import gc
import time
from datetime import datetime, timedelta
import pymongo
from pymongo.errors import PyMongoError, ServerSelectionTimeoutError
import pandas as pd
import os
import random
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import font_manager
import matplotlib.dates as mdates
from config import mongodb_config, vj_flight_route_list, vj_flight_route_list_hot, vj_flight_route_list_nothot, \
    CLEAN_VJ_HOT_NEAR_INFO_TAB, CLEAN_VJ_HOT_FAR_INFO_TAB, CLEAN_VJ_NOTHOT_NEAR_INFO_TAB, CLEAN_VJ_NOTHOT_FAR_INFO_TAB


font_path = "./simhei.ttf"
font_prop = font_manager.FontProperties(fname=font_path)

def mongo_con_parse(config=None):
    if config is None:
        config = mongodb_config.copy()

    try:
        if config.get("URI", ""):
            motor_uri = config["URI"]
            client = pymongo.MongoClient(motor_uri, maxPoolSize=100)
            db = client[config['db']]
            print("motor_uri: ", motor_uri)
        else:
            client = pymongo.MongoClient(
                config['host'],
                config['port'],
                serverSelectionTimeoutMS=15000,  # 6秒
                connectTimeoutMS=15000,  # 6秒
                socketTimeoutMS=15000,  # 6秒,
                retryReads=True,    # 开启重试
                maxPoolSize=50
            )
            db = client[config['db']]
            if config.get('user'):
                db.authenticate(config['user'], config['pwd'])

        print(f"✅ MongoDB 连接对象创建成功")

    except Exception as e:
        print(f"❌ 创建 MongoDB 连接对象时发生错误: {e}")
        raise
    return client, db


def test_mongo_connection(db):
    try:
        # 获取客户端对象
        client = db.client

        # 方法1：使用 server_info() 测试连接
        info = client.server_info()
        print(f"✅ MongoDB 连接测试成功!")
        print(f"  服务器版本: {info.get('version')}")
        print(f"  数据库: {db.name}")
        return True

    except Exception as e:
        print(f"❌ 数据库连接测试失败: {e}")
        return False


def query_flight_range_status(db, table_name, from_city, to_city, dep_date_begin, dep_date_end, flight_nums, 
                              limit=0, max_retries=3, base_sleep=1.0, thread_id=0):
    """
    从指定表(4类)查询数据(指定起飞天的范围) （失败自动重试）
    """
    for attempt in range(1, max_retries + 1):
        try:
            print(f"🔁 第 {attempt}/{max_retries} 次尝试查询")
            # 构建查询条件
            query_condition = {
                "from_city_code": from_city,
                "to_city_code": to_city,
                "search_dep_time": {
                    "$gte": dep_date_begin,
                    "$lte": dep_date_end,
                },
                "segments.baggage": {"$in": ["1-20", "1-30"]}  # 只查20公斤和30公斤行李的
            }
            # 动态添加航班号条件
            for i, flight_num in enumerate(flight_nums):
                query_condition[f"segments.{i}.flight_number"] = flight_num
    
            print(f"   查询条件: {query_condition}")
            # 定义要查询的字段
            projection = {
                # "_id": 1,
                "from_city_code": 1,
                "search_dep_time": 1,
                "to_city_code": 1,
                "currency": 1,
                "adult_price": 1,
                "adult_tax": 1,
                "adult_total_price": 1,
                "seats_remaining": 1,
                "segments": 1,
                "source_website": 1,
                "crawl_date": 1
            }
            # 执行查询
            cursor = db.get_collection(table_name).find(
                query_condition,
                projection=projection  # 添加投影参数
            ).sort(
                [
                    ("search_dep_time", 1),  # 多级排序要用列表+元组的格式
                    ("segments.0.baggage", 1),
                    ("crawl_date", 1)
                ]
            )
            if limit > 0:
                cursor = cursor.limit(limit)

            # 将结果转换为列表
            results = list(cursor)
            print(f"✅ 查询成功，找到 {len(results)} 条记录")

            if results:
                df = pd.DataFrame(results)
                # 处理特殊的 ObjectId 类型
                if '_id' in df.columns:
                    df = df.drop(columns=['_id'])
                print(f"📊 已转换为 DataFrame，形状: {df.shape}")

                # 1️⃣ 展开 segments
                print(f"📊 开始扩展segments 稍等...")
                t1 = time.time()
                df = expand_segments_columns_optimized(df)  # 改为调用优化版
                t2 = time.time()
                rt = round(t2 - t1, 3)
                print(f"用时: {rt} 秒")
                print(f"📊 已将segments扩展成字段，形状: {df.shape}")

                # 不用排序，因为mongo语句已经排好
                return df

            else:
                print("⚠️  查询结果为空")
                return pd.DataFrame()
            
        except (ServerSelectionTimeoutError, PyMongoError) as e:
            print(f"⚠️ Mongo 查询失败: {e}")
            if attempt == max_retries:
                print("❌ 达到最大重试次数，放弃")
                return pd.DataFrame()
            
            # 指数退避 + 随机抖动
            sleep_time = base_sleep * (2 ** (attempt - 1)) + random.random()
            print(f"⏳ {sleep_time:.2f}s 后重试...")
            time.sleep(sleep_time)


# def expand_segments_columns(df):
#     """展开 segments"""
#     df = df.copy()

#     # 定义要展开的列
#     seg1_cols = ['flight_number', 'dep_air_port', 'dep_time', 'arr_air_port', 'arr_time', 'cabin', 'baggage']
#     seg2_cols = ['flight_number', 'dep_air_port', 'dep_time', 'arr_air_port', 'arr_time']

#     # 定义 apply 函数一次返回字典
#     def extract_segments(row):
#         segments = row.get('segments')
#         result = {}
#         # 默认缺失使用 pd.NA（对字符串友好）
#         missing = pd.NA
#         if isinstance(segments, list):
#             # 第一段
#             if len(segments) >= 1 and isinstance(segments[0], dict):
#                 for col in seg1_cols:
#                     result[f'seg1_{col}'] = segments[0].get(col)
#             else:
#                 for col in seg1_cols:
#                     result[f'seg1_{col}'] = missing
#             # 第二段
#             if len(segments) >= 2 and isinstance(segments[1], dict):
#                 for col in seg2_cols:
#                     result[f'seg2_{col}'] = segments[1].get(col)
#             else:
#                 for col in seg2_cols:
#                     result[f'seg2_{col}'] = missing
#         else:
#             # segments 不是 list，全都置空
#             for col in seg1_cols:
#                 result[f'seg1_{col}'] = missing
#             for col in seg2_cols:
#                 result[f'seg2_{col}'] = missing

#         return pd.Series(result)

#     # 一次 apply
#     df_segments = df.apply(extract_segments, axis=1)

#     # 拼回原 df
#     df = pd.concat([df.drop(columns=['segments'], errors='ignore'), df_segments], axis=1)

#     # 统一转换时间字段为 datetime
#     time_cols = [
#         'seg1_dep_time', 'seg1_arr_time',
#         'seg2_dep_time', 'seg2_arr_time'
#     ]
#     for col in time_cols:
#         if col in df.columns:
#             df[col] = pd.to_datetime(
#                 df[col],
#                 format='%Y%m%d%H%M%S',
#                 errors='coerce'
#             )

#     # 站点来源 -> 是否近期
#     df['source_website'] = np.where(
#         df['source_website'].str.contains('7_30'),
#         0,  # 远期 -> 0
#         np.where(df['source_website'].str.contains('0_7'),
#                  1,  # 近期 -> 1
#                  df['source_website'])  # 其他情况保持原值
#     )

#     # 行李配额字符 -> 数字
#     conditions = [
#         df['seg1_baggage'] == '-;-;-;-',
#         df['seg1_baggage'] == '1-20',
#         df['seg1_baggage'] == '1-30',
#         df['seg1_baggage'] == '1-40',
#     ]
#     choices = [0, 20, 30, 40]
#     df['seg1_baggage'] = np.select(conditions, choices, default=df['seg1_baggage'])

#     # 重命名字段
#     df = df.rename(columns={
#         'seg1_cabin': 'cabin',
#         'seg1_baggage': 'baggage',
#         'source_website': 'is_near',
#     })

#     return df

def expand_segments_columns_optimized(df):
    """优化版的展开segments函数（避免逐行apply）"""
    if df.empty:
        return df
    
    df = df.copy()

    # 直接操作segments列表，避免逐行apply
    if 'segments' in df.columns:
        # 提取第一段信息
        seg1_cols = ['flight_number', 'dep_air_port', 'dep_time', 'arr_air_port', 'arr_time', 'cabin', 'baggage']
        # 提取第二段信息
        seg2_cols = ['flight_number', 'dep_air_port', 'dep_time', 'arr_air_port', 'arr_time']
    
        # 使用列表推导式替代apply，大幅提升性能
        seg1_data = []
        seg2_data = []

        for segments in df['segments']:
            seg1_dict = {}
            seg2_dict = {}

            if isinstance(segments, list) and len(segments) >= 1 and isinstance(segments[0], dict):
                for col in seg1_cols:
                    seg1_dict[f'seg1_{col}'] = segments[0].get(col)
            else:
                for col in seg1_cols:
                    seg1_dict[f'seg1_{col}'] = pd.NA
            
            if isinstance(segments, list) and len(segments) >= 2 and isinstance(segments[1], dict):
                for col in seg2_cols:
                    seg2_dict[f'seg2_{col}'] = segments[1].get(col)
            else:
                for col in seg2_cols:
                    seg2_dict[f'seg2_{col}'] = pd.NA
                    
            seg1_data.append(seg1_dict)
            seg2_data.append(seg2_dict)

        # 创建DataFrame
        df_seg1 = pd.DataFrame(seg1_data, index=df.index)
        df_seg2 = pd.DataFrame(seg2_data, index=df.index)

        # 合并到原DataFrame
        df = pd.concat([df.drop(columns=['segments'], errors='ignore'), df_seg1, df_seg2], axis=1)

    # 后续处理保持不变
    time_cols = ['seg1_dep_time', 'seg1_arr_time', 'seg2_dep_time', 'seg2_arr_time']
    for col in time_cols:
        if col in df.columns:
            df[col] = pd.to_datetime(df[col], format='%Y%m%d%H%M%S', errors='coerce')
    
    df['source_website'] = np.where(
        df['source_website'].str.contains('7_30'), 0,
        np.where(df['source_website'].str.contains('0_7'), 1, df['source_website'])
    )

    conditions = [
        df['seg1_baggage'] == '-;-;-;-',
        df['seg1_baggage'] == '1-20',
        df['seg1_baggage'] == '1-30',
        df['seg1_baggage'] == '1-40',
    ]
    choices = [0, 20, 30, 40]
    df['seg1_baggage'] = np.select(conditions, choices, default=df['seg1_baggage'])

    df = df.rename(columns={
        'seg1_cabin': 'cabin',
        'seg1_baggage': 'baggage',
        'source_website': 'is_near',
    })
    
    return df


def fill_hourly_crawl_date(df, head_fill=0, rear_fill=0):
    """补齐成小时粒度数据"""
    df = df.copy()

    # 1. 转 datetime
    df['crawl_date'] = pd.to_datetime(df['crawl_date'])

    # 添加一个用于分组的小时字段
    df['update_hour'] = df['crawl_date'].dt.floor('h')

    # 2. 排序规则：同一小时内，按原始时间戳排序
    # 假设你想保留最早的一条
    df = df.sort_values(['update_hour', 'crawl_date'])

    # 3. 按小时去重，保留该小时内最早的一条
    df = df.drop_duplicates(subset=['update_hour'], keep='first')

    # 删除原始时间戳列
    # df = df.drop(columns=['crawl_date'])
    # df = df.drop(columns=['_id'])

    # 4. 标记原始数据
    df['is_filled'] = 0

    # 5. 排序 + 设索引
    df = df.sort_values('update_hour').set_index('update_hour')

    # 6. 构造完整小时轴
    start_of_day = df.index.min()  # 默认 第一天 最早 开始
    if head_fill == 1:
        start_of_day = df.index.min().normalize()  # 强制 第一天 00:00 开始

    end_of_day = df.index.max()  # 默认 最后一天 最晚 结束
    if rear_fill == 1:
        end_of_day = df.index.max().normalize() + pd.Timedelta(hours=23)  # 强制 最后一天 23:00 结束
    elif rear_fill == 2:
        if 'seg1_dep_time' in df.columns:
            last_dep_time = df['seg1_dep_time'].iloc[-1]
            if pd.notna(last_dep_time):
                # 对齐到整点小时（向下取整）
                end_of_day = last_dep_time.floor('h')

    full_index = pd.date_range(
        start=start_of_day,
        end=end_of_day,
        freq='1h'
    )

    # 7. 按小时补齐
    df = df.reindex(full_index)

    # 先恢复 dtype（关键！）
    df = df.infer_objects(copy=False)

    # 8. 新增出来的行标记为 1
    df['is_filled'] = df['is_filled'].fillna(1)

    # 9. 前向填充
    df = df.ffill()

    # 10. 还原整型字段
    int_cols = [
        'seats_remaining',
        'is_near',
        'baggage',
        'is_filled',
    ]
    for col in int_cols:
        if col in df.columns:
            df[col] = df[col].astype('int64')

    # 10.5 价格字段统一保留两位小数
    price_cols = [
        'adult_price',
        'adult_tax',
        'adult_total_price'
    ]
    for col in price_cols:
        if col in df.columns:
            df[col] = df[col].astype('float64').round(2)

    # 10.6 新增：距离起飞还有多少小时
    if 'seg1_dep_time' in df.columns:
        # 创建临时字段（整点）
        df['seg1_dep_hour'] = df['seg1_dep_time'].dt.floor('h')

        # 计算小时差 df.index 此时就是 update_hour
        df['hours_until_departure'] = (
                (df['seg1_dep_hour'] - df.index) / pd.Timedelta(hours=1)
        ).astype('int64')

        # 新增：距离起飞还有多少天
        df['days_to_departure'] = (df['hours_until_departure'] // 24).astype('int64')

        # 删除临时字段
        df = df.drop(columns=['seg1_dep_hour'])

    # 11. 写回 update_hour
    df['update_hour'] = df.index

    # 12. 恢复普通索引
    df = df.reset_index(drop=True)

    return df


def query_groups_of_city_code(db, from_city, to_city, table_name, min_days=20, max_retries=3, base_sleep=1.0):
    """
    从一组城市对中查找所有分组(航班号与起飞时间)的组合
    按：第一段航班号 → 第二段航班号 → 起飞时间 排序
    （失败自动重试） 保证2个月内至少有20天起飞的航线
    """
    print(f"{from_city}-{to_city} 查找所有分组")
    date_begin = (datetime.today() - timedelta(days=60)).strftime("%Y%m%d")
    date_end = datetime.today().strftime("%Y%m%d")
    pipeline = [
        # 1️⃣ 先筛选城市对
        {
            "$match": {
                "from_city_code": from_city,
                "to_city_code": to_city,
                "search_dep_time": {
                    "$gte": date_begin,
                    "$lte": date_end
                }
            }
        },
        # 2️⃣ 投影字段 + 拆第一、第二段航班号用于排序
        {
            "$project": {
                "flight_numbers": "$segments.flight_number",
                "search_dep_time": 1,
                "fn1": {"$arrayElemAt": ["$segments.flight_number", 0]},
                "fn2": {"$arrayElemAt": ["$segments.flight_number", 1]}
            }
        },
        # 3️⃣ 第一级分组：组合 + 每一天
        {
            "$group": {
                "_id": {
                    "flight_numbers": "$flight_numbers",
                    "search_dep_time": "$search_dep_time",
                    "fn1": "$fn1",
                    "fn2": "$fn2"
                },
                "count": {"$sum": 1}
            }
        },
        # 关键修复点：这里先按【时间】排好序！
        {
            "$sort": {
                "_id.fn1": 1,
                "_id.fn2": 1,
                "_id.search_dep_time": 1  # 确保 push 进去时是按天递增
            }
        },
        # 4️⃣ 第二级分组：只按【航班组合】聚合 → 统计“有多少天”
        {
            "$group": {
                "_id": {
                    "flight_numbers": "$_id.flight_numbers",
                    "fn1": "$_id.fn1",
                    "fn2": "$_id.fn2"
                },
                "days": {"$sum": 1},  # 不同起飞天数
                "details": {
                    "$push": {
                        "search_dep_time": "$_id.search_dep_time",
                        "count": "$count"
                    }
                }
            }
        },
        # 5️⃣ 关键：按“天数阈值”过滤
        {
            "$match": {
                "days": {"$gte": min_days}
            }
        },
        # 6️⃣ ✅ 按“第一段 → 第二段”排序
        {
            "$sort": {
                "_id.fn1": 1,
                "_id.fn2": 1,
            }
        }
    ]
    for attempt in range(1, max_retries + 1):
        try:
            print(f"  第 {attempt}/{max_retries} 次尝试查询")

            # 执行聚合查询
            collection = db[table_name]
            results = list(collection.aggregate(pipeline))

            # 格式化结果，将 _id 中的字段提取到外层
            formatted_results = []
            for item in results:
                formatted_item = {
                    "flight_numbers": item["_id"]["flight_numbers"],
                    "days": item["days"],       # 这个组合一共有多少天
                    "details": item["details"]  # 每一天的 count 明细
                }
                formatted_results.append(formatted_item)

            return formatted_results

        except (ServerSelectionTimeoutError, PyMongoError) as e:
            print(f"⚠️ Mongo 查询失败: {e}")
            if attempt == max_retries:
                print("❌ 达到最大重试次数，放弃")
                return []

            # 指数退避 + 随机抖动
            sleep_time = base_sleep * (2 ** (attempt - 1)) + random.random()
            print(f"⏳ {sleep_time:.2f}s 后重试...")
            time.sleep(sleep_time)


def plot_c12_trend(df, output_dir="."):
    """
    根据传入的 dataframe 绘制 adult_total_price 随 update_hour 的趋势图，
    并按照 baggage 分类进行分组绘制。
    """
    # output_dir_photo = output_dir

    # 颜色与线型配置（按顺序循环使用）
    colors = ['green', 'blue', 'red', 'brown']
    linestyles = ['--', '--', '--', '--']

    # 确保时间字段为 datetime 类型
    if not hasattr(df['update_hour'], 'dt'):
        df['update_hour'] = pd.to_datetime(df['update_hour'])

    from_city = df['from_city_code'].mode().iloc[0]
    to_city = df['to_city_code'].mode().iloc[0]
    flight_number_1 = df['seg1_flight_number'].mode().iloc[0]
    flight_number_2 = df['seg2_flight_number'].mode().get(0, "")
    dep_time = df['seg1_dep_time'].mode().iloc[0]

    route = f"{from_city}-{to_city}"
    flight_number = f"{flight_number_1},{flight_number_2}" if flight_number_2 else f"{flight_number_1}"

    output_dir_photo = os.path.join(output_dir, route)
    os.makedirs(output_dir_photo, exist_ok=True)

    # 创建图表对象
    fig = plt.figure(figsize=(14, 8))

    # 按 baggage 分类绘制
    for i, (baggage_value, group) in enumerate(df.groupby('baggage')):
        # 按时间排序
        g = group.sort_values('update_hour').reset_index(drop=True)

        # 找价格变化点：与前一行不同的价格即为变化点
        # keep first row + change rows + last row
        change_points = g.loc[
            (g['adult_total_price'] != g['adult_total_price'].shift(1)) |
            (g.index == 0) |
            (g.index == len(g) - 1)  # 终点
        ].drop_duplicates(subset=['update_hour'])

        # 绘制点和线条
        plt.plot(
            change_points['update_hour'],
            change_points['adult_total_price'],
            marker='o',
            color=colors[i % len(colors)],
            linestyle=linestyles[i % len(linestyles)],
            linewidth=2, markersize=6,
            markerfacecolor='white', markeredgewidth=2,
            label=f"Baggage {baggage_value}"
        )

        # 添加注释 (小时数, 价格)
        for _, row in change_points.iterrows():
            text = f"({row['hours_until_departure']}, {row['adult_total_price']})"
            plt.annotate(
                text,
                xy=(row['update_hour'], row['adult_total_price']),
                xytext=(0, 0),  # 向右偏移
                textcoords="offset points",
                ha='left',
                va='center',
                fontsize=5,  # 字体稍小
                color='gray',
                alpha=0.8,
                rotation=25,
            )

    # 自动优化日期显示
    plt.gcf().autofmt_xdate()
    plt.xlabel('时刻', fontsize=12, fontproperties=font_prop)
    plt.ylabel('价格', fontsize=12, fontproperties=font_prop)
    plt.title(f'价格变化趋势 - 航线: {route} 航班号: {flight_number}\n起飞时间: {dep_time}',
              fontsize=14, fontweight='bold', fontproperties=font_prop)

    # 设置 x 轴刻度为每天
    ax = plt.gca()
    ax.xaxis.set_major_locator(mdates.DayLocator(interval=1))    # 每天一个主刻度
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))  # 显示月-日
    ax.xaxis.set_minor_locator(mdates.HourLocator(byhour=[12]))  # 指定在12:00显示副刻度
    ax.xaxis.set_minor_formatter(mdates.DateFormatter(''))       # 输出空字符串
    # ax.tick_params(axis='x', which='minor', labelsize=8, rotation=30)

    # 添加图例
    plt.legend(bbox_to_anchor=(1.05, 1), loc='best', prop=font_prop)
    plt.grid(True, alpha=0.3)
    plt.tight_layout()

    safe_flight = flight_number.replace(",", "_")
    safe_dep_time = dep_time.strftime("%Y-%m-%d %H%M%S")
    save_file = f"{route} {safe_flight} {safe_dep_time}.png"
    output_path = os.path.join(output_dir_photo, save_file)
    # 保存图片（在显示之前）
    plt.savefig(output_path, dpi=300, bbox_inches='tight', facecolor='white')

    # 关闭图形释放内存
    plt.close(fig)


def process_flight_group(args):
    """处理单个航班号的进程函数（独立数据库连接）"""
    process_id, db_config, each_group, from_city, to_city, date_begin_s, date_end_s, is_hot, plot_flag, output_dir = args
    flight_nums = each_group.get("flight_numbers")
    details = each_group.get("details")

    print(f"[进程{process_id}] 开始处理航班号: {flight_nums}")

    # 为每个进程创建独立的数据库连接
    try:
        client, db = mongo_con_parse(db_config)
        print(f"[进程{process_id}] ✅ 数据库连接创建成功")
    except Exception as e:
        print(f"[进程{process_id}] ❌ 数据库连接创建失败: {e}")
        return pd.DataFrame()

    try:
        # 查询远期表
        if is_hot == 1:
            df1 = query_flight_range_status(db, CLEAN_VJ_HOT_FAR_INFO_TAB, from_city, to_city,
                                            date_begin_s, date_end_s, flight_nums)
        else:
            df1 = query_flight_range_status(db, CLEAN_VJ_NOTHOT_FAR_INFO_TAB, from_city, to_city,
                                            date_begin_s, date_end_s, flight_nums)
        
        # 保证远期表里有数据
        if df1.empty:
            print(f"[进程{process_id}] 航班号:{flight_nums} 远期表无数据, 跳过")
            return pd.DataFrame()
        
        # 查询近期表
        if is_hot == 1:
            df2 = query_flight_range_status(db, CLEAN_VJ_HOT_NEAR_INFO_TAB, from_city, to_city,
                                            date_begin_s, date_end_s, flight_nums)
        else:
            df2 = query_flight_range_status(db, CLEAN_VJ_NOTHOT_NEAR_INFO_TAB, from_city, to_city,
                                            date_begin_s, date_end_s, flight_nums)
            
        # 保证近期表里有数据
        if df2.empty:
            print(f"[进程{process_id}] 航班号:{flight_nums} 近期表无数据, 跳过")
            return pd.DataFrame()
        
        # 起飞天数、行李配额以近期表的为主
        if df2.empty:
            common_dep_dates = []
            common_baggages = []
        else:
            common_dep_dates = df2['search_dep_time'].unique()
            common_baggages = df2['baggage'].unique()

        list_mid = []
        for dep_date in common_dep_dates:
            # 起飞日期筛选
            df_d1 = df1[df1["search_dep_time"] == dep_date].copy()
            if not df_d1.empty:
                for col in ["seg1_dep_time", "seg1_arr_time", "seg2_dep_time", "seg2_arr_time"]:
                    mode_series_1 = df_d1[col].mode()
                    if mode_series_1.empty:
                        zong_1 = pd.NaT
                    else:
                        zong_1 = mode_series_1.iloc[0]
                    df_d1[col] = zong_1

            df_d2 = df2[df2["search_dep_time"] == dep_date].copy()
            if not df_d2.empty:
                for col in ["seg1_dep_time", "seg1_arr_time", "seg2_dep_time", "seg2_arr_time"]:
                    mode_series_2 = df_d2[col].mode()
                    if mode_series_2.empty:
                        zong_2 = pd.NaT
                    else:
                        zong_2 = mode_series_2.iloc[0]
                    df_d2[col] = zong_2

            list_12 = []
            for baggage in common_baggages:
                # 行李配额筛选
                df_b1 = df_d1[df_d1["baggage"] == baggage].copy()
                df_b2 = df_d2[df_d2["baggage"] == baggage].copy()

                # 合并前检查是否都有数据
                if df_b1.empty and df_b2.empty:
                    print(f"[进程{process_id}] ⚠️ dep_date:{dep_date}, baggage:{baggage} 远期表和近期表都为空，跳过")
                    continue

                cols = ["seg1_flight_number", "seg1_dep_air_port", "seg1_arr_air_port",
                        "seg2_flight_number", "seg2_dep_air_port", "seg2_arr_air_port"]
                df_b1[cols] = df_b1[cols].astype("string")
                df_b2[cols] = df_b2[cols].astype("string")

                df_b12 = pd.concat([df_b1, df_b2]).reset_index(drop=True)
                # print(f"📊 dep_date:{dep_date}, baggage:{baggage} 已将远期表和近期表合并，形状: {df_b12.shape}")
                df_b12 = fill_hourly_crawl_date(df_b12, rear_fill=2)
                # print(f"📊 dep_date:{dep_date}, baggage:{baggage} 已合并且补齐为完整小时序列，形状: {df_b12.shape}")
                list_12.append(df_b12)

                del df_b12
                del df_b2
                del df_b1

            if list_12:
                df_c12 = pd.concat(list_12, ignore_index=True)
                if plot_flag:
                    print(f"[进程{process_id}] ✅ dep_date:{dep_date}, 所有 baggage 数据合并完成，总形状: {df_c12.shape}")
                    plot_c12_trend(df_c12, output_dir)
                    print(f"[进程{process_id}] ✅ dep_date:{dep_date}, 所有 baggage 数据绘图完成")
            else:
                df_c12 = pd.DataFrame()
                if plot_flag:
                    print(f"[进程{process_id}] ⚠️ dep_date:{dep_date}, 所有 baggage 数据合并为空")

            del list_12
            list_mid.append(df_c12)

            del df_c12
            del df_d1
            del df_d2
            # print(f"结束处理起飞日期: {dep_date}")

        if list_mid:
            df_mid = pd.concat(list_mid, ignore_index=True)
            print(f"[进程{process_id}] ✅ 航班号:{flight_nums} 所有 起飞日期 数据合并完成，总形状: {df_mid.shape}")
        else:
            df_mid = pd.DataFrame()
            print(f"[进程{process_id}] ⚠️ 航班号:{flight_nums} 所有 起飞日期 数据合并为空")
        
        del list_mid
        del df1
        del df2
        gc.collect()
        print(f"[进程{process_id}] 结束处理航班号: {flight_nums}")
        return df_mid
    
    except Exception as e:
        print(f"[进程{process_id}] ❌ 处理航班号:{flight_nums} 时发生异常: {e}")
        return pd.DataFrame()
    finally:
        # 确保关闭数据库连接
        try:
            client.close()
            print(f"[进程{process_id}] ✅ 数据库连接已关闭")
        except:
            pass


def load_train_data(db_config, flight_route_list, table_name, date_begin, date_end, output_dir='.', is_hot=1, plot_flag=False,
                    use_multiprocess=False, max_workers=None):
    """加载训练数据（支持多进程）"""
    timestamp_str = datetime.now().strftime("%Y%m%d%H%M%S")
    date_begin_s = datetime.strptime(date_begin, "%Y-%m-%d").strftime("%Y%m%d")  # 查询时的格式
    date_end_s = datetime.strptime(date_end, "%Y-%m-%d").strftime("%Y%m%d")
    list_all = []

    # 每一航线对
    for flight_route in flight_route_list:
        from_city = flight_route.split('-')[0]
        to_city = flight_route.split('-')[1]
        route = f"{from_city}-{to_city}"
        print(f"开始处理航线: {route}")

        # 在主进程中查询航班号分组（避免多进程重复查询）
        main_client, main_db = mongo_con_parse(db_config)
        all_groups = query_groups_of_city_code(main_db, from_city, to_city, table_name)
        main_client.close()

        all_groups_len = len(all_groups)
        print(f"该航线共有{all_groups_len}个航班号")
        
        if use_multiprocess and all_groups_len > 1:
            print(f"启用多线程处理，最大线程数: {max_workers}")
            # 多进程处理
            process_args = []
            process_id = 0
            for each_group in all_groups:
                process_id += 1
                args = (process_id, db_config, each_group, from_city, to_city, date_begin_s, date_end_s, is_hot, plot_flag, output_dir)
                process_args.append(args)
            
            with ProcessPoolExecutor(max_workers=max_workers) as executor:
                future_to_group = {executor.submit(process_flight_group, args): each_group for args, each_group in zip(process_args, all_groups)}
                
                for future in as_completed(future_to_group):
                    each_group = future_to_group[future]
                    flight_nums = each_group.get("flight_numbers", "未知")
                    try:
                        df_mid = future.result()
                        if not df_mid.empty:
                            list_all.append(df_mid)
                            print(f"✅ 航班号:{flight_nums} 处理完成")
                        else:
                            print(f"⚠️ 航班号:{flight_nums} 处理结果为空")
                    except Exception as e:
                        print(f"❌ 航班号:{flight_nums} 处理异常: {e}")

        else:
            # 单进程处理（进程编号为0）
            print("使用单进程处理")
            process_id = 0
            for each_group in all_groups:
                args = (process_id, db_config, each_group, from_city, to_city, date_begin_s, date_end_s, is_hot, plot_flag, output_dir)
                flight_nums = each_group.get("flight_numbers", "未知")
                try:
                    df_mid = process_flight_group(args)
                    if not df_mid.empty:
                        list_all.append(df_mid)
                        print(f"✅ 航班号:{flight_nums} 处理完成")
                    else:
                        print(f"⚠️ 航班号:{flight_nums} 处理结果为空")
                except Exception as e:
                    print(f"❌ 航班号:{flight_nums} 处理异常: {e}")
                
        print(f"结束处理航线: {from_city}-{to_city}")

    if list_all:
        df_all = pd.concat(list_all, ignore_index=True)
    else:
        df_all = pd.DataFrame()

    print(f"本批次数据加载完毕, 总形状: {df_all.shape}")
    del list_all
    gc.collect()

    return df_all


def query_all_flight_number(db, table_name):
    print(f"{table_name} 查找所有航班号")
    pipeline = [
        {
            "$project": {
                "flight_numbers": "$segments.flight_number"
            }
        },
        {
            "$group": {
                "_id": "$flight_numbers",
                "count": { "$sum": 1 }
            }
        },
    ]
    # 执行聚合查询
    collection = db[table_name]
    results = list(collection.aggregate(pipeline))

    list_flight_number = []
    for item in results:
        item_li = item.get("_id", [])
        list_flight_number.extend(item_li)

    list_flight_number = list(set(list_flight_number))
    
    return list_flight_number


def validate_one_line(db, city_pair, flight_day, flight_number_1, flight_number_2, baggage, valid_begin_hour, 
                      limit=0, max_retries=3, base_sleep=1.0):
    """验证预测结果的一行"""
    
    if city_pair in vj_flight_route_list_hot:
        table_name = CLEAN_VJ_HOT_NEAR_INFO_TAB
    elif city_pair in vj_flight_route_list_nothot: 
        table_name = CLEAN_VJ_NOTHOT_NEAR_INFO_TAB
    else:
        print(f"城市对{city_pair}不在热门航线与冷门航线, 返回")
        return pd.DataFrame()
        
    city_pair_split = city_pair.split('-')
    from_city_code = city_pair_split[0]
    to_city_code = city_pair_split[1]
    flight_day_str = datetime.strptime(flight_day, "%Y-%m-%d").strftime("%Y%m%d") 
    baggage_str = f"1-{baggage}"

    for attempt in range(1, max_retries + 1):
        try:
            print(f"🔁 第 {attempt}/{max_retries} 次尝试查询") 
            # 构建查询条件
            query_condition = {
                "from_city_code": from_city_code,
                "to_city_code": to_city_code,
                "search_dep_time": flight_day_str,
                "segments.baggage": baggage_str,
                "crawl_date": {"$gte": valid_begin_hour},
                "segments.0.flight_number": flight_number_1,
            }
            # 如果有第二段
            if flight_number_2 != "VJ":
                query_condition["segments.1.flight_number"] = flight_number_2
            print(f"   查询条件: {query_condition}")
            # 定义要查询的字段
            projection = {
                # "_id": 1,
                "from_city_code": 1,
                "search_dep_time": 1,
                "to_city_code": 1,
                "currency": 1,
                "adult_price": 1,
                "adult_tax": 1,
                "adult_total_price": 1,
                "seats_remaining": 1,
                "segments": 1,
                "source_website": 1,
                "crawl_date": 1
            }
            # 执行查询
            cursor = db.get_collection(table_name).find(
                query_condition,
                projection=projection  # 添加投影参数
            ).sort(
                [
                    ("crawl_date", 1)
                ]
            )
            if limit > 0:
                cursor = cursor.limit(limit)

            # 将结果转换为列表
            results = list(cursor)
            print(f"✅ 查询成功，找到 {len(results)} 条记录")

            if results:
                df = pd.DataFrame(results)
                # 处理特殊的 ObjectId 类型
                if '_id' in df.columns:
                    df = df.drop(columns=['_id'])
                print(f"📊 已转换为 DataFrame，形状: {df.shape}")

                # 1️⃣ 展开 segments
                print(f"📊 开始扩展segments 稍等...")
                t1 = time.time()
                df = expand_segments_columns_optimized(df)
                t2 = time.time()
                rt = round(t2 - t1, 3)
                print(f"用时: {rt} 秒")
                print(f"📊 已将segments扩展成字段，形状: {df.shape}")

                # 不用排序，因为mongo语句已经排好
                return df

            else:
                print("⚠️  查询结果为空")
                return pd.DataFrame()

        except (ServerSelectionTimeoutError, PyMongoError) as e:
            print(f"⚠️ Mongo 查询失败: {e}")
            if attempt == max_retries:
                print("❌ 达到最大重试次数，放弃")
                return pd.DataFrame()
            
            # 指数退避 + 随机抖动
            sleep_time = base_sleep * (2 ** (attempt - 1)) + random.random()
            print(f"⏳ {sleep_time:.2f}s 后重试...")
            time.sleep(sleep_time)


if __name__ == "__main__":

    # test_mongo_connection(db)
    from utils import chunk_list_with_index

    cpu_cores = os.cpu_count()  # 你的系统是72
    max_workers = min(8, cpu_cores)  # 最大不超过8个进程

    output_dir = f"./output"
    os.makedirs(output_dir, exist_ok=True)

    # 加载热门航线数据
    date_begin = "2026-01-08"
    date_end = datetime.today().strftime("%Y-%m-%d")

    flight_route_list = vj_flight_route_list_hot[:]  # 热门 vj_flight_route_list_hot  冷门 vj_flight_route_list_nothot
    table_name = CLEAN_VJ_HOT_NEAR_INFO_TAB  # 热门 CLEAN_VJ_HOT_NEAR_INFO_TAB  冷门 CLEAN_VJ_NOTHOT_NEAR_INFO_TAB
    is_hot = 1   # 1 热门 0 冷门
    group_size = 1
    chunks = chunk_list_with_index(flight_route_list, group_size)

    for idx, (_, group_route_list) in enumerate(chunks, 1):
        # 使用默认配置
        # client, db = mongo_con_parse()
        print(f"第 {idx} 组 :", group_route_list)
        start_time = time.time()
        load_train_data(mongodb_config, group_route_list, table_name, date_begin, date_end, output_dir, is_hot, plot_flag=False,
                        use_multiprocess=True, max_workers=max_workers)
        end_time = time.time()
        run_time = round(end_time - start_time, 3)
        print(f"用时: {run_time} 秒")

        # client.close()
        time.sleep(3)

    print("整体结束")

    # client, db = mongo_con_parse()
    # list_flight_number_1 = query_all_flight_number(db, CLEAN_VJ_HOT_NEAR_INFO_TAB)
    # list_flight_number_2 = query_all_flight_number(db, CLEAN_VJ_NOTHOT_NEAR_INFO_TAB)

    # list_flight_number_all = list_flight_number_1 + list_flight_number_2
    # list_flight_number_all = list(set(list_flight_number_all))
    # list_flight_number_all.sort()
    
    # print(list_flight_number_all)
    # print(len(list_flight_number_all))

    # flight_map = {v: i for i, v in enumerate(list_flight_number_all, start=1)}
    # print(flight_map)