把pgvector的pg_conn改为db_connection,准备发布到公司服务器.

data_pipeline/api/vector_restore_manager.py

@@ -99,7 +99,7 @@ class VectorRestoreManager:
             raise
     
     def restore_from_backup(self, backup_path: str, timestamp: str, 
-                          tables: List[str] = None, pg_conn: str = None,
+                          tables: List[str] = None, db_connection: str = None,
                           truncate_before_restore: bool = False) -> Dict[str, Any]:
         """
         从备份文件恢复数据
@@ -108,7 +108,7 @@ class VectorRestoreManager:
             backup_path: 备份文件所在的目录路径（相对路径）
             timestamp: 备份文件的时间戳
             tables: 要恢复的表名列表，None表示恢复所有表
-            pg_conn: PostgreSQL连接字符串，None则从config获取
+            db_connection: PostgreSQL连接字符串，None则从config获取
             truncate_before_restore: 恢复前是否清空目标表
             
         Returns:
@@ -165,10 +165,10 @@ class VectorRestoreManager:
         
         # 临时修改数据库连接配置
         original_config = None
-        if pg_conn:
+        if db_connection:
             from data_pipeline.config import SCHEMA_TOOLS_CONFIG
             original_config = SCHEMA_TOOLS_CONFIG.get("default_db_connection")
-            SCHEMA_TOOLS_CONFIG["default_db_connection"] = pg_conn
+            SCHEMA_TOOLS_CONFIG["default_db_connection"] = db_connection
         
         try:
             # 执行清空操作（如果需要）

data_pipeline/schema_workflow.py

@@ -896,6 +896,12 @@ def setup_argument_parser():
         help="清空vector表数据（自动启用备份）"
     )
     
+    parser.add_argument(
+        "--skip-training",
+        action="store_true",
+        help="跳过训练文件处理，仅执行Vector表管理"
+    )
+    
     parser.add_argument(
         "--verbose", "-v",
         action="store_true",
@@ -947,7 +953,8 @@ async def main():
             modify_original_file=not args.no_modify_file,
             enable_training_data_load=True,
             backup_vector_tables=args.backup_vector_tables,
-            truncate_vector_tables=args.truncate_vector_tables
+            truncate_vector_tables=args.truncate_vector_tables,
+            skip_training=args.skip_training
         )
         
         # 获取logger用于启动信息
@@ -964,7 +971,7 @@ async def main():
         logger.info(f"💾 数据库: {orchestrator.db_name}")
         logger.info(f"🔍 SQL验证: {'启用' if not args.skip_validation else '禁用'}")
         logger.info(f"🔧 LLM修复: {'启用' if not args.disable_llm_repair else '禁用'}")
-        logger.info(f"🎯 训练数据加载: {'启用' if not args.skip_training_load else '禁用'}")
+        logger.info(f"🎯 训练数据加载: {'启用' if not args.skip_training else '禁用'}")
         
         # 执行完整工作流程
         report = await orchestrator.execute_complete_workflow()

data_pipeline/training_data/manual_20250721_002320/bss_business_day_data.ddl

@@ -1,31 +0,0 @@
--- 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。
-create table public.bss_business_day_data (
-  id varchar(32) not null     -- 主键ID，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,
-  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            -- 行吧订单数量,
-  jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
-  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,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_002320/bss_business_day_data_detail.md

@@ -1,32 +0,0 @@
-## 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。
-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]
-- 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) - 行吧订单数量 [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]
-- 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]
-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/manual_20250721_002320/bss_car_day_count.ddl

@@ -1,17 +0,0 @@
--- 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。
-create table public.bss_car_day_count (
-  id varchar(32) not null     -- 主键ID，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,
-  customer_count bigint       -- 车辆数量,
-  car_type varchar(100)       -- 车辆类别,
-  count_date date             -- 统计日期,
-  service_area_id varchar(32) -- 服务区ID,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_002320/bss_car_day_count_detail.md

@@ -1,18 +0,0 @@
-## 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。
-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]
-- 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]
-- 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_20250721_002320/bss_company.ddl

@@ -1,15 +0,0 @@
--- 中文名: **表注释:** 公司信息表
--- 描述: **表注释:** 公司信息表，存储服务区合作企业基础信息。
-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/manual_20250721_002320/bss_company_detail.md

@@ -1,16 +0,0 @@
-## bss_company（**表注释:** 公司信息表）
-bss_company 表**表注释:** 公司信息表，存储服务区合作企业基础信息。
-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]
-- 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/manual_20250721_002320/bss_section_route.ddl

@@ -1,16 +0,0 @@
--- 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。
-create table public.bss_section_route (
-  id varchar(32) not null     -- 主键ID，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,
-  section_name varchar(255)   -- 路段名称,
-  route_name varchar(255)     -- 路线名称,
-  code varchar(255)           -- 编号,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_002320/bss_section_route_area_link.ddl

@@ -1,7 +0,0 @@
--- 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,
-  primary key (section_route_id, service_area_id)
-);

data_pipeline/training_data/manual_20250721_002320/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_20250721_002320/bss_section_route_detail.md

@@ -1,16 +0,0 @@
-## 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。
-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]
-- 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]
-字段补充说明：
-- id 为主键

data_pipeline/training_data/manual_20250721_002320/bss_service_area.ddl

@@ -1,19 +0,0 @@
--- 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，如名称、编码及操作记录，为核心业务提供数据支撑。
-create table public.bss_service_area (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  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) -- 服务区经纬度,
-  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/manual_20250721_002320/bss_service_area_detail.md

@@ -1,21 +0,0 @@
-## 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，如名称、编码及操作记录，为核心业务提供数据支撑。
-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]
-- 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]
-- updated_by (varchar(50)) - 更新人 [示例: admin]
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人 [示例: ]
-- 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_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：
-- 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
-- service_state 为枚举字段，包含取值：开放、关闭、上传数据

data_pipeline/training_data/manual_20250721_002320/bss_service_area_mapper.ddl

@@ -1,18 +0,0 @@
--- 中文名: 服务区基础信息表
--- 描述: 服务区基础信息表，记录全国高速公路服务区的编码、名称及生命周期信息。
-create table public.bss_service_area_mapper (
-  id varchar(32) not null     -- 主键ID，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,
-  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,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_002320/bss_service_area_mapper_detail.md

@@ -1,20 +0,0 @@
-## bss_service_area_mapper（服务区基础信息表）
-bss_service_area_mapper 表服务区基础信息表，记录全国高速公路服务区的编码、名称及生命周期信息。
-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]
-- 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]
-- updated_by (varchar(50)) - 更新人
-- delete_ts (timestamp) - 删除时间
-- 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]
-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
-- source_type 为枚举字段，包含取值：5、0、1、3、4

data_pipeline/training_data/manual_20250721_002320/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，主要涉及经营流水、车流统计、公司信息、路段路线、服务区基础信息等，包含以下业务数据：
-核心业务实体：
-- 服务区：描述高速公路沿线提供停车、加油、餐饮等服务的地点，主要字段：service_area_name、service_area_no、service_state、service_position
-- 经营档口：描述服务区内的具体经营单元，主要字段：branch_no、branch_name
-- 车辆：描述经过服务区的车辆类型与数量，主要字段：car_type、customer_count
-- 公司：描述服务区所属的管理公司，主要字段：company_name、company_no
-- 支付方式：描述经营数据中的支付类型，主要字段：wx、zfb、rmb、xs、jd
-关键业务指标：
-- 日经营金额：记录每个档口或服务区每日的支付金额总和，可用于分析经营趋势，字段：pay_sum
-- 日订单数量：记录每个档口或服务区每日的订单数量，可用于分析消费频次，字段：order_sum

data_pipeline/training_data/manual_20250721_002320/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 00:27:47
--- 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',
-  '基于 bss_business_day_data 表，分析各服务区每日营收、订单结构及支付方式分布，用于经营优化。',
-  'bss_business_day_data,bss_service_area',
-  '服务区,档口,支付方式,统计日期',
-  '总营收,订单数量,支付方式占比,日营收趋势'
-);
-
-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类型及数量变化，用于车流与运营关联分析。',
-  'bss_car_day_count,bss_service_area',
-  '服务区,车辆类型,统计日期',
-  '车流量趋势,车辆类型分布,车流量排名'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '公司业绩对比',
-  '结合 bss_company 和 bss_service_area，分析各公司下属服务区营收与车流表现，支持公司级管理决策。',
-  'bss_company,bss_service_area,bss_business_day_data,bss_car_day_count',
-  '公司,服务区,统计日期',
-  '平均营收,车流总量,营收排名,车流排名'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '路线关联分析',
-  '基于 bss_section_route 和 bss_section_route_area_link，分析路线与服务区的关联关系，支持路网运营优化。',
-  'bss_section_route,bss_section_route_area_link,bss_service_area',
-  '路段,路线,服务区',
-  '服务区数量,路段覆盖,路线关联度'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '服务区状态分析',
-  '基于 bss_service_area 表，分析服务区类型、状态分布及地理位置，支持服务区布局与运营策略制定。',
-  'bss_service_area,bss_company',
-  '服务区,类型,状态,公司',
-  '服务区数量,开放比例,区域分布,公司覆盖率'
-);
-

data_pipeline/training_data/manual_20250721_002320/metadata_detail.md

@@ -1,20 +0,0 @@
-## 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_section_route]
-- `biz_entities` (text[]) - 主要业务实体名称 [示例: 服务区, 路段, 状态]
-- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 车流量排名, 平均营收, 车流总量]
-- `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/manual_20250721_002320/qs_highway_db_20250721_002747_pair.json

@@ -1,202 +0,0 @@
-[
-  {
-    "question": "查询2023年4月1日各服务区的总营收和订单数量，并按总营收降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, pay_sum AS 总营收, order_sum AS 订单数量 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY pay_sum DESC;"
-  },
-  {
-    "question": "统计2023年4月各服务区每日总营收，按日期和服务区分组。",
-    "sql": "SELECT oper_date AS 统计日期, service_name AS 服务区名称, SUM(pay_sum) AS 日总营收 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY oper_date, service_name ORDER BY oper_date;"
-  },
-  {
-    "question": "查询2023年4月1日各支付方式的总金额及订单数量，并按支付方式分类。",
-    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 支付金额, SUM(wx_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '支付宝', SUM(zfb), SUM(zf_order) FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '现金', SUM(rmb), SUM(rmb_order) FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计2023年4月各档口的总营收TOP 10，并显示对应的支付方式占比。",
-    "sql": "SELECT branch_name AS 档口名称, pay_sum AS 总营收, wx / pay_sum * 100 AS 微信占比, zfb / pay_sum * 100 AS 支付宝占比, rmb / pay_sum * 100 AS 现金占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL ORDER BY pay_sum DESC LIMIT 10;"
-  },
-  {
-    "question": "查询2023年4月各服务区现金支付金额占比，并按占比降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付占比 DESC;"
-  },
-  {
-    "question": "统计2023年4月各周的总营收趋势，按周分组。",
-    "sql": "SELECT date_trunc('week', oper_date) AS 周, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY 周 ORDER BY 周;"
-  },
-  {
-    "question": "查询2023年4月每日订单数量趋势，并按日期升序排序。",
-    "sql": "SELECT oper_date AS 统计日期, SUM(order_sum) AS 订单数量 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY oper_date ORDER BY oper_date ASC;"
-  },
-  {
-    "question": "统计2023年4月各服务区的微信支付占比，并按占比降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx) / SUM(pay_sum) * 100 AS 微信支付占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 微信支付占比 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各服务区现金支付金额超过1000元的服务区名称及金额。",
-    "sql": "SELECT service_name AS 服务区名称, rmb AS 现金支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND rmb > 1000 AND delete_ts IS NULL ORDER BY rmb DESC;"
-  },
-  {
-    "question": "统计2023年4月各服务区的平均每日营收，并按平均值降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, AVG(pay_sum) AS 平均每日营收 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 平均每日营收 DESC;"
-  },
-  {
-    "question": "统计各服务区每日总车流量，并按日期和服务区排序。",
-    "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 GROUP BY count_date, service_area_id ORDER BY count_date, service_area_id;"
-  },
-  {
-    "question": "查询2023年4月各服务区每日车流趋势，仅显示城际车辆。",
-    "sql": "SELECT count_date AS 统计日期, service_area_id AS 服务区ID, customer_count AS 车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND car_type = '城际' AND count_date BETWEEN '2023-04-01' AND '2023-04-30' ORDER BY count_date;"
-  },
-  {
-    "question": "统计各车辆类型在所有服务区的总占比。",
-    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count) * 100.0 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE delete_ts IS NULL), 2) AS 占比百分比 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY car_type;"
-  },
-  {
-    "question": "找出2023年4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 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": "分析某特定服务区（如ID为'17461166e7fa3ecda03534a5795ce985'）2023年4月每日车流量变化趋势。",
-    "sql": "SELECT count_date AS 统计日期, customer_count AS 车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND service_area_id = '17461166e7fa3ecda03534a5795ce985' AND count_date BETWEEN '2023-04-01' AND '2023-04-30' ORDER BY count_date;"
-  },
-  {
-    "question": "比较不同车辆类型在2023年4月的平均日车流量。",
-    "sql": "SELECT car_type AS 车辆类型, AVG(customer_count) AS 平均日车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY car_type ORDER BY 平均日车流量 DESC;"
-  },
-  {
-    "question": "统计各服务区在2023年4月每日车流量的标准差，以评估车流波动性。",
-    "sql": "SELECT service_area_id AS 服务区ID, STDDEV(customer_count) AS 车流标准差 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 车流标准差 DESC;"
-  },
-  {
-    "question": "查询2023年4月每天总车流量的变化趋势。",
-    "sql": "SELECT count_date AS 统计日期, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY count_date ORDER BY count_date;"
-  },
-  {
-    "question": "找出2023年4月平均日车流量最高的5个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, AVG(customer_count) AS 平均日车流量 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 平均日车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月各服务区每日车流量与经营数据的关联性（需联合bss_business_day_data表）。",
-    "sql": "SELECT a.count_date AS 统计日期, a.service_area_id AS 服务区ID, SUM(a.customer_count) AS 总车流量, SUM(b.pay_sum) AS 总支付金额 FROM bss_car_day_count a LEFT JOIN bss_business_day_data b ON a.service_area_id = b.service_no AND a.count_date = b.oper_date WHERE a.delete_ts IS NULL AND a.count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY a.count_date, a.service_area_id ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日的平均营收，并按平均营收降序排列。",
-    "sql": "SELECT bco.company_name AS 公司名称, AVG(bbd.pay_sum) AS 平均营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL GROUP BY bco.company_name ORDER BY 平均营收 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各公司的车流总量，并按车流总量降序排列。",
-    "sql": "SELECT bco.company_name AS 公司名称, SUM(bcc.customer_count) AS 车流总量 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date = '2023-04-01' AND bcc.delete_ts IS NULL GROUP BY bco.company_name ORDER BY 车流总量 DESC;"
-  },
-  {
-    "question": "列出2023年4月1日营收排名前5的服务区及其所属公司。",
-    "sql": "SELECT bbd.service_name AS 服务区名称, bco.company_name AS 公司名称, SUM(bbd.pay_sum) AS 营收总额 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL GROUP BY bbd.service_name, bco.company_name ORDER BY 营收总额 DESC LIMIT 5;"
-  },
-  {
-    "question": "列出2023年4月1日车流排名前5的服务区及其所属公司。",
-    "sql": "SELECT bsa.service_area_name AS 服务区名称, bco.company_name AS 公司名称, SUM(bcc.customer_count) AS 车流总量 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date = '2023-04-01' AND bcc.delete_ts IS NULL GROUP BY bsa.service_area_name, bco.company_name ORDER BY 车流总量 DESC LIMIT 5;"
-  },
-  {
-    "question": "分析2023年4月1日各公司平均营收与车流总量的关系，按公司分组。",
-    "sql": "SELECT bco.company_name AS 公司名称, AVG(bbd.pay_sum) AS 平均营收, SUM(bcc.customer_count) AS 车流总量 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id JOIN bss_car_day_count bcc ON bsa.id = bcc.service_area_id AND bbd.oper_date = bcc.count_date WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL AND bcc.delete_ts IS NULL GROUP BY bco.company_name;"
-  },
-  {
-    "question": "统计2023年4月各公司每日平均营收，并按日期升序、平均营收降序排列。",
-    "sql": "SELECT bbd.oper_date AS 统计日期, bco.company_name AS 公司名称, AVG(bbd.pay_sum) AS 平均营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND bbd.delete_ts IS NULL GROUP BY bbd.oper_date, bco.company_name ORDER BY 统计日期 ASC, 平均营收 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各公司服务区的营收明细，并按营收降序排列。",
-    "sql": "SELECT bbd.service_name AS 服务区名称, bco.company_name AS 公司名称, bbd.pay_sum AS 营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL ORDER BY 营收 DESC;"
-  },
-  {
-    "question": "统计2023年4月各公司总营收与车流总量，并计算营收占比。",
-    "sql": "WITH company_revenue AS (SELECT bco.company_name AS 公司名称, SUM(bbd.pay_sum) AS 总营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND bbd.delete_ts IS NULL GROUP BY bco.company_name), company_traffic AS (SELECT bco.company_name AS 公司名称, SUM(bcc.customer_count) AS 总车流 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND bcc.delete_ts IS NULL GROUP BY bco.company_name) SELECT cr.公司名称, cr.总营收, ct.总车流, (cr.总营收 / SUM(cr.总营收) OVER ()) * 100 AS 营收占比百分比 FROM company_revenue cr JOIN company_traffic ct ON cr.公司名称 = ct.公司名称;"
-  },
-  {
-    "question": "列出2023年4月1日各公司下辖服务区的营收排名。",
-    "sql": "SELECT bco.company_name AS 公司名称, bbd.service_name AS 服务区名称, SUM(bbd.pay_sum) AS 营收总额, RANK() OVER (PARTITION BY bco.company_name ORDER BY SUM(bbd.pay_sum) DESC) AS 排名 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL GROUP BY bco.company_name, bbd.service_name;"
-  },
-  {
-    "question": "列出2023年4月1日各公司下辖服务区的车流排名。",
-    "sql": "SELECT bco.company_name AS 公司名称, bsa.service_area_name AS 服务区名称, SUM(bcc.customer_count) AS 车流总量, RANK() OVER (PARTITION BY bco.company_name ORDER BY SUM(bcc.customer_count) DESC) AS 排名 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date = '2023-04-01' AND bcc.delete_ts IS NULL GROUP BY bco.company_name, bsa.service_area_name;"
-  },
-  {
-    "question": "统计每条路线关联的服务区数量，并按数量降序排列。",
-    "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 AND bsral.service_area_id IS NOT NULL GROUP BY bsr.route_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "查询每个路段覆盖的服务区数量，并列出覆盖最少的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 AND bsral.service_area_id IS NOT NULL GROUP BY bsr.section_name ORDER BY 服务区数量 ASC LIMIT 5;"
-  },
-  {
-    "question": "找出与最多服务区关联的路线，并列出其关联的服务区名称。",
-    "sql": "SELECT bsr.route_name AS 路线名称, bsa.service_area_name AS 服务区名称 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE (bsr.id IN (SELECT section_route_id FROM bss_section_route_area_link GROUP BY section_route_id ORDER BY COUNT(service_area_id) DESC LIMIT 1)) ORDER BY 服务区名称;"
-  },
-  {
-    "question": "统计每条路线在2023年4月1日当天的总支付金额，并按路线名称排序。",
-    "sql": "SELECT bsr.route_name AS 路线名称, SUM(bdd.pay_sum) AS 总支付金额 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no WHERE bdd.oper_date = '2023-04-01' GROUP BY bsr.route_name ORDER BY 路线名称;"
-  },
-  {
-    "question": "列出2022年3月2日当天车辆数量最多的前5个服务区及其关联路线。",
-    "sql": "SELECT bsa.service_area_name AS 服务区名称, bsr.route_name AS 路线名称 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 JOIN bss_car_day_count bcdc ON bsa.id = bcdc.service_area_id WHERE bcdc.count_date = '2022-03-02' ORDER BY bcdc.customer_count DESC LIMIT 5;"
-  },
-  {
-    "question": "统计每个服务区所属公司的路线覆盖情况，并列出覆盖最少的公司。",
-    "sql": "SELECT bc.company_name AS 公司名称, COUNT(DISTINCT bsr.route_name) AS 路线数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_company bc ON bsa.company_id = bc.id GROUP BY bc.company_name ORDER BY 路线数量 ASC LIMIT 1;"
-  },
-  {
-    "question": "查询2023年4月1日当天，每条路线的订单总数，并按订单总数降序排列。",
-    "sql": "SELECT bsr.route_name AS 路线名称, SUM(bdd.order_sum) AS 订单总数 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no WHERE bdd.oper_date = '2023-04-01' GROUP BY bsr.route_name ORDER BY 订单总数 DESC;"
-  },
-  {
-    "question": "列出所有路线及其关联的服务区数量，仅包括服务区状态为开放的记录。",
-    "sql": "SELECT bsr.route_name AS 路线名称, COUNT(bsa.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 JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE bsa.service_state = '开放' GROUP BY bsr.route_name;"
-  },
-  {
-    "question": "统计每个服务区的微信支付金额占比，并按路线分组列出占比最高的服务区。",
-    "sql": "WITH wx_sum_per_route AS (SELECT bsr.route_name, SUM(bdd.wx) AS total_wx FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no GROUP BY bsr.route_name), wx_per_area AS (SELECT bsr.route_name, bsa.service_area_name, SUM(bdd.wx) AS area_wx FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no GROUP BY bsr.route_name, bsa.service_area_name) SELECT wpa.route_name AS 路线名称, wpa.service_area_name AS 服务区名称, (wpa.area_wx / wsp.total_wx * 100) AS 微信占比 FROM wx_per_area wpa JOIN wx_sum_per_route wsp ON wpa.route_name = wsp.route_name ORDER BY 微信占比 DESC;"
-  },
-  {
-    "question": "列出2022年2月2日当天，车辆类型为过境的车流数量超过1000的路线及其服务区。",
-    "sql": "SELECT bsr.route_name AS 路线名称, bsa.service_area_name AS 服务区名称, bcdc.customer_count AS 车辆数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_car_day_count bcdc ON bsa.id = bcdc.service_area_id WHERE bcdc.count_date = '2022-02-02' AND bcdc.car_type = '过境' AND bcdc.customer_count > 1000;"
-  },
-  {
-    "question": "统计各类型服务区的数量及占比，仅考虑未删除的服务区",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 服务区数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area WHERE delete_ts IS NULL), 2) AS 占比 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "统计各公司管理的服务区数量及开放比例",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(sa.id) AS 服务区总数, SUM(CASE WHEN sa.service_state = '开放' THEN 1 ELSE 0 END) AS 开放数量, ROUND(SUM(CASE WHEN sa.service_state = '开放' THEN 1 ELSE 0 END) * 100.0 / COUNT(sa.id), 2) AS 开放比例 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 GROUP BY c.company_name;"
-  },
-  {
-    "question": "查询最近一周新增的服务区列表及其所属公司",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司, sa.create_ts AS 创建时间 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.create_ts >= NOW() - INTERVAL '7 days' AND sa.delete_ts IS NULL ORDER BY sa.create_ts DESC;"
-  },
-  {
-    "question": "统计不同状态的服务区数量分布",
-    "sql": "SELECT service_state AS 服务区状态, COUNT(*) AS 数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_state;"
-  },
-  {
-    "question": "列出所有关闭的服务区及其所属公司名称",
-    "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": "按省份划分服务区数量（假设服务区编码前两位代表省份）",
-    "sql": "SELECT LEFT(service_area_no, 2) AS 省份编码, COUNT(*) AS 服务区数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY LEFT(service_area_no, 2) ORDER BY 服务区数量 DESC LIMIT 10;"
-  },
-  {
-    "question": "找出最近一个月更新过的服务区及其最后更新时间",
-    "sql": "SELECT service_area_name AS 服务区名称, update_ts AS 最后更新时间 FROM bss_service_area WHERE update_ts >= NOW() - INTERVAL '1 month' AND delete_ts IS NULL ORDER BY update_ts DESC;"
-  },
-  {
-    "question": "列出所有服务区的经纬度信息及其所属公司名称",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_position AS 经纬度, 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;"
-  },
-  {
-    "question": "按公司统计其管理的服务区数量，并按数量降序排列",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(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 GROUP BY c.company_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "统计各类型服务区中关闭的数量及占比",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 总数量, SUM(CASE WHEN service_state = '关闭' THEN 1 ELSE 0 END) AS 关闭数量, ROUND(SUM(CASE WHEN service_state = '关闭' THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS 关闭比例 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  }
-]

data_pipeline/training_data/manual_20250721_002320/qs_highway_db_20250721_002747_pair.json.backup

@@ -1,202 +0,0 @@
-[
-  {
-    "question": "查询2023年4月1日各服务区的总营收和订单数量，并按总营收降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, pay_sum AS 总营收, order_sum AS 订单数量 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY pay_sum DESC;"
-  },
-  {
-    "question": "统计2023年4月各服务区每日总营收，按日期和服务区分组。",
-    "sql": "SELECT oper_date AS 统计日期, service_name AS 服务区名称, SUM(pay_sum) AS 日总营收 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY oper_date, service_name ORDER BY oper_date;"
-  },
-  {
-    "question": "查询2023年4月1日各支付方式的总金额及订单数量，并按支付方式分类。",
-    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 支付金额, SUM(wx_order) AS 订单数量 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '支付宝', SUM(zfb), SUM(zf_order) FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '现金', SUM(rmb), SUM(rmb_order) FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "统计2023年4月各档口的总营收TOP 10，并显示对应的支付方式占比。",
-    "sql": "SELECT branch_name AS 档口名称, pay_sum AS 总营收, wx / pay_sum * 100 AS 微信占比, zfb / pay_sum * 100 AS 支付宝占比, rmb / pay_sum * 100 AS 现金占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL ORDER BY pay_sum DESC LIMIT 10;"
-  },
-  {
-    "question": "查询2023年4月各服务区现金支付金额占比，并按占比降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) / SUM(pay_sum) * 100 AS 现金支付占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付占比 DESC;"
-  },
-  {
-    "question": "统计2023年4月各周的总营收趋势，按周分组。",
-    "sql": "SELECT date_trunc('week', oper_date) AS 周, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY 周 ORDER BY 周;"
-  },
-  {
-    "question": "查询2023年4月每日订单数量趋势，并按日期升序排序。",
-    "sql": "SELECT oper_date AS 统计日期, SUM(order_sum) AS 订单数量 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY oper_date ORDER BY oper_date ASC;"
-  },
-  {
-    "question": "统计2023年4月各服务区的微信支付占比，并按占比降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx) / SUM(pay_sum) * 100 AS 微信支付占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 微信支付占比 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各服务区现金支付金额超过1000元的服务区名称及金额。",
-    "sql": "SELECT service_name AS 服务区名称, rmb AS 现金支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND rmb > 1000 AND delete_ts IS NULL ORDER BY rmb DESC;"
-  },
-  {
-    "question": "统计2023年4月各服务区的平均每日营收，并按平均值降序排序。",
-    "sql": "SELECT service_name AS 服务区名称, AVG(pay_sum) AS 平均每日营收 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 平均每日营收 DESC;"
-  },
-  {
-    "question": "统计各服务区每日总车流量，并按日期和服务区排序。",
-    "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 GROUP BY count_date, service_area_id ORDER BY count_date, service_area_id;"
-  },
-  {
-    "question": "查询2023年4月各服务区每日车流趋势，仅显示城际车辆。",
-    "sql": "SELECT count_date AS 统计日期, service_area_id AS 服务区ID, customer_count AS 车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND car_type = '城际' AND count_date BETWEEN '2023-04-01' AND '2023-04-30' ORDER BY count_date;"
-  },
-  {
-    "question": "统计各车辆类型在所有服务区的总占比。",
-    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count) * 100.0 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE delete_ts IS NULL), 2) AS 占比百分比 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY car_type;"
-  },
-  {
-    "question": "找出2023年4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 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": "分析某特定服务区（如ID为'17461166e7fa3ecda03534a5795ce985'）2023年4月每日车流量变化趋势。",
-    "sql": "SELECT count_date AS 统计日期, customer_count AS 车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND service_area_id = '17461166e7fa3ecda03534a5795ce985' AND count_date BETWEEN '2023-04-01' AND '2023-04-30' ORDER BY count_date;"
-  },
-  {
-    "question": "比较不同车辆类型在2023年4月的平均日车流量。",
-    "sql": "SELECT car_type AS 车辆类型, AVG(customer_count) AS 平均日车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY car_type ORDER BY 平均日车流量 DESC;"
-  },
-  {
-    "question": "统计各服务区在2023年4月每日车流量的标准差，以评估车流波动性。",
-    "sql": "SELECT service_area_id AS 服务区ID, STDDEV(customer_count) AS 车流标准差 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 车流标准差 DESC;"
-  },
-  {
-    "question": "查询2023年4月每天总车流量的变化趋势。",
-    "sql": "SELECT count_date AS 统计日期, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE delete_ts IS NULL AND count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY count_date ORDER BY count_date;"
-  },
-  {
-    "question": "找出2023年4月平均日车流量最高的5个服务区。",
-    "sql": "SELECT service_area_id AS 服务区ID, AVG(customer_count) AS 平均日车流量 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 平均日车流量 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月各服务区每日车流量与经营数据的关联性（需联合bss_business_day_data表）。",
-    "sql": "SELECT a.count_date AS 统计日期, a.service_area_id AS 服务区ID, SUM(a.customer_count) AS 总车流量, SUM(b.pay_sum) AS 总支付金额 FROM bss_car_day_count a LEFT JOIN bss_business_day_data b ON a.service_area_id = b.service_no AND a.count_date = b.oper_date WHERE a.delete_ts IS NULL AND a.count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY a.count_date, a.service_area_id ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日的平均营收，并按平均营收降序排列。",
-    "sql": "SELECT bco.company_name AS 公司名称, AVG(bbd.pay_sum) AS 平均营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL GROUP BY bco.company_name ORDER BY 平均营收 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各公司的车流总量，并按车流总量降序排列。",
-    "sql": "SELECT bco.company_name AS 公司名称, SUM(bcc.customer_count) AS 车流总量 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date = '2023-04-01' AND bcc.delete_ts IS NULL GROUP BY bco.company_name ORDER BY 车流总量 DESC;"
-  },
-  {
-    "question": "列出2023年4月1日营收排名前5的服务区及其所属公司。",
-    "sql": "SELECT bbd.service_name AS 服务区名称, bco.company_name AS 公司名称, SUM(bbd.pay_sum) AS 营收总额 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL GROUP BY bbd.service_name, bco.company_name ORDER BY 营收总额 DESC LIMIT 5;"
-  },
-  {
-    "question": "列出2023年4月1日车流排名前5的服务区及其所属公司。",
-    "sql": "SELECT bsa.service_area_name AS 服务区名称, bco.company_name AS 公司名称, SUM(bcc.customer_count) AS 车流总量 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date = '2023-04-01' AND bcc.delete_ts IS NULL GROUP BY bsa.service_area_name, bco.company_name ORDER BY 车流总量 DESC LIMIT 5;"
-  },
-  {
-    "question": "分析2023年4月1日各公司平均营收与车流总量的关系，按公司分组。",
-    "sql": "SELECT bco.company_name AS 公司名称, AVG(bbd.pay_sum) AS 平均营收, SUM(bcc.customer_count) AS 车流总量 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id JOIN bss_car_day_count bcc ON bsa.id = bcc.service_area_id AND bbd.oper_date = bcc.count_date WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL AND bcc.delete_ts IS NULL GROUP BY bco.company_name;"
-  },
-  {
-    "question": "统计2023年4月各公司每日平均营收，并按日期升序、平均营收降序排列。",
-    "sql": "SELECT bbd.oper_date AS 统计日期, bco.company_name AS 公司名称, AVG(bbd.pay_sum) AS 平均营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND bbd.delete_ts IS NULL GROUP BY bbd.oper_date, bco.company_name ORDER BY 统计日期 ASC, 平均营收 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各公司服务区的营收明细，并按营收降序排列。",
-    "sql": "SELECT bbd.service_name AS 服务区名称, bco.company_name AS 公司名称, bbd.pay_sum AS 营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL ORDER BY 营收 DESC;"
-  },
-  {
-    "question": "统计2023年4月各公司总营收与车流总量，并计算营收占比。",
-    "sql": "WITH company_revenue AS (SELECT bco.company_name AS 公司名称, SUM(bbd.pay_sum) AS 总营收 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND bbd.delete_ts IS NULL GROUP BY bco.company_name), company_traffic AS (SELECT bco.company_name AS 公司名称, SUM(bcc.customer_count) AS 总车流 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND bcc.delete_ts IS NULL GROUP BY bco.company_name) SELECT cr.公司名称, cr.总营收, ct.总车流, (cr.总营收 / SUM(cr.总营收) OVER ()) * 100 AS 营收占比百分比 FROM company_revenue cr JOIN company_traffic ct ON cr.公司名称 = ct.公司名称;"
-  },
-  {
-    "question": "列出2023年4月1日各公司下辖服务区的营收排名。",
-    "sql": "SELECT bco.company_name AS 公司名称, bbd.service_name AS 服务区名称, SUM(bbd.pay_sum) AS 营收总额, RANK() OVER (PARTITION BY bco.company_name ORDER BY SUM(bbd.pay_sum) DESC) AS 排名 FROM bss_business_day_data bbd JOIN bss_service_area bsa ON bbd.service_no = bsa.service_area_no JOIN bss_company bco ON bsa.company_id = bco.id WHERE bbd.oper_date = '2023-04-01' AND bbd.delete_ts IS NULL GROUP BY bco.company_name, bbd.service_name;"
-  },
-  {
-    "question": "列出2023年4月1日各公司下辖服务区的车流排名。",
-    "sql": "SELECT bco.company_name AS 公司名称, bsa.service_area_name AS 服务区名称, SUM(bcc.customer_count) AS 车流总量, RANK() OVER (PARTITION BY bco.company_name ORDER BY SUM(bcc.customer_count) DESC) AS 排名 FROM bss_car_day_count bcc JOIN bss_service_area bsa ON bcc.service_area_id = bsa.id JOIN bss_company bco ON bsa.company_id = bco.id WHERE bcc.count_date = '2023-04-01' AND bcc.delete_ts IS NULL GROUP BY bco.company_name, bsa.service_area_name;"
-  },
-  {
-    "question": "统计每条路线关联的服务区数量，并按数量降序排列。",
-    "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 AND bsral.service_area_id IS NOT NULL GROUP BY bsr.route_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "查询每个路段覆盖的服务区数量，并列出覆盖最少的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 AND bsral.service_area_id IS NOT NULL GROUP BY bsr.section_name ORDER BY 服务区数量 ASC LIMIT 5;"
-  },
-  {
-    "question": "找出与最多服务区关联的路线，并列出其关联的服务区名称。",
-    "sql": "SELECT bsr.route_name AS 路线名称, bsa.service_area_name AS 服务区名称 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE (bsr.id IN (SELECT section_route_id FROM bss_section_route_area_link GROUP BY section_route_id ORDER BY COUNT(service_area_id) DESC LIMIT 1)) ORDER BY 服务区名称;"
-  },
-  {
-    "question": "统计每条路线在2023年4月1日当天的总支付金额，并按路线名称排序。",
-    "sql": "SELECT bsr.route_name AS 路线名称, SUM(bdd.pay_sum) AS 总支付金额 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no WHERE bdd.oper_date = '2023-04-01' GROUP BY bsr.route_name ORDER BY 路线名称;"
-  },
-  {
-    "question": "列出2022年3月2日当天车辆数量最多的前5个服务区及其关联路线。",
-    "sql": "SELECT bsa.service_area_name AS 服务区名称, bsr.route_name AS 路线名称 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 JOIN bss_car_day_count bcdc ON bsa.id = bcdc.service_area_id WHERE bcdc.count_date = '2022-03-02' ORDER BY bcdc.customer_count DESC LIMIT 5;"
-  },
-  {
-    "question": "统计每个服务区所属公司的路线覆盖情况，并列出覆盖最少的公司。",
-    "sql": "SELECT bc.company_name AS 公司名称, COUNT(DISTINCT bsr.route_name) AS 路线数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_company bc ON bsa.company_id = bc.id GROUP BY bc.company_name ORDER BY 路线数量 ASC LIMIT 1;"
-  },
-  {
-    "question": "查询2023年4月1日当天，每条路线的订单总数，并按订单总数降序排列。",
-    "sql": "SELECT bsr.route_name AS 路线名称, SUM(bdd.order_sum) AS 订单总数 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no WHERE bdd.oper_date = '2023-04-01' GROUP BY bsr.route_name ORDER BY 订单总数 DESC;"
-  },
-  {
-    "question": "列出所有路线及其关联的服务区数量，仅包括服务区状态为开放的记录。",
-    "sql": "SELECT bsr.route_name AS 路线名称, COUNT(bsa.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 JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id WHERE bsa.service_state = '开放' GROUP BY bsr.route_name;"
-  },
-  {
-    "question": "统计每个服务区的微信支付金额占比，并按路线分组列出占比最高的服务区。",
-    "sql": "WITH wx_sum_per_route AS (SELECT bsr.route_name, SUM(bdd.wx) AS total_wx FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no GROUP BY bsr.route_name), wx_per_area AS (SELECT bsr.route_name, bsa.service_area_name, SUM(bdd.wx) AS area_wx FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_business_day_data bdd ON bsa.service_area_no = bdd.service_no GROUP BY bsr.route_name, bsa.service_area_name) SELECT wpa.route_name AS 路线名称, wpa.service_area_name AS 服务区名称, (wpa.area_wx / wsp.total_wx * 100) AS 微信占比 FROM wx_per_area wpa JOIN wx_sum_per_route wsp ON wpa.route_name = wsp.route_name ORDER BY 微信占比 DESC;"
-  },
-  {
-    "question": "列出2022年2月2日当天，车辆类型为过境的车流数量超过1000的路线及其服务区。",
-    "sql": "SELECT bsr.route_name AS 路线名称, bsa.service_area_name AS 服务区名称, bcdc.customer_count AS 车辆数量 FROM bss_section_route bsr JOIN bss_section_route_area_link bsral ON bsr.id = bsral.section_route_id JOIN bss_service_area bsa ON bsral.service_area_id = bsa.id JOIN bss_car_day_count bcdc ON bsa.id = bcdc.service_area_id WHERE bcdc.count_date = '2022-02-02' AND bcdc.car_type = '过境' AND bcdc.customer_count > 1000;"
-  },
-  {
-    "question": "统计各类型服务区的数量及占比，仅考虑未删除的服务区",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 服务区数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area WHERE delete_ts IS NULL), 2) AS 占比 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  },
-  {
-    "question": "统计各公司管理的服务区数量及开放比例",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(sa.id) AS 服务区总数, SUM(CASE WHEN sa.service_state = '开放' THEN 1 ELSE 0 END) AS 开放数量, ROUND(SUM(CASE WHEN sa.service_state = '开放' THEN 1 ELSE 0 END) * 100.0 / COUNT(sa.id), 2) AS 开放比例 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 GROUP BY c.company_name;"
-  },
-  {
-    "question": "查询最近一周新增的服务区列表及其所属公司",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司, sa.create_ts AS 创建时间 FROM bss_service_area sa LEFT JOIN bss_company c ON sa.company_id = c.id WHERE sa.create_ts >= NOW() - INTERVAL '7 days' AND sa.delete_ts IS NULL ORDER BY sa.create_ts DESC;"
-  },
-  {
-    "question": "统计不同状态的服务区数量分布",
-    "sql": "SELECT service_state AS 服务区状态, COUNT(*) AS 数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_state;"
-  },
-  {
-    "question": "列出所有关闭的服务区及其所属公司名称",
-    "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": "按省份划分服务区数量（假设服务区编码前两位代表省份）",
-    "sql": "SELECT LEFT(service_area_no, 2) AS 省份编码, COUNT(*) AS 服务区数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY LEFT(service_area_no, 2) ORDER BY 服务区数量 DESC LIMIT 10;"
-  },
-  {
-    "question": "找出最近一个月更新过的服务区及其最后更新时间",
-    "sql": "SELECT service_area_name AS 服务区名称, update_ts AS 最后更新时间 FROM bss_service_area WHERE update_ts >= NOW() - INTERVAL '1 month' AND delete_ts IS NULL ORDER BY update_ts DESC;"
-  },
-  {
-    "question": "列出所有服务区的经纬度信息及其所属公司名称",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_position AS 经纬度, 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;"
-  },
-  {
-    "question": "按公司统计其管理的服务区数量，并按数量降序排列",
-    "sql": "SELECT c.company_name AS 公司名称, COUNT(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 GROUP BY c.company_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "统计各类型服务区中关闭的数量及占比",
-    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 总数量, SUM(CASE WHEN service_state = '关闭' THEN 1 ELSE 0 END) AS 关闭数量, ROUND(SUM(CASE WHEN service_state = '关闭' THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS 关闭比例 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_area_type;"
-  }
-]

data_pipeline/training_data/manual_20250721_002320/vector_bak/vector_backup_log.txt

@@ -1,11 +0,0 @@
-=== Vector Table Backup Log ===
-Backup Time: 2025-07-21 00:27:58
-Task ID: manual_20250721_002320
-Duration: 0.00s
-
-Tables Backup Status:
-✓ langchain_pg_collection: 4 rows -> langchain_pg_collection_20250721_002757.csv (209.0 B)
-✓ langchain_pg_embedding: 814 rows -> langchain_pg_embedding_20250721_002757.csv (10.4 MB)
-
-Truncate Status:
-✓ langchain_pg_embedding: TRUNCATED (814 rows removed)

data_pipeline/training_data/manual_20250721_010214/bss_business_day_data.ddl

@@ -1,31 +0,0 @@
--- 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。
-create table public.bss_business_day_data (
-  id varchar(32) not null     -- 主键ID，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,
-  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            -- 行吧订单数量,
-  jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
-  order_sum integer           -- 订单总数,
-  pay_sum numeric(19,4)       -- 支付总金额,
-  source_type integer         -- 数据来源类别,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_010214/bss_business_day_data_detail.md

@@ -1,32 +0,0 @@
-## 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。
-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]
-- 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) - 行吧订单数量 [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]
-- 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]
-- source_type (integer) - 数据来源类别 [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/manual_20250721_010214/bss_car_day_count.ddl

@@ -1,17 +0,0 @@
--- 中文名: `bss_car_day_count` 表用于按天统计进入服务区的车辆数量及类型
--- 描述: `bss_car_day_count` 表用于按天统计进入服务区的车辆数量及类型，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    -- 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,
-  count_date date             -- 统计日期,
-  service_area_id varchar(32) -- 服务区ID,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_010214/bss_car_day_count_detail.md

@@ -1,18 +0,0 @@
-## bss_car_day_count（`bss_car_day_count` 表用于按天统计进入服务区的车辆数量及类型）
-bss_car_day_count 表`bss_car_day_count` 表用于按天统计进入服务区的车辆数量及类型，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) - 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]
-- 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_20250721_010214/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。
-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/manual_20250721_010214/bss_company_detail.md

@@ -1,19 +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。
-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
-- 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_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/manual_20250721_010214/bss_section_route.ddl

@@ -1,16 +0,0 @@
--- 中文名: 路段与路线信息表
--- 描述: 路段与路线信息表，用于管理高速公路服务区所属路段及路线名称。
-create table public.bss_section_route (
-  id varchar(32) not null     -- 主键ID，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,
-  section_name varchar(255)   -- 路段名称,
-  route_name varchar(255)     -- 路线名称,
-  code varchar(255)           -- 编号,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_010214/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，记录路线与服务区之间的绑定关系。
-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/manual_20250721_010214/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，记录路线与服务区之间的绑定关系。
-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/manual_20250721_010214/bss_section_route_detail.md

@@ -1,16 +0,0 @@
-## bss_section_route（路段与路线信息表）
-bss_section_route 表路段与路线信息表，用于管理高速公路服务区所属路段及路线名称。
-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]
-- 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]
-字段补充说明：
-- id 为主键

data_pipeline/training_data/manual_20250721_010214/bss_service_area.ddl

@@ -1,19 +0,0 @@
--- 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、编码及操作记录，为核心业务提供数据支撑。
-create table public.bss_service_area (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  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) -- 服务区经纬度,
-  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/manual_20250721_010214/bss_service_area_detail.md

@@ -1,21 +0,0 @@
-## 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、编码及操作记录，为核心业务提供数据支撑。
-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]
-- 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]
-- updated_by (varchar(50)) - 更新人 [示例: admin]
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人 [示例: ]
-- 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_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：
-- 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
-- service_state 为枚举字段，包含取值：开放、关闭、上传数据

data_pipeline/training_data/manual_20250721_010214/bss_service_area_mapper.ddl

@@ -1,18 +0,0 @@
--- 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，用于统一服务区名称与编码的关联关系及生命周期管理。
-create table public.bss_service_area_mapper (
-  id varchar(32) not null     -- 主键ID，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,
-  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,
-  primary key (id)
-);

data_pipeline/training_data/manual_20250721_010214/bss_service_area_mapper_detail.md

@@ -1,20 +0,0 @@
-## 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，用于统一服务区名称与编码的关联关系及生命周期管理。
-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]
-- 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]
-- updated_by (varchar(50)) - 更新人
-- delete_ts (timestamp) - 删除时间
-- 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]
-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
-- source_type 为枚举字段，包含取值：5、0、1、3、4

data_pipeline/training_data/manual_20250721_010214/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，主要涉及服务区经营、车辆统计、公司管理及路段路线关联，包含以下业务数据：
-核心业务实体：
-- 服务区：表示高速公路的服务区域，主要字段：service_area_name、service_area_no、service_state
-- 档口：表示服务区内的具体经营点，主要字段：branch_no、branch_name
-- 公司：表示服务区所属的管理公司，主要字段：company_name、company_no
-- 车辆：表示进入服务区的车辆类型，主要字段：car_type、customer_count
-- 路段与路线：表示服务区所属的路段和路线信息，主要字段：section_name、route_name
-关键业务指标：
-- 经营数据：包括支付总金额(pay_sum)、订单总数(order_sum)、各类支付方式金额(wx、zfb、rmb)及订单数(wx_order、zf_order、rmb_order)
-- 车流统计：进入服务区的各类车辆数量(customer_count)，按车辆类别(car_type)进行统计

data_pipeline/training_data/manual_20250721_010214/filename_mapping.txt

@@ -1,10 +0,0 @@
-# 文件名映射报告
-# 格式: 原始表名 -> 实际文件名
-
-public.bss_business_day_data -> bss_business_day_data_detail.md
-public.bss_car_day_count -> bss_car_day_count_detail.md
-public.bss_company -> bss_company_detail.md
-public.bss_section_route -> bss_section_route_detail.md
-public.bss_section_route_area_link -> bss_section_route_area_link_detail.md
-public.bss_service_area -> bss_service_area_detail.md
-public.bss_service_area_mapper -> bss_service_area_mapper_detail.md

data_pipeline/training_data/manual_20250721_010214/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 01:06:58
--- 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每日的营业收入、订单数量及支付方式分布，支撑经营优化决策。',
-  'bss_business_day_data',
-  '服务区,档口,支付方式,统计日期',
-  '收入趋势,订单数量,支付方式占比,服务区对比'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '车流统计分析',
-  '基于车辆进入服务区的数据，分析车流数量及类型分布，为设施规划和运营管理提供依据。',
-  'bss_car_day_count,bss_service_area',
-  '服务区,车辆类型,统计日期',
-  '车流趋势,车辆类型占比,服务区车流排名'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '公司运营对比',
-  '对比不同公司管辖服务区的营业收入和车流数据，评估各公司的运营绩效。',
-  'bss_company,bss_service_area,bss_business_day_data,bss_car_day_count',
-  '公司,服务区,支付方式',
-  '公司营收排名,公司车流排名,单位车流营收对比'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '服务区路段关联分析',
-  '分析服务区与路段路线的关联关系，评估路段车流对服务区业务的影响。',
-  'bss_section_route,bss_section_route_area_link,bss_service_area,bss_car_day_count',
-  '路段,路线,服务区',
-  '路段服务区数量,路段车流总量,服务区车流分布'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '服务区状态与营收关系',
-  '分析服务区开放与关闭状态对营业收入的影响，优化服务区运营策略。',
-  'bss_service_area,bss_business_day_data',
-  '服务区,服务状态,统计日期',
-  '开放状态营收,关闭状态营收,营收状态对比'
-);
-

data_pipeline/training_data/manual_20250721_010214/qs_highway_db_20250721_010658_pair.json

@@ -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列。",
-    "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 - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各档口的现金支付金额及订单数量，并按金额降序排序。",
-    "sql": "SELECT branch_name AS 档口名称, rmb AS 现金支付金额, 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 ORDER BY 现金支付金额 DESC;"
-  },
-  {
-    "question": "分析2023年4月各服务区微信支付与支付宝支付的占比情况。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx) / SUM(pay_sum) * 100 AS 微信支付占比, SUM(zfb) / SUM(pay_sum) * 100 AS 支付宝支付占比 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "找出2023年4月营业收入最高的前5个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月每天的营业收入趋势，显示每日总收入。",
-    "sql": "SELECT oper_date AS 统计日期, SUM(pay_sum) AS 日营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "比较不同服务区2023年4月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 >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 平均每日营业收入 DESC;"
-  },
-  {
-    "question": "查询2023年4月营业收入最低的3个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 ASC LIMIT 3;"
-  },
-  {
-    "question": "统计2023年4月各服务区不同支付方式的订单数量分布。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx_order) AS 微信订单数, SUM(zf_order) AS 支付宝订单数, SUM(rmb_order) AS 现金订单数 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "查询2023年4月营业收入超过10000元的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name HAVING SUM(pay_sum) > 10000 ORDER BY 总营业收入 DESC;"
-  },
-  {
-    "question": "统计2023年4月各服务区每日营业收入的波动情况。",
-    "sql": "SELECT service_name AS 服务区名称, oper_date AS 统计日期, SUM(pay_sum) AS 日营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name, oper_date ORDER BY 服务区名称, 统计日期;"
-  },
-  {
-    "question": "统计2023年每个月进入宜春服务区的车辆总数及平均每日车流量，并按月份排序。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '宜春服务区') AND delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date) ORDER BY 月份;"
-  },
-  {
-    "question": "2023年4月，各服务区车辆总数排名前5的服务区名称及车流量。",
-    "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 BETWEEN '2023-04-01' AND '2023-04-30' 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，各车辆类型在所有服务区的占比情况。",
-    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count) * 100.0 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL), 2) AS 占比百分比 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "2023年，平均每日车流量最低的3个服务区名称及平均车流量。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cc.customer_count) AS 日均车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 ASC LIMIT 3;"
-  },
-  {
-    "question": "2023年4月，每天进入庐山服务区的车流量趋势变化。",
-    "sql": "SELECT count_date AS 统计日期, customer_count AS 车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-30' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '庐山服务区') AND delete_ts IS NULL ORDER BY count_date;"
-  },
-  {
-    "question": "2023年4月，各公司管辖的服务区平均每日车流量对比。",
-    "sql": "SELECT co.company_name AS 公司名称, AVG(cc.customer_count) AS 日均车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id JOIN bss_company co ON sa.company_id = co.id WHERE cc.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL AND co.delete_ts IS NULL GROUP BY co.company_name ORDER BY 日均车流量 DESC;"
-  },
-  {
-    "question": "2023年4月，过境车辆最多的前3个服务区名称及数量。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cc.customer_count) AS 过境车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND cc.car_type = '过境' AND cc.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 3;"
-  },
-  {
-    "question": "2023年4月，每个星期几在宜春服务区的平均车流量，并按星期排序。",
-    "sql": "SELECT EXTRACT(DOW FROM count_date) AS 星期, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-30' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '宜春服务区') AND delete_ts IS NULL GROUP BY EXTRACT(DOW FROM count_date) ORDER BY 星期;"
-  },
-  {
-    "question": "2023年4月，所有服务区中，每日车流量超过1000的日期及服务区名称。",
-    "sql": "SELECT cc.count_date AS 统计日期, sa.service_area_name AS 服务区名称, cc.customer_count AS 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 BETWEEN '2023-04-01' AND '2023-04-30' AND cc.customer_count > 1000 AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL ORDER BY cc.count_date;"
-  },
-  {
-    "question": "2023年4月，各车辆类型在宜春服务区的每日车流量明细。",
-    "sql": "SELECT count_date AS 统计日期, car_type AS 车辆类型, customer_count AS 车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-30' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '宜春服务区') AND delete_ts IS NULL ORDER BY count_date, car_type;"
-  },
-  {
-    "question": "统计各公司2023年4月1日的总营业收入，并按公司名称分组。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.pay_sum) AS 总营业收入 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "统计各公司2023年4月1日的车辆总数，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(car_count) AS 车辆总数 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id GROUP BY b.company_name ORDER BY 车辆总数 DESC;"
-  },
-  {
-    "question": "计算各公司单位车流的平均营业收入，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.pay_sum) / SUM(car_count) AS 单位车流营收 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id JOIN bss_business_day_data a ON s.service_area_no = a.service_no WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "找出2023年4月1日营业收入最高的前5家公司。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.pay_sum) AS 总营业收入 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 总营业收入 DESC LIMIT 5;"
-  },
-  {
-    "question": "找出2023年4月1日车流量最高的前5家公司。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(car_count) AS 车辆总数 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id GROUP BY b.company_name ORDER BY 车辆总数 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日的现金支付总金额，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.rmb) AS 现金支付总额 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 现金支付总额 DESC;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日使用微信支付的订单数量，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.wx_order) AS 微信订单数量 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "比较各公司在2023年4月1日的支付宝支付金额占比。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.zfb) / SUM(a.pay_sum) * 100 AS 支付宝占比百分比 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日的订单总数，并按订单数量排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.order_sum) AS 订单总数 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 订单总数 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日车流和营收均排名前五的公司。",
-    "sql": "WITH car_count_rank AS (SELECT b.company_name AS 公司名称, SUM(car_count) AS 车辆总数 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id GROUP BY b.company_name ORDER BY 车辆总数 DESC LIMIT 5), revenue_rank AS (SELECT b.company_name AS 公司名称, SUM(a.pay_sum) AS 总营业收入 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 总营业收入 DESC LIMIT 5) SELECT car_count_rank.公司名称 FROM car_count_rank INNER JOIN revenue_rank ON car_count_rank.公司名称 = revenue_rank.公司名称;"
-  },
-  {
-    "question": "统计各路段关联的服务区数量，并按数量降序排列。",
-    "sql": "SELECT section_name AS 路段名称, COUNT(service_area_id) AS 服务区数量 FROM bss_section_route LEFT JOIN bss_section_route_area_link ON bss_section_route.id = bss_section_route_area_link.section_route_id WHERE bss_section_route.delete_ts IS NULL GROUP BY section_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各服务区的车流总量，并按车流降序排列。",
-    "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 = '2023-04-01' GROUP BY service_area_id ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日车流最少的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 = '2023-04-01' GROUP BY service_area_id ORDER BY 总车流量 ASC LIMIT 5;"
-  },
-  {
-    "question": "分析2022年各路段的月均车流情况，并按路段排序。",
-    "sql": "SELECT bss_section_route.section_name AS 路段名称, AVG(monthly_count) AS 月均车流量 FROM (SELECT bss_section_route_area_link.section_route_id, DATE_TRUNC('month', count_date) AS 月份, SUM(customer_count) AS monthly_count FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id WHERE count_date BETWEEN '2022-01-01' AND '2022-12-31' GROUP BY bss_section_route_area_link.section_route_id, 月份) AS monthly_data JOIN bss_section_route ON monthly_data.section_route_id = bss_section_route.id GROUP BY bss_section_route.section_name ORDER BY 路段名称;"
-  },
-  {
-    "question": "查询所有开放状态的服务区及其所属路段。",
-    "sql": "SELECT bss_service_area.service_area_name AS 服务区名称, bss_section_route.section_name AS 路段名称 FROM bss_service_area JOIN bss_section_route_area_link ON bss_service_area.id = bss_section_route_area_link.service_area_id JOIN bss_section_route ON bss_section_route_area_link.section_route_id = bss_section_route.id WHERE bss_service_area.delete_ts IS NULL AND bss_service_area.service_state = '开放';"
-  },
-  {
-    "question": "统计2023年4月1日各车辆类型在各服务区的分布情况。",
-    "sql": "SELECT service_area_id AS 服务区ID, car_type AS 车辆类型, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date = '2023-04-01' GROUP BY service_area_id, car_type ORDER BY 服务区ID, 总车流量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日车流最多的3个路段及其总车流量。",
-    "sql": "SELECT bss_section_route.section_name AS 路段名称, SUM(bss_car_day_count.customer_count) AS 总车流量 FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id JOIN bss_section_route ON bss_section_route_area_link.section_route_id = bss_section_route.id WHERE bss_car_day_count.count_date = '2023-04-01' GROUP BY bss_section_route.section_name ORDER BY 总车流量 DESC LIMIT 3;"
-  },
-  {
-    "question": "计算2023年4月1日各服务区车流占所属路段车流的百分比。",
-    "sql": "WITH section_total AS (SELECT bss_section_route.id AS section_id, SUM(bss_car_day_count.customer_count) AS total_count FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id JOIN bss_section_route ON bss_section_route_area_link.section_route_id = bss_section_route.id WHERE bss_car_day_count.count_date = '2023-04-01' GROUP BY bss_section_route.id), area_count AS (SELECT bss_section_route_area_link.section_route_id, bss_car_day_count.service_area_id, SUM(bss_car_day_count.customer_count) AS area_count FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id WHERE bss_car_day_count.count_date = '2023-04-01' GROUP BY bss_section_route_area_link.section_route_id, bss_car_day_count.service_area_id) SELECT area_count.service_area_id AS 服务区ID, bss_section_route.section_name AS 路段名称, area_count.area_count AS 服务区车流量, section_total.total_count AS 路段总车流量, (area_count.area_count::numeric / section_total.total_count) * 100 AS 占比百分比 FROM area_count JOIN section_total ON area_count.section_route_id = section_total.section_id JOIN bss_section_route ON area_count.section_route_id = bss_section_route.id;"
-  },
-  {
-    "question": "统计各公司管辖路段的平均服务区数量。",
-    "sql": "SELECT bss_company.company_name AS 公司名称, AVG(area_count) AS 平均服务区数量 FROM (SELECT bss_section_route.section_name, COUNT(bss_section_route_area_link.service_area_id) AS area_count FROM bss_section_route LEFT JOIN bss_section_route_area_link ON bss_section_route.id = bss_section_route_area_link.section_route_id GROUP BY bss_section_route.section_name) AS section_area_count JOIN bss_section_route ON section_area_count.section_name = bss_section_route.section_name JOIN bss_service_area ON bss_section_route.id = (SELECT section_route_id FROM bss_section_route_area_link WHERE bss_section_route_area_link.service_area_id = bss_service_area.id LIMIT 1) JOIN bss_company ON bss_service_area.company_id = bss_company.id GROUP BY bss_company.company_name;"
-  },
-  {
-    "question": "查询2023年4月1日所有服务区的车流数据，包括车辆类型明细。",
-    "sql": "SELECT service_area_id AS 服务区ID, car_type AS 车辆类型, customer_count AS 车流量 FROM bss_car_day_count WHERE count_date = '2023-04-01' ORDER BY 服务区ID, 车流量 DESC;"
-  },
-  {
-    "question": "统计最近7天内每天各服务区的营业总额，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 营业总额 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, service_name;"
-  },
-  {
-    "question": "统计各服务区在2023年4月期间的总营收，并按营收从高到低排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY service_name ORDER BY 总营收 DESC;"
-  },
-  {
-    "question": "统计2023年4月期间，开放状态和关闭状态的服务区的平均日营收，并按状态排序。",
-    "sql": "SELECT sa.service_state AS 服务区状态, AVG(bd.pay_sum) AS 平均日营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY sa.service_state ORDER BY 平均日营收 DESC;"
-  },
-  {
-    "question": "找出2023年4月营收最高的前5个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY service_name ORDER BY 总营收 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月每天开放状态服务区的总营收，并按日期排序。",
-    "sql": "SELECT bd.oper_date AS 统计日期, SUM(bd.pay_sum) AS 开放状态总营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE sa.service_state = '开放' AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY bd.oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "统计2023年4月每天关闭状态服务区的总营收，并按日期排序。",
-    "sql": "SELECT bd.oper_date AS 统计日期, SUM(bd.pay_sum) AS 关闭状态总营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE sa.service_state = '关闭' AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY bd.oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "比较2023年4月开放和关闭状态服务区的总营收差异。",
-    "sql": "SELECT sa.service_state AS 服务区状态, SUM(bd.pay_sum) AS 总营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY sa.service_state;"
-  },
-  {
-    "question": "统计2023年4月每天所有服务区的总营收，并按日期排序。",
-    "sql": "SELECT oper_date AS 统计日期, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "列出2023年4月期间，每个服务区的总营收、订单总数，并按总营收从高到低排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收, SUM(order_sum) AS 订单总数 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY service_name ORDER BY 总营收 DESC;"
-  },
-  {
-    "question": "统计2023年4月期间，开放状态服务区的总营收、订单总数，并按总营收从高到低排序。",
-    "sql": "SELECT bd.service_name AS 服务区名称, SUM(bd.pay_sum) AS 总营收, SUM(bd.order_sum) AS 订单总数 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE sa.service_state = '开放' AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY bd.service_name ORDER BY 总营收 DESC;"
-  }
-]

data_pipeline/training_data/manual_20250721_010214/qs_highway_db_20250721_010658_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列。",
-    "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 - INTERVAL '7 days' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各档口的现金支付金额及订单数量，并按金额降序排序。",
-    "sql": "SELECT branch_name AS 档口名称, rmb AS 现金支付金额, 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 ORDER BY 现金支付金额 DESC;"
-  },
-  {
-    "question": "分析2023年4月各服务区微信支付与支付宝支付的占比情况。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx) / SUM(pay_sum) * 100 AS 微信支付占比, SUM(zfb) / SUM(pay_sum) * 100 AS 支付宝支付占比 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "找出2023年4月营业收入最高的前5个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月每天的营业收入趋势，显示每日总收入。",
-    "sql": "SELECT oper_date AS 统计日期, SUM(pay_sum) AS 日营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "比较不同服务区2023年4月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 >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 平均每日营业收入 DESC;"
-  },
-  {
-    "question": "查询2023年4月营业收入最低的3个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营业收入 ASC LIMIT 3;"
-  },
-  {
-    "question": "统计2023年4月各服务区不同支付方式的订单数量分布。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(wx_order) AS 微信订单数, SUM(zf_order) AS 支付宝订单数, SUM(rmb_order) AS 现金订单数 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name;"
-  },
-  {
-    "question": "查询2023年4月营业收入超过10000元的服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name HAVING SUM(pay_sum) > 10000 ORDER BY 总营业收入 DESC;"
-  },
-  {
-    "question": "统计2023年4月各服务区每日营业收入的波动情况。",
-    "sql": "SELECT service_name AS 服务区名称, oper_date AS 统计日期, SUM(pay_sum) AS 日营业收入 FROM bss_business_day_data WHERE oper_date >= '2023-04-01' AND oper_date <= '2023-04-30' AND delete_ts IS NULL GROUP BY service_name, oper_date ORDER BY 服务区名称, 统计日期;"
-  },
-  {
-    "question": "统计2023年每个月进入宜春服务区的车辆总数及平均每日车流量，并按月份排序。",
-    "sql": "SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '宜春服务区') AND delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date) ORDER BY 月份;"
-  },
-  {
-    "question": "2023年4月，各服务区车辆总数排名前5的服务区名称及车流量。",
-    "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 BETWEEN '2023-04-01' AND '2023-04-30' 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，各车辆类型在所有服务区的占比情况。",
-    "sql": "SELECT car_type AS 车辆类型, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count) * 100.0 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL), 2) AS 占比百分比 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "2023年，平均每日车流量最低的3个服务区名称及平均车流量。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cc.customer_count) AS 日均车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 ASC LIMIT 3;"
-  },
-  {
-    "question": "2023年4月，每天进入庐山服务区的车流量趋势变化。",
-    "sql": "SELECT count_date AS 统计日期, customer_count AS 车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-30' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '庐山服务区') AND delete_ts IS NULL ORDER BY count_date;"
-  },
-  {
-    "question": "2023年4月，各公司管辖的服务区平均每日车流量对比。",
-    "sql": "SELECT co.company_name AS 公司名称, AVG(cc.customer_count) AS 日均车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id JOIN bss_company co ON sa.company_id = co.id WHERE cc.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL AND co.delete_ts IS NULL GROUP BY co.company_name ORDER BY 日均车流量 DESC;"
-  },
-  {
-    "question": "2023年4月，过境车辆最多的前3个服务区名称及数量。",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cc.customer_count) AS 过境车流量 FROM bss_car_day_count cc JOIN bss_service_area sa ON cc.service_area_id = sa.id WHERE cc.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND cc.car_type = '过境' AND cc.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 3;"
-  },
-  {
-    "question": "2023年4月，每个星期几在宜春服务区的平均车流量，并按星期排序。",
-    "sql": "SELECT EXTRACT(DOW FROM count_date) AS 星期, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-30' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '宜春服务区') AND delete_ts IS NULL GROUP BY EXTRACT(DOW FROM count_date) ORDER BY 星期;"
-  },
-  {
-    "question": "2023年4月，所有服务区中，每日车流量超过1000的日期及服务区名称。",
-    "sql": "SELECT cc.count_date AS 统计日期, sa.service_area_name AS 服务区名称, cc.customer_count AS 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 BETWEEN '2023-04-01' AND '2023-04-30' AND cc.customer_count > 1000 AND cc.delete_ts IS NULL AND sa.delete_ts IS NULL ORDER BY cc.count_date;"
-  },
-  {
-    "question": "2023年4月，各车辆类型在宜春服务区的每日车流量明细。",
-    "sql": "SELECT count_date AS 统计日期, car_type AS 车辆类型, customer_count AS 车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-30' AND service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '宜春服务区') AND delete_ts IS NULL ORDER BY count_date, car_type;"
-  },
-  {
-    "question": "统计各公司2023年4月1日的总营业收入，并按公司名称分组。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.pay_sum) AS 总营业收入 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "统计各公司2023年4月1日的车辆总数，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(car_count) AS 车辆总数 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id GROUP BY b.company_name ORDER BY 车辆总数 DESC;"
-  },
-  {
-    "question": "计算各公司单位车流的平均营业收入，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.pay_sum) / SUM(car_count) AS 单位车流营收 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id JOIN bss_business_day_data a ON s.service_area_no = a.service_no WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "找出2023年4月1日营业收入最高的前5家公司。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.pay_sum) AS 总营业收入 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 总营业收入 DESC LIMIT 5;"
-  },
-  {
-    "question": "找出2023年4月1日车流量最高的前5家公司。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(car_count) AS 车辆总数 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id GROUP BY b.company_name ORDER BY 车辆总数 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日的现金支付总金额，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.rmb) AS 现金支付总额 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 现金支付总额 DESC;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日使用微信支付的订单数量，并按公司名称排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.wx_order) AS 微信订单数量 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "比较各公司在2023年4月1日的支付宝支付金额占比。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.zfb) / SUM(a.pay_sum) * 100 AS 支付宝占比百分比 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name;"
-  },
-  {
-    "question": "统计各公司在2023年4月1日的订单总数，并按订单数量排序。",
-    "sql": "SELECT b.company_name AS 公司名称, SUM(a.order_sum) AS 订单总数 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 订单总数 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日车流和营收均排名前五的公司。",
-    "sql": "WITH car_count_rank AS (SELECT b.company_name AS 公司名称, SUM(car_count) AS 车辆总数 FROM (SELECT service_area_id, SUM(customer_count) AS car_count FROM bss_car_day_count WHERE count_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_area_id) t JOIN bss_service_area s ON t.service_area_id = s.id JOIN bss_company b ON s.company_id = b.id GROUP BY b.company_name ORDER BY 车辆总数 DESC LIMIT 5), revenue_rank AS (SELECT b.company_name AS 公司名称, SUM(a.pay_sum) AS 总营业收入 FROM bss_business_day_data a JOIN bss_service_area c ON a.service_no = c.service_area_no JOIN bss_company b ON c.company_id = b.id WHERE a.oper_date = '2023-04-01' AND a.delete_ts IS NULL GROUP BY b.company_name ORDER BY 总营业收入 DESC LIMIT 5) SELECT car_count_rank.公司名称 FROM car_count_rank INNER JOIN revenue_rank ON car_count_rank.公司名称 = revenue_rank.公司名称;"
-  },
-  {
-    "question": "统计各路段关联的服务区数量，并按数量降序排列。",
-    "sql": "SELECT section_name AS 路段名称, COUNT(service_area_id) AS 服务区数量 FROM bss_section_route LEFT JOIN bss_section_route_area_link ON bss_section_route.id = bss_section_route_area_link.section_route_id WHERE bss_section_route.delete_ts IS NULL GROUP BY section_name ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日各服务区的车流总量，并按车流降序排列。",
-    "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 = '2023-04-01' GROUP BY service_area_id ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "找出2023年4月1日车流最少的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 = '2023-04-01' GROUP BY service_area_id ORDER BY 总车流量 ASC LIMIT 5;"
-  },
-  {
-    "question": "分析2022年各路段的月均车流情况，并按路段排序。",
-    "sql": "SELECT bss_section_route.section_name AS 路段名称, AVG(monthly_count) AS 月均车流量 FROM (SELECT bss_section_route_area_link.section_route_id, DATE_TRUNC('month', count_date) AS 月份, SUM(customer_count) AS monthly_count FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id WHERE count_date BETWEEN '2022-01-01' AND '2022-12-31' GROUP BY bss_section_route_area_link.section_route_id, 月份) AS monthly_data JOIN bss_section_route ON monthly_data.section_route_id = bss_section_route.id GROUP BY bss_section_route.section_name ORDER BY 路段名称;"
-  },
-  {
-    "question": "查询所有开放状态的服务区及其所属路段。",
-    "sql": "SELECT bss_service_area.service_area_name AS 服务区名称, bss_section_route.section_name AS 路段名称 FROM bss_service_area JOIN bss_section_route_area_link ON bss_service_area.id = bss_section_route_area_link.service_area_id JOIN bss_section_route ON bss_section_route_area_link.section_route_id = bss_section_route.id WHERE bss_service_area.delete_ts IS NULL AND bss_service_area.service_state = '开放';"
-  },
-  {
-    "question": "统计2023年4月1日各车辆类型在各服务区的分布情况。",
-    "sql": "SELECT service_area_id AS 服务区ID, car_type AS 车辆类型, SUM(customer_count) AS 总车流量 FROM bss_car_day_count WHERE count_date = '2023-04-01' GROUP BY service_area_id, car_type ORDER BY 服务区ID, 总车流量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日车流最多的3个路段及其总车流量。",
-    "sql": "SELECT bss_section_route.section_name AS 路段名称, SUM(bss_car_day_count.customer_count) AS 总车流量 FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id JOIN bss_section_route ON bss_section_route_area_link.section_route_id = bss_section_route.id WHERE bss_car_day_count.count_date = '2023-04-01' GROUP BY bss_section_route.section_name ORDER BY 总车流量 DESC LIMIT 3;"
-  },
-  {
-    "question": "计算2023年4月1日各服务区车流占所属路段车流的百分比。",
-    "sql": "WITH section_total AS (SELECT bss_section_route.id AS section_id, SUM(bss_car_day_count.customer_count) AS total_count FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id JOIN bss_section_route ON bss_section_route_area_link.section_route_id = bss_section_route.id WHERE bss_car_day_count.count_date = '2023-04-01' GROUP BY bss_section_route.id), area_count AS (SELECT bss_section_route_area_link.section_route_id, bss_car_day_count.service_area_id, SUM(bss_car_day_count.customer_count) AS area_count FROM bss_car_day_count JOIN bss_section_route_area_link ON bss_car_day_count.service_area_id = bss_section_route_area_link.service_area_id WHERE bss_car_day_count.count_date = '2023-04-01' GROUP BY bss_section_route_area_link.section_route_id, bss_car_day_count.service_area_id) SELECT area_count.service_area_id AS 服务区ID, bss_section_route.section_name AS 路段名称, area_count.area_count AS 服务区车流量, section_total.total_count AS 路段总车流量, (area_count.area_count::numeric / section_total.total_count) * 100 AS 占比百分比 FROM area_count JOIN section_total ON area_count.section_route_id = section_total.section_id JOIN bss_section_route ON area_count.section_route_id = bss_section_route.id;"
-  },
-  {
-    "question": "统计各公司管辖路段的平均服务区数量。",
-    "sql": "SELECT bss_company.company_name AS 公司名称, AVG(area_count) AS 平均服务区数量 FROM (SELECT bss_section_route.section_name, COUNT(bss_section_route_area_link.service_area_id) AS area_count FROM bss_section_route LEFT JOIN bss_section_route_area_link ON bss_section_route.id = bss_section_route_area_link.section_route_id GROUP BY bss_section_route.section_name) AS section_area_count JOIN bss_section_route ON section_area_count.section_name = bss_section_route.section_name JOIN bss_service_area ON bss_section_route.id = (SELECT section_route_id FROM bss_section_route_area_link WHERE bss_section_route_area_link.service_area_id = bss_service_area.id LIMIT 1) JOIN bss_company ON bss_service_area.company_id = bss_company.id GROUP BY bss_company.company_name;"
-  },
-  {
-    "question": "查询2023年4月1日所有服务区的车流数据，包括车辆类型明细。",
-    "sql": "SELECT service_area_id AS 服务区ID, car_type AS 车辆类型, customer_count AS 车流量 FROM bss_car_day_count WHERE count_date = '2023-04-01' ORDER BY 服务区ID, 车流量 DESC;"
-  },
-  {
-    "question": "统计最近7天内每天各服务区的营业总额，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 营业总额 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, service_name;"
-  },
-  {
-    "question": "统计各服务区在2023年4月期间的总营收，并按营收从高到低排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY service_name ORDER BY 总营收 DESC;"
-  },
-  {
-    "question": "统计2023年4月期间，开放状态和关闭状态的服务区的平均日营收，并按状态排序。",
-    "sql": "SELECT sa.service_state AS 服务区状态, AVG(bd.pay_sum) AS 平均日营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY sa.service_state ORDER BY 平均日营收 DESC;"
-  },
-  {
-    "question": "找出2023年4月营收最高的前5个服务区。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY service_name ORDER BY 总营收 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月每天开放状态服务区的总营收，并按日期排序。",
-    "sql": "SELECT bd.oper_date AS 统计日期, SUM(bd.pay_sum) AS 开放状态总营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE sa.service_state = '开放' AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY bd.oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "统计2023年4月每天关闭状态服务区的总营收，并按日期排序。",
-    "sql": "SELECT bd.oper_date AS 统计日期, SUM(bd.pay_sum) AS 关闭状态总营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE sa.service_state = '关闭' AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY bd.oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "比较2023年4月开放和关闭状态服务区的总营收差异。",
-    "sql": "SELECT sa.service_state AS 服务区状态, SUM(bd.pay_sum) AS 总营收 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY sa.service_state;"
-  },
-  {
-    "question": "统计2023年4月每天所有服务区的总营收，并按日期排序。",
-    "sql": "SELECT oper_date AS 统计日期, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY oper_date ORDER BY 统计日期;"
-  },
-  {
-    "question": "列出2023年4月期间，每个服务区的总营收、订单总数，并按总营收从高到低排序。",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收, SUM(order_sum) AS 订单总数 FROM bss_business_day_data WHERE delete_ts IS NULL AND EXTRACT(YEAR FROM oper_date) = 2023 AND EXTRACT(MONTH FROM oper_date) = 4 GROUP BY service_name ORDER BY 总营收 DESC;"
-  },
-  {
-    "question": "统计2023年4月期间，开放状态服务区的总营收、订单总数，并按总营收从高到低排序。",
-    "sql": "SELECT bd.service_name AS 服务区名称, SUM(bd.pay_sum) AS 总营收, SUM(bd.order_sum) AS 订单总数 FROM bss_business_day_data bd JOIN bss_service_area sa ON bd.service_no = sa.service_area_no WHERE sa.service_state = '开放' AND bd.delete_ts IS NULL AND sa.delete_ts IS NULL AND EXTRACT(YEAR FROM bd.oper_date) = 2023 AND EXTRACT(MONTH FROM bd.oper_date) = 4 GROUP BY bd.service_name ORDER BY 总营收 DESC;"
-  }
-]

data_pipeline/training_data/manual_20250721_010214/vector_bak/langchain_pg_embedding_20250721_010708.csv

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

data_pipeline/training_data/manual_20250721_010214/vector_bak/vector_backup_log.txt

@@ -1,11 +0,0 @@
-=== Vector Table Backup Log ===
-Backup Time: 2025-07-21 01:07:08
-Task ID: manual_20250721_010214
-Duration: 0.00s
-
-Tables Backup Status:
-✓ langchain_pg_collection: 4 rows -> langchain_pg_collection_20250721_010708.csv (209.0 B)
-✓ langchain_pg_embedding: 0 rows -> langchain_pg_embedding_20250721_010708.csv (47.0 B)
-
-Truncate Status:
-✓ langchain_pg_embedding: TRUNCATED (0 rows removed)

data_pipeline/training_data/task_20250721_183935/bss_business_day_data.ddl → data_pipeline/training_data/manual_20250722_164749/bss_business_day_data.ddl

@@ -4,11 +4,11 @@ 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,
+  created_by varchar(50)      -- 创建人账号,
   update_ts timestamp         -- 更新时间,
-  updated_by varchar(50)      -- 更新人,
+  updated_by varchar(50)      -- 更新人账号,
   delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除人,
+  deleted_by varchar(50)      -- 删除人账号,
   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,

data_pipeline/training_data/task_20250721_183935/bss_business_day_data_detail.md → data_pipeline/training_data/manual_20250722_164749/bss_business_day_data_detail.md

@@ -4,11 +4,11 @@ bss_business_day_data 表高速公路服务区每日经营数据记录表，用
 - 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]
+- created_by (varchar(50)) - 创建人账号 [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
+- updated_by (varchar(50)) - 更新人账号
 - delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人
+- deleted_by (varchar(50)) - 删除人账号
 - 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]

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_194637/bss_car_day_count.ddl → data_pipeline/training_data/manual_20250722_164749/bss_car_day_count.ddl

@@ -1,8 +1,8 @@
--- 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，记录各类型车辆数量及变更历史。
+-- 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，记录各类型车辆进出数量及操作审计信息。
 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_183935/file_bak_20250721_194637/bss_car_day_count_detail.md → data_pipeline/training_data/manual_20250722_164749/bss_car_day_count_detail.md

@@ -1,8 +1,8 @@
-## 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，记录各类型车辆数量及变更历史。
+## 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，记录各类型车辆进出数量及操作审计信息。
 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_183935/file_bak_20250721_200934/bss_company.ddl → data_pipeline/training_data/manual_20250722_164749/bss_company.ddl

@@ -1,8 +1,8 @@
 -- 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操作审计字段。
 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,
+  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_183935/file_bak_20250721_200934/bss_company_detail.md → data_pipeline/training_data/manual_20250722_164749/bss_company_detail.md

@@ -1,8 +1,8 @@
 ## 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操作审计字段。
 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]
+- 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]

data_pipeline/training_data/task_20250721_183935/bss_section_route.ddl → data_pipeline/training_data/manual_20250722_164749/bss_section_route.ddl

@@ -2,7 +2,7 @@
 -- 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,
+  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,
@@ -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)           -- 路段编号,
+  code varchar(255)           -- 编号,
   primary key (id)
 );

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_section_route_area_link.ddl → data_pipeline/training_data/manual_20250722_164749/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: 高速公路服务区与路线关联表
+-- 描述: 高速公路服务区与路线关联表，记录服务区所属路段关系。
 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 → data_pipeline/training_data/manual_20250722_164749/bss_section_route_area_link_detail.md

@@ -1,5 +1,5 @@
-## 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。
+## bss_section_route_area_link（高速公路服务区与路线关联表）
+bss_section_route_area_link 表高速公路服务区与路线关联表，记录服务区所属路段关系。
 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 → data_pipeline/training_data/manual_20250722_164749/bss_section_route_detail.md

@@ -2,16 +2,15 @@
 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]
+- version (integer) - 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]
+- 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]
 字段补充说明：
-- id 为主键
-- created_by 为枚举字段，包含取值：admin
+- id 为主键

data_pipeline/training_data/task_20250721_113010/bss_service_area.ddl → data_pipeline/training_data/manual_20250722_164749/bss_service_area.ddl

@@ -1,5 +1,5 @@
--- 中文名: 高速公路服务区信息表
--- 描述: 高速公路服务区信息表，存储服务区基础信息及变更记录。
+-- 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、编码及管理元数据。
 create table public.bss_service_area (
   id varchar(32) not null     -- 主键ID，主键,
   version integer not null    -- 版本号,

data_pipeline/training_data/task_20250721_113010/bss_service_area_detail.md → data_pipeline/training_data/manual_20250722_164749/bss_service_area_detail.md

@@ -1,5 +1,5 @@
-## bss_service_area（高速公路服务区信息表）
-bss_service_area 表高速公路服务区信息表，存储服务区基础信息及变更记录。
+## 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、编码及管理元数据。
 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]

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/bss_service_area_mapper_1.ddl → data_pipeline/training_data/manual_20250722_164749/bss_service_area_mapper.ddl

@@ -1,5 +1,5 @@
 -- 中文名: 服务区信息映射表
--- 描述: 服务区信息映射表，用于统一管理全国高速公路服务区基础数据。
+-- 描述: 服务区信息映射表，用于管理高速公路服务区的基础信息及变更记录。
 create table public.bss_service_area_mapper (
   id varchar(32) not null     -- 主键ID，主键,
   version integer not null    -- 版本号,
@@ -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) -- 服务区系统ID,
+  source_system_type varchar(50) -- 数据来源系统,
   source_type integer         -- 数据来源类别ID,
   primary key (id)
 );

data_pipeline/training_data/task_20250721_183935/file_bak_20250721_200934/bss_service_area_mapper_detail.md → data_pipeline/training_data/manual_20250722_164749/bss_service_area_mapper_detail.md

@@ -1,5 +1,5 @@
 ## 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信息及变更记录。
 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]
@@ -11,8 +11,8 @@ 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]
+- service_area_id (varchar(32)) - 服务区系统ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f]
+- source_system_type (varchar(50)) - 数据来源系统 [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/manual_20250722_164749/db_query_decision_prompt.txt

@@ -0,0 +1,35 @@
+{
+  "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收入、车辆流量统计及基础信息管理，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）、车辆流量数据（按车类统计的数量）、服务区基础属性（类型、状态、所属公司）及路段关联关系",
+  "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，是业务统计的核心维度",
+      "主要字段": [
+        "service_area_name",
+        "service_area_no",
+        "service_state",
+        "company_id"
+      ]
+    },
+    {
+      "实体类型": "经营档口",
+      "描述": "服务区内的具体商户或功能分区（如南区、鲜徕客东区），用于细化收入来源分析",
+      "主要字段": [
+        "branch_name",
+        "branch_no",
+        "service_no"
+      ]
+    }
+  ],
+  "关键业务指标": [
+    {
+      "指标类型": "日均营业额",
+      "描述": "通过pay_sum字段计算各服务区或档口每日总交易额，可用于趋势分析和横向对比"
+    },
+    {
+      "指标类型": "车类分布结构",
+      "描述": "基于car_type和customer_count字段分析不同类型车辆（危化品、城际等）在服务区的占比情况"
+    }
+  ]
+}

data_pipeline/training_data/manual_20250722_164749/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: 2025-07-22 16:55:43
+-- 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
+(
+  '营收分析',
+  '基于每日经营数据，分析各服务区及档口的收入趋势、订单分布与支付方式结构，支撑经营决策优化。',
+  'bss_business_day_data,bss_service_area,bss_company',
+  '服务区,档口,公司,支付方式',
+  '总支付金额,订单总数,收入趋势,支付方式占比,服务区对比'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '车流分析',
+  '结合车辆类型与日流量数据，分析各服务区车流构成及时序变化，辅助服务资源配置与营销策略制定。',
+  'bss_car_day_count,bss_service_area,bss_section_route,bss_company',
+  '服务区,车辆类别,路段,公司',
+  '日均车流量,车类分布,车流趋势,服务区排名,跨路段对比'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '公司绩效',
+  '从所属公司维度汇总营收与车流数据，评估各分公司管理效能与市场表现，支持绩效考核与资源分配。',
+  'bss_business_day_data,bss_car_day_count,bss_service_area,bss_company',
+  '公司,服务区,统计日期',
+  '公司总营收,平均单区产出,车流覆盖率,同比增长率,公司间对比'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '路段关联',
+  '分析不同路段路线下的服务区运营与车流情况，识别高价值路线，优化路网服务布局与招商策略。',
+  'bss_section_route,bss_section_route_area_link,bss_service_area,bss_business_day_data,bss_car_day_count',
+  '路段,路线,服务区,公司',
+  '路段总营收,单区平均车流,路线密度,服务区覆盖率,路段排名'
+);
+
+INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
+(
+  '状态监控',
+  '统计不同类型与状态的服务区数量分布及其运营数据差异，掌握开放服务能力与系统覆盖情况。',
+  'bss_service_area,bss_business_day_data,bss_car_day_count',
+  '服务区状态,服务区类型,服务区,公司',
+  '开放服务区数,状态分布,运营率,异常状态预警,类型对比'
+);
+

data_pipeline/training_data/manual_20250721_010214/metadata_detail.md → data_pipeline/training_data/manual_20250722_164749/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_section_route_area_link, bss_company]
-- `biz_entities` (text[]) - 主要业务实体名称 [示例: 路段, 公司, 路线]
-- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 路段服务区数量, 车流趋势, 关闭状态营收]
+- `related_tables` (text[]) - 涉及的数据表 [示例: bss_service_area, bss_section_route]
+- `biz_entities` (text[]) - 主要业务实体名称 [示例: 统计日期, 服务区类型, 路段]
+- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 收入趋势, 总支付金额, 车类分布]
 - `created_at` (timestamp) - 插入时间 [默认值: `CURRENT_TIMESTAMP`]
 
 字段补充说明：

data_pipeline/training_data/manual_20250722_164749/qs_highway_db_20250722_165543_pair.json

@@ -0,0 +1,198 @@
+[
+  {
+    "question": "统计2023年4月1日各服务区的总支付金额和订单总数，并按收入降序排列？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总支付金额, SUM(order_sum) AS 订单总数 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总支付金额 DESC;"
+  },
+  {
+    "question": "查询2023年4月1日微信支付金额占比超过50%的服务区及其占比？",
+    "sql": "SELECT service_name AS 服务区名称, (SUM(wx) / SUM(pay_sum)) AS 微信支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name HAVING (SUM(wx) / SUM(pay_sum)) > 0.5 ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "列出2023年4月1日订单总数最多的前5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, order_sum AS 订单总数 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY order_sum DESC LIMIT 5;"
+  },
+  {
+    "question": "分析2023年4月1日各支付方式的总金额分布情况？",
+    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '支付宝' AS 支付方式, SUM(zfb) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '现金' AS 支付方式, SUM(rmb) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '行吧' AS 支付方式, SUM(xs) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '金豆' AS 支付方式, SUM(jd) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY 总金额 DESC;"
+  },
+  {
+    "question": "计算各公司在2023年4月1日的总营收并按公司名称排序？",
+    "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 = '2023-04-01' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 公司名称;"
+  },
+  {
+    "question": "找出2023年4月1日平均客单价最高的前3个服务区（总支付金额/订单总数）？",
+    "sql": "SELECT service_name AS 服务区名称, (SUM(pay_sum) / NULLIF(SUM(order_sum), 0)) AS 平均客单价 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 平均客单价 DESC LIMIT 3;"
+  },
+  {
+    "question": "对比2023年4月1日各服务区现金支付与非现金支付的金额差异？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) AS 现金支付总额, (SUM(wx) + SUM(zfb) + SUM(xs) + SUM(jd)) AS 非现金支付总额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付总额 DESC;"
+  },
+  {
+    "question": "查询宜春分公司下所有服务区在2023年4月1日的营收汇总？",
+    "sql": "SELECT s.company_name AS 公司名称, SUM(b.pay_sum) AS 营收总额, SUM(b.order_sum) AS 订单总数 FROM bss_business_day_data b JOIN bss_service_area a ON b.service_no = a.service_area_no JOIN bss_company s ON a.company_id = s.id WHERE s.company_name = '宜春分公司' AND b.oper_date = '2023-04-01' AND b.delete_ts IS NULL AND a.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.company_name;"
+  },
+  {
+    "question": "统计2023年4月1日各服务区支付宝订单数量占总订单比例，并筛选高于10%的服务区？",
+    "sql": "SELECT service_name AS 服务区名称, (SUM(zf_order) * 1.0 / SUM(order_sum)) AS 支付宝订单占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name HAVING (SUM(zf_order) * 1.0 / SUM(order_sum)) > 0.1 ORDER BY 支付宝订单占比 DESC;"
+  },
+  {
+    "question": "获取2023年4月1日所有开放状态的服务区经营数据，包括总支付金额、订单数及支付方式明细？",
+    "sql": "SELECT b.service_name AS 服务区名称, b.branch_name AS 档口名称, b.pay_sum AS 总支付金额, b.order_sum AS 订单总数, b.wx AS 微信金额, b.zfb AS 支付宝金额, b.rmb AS 现金金额 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' AND s.service_state = '开放' AND b.delete_ts IS NULL AND s.delete_ts IS NULL ORDER BY b.pay_sum DESC;"
+  },
+  {
+    "question": "各服务区2023年日均车流量排名（前10名）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id WHERE cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.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 10;"
+  },
+  {
+    "question": "2023年各类车型在所有服务区的总流量分布占比？",
+    "sql": "SELECT car_type AS 车辆类别, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count)::numeric * 100 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL), 2) AS 占比百分比 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
+  },
+  {
+    "question": "2023年每月总车流量趋势变化情况？",
+    "sql": "SELECT EXTRACT(YEAR FROM count_date) AS 年份, EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 月总车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY 年份, 月份 ORDER BY 年份, 月份;"
+  },
+  {
+    "question": "昌九路段下各服务区2023年日均车流量对比？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE sr.section_name = '昌九' AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 DESC;"
+  },
+  {
+    "question": "宜春分公司所属服务区2023年车流总量及平均值？",
+    "sql": "SELECT co.company_name AS 公司名称, COUNT(*) AS 统计天数, SUM(cdc.customer_count) AS 总车流量, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_company co ON sa.company_id = co.id WHERE co.company_name = '宜春分公司' AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND co.delete_ts IS NULL GROUP BY 公司名称;"
+  },
+  {
+    "question": "2023年危化品车辆通行量最高的前5个服务区？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cdc.customer_count) AS 危化品车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id WHERE cdc.car_type = '危化品' AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 危化品车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "2023年每个季度各公司下属服务区的总车流量对比？",
+    "sql": "SELECT co.company_name AS 公司名称, EXTRACT(YEAR FROM cdc.count_date) AS 年份, EXTRACT(QUARTER FROM cdc.count_date) AS 季度, SUM(cdc.customer_count) AS 季度总车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_company co ON sa.company_id = co.id WHERE cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND co.delete_ts IS NULL GROUP BY 公司名称, 年份, 季度 ORDER BY 年份, 季度, 季度总车流量 DESC;"
+  },
+  {
+    "question": "2023年‘城际’类车辆日均车流量时间趋势（按月）？",
+    "sql": "SELECT EXTRACT(YEAR FROM count_date) AS 年份, EXTRACT(MONTH FROM count_date) AS 月, AVG(customer_count) AS 日均城际车流量 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 年份, 月 ORDER BY 年份, 月;"
+  },
+  {
+    "question": "哪些服务区在2023年存在单日车流量超过10000的记录？列出其名称及最高单日流量。",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, MAX(cdc.customer_count) AS 最高单日车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id WHERE cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name HAVING MAX(cdc.customer_count) > 10000 ORDER BY 最高单日车流量 DESC;"
+  },
+  {
+    "question": "2023年‘过境’与‘城际’车辆在各路段的日均车流对比分析？",
+    "sql": "SELECT sr.section_name AS 路段名称, cdc.car_type AS 车辆类型, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE cdc.car_type IN ('过境', '城际') AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY sr.section_name, cdc.car_type ORDER BY 路段名称, 车辆类型;"
+  },
+  {
+    "question": "各公司2023年4月总营收是多少？按营收降序排列。",
+    "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 BETWEEN '2023-04-01' AND '2023-04-30' AND c.delete_ts IS NULL AND sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY c.company_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "各公司平均单个服务区的日均营收（2023年4月）排名如何？",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(company_area_daily.avg_daily_revenue) AS 平均单区日均产出 FROM (SELECT sa.company_id, sa.service_area_no, AVG(b.pay_sum) AS avg_daily_revenue FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id, sa.service_area_no) AS company_area_daily JOIN bss_company c ON company_area_daily.company_id = c.id WHERE c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 平均单区日均产出 DESC;"
+  },
+  {
+    "question": "各公司在2023年4月的服务区车流覆盖率（有车流数据的服务区占比）是多少？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(DISTINCT car.service_area_id) * 1.0 / COUNT(DISTINCT sa.id) AS 车流覆盖率 FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id AND car.count_date BETWEEN '2023-04-01' AND '2023-04-30' WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY c.company_name ORDER BY 车流覆盖率 DESC;"
+  },
+  {
+    "question": "与2022年4月相比，各公司2023年4月营收的同比增长率是多少？",
+    "sql": "WITH revenue_2022 AS (SELECT sa.company_id, SUM(b.pay_sum) AS total_2022 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2022-04-01' AND '2022-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id), revenue_2023 AS (SELECT sa.company_id, SUM(b.pay_sum) AS total_2023 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id) SELECT c.company_name AS 公司名称, COALESCE((r2023.total_2023 - r2022.total_2022) * 100.0 / NULLIF(r2022.total_2022, 0), 0) AS 同比增长率 FROM bss_company c LEFT JOIN revenue_2022 r2022 ON c.id = r2022.company_id LEFT JOIN revenue_2023 r2023 ON c.id = r2023.company_id WHERE c.delete_ts IS NULL ORDER BY 同比增长率 DESC;"
+  },
+  {
+    "question": "哪些公司的平均单区日均营收高于整体平均水平（2023年4月）？",
+    "sql": "WITH area_avg AS (SELECT sa.company_id, sa.service_area_no, AVG(b.pay_sum) AS daily_avg FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id, sa.service_area_no), company_avg AS (SELECT company_id, AVG(daily_avg) AS company_daily_avg FROM area_avg GROUP BY company_id), overall_avg AS (SELECT AVG(company_daily_avg) AS global_avg FROM company_avg) SELECT c.company_name AS 公司名称, ca.company_daily_avg AS 平均单区日均产出 FROM company_avg ca JOIN bss_company c ON ca.company_id = c.id CROSS JOIN overall_avg o WHERE ca.company_daily_avg > o.global_avg AND c.delete_ts IS NULL ORDER BY company_daily_avg DESC;"
+  },
+  {
+    "question": "各公司2023年4月微信支付占总支付金额的比例是多少？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.wx) * 100.0 / SUM(b.pay_sum) AS 微信支付占比 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 BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "车流量最高的前5个服务区及其所属公司是哪些（2023年4月）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司, SUM(car.customer_count) AS 总车流量 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 BETWEEN '2023-04-01' AND '2023-04-30' AND car.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY sa.service_area_name, c.company_name ORDER BY 总车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "各公司2023年4月每日平均订单总数是多少？按从高到低排序。",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(b.order_sum) AS 日均订单总数 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 BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 日均订单总数 DESC;"
+  },
+  {
+    "question": "宜春分公司在2023年4月每天的总营收趋势如何？",
+    "sql": "SELECT b.oper_date AS 统计日期, SUM(b.pay_sum) AS 日总营收 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 c.company_name = '宜春分公司' AND b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND c.delete_ts IS NULL AND sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY b.oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "各公司在2023年4月的现金支付总额占比分布情况如何？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.rmb) * 100.0 / SUM(b.pay_sum) AS 现金支付占比 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 BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 现金支付占比 DESC;"
+  },
+  {
+    "question": "各路段路线的总营收排名（近30天），用于识别高价值路线？",
+    "sql": "SELECT sr.route_name AS 路线名称, SUM(bdd.pay_sum) AS 总营收 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 bdd ON sa.service_area_no = bdd.service_no WHERE bdd.oper_date >= CURRENT_DATE - 30 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND bdd.delete_ts IS NULL GROUP BY sr.route_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "每条路线下的平均单区车流量（近7天），用于评估路线吸引力？",
+    "sql": "SELECT sr.route_name AS 路线名称, AVG(car.customer_count) AS 单区平均车流 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 car ON sa.id = car.service_area_id WHERE car.count_date >= CURRENT_DATE - 7 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND car.delete_ts IS NULL GROUP BY sr.route_name;"
+  },
+  {
+    "question": "各路段路线的服务区数量及覆盖率（开放状态），辅助招商布局决策？",
+    "sql": "SELECT sr.section_name AS 路段名称, sr.route_name AS 路线名称, COUNT(sa.id) AS 服务区数量, ROUND(COUNT(sa.id)::numeric / (SELECT COUNT(*) FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL), 4) AS 服务区覆盖率 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 sa.service_state = '开放' AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sr.section_name, sr.route_name ORDER BY 服务区数量 DESC;"
+  },
+  {
+    "question": "昌九路段下各服务区近一周日均车流量TOP5？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(car.customer_count) AS 日均车流量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sr.section_name = '昌九' AND car.count_date >= CURRENT_DATE - 7 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND car.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "不同公司管理的路段路线数量分布，用于资源均衡分析？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(DISTINCT sr.id) AS 管辖路线数 FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id JOIN bss_section_route_area_link link ON sa.id = link.service_area_id JOIN bss_section_route sr ON link.section_route_id = sr.id WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY c.company_name ORDER BY 管辖路线数 DESC;"
+  },
+  {
+    "question": "近一个月微信支付金额最高的服务区TOP3及其所属路线？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, sr.route_name AS 所属路线, SUM(bdd.wx) AS 微信总金额 FROM bss_service_area sa JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no JOIN bss_section_route_area_link link ON sa.id = link.service_area_id JOIN bss_section_route sr ON link.section_route_id = sr.id WHERE bdd.oper_date >= CURRENT_DATE - 30 AND bdd.delete_ts IS NULL AND sa.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY sa.service_area_name, sr.route_name ORDER BY 微信总金额 DESC LIMIT 3;"
+  },
+  {
+    "question": "昌栗路段每日总营收趋势（最近7天），用于短期运营监控？",
+    "sql": "SELECT bdd.oper_date AS 统计日期, SUM(bdd.pay_sum) AS 日总营收 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 bdd ON sa.service_area_no = bdd.service_no WHERE sr.section_name = '昌栗' AND bdd.oper_date >= CURRENT_DATE - 7 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND bdd.delete_ts IS NULL GROUP BY bdd.oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "哪些路线没有关联任何服务区？用于数据完整性校验？",
+    "sql": "SELECT sr.route_name AS 无服务区路线 FROM bss_section_route sr LEFT JOIN bss_section_route_area_link link ON sr.id = link.section_route_id WHERE link.section_route_id IS NULL AND sr.delete_ts IS NULL;"
+  },
+  {
+    "question": "各路段路线的订单总数与平均客单价（近30天），综合评估消费活跃度？",
+    "sql": "SELECT sr.section_name AS 路段名称, sr.route_name AS 路线名称, SUM(bdd.order_sum) AS 订单总数, ROUND(SUM(bdd.pay_sum) / NULLIF(SUM(bdd.order_sum), 0), 2) AS 平均客单价 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 bdd ON sa.service_area_no = bdd.service_no WHERE bdd.oper_date >= CURRENT_DATE - 30 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND bdd.delete_ts IS NULL GROUP BY sr.section_name, sr.route_name ORDER BY 订单总数 DESC;"
+  },
+  {
+    "question": "统计当前各服务区状态的分布情况，包括开放、关闭和上传数据的服务区数量？",
+    "sql": "SELECT service_state AS 服务区状态, COUNT(*) AS 服务区间数 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_state ORDER BY 服务区间数 DESC;"
+  },
+  {
+    "question": "按服务区类型统计各类别下处于开放状态的服务区数量及占比？",
+    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 开放数量, ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER(), 2) AS 占比百分比 FROM bss_service_area WHERE delete_ts IS NULL AND service_state = '开放' GROUP BY service_area_type;"
+  },
+  {
+    "question": "查询最近7天内有经营数据记录的开放服务区列表及其所属公司名称？",
+    "sql": "SELECT DISTINCT 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 JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bd.oper_date >= CURRENT_DATE - INTERVAL '7 days' AND sa.service_state = '开放' ORDER BY 所属公司, 服务区名称;"
+  },
+  {
+    "question": "列出所有未产生任何车辆流量数据的服务区（可能异常）及其基本信息？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_area_no AS 服务区编码, sa.service_state AS 状态, c.company_name AS 所属公司 FROM bss_service_area sa LEFT JOIN bss_car_day_count cc ON sa.id = cc.service_area_id JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND cc.id IS NULL ORDER BY 所属公司;"
+  },
+  {
+    "question": "统计各公司下属服务区的总数、开放数量及运营率（开放/总数）？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(sa.id) AS 总服务区数, COUNT(CASE WHEN sa.service_state = '开放' THEN 1 END) AS 开放服务区数, ROUND(COUNT(CASE WHEN sa.service_state = '开放' THEN 1 END) * 100.0 / COUNT(sa.id), 2) AS 运营率 FROM bss_company c LEFT JOIN bss_service_area sa ON c.id = sa.company_id AND sa.delete_ts IS NULL WHERE c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 运营率 DESC;"
+  },
+  {
+    "question": "找出过去30天日均支付总额最高的前5个开放服务区？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(bd.pay_sum) AS 日均支付金额 FROM bss_service_area sa JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sa.delete_ts IS NULL AND sa.service_state = '开放' AND bd.oper_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY sa.service_area_name ORDER BY 日均支付金额 DESC LIMIT 5;"
+  },
+  {
+    "question": "分析不同类型服务区在最近一周的平均每日车辆流量差异？",
+    "sql": "SELECT sa.service_area_type AS 服务区类型, AVG(cd.customer_count) AS 平均每日车流量 FROM bss_service_area sa JOIN bss_car_day_count cd ON sa.id = cd.service_area_id WHERE sa.delete_ts IS NULL AND cd.count_date >= CURRENT_DATE - INTERVAL '7 days' GROUP BY sa.service_area_type ORDER BY 平均每日车流量 DESC;"
+  },
+  {
+    "question": "哪些服务区虽标记为‘开放’但近7天无任何经营数据记录（可能存在数据异常）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_area_no AS 服务区编码, c.company_name AS 所属公司 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' AND NOT EXISTS (SELECT 1 FROM bss_business_day_data bd WHERE bd.service_no = sa.service_area_no AND bd.oper_date >= CURRENT_DATE - INTERVAL '7 days') ORDER BY 所属公司;"
+  },
+  {
+    "question": "统计每种车辆类型在过去一个月中出现频率最高的服务区？",
+    "sql": "SELECT car_type AS 车辆类型, service_area_name AS 服务区名称, customer_count AS 车流量 FROM (SELECT cd.car_type, sa.service_area_name, cd.customer_count, ROW_NUMBER() OVER (PARTITION BY cd.car_type ORDER BY cd.customer_count DESC) AS rn FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date >= CURRENT_DATE - INTERVAL '1 month' AND sa.delete_ts IS NULL) t WHERE rn = 1;"
+  },
+  {
+    "question": "汇总各公司在上一个自然月内的总订单量和总支付金额，并按金额排序？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bd.order_sum) AS 总订单量, SUM(bd.pay_sum) AS 总支付金额 FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND bd.oper_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') AND bd.oper_date < DATE_TRUNC('month', CURRENT_DATE) GROUP BY c.company_name ORDER BY 总支付金额 DESC;"
+  }
+]

data_pipeline/training_data/manual_20250722_164749/qs_highway_db_20250722_165543_pair.json.backup

@@ -0,0 +1,202 @@
+[
+  {
+    "question": "统计2023年4月1日各服务区的总支付金额和订单总数，并按收入降序排列？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总支付金额, SUM(order_sum) AS 订单总数 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总支付金额 DESC;"
+  },
+  {
+    "question": "查询2023年4月1日微信支付金额占比超过50%的服务区及其占比？",
+    "sql": "SELECT service_name AS 服务区名称, (SUM(wx) / SUM(pay_sum)) AS 微信支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name HAVING (SUM(wx) / SUM(pay_sum)) > 0.5 ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "列出2023年4月1日订单总数最多的前5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, order_sum AS 订单总数 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY order_sum DESC LIMIT 5;"
+  },
+  {
+    "question": "分析2023年4月1日各支付方式的总金额分布情况？",
+    "sql": "SELECT '微信' AS 支付方式, SUM(wx) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '支付宝' AS 支付方式, SUM(zfb) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '现金' AS 支付方式, SUM(rmb) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '行吧' AS 支付方式, SUM(xs) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL UNION ALL SELECT '金豆' AS 支付方式, SUM(jd) AS 总金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL ORDER BY 总金额 DESC;"
+  },
+  {
+    "question": "计算各公司在2023年4月1日的总营收并按公司名称排序？",
+    "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 = '2023-04-01' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 公司名称;"
+  },
+  {
+    "question": "找出2023年4月1日平均客单价最高的前3个服务区（总支付金额/订单总数）？",
+    "sql": "SELECT service_name AS 服务区名称, (SUM(pay_sum) / NULLIF(SUM(order_sum), 0)) AS 平均客单价 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 平均客单价 DESC LIMIT 3;"
+  },
+  {
+    "question": "对比2023年4月1日各服务区现金支付与非现金支付的金额差异？",
+    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) AS 现金支付总额, (SUM(wx) + SUM(zfb) + SUM(xs) + SUM(jd)) AS 非现金支付总额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付总额 DESC;"
+  },
+  {
+    "question": "查询宜春分公司下所有服务区在2023年4月1日的营收汇总？",
+    "sql": "SELECT s.company_name AS 公司名称, SUM(b.pay_sum) AS 营收总额, SUM(b.order_sum) AS 订单总数 FROM bss_business_day_data b JOIN bss_service_area a ON b.service_no = a.service_area_no JOIN bss_company s ON a.company_id = s.id WHERE s.company_name = '宜春分公司' AND b.oper_date = '2023-04-01' AND b.delete_ts IS NULL AND a.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.company_name;"
+  },
+  {
+    "question": "统计2023年4月1日各服务区支付宝订单数量占总订单比例，并筛选高于10%的服务区？",
+    "sql": "SELECT service_name AS 服务区名称, (SUM(zf_order) * 1.0 / SUM(order_sum)) AS 支付宝订单占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name HAVING (SUM(zf_order) * 1.0 / SUM(order_sum)) > 0.1 ORDER BY 支付宝订单占比 DESC;"
+  },
+  {
+    "question": "获取2023年4月1日所有开放状态的服务区经营数据，包括总支付金额、订单数及支付方式明细？",
+    "sql": "SELECT b.service_name AS 服务区名称, b.branch_name AS 档口名称, b.pay_sum AS 总支付金额, b.order_sum AS 订单总数, b.wx AS 微信金额, b.zfb AS 支付宝金额, b.rmb AS 现金金额 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' AND s.service_state = '开放' AND b.delete_ts IS NULL AND s.delete_ts IS NULL ORDER BY b.pay_sum DESC;"
+  },
+  {
+    "question": "各服务区2023年日均车流量排名（前10名）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id WHERE cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.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 10;"
+  },
+  {
+    "question": "2023年各类车型在所有服务区的总流量分布占比？",
+    "sql": "SELECT car_type AS 车辆类别, SUM(customer_count) AS 总车流量, ROUND(SUM(customer_count)::numeric * 100 / (SELECT SUM(customer_count) FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL), 2) AS 占比百分比 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY car_type ORDER BY 总车流量 DESC;"
+  },
+  {
+    "question": "2023年每月总车流量趋势变化情况？",
+    "sql": "SELECT EXTRACT(YEAR FROM count_date) AS 年份, EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 月总车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-01-01' AND '2023-12-31' AND delete_ts IS NULL GROUP BY 年份, 月份 ORDER BY 年份, 月份;"
+  },
+  {
+    "question": "昌九路段下各服务区2023年日均车流量对比？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE sr.section_name = '昌九' AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 DESC;"
+  },
+  {
+    "question": "宜春分公司所属服务区2023年车流总量及平均值？",
+    "sql": "SELECT co.company_name AS 公司名称, COUNT(*) AS 统计天数, SUM(cdc.customer_count) AS 总车流量, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_company co ON sa.company_id = co.id WHERE co.company_name = '宜春分公司' AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND co.delete_ts IS NULL GROUP BY 公司名称;"
+  },
+  {
+    "question": "2023年危化品车辆通行量最高的前5个服务区？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, SUM(cdc.customer_count) AS 危化品车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id WHERE cdc.car_type = '危化品' AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 危化品车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "2023年每个季度各公司下属服务区的总车流量对比？",
+    "sql": "SELECT co.company_name AS 公司名称, EXTRACT(YEAR FROM cdc.count_date) AS 年份, EXTRACT(QUARTER FROM cdc.count_date) AS 季度, SUM(cdc.customer_count) AS 季度总车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_company co ON sa.company_id = co.id WHERE cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND co.delete_ts IS NULL GROUP BY 公司名称, 年份, 季度 ORDER BY 年份, 季度, 季度总车流量 DESC;"
+  },
+  {
+    "question": "2023年‘城际’类车辆日均车流量时间趋势（按月）？",
+    "sql": "SELECT EXTRACT(YEAR FROM count_date) AS 年份, EXTRACT(MONTH FROM count_date) AS 月, AVG(customer_count) AS 日均城际车流量 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 年份, 月 ORDER BY 年份, 月;"
+  },
+  {
+    "question": "哪些服务区在2023年存在单日车流量超过10000的记录？列出其名称及最高单日流量。",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, MAX(cdc.customer_count) AS 最高单日车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id WHERE cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name HAVING MAX(cdc.customer_count) > 10000 ORDER BY 最高单日车流量 DESC;"
+  },
+  {
+    "question": "2023年‘过境’与‘城际’车辆在各路段的日均车流对比分析？",
+    "sql": "SELECT sr.section_name AS 路段名称, cdc.car_type AS 车辆类型, AVG(cdc.customer_count) AS 日均车流量 FROM bss_car_day_count cdc JOIN bss_service_area sa ON cdc.service_area_id = sa.id JOIN bss_section_route_area_link sral ON sa.id = sral.service_area_id JOIN bss_section_route sr ON sral.section_route_id = sr.id WHERE cdc.car_type IN ('过境', '城际') AND cdc.count_date BETWEEN '2023-01-01' AND '2023-12-31' AND cdc.delete_ts IS NULL AND sa.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY sr.section_name, cdc.car_type ORDER BY 路段名称, 车辆类型;"
+  },
+  {
+    "question": "各公司2023年4月总营收是多少？按营收降序排列。",
+    "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 BETWEEN '2023-04-01' AND '2023-04-30' AND c.delete_ts IS NULL AND sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY c.company_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "各公司平均单个服务区的日均营收（2023年4月）排名如何？",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(company_area_daily.avg_daily_revenue) AS 平均单区日均产出 FROM (SELECT sa.company_id, sa.service_area_no, AVG(b.pay_sum) AS avg_daily_revenue FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id, sa.service_area_no) AS company_area_daily JOIN bss_company c ON company_area_daily.company_id = c.id WHERE c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 平均单区日均产出 DESC;"
+  },
+  {
+    "question": "各公司在2023年4月的服务区车流覆盖率（有车流数据的服务区占比）是多少？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(DISTINCT car.service_area_id) * 1.0 / COUNT(DISTINCT sa.id) AS 车流覆盖率 FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id LEFT JOIN bss_car_day_count car ON sa.id = car.service_area_id AND car.count_date BETWEEN '2023-04-01' AND '2023-04-30' WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY c.company_name ORDER BY 车流覆盖率 DESC;"
+  },
+  {
+    "question": "与2022年4月相比，各公司2023年4月营收的同比增长率是多少？",
+    "sql": "WITH revenue_2022 AS (SELECT sa.company_id, SUM(b.pay_sum) AS total_2022 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2022-04-01' AND '2022-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id), revenue_2023 AS (SELECT sa.company_id, SUM(b.pay_sum) AS total_2023 FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id) SELECT c.company_name AS 公司名称, COALESCE((r2023.total_2023 - r2022.total_2022) * 100.0 / NULLIF(r2022.total_2022, 0), 0) AS 同比增长率 FROM bss_company c LEFT JOIN revenue_2022 r2022 ON c.id = r2022.company_id LEFT JOIN revenue_2023 r2023 ON c.id = r2023.company_id WHERE c.delete_ts IS NULL ORDER BY 同比增长率 DESC;"
+  },
+  {
+    "question": "哪些公司的平均单区日均营收高于整体平均水平（2023年4月）？",
+    "sql": "WITH area_avg AS (SELECT sa.company_id, sa.service_area_no, AVG(b.pay_sum) AS daily_avg FROM bss_business_day_data b JOIN bss_service_area sa ON b.service_no = sa.service_area_no WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.company_id, sa.service_area_no), company_avg AS (SELECT company_id, AVG(daily_avg) AS company_daily_avg FROM area_avg GROUP BY company_id), overall_avg AS (SELECT AVG(company_daily_avg) AS global_avg FROM company_avg) SELECT c.company_name AS 公司名称, ca.company_daily_avg AS 平均单区日均产出 FROM company_avg ca JOIN bss_company c ON ca.company_id = c.id CROSS JOIN overall_avg o WHERE ca.company_daily_avg > o.global_avg AND c.delete_ts IS NULL ORDER BY company_daily_avg DESC;"
+  },
+  {
+    "question": "各公司2023年4月微信支付占总支付金额的比例是多少？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.wx) * 100.0 / SUM(b.pay_sum) AS 微信支付占比 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 BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 微信支付占比 DESC;"
+  },
+  {
+    "question": "车流量最高的前5个服务区及其所属公司是哪些（2023年4月）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, c.company_name AS 所属公司, SUM(car.customer_count) AS 总车流量 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 BETWEEN '2023-04-01' AND '2023-04-30' AND car.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY sa.service_area_name, c.company_name ORDER BY 总车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "各公司2023年4月每日平均订单总数是多少？按从高到低排序。",
+    "sql": "SELECT c.company_name AS 公司名称, AVG(b.order_sum) AS 日均订单总数 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 BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 日均订单总数 DESC;"
+  },
+  {
+    "question": "宜春分公司在2023年4月每天的总营收趋势如何？",
+    "sql": "SELECT b.oper_date AS 统计日期, SUM(b.pay_sum) AS 日总营收 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 c.company_name = '宜春分公司' AND b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND c.delete_ts IS NULL AND sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY b.oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "各公司在2023年4月的现金支付总额占比分布情况如何？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(b.rmb) * 100.0 / SUM(b.pay_sum) AS 现金支付占比 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 BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND sa.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 现金支付占比 DESC;"
+  },
+  {
+    "question": "各路段路线的总营收排名（近30天），用于识别高价值路线？",
+    "sql": "SELECT sr.route_name AS 路线名称, SUM(bdd.pay_sum) AS 总营收 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 bdd ON sa.service_area_no = bdd.service_no WHERE bdd.oper_date >= CURRENT_DATE - 30 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND bdd.delete_ts IS NULL GROUP BY sr.route_name ORDER BY 总营收 DESC;"
+  },
+  {
+    "question": "每条路线下的平均单区车流量（近7天），用于评估路线吸引力？",
+    "sql": "SELECT sr.route_name AS 路线名称, AVG(car.customer_count) AS 单区平均车流 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 car ON sa.id = car.service_area_id WHERE car.count_date >= CURRENT_DATE - 7 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND car.delete_ts IS NULL GROUP BY sr.route_name;"
+  },
+  {
+    "question": "各路段路线的服务区数量及覆盖率（开放状态），辅助招商布局决策？",
+    "sql": "SELECT sr.section_name AS 路段名称, sr.route_name AS 路线名称, COUNT(sa.id) AS 服务区数量, ROUND(COUNT(sa.id)::numeric / (SELECT COUNT(*) FROM bss_service_area WHERE service_state = '开放' AND delete_ts IS NULL), 4) AS 服务区覆盖率 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 sa.service_state = '开放' AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sr.section_name, sr.route_name ORDER BY 服务区数量 DESC;"
+  },
+  {
+    "question": "昌九路段下各服务区近一周日均车流量TOP5？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(car.customer_count) AS 日均车流量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_car_day_count car ON sa.id = car.service_area_id WHERE sr.section_name = '昌九' AND car.count_date >= CURRENT_DATE - 7 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND car.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 日均车流量 DESC LIMIT 5;"
+  },
+  {
+    "question": "不同公司管理的路段路线数量分布，用于资源均衡分析？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(DISTINCT sr.id) AS 管辖路线数 FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id JOIN bss_section_route_area_link link ON sa.id = link.service_area_id JOIN bss_section_route sr ON link.section_route_id = sr.id WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND link.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY c.company_name ORDER BY 管辖路线数 DESC;"
+  },
+  {
+    "question": "近一个月微信支付金额最高的服务区TOP3及其所属路线？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, sr.route_name AS 所属路线, SUM(bdd.wx) AS 微信总金额 FROM bss_service_area sa JOIN bss_business_day_data bdd ON sa.service_area_no = bdd.service_no JOIN bss_section_route_area_link link ON sa.id = link.service_area_id JOIN bss_section_route sr ON link.section_route_id = sr.id WHERE bdd.oper_date >= CURRENT_DATE - 30 AND bdd.delete_ts IS NULL AND sa.delete_ts IS NULL AND link.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY sa.service_area_name, sr.route_name ORDER BY 微信总金额 DESC LIMIT 3;"
+  },
+  {
+    "question": "各路线危化品车辆占比（近30天），用于安全与服务策略优化？",
+    "sql": "SELECT sr.route_name AS 路线名称, SUM(CASE WHEN car.car_type = '危化品' THEN car.customer_count ELSE 0 END)::numeric / SUM(car.customer_count) AS 危化品占比 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 car ON sa.id = car.service_area_id WHERE car.count_date >= CURRENT_DATE - 30 AND car.delete_ts IS NULL AND sa.delete_ts IS NULL AND link.delete_ts IS NULL AND sr.delete_ts IS NULL GROUP BY sr.route_name ORDER BY 危化品占比 DESC;"
+  },
+  {
+    "question": "昌栗路段每日总营收趋势（最近7天），用于短期运营监控？",
+    "sql": "SELECT bdd.oper_date AS 统计日期, SUM(bdd.pay_sum) AS 日总营收 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 bdd ON sa.service_area_no = bdd.service_no WHERE sr.section_name = '昌栗' AND bdd.oper_date >= CURRENT_DATE - 7 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND bdd.delete_ts IS NULL GROUP BY bdd.oper_date ORDER BY 统计日期;"
+  },
+  {
+    "question": "哪些路线没有关联任何服务区？用于数据完整性校验？",
+    "sql": "SELECT sr.route_name AS 无服务区路线 FROM bss_section_route sr LEFT JOIN bss_section_route_area_link link ON sr.id = link.section_route_id WHERE link.section_route_id IS NULL AND sr.delete_ts IS NULL;"
+  },
+  {
+    "question": "各路段路线的订单总数与平均客单价（近30天），综合评估消费活跃度？",
+    "sql": "SELECT sr.section_name AS 路段名称, sr.route_name AS 路线名称, SUM(bdd.order_sum) AS 订单总数, ROUND(SUM(bdd.pay_sum) / NULLIF(SUM(bdd.order_sum), 0), 2) AS 平均客单价 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 bdd ON sa.service_area_no = bdd.service_no WHERE bdd.oper_date >= CURRENT_DATE - 30 AND sr.delete_ts IS NULL AND sa.delete_ts IS NULL AND bdd.delete_ts IS NULL GROUP BY sr.section_name, sr.route_name ORDER BY 订单总数 DESC;"
+  },
+  {
+    "question": "统计当前各服务区状态的分布情况，包括开放、关闭和上传数据的服务区数量？",
+    "sql": "SELECT service_state AS 服务区状态, COUNT(*) AS 服务区间数 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_state ORDER BY 服务区间数 DESC;"
+  },
+  {
+    "question": "按服务区类型统计各类别下处于开放状态的服务区数量及占比？",
+    "sql": "SELECT service_area_type AS 服务区类型, COUNT(*) AS 开放数量, ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER(), 2) AS 占比百分比 FROM bss_service_area WHERE delete_ts IS NULL AND service_state = '开放' GROUP BY service_area_type;"
+  },
+  {
+    "question": "查询最近7天内有经营数据记录的开放服务区列表及其所属公司名称？",
+    "sql": "SELECT DISTINCT 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 JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND bd.oper_date >= CURRENT_DATE - INTERVAL '7 days' AND sa.service_state = '开放' ORDER BY 所属公司, 服务区名称;"
+  },
+  {
+    "question": "列出所有未产生任何车辆流量数据的服务区（可能异常）及其基本信息？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_area_no AS 服务区编码, sa.service_state AS 状态, c.company_name AS 所属公司 FROM bss_service_area sa LEFT JOIN bss_car_day_count cc ON sa.id = cc.service_area_id JOIN bss_company c ON sa.company_id = c.id WHERE sa.delete_ts IS NULL AND c.delete_ts IS NULL AND cc.id IS NULL ORDER BY 所属公司;"
+  },
+  {
+    "question": "统计各公司下属服务区的总数、开放数量及运营率（开放/总数）？",
+    "sql": "SELECT c.company_name AS 公司名称, COUNT(sa.id) AS 总服务区数, COUNT(CASE WHEN sa.service_state = '开放' THEN 1 END) AS 开放服务区数, ROUND(COUNT(CASE WHEN sa.service_state = '开放' THEN 1 END) * 100.0 / COUNT(sa.id), 2) AS 运营率 FROM bss_company c LEFT JOIN bss_service_area sa ON c.id = sa.company_id AND sa.delete_ts IS NULL WHERE c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 运营率 DESC;"
+  },
+  {
+    "question": "找出过去30天日均支付总额最高的前5个开放服务区？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, AVG(bd.pay_sum) AS 日均支付金额 FROM bss_service_area sa JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE sa.delete_ts IS NULL AND sa.service_state = '开放' AND bd.oper_date >= CURRENT_DATE - INTERVAL '30 days' GROUP BY sa.service_area_name ORDER BY 日均支付金额 DESC LIMIT 5;"
+  },
+  {
+    "question": "分析不同类型服务区在最近一周的平均每日车辆流量差异？",
+    "sql": "SELECT sa.service_area_type AS 服务区类型, AVG(cd.customer_count) AS 平均每日车流量 FROM bss_service_area sa JOIN bss_car_day_count cd ON sa.id = cd.service_area_id WHERE sa.delete_ts IS NULL AND cd.count_date >= CURRENT_DATE - INTERVAL '7 days' GROUP BY sa.service_area_type ORDER BY 平均每日车流量 DESC;"
+  },
+  {
+    "question": "哪些服务区虽标记为‘开放’但近7天无任何经营数据记录（可能存在数据异常）？",
+    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_area_no AS 服务区编码, c.company_name AS 所属公司 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' AND NOT EXISTS (SELECT 1 FROM bss_business_day_data bd WHERE bd.service_no = sa.service_area_no AND bd.oper_date >= CURRENT_DATE - INTERVAL '7 days') ORDER BY 所属公司;"
+  },
+  {
+    "question": "统计每种车辆类型在过去一个月中出现频率最高的服务区？",
+    "sql": "SELECT car_type AS 车辆类型, service_area_name AS 服务区名称, customer_count AS 车流量 FROM (SELECT cd.car_type, sa.service_area_name, cd.customer_count, ROW_NUMBER() OVER (PARTITION BY cd.car_type ORDER BY cd.customer_count DESC) AS rn FROM bss_car_day_count cd JOIN bss_service_area sa ON cd.service_area_id = sa.id WHERE cd.count_date >= CURRENT_DATE - INTERVAL '1 month' AND sa.delete_ts IS NULL) t WHERE rn = 1;"
+  },
+  {
+    "question": "汇总各公司在上一个自然月内的总订单量和总支付金额，并按金额排序？",
+    "sql": "SELECT c.company_name AS 公司名称, SUM(bd.order_sum) AS 总订单量, SUM(bd.pay_sum) AS 总支付金额 FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id JOIN bss_business_day_data bd ON sa.service_area_no = bd.service_no WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND bd.oper_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') AND bd.oper_date < DATE_TRUNC('month', CURRENT_DATE) GROUP BY c.company_name ORDER BY 总支付金额 DESC;"
+  }
+]

data_pipeline/training_data/manual_20250722_164749/vector_bak/vector_backup_log.txt

@@ -0,0 +1,11 @@
+=== Vector Table Backup Log ===
+Backup Time: 2025-07-22 16:56:19
+Task ID: manual_20250722_164749
+Duration: 0.00s
+
+Tables Backup Status:
+✓ langchain_pg_collection: 4 rows -> langchain_pg_collection_20250722_165619.csv (209.0 B)
+✓ langchain_pg_embedding: 62 rows -> langchain_pg_embedding_20250722_165619.csv (818.9 KB)
+
+Truncate Status:
+- Not performed

data_pipeline/training_data/task_20250721_113010/bss_business_day_data.ddl

@@ -1,31 +0,0 @@
--- 中文名: `bss_business_day_data` 表用于记录高速公路服务区每日业务统计数据
--- 描述: `bss_business_day_data` 表用于记录高速公路服务区每日业务统计数据，包含服务区间、操作日期及数据变更轨迹，为核心业务分析提供数据支撑。
-create table public.bss_business_day_data (
-  id varchar(32) not null     -- 主键ID，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,
-  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            -- 行吧订单数量,
-  jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
-  order_sum integer           -- 订单总数,
-  pay_sum numeric(19,4)       -- 支付总金额,
-  source_type integer         -- 数据来源类别,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_113010/bss_business_day_data_detail.md

@@ -1,32 +0,0 @@
-## bss_business_day_data（`bss_business_day_data` 表用于记录高速公路服务区每日业务统计数据）
-bss_business_day_data 表`bss_business_day_data` 表用于记录高速公路服务区每日业务统计数据，包含服务区间、操作日期及数据变更轨迹，为核心业务分析提供数据支撑。
-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]
-- 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) - 行吧订单数量 [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]
-- 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]
-- source_type (integer) - 数据来源类别 [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_113010/bss_car_day_count.ddl

@@ -1,17 +0,0 @@
--- 中文名: `bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型
--- 描述: `bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型，辅助交通流量分析与运营管理。
-create table public.bss_car_day_count (
-  id varchar(32) not null     -- 主键ID，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,
-  customer_count bigint       -- 车辆数量,
-  car_type varchar(100)       -- 车辆类别,
-  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_113010/bss_car_day_count_detail.md

@@ -1,18 +0,0 @@
-## bss_car_day_count（`bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型）
-bss_car_day_count 表`bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型，辅助交通流量分析与运营管理。
-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]
-- 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]
-- 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/task_20250721_113010/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，为核心业务数据表。
-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_113010/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，为核心业务数据表。
-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_113010/bss_section_route.ddl

@@ -1,16 +0,0 @@
--- 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,
-  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)           -- 编号,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_113010/bss_section_route_area_link.ddl

@@ -1,7 +0,0 @@
--- 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,
-  primary key (section_route_id, service_area_id)
-);

data_pipeline/training_data/task_20250721_113010/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/task_20250721_113010/bss_section_route_detail.md

@@ -1,16 +0,0 @@
-## 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]
-- 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]
-字段补充说明：
-- id 为主键

data_pipeline/training_data/task_20250721_113010/bss_service_area_mapper.ddl

@@ -1,18 +0,0 @@
--- 中文名: `bss_service_area_mapper` 表用于映射和管理高速公路服务区的基本信息
--- 描述: `bss_service_area_mapper` 表用于映射和管理高速公路服务区的基本信息，包括服务区名称、编码及操作记录，支撑服务区相关业务的数据管理与追溯。
-create table public.bss_service_area_mapper (
-  id varchar(32) not null     -- 主键ID，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,
-  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,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_113010/bss_service_area_mapper_detail.md

@@ -1,20 +0,0 @@
-## bss_service_area_mapper（`bss_service_area_mapper` 表用于映射和管理高速公路服务区的基本信息）
-bss_service_area_mapper 表`bss_service_area_mapper` 表用于映射和管理高速公路服务区的基本信息，包括服务区名称、编码及操作记录，支撑服务区相关业务的数据管理与追溯。
-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]
-- 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]
-- updated_by (varchar(50)) - 更新人
-- delete_ts (timestamp) - 删除时间
-- 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]
-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
-- source_type 为枚举字段，包含取值：5、0、1、3、4

data_pipeline/training_data/task_20250721_113010/db_query_decision_prompt.txt

@@ -1,14 +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，主要涉及服务区业务交易、车辆流量、企业信息、路段路线及服务区基础信息，包含以下业务数据：
-核心业务实体：
-- 服务区：提供休息、加油、购物等功能的高速公路沿线设施，主要字段：service_name、service_no、service_area_name、service_area_no
-- 档口：服务区内的商业经营单位，主要字段：branch_name、branch_no
-- 支付方式：记录交易支付类型，主要字段：wx、zfb、rmb、xs、jd
-- 车辆类型：进入服务区的车辆分类，主要字段：car_type
-- 公司：负责服务区管理的分公司，主要字段：company_name、company_no
-- 路段路线：高速公路的路段与路线信息，主要字段：section_name、route_name
-关键业务指标：
-- 支付金额与订单数量：按支付方式统计的交易金额和订单数，如微信、支付宝、现金等
-- 车流量：按日期和车辆类型统计进入服务区的车辆数量
-- 营收汇总：每日支付总金额与订单总数的统计
-- 服务区运营状态：服务区是否开放、关闭或数据上传中

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/backup_info.json

@@ -1,51 +0,0 @@
-{
-  "backup_time": "2025-07-21T12:02:36.094246",
-  "backup_directory": "file_bak_20250721_120236",
-  "moved_files": [
-    "bss_business_day_data.ddl",
-    "bss_business_day_data_1.ddl",
-    "bss_business_day_data_detail.md",
-    "bss_business_day_data_detail_1.md",
-    "bss_car_day_count.ddl",
-    "bss_car_day_count_1.ddl",
-    "bss_car_day_count_detail.md",
-    "bss_car_day_count_detail_1.md",
-    "bss_company.ddl",
-    "bss_company_1.ddl",
-    "bss_company_detail.md",
-    "bss_company_detail_1.md",
-    "bss_section_route.ddl",
-    "bss_section_route_1.ddl",
-    "bss_section_route_area_link.ddl",
-    "bss_section_route_area_link_1.ddl",
-    "bss_section_route_area_link_detail.md",
-    "bss_section_route_area_link_detail_1.md",
-    "bss_section_route_detail.md",
-    "bss_section_route_detail_1.md",
-    "bss_service_area.ddl",
-    "bss_service_area_1.ddl",
-    "bss_service_area_detail.md",
-    "bss_service_area_detail_1.md",
-    "bss_service_area_mapper.ddl",
-    "bss_service_area_mapper_1.ddl",
-    "bss_service_area_mapper_detail.md",
-    "bss_service_area_mapper_detail_1.md",
-    "db_query_decision_prompt.txt",
-    "filename_mapping.txt",
-    "file_modifications_20250721_114134.log",
-    "metadata.txt",
-    "metadata_detail.md",
-    "qs_highway_db_20250721_114123_pair.json",
-    "qs_highway_db_20250721_114123_pair.json.backup",
-    "sql_validation_20250721_114134_summary.log",
-    "task_config.json",
-    "task_result.json"
-  ],
-  "failed_files": [
-    {
-      "file": "data_pipeline.log",
-      "error": "[WinError 32] 另一个程序正在使用此文件，进程无法访问。: 'C:\\\\Projects\\\\cursor_projects\\\\Vanna-Chainlit-Chromadb\\\\data_pipeline\\\\training_data\\\\task_20250721_113010\\\\data_pipeline.log'"
-    }
-  ],
-  "task_id": "task_20250721_113010"
-}

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/bss_business_day_data.ddl

@@ -1,31 +0,0 @@
--- 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。
-create table public.bss_business_day_data (
-  id varchar(32) not null     -- 主键ID，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,
-  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            -- 行吧订单数量,
-  jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
-  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,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/bss_business_day_data_1.ddl

@@ -1,31 +0,0 @@
--- 中文名: 业务日数据表
--- 描述: 业务日数据表，记录高速公路服务区每日经营统计信息。
-create table public.bss_business_day_data (
-  id varchar(32) not null     -- 主键ID，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,
-  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            -- 行吧订单数量,
-  jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
-  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,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/bss_business_day_data_detail.md

@@ -1,32 +0,0 @@
-## 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。
-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]
-- 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) - 行吧订单数量 [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]
-- 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]
-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_113010/file_bak_20250721_120236/bss_business_day_data_detail_1.md

@@ -1,32 +0,0 @@
-## bss_business_day_data（业务日数据表）
-bss_business_day_data 表业务日数据表，记录高速公路服务区每日经营统计信息。
-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]
-- 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) - 行吧订单数量 [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]
-- 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]
-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_113010/file_bak_20250721_120236/bss_car_day_count.ddl

@@ -1,17 +0,0 @@
--- 中文名: `bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型
--- 描述: `bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型，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    -- 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,
-  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_113010/file_bak_20250721_120236/bss_car_day_count_1.ddl

@@ -1,17 +0,0 @@
--- 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，记录车辆类别与数量统计信息。
-create table public.bss_car_day_count (
-  id varchar(32) not null     -- 主键ID，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,
-  customer_count bigint       -- 车辆数量,
-  car_type varchar(100)       -- 车辆类别,
-  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_113010/file_bak_20250721_120236/bss_car_day_count_detail.md

@@ -1,18 +0,0 @@
-## bss_car_day_count（`bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型）
-bss_car_day_count 表`bss_car_day_count` 表用于按日统计进入服务区的车辆数量及类型，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) - 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]
-- 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/task_20250721_113010/file_bak_20250721_120236/bss_car_day_count_detail_1.md

@@ -1,18 +0,0 @@
-## 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，记录车辆类别与数量统计信息。
-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]
-- 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]
-- 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/task_20250721_113010/file_bak_20250721_120236/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，支撑服务区运营管理。
-create table public.bss_company (
-  id varchar(32) not null     -- 公司ID，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_113010/file_bak_20250721_120236/bss_company_1.ddl

@@ -1,15 +0,0 @@
--- 中文名: 公司信息表
--- 描述: 公司信息表，用于存储高速公路服务区合作公司的基础信息与变更记录。
-create table public.bss_company (
-  id varchar(32) not null     -- 公司唯一标识符，主键,
-  version integer not null    -- 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_113010/file_bak_20250721_120236/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，支撑服务区运营管理。
-字段列表：
-- id (varchar(32)) - 公司ID [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_113010/file_bak_20250721_120236/bss_company_detail_1.md

@@ -1,17 +0,0 @@
-## bss_company（公司信息表）
-bss_company 表公司信息表，用于存储高速公路服务区合作公司的基础信息与变更记录。
-字段列表：
-- id (varchar(32)) - 公司唯一标识符 [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
-- version (integer) - 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_113010/file_bak_20250721_120236/bss_section_route.ddl

@@ -1,16 +0,0 @@
--- 中文名: 路段与路线信息表
--- 描述: 路段与路线信息表，用于管理高速公路服务区所属路段及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,
-  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)           -- 编号,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/bss_section_route_1.ddl

@@ -1,16 +0,0 @@
--- 中文名: **表注释:** 路段路线信息表
--- 描述: **表注释:** 路段路线信息表，用于管理高速公路各路段与对应路线的基本信息。
-create table public.bss_section_route (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  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)           -- 编号,
-  primary key (id)
-);

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/bss_section_route_area_link.ddl

@@ -1,7 +0,0 @@
--- 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,
-  primary key (section_route_id, service_area_id)
-);

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/bss_section_route_area_link_1.ddl

@@ -1,7 +0,0 @@
--- 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,
-  primary key (section_route_id, service_area_id)
-);

data_pipeline/training_data/task_20250721_113010/file_bak_20250721_120236/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/task_20250721_113010/file_bak_20250721_120236/bss_section_route_area_link_detail_1.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/task_20250721_113010/file_bak_20250721_120236/bss_section_route_detail.md

@@ -1,16 +0,0 @@
-## bss_section_route（路段与路线信息表）
-bss_section_route 表路段与路线信息表，用于管理高速公路服务区所属路段及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]
-- 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]
-字段补充说明：
-- id 为主键