"""
数据模型核心业务逻辑模块

本模块包含了数据模型相关的所有核心业务逻辑函数，包括：
- 数据模型的创建、更新、删除
- 数据模型与数据资源、元数据之间的关系处理
- 数据模型血缘关系管理
- 数据模型图谱生成
- 数据模型层级计算等功能
"""

import math
import threading
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
from py2neo import Relationship
import logging
import json

from app.core.graph.graph_operations import relationship_exists
from app.core.graph.graph_operations import connect_graph,create_or_get_node,get_node
from app.services.neo4j_driver import neo4j_driver
from app.core.meta_data import get_formatted_time, handle_id_unstructured
from app.core.common import delete_relationships, update_or_create_node, get_node_by_id_no_label
from app.core.data_resource.resource import get_node_by_id


# 根据child关系计算数据模型当前的level自动保存
def calculate_model_level(id):
    """
    根据child关系计算数据模型当前的level并自动保存
    
    Args:
        id: 数据模型的节点ID(整数)
        
    Returns:
        None
    """
    # 确保id是整数类型
    node_id = int(id) if id is not None else None
    
    cql = """
    MATCH (start_node:data_model)
    WHERE id(start_node) = $nodeId
    CALL {
        WITH start_node
        OPTIONAL MATCH path = (start_node)-[:child*]->(end_node)
        RETURN length(path) AS level
    }
    WITH coalesce(max(level), 0) AS max_level
    RETURN max_level
    """
    
    with connect_graph().session() as session:
        result = session.run(cql, nodeId=node_id)
        record = result.single()
        data = record["max_level"] if record and "max_level" in record else 0
        
    # 更新level属性
    update_query = """
    MATCH (n:data_model)
    WHERE id(n) = $nodeId
    SET n.level = $level
    RETURN n
    """
    
    with connect_graph().session() as session:
        session.run(update_query, nodeId=node_id, level=data)


# 处理数据模型血缘关系
def handle_model_relation(resource_ids):
    """
    处理数据模型血缘关系
    
    Args:
        resource_ids: 数据资源ID
        
    Returns:
        血缘关系数据
    """
    query = """
            MATCH (search:data_resource)-[:connection]->(common_node:meta_node)<-[:connection]-(connect:data_resource)
            WHERE id(search) = $resource_Ids
            WITH search, connect, common_node
            MATCH (search)-[:connection]->(search_node:meta_node)
            WITH search, connect, common_node, collect(DISTINCT id(search_node)) AS search_nodes
            MATCH (connect)-[:connection]->(connect_node:meta_node)
            WITH search, connect, common_node, search_nodes, collect(DISTINCT id(connect_node)) AS connect_nodes
            WITH search, connect, search_nodes, connect_nodes, collect(DISTINCT id(common_node)) AS common_nodes

            // 剔除 search_nodes 和 connect_nodes 中包含在 common_nodes 中的内容
            WITH search, connect, common_nodes,
                 [node IN search_nodes WHERE NOT node IN common_nodes] AS filtered_search_nodes,
                 [node IN connect_nodes WHERE NOT node IN common_nodes] AS filtered_connect_nodes

            RETURN  id(connect) as blood_resources, common_nodes, 
            filtered_search_nodes as origin_nodes, filtered_connect_nodes as blood_nodes
            """

    with connect_graph().session() as session:
        result = session.run(query, resource_Ids=resource_ids)
    return result.data()


# 创建一个数据模型节点
def handle_data_model(data_model, result_list, result, receiver):
    """
    创建一个数据模型节点
    
    Args:
        data_model: 数据模型名称
        result_list: 数据模型英文名列表
        result: 序列化的ID列表
        receiver: 接收到的请求参数
        
    Returns:
        tuple: (id, data_model_node)
    """
    try:
        # 添加数据资源 血缘关系的字段 blood_resource
        data_model_en = result_list[0] if result_list and len(result_list) > 0 else ""
        receiver['id_list'] = result
        add_attribute = {
            'time': get_formatted_time(),
            'en_name': data_model_en
        }
        receiver.update(add_attribute)
        data_model_node = get_node('data_model', name=data_model) or create_or_get_node('data_model', **receiver)

        # 安全地处理子节点关系
        child_list = receiver.get('childrenId', [])
        for child_id in child_list:
            child_node = get_node_by_id_no_label(child_id)
            if child_node and not relationship_exists(data_model_node, 'child', child_node):
                with connect_graph().session() as session:
                    session.execute_write(
                        lambda tx: tx.run(
                            "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:child]->(b)",
                            a_id=data_model_node.id, b_id=child_node.id
                        )
                    )

        # 根据传入参数id，和数据标签建立关系
        if receiver.get('tag'):
            tag = get_node_by_id('data_label', receiver['tag'])
            if tag and not relationship_exists(data_model_node, 'label', tag):
                with connect_graph().session() as session:
                    session.execute_write(
                        lambda tx: tx.run(
                            "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:label]->(b)",
                            a_id=data_model_node.id, b_id=tag.id
                        )
                    )

        # 获取节点ID
        node_id = None
        if hasattr(data_model_node, 'id'):
            node_id = data_model_node.id
        else:
            # 如果节点没有identity属性，尝试通过查询获取
            query = """
            MATCH (n:data_model {name: $name})
            RETURN id(n) as node_id
            """
            with connect_graph().session() as session:
                result = session.run(query, name=data_model)
                record = result.single()
                if record and "node_id" in record:
                    node_id = record["node_id"]

        return node_id, data_model_node
    except Exception as e:
        logging.error(f"Error in handle_data_model: {str(e)}")
        raise


# (从数据资源中选取）
def resource_handle_meta_data_model(id_lists, data_model_node_id):
    """
    处理从数据资源中选取的数据模型与元数据的关系
    
    Args:
        id_lists: ID列表
        data_model_node_id: 数据模型节点ID
        
    Returns:
        None
    """
    # 构建meta_id和resouce_id的列表
    resouce_ids = [record['resource_id'] for record in id_lists]
    meta_ids = [record['id'] for id_list in id_lists for record in id_list['metaData']]
    metaData = [record['data_standard'] for id_list in id_lists for record in id_list['metaData']]
    
    # 创建与meta_node的关系 组成关系
    if meta_ids:
        query = """
        MATCH (source:data_model), (target:meta_node)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:component]->(target)
        """
        with neo4j_driver.get_session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=meta_ids)

    # 创建与data_resource的关系 资源关系
    if resouce_ids:
        query = """
        MATCH (source:data_model), (target:data_resource)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:resource]->(target)
        """
        with neo4j_driver.get_session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=resouce_ids)


# (从数据模型中选取）
def model_handle_meta_data_model(id_lists, data_model_node_id):
    """
    处理从数据模型中选取的数据模型与元数据的关系
    
    Args:
        id_lists: ID列表
        data_model_node_id: 数据模型节点ID
        
    Returns:
        None
    """
    # 构建meta_id和model_id的列表
    model_ids = [record['model_id'] for record in id_lists]
    meta_ids = [record['id'] for id_list in id_lists for record in id_list['metaData']]
    
    # 创建与meta_node的关系 组成关系
    if meta_ids:
        query = """
        MATCH (source:data_model), (target:meta_node)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:component]->(target)
        """
        with neo4j_driver.get_session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=meta_ids)

    # 创建与data_model的关系 模型关系
    if model_ids:
        query = """
        MATCH (source:data_model), (target:data_model)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:use]->(target)
        """
        with neo4j_driver.get_session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=model_ids)


# (从DDL中选取）
def handle_no_meta_data_model(id_lists, receiver, data_model_node):
    """
    处理从DDL中选取的没有元数据的数据模型
    
    Args:
        id_lists: ID列表
        receiver: 接收到的请求参数
        data_model_node: 数据模型节点
        
    Returns:
        None
    """
    # 构建meta_id和resouce_id的列表
    resouce_ids = [record['resource_id'] for record in id_lists]
    meta_ids = [record['id'] for id_list in id_lists for record in id_list['metaData']]
    
    # 获取数据模型节点ID
    data_model_node_id = None
    if hasattr(data_model_node, 'id'):
        data_model_node_id = data_model_node.id
    else:
        # 如果节点没有id属性，尝试通过查询获取
        query = """
        MATCH (n:data_model {name: $name})
        RETURN id(n) as node_id
        """
        with connect_graph().session() as session:
            result = session.run(query, name=data_model_node.get('name'))
            record = result.single()
            if record:
                data_model_node_id = record["node_id"]
    
    if not data_model_node_id:
        return
    
    # 创建与data_resource的关系 资源关系
    if resouce_ids:
        query = """
        MATCH (source:data_model), (target:data_resource)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:resource]->(target)
        """
        with connect_graph().session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=resouce_ids)

    if meta_ids:
        meta_node_list = []
        for id in meta_ids:
            query = """
            MATCH (n)
            WHERE id(n) = $node_id
            RETURN n
            """
            with connect_graph().session() as session:
                result = session.run(query, node_id=id)
            if result:
                record = result.data()
                if record:
                    meta_node_list.append(record[0]['n'])
        
        # 提取接收到的数据并创建meta_node节点
        meta_node = None
        resource_ids = []
        
        for item in id_lists:
            resource_id = item['resource_id']
            resource_ids.append(resource_id)
            
            for meta_item in item['metaData']:
                meta_id = meta_item['id']
                data_standard = meta_item.get('data_standard', '')
                en_name_zh = meta_item.get('en_name_zh', '')
                data_name = meta_item.get('data_name', '')
                
                # 使用传递的参数创建meta_node节点
                meta_params = {
                    'name': data_name,
                    'cn_name': en_name_zh,
                    'standard': data_standard,
                    'time': get_formatted_time()
                }
                
                # 创建meta_node节点
                meta_node = create_or_get_node('meta_node', **meta_params)
                
                # 创建与data_model的关系
                if meta_node and not relationship_exists(data_model_node, 'component', meta_node):
                    with connect_graph().session() as session:
                        session.execute_write(
                            lambda tx: tx.run(
                                "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:component]->(b)",
                                a_id=data_model_node.id, b_id=meta_node.id
                            )
                        )


# 数据模型-详情接口
def handle_id_model(id):
    """
    获取数据模型详情
    
    Args:
        id: 数据模型的节点ID
        
    Returns:
        dict: 包含数据模型详情的字典，格式为：
        {"data_model": {
            "resource_selected": [...],
            "leader": ...,
            "origin": ...,
            "frequency": ...,
            "childrenId": [...],
            "organization": ...,
            "name": ...,
            "en_name": ...,
            "data_sensitivity": ...,
            "describe": ...,
            "tag": ...,
            "time": ...,
            "category": ...,
            "status": ...
        }}
    """
    node_id = id
    cql = """
        MATCH (n:data_model) WHERE id(n) = $nodeId
        OPTIONAL MATCH (n)-[:connection]->(meta:meta_node)
        OPTIONAL MATCH (n)<-[:belongs_to]-(resource:data_resource)
        OPTIONAL MATCH (n)-[:label]->(tag:data_label)
        OPTIONAL MATCH (uses:model_use)-[:use]->(n)
        OPTIONAL MATCH (n)-[:has_component]->(component)
        WITH n, 
            collect(DISTINCT meta) as meta_nodes,
            collect(DISTINCT resource) as resources,
            collect(DISTINCT component) as components,
            collect(DISTINCT uses) as uses,
            collect(DISTINCT tag) as tags,
            CASE WHEN n.childrenId IS NOT NULL THEN n.childrenId ELSE [] END as children
        RETURN {
            // 基本信息
            id: id(n),
            name: n.name,
            en_name: n.en_name,
            time: n.time,
            description: n.description,
            describe: n.description,  // 使用description作为describe字段
            category: n.category,
            level: n.level,
            tag: CASE WHEN size(tags) > 0 AND tags[0] IS NOT NULL THEN {id: id(tags[0]), name: tags[0].name} ELSE null END,
            // 添加其他必需字段
            leader: n.leader,
            origin: n.origin,
            blood_resource: n.blood_resource,
            frequency: n.frequency,
            organization: n.organization,
            data_sensitivity: n.data_sensitivity,
            status: n.status,
            // 子节点列表
            childrenId: children
        } AS result,
        // 资源列表
        [{
            data_resource: [resource IN resources WHERE resource IS NOT NULL | {
                id: id(resource),
                name: resource.name,
                en_name: resource.en_name,
                description: resource.description
            }],
            resource_id: [resource IN resources WHERE resource IS NOT NULL | id(resource)],
            meta_ids: [meta IN meta_nodes WHERE meta IS NOT NULL | {
                id: id(meta),
                name: meta.name,
                en_name: meta.en_name,
                data_type: meta.data_type
            }]
        }] AS resource_selected
        """
        
    with connect_graph().session() as session:
        result = session.run(cql, nodeId=node_id)
        
        # 处理查询结果
        record = result.single()
        logging.info(f"获得查询结果---------->>>{record}")
        
        if record:
            # 获取基本属性和资源选择列表
            properties = record["result"]
            resource_selected = record["resource_selected"]
            
            # 确保所有必需字段都有默认值，避免空值
            required_fields = ['tag', 'description', 'leader', 'origin', 'blood_resource', 
                              'frequency', 'describe', 'organization', 'name', 'en_name', 
                              'data_sensitivity', 'time', 'category', 'status', 'childrenId']
            
            for field in required_fields:
                if field not in properties or properties[field] is None:
                    if field == 'tag':
                        properties[field] = {}
                    elif field == 'childrenId':
                        properties[field] = []
                    else:
                        properties[field] = ""
            
            # 构建最终返回格式
            final_data = {
                "resource_selected": resource_selected,
                **properties
            }
            
            return {"data_model": final_data}
        else:
            # 如果没有查询到结果，返回空的结构
            return {"data_model": {
                "resource_selected": [{"meta_ids": [], "data_resource": None, "resource_id": None}],
                "leader": None, "origin": None, "frequency": None, "childrenId": [],
                "organization": None, "name": None, "en_name": None, "data_sensitivity": None,
                "describe": None, "tag": {}, "time": None, "category": None, "status": None
            }}


# 数据模型列表
def model_list(skip_count, page_size, en_name_filter=None, name_filter=None, 
               category=None, tag=None, level=None):
    """
    获取数据模型列表
    
    Args:
        skip_count: 跳过的数量
        page_size: 页面大小
        en_name_filter: 英文名称过滤条件
        name_filter: 名称过滤条件
        category: 类别过滤条件
        tag: 标签过滤条件
        level: 层级过滤条件
        
    Returns:
        tuple: (数据模型列表, 总数量)
    """
    try:
        # 构建where子句
        where_clause = []
        params = {}

        if name_filter is not None:
            where_clause.append("n.name =~ $name")
            params['name'] = f".*{name_filter}.*"
        
        if en_name_filter is not None:
            where_clause.append("n.en_name =~ $en_name")
            params['en_name'] = f".*{en_name_filter}.*"

        if category is not None:
            where_clause.append("n.category = $category")
            params['category'] = category
    
        if level is not None:
            where_clause.append("n.level = $level")
            params['level'] = level
    
        if tag is not None:
            where_clause.append("id(t) = $tag")
            params['tag'] = tag

        # At the end of where_clause construction
        where_str = " AND ".join(where_clause)
        if where_str:
            where_str = f"WHERE {where_str}"
    
        # 构建查询
        with connect_graph().session() as session:
            # 计算总数量
            count_query = f"""
            MATCH (n:data_model)
            OPTIONAL MATCH (n)-[:label]->(t)
            {where_str}
            RETURN COUNT(DISTINCT n) AS count
            """
            count_result = session.run(count_query, **params)
            count_record = count_result.single()
            total = count_record['count'] if count_record else 0

            # 查询数据
            query = f"""
            MATCH (n:data_model)
            OPTIONAL MATCH (n)-[:label]->(t)
            {where_str}
            RETURN DISTINCT 
            id(n) as id, 
            n.name as name, 
            n.en_name as en_name, 
            n.time as time,
            n.description as description,
            n.level as level,
            id(t) as tag_id,
            t.name as tag_name
            ORDER BY n.time DESC
            SKIP $skip
            LIMIT $limit
            """
        
            result = session.run(query, skip=skip_count, limit=page_size, **params)
            
            # 处理结果
            data = []
            for record in result:
                item = {
                    "id": record['id'],
                    "name": record['name'],
                    "en_name": record['en_name'],
                    "time": record['time'],
                    "description": record['description'],
                    "level": record['level'],
                    "tag": {"id": record['tag_id'], "name": record['tag_name']} if record['tag_id'] is not None else None
                }
                data.append(item)
            
            return data, total
    except Exception as e:
        print(f"Error in model_list: {str(e)}")
        import traceback
        traceback.print_exc()
        return [], 0


# 有血缘关系的数据资源列表
def model_resource_list(skip_count, page_size, name_filter=None, id=None, 
                        category=None, time=None):
    """
    获取数据模型相关的数据资源列表
    
    Args:
        skip_count: 跳过的数量
        page_size: 页面大小
        name_filter: 名称过滤条件
        id: 数据模型ID
        category: 类别过滤条件
        time: 时间过滤条件
        
    Returns:
        tuple: (数据资源列表, 总数量)
    """
    try:
        # 构建基础查询
        base_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId
        MATCH (n)-[:children]->(m:data_resource)
        """
        
        # 计算总数量
        count_query = base_query + """
        RETURN COUNT(m) as count
        """
        
        with connect_graph().session() as session:
            # 执行计数查询
            count_result = session.run(count_query, nodeId=id)
            count_record = count_result.single()
            total = count_record['count'] if count_record else 0
            
            # 使用分页和筛选条件构建主查询
            main_query = base_query + """
            MATCH (m)-[:label]->(l)
            WHERE id(n) = $nodeId and labels(m) <> ['meta_node']
            RETURN m.name as name, 
                   m.en_name as en_name,
                   id(m) as id,
                   l.name as label,
                   m.time as time,
                   m.description as description,
                   m.category as category 
            ORDER BY m.time DESC
            SKIP $skip LIMIT $limit
            """
            
            # 执行主查询
            result = session.run(main_query, nodeId=id, skip=skip_count, limit=page_size)
            
            # 处理结果
            data = []
            for record in result:
                item = {
                    "name": record['name'],
                    "en_name": record['en_name'],
                    "id": record['id'],
                    "label": record['label'],
                    "time": record['time'],
                    "description": record['description'],
                    "category": record['category']
                }
                data.append(item)
            
            return data, total
    except Exception as e:
        print(f"Error in model_resource_list: {str(e)}")
        import traceback
        traceback.print_exc()
        return [], 0


# 数据模型血缘图谱
def model_kinship_graph(nodeid, meta=False):
    """
    生成数据模型的血缘关系图谱
    
    Args:
        nodeid: 节点ID
        meta: 是否包含元数据
        
    Returns:
        dict: 包含节点和连线信息的图谱数据
    """
    result = {}
    
    with connect_graph().session() as session:
        # 查询起始模型节点
        start_node_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId
        RETURN n.name as name, n.en_name as en_name
        """
        
        start_result = session.run(start_node_query, nodeId=nodeid)
        start_record = start_result.single()
        
        if not start_record:
            return {"nodes": [], "lines": []}
        
        # 查询与模型关联的数据资源
        resource_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId
        MATCH p = (n)-[:children]->(resource:data_resource)
        RETURN resource
        """
        
        resource_result = session.run(resource_query, nodeId=nodeid)
        
        nodes = [{"id": str(nodeid), "text": start_record['name'], "type": "model"}]
        lines = []
        
        # 处理资源节点
        for record in resource_result:
            if 'resource' in record:
                resource = record['resource']
                resource_id = str(resource.id)
                resource_name = resource.get('name', '')
                resource_en_name = resource.get('en_name', '')
                
                # 创建资源节点
                resource_node = {
                    "id": resource_id,
                    "text": resource_name,
                    "type": "resource"
                }
                nodes.append(resource_node)
                
                # 创建资源到模型的关系
                line = {
                    "from": str(nodeid),
                    "to": resource_id,
                    "text": "resource"
                }
                lines.append(line)

        # 处理元数据节点
        if meta:
            meta_query = """
            MATCH (n:data_model)
            WHERE id(n) = $nodeId and labels(m) <> ['meta_node']
            MATCH p = (n)-[:meta]->(meta:meta_node)
            RETURN meta
            """
            meta_result = session.run(meta_query, nodeId=nodeid)
            
            for record in meta_result:
                if 'meta' in record:
                    meta_node = record['meta']
                    meta_id = str(meta.id)
                    meta_name = meta.get('name', '')
                    meta_en_name = meta.get('en_name', '')
                    
                    # 创建元数据节点
                    meta_node = {
                        "id": meta_id,
                        "text": meta_name,
                        "type": "meta"
                    }
                    nodes.append(meta_node)
                    
                    # 创建模型到元数据的标签关系
                    tag_line = {
                        "from": str(nodeid),
                        "to": meta_id,
                        "text": "component"
                    }
                    lines.append(tag_line)

        # 构建结果
        result = {
            "nodes": nodes,
            "lines": lines
        }
        return result


# 数据模型影响图谱
def model_impact_graph(nodeid, meta=False):
    """
    生成数据模型的影响关系图谱
    
    Args:
        nodeid: 节点ID
        meta: 是否包含元数据
        
    Returns:
        dict: 包含节点和连线信息的图谱数据
    """
    result = {}
    
    with connect_graph().session() as session:
        # 查询起始模型节点
        start_node_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId
        RETURN n.name as name, n.en_name as en_name
        """
        
        start_result = session.run(start_node_query, nodeId=nodeid)
        start_record = start_result.single()
        
        if not start_record:
            return {"nodes": [], "lines": []}
        
        # 查询影响模型的数据资源
        resource_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId
        MATCH p = (n)-[:children]->(resource:data_resource)
        RETURN resource
        """
        
        resource_result = session.run(resource_query, nodeId=nodeid)
        
        nodes = [{"id": str(nodeid), "text": start_record['name'], "type": "model"}]
        lines = []
        
        # 处理资源节点
        for record in resource_result:
            if 'resource' in record:
                resource = record['resource']
                resource_id = str(resource.id)
                resource_name = resource.get('name', '')
                resource_en_name = resource.get('en_name', '')
                
                # 创建资源节点
                resource_node = {
                    "id": resource_id,
                    "text": resource_name,
                    "type": "resource"
                }
                nodes.append(resource_node)
                
                # 创建资源到模型的关系
                line = {
                    "from": str(nodeid),
                    "to": resource_id,
                    "text": "resource"
                }
                lines.append(line)

        # 处理元数据节点
        if meta:
            meta_query = """
            MATCH (n:data_model)
            WHERE id(n) = $nodeId and labels(m) <> ['meta_node']
            MATCH p = (n)-[:meta]->(meta:meta_node)
            RETURN meta
            """
            meta_result = session.run(meta_query, nodeId=nodeid)
            
            for record in meta_result:
                if 'meta' in record:
                    meta_node = record['meta']
                    meta_id = str(meta.id)
                    meta_name = meta.get('name', '')
                    meta_en_name = meta.get('en_name', '')
                    
                    # 创建元数据节点
                    meta_node = {
                        "id": meta_id,
                        "text": meta_name,
                        "type": "meta"
                    }
                    nodes.append(meta_node)
                    
                    # 创建模型到元数据的标签关系
                    tag_line = {
                        "from": str(nodeid),
                        "to": meta_id,
                        "text": "component"
                    }
                    lines.append(tag_line)

        # 构建结果
        result = {
            "nodes": nodes,
            "lines": lines
        }
        return result


# 数据模型全部图谱
def model_all_graph(nodeid, meta=False):
    """
    生成数据模型的所有关系图谱
    
    Args:
        nodeid: 节点ID
        meta: 是否包含元数据
        
    Returns:
        dict: 包含节点和连线信息的图谱数据
    """
    result = {}
    
    with connect_graph().session() as session:
        # 查询起始模型节点
        start_node_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId
        RETURN n.name as name, n.en_name as en_name
        """
        
        start_result = session.run(start_node_query, nodeId=nodeid)
        start_record = start_result.single()
        
        if not start_record:
            return {"nodes": [], "lines": []}
        
        # 查询与模型关联的数据资源
        resource_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId
        MATCH p = (n)-[:children]->(resource:data_resource)
        RETURN resource
        """
        
        resource_result = session.run(resource_query, nodeId=nodeid)
        
        # 查询与模型关联的元数据
        meta_query = """
        MATCH (n:data_model)
        WHERE id(n) = $nodeId and labels(m) <> ['meta_node']
        MATCH p = (n)-[:meta]->(meta:meta_node)
        RETURN meta
        """
        
        nodes = [{"id": str(nodeid), "text": start_record['name'], "type": "model"}]
        lines = []
        
        # 处理资源节点
        for record in resource_result:
            if 'resource' in record:
                resource = record['resource']
                resource_id = str(resource.id)
                resource_name = resource.get('name', '')
                resource_en_name = resource.get('en_name', '')
                
                # 创建资源节点
                resource_node = {
                    "id": resource_id,
                    "text": resource_name,
                    "type": "resource"
                }
                nodes.append(resource_node)
                
                # 创建资源到模型的关系
                line = {
                    "from": str(nodeid),
                    "to": resource_id,
                    "text": "resource"
                }
                lines.append(line)

        # 处理元数据节点
        if meta:
            meta_result = session.run(meta_query, nodeId=nodeid)
            
            for record in meta_result:
                if 'meta' in record:
                    meta_node = record['meta']
                    meta_id = str(meta.id)
                    meta_name = meta.get('name', '')
                    meta_en_name = meta.get('en_name', '')
                    
                    # 创建元数据节点
                    meta_node = {
                        "id": meta_id,
                        "text": meta_name,
                        "type": "meta"
                    }
                    nodes.append(meta_node)
                    
                    # 创建模型到元数据的标签关系
                    tag_line = {
                        "from": str(nodeid),
                        "to": meta_id,
                        "text": "component"
                    }
                    lines.append(tag_line)

        # 构建结果
        result = {
            "nodes": nodes,
            "lines": lines
        }
        return result


# 更新数据模型
def data_model_edit(receiver):
    """
    更新数据模型
    
    Args:
        receiver: 接收到的请求参数
        
    Returns:
        更新结果
    """
    id = receiver.get('id')
    name = receiver.get('name')
    en_name = receiver.get('en_name')
    category = receiver.get('category')
    description = receiver.get('description')
    tag = receiver.get('tag')
    
    # 更新数据模型节点
    query = """
    MATCH (n:data_model) WHERE id(n) = $id
    SET n.name = $name, n.en_name = $en_name, n.category = $category, n.description = $description
    RETURN n
    """
    
    with connect_graph().session() as session:
        result = session.run(query, id=id, name=name, en_name=en_name, 
                             category=category, description=description).data()
    
    # 处理标签关系
    if tag:
        # 先删除所有标签关系
        delete_query = """
        MATCH (n:data_model)-[r:label]->() WHERE id(n) = $id
        DELETE r
        """
        with connect_graph().session() as session:
            session.run(delete_query, id=id)
        
        # 再创建新的标签关系
        tag_node = get_node_by_id('data_label', tag)
        if tag_node:
            model_node = get_node_by_id_no_label(id)
            if model_node and not relationship_exists(model_node, 'label', tag_node):
                with connect_graph().session() as session:
                    session.execute_write(
                        lambda tx: tx.run(
                            "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:label]->(b)",
                            a_id=model_node.id, b_id=tag_node.id
                        )
                    )
    
    return {"message": "数据模型更新成功"}