },
+  {
+    "question": "2023年危化品车辆占比最高的服务区是哪个？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(CASE WHEN cd.car_type = '危化品' THEN cd.customer_count ELSE 0 END)::DECIMAL / SUM(cd.customer_count) AS 危化品占比 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 危化品占比 DESC LIMIT 1;"
+  },
+  {
+    "question": "近30天内各车型（过境、城际、危化品、其他）的总车流量对比情况如何？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - 30 AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
+  },
+  {
+    "question": "宜春分公司下属服务区2023年Q1车流量总和是多少？",
+    "sql": "SELECT SUM(cd.customer_count) AS 总车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '宜春分公司' AND cd.count_date BETWEEN '2023-01-01' AND '2023-03-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL;"
+  },
+  {
+    "question": "最近一周每天的过境与城际车辆数量变化趋势是怎样的？",
+    "sql": "SELECT count_date AS 统计日期, SUM(CASE WHEN car_type = '过境' THEN customer_count ELSE 0 END) AS 过境车流量, SUM(CASE WHEN car_type = '城际' THEN customer_count ELSE 0 END) AS 城际车流量 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY count_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "哪些服务区在过去一个月中从未出现危化品车辆？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称 FROM bss_service_area sa WHERE sa.id NOT IN (SELECT DISTINCT service_area_id FROM bss_car_day_count WHERE car_type = '危化品' AND count_date >= CURRENT_DATE - 30 AND delete_ts IS NULL) AND sa.delete_ts IS NULL;"
+  },
+  {
+    "question": "2023年单日车流量最大值出现在哪一天？对应的服务区和车流量是多少？",
+    "sql": "SELECT cd.count_date AS 统计日期, sa.service_area_name AS 服务区名称, cd.customer_count AS 车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL ORDER BY cd.customer_count DESC LIMIT 1;"
+  },
+  {
+    "question": "每个服务区2023年各类车型的车流量分布明细（按服务区分组）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, cd.car_type AS 车辆类型, SUM(cd.customer_count) AS 车流量 FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cd.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name, cd.car_type ORDER BY 服务区名称, 车流量 DESC;"
+  },
+  {
+    "question": "各公司近30天总营收排名（万元）？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.pay_sum) / 10000 AS 总营收_万元 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND b.oper_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY c.id, c.company_name ORDER BY 总营收_万元 DESC;"
+  },
+  {
+    "question": "各公司最近一天订单量与人均消费对比（排除无订单公司）？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.order_sum) AS 订单总量, CASE WHEN SUM(b.order_sum) > 0 THEN ROUND(SUM(b.pay_sum) / SUM(b.order_sum), 2) ELSE 0 END AS 人均消费 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND b.oper_date = (SELECT MAX(oper_date) FROM bss_business_day_data WHERE delete_ts IS NULL) GROUP BY c.id, c.company_name HAVING SUM(b.order_sum) > 0 ORDER BY 订单总量 DESC;"
+  },
+  {
+    "question": "上月各公司现金支付占比最高的前三名？",
+    "sql": "SELECT company_name AS 公司名称, ROUND(SUM(rmb) / NULLIF(SUM(pay_sum), 0) * 100, 2) AS 现金支付占比 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND DATE_TRUNC('month', b.oper_date) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') GROUP BY c.id, c.company_name ORDER BY 现金支付占比 DESC LIMIT 3;"
+  },
+  {
+    "question": "当前开放的服务区中，各公司平均每日营收（万元）？",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(daily_revenue) AS 平均日营收_万元 FROM (SELECT s.company_id, b.oper_date, SUM(b.pay_sum) / 10000 AS daily_revenue FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND s.service_state = '开放' GROUP BY s.company_id, b.oper_date) sub JOIN bss_company c ON sub.company_id = c.id WHERE c.delete_ts IS NULL GROUP BY c.id, c.company_name ORDER BY 平均日营收_万元 DESC;"
+  },
+  {
+    "question": "本月各公司微信支付订单量环比上月变化率（%）？",
+    "sql": "WITH current_month AS (SELECT c.company_name, SUM(wx_order) AS wx_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND DATE_TRUNC('month', b.oper_date) = DATE_TRUNC('month', CURRENT_DATE) GROUP BY c.company_name), last_month AS (SELECT c.company_name, SUM(wx_order) AS wx_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL AND DATE_TRUNC('month', b.oper_date) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') GROUP BY c.company_name) SELECT cm.company_name AS 公司名称, ROUND((cm.wx_orders - COALESCE(lm.wx_orders, 0)) * 100.0 / NULLIF(lm.wx_orders, 0), 2) AS 微信订单环比变化率 FROM current_month cm LEFT JOIN last_month lm ON cm.company_name = lm.company_name ORDER BY 微信订单环比变化率 DESC;"
+  },
+  {
+    "question": "各公司订单总量前五的服务区明细（公司+服务区+订单量）？",
+    "sql": "SELECT c.company_name AS 公司名称, b.service_name AS 服务区名称, SUM(b.order_sum) AS 订单总量 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.id, c.company_name, b.service_name ORDER BY 订单总量 DESC LIMIT 5;"
+  },
+  {
+    "question": "各公司最近7天日均订单量与历史日均订单量对比（%）？",
+    "sql": "WITH recent AS (SELECT s.company_id, AVG(b.order_sum) AS avg_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND b.oper_date >= CURRENT_DATE - 7 GROUP BY s.company_id), historical AS (SELECT s.company_id, AVG(b.order_sum) AS avg_orders FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.company_id) SELECT c.company_name AS 公司名称, ROUND((r.avg_orders - h.avg_orders) * 100.0 / NULLIF(h.avg_orders, 0), 2) AS 近七天相对历史变化率 FROM recent r JOIN historical h ON r.company_id = h.company_id JOIN bss_company c ON r.company_id = c.id WHERE c.delete_ts IS NULL ORDER BY 近七天相对历史变化率 DESC;"
+  },
+  {
+    "question": "各公司支付宝支付金额占总支付金额比例超过10%的公司列表？",
+    "sql": "SELECT c.company_name AS 公司名称, ROUND(SUM(b.zfb) / SUM(b.pay_sum) * 100, 2) AS 支付宝占比 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.id, c.company_name HAVING SUM(b.pay_sum) > 0 AND SUM(b.zfb) / SUM(b.pay_sum) > 0.1 ORDER BY 支付宝占比 DESC;"
+  },
+  {
+    "question": "各公司最近一个月无营收记录的服务区数量？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(DISTINCT s.service_area_no) AS 零营收服务区数 FROM bss_service_area s JOIN bss_company c ON s.company_id = c.id LEFT JOIN bss_business_day_data b ON s.service_area_no = b.service_no AND b.oper_date >= CURRENT_DATE - INTERVAL '1 month' AND b.delete_ts IS NULL WHERE s.delete_ts IS NULL AND c.delete_ts IS NULL AND b.id IS NULL GROUP BY c.id, c.company_name ORDER BY 零营收服务区数 DESC;"
+  },
+  {
+    "question": "各公司订单量最高的单日数据（公司+日期+订单量）？",
+    "sql": "SELECT company_name AS 公司名称, oper_date AS 日期, max_orders AS 最高单日订单量 FROM (SELECT c.company_name, b.oper_date, SUM(b.order_sum) AS max_orders, ROW_NUMBER() OVER (PARTITION BY c.id ORDER BY SUM(b.order_sum) DESC) AS rn FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.id, c.company_name, b.oper_date) sub WHERE rn = 1 ORDER BY 最高单日订单量 DESC;"
+  },
+  {
+    "question": "各路段下辖服务区的平均车流量是多少？按车流量从高到低排序。",
+    "sql": "SELECT sr.section_name AS 路段名称, AVG(cd.customer_count) AS 平均车流量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND cd.delete_ts IS NULL GROUP BY sr.section_name ORDER BY 平均车流量 DESC;"
+  },
+  {
+    "question": "每条路线中单位车流转化收入最高的服务区是哪个？（仅统计开放状态的服务区）",
+    "sql": "SELECT t.route_name AS 路线名称, t.service_name AS 服务区名称, t.unit_income AS 单位车流转化收入 FROM (SELECT sr.route_name, sa.service_area_name AS service_name, SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) AS unit_income, ROW_NUMBER() OVER (PARTITION BY sr.route_name ORDER BY SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) DESC) AS rn FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_car_day_count cd ON sa.id = cd.service_area_id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND sa.delete_ts IS NULL AND cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY sr.route_name, sa.service_area_name) t WHERE t.rn = 1;"
+  },
+  {
+    "question": "近30天内哪些路段的综合活跃度（车流+消费）最高？取前5名。",
+    "sql": "SELECT sr.section_name AS 路段名称, SUM(cd.customer_count) AS 总车流量, SUM(bd.pay_sum) AS 总收入, (SUM(cd.customer_count) + SUM(bd.pay_sum)) AS 综合活跃度 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id JOIN bss_business_day_data bd ON cd.count_date = bd.oper_date JOIN bss_service_area sa ON link.service_area_id = sa.id WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND cd.count_date >= CURRENT_DATE - 30 AND bd.oper_date >= CURRENT_DATE - 30 GROUP BY sr.section_name ORDER BY 综合活跃度 DESC LIMIT 5;"
+  },
+  {
+    "question": "昌栗路段下辖各服务区的日均车流量和日均收入对比如何？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cd.customer_count) AS 日均车流量, AVG(bd.pay_sum) AS 日均收入 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_car_day_count cd ON sa.id = cd.service_area_id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND sa.delete_ts IS NULL AND cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sr.section_name = '昌栗' GROUP BY sa.service_area_name;"
+  },
+  {
+    "question": "不同车辆类型的车流量分布情况如何？按路段分类统计。",
+    "sql": "SELECT sr.section_name AS 路段名称, cd.car_type AS 车辆类型, SUM(cd.customer_count) AS 总车流量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND cd.delete_ts IS NULL GROUP BY sr.section_name, cd.car_type ORDER BY 路段名称, 总车流量 DESC;"
+  },
+  {
+    "question": "本月各路线的总车流量和总收入趋势变化（按日聚合）？",
+    "sql": "SELECT sr.route_name AS 路线名称, cd.count_date AS 统计日期, SUM(cd.customer_count) AS 日车流量, SUM(bd.pay_sum) AS 日收入 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id JOIN bss_business_day_data bd ON cd.count_date = bd.oper_date AND cd.service_area_id IN (SELECT service_area_id FROM bss_service_area_mapper WHERE service_no = bd.service_no) WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND cd.count_date >= DATE_TRUNC('month', CURRENT_DATE) GROUP BY sr.route_name, cd.count_date ORDER BY 路线名称, 统计日期;"
+  },
+  {
+    "question": "哪些服务区的单位车流转化收入低于整体平均水平？列出具体数值。",
+    "sql": "WITH overall_avg AS (SELECT AVG(SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0)) AS avg_unit_income FROM bss_car_day_count cd JOIN bss_business_day_data bd ON cd.count_date = bd.oper_date JOIN bss_service_area sa ON cd.service_area_id = sa.id JOIN bss_service_area_mapper sm ON sa.id = sm.service_area_id WHERE cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND sm.delete_ts IS NULL GROUP BY sa.service_area_name) SELECT sa.service_area_name AS 服务区名称, SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) AS 单位车流转化收入 FROM bss_car_day_count cd JOIN bss_business_day_data bd ON cd.count_date = bd.oper_date JOIN bss_service_area sa ON cd.service_area_id = sa.id JOIN bss_service_area_mapper sm ON sa.id = sm.service_area_id WHERE cd.delete_ts IS NULL AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND sm.delete_ts IS NULL GROUP BY sa.service_area_name HAVING SUM(bd.pay_sum) / NULLIF(SUM(cd.customer_count), 0) < (SELECT avg_unit_income FROM overall_avg);"
+  },
+  {
+    "question": "昌宁路段下辖服务区中，订单总数排名前三的档口是哪些？",
+    "sql": "SELECT bd.branch_name AS 档口名称, SUM(bd.order_sum) AS 订单总数 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND sa.delete_ts IS NULL AND bd.delete_ts IS NULL AND sr.section_name = '昌宁' GROUP BY bd.branch_name ORDER BY 订单总数 DESC LIMIT 3;"
+  },
+  {
+    "question": "最近一周内，各路段的日均车流量波动幅度（标准差）是多少？",
+    "sql": "SELECT sr.section_name AS 路段名称, STDDEV(cd.customer_count) AS 车流量标准差 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_car_day_count cd ON link.service_area_id = cd.service_area_id WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND cd.delete_ts IS NULL AND cd.count_date >= CURRENT_DATE - 7 GROUP BY sr.section_name ORDER BY 车流量标准差 DESC;"
+  },
+  {
+    "question": "哪些路段存在服务区处于关闭状态？列出这些路段及其关闭的服务区数量。",
+    "sql": "SELECT sr.section_name AS 路段名称, COUNT(sa.id) AS 关闭服务区数量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id WHERE sr.delete_ts IS NULL AND link.delete_ts IS NULL AND sa.delete_ts IS NULL AND sa.service_state = '关闭' GROUP BY sr.section_name HAVING COUNT(sa.id) > 0;"
+  },
+  {
+    "question": "不同数据来源系统（如驿购、驿美）每日记录数分布情况如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, oper_date AS 统计日期, COUNT(*) AS 记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type, oper_date ORDER BY 统计日期 DESC, 记录数 DESC;"
+  },
+  {
+    "question": "各数据来源系统的平均每日更新频率（记录数/天）是多少？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*)::float / COUNT(DISTINCT oper_date) AS 平均每日记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type ORDER BY 平均每日记录数 DESC;"
+  },
+  {
+    "question": "哪些服务区在所有数据来源系统中均有覆盖？即来源系统覆盖率100%的服务区",
+    "sql": "SELECT service_name AS 服务区名称 FROM (SELECT service_name, COUNT(DISTINCT source_system_type) AS 系统数量 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY service_name) t WHERE 系统数量 = (SELECT COUNT(DISTINCT source_system_type) FROM bss_service_area_mapper WHERE delete_ts IS NULL);"
+  },
+  {
+    "question": "最近7天内，哪个数据来源系统新增记录最多？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 新增记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL AND oper_date >= CURRENT_DATE - 7 GROUP BY source_system_type ORDER BY 新增记录数 DESC LIMIT 1;"
+  },
+  {
+    "question": "各数据来源系统的订单总数和总支付金额对比如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, SUM(order_sum) AS 总订单数, SUM(pay_sum) AS 总支付金额 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type ORDER BY 总支付金额 DESC;"
+  },
+  {
+    "question": "过去30天内，数据来源系统为'驿购'的日均记录数变化趋势是怎样的？",
+    "sql": "SELECT oper_date AS 统计日期, COUNT(*) AS 日记录数 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL AND source_system_type = '驿购' AND oper_date >= CURRENT_DATE - 30 GROUP BY oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "数据来源系统中，哪个系统的记录缺失率最高（即未填充值字段比例）？",
+    "sql": "SELECT source_system_type AS 数据来源系统, AVG(CASE WHEN wx IS NULL THEN 1 ELSE 0 END) AS 微信缺失率, AVG(CASE WHEN zfb IS NULL THEN 1 ELSE 0 END) AS 支付宝缺失率 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type ORDER BY 微信缺失率 DESC LIMIT 1;"
+  },
+  {
+    "question": "每个数据来源系统下，记录数最多的前3个服务区是哪些？",
+    "sql": "WITH ranked AS (SELECT source_system_type AS 数据来源系统, service_name AS 服务区名称, COUNT(*) AS 记录数, ROW_NUMBER() OVER (PARTITION BY source_system_type ORDER BY COUNT(*) DESC) AS rn FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY source_system_type, service_name) SELECT 数据来源系统, 服务区名称, 记录数 FROM ranked WHERE rn <= 3 ORDER BY 数据来源系统, rn;"
+  },
+  {
+    "question": "不同数据来源系统的更新活跃度（最近30天有数据的天数占比）如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(DISTINCT oper_date)::float / 30 AS 活跃度 FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL AND oper_date >= CURRENT_DATE - 30 GROUP BY source_system_type ORDER BY 活跃度 DESC;"
+  },
+  {
+    "question": "是否存在某些服务区仅由单一数据来源系统提供数据？这些服务区名称及对应系统是什么？",
+    "sql": "SELECT service_name AS 服务区名称, MAX(source_system_type) AS 唯一来源系统 FROM (SELECT service_name, source_system_type FROM bss_business_day_data b JOIN bss_service_area_mapper m ON b.service_no = m.service_no WHERE b.delete_ts IS NULL AND m.delete_ts IS NULL GROUP BY service_name, source_system_type) t GROUP BY service_name HAVING COUNT(*) = 1;"
+  }
+]

data_pipeline/training_data/task_20250721_183935/task_result.json

@@ -0,0 +1,169 @@
+{
+  "success": true,
+  "workflow_state": {
+    "start_time": null,
+    "end_time": null,
+    "current_step": "training_data_load",
+    "completed_steps": [
+      "ddl_md_generation",
+      "question_sql_generation",
+      "sql_validation",
+      "training_data_load"
+    ],
+    "failed_steps": [],
+    "artifacts": {
+      "ddl_md_generation": {
+        "total_tables": 7,
+        "processed_successfully": 7,
+        "failed": 0,
+        "files_generated": 14,
+        "duration": 90.35672664642334
+      },
+      "question_sql_generation": {
+        "output_file": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_183935\\qs_highway_db_20250721_201355_pair.json",
+        "total_questions": 50,
+        "total_themes": 5,
+        "successful_themes": 5,
+        "failed_themes": [],
+        "duration": 177.6981644630432
+      },
+      "sql_validation": {
+        "original_sql_count": 50,
+        "valid_sql_count": 47,
+        "invalid_sql_count": 3,
+        "success_rate": 0.94,
+        "repair_stats": {
+          "attempted": 14,
+          "successful": 11,
+          "failed": 3
+        },
+        "file_modification_stats": {
+          "modified": 11,
+          "deleted": 3,
+          "failed_modifications": 0
+        },
+        "average_execution_time": 0.0292741060256958,
+        "total_retries": 0,
+        "duration": 40.420435190200806
+      },
+      "vector_management": {
+        "backup_performed": true,
+        "truncate_performed": true,
+        "tables_backed_up": {
+          "langchain_pg_collection": {
+            "success": true,
+            "row_count": 4,
+            "file_size": "209.0 B",
+            "backup_file": "langchain_pg_collection_20250721_201447.csv",
+            "duration": 0.10566401481628418
+          },
+          "langchain_pg_embedding": {
+            "success": true,
+            "row_count": 251,
+            "file_size": "3.2 MB",
+            "backup_file": "langchain_pg_embedding_20250721_201447.csv",
+            "duration": 0.31218457221984863
+          }
+        },
+        "truncate_results": {
+          "langchain_pg_embedding": {
+            "success": true,
+            "rows_before": 251,
+            "rows_after": 0,
+            "duration": 0.06867551803588867
+          }
+        },
+        "errors": [],
+        "backup_directory": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_183935\\vector_bak",
+        "duration": 0.498615026473999
+      },
+      "training_data_load": {
+        "training_data_dir": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_183935",
+        "load_successful": true,
+        "total_records": 0,
+        "data_type_counts": {},
+        "duration": 4.831601619720459
+      }
+    },
+    "statistics": {
+      "step1_duration": 90.35672664642334,
+      "step2_duration": 177.6981644630432,
+      "step3_duration": 40.420435190200806,
+      "step4_duration": 4.831601619720459
+    }
+  },
+  "artifacts": {
+    "ddl_md_generation": {
+      "total_tables": 7,
+      "processed_successfully": 7,
+      "failed": 0,
+      "files_generated": 14,
+      "duration": 90.35672664642334
+    },
+    "question_sql_generation": {
+      "output_file": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_183935\\qs_highway_db_20250721_201355_pair.json",
+      "total_questions": 50,
+      "total_themes": 5,
+      "successful_themes": 5,
+      "failed_themes": [],
+      "duration": 177.6981644630432
+    },
+    "sql_validation": {
+      "original_sql_count": 50,
+      "valid_sql_count": 47,
+      "invalid_sql_count": 3,
+      "success_rate": 0.94,
+      "repair_stats": {
+        "attempted": 14,
+        "successful": 11,
+        "failed": 3
+      },
+      "file_modification_stats": {
+        "modified": 11,
+        "deleted": 3,
+        "failed_modifications": 0
+      },
+      "average_execution_time": 0.0292741060256958,
+      "total_retries": 0,
+      "duration": 40.420435190200806
+    },
+    "vector_management": {
+      "backup_performed": true,
+      "truncate_performed": true,
+      "tables_backed_up": {
+        "langchain_pg_collection": {
+          "success": true,
+          "row_count": 4,
+          "file_size": "209.0 B",
+          "backup_file": "langchain_pg_collection_20250721_201447.csv",
+          "duration": 0.10566401481628418
+        },
+        "langchain_pg_embedding": {
+          "success": true,
+          "row_count": 251,
+          "file_size": "3.2 MB",
+          "backup_file": "langchain_pg_embedding_20250721_201447.csv",
+          "duration": 0.31218457221984863
+        }
+      },
+      "truncate_results": {
+        "langchain_pg_embedding": {
+          "success": true,
+          "rows_before": 251,
+          "rows_after": 0,
+          "duration": 0.06867551803588867
+        }
+      },
+      "errors": [],
+      "backup_directory": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_183935\\vector_bak",
+      "duration": 0.498615026473999
+    },
+    "training_data_load": {
+      "training_data_dir": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_183935",
+      "load_successful": true,
+      "total_records": 0,
+      "data_type_counts": {},
+      "duration": 4.831601619720459
+    }
+  }
+}

data_pipeline/training_data/task_20250721_183935/vector_bak/langchain_pg_collection_20250721_201447.csv

@@ -0,0 +1,5 @@
+uuid,name,cmetadata
+f4e11877-44e7-4741-b511-fa0e2e399395,sql,
+f0b714ca-44a9-433a-8768-390740bd1a18,ddl,
+98b97e3a-752d-4115-9667-7635687dbc1c,documentation,
+ab83ab0a-5649-4722-984d-b093227cdb02,error_sql,

data_pipeline/training_data/task_20250721_183935/vector_bak/vector_backup_log.txt

@@ -0,0 +1,11 @@
+=== Vector Table Backup Log ===
+Backup Time: 2025-07-21 20:14:47
+Task ID: task_20250721_183935
+Duration: 0.00s
+
+Tables Backup Status:
+✓ langchain_pg_collection: 4 rows -> langchain_pg_collection_20250721_201447.csv (209.0 B)
+✓ langchain_pg_embedding: 251 rows -> langchain_pg_embedding_20250721_201447.csv (3.2 MB)
+
+Truncate Status:
+✓ langchain_pg_embedding: TRUNCATED (251 rows removed)

data_pipeline/training_data/manual_20250720_130541/bss_business_day_data.ddl → data_pipeline/training_data/task_20250721_202929/bss_business_day_data.ddl

@@ -1,8 +1,8 @@
--- 中文名: `bss_business_day_data` 表用于记录高速公路服务区每日经营数据
--- 描述: `bss_business_day_data` 表用于记录高速公路服务区每日经营数据，支持业务分析与统计。
+-- 中文名: 高速公路服务区每日经营数据记录表
+-- 描述: 高速公路服务区每日经营数据记录表，用于统计各服务区按日维度的运营情况。
 create table public.bss_business_day_data (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -21,11 +21,11 @@ create table public.bss_business_day_data (
   rmb numeric(19,4)           -- 现金支付金额,
   rmb_order integer           -- 现金订单数量,
   xs numeric(19,4)            -- 行吧支付金额,
-  xs_order integer            -- 行吧订单数量,
+  xs_order integer            -- 行吧支付订单数,
   jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
+  jd_order integer            -- 金豆支付订单数,
   order_sum integer           -- 订单总数,
-  pay_sum numeric(19,4)       -- 支付总金额,
+  pay_sum numeric(19,4)       -- 总支付金额,
   source_type integer         -- 数据来源类别,
   primary key (id)
 );

data_pipeline/training_data/manual_20250720_130541/bss_business_day_data_detail.md → data_pipeline/training_data/task_20250721_202929/bss_business_day_data_detail.md

@@ -1,8 +1,8 @@
-## bss_business_day_data（`bss_business_day_data` 表用于记录高速公路服务区每日经营数据）
-bss_business_day_data 表`bss_business_day_data` 表用于记录高速公路服务区每日经营数据，支持业务分析与统计。
+## bss_business_day_data（高速公路服务区每日经营数据记录表）
+bss_business_day_data 表高速公路服务区每日经营数据记录表，用于统计各服务区按日维度的运营情况。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820]
-- version (integer) - 版本号 [非空] [示例: 1]
+- version (integer) - 数据版本号 [非空] [示例: 1]
 - create_ts (timestamp) - 创建时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
 - created_by (varchar(50)) - 创建人 [示例: xingba]
 - update_ts (timestamp) - 更新时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
@@ -21,11 +21,11 @@ bss_business_day_data 表`bss_business_day_data` 表用于记录高速公路服
 - rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000]
 - rmb_order (integer) - 现金订单数量 [示例: 56, 12]
 - xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000]
-- xs_order (integer) - 行吧订单数量 [示例: 0, 1]
+- xs_order (integer) - 行吧支付订单数 [示例: 0, 1]
 - jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000]
-- jd_order (integer) - 金豆订单数量 [示例: 0]
+- jd_order (integer) - 金豆支付订单数 [示例: 0]
 - order_sum (integer) - 订单总数 [示例: 324, 146]
-- pay_sum (numeric(19,4)) - 支付总金额 [示例: 6077.5000, 2687.0000]
+- pay_sum (numeric(19,4)) - 总支付金额 [示例: 6077.5000, 2687.0000]
 - source_type (integer) - 数据来源类别 [示例: 1, 0, 4]
 字段补充说明：
 - id 为主键

data_pipeline/training_data/manual_20250720_130541/bss_car_day_count.ddl → data_pipeline/training_data/task_20250721_202929/bss_car_day_count.ddl

@@ -1,16 +1,16 @@
--- 中文名: `bss_car_day_count` 表用于**按天统计高速公路服务区车辆数量及类型**
--- 描述: `bss_car_day_count` 表用于**按天统计高速公路服务区车辆数量及类型**，支持车流分析与运营决策。
+-- 中文名: 高速公路服务区每日车辆统计表
+-- 描述: 高速公路服务区每日车辆统计表，记录各类型车辆流量数据。
 create table public.bss_car_day_count (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
   updated_by varchar(50)      -- 更新人,
   delete_ts timestamp         -- 删除时间,
   deleted_by varchar(50)      -- 删除人,
-  customer_count bigint       -- 车辆数量,
-  car_type varchar(100)       -- 车辆类别,
+  customer_count bigint       -- 车辆通行数量,
+  car_type varchar(100)       -- 车辆类型,
   count_date date             -- 统计日期,
   service_area_id varchar(32) -- 服务区ID,
   primary key (id)

data_pipeline/training_data/manual_20250720_130541/bss_car_day_count_detail.md → data_pipeline/training_data/task_20250721_202929/bss_car_day_count_detail.md

@@ -1,16 +1,16 @@
-## bss_car_day_count（`bss_car_day_count` 表用于**按天统计高速公路服务区车辆数量及类型**）
-bss_car_day_count 表`bss_car_day_count` 表用于**按天统计高速公路服务区车辆数量及类型**，支持车流分析与运营决策。
+## bss_car_day_count（高速公路服务区每日车辆统计表）
+bss_car_day_count 表高速公路服务区每日车辆统计表，记录各类型车辆流量数据。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc]
-- version (integer) - 版本号 [非空] [示例: 1]
+- version (integer) - 数据版本号 [非空] [示例: 1]
 - create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
 - created_by (varchar(50)) - 创建人
 - update_ts (timestamp) - 更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
 - updated_by (varchar(50)) - 更新人
 - delete_ts (timestamp) - 删除时间
 - deleted_by (varchar(50)) - 删除人
-- customer_count (bigint) - 车辆数量 [示例: 1114, 295]
-- car_type (varchar(100)) - 车辆类别 [示例: 其他]
+- customer_count (bigint) - 车辆通行数量 [示例: 1114, 295]
+- car_type (varchar(100)) - 车辆类型 [示例: 其他]
 - count_date (date) - 统计日期 [示例: 2022-03-02, 2022-02-02]
 - service_area_id (varchar(32)) - 服务区ID [示例: 17461166e7fa3ecda03534a5795ce985, 81f4eb731fb0728aef17ae61f1f1daef]
 字段补充说明：

data_pipeline/training_data/task_20250721_202929/bss_company.ddl

@@ -0,0 +1,15 @@
+-- 中文名: 高速公路服务区管理系统中的公司信息表
+-- 描述: 高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及操作审计字段。
+create table public.bss_company (
+  id varchar(32) not null     -- 公司唯一标识符，主键,
+  version integer not null    -- 数据版本号,
+  create_ts timestamp         -- 创建时间,
+  created_by varchar(50)      -- 创建人,
+  update_ts timestamp         -- 更新时间,
+  updated_by varchar(50)      -- 最后更新人,
+  delete_ts timestamp         -- 删除时间,
+  deleted_by varchar(50)      -- 删除操作人,
+  company_name varchar(255)   -- 公司名称,
+  company_no varchar(255)     -- 公司编码,
+  primary key (id)
+);

data_pipeline/training_data/task_20250721_202929/bss_company_detail.md

@@ -0,0 +1,16 @@
+## bss_company（高速公路服务区管理系统中的公司信息表）
+bss_company 表高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及操作审计字段。
+字段列表：
+- id (varchar(32)) - 公司唯一标识符 [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
+- version (integer) - 数据版本号 [非空] [示例: 1, 2]
+- create_ts (timestamp) - 创建时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
+- created_by (varchar(50)) - 创建人 [示例: admin]
+- update_ts (timestamp) - 更新时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
+- updated_by (varchar(50)) - 最后更新人 [示例: admin]
+- delete_ts (timestamp) - 删除时间
+- deleted_by (varchar(50)) - 删除操作人
+- company_name (varchar(255)) - 公司名称 [示例: 上饶分公司, 宜春分公司]
+- company_no (varchar(255)) - 公司编码 [示例: H03, H02, H07]
+字段补充说明：
+- id 为主键
+- company_no 为枚举字段，包含取值：H01、H02、H03、H04、H05、H06、H07、H08、Q01

data_pipeline/training_data/task_20250721_202929/bss_section_route.ddl

@@ -0,0 +1,16 @@
+-- 中文名: 高速公路路段与路线关联信息表
+-- 描述: 高速公路路段与路线关联信息表，用于管理服务区所属路段及路线关系。
+create table public.bss_section_route (
+  id varchar(32) not null     -- 主键ID，主键,
+  version integer not null    -- 数据版本号,
+  create_ts timestamp         -- 创建时间,
+  created_by varchar(50)      -- 创建人,
+  update_ts timestamp         -- 更新时间,
+  updated_by varchar(50)      -- 更新人,
+  delete_ts timestamp         -- 删除时间,
+  deleted_by varchar(50)      -- 删除人,
+  section_name varchar(255)   -- 路段名称,
+  route_name varchar(255)     -- 路线名称,
+  code varchar(255)           -- 路段编号,
+  primary key (id)
+);

data_pipeline/training_data/task_20250721_202929/bss_section_route_area_link.ddl

@@ -0,0 +1,7 @@
+-- 中文名: 高速公路路线与服务区关联表
+-- 描述: 高速公路路线与服务区关联表，用于管理路段和服务区的对应关系。
+create table public.bss_section_route_area_link (
+  section_route_id varchar(32) not null -- 路段路线唯一标识，主键,
+  service_area_id varchar(32) not null -- 服务区唯一标识，主键,
+  primary key (section_route_id, service_area_id)
+);

data_pipeline/training_data/task_20250721_202929/bss_section_route_area_link_detail.md

@@ -0,0 +1,7 @@
+## bss_section_route_area_link（高速公路路线与服务区关联表）
+bss_section_route_area_link 表高速公路路线与服务区关联表，用于管理路段和服务区的对应关系。
+字段列表：
+- section_route_id (varchar(32)) - 路段路线唯一标识 [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp]
+- service_area_id (varchar(32)) - 服务区唯一标识 [主键, 非空] [示例: 08e01d7402abd1d6a4d9fdd5df855ef8, 091662311d2c737029445442ff198c4c]
+字段补充说明：
+- 复合主键：section_route_id, service_area_id

data_pipeline/training_data/task_20250721_202929/bss_section_route_detail.md

@@ -0,0 +1,17 @@
+## bss_section_route（高速公路路段与路线关联信息表）
+bss_section_route 表高速公路路段与路线关联信息表，用于管理服务区所属路段及路线关系。
+字段列表：
+- id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7]
+- version (integer) - 数据版本号 [非空] [示例: 1, 0]
+- create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16]
+- created_by (varchar(50)) - 创建人 [示例: admin]
+- update_ts (timestamp) - 更新时间
+- updated_by (varchar(50)) - 更新人
+- delete_ts (timestamp) - 删除时间
+- deleted_by (varchar(50)) - 删除人
+- section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁]
+- route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶]
+- code (varchar(255)) - 路段编号 [示例: SR0001, SR0002]
+字段补充说明：
+- id 为主键
+- created_by 为枚举字段，包含取值：admin

data_pipeline/training_data/manual_20250720_130541/bss_service_area.ddl → data_pipeline/training_data/task_20250721_202929/bss_service_area.ddl

@@ -1,8 +1,8 @@
--- 中文名: `bss_service_area` 表用于存储高速公路服务区基本信息
--- 描述: `bss_service_area` 表用于存储高速公路服务区基本信息，包括名称、编码及操作记录，为核心业务管理提供数据支撑。
+-- 中文名: 高速公路服务区基础信息表
+-- 描述: 高速公路服务区基础信息表，存储服务区名称、编码及变更记录。
 create table public.bss_service_area (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  id varchar(32) not null     -- 服务区唯一标识，主键,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -12,8 +12,8 @@ create table public.bss_service_area (
   service_area_name varchar(255) -- 服务区名称,
   service_area_no varchar(255) -- 服务区编码,
   company_id varchar(32)      -- 所属公司ID,
-  service_position varchar(255) -- 服务区经纬度,
+  service_position varchar(255) -- 经纬度坐标,
   service_area_type varchar(50) -- 服务区类型,
-  service_state varchar(50)   -- 服务区状态,
+  service_state varchar(50)   -- 运营状态,
   primary key (id)
 );

data_pipeline/training_data/manual_20250720_130541/bss_service_area_detail.md → data_pipeline/training_data/task_20250721_202929/bss_service_area_detail.md

@@ -1,8 +1,8 @@
-## bss_service_area（`bss_service_area` 表用于存储高速公路服务区基本信息）
-bss_service_area 表`bss_service_area` 表用于存储高速公路服务区基本信息，包括名称、编码及操作记录，为核心业务管理提供数据支撑。
+## bss_service_area（高速公路服务区基础信息表）
+bss_service_area 表高速公路服务区基础信息表，存储服务区名称、编码及变更记录。
 字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8]
-- version (integer) - 版本号 [非空] [示例: 3, 6]
+- id (varchar(32)) - 服务区唯一标识 [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8]
+- version (integer) - 数据版本号 [非空] [示例: 3, 6]
 - create_ts (timestamp) - 创建时间 [示例: 2021-05-21 13:26:40.589000, 2021-05-20 19:51:46.314000]
 - created_by (varchar(50)) - 创建人 [示例: admin]
 - update_ts (timestamp) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000]
@@ -12,9 +12,9 @@ bss_service_area 表`bss_service_area` 表用于存储高速公路服务区基
 - service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区, 南昌南服务区]
 - service_area_no (varchar(255)) - 服务区编码 [示例: H0814, H0105]
 - company_id (varchar(32)) - 所属公司ID [示例: b1629f07c8d9ac81494fbc1de61f1ea5, ee9bf1180a2b45003f96e597a4b7f15a]
-- service_position (varchar(255)) - 服务区经纬度 [示例: 114.574721,26.825584, 115.910549,28.396355]
+- service_position (varchar(255)) - 经纬度坐标 [示例: 114.574721,26.825584, 115.910549,28.396355]
 - service_area_type (varchar(50)) - 服务区类型 [示例: 信息化服务区]
-- service_state (varchar(50)) - 服务区状态 [示例: 开放, 关闭]
+- service_state (varchar(50)) - 运营状态 [示例: 开放, 关闭]
 字段补充说明：
 - id 为主键
 - service_area_type 为枚举字段，包含取值：信息化服务区、智能化服务区

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_service_area_mapper.ddl → data_pipeline/training_data/task_20250721_202929/bss_service_area_mapper.ddl

@@ -1,8 +1,8 @@
--- 中文名: 服务区基础信息映射表
--- 描述: 服务区基础信息映射表，用于统一管理全国高速公路服务区的名称与编码对应关系。
+-- 中文名: 服务区信息映射表
+-- 描述: 服务区信息映射表，用于管理高速公路上各服务区的唯一编码与名称对应关系。
 create table public.bss_service_area_mapper (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -11,8 +11,8 @@ create table public.bss_service_area_mapper (
   deleted_by varchar(50)      -- 删除人,
   service_name varchar(255)   -- 服务区名称,
   service_no varchar(255)     -- 服务区编码,
-  service_area_id varchar(32) -- 服务区ID,
-  source_system_type varchar(50) -- 数据来源类别名称,
+  service_area_id varchar(32) -- 服务区业务ID,
+  source_system_type varchar(50) -- 数据来源系统类型,
   source_type integer         -- 数据来源类别ID,
   primary key (id)
 );

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_service_area_mapper_detail.md → data_pipeline/training_data/task_20250721_202929/bss_service_area_mapper_detail.md

@@ -1,8 +1,8 @@
-## bss_service_area_mapper（服务区基础信息映射表）
-bss_service_area_mapper 表服务区基础信息映射表，用于统一管理全国高速公路服务区的名称与编码对应关系。
+## bss_service_area_mapper（服务区信息映射表）
+bss_service_area_mapper 表服务区信息映射表，用于管理高速公路上各服务区的唯一编码与名称对应关系。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00e1e893909211ed8ee6fa163eaf653f, 013867f5962211ed8ee6fa163eaf653f]
-- version (integer) - 版本号 [非空] [示例: 1]
+- version (integer) - 数据版本号 [非空] [示例: 1]
 - create_ts (timestamp) - 创建时间 [示例: 2023-01-10 10:54:03, 2023-01-17 12:47:29]
 - created_by (varchar(50)) - 创建人 [示例: admin]
 - update_ts (timestamp) - 更新时间 [示例: 2023-01-10 10:54:07, 2023-01-17 12:47:32]
@@ -11,8 +11,8 @@ bss_service_area_mapper 表服务区基础信息映射表，用于统一管理
 - deleted_by (varchar(50)) - 删除人
 - service_name (varchar(255)) - 服务区名称 [示例: 信丰西服务区, 南康北服务区]
 - service_no (varchar(255)) - 服务区编码 [示例: 1067, 1062]
-- service_area_id (varchar(32)) - 服务区ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f]
-- source_system_type (varchar(50)) - 数据来源类别名称 [示例: 驿美, 驿购]
+- service_area_id (varchar(32)) - 服务区业务ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f]
+- source_system_type (varchar(50)) - 数据来源系统类型 [示例: 驿美, 驿购]
 - source_type (integer) - 数据来源类别ID [示例: 3, 1]
 字段补充说明：
 - id 为主键

data_pipeline/training_data/task_20250721_202929/db_query_decision_prompt.txt

@@ -0,0 +1,54 @@
+{
+  "业务范围": "当前数据库存储的是高速公路服务区运营管理的相关数据，主要涉及服务区每日经营流水、车辆通行流量、公司与路段归属关系及数据来源映射，包含以下业务数据：",
+  "数据范围": "交易类数据（微信/支付宝/现金等支付方式的金额与订单数）、车流统计类数据（按车型分类的通行数量）、组织架构数据（公司编码与名称）、地理与运营属性（服务区坐标、状态）、以及多源系统数据映射关系。",
+  "核心业务实体": [
+    {
+      "实体类型": "服务区",
+      "描述": "高速公路沿线提供休息、餐饮、购物等服务的物理区域，是经营与车流统计的基本单位",
+      "主要字段": [
+        "service_no",
+        "service_name",
+        "service_state",
+        "company_id"
+      ]
+    },
+    {
+      "实体类型": "档口",
+      "描述": "服务区内具体经营单元（如餐厅、便利店），用于细化收入来源",
+      "主要字段": [
+        "branch_no",
+        "branch_name"
+      ]
+    },
+    {
+      "实体类型": "公司",
+      "描述": "管理多个服务区的运营主体，用于区域化管理和业绩对比",
+      "主要字段": [
+        "company_no",
+        "company_name"
+      ]
+    },
+    {
+      "实体类型": "路段与路线",
+      "描述": "服务区所属的高速公路路段及路线信息，用于地理分布分析",
+      "主要字段": [
+        "section_name",
+        "route_name"
+      ]
+    }
+  ],
+  "关键业务指标": [
+    {
+      "指标类型": "日经营总额与订单量",
+      "描述": "基于pay_sum、order_sum字段，可分析单日营收能力及客户活跃度趋势"
+    },
+    {
+      "指标类型": "支付方式结构占比",
+      "描述": "基于wx、zfb、rmb等各支付渠道金额与订单数，可计算不同支付方式的渗透率与偏好"
+    },
+    {
+      "指标类型": "车流构成分析",
+      "描述": "基于car_type与customer_count字段，可识别危化品车、过境车等特殊车辆比例，辅助安全管理与服务配置"
+    }
+  ]
+}

data_pipeline/training_data/task_20250721_202929/metadata.txt

@@ -0,0 +1,62 @@
+-- Schema Tools生成的主题元数据
+-- 业务背景: 高速公路服务区管理系统
+-- 生成时间: 2025-07-21 20:34:52
+-- 数据库: highway_db
+
+-- 创建表（如果不存在）
+CREATE TABLE IF NOT EXISTS metadata (
+    id SERIAL PRIMARY KEY,    -- 主键
+    topic_name VARCHAR(100) NOT NULL,  -- 业务主题名称
+    description TEXT,                  -- 业务主体说明
+    related_tables TEXT[],			  -- 相关表名
+    biz_entities TEXT[],               -- 主要业务实体名称
+    biz_metrics TEXT[],                -- 主要业务指标名称
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP    -- 插入时间
+);
+
+-- 插入主题数据
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '日营业分析',
+  '基于bss_business_day_data表分析各服务区每日营收、订单量及支付方式结构，辅助经营决策和档口优化。',
+  'bss_business_day_data',
+  '服务区,档口,支付方式',
+  '收入趋势,订单数量变化,支付方式占比'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '车流特征分析',
+  '结合bss_car_day_count表统计不同类型车辆在各服务区的通行频次及时序分布，识别高峰规律与用户群体特征。',
+  'bss_car_day_count,bss_service_area',
+  '车辆类型,服务区,日期',
+  '车流量趋势,车型占比,服务区车流排名'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '公司绩效对比',
+  '关联bss_service_area和bss_company表，按所属公司汇总营收与车流数据，评估不同区域公司的运营效率差异。',
+  'bss_service_area,bss_company,bss_business_day_data',
+  '公司,服务区,日期',
+  '平均日收入,公司间对比,单位车流转化收入'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '路段流量关联',
+  '通过bss_section_route_area_link和bss_section_route表分析各路段下辖服务区的车流分布，发现热点路线与潜力区域。',
+  'bss_section_route,bss_section_route_area_link,bss_car_day_count',
+  '路段名称,路线名称,服务区',
+  '路段总车流,服务区密度分布,路段间对比'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '数据来源质量',
+  '利用bss_service_area_mapper表统计不同系统来源的服务区数据占比及更新频率，识别主数据一致性问题。',
+  'bss_service_area_mapper,bss_service_area',
+  '数据来源系统,服务区,编码一致性',
+  '来源分布,编码覆盖率,多源冲突识别'
+);
+

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/metadata_detail.md → data_pipeline/training_data/task_20250721_202929/metadata_detail.md

@@ -7,9 +7,9 @@
 - `id` (serial) - 主键ID [主键, 非空]
 - `topic_name` (varchar(100)) - 业务主题名称 [非空]
 - `description` (text) - 业务主题说明
-- `related_tables` (text[]) - 涉及的数据表 [示例: bss_service_area, bss_section_route_area_link]
-- `biz_entities` (text[]) - 主要业务实体名称 [示例: 营收, 路段, 档口]
-- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 平均营收, 开放率, 车辆占比]
+- `related_tables` (text[]) - 涉及的数据表 [示例: bss_car_day_count, bss_section_route]
+- `biz_entities` (text[]) - 主要业务实体名称 [示例: 公司, 路段名称, 日期]
+- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 支付方式占比, 公司间对比, 车流量趋势]
 - `created_at` (timestamp) - 插入时间 [默认值: `CURRENT_TIMESTAMP`]
 
 字段补充说明：

data_pipeline/training_data/task_20250721_202929/qs_highway_db_20250721_203452_pair.json

@@ -0,0 +1,182 @@
+[
+  {
+    "question": "最近7天各服务区总营收排名（从高到低）？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "昨日各支付方式订单量占比情况（微信、支付宝、现金等）？",
+    "sql": "SELECT '微信' AS 支付方式, SUM(wx_order) AS 订单数 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '支付宝', SUM(zf_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '现金', SUM(rmb_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '行吧', SUM(xs_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '金豆', SUM(jd_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL;"
+  },
+  {
+    "question": "本月至今订单总量最多的前3个档口及其所属服务区？",
+    "sql": "SELECT branch_name AS 档口名称, service_name AS 服务区名称, SUM(order_sum) AS 总订单量 FROM bss_business_day_data WHERE oper_date >= DATE_TRUNC('month', CURRENT_DATE) AND delete_ts IS NULL GROUP BY branch_name, service_name ORDER BY 总订单量 DESC LIMIT 3;"
+  },
+  {
+    "question": "宜春服务区过去30天日均订单数量和日均收入是多少？",
+    "sql": "SELECT AVG(order_sum) AS 日均订单数, AVG(pay_sum) AS 日均收入 FROM bss_business_day_data WHERE service_name = '宜春服务区' AND oper_date BETWEEN CURRENT_DATE - 30 AND CURRENT_DATE - 1 AND delete_ts IS NULL;"
+  },
+  {
+    "question": "哪些服务区在最近一周出现过单日订单量低于50的异常情况？",
+    "sql": "SELECT DISTINCT service_name AS 异常服务区 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND order_sum < 50 AND delete_ts IS NULL;"
+  },
+  {
+    "question": "按公司维度统计最近一个月各公司旗下服务区总营收（需关联公司表）？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.pay_sum) AS 总营收 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no JOIN bss_company c ON sa.company_id = c.id WHERE b.oper_date >= CURRENT_DATE - 30 AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY c.company_name;"
+  },
+  {
+    "question": "庐山服务区最近一周每天的收入趋势变化（按日期排序）？",
+    "sql": "SELECT oper_date AS 统计日期, pay_sum AS 当日收入 FROM bss_business_day_data WHERE service_name = '庐山服务区' AND oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL ORDER BY 统计日期;"
+  },
+  {
+    "question": "过去30天现金支付金额占比超过总收入20%的服务区有哪些？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 30 AND delete_ts IS NULL GROUP BY service_name HAVING SUM(rmb) / SUM(pay_sum) > 0.2;"
+  },
+  {
+    "question": "昨日订单总数最高的服务区及具体数值？",
+    "sql": "SELECT service_name AS 服务区名称, order_sum AS 订单总数 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL ORDER BY order_sum DESC LIMIT 1;"
+  },
+  {
+    "question": "各服务区最近一天的数据完整度（字段非空率）如何？",
+    "sql": "SELECT service_name AS 服务区名称, (CASE WHEN wx IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN zfb IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN rmb IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN xs IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN jd IS NOT NULL THEN 1 ELSE 0 END) * 20 AS 数据完整度百分比 FROM bss_business_day_data WHERE (oper_date, service_name) IN (SELECT MAX(oper_date), service_name FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name);"
+  },
+  {
+    "question": "近30天各服务区总车流量排名前5的服务区是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cc.customer_count) AS 总车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.count_date >= CURRENT_DATE - INTERVAL '30 days' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 总车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "2023年每月各类车型在所有服务区的平均日车流量趋势如何？",
+    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, car_type AS 车辆类型, AVG(customer_count) AS 平均日车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY 月份, car_type ORDER BY 月份, 车辆类型;"
+  },
+  {
+    "question": "当前开放状态的服务区中，哪种车型占比最高？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总通行量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE sa.service_state = '开放' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY car_type ORDER BY 总通行量 DESC LIMIT 1;"
+  },
+  {
+    "question": "宜春分公司下属各服务区最近一周车流总量及日均值是多少？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cc.customer_count) AS 一周总车流, ROUND(AVG(cc.customer_count), 2) AS 日均车流 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '宜春分公司' AND cc.count_date >= CURRENT_DATE - 7 AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY sa.service_area_name;"
+  },
+  {
+    "question": "危化品车辆在各服务区的日均通行频次排名前3的服务区是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cc.customer_count) AS 日均危化品车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.car_type = '危化品' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均危化品车流量 DESC LIMIT 3;"
+  },
+  {
+    "question": "过去7天内每天的总车流量变化趋势是怎样的？",
+    "sql": "SELECT count_date AS 日期, SUM(customer_count) AS 日总车流量 FROM bss_car_day_count WHERE count_date BETWEEN CURRENT_DATE - 7 AND CURRENT_DATE - 1 AND delete_ts IS NULL GROUP BY count_date ORDER BY 日期;"
+  },
+  {
+    "question": "城际车辆与过境车辆在庐山服务区的车流对比情况如何？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总通行量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE sa.service_area_name = '庐山服务区' AND car_type IN ('城际', '过境') AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY car_type;"
+  },
+  {
+    "question": "每个服务区中车流量最高的单日记录及其对应日期是什么？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, MAX(cc.customer_count) AS 最高单日车流量, cc.count_date AS 日期 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name, cc.count_date ORDER BY 最高单日车流量 DESC LIMIT 10;"
+  },
+  {
+    "question": "不同车辆类型在过去一个月内的总车流量占比分布是怎样的？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count) * 100.0 / SUM(SUM(customer_count)) OVER (), 2) AS 占比百分比 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - INTERVAL '1 month' AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
+  },
+  {
+    "question": "南康北服务区最近一天的各车型详细车流数据有哪些？",
+    "sql": "SELECT cc.car_type AS 车辆类型, cc.customer_count AS 通行数量 FROM bss_car_day_count cc JOIN bss_service_area_mapper sm ON cc.service_area_id = sm.service_area_id WHERE sm.service_name = '南康北服务区' AND cc.count_date = (SELECT MAX(count_date) FROM bss_car_day_count WHERE delete_ts IS NULL) AND cc.delete_ts IS NULL LIMIT 10;"
+  },
+  {
+    "question": "各公司平均日收入排名（仅统计开放状态服务区），按收入从高到低排序",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(bdd.pay_sum) AS 平均日收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY c.id, c.company_name ORDER BY 平均日收入 DESC;"
+  },
+  {
+    "question": "每万辆车流产生的平均收入最高的前三家公司（仅开放服务区）",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bdd.pay_sum) / (SUM(car.customer_count) / 10000) AS 单位车流转化收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' AND car.delete_ts IS NULL GROUP BY c.id, c.company_name ORDER BY 单位车流转化收入 DESC LIMIT 3;"
+  },
+  {
+    "question": "2023年4月各公司总营收对比（仅开放服务区）",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bdd.pay_sum) AS 总营收 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bdd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND sa.service_state = '开放' GROUP BY c.id, c.company_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "宜春分公司和上饶分公司在2023年4月的日均车流量对比",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(car.customer_count) AS 日均车流量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND car.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND c.company_name IN ('宜春分公司', '上饶分公司') AND sa.service_state = '开放' GROUP BY c.id, c.company_name;"
+  },
+  {
+    "question": "各公司管理的服务区数量统计（仅开放状态）",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(sa.id) AS 服务区数量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY c.id, c.company_name ORDER BY 服务区数量 DESC;"
+  },
+  {
+    "question": "哪家公司的单个服务区平均日收入最低？（仅开放服务区且至少有一个数据记录）",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(bdd.pay_sum) AS 平均日收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' AND bdd.id IS NOT NULL GROUP BY c.id, c.company_name ORDER BY 平均日收入 ASC LIMIT 1;"
+  },
+  {
+    "question": "统计所有公司2023年4月的总收入与总车流量用于整体效率评估",
+    "sql": "SELECT '全部公司' AS 统计维度, SUM(bdd.pay_sum) AS 总收入, SUM(car.customer_count) AS 总车流量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bdd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND sa.service_state = '开放';"
+  },
+  {
+    "question": "列出上饶分公司下每个服务区的平均日收入明细（仅开放状态）",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(bdd.pay_sum) AS 平均日收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND c.company_name = '上饶分公司' AND sa.service_state = '开放' GROUP BY sa.id, sa.service_area_name ORDER BY 平均日收入 DESC;"
+  },
+  {
+    "question": "比较不同公司2023年4月微信支付占总收入的比例",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bdd.wx) / SUM(bdd.pay_sum) * 100 AS 微信支付占比 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bdd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND sa.service_state = '开放' AND bdd.pay_sum > 0 GROUP BY c.id, c.company_name ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "找出过去30天内没有经营数据的公司（可能存在运营异常）",
+    "sql": "SELECT c.company_name AS 公司名称 FROM bss_company c LEFT JOIN bss_service_area sa ON c.id = sa.company_id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no AND bdd.oper_date >= CURRENT_DATE - 30 WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY c.id, c.company_name HAVING COUNT(bdd.id) = 0;"
+  },
+  {
+    "question": "最近30天内哪些服务区车流量最高（TOP 10）及其所属路段？",
+    "sql": "SELECT bsa.service_area_name AS 服务区名称, bsr.section_name AS 路段名称, SUM(bcd.customer_count) AS 总车流量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE bsr.delete_ts IS NULL AND bsa.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY bsa.service_area_name, bsr.section_name ORDER BY 总车流量 DESC LIMIT 10;"
+  },
+  {
+    "question": "昌栗路段下各服务区的日均车流量是多少？",
+    "sql": "SELECT bsa.service_area_name AS 服务区名称, AVG(bcd.customer_count) AS 日均车流量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE bsr.delete_ts IS NULL AND bsa.delete_ts IS NULL AND bsr.section_name = '昌栗' GROUP BY bsa.service_area_name;"
+  },
+  {
+    "question": "不同车辆类型在各路段的分布情况如何？",
+    "sql": "SELECT bsr.section_name AS 路段名称, bcd.car_type AS 车辆类型, SUM(bcd.customer_count) AS 总数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id WHERE bsr.delete_ts IS NULL AND bsr.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '7 days' GROUP BY bsr.section_name, bcd.car_type ORDER BY 路段名称, 总数量 DESC;"
+  },
+  {
+    "question": "哪些路段没有关联任何服务区？",
+    "sql": "SELECT section_name AS 路段名称 FROM bss_section_route WHERE delete_ts IS NULL AND id NOT IN (SELECT section_route_id FROM bss_section_route_area_link WHERE delete_ts IS NULL);"
+  },
+  {
+    "question": "最近一周各路段每日总车流量趋势（按日期和路段分组）？",
+    "sql": "SELECT bsr.section_name AS 路段名称, bcd.count_date AS 统计日期, SUM(bcd.customer_count) AS 日总车流量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id WHERE bsr.delete_ts IS NULL AND bsr.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '7 days' GROUP BY bsr.section_name, bcd.count_date ORDER BY 统计日期 DESC, 日总车流量 DESC;"
+  },
+  {
+    "question": "各数据来源系统的服务区数量分布情况如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 服务区数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY source_system_type ORDER BY 服务区数量 DESC;"
+  },
+  {
+    "question": "哪些数据来源系统的编码覆盖率最高（即关联到bss_service_area的记录占比）？",
+    "sql": "SELECT mapper.source_system_type AS 数据来源系统, COUNT(mapper.id) AS 总记录数, COUNT(sa.id) AS 已关联记录数, ROUND(COUNT(sa.id)::numeric / COUNT(mapper.id), 4) AS 编码覆盖率 FROM bss_service_area_mapper mapper LEFT JOIN bss_service_area sa ON mapper.service_area_id = sa.id WHERE mapper.delete_ts IS NULL GROUP BY mapper.source_system_type ORDER BY 编码覆盖率 DESC;"
+  },
+  {
+    "question": "存在多源冲突的服务区有哪些（同一服务区被多个系统录入）？",
+    "sql": "SELECT service_area_id AS 服务区业务ID, COUNT(DISTINCT source_system_type) AS 数据源数量, STRING_AGG(DISTINCT source_system_type, ',') AS 数据来源列表 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY service_area_id HAVING COUNT(DISTINCT source_system_type) > 1 LIMIT 10;"
+  },
+  {
+    "question": "最近一个月内各系统新增的服务区数量是多少？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 新增数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND create_ts >= CURRENT_DATE - INTERVAL '1 month' GROUP BY source_system_type ORDER BY 新增数量 DESC;"
+  },
+  {
+    "question": "哪个数据来源系统更新最频繁（基于update_ts非空且不同于create_ts的记录数）？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 更新次数 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND update_ts IS NOT NULL AND update_ts != create_ts GROUP BY source_system_type ORDER BY 更新次数 DESC LIMIT 5;"
+  },
+  {
+    "question": "手工录入的数据与其他系统相比，其编码一致性如何（是否能匹配service_area_id）？",
+    "sql": "SELECT CASE WHEN sa.id IS NOT NULL THEN '一致' ELSE '不一致' END AS 编码一致性, COUNT(*) AS 数量 FROM bss_service_area_mapper mapper LEFT JOIN bss_service_area sa ON mapper.service_area_id = sa.id WHERE mapper.delete_ts IS NULL AND mapper.source_system_type = '手工录入' GROUP BY 编码一致性;"
+  },
+  {
+    "question": "每个数据来源系统最新一条记录的创建时间是什么时候？",
+    "sql": "SELECT source_system_type AS 数据来源系统, MAX(create_ts) AS 最新创建时间 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY source_system_type ORDER BY 最新创建时间 DESC;"
+  },
+  {
+    "question": "哪些服务区在多个系统中使用了不同的编码（编码冲突）？",
+    "sql": "SELECT service_name AS 服务区名称, COUNT(DISTINCT service_no) AS 不同编码数量, STRING_AGG(DISTINCT service_no, ',') AS 所有编码 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY service_name HAVING COUNT(DISTINCT service_no) > 1 LIMIT 10;"
+  },
+  {
+    "question": "按月统计各系统新增数据趋势（最近6个月）？",
+    "sql": "SELECT TO_CHAR(create_ts, 'YYYY-MM') AS 年月, source_system_type AS 数据来源系统, COUNT(*) AS 新增数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND create_ts >= CURRENT_DATE - INTERVAL '6 months' GROUP BY 年月, source_system_type ORDER BY 年月 DESC, 新增数量 DESC;"
+  },
+  {
+    "question": "数据来源类别为1和3的系统分别贡献了多少条有效数据？",
+    "sql": "SELECT source_system_type AS 数据来源系统, source_type AS 来源类别, COUNT(*) AS 数据条数 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND source_type IN (1, 3) GROUP BY source_system_type, source_type ORDER BY 来源类别, 数据条数 DESC;"
+  }
+]

data_pipeline/training_data/task_20250721_202929/qs_highway_db_20250721_203452_pair.json.backup

@@ -0,0 +1,202 @@
+[
+  {
+    "question": "最近7天各服务区总营收排名（从高到低）？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "昨日各支付方式订单量占比情况（微信、支付宝、现金等）？",
+    "sql": "SELECT '微信' AS 支付方式, SUM(wx_order) AS 订单数 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '支付宝', SUM(zf_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '现金', SUM(rmb_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '行吧', SUM(xs_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL UNION ALL SELECT '金豆', SUM(jd_order) FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL;"
+  },
+  {
+    "question": "本月至今订单总量最多的前3个档口及其所属服务区？",
+    "sql": "SELECT branch_name AS 档口名称, service_name AS 服务区名称, SUM(order_sum) AS 总订单量 FROM bss_business_day_data WHERE oper_date >= DATE_TRUNC('month', CURRENT_DATE) AND delete_ts IS NULL GROUP BY branch_name, service_name ORDER BY 总订单量 DESC LIMIT 3;"
+  },
+  {
+    "question": "宜春服务区过去30天日均订单数量和日均收入是多少？",
+    "sql": "SELECT AVG(order_sum) AS 日均订单数, AVG(pay_sum) AS 日均收入 FROM bss_business_day_data WHERE service_name = '宜春服务区' AND oper_date BETWEEN CURRENT_DATE - 30 AND CURRENT_DATE - 1 AND delete_ts IS NULL;"
+  },
+  {
+    "question": "哪些服务区在最近一周出现过单日订单量低于50的异常情况？",
+    "sql": "SELECT DISTINCT service_name AS 异常服务区 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND order_sum < 50 AND delete_ts IS NULL;"
+  },
+  {
+    "question": "按公司维度统计最近一个月各公司旗下服务区总营收（需关联公司表）？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.pay_sum) AS 总营收 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no JOIN bss_company c ON sa.company_id = c.id WHERE b.oper_date >= CURRENT_DATE - 30 AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY c.company_name;"
+  },
+  {
+    "question": "庐山服务区最近一周每天的收入趋势变化（按日期排序）？",
+    "sql": "SELECT oper_date AS 统计日期, pay_sum AS 当日收入 FROM bss_business_day_data WHERE service_name = '庐山服务区' AND oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL ORDER BY 统计日期;"
+  },
+  {
+    "question": "过去30天现金支付金额占比超过总收入20%的服务区有哪些？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 30 AND delete_ts IS NULL GROUP BY service_name HAVING SUM(rmb) / SUM(pay_sum) > 0.2;"
+  },
+  {
+    "question": "昨日订单总数最高的服务区及具体数值？",
+    "sql": "SELECT service_name AS 服务区名称, order_sum AS 订单总数 FROM bss_business_day_data WHERE oper_date = CURRENT_DATE - 1 AND delete_ts IS NULL ORDER BY order_sum DESC LIMIT 1;"
+  },
+  {
+    "question": "各服务区最近一天的数据完整度（字段非空率）如何？",
+    "sql": "SELECT service_name AS 服务区名称, (CASE WHEN wx IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN zfb IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN rmb IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN xs IS NOT NULL THEN 1 ELSE 0 END + CASE WHEN jd IS NOT NULL THEN 1 ELSE 0 END) * 20 AS 数据完整度百分比 FROM bss_business_day_data WHERE (oper_date, service_name) IN (SELECT MAX(oper_date), service_name FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name);"
+  },
+  {
+    "question": "近30天各服务区总车流量排名前5的服务区是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cc.customer_count) AS 总车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.count_date >= CURRENT_DATE - INTERVAL '30 days' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 总车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "2023年每月各类车型在所有服务区的平均日车流量趋势如何？",
+    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, car_type AS 车辆类型, AVG(customer_count) AS 平均日车流量 FROM bss_car_day_count WHERE EXTRACT(YEAR FROM count_date) = 2023 AND delete_ts IS NULL GROUP BY 月份, car_type ORDER BY 月份, 车辆类型;"
+  },
+  {
+    "question": "当前开放状态的服务区中，哪种车型占比最高？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总通行量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE sa.service_state = '开放' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY car_type ORDER BY 总通行量 DESC LIMIT 1;"
+  },
+  {
+    "question": "宜春分公司下属各服务区最近一周车流总量及日均值是多少？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cc.customer_count) AS 一周总车流, ROUND(AVG(cc.customer_count), 2) AS 日均车流 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id JOIN bss_company c ON sa.company_id = c.id WHERE c.company_name = '宜春分公司' AND cc.count_date >= CURRENT_DATE - 7 AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY sa.service_area_name;"
+  },
+  {
+    "question": "危化品车辆在各服务区的日均通行频次排名前3的服务区是哪些？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cc.customer_count) AS 日均危化品车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.car_type = '危化品' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均危化品车流量 DESC LIMIT 3;"
+  },
+  {
+    "question": "过去7天内每天的总车流量变化趋势是怎样的？",
+    "sql": "SELECT count_date AS 日期, SUM(customer_count) AS 日总车流量 FROM bss_car_day_count WHERE count_date BETWEEN CURRENT_DATE - 7 AND CURRENT_DATE - 1 AND delete_ts IS NULL GROUP BY count_date ORDER BY 日期;"
+  },
+  {
+    "question": "城际车辆与过境车辆在庐山服务区的车流对比情况如何？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总通行量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE sa.service_area_name = '庐山服务区' AND car_type IN ('城际', '过境') AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY car_type;"
+  },
+  {
+    "question": "每个服务区中车流量最高的单日记录及其对应日期是什么？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, MAX(cc.customer_count) AS 最高单日车流量, cc.count_date AS 日期 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name, cc.count_date ORDER BY 最高单日车流量 DESC LIMIT 10;"
+  },
+  {
+    "question": "不同车辆类型在过去一个月内的总车流量占比分布是怎样的？",
+    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count) * 100.0 / SUM(SUM(customer_count)) OVER (), 2) AS 占比百分比 FROM bss_car_day_count WHERE count_date >= CURRENT_DATE - INTERVAL '1 month' AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
+  },
+  {
+    "question": "南康北服务区最近一天的各车型详细车流数据有哪些？",
+    "sql": "SELECT cc.car_type AS 车辆类型, cc.customer_count AS 通行数量 FROM bss_car_day_count cc JOIN bss_service_area_mapper sm ON cc.service_area_id = sm.service_area_id WHERE sm.service_name = '南康北服务区' AND cc.count_date = (SELECT MAX(count_date) FROM bss_car_day_count WHERE delete_ts IS NULL) AND cc.delete_ts IS NULL LIMIT 10;"
+  },
+  {
+    "question": "各公司平均日收入排名（仅统计开放状态服务区），按收入从高到低排序",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(bdd.pay_sum) AS 平均日收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY c.id, c.company_name ORDER BY 平均日收入 DESC;"
+  },
+  {
+    "question": "每万辆车流产生的平均收入最高的前三家公司（仅开放服务区）",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bdd.pay_sum) / (SUM(car.customer_count) / 10000) AS 单位车流转化收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' AND car.delete_ts IS NULL GROUP BY c.id, c.company_name ORDER BY 单位车流转化收入 DESC LIMIT 3;"
+  },
+  {
+    "question": "2023年4月各公司总营收对比（仅开放服务区）",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bdd.pay_sum) AS 总营收 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bdd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND sa.service_state = '开放' GROUP BY c.id, c.company_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "宜春分公司和上饶分公司在2023年4月的日均车流量对比",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(car.customer_count) AS 日均车流量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND car.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND c.company_name IN ('宜春分公司', '上饶分公司') AND sa.service_state = '开放' GROUP BY c.id, c.company_name;"
+  },
+  {
+    "question": "各公司管理的服务区数量统计（仅开放状态）",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(sa.id) AS 服务区数量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY c.id, c.company_name ORDER BY 服务区数量 DESC;"
+  },
+  {
+    "question": "哪家公司的单个服务区平均日收入最低？（仅开放服务区且至少有一个数据记录）",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(bdd.pay_sum) AS 平均日收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND sa.service_state = '开放' AND bdd.id IS NOT NULL GROUP BY c.id, c.company_name ORDER BY 平均日收入 ASC LIMIT 1;"
+  },
+  {
+    "question": "统计所有公司2023年4月的总收入与总车流量用于整体效率评估",
+    "sql": "SELECT '全部公司' AS 统计维度, SUM(bdd.pay_sum) AS 总收入, SUM(car.customer_count) AS 总车流量 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bdd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND sa.service_state = '开放';"
+  },
+  {
+    "question": "列出上饶分公司下每个服务区的平均日收入明细（仅开放状态）",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(bdd.pay_sum) AS 平均日收入 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND c.company_name = '上饶分公司' AND sa.service_state = '开放' GROUP BY sa.id, sa.service_area_name ORDER BY 平均日收入 DESC;"
+  },
+  {
+    "question": "比较不同公司2023年4月微信支付占总收入的比例",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bdd.wx) / SUM(bdd.pay_sum) * 100 AS 微信支付占比 FROM bss_service_area sa JOIN bss_company c ON sa.company_id = c.id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bdd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND sa.service_state = '开放' AND bdd.pay_sum > 0 GROUP BY c.id, c.company_name ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "找出过去30天内没有经营数据的公司（可能存在运营异常）",
+    "sql": "SELECT c.company_name AS 公司名称 FROM bss_company c LEFT JOIN bss_service_area sa ON c.id = sa.company_id LEFT JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no AND bdd.oper_date >= CURRENT_DATE - 30 WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND sa.service_state = '开放' GROUP BY c.id, c.company_name HAVING COUNT(bdd.id) = 0;"
+  },
+  {
+    "question": "各路段总车流量排名前5的路段名称及总车流数量？",
+    "sql": "SELECT bsr.section_name AS 路段名称, SUM(bcd.customer_count) AS 总车流数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id WHERE bsr.delete_ts IS NULL AND bsral.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY bsr.section_name ORDER BY 总车流数量 DESC LIMIT 5;"
+  },
+  {
+    "question": "每条路线平均关联的服务区数量是多少？",
+    "sql": "SELECT route_name AS 路线名称, COUNT(service_area_id) AS 服务区数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id WHERE bsr.delete_ts IS NULL AND bsral.delete_ts IS NULL GROUP BY route_name ORDER BY 服务区数量 DESC;"
+  },
+  {
+    "question": "最近30天内哪些服务区车流量最高（TOP 10）及其所属路段？",
+    "sql": "SELECT bsa.service_area_name AS 服务区名称, bsr.section_name AS 路段名称, SUM(bcd.customer_count) AS 总车流量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE bsr.delete_ts IS NULL AND bsral.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY bsa.service_area_name, bsr.section_name ORDER BY 总车流量 DESC LIMIT 10;"
+  },
+  {
+    "question": "昌栗路段下各服务区的日均车流量是多少？",
+    "sql": "SELECT bsa.service_area_name AS 服务区名称, AVG(bcd.customer_count) AS 日均车流量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE bsr.delete_ts IS NULL AND bsral.delete_ts IS NULL AND bsr.section_name = '昌栗' GROUP BY bsa.service_area_name;"
+  },
+  {
+    "question": "不同车辆类型在各路段的分布情况如何？",
+    "sql": "SELECT bsr.section_name AS 路段名称, bcd.car_type AS 车辆类型, SUM(bcd.customer_count) AS 总数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id WHERE bsr.delete_ts IS NULL AND bsral.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '7 days' GROUP BY bsr.section_name, bcd.car_type ORDER BY 路段名称, 总数量 DESC;"
+  },
+  {
+    "question": "哪些路段没有关联任何服务区？",
+    "sql": "SELECT section_name AS 路段名称 FROM bss_section_route WHERE delete_ts IS NULL AND id NOT IN (SELECT section_route_id FROM bss_section_route_area_link WHERE delete_ts IS NULL);"
+  },
+  {
+    "question": "最近一周各路段每日总车流量趋势（按日期和路段分组）？",
+    "sql": "SELECT bsr.section_name AS 路段名称, bcd.count_date AS 统计日期, SUM(bcd.customer_count) AS 日总车流量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id WHERE bsr.delete_ts IS NULL AND bsral.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '7 days' GROUP BY bsr.section_name, bcd.count_date ORDER BY 统计日期 DESC, 日总车流量 DESC;"
+  },
+  {
+    "question": "各路段下辖服务区数量与总车流量对比分析（双指标）？",
+    "sql": "SELECT bsr.section_name AS 路段名称, COUNT(bsral.service_area_id) AS 服务区数量, COALESCE(SUM(bcd.customer_count), 0) AS 总车流量 FROM bss_section_route bsr LEFT JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id LEFT JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id AND bcd.count_date >= CURRENT_DATE - INTERVAL '30 days' WHERE bsr.delete_ts IS NULL AND (bsral.delete_ts IS NULL OR bsral.delete_ts IS NOT NULL) GROUP BY bsr.section_name ORDER BY 总车流量 DESC;"
+  },
+  {
+    "question": "车流量最低的5个服务区及其所属路线和路段？",
+    "sql": "SELECT bsa.service_area_name AS 服务区名称, bsr.route_name AS 路线名称, bsr.section_name AS 路段名称, SUM(bcd.customer_count) AS 总车流量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_car_day_count bcd ON bsral.service_area_id = bcd.service_area_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE bsr.delete_ts IS NULL AND bsral.delete_ts IS NULL AND bcd.count_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY bsa.service_area_name, bsr.route_name, bsr.section_name ORDER BY 总车流量 ASC LIMIT 5;"
+  },
+  {
+    "question": "本月新开通（运营状态为开放）的服务区分布在哪些路段？",
+    "sql": "SELECT bsa.service_area_name AS 服务区名称, bsr.section_name AS 路段名称 FROM bss_service_area bsa JOIN bss_section_route_area_link bsral ON bsa.id = bsral.service_area_id JOIN bss_section_route bsr ON bsral.section_route_id = bsr.id WHERE bsa.service_state = '开放' AND bsa.create_ts >= DATE_TRUNC('month', CURRENT_DATE) AND bsa.delete_ts IS NULL AND bsral.delete_ts IS NULL AND bsr.delete_ts IS NULL;"
+  },
+  {
+    "question": "各数据来源系统的服务区数量分布情况如何？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 服务区数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY source_system_type ORDER BY 服务区数量 DESC;"
+  },
+  {
+    "question": "哪些数据来源系统的编码覆盖率最高（即关联到bss_service_area的记录占比）？",
+    "sql": "SELECT mapper.source_system_type AS 数据来源系统, COUNT(mapper.id) AS 总记录数, COUNT(sa.id) AS 已关联记录数, ROUND(COUNT(sa.id)::numeric / COUNT(mapper.id), 4) AS 编码覆盖率 FROM bss_service_area_mapper mapper LEFT JOIN bss_service_area sa ON mapper.service_area_id = sa.id WHERE mapper.delete_ts IS NULL GROUP BY mapper.source_system_type ORDER BY 编码覆盖率 DESC;"
+  },
+  {
+    "question": "存在多源冲突的服务区有哪些（同一服务区被多个系统录入）？",
+    "sql": "SELECT service_area_id AS 服务区业务ID, COUNT(DISTINCT source_system_type) AS 数据源数量, STRING_AGG(DISTINCT source_system_type, ',') AS 数据来源列表 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY service_area_id HAVING COUNT(DISTINCT source_system_type) > 1 LIMIT 10;"
+  },
+  {
+    "question": "最近一个月内各系统新增的服务区数量是多少？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 新增数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND create_ts >= CURRENT_DATE - INTERVAL '1 month' GROUP BY source_system_type ORDER BY 新增数量 DESC;"
+  },
+  {
+    "question": "哪个数据来源系统更新最频繁（基于update_ts非空且不同于create_ts的记录数）？",
+    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 更新次数 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND update_ts IS NOT NULL AND update_ts != create_ts GROUP BY source_system_type ORDER BY 更新次数 DESC LIMIT 5;"
+  },
+  {
+    "question": "手工录入的数据与其他系统相比，其编码一致性如何（是否能匹配service_area_id）？",
+    "sql": "SELECT CASE WHEN sa.id IS NOT NULL THEN '一致' ELSE '不一致' END AS 编码一致性, COUNT(*) AS 数量 FROM bss_service_area_mapper mapper LEFT JOIN bss_service_area sa ON mapper.service_area_id = sa.id WHERE mapper.delete_ts IS NULL AND mapper.source_system_type = '手工录入' GROUP BY 编码一致性;"
+  },
+  {
+    "question": "每个数据来源系统最新一条记录的创建时间是什么时候？",
+    "sql": "SELECT source_system_type AS 数据来源系统, MAX(create_ts) AS 最新创建时间 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY source_system_type ORDER BY 最新创建时间 DESC;"
+  },
+  {
+    "question": "哪些服务区在多个系统中使用了不同的编码（编码冲突）？",
+    "sql": "SELECT service_name AS 服务区名称, COUNT(DISTINCT service_no) AS 不同编码数量, STRING_AGG(DISTINCT service_no, ',') AS 所有编码 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY service_name HAVING COUNT(DISTINCT service_no) > 1 LIMIT 10;"
+  },
+  {
+    "question": "按月统计各系统新增数据趋势（最近6个月）？",
+    "sql": "SELECT TO_CHAR(create_ts, 'YYYY-MM') AS 年月, source_system_type AS 数据来源系统, COUNT(*) AS 新增数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND create_ts >= CURRENT_DATE - INTERVAL '6 months' GROUP BY 年月, source_system_type ORDER BY 年月 DESC, 新增数量 DESC;"
+  },
+  {
+    "question": "数据来源类别为1和3的系统分别贡献了多少条有效数据？",
+    "sql": "SELECT source_system_type AS 数据来源系统, source_type AS 来源类别, COUNT(*) AS 数据条数 FROM bss_service_area_mapper WHERE delete_ts IS NULL AND source_type IN (1, 3) GROUP BY source_system_type, source_type ORDER BY 来源类别, 数据条数 DESC;"
+  }
+]

data_pipeline/training_data/task_20250721_202929/task_config.json

@@ -0,0 +1,15 @@
+{
+  "task_id": "task_20250721_202929",
+  "created_at": "2025-07-21T20:29:29.262473",
+  "parameters": {
+    "db_connection": "postgresql://postgres:postgres@192.168.67.1:6432/highway_db",
+    "table_list_file": "{task_directory}/table_list.txt",
+    "business_context": "高速公路服务区管理系统",
+    "file_upload_mode": true,
+    "enable_llm_repair": true,
+    "modify_original_file": true,
+    "enable_sql_validation": true,
+    "enable_training_data_load": true
+  },
+  "output_directory": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_202929"
+}

data_pipeline/training_data/task_20250721_202929/task_result.json

@@ -0,0 +1,177 @@
+{
+  "success": true,
+  "workflow_state": {
+    "start_time": null,
+    "end_time": null,
+    "current_step": "training_data_load",
+    "completed_steps": [
+      "ddl_md_generation",
+      "question_sql_generation",
+      "sql_validation",
+      "training_data_load"
+    ],
+    "failed_steps": [],
+    "artifacts": {
+      "ddl_md_generation": {
+        "total_tables": 7,
+        "processed_successfully": 7,
+        "failed": 0,
+        "files_generated": 14,
+        "duration": 94.89018130302429
+      },
+      "question_sql_generation": {
+        "output_file": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_202929\\qs_highway_db_20250721_203452_pair.json",
+        "total_questions": 50,
+        "total_themes": 5,
+        "successful_themes": 5,
+        "failed_themes": [],
+        "duration": 173.30390071868896
+      },
+      "sql_validation": {
+        "original_sql_count": 50,
+        "valid_sql_count": 45,
+        "invalid_sql_count": 5,
+        "success_rate": 0.9,
+        "repair_stats": {
+          "attempted": 9,
+          "successful": 4,
+          "failed": 5
+        },
+        "file_modification_stats": {
+          "modified": 4,
+          "deleted": 5,
+          "failed_modifications": 0
+        },
+        "average_execution_time": 0.024708290100097657,
+        "total_retries": 0,
+        "duration": 29.800068616867065
+      },
+      "vector_management": {
+        "backup_performed": true,
+        "truncate_performed": true,
+        "tables_backed_up": {
+          "langchain_pg_collection": {
+            "success": true,
+            "row_count": 4,
+            "file_size": "209.0 B",
+            "backup_file": "langchain_pg_collection_20250721_203539.csv",
+            "duration": 0.02439570426940918
+          },
+          "langchain_pg_embedding": {
+            "success": true,
+            "row_count": 0,
+            "file_size": "47.0 B",
+            "backup_file": "langchain_pg_embedding_20250721_203539.csv",
+            "duration": 0.02391791343688965
+          }
+        },
+        "truncate_results": {
+          "langchain_pg_embedding": {
+            "success": true,
+            "rows_before": 0,
+            "rows_after": 0,
+            "duration": 0.02629232406616211
+          }
+        },
+        "errors": [],
+        "backup_directory": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_202929\\vector_bak",
+        "duration": 0.07828950881958008
+      },
+      "training_data_load": {
+        "training_data_dir": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_202929",
+        "load_successful": true,
+        "total_records": 58,
+        "data_type_counts": {
+          "sql": 43,
+          "documentation": 8,
+          "ddl": 7
+        },
+        "duration": 69.35857605934143
+      }
+    },
+    "statistics": {
+      "step1_duration": 94.89018130302429,
+      "step2_duration": 173.30390071868896,
+      "step3_duration": 29.800068616867065,
+      "step4_duration": 69.35857605934143
+    }
+  },
+  "artifacts": {
+    "ddl_md_generation": {
+      "total_tables": 7,
+      "processed_successfully": 7,
+      "failed": 0,
+      "files_generated": 14,
+      "duration": 94.89018130302429
+    },
+    "question_sql_generation": {
+      "output_file": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_202929\\qs_highway_db_20250721_203452_pair.json",
+      "total_questions": 50,
+      "total_themes": 5,
+      "successful_themes": 5,
+      "failed_themes": [],
+      "duration": 173.30390071868896
+    },
+    "sql_validation": {
+      "original_sql_count": 50,
+      "valid_sql_count": 45,
+      "invalid_sql_count": 5,
+      "success_rate": 0.9,
+      "repair_stats": {
+        "attempted": 9,
+        "successful": 4,
+        "failed": 5
+      },
+      "file_modification_stats": {
+        "modified": 4,
+        "deleted": 5,
+        "failed_modifications": 0
+      },
+      "average_execution_time": 0.024708290100097657,
+      "total_retries": 0,
+      "duration": 29.800068616867065
+    },
+    "vector_management": {
+      "backup_performed": true,
+      "truncate_performed": true,
+      "tables_backed_up": {
+        "langchain_pg_collection": {
+          "success": true,
+          "row_count": 4,
+          "file_size": "209.0 B",
+          "backup_file": "langchain_pg_collection_20250721_203539.csv",
+          "duration": 0.02439570426940918
+        },
+        "langchain_pg_embedding": {
+          "success": true,
+          "row_count": 0,
+          "file_size": "47.0 B",
+          "backup_file": "langchain_pg_embedding_20250721_203539.csv",
+          "duration": 0.02391791343688965
+        }
+      },
+      "truncate_results": {
+        "langchain_pg_embedding": {
+          "success": true,
+          "rows_before": 0,
+          "rows_after": 0,
+          "duration": 0.02629232406616211
+        }
+      },
+      "errors": [],
+      "backup_directory": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_202929\\vector_bak",
+      "duration": 0.07828950881958008
+    },
+    "training_data_load": {
+      "training_data_dir": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250721_202929",
+      "load_successful": true,
+      "total_records": 58,
+      "data_type_counts": {
+        "sql": 43,
+        "documentation": 8,
+        "ddl": 7
+      },
+      "duration": 69.35857605934143
+    }
+  }
+}

data_pipeline/training_data/task_20250721_202929/vector_bak/langchain_pg_collection_20250721_203539.csv

@@ -0,0 +1,5 @@
+uuid,name,cmetadata
+f4e11877-44e7-4741-b511-fa0e2e399395,sql,
+f0b714ca-44a9-433a-8768-390740bd1a18,ddl,
+98b97e3a-752d-4115-9667-7635687dbc1c,documentation,
+ab83ab0a-5649-4722-984d-b093227cdb02,error_sql,

data_pipeline/training_data/task_20250721_202929/vector_bak/langchain_pg_embedding_20250721_203539.csv

@@ -0,0 +1 @@
+id,collection_id,embedding,document,cmetadata

data_pipeline/training_data/task_20250721_202929/vector_bak/vector_backup_log.txt

@@ -0,0 +1,11 @@
+=== Vector Table Backup Log ===
+Backup Time: 2025-07-21 20:35:39
+Task ID: task_20250721_202929
+Duration: 0.00s
+
+Tables Backup Status:
+✓ langchain_pg_collection: 4 rows -> langchain_pg_collection_20250721_203539.csv (209.0 B)
+✓ langchain_pg_embedding: 0 rows -> langchain_pg_embedding_20250721_203539.csv (47.0 B)
+
+Truncate Status:
+✓ langchain_pg_embedding: TRUNCATED (0 rows removed)

data_pipeline/training_data/task_20250721_213627/bss_business_day_data.ddl

@@ -0,0 +1,31 @@
+-- 中文名: 高速公路服务区每日经营数据统计表
+-- 描述: 高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。
+create table public.bss_business_day_data (
+  id varchar(32) not null     -- 主键ID，主键,
+  version integer not null    -- 数据版本号,
+  create_ts timestamp         -- 创建时间,
+  created_by varchar(50)      -- 创建人,
+  update_ts timestamp         -- 更新时间,
+  updated_by varchar(50)      -- 更新人,
+  delete_ts timestamp         -- 删除时间,
+  deleted_by varchar(50)      -- 删除人,
+  oper_date date              -- 统计日期,
+  service_no varchar(255)     -- 服务区编码,
+  service_name varchar(255)   -- 服务区名称,
+  branch_no varchar(255)      -- 档口编码,
+  branch_name varchar(255)    -- 档口名称,
+  wx numeric(19,4)            -- 微信支付金额,
+  wx_order integer            -- 微信订单数量,
+  zfb numeric(19,4)           -- 支付宝支付金额,
+  zf_order integer            -- 支付宝订单数量,
+  rmb numeric(19,4)           -- 现金支付金额,
+  rmb_order integer           -- 现金订单数量,
+  xs numeric(19,4)            -- 行吧支付金额,
+  xs_order integer            -- 行吧支付订单数,
+  jd numeric(19,4)            -- 金豆支付金额,
+  jd_order integer            -- 金豆支付订单数,
+  order_sum integer           -- 订单总数,
+  pay_sum numeric(19,4)       -- 总支付金额,
+  source_type integer         -- 数据来源类型,
+  primary key (id)
+);

data_pipeline/training_data/task_20250721_213627/bss_business_day_data_detail.md

@@ -0,0 +1,32 @@
+## bss_business_day_data（高速公路服务区每日经营数据统计表）
+bss_business_day_data 表高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。
+字段列表：
+- id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820]
+- version (integer) - 数据版本号 [非空] [示例: 1]
+- create_ts (timestamp) - 创建时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
+- created_by (varchar(50)) - 创建人 [示例: xingba]
+- update_ts (timestamp) - 更新时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
+- updated_by (varchar(50)) - 更新人
+- delete_ts (timestamp) - 删除时间
+- deleted_by (varchar(50)) - 删除人
+- oper_date (date) - 统计日期 [示例: 2023-04-01]
+- service_no (varchar(255)) - 服务区编码 [示例: 1028, H0501]
+- service_name (varchar(255)) - 服务区名称 [示例: 宜春服务区, 庐山服务区]
+- branch_no (varchar(255)) - 档口编码 [示例: 1, H05016]
+- branch_name (varchar(255)) - 档口名称 [示例: 宜春南区, 庐山鲜徕客东区]
+- wx (numeric(19,4)) - 微信支付金额 [示例: 4790.0000, 2523.0000]
+- wx_order (integer) - 微信订单数量 [示例: 253, 133]
+- zfb (numeric(19,4)) - 支付宝支付金额 [示例: 229.0000, 0.0000]
+- zf_order (integer) - 支付宝订单数量 [示例: 15, 0]
+- rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000]
+- rmb_order (integer) - 现金订单数量 [示例: 56, 12]
+- xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000]
+- xs_order (integer) - 行吧支付订单数 [示例: 0, 1]
+- jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000]
+- jd_order (integer) - 金豆支付订单数 [示例: 0]
+- order_sum (integer) - 订单总数 [示例: 324, 146]
+- pay_sum (numeric(19,4)) - 总支付金额 [示例: 6077.5000, 2687.0000]
+- source_type (integer) - 数据来源类型 [示例: 1, 0, 4]
+字段补充说明：
+- id 为主键
+- source_type 为枚举字段，包含取值：0、4、1、2、3

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_car_day_count.ddl → data_pipeline/training_data/task_20250721_213627/bss_car_day_count.ddl

@@ -1,8 +1,8 @@
--- 中文名: 记录高速公路服务区每日车辆统计信息
--- 描述: 记录高速公路服务区每日车辆统计信息，用于车流分析与运营决策。
+-- 中文名: 高速公路服务区每日车辆流量统计表
+-- 描述: 高速公路服务区每日车辆流量统计表，记录各类型车辆数量及变更历史。
 create table public.bss_car_day_count (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,

data_pipeline/training_data/task_20250721_083557/task_20250721_083557/bss_car_day_count_detail.md → data_pipeline/training_data/task_20250721_213627/bss_car_day_count_detail.md

@@ -1,8 +1,8 @@
-## bss_car_day_count（记录高速公路服务区每日车辆统计信息）
-bss_car_day_count 表记录高速公路服务区每日车辆统计信息，用于车流分析与运营决策。
+## bss_car_day_count（高速公路服务区每日车辆流量统计表）
+bss_car_day_count 表高速公路服务区每日车辆流量统计表，记录各类型车辆数量及变更历史。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc]
-- version (integer) - 版本号 [非空] [示例: 1]
+- version (integer) - 数据版本号 [非空] [示例: 1]
 - create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
 - created_by (varchar(50)) - 创建人
 - update_ts (timestamp) - 更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]

data_pipeline/training_data/task_20250721_213627/bss_company.ddl

@@ -0,0 +1,15 @@
+-- 中文名: 高速公路服务区管理系统中的公司信息表
+-- 描述: 高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及变更记录。
+create table public.bss_company (
+  id varchar(32) not null     -- 公司唯一标识符，主键,
+  version integer not null    -- 数据版本号,
+  create_ts timestamp         -- 创建时间,
+  created_by varchar(50)      -- 创建人,
+  update_ts timestamp         -- 最后更新时间,
+  updated_by varchar(50)      -- 更新人,
+  delete_ts timestamp         -- 删除时间,
+  deleted_by varchar(50)      -- 删除人,
+  company_name varchar(255)   -- 公司名称,
+  company_no varchar(255)     -- 公司编码,
+  primary key (id)
+);

data_pipeline/training_data/task_20250721_213627/bss_company_detail.md

@@ -0,0 +1,18 @@
+## bss_company（高速公路服务区管理系统中的公司信息表）
+bss_company 表高速公路服务区管理系统中的公司信息表，存储服务区所属公司的基本信息及变更记录。
+字段列表：
+- id (varchar(32)) - 公司唯一标识符 [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
+- version (integer) - 数据版本号 [非空] [示例: 1, 2]
+- create_ts (timestamp) - 创建时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
+- created_by (varchar(50)) - 创建人 [示例: admin]
+- update_ts (timestamp) - 最后更新时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
+- updated_by (varchar(50)) - 更新人 [示例: admin]
+- delete_ts (timestamp) - 删除时间
+- deleted_by (varchar(50)) - 删除人
+- company_name (varchar(255)) - 公司名称 [示例: 上饶分公司, 宜春分公司]
+- company_no (varchar(255)) - 公司编码 [示例: H03, H02, H07]
+字段补充说明：
+- id 为主键
+- created_by 为枚举字段，包含取值：admin
+- updated_by 为枚举字段，包含取值：admin
+- company_no 为枚举字段，包含取值：H01、H02、H03、H04、H05、H06、H07、H08、Q01

data_pipeline/training_data/manual_20250720_130541/bss_section_route.ddl → data_pipeline/training_data/task_20250721_213627/bss_section_route.ddl

@@ -1,8 +1,8 @@
--- 中文名: 路段与路线信息关联表
--- 描述: 路段与路线信息关联表，用于高速公路服务区路线管理。
+-- 中文名: 路段路线信息表
+-- 描述: 路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。
 create table public.bss_section_route (
   id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
+  version integer not null    -- 数据版本号,
   create_ts timestamp         -- 创建时间,
   created_by varchar(50)      -- 创建人,
   update_ts timestamp         -- 更新时间,
@@ -11,6 +11,6 @@ create table public.bss_section_route (
   deleted_by varchar(50)      -- 删除人,
   section_name varchar(255)   -- 路段名称,
   route_name varchar(255)     -- 路线名称,
-  code varchar(255)           -- 编号,
+  code varchar(255)           -- 路段编号,
   primary key (id)
 );

data_pipeline/training_data/task_20250721_213627/bss_section_route_area_link.ddl

@@ -0,0 +1,7 @@
+-- 中文名: 高速公路路线与服务区关联表
+-- 描述: 高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。
+create table public.bss_section_route_area_link (
+  section_route_id varchar(32) not null -- 路段路线唯一标识，主键,
+  service_area_id varchar(32) not null -- 服务区唯一标识，主键,
+  primary key (section_route_id, service_area_id)
+);

data_pipeline/training_data/task_20250721_213627/bss_section_route_area_link_detail.md

@@ -0,0 +1,7 @@
+## bss_section_route_area_link（高速公路路线与服务区关联表）
+bss_section_route_area_link 表高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。
+字段列表：
+- section_route_id (varchar(32)) - 路段路线唯一标识 [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp]
+- service_area_id (varchar(32)) - 服务区唯一标识 [主键, 非空] [示例: 08e01d7402abd1d6a4d9fdd5df855ef8, 091662311d2c737029445442ff198c4c]
+字段补充说明：
+- 复合主键：section_route_id, service_area_id

data_pipeline/training_data/manual_20250720_130541/bss_section_route_detail.md → data_pipeline/training_data/task_20250721_213627/bss_section_route_detail.md

@@ -1,16 +1,17 @@
-## bss_section_route（路段与路线信息关联表）
-bss_section_route 表路段与路线信息关联表，用于高速公路服务区路线管理。
+## bss_section_route（路段路线信息表）
+bss_section_route 表路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。
 字段列表：
 - id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7]
-- version (integer) - 版本号 [非空] [示例: 1, 0]
+- version (integer) - 数据版本号 [非空] [示例: 1, 0]
 - create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16]
 - created_by (varchar(50)) - 创建人 [示例: admin]
 - update_ts (timestamp) - 更新时间
 - updated_by (varchar(50)) - 更新人
 - delete_ts (timestamp) - 删除时间
 - deleted_by (varchar(50)) - 删除人
-- section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁, 昌九]
-- route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶, /]
-- code (varchar(255)) - 编号 [示例: SR0001, SR0002, SR0147]
+- section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁]
+- route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶]
+- code (varchar(255)) - 路段编号 [示例: SR0001, SR0002]
 字段补充说明：
-- id 为主键
+- id 为主键
+- created_by 为枚举字段，包含取值：admin

data_pipeline/training_data/task_20250721_213627/bss_service_area.ddl

@@ -0,0 +1,19 @@
+-- 中文名: 高速公路服务区基础信息表
+-- 描述: 高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。
+create table public.bss_service_area (
+  id varchar(32) not null     -- 唯一标识符，主键,
+  version integer not null    -- 数据版本号,
+  create_ts timestamp         -- 创建时间,
+  created_by varchar(50)      -- 创建人,
+  update_ts timestamp         -- 更新时间,
+  updated_by varchar(50)      -- 更新人,
+  delete_ts timestamp         -- 删除时间,
+  deleted_by varchar(50)      -- 删除人,
+  service_area_name varchar(255) -- 服务区名称,
+  service_area_no varchar(255) -- 服务区编码,
+  company_id varchar(32)      -- 所属公司ID,
+  service_position varchar(255) -- 经纬度坐标,
+  service_area_type varchar(50) -- 服务区类型,
+  service_state varchar(50)   -- 运营状态,
+  primary key (id)
+);