20260305 对近期改动做一次线上备份

backfill_mysql.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+申万行业热度数据回溯补录工具
+读取本地CSV文件，存入MySQL sw_heat_daily表
+用于历史数据补录或重新导入
+"""
+
+import pandas as pd
+import pymysql
+import os
+import re
+from datetime import datetime
+import argparse
+
+
+def extract_date_from_filename(filename):
+    """
+    从文件名提取日期
+    例如: heat_ranking_2026-03-04_5d.csv -> 2026-03-04 -> 20260304
+    """
+    # 匹配日期格式 YYYY-MM-DD
+    pattern = r'(\d{4}-\d{2}-\d{2})'
+    match = re.search(pattern, filename)
+    
+    if match:
+        date_str = match.group(1)  # 2026-03-04
+        # 转换为 YYYYMMDD 格式
+        calc_date = date_str.replace('-', '')  # 20260304
+        return calc_date, date_str
+    else:
+        raise ValueError(f"无法从文件名提取日期: {filename}")
+
+
+def read_heat_csv(file_path):
+    """
+    读取热度排名CSV文件
+    """
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"文件不存在: {file_path}")
+    
+    print(f"📂 读取文件: {file_path}")
+    df = pd.read_csv(file_path, encoding='utf-8-sig')
+    
+    # 检查必要列是否存在
+    required_cols = ['code', 'name', 'total_return', 'avg_turnover', 
+                     'volume_ratio', 'momentum', 'total_return_score',
+                     'avg_turnover_score', 'volume_ratio_score', 
+                     'momentum_score', 'heat_score']
+    
+    missing_cols = [col for col in required_cols if col not in df.columns]
+    if missing_cols:
+        # 尝试兼容旧版列名（如果有的话）
+        col_mapping = {
+            '行业代码': 'code',
+            '行业名称': 'name',
+            # 添加其他可能的映射
+        }
+        for old_col, new_col in col_mapping.items():
+            if old_col in df.columns and new_col not in df.columns:
+                df[new_col] = df[old_col]
+        
+        # 再次检查
+        still_missing = [col for col in required_cols if col not in df.columns]
+        if still_missing:
+            raise ValueError(f"CSV文件缺少必要列: {still_missing}")
+    
+    print(f"✅ 成功读取 {len(df)} 条行业数据")
+    return df
+
+
+def save_to_mysql(df, calc_date, db_config, dry_run=False):
+    """
+    将DataFrame存入MySQL
+    :param calc_date: YYYYMMDD 格式的日期字符串
+    :param dry_run: 如果为True，只打印SQL不执行
+    """
+    if dry_run:
+        print(f"\n🔍 [DRY RUN] 预览模式，不实际写入数据库")
+        print(f"📅 目标日期: {calc_date}")
+        print(f"📊 数据预览:")
+        print(df[['code', 'name', 'heat_score']].head())
+        return True
+    
+    conn = None
+    try:
+        conn = pymysql.connect(**db_config)
+        cursor = conn.cursor()
+        
+        # 检查该日期是否已有数据
+        cursor.execute(
+            "SELECT COUNT(*) FROM sw_heat_daily WHERE calc_date = %s", 
+            (calc_date,)
+        )
+        existing = cursor.fetchone()[0]
+        
+        if existing > 0:
+            print(f"⚠️  日期 {calc_date} 已有 {existing} 条记录，将执行覆盖更新")
+        
+        # 准备插入SQL
+        insert_sql = """
+        INSERT INTO sw_heat_daily 
+        (calc_date, code, name, total_return, avg_turnover, volume_ratio, momentum,
+         total_return_score, avg_turnover_score, volume_ratio_score, momentum_score, heat_score)
+        VALUES 
+        (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+        ON DUPLICATE KEY UPDATE
+        name = VALUES(name),
+        total_return = VALUES(total_return),
+        avg_turnover = VALUES(avg_turnover),
+        volume_ratio = VALUES(volume_ratio),
+        momentum = VALUES(momentum),
+        total_return_score = VALUES(total_return_score),
+        avg_turnover_score = VALUES(avg_turnover_score),
+        volume_ratio_score = VALUES(volume_ratio_score),
+        momentum_score = VALUES(momentum_score),
+        heat_score = VALUES(heat_score),
+        created_at = CURRENT_TIMESTAMP;
+        """
+        
+        # 转换数据为列表元组
+        data_tuples = []
+        for _, row in df.iterrows():
+            data_tuples.append((
+                calc_date,
+                str(row['code']),
+                str(row['name']),
+                float(row['total_return']),
+                float(row['avg_turnover']),
+                float(row['volume_ratio']),
+                float(row['momentum']),
+                float(row['total_return_score']),
+                float(row['avg_turnover_score']),
+                float(row['volume_ratio_score']),
+                float(row['momentum_score']),
+                float(row['heat_score'])
+            ))
+        
+        # 执行批量插入
+        cursor.executemany(insert_sql, data_tuples)
+        conn.commit()
+        
+        action = "覆盖更新" if existing > 0 else "新增"
+        print(f"✅ MySQL{action}成功: {calc_date} 共 {len(data_tuples)} 条记录")
+        return True
+        
+    except Exception as e:
+        print(f"❌ MySQL操作失败: {e}")
+        if conn:
+            conn.rollback()
+        return False
+    finally:
+        if conn:
+            conn.close()
+
+
+def batch_import(data_dir, db_config, pattern=None, dry_run=False):
+    """
+    批量导入目录下所有符合格式的CSV文件
+    """
+    if pattern is None:
+        pattern = r'heat_ranking_\d{4}-\d{2}-\d{2}_.*\.csv'
+    
+    files = [f for f in os.listdir(data_dir) if re.match(pattern, f)]
+    files.sort()
+    
+    print(f"📁 发现 {len(files)} 个待导入文件")
+    
+    success_count = 0
+    for filename in files:
+        file_path = os.path.join(data_dir, filename)
+        try:
+            calc_date, _ = extract_date_from_filename(filename)
+            df = read_heat_csv(file_path)
+            
+            if save_to_mysql(df, calc_date, db_config, dry_run):
+                success_count += 1
+                
+        except Exception as e:
+            print(f"❌ 处理 {filename} 失败: {e}")
+            continue
+    
+    print(f"\n📊 导入完成: {success_count}/{len(files)} 个文件成功")
+
+
+def main():
+    # 数据库配置（请根据实际情况修改）
+    config = {
+        'host': '10.127.2.207',
+        'user': 'financial_prod',
+        'password': 'mmTFncqmDal5HLRGY0BV',
+        'database': 'reference',
+        'port': 3306,
+        'charset': 'utf8mb4'
+    }
+    
+    # 命令行参数解析
+    parser = argparse.ArgumentParser(description='申万行业热度数据回溯补录工具')
+    parser.add_argument('file', nargs='?', help='单个CSV文件路径')
+    parser.add_argument('--date', '-d', help='指定日期(YYYYMMDD)，覆盖文件名中的日期')
+    parser.add_argument('--batch', '-b', action='store_true', help='批量导入目录下所有文件')
+    parser.add_argument('--dir', default='./sw_data', help='数据目录路径(默认: ./sw_data)')
+    parser.add_argument('--dry-run', action='store_true', help='预览模式，不实际写入数据库')
+    
+    args = parser.parse_args()
+    
+    # 批量模式
+    if args.batch:
+        batch_import(args.dir, config, dry_run=args.dry_run)
+        return
+    
+    # 单文件模式
+    if args.file:
+        file_path = args.file
+    else:
+        # 默认示例文件
+        file_path = './sw_data/heat_ranking_2026-03-04_5d.csv'
+        if not os.path.exists(file_path):
+            print("请提供CSV文件路径，或使用 --batch 批量导入")
+            parser.print_help()
+            return
+    
+    try:
+        # 读取CSV
+        df = read_heat_csv(file_path)
+        
+        # 确定日期
+        if args.date:
+            calc_date = args.date  # 命令行指定的日期
+            print(f"📅 使用指定日期: {calc_date}")
+        else:
+            calc_date, original_date = extract_date_from_filename(file_path)
+            print(f"📅 从文件名提取日期: {original_date} -> {calc_date}")
+        
+        # 存入MySQL
+        save_to_mysql(df, calc_date, config, dry_run=args.dry_run)
+        
+    except Exception as e:
+        print(f"❌ 错误: {e}")
+        raise
+
+
+if __name__ == "__main__":
+    main()

industry_heat_task.py

@@ -0,0 +1,405 @@
+import akshare as ak
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import os
+import time
+import pymysql
+import warnings
+warnings.filterwarnings('ignore')
+
+class SWIndustryHeatTracker:
+    """
+    申万一级行业热度追踪器
+    使用 index_analysis_daily_sw 接口（收盘后官方数据）
+    支持自定义热度计算周期和权重，数据自动存入MySQL
+    """
+    
+    def __init__(self, data_dir="./sw_data", lookback_days=5, db_config=None):
+        self.data_dir = data_dir
+        self.lookback_days = lookback_days
+        self.db_config = db_config  # MySQL配置
+        os.makedirs(data_dir, exist_ok=True)
+        
+        # 初始化交易日历
+        self.trading_dates = self._load_trading_calendar()
+        
+        # 申万一级行业代码映射（31个行业）
+        self.industry_map = {
+            '801010': '农林牧渔', '801030': '基础化工', '801040': '钢铁',
+            '801050': '有色金属', '801080': '电子', '801110': '家用电器',
+            '801120': '食品饮料', '801130': '纺织服饰', '801140': '轻工制造',
+            '801150': '医药生物', '801160': '公用事业', '801170': '交通运输',
+            '801180': '房地产', '801200': '商贸零售', '801210': '社会服务',
+            '801230': '综合', '801710': '建筑材料', '801720': '建筑装饰',
+            '801730': '电力设备', '801740': '国防军工', '801750': '计算机',
+            '801760': '传媒', '801770': '通信', '801780': '银行',
+            '801790': '非银金融', '801880': '汽车', '801890': '机械设备',
+            '801950': '煤炭', '801960': '石油石化', '801970': '环保',
+            '801980': '美容护理'
+        }
+        
+        # 热度指标权重配置
+        self.weights = {
+            'return': 0.35,
+            'turnover': 0.25,
+            'volume_ratio': 0.25,
+            'momentum': 0.15
+        }
+        
+        # 初始化数据库表
+        if self.db_config:
+            self._init_database()
+    
+    def _init_database(self):
+        """初始化MySQL数据库和表"""
+        conn = None
+        try:
+            conn = pymysql.connect(**self.db_config)
+            with conn.cursor() as cursor:
+                # 创建表（如果不存在）
+                create_table_sql = """
+                CREATE TABLE IF NOT EXISTS sw_heat_daily (
+                    id INT AUTO_INCREMENT PRIMARY KEY,
+                    calc_date VARCHAR(8) NOT NULL COMMENT '计算日期(YYYYMMDD)',
+                    code VARCHAR(10) NOT NULL COMMENT '行业代码',
+                    name VARCHAR(20) NOT NULL COMMENT '行业名称',
+                    total_return DECIMAL(10,4) COMMENT '累计涨跌幅(%)',
+                    avg_turnover DECIMAL(10,4) COMMENT '平均换手率(%)',
+                    volume_ratio DECIMAL(10,4) COMMENT '量比',
+                    momentum DECIMAL(5,4) COMMENT '动量(上涨天数占比)',
+                    total_return_score DECIMAL(5,4) COMMENT '收益因子得分(0-1)',
+                    avg_turnover_score DECIMAL(5,4) COMMENT '换手因子得分(0-1)',
+                    volume_ratio_score DECIMAL(5,4) COMMENT '量比因子得分(0-1)',
+                    momentum_score DECIMAL(5,4) COMMENT '动量因子得分(0-1)',
+                    heat_score DECIMAL(6,2) COMMENT '热度综合得分(0-100)',
+                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                    UNIQUE KEY uk_date_code (calc_date, code),
+                    INDEX idx_date (calc_date),
+                    INDEX idx_heat_score (calc_date, heat_score)
+                ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='申万一级行业热度日表';
+                """
+                cursor.execute(create_table_sql)
+                conn.commit()
+                print("✅ 数据库表 sw_heat_daily 初始化成功")
+        except Exception as e:
+            print(f"❌ 数据库初始化失败: {e}")
+            raise
+        finally:
+            if conn:
+                conn.close()
+    
+    def _load_trading_calendar(self):
+        """加载交易日历"""
+        cache_file = f"{self.data_dir}/trading_calendar.csv"
+        
+        if os.path.exists(cache_file):
+            df = pd.read_csv(cache_file)
+            dates = df['trade_date'].astype(str).tolist()
+            cache_time = datetime.fromtimestamp(os.path.getmtime(cache_file))
+            if (datetime.now() - cache_time).days > 7:
+                self._update_calendar_cache(cache_file)
+        else:
+            dates = self._update_calendar_cache(cache_file)
+        
+        return set(dates)
+    
+    def _update_calendar_cache(self, cache_file):
+        """更新交易日历缓存"""
+        print("正在更新交易日历...")
+        df = ak.tool_trade_date_hist_sina()
+        df.to_csv(cache_file, index=False)
+        return df['trade_date'].astype(str).tolist()
+    
+    def get_recent_trading_dates(self, n_days, end_date=None):
+        """获取最近N个交易日"""
+        if end_date is None:
+            end_date = datetime.now().strftime('%Y-%m-%d')
+        
+        valid_dates = [d for d in self.trading_dates if d <= end_date]
+        return sorted(valid_dates)[-n_days:]
+    
+    def fetch_daily_data(self, trade_date):
+        """获取指定交易日的申万行业日报数据"""
+        date_str = trade_date.replace('-', '')
+        
+        try:
+            df = ak.index_analysis_daily_sw(
+                symbol="一级行业",
+                start_date=date_str,
+                end_date=date_str
+            )
+            
+            if df.empty:
+                print(f"⚠️  {trade_date} 无数据")
+                return None
+            
+            df = df.rename(columns={
+                '指数代码': 'code',
+                '指数名称': 'name',
+                '发布日期': 'date',
+                '收盘指数': 'close',
+                '涨跌幅': 'return',
+                '换手率': 'turnover',
+                '成交量': 'volume',
+                '成交额': 'amount',
+                '市盈率': 'pe',
+                '市净率': 'pb'
+            })
+            
+            df['trade_date'] = trade_date
+            df['code'] = df['code'].astype(str)
+            
+            return df
+            
+        except Exception as e:
+            print(f"❌ 获取 {trade_date} 数据失败: {e}")
+            return None
+    
+    def fetch_multi_days_data(self, n_days=None):
+        """获取最近N天的数据"""
+        if n_days is None:
+            n_days = self.lookback_days + 5
+        
+        dates = self.get_recent_trading_dates(n_days)
+        print(f"📅 获取数据区间: {dates[0]} 至 {dates[-1]}")
+        
+        all_data = []
+        for date in dates:
+            df = self.fetch_daily_data(date)
+            if df is not None:
+                all_data.append(df)
+            time.sleep(0.5)
+        
+        if not all_data:
+            return None
+        
+        combined = pd.concat(all_data, ignore_index=True)
+        combined['trade_date'] = pd.to_datetime(combined['trade_date'])
+        return combined.sort_values(['code', 'trade_date'])
+    
+    def calculate_heat_score(self, df, lookback_days=None):
+        """计算板块热度复合指标"""
+        if lookback_days is None:
+            lookback_days = self.lookback_days
+        
+        latest_dates = df['trade_date'].unique()[-lookback_days:]
+        recent_df = df[df['trade_date'].isin(latest_dates)].copy()
+        
+        all_dates = df['trade_date'].unique()
+        if len(all_dates) >= lookback_days * 2:
+            hist_dates = all_dates[-lookback_days*2:-lookback_days]
+            hist_df = df[df['trade_date'].isin(hist_dates)]
+        else:
+            hist_df = None
+        
+        heat_data = []
+        
+        for code in recent_df['code'].unique():
+            industry_data = recent_df[recent_df['code'] == code]
+            
+            if len(industry_data) < lookback_days:
+                continue
+            
+            name = industry_data['name'].iloc[0]
+            total_return = industry_data['return'].sum()
+            avg_turnover = industry_data['turnover'].mean()
+            recent_volume = industry_data['volume'].mean()
+            
+            if hist_df is not None and code in hist_df['code'].values:
+                hist_volume = hist_df[hist_df['code'] == code]['volume'].mean()
+                volume_ratio = recent_volume / hist_volume if hist_volume > 0 else 1.0
+            else:
+                volume_ratio = 1.0
+            
+            up_days = (industry_data['return'] > 0).sum()
+            momentum = up_days / lookback_days
+            
+            heat_data.append({
+                'code': code,
+                'name': name,
+                'total_return': total_return,
+                'avg_turnover': avg_turnover,
+                'volume_ratio': volume_ratio,
+                'momentum': momentum,
+                'latest_close': industry_data['close'].iloc[-1],
+                'latest_return': industry_data['return'].iloc[-1],
+                'up_days': up_days
+            })
+        
+        heat_df = pd.DataFrame(heat_data)
+        
+        for col in ['total_return', 'avg_turnover', 'volume_ratio', 'momentum']:
+            heat_df[f'{col}_score'] = heat_df[col].rank(pct=True)
+        
+        heat_df['heat_score'] = (
+            heat_df['total_return_score'] * self.weights['return'] +
+            heat_df['avg_turnover_score'] * self.weights['turnover'] +
+            heat_df['volume_ratio_score'] * self.weights['volume_ratio'] +
+            heat_df['momentum_score'] * self.weights['momentum']
+        ) * 100
+        
+        heat_df['heat_level'] = pd.cut(
+            heat_df['heat_score'],
+            bins=[0, 20, 40, 60, 80, 100],
+            labels=['极冷', '偏冷', '温和', '偏热', '极热']
+        )
+        
+        heat_df['rank'] = heat_df['heat_score'].rank(ascending=False, method='min').astype(int)
+        
+        return heat_df.sort_values('heat_score', ascending=False)
+    
+    def save_to_mysql(self, heat_df, calc_date):
+        """
+        将热度数据存入MySQL
+        :param heat_df: 热度数据DataFrame
+        :param calc_date: 计算日期，格式'YYYYMMDD'
+        """
+        if not self.db_config:
+            print("⚠️  未配置数据库，跳过MySQL存储")
+            return False
+        
+        conn = None
+        try:
+            conn = pymysql.connect(**self.db_config)
+            cursor = conn.cursor()
+            
+            # 准备插入数据
+            insert_sql = """
+            INSERT INTO sw_heat_daily 
+            (calc_date, code, name, total_return, avg_turnover, volume_ratio, momentum,
+             total_return_score, avg_turnover_score, volume_ratio_score, momentum_score, heat_score)
+            VALUES 
+            (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+            ON DUPLICATE KEY UPDATE
+            name = VALUES(name),
+            total_return = VALUES(total_return),
+            avg_turnover = VALUES(avg_turnover),
+            volume_ratio = VALUES(volume_ratio),
+            momentum = VALUES(momentum),
+            total_return_score = VALUES(total_return_score),
+            avg_turnover_score = VALUES(avg_turnover_score),
+            volume_ratio_score = VALUES(volume_ratio_score),
+            momentum_score = VALUES(momentum_score),
+            heat_score = VALUES(heat_score),
+            created_at = CURRENT_TIMESTAMP;
+            """
+            
+            # 转换数据为列表元组
+            data_tuples = []
+            for _, row in heat_df.iterrows():
+                data_tuples.append((
+                    calc_date,
+                    row['code'],
+                    row['name'],
+                    float(row['total_return']),
+                    float(row['avg_turnover']),
+                    float(row['volume_ratio']),
+                    float(row['momentum']),
+                    float(row['total_return_score']),
+                    float(row['avg_turnover_score']),
+                    float(row['volume_ratio_score']),
+                    float(row['momentum_score']),
+                    float(row['heat_score'])
+                ))
+            
+            # 批量插入
+            cursor.executemany(insert_sql, data_tuples)
+            conn.commit()
+            
+            print(f"✅ MySQL存储成功: {calc_date} 共 {len(data_tuples)} 条记录")
+            return True
+            
+        except Exception as e:
+            print(f"❌ MySQL存储失败: {e}")
+            if conn:
+                conn.rollback()
+            return False
+        finally:
+            if conn:
+                conn.close()
+    
+    def generate_report(self, save=True, to_mysql=True):
+        """生成热度分析报告并存储"""
+        calc_date = datetime.now().strftime('%Y%m%d')  # YYYYMMDD格式
+        
+        print("="*80)
+        print(f"🔥 申万一级行业热度报告 ({self.lookback_days}日复合指标)")
+        print(f"📅 计算日期: {calc_date}")
+        print(f"⏰ 生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        print("="*80)
+        
+        # 获取数据
+        raw_data = self.fetch_multi_days_data()
+        if raw_data is None:
+            print("❌ 数据获取失败")
+            return None
+        
+        # 计算热度
+        heat_df = self.calculate_heat_score(raw_data)
+        
+        # 打印报告（省略，与之前相同）...
+        print(f"\n🏆 热度 TOP 10 (近{self.lookback_days}日):")
+        print("-"*80)
+        top10 = heat_df.head(10)[['rank', 'name', 'heat_score', 'heat_level', 
+                                  'total_return', 'avg_turnover', 'volume_ratio', 'momentum']]
+        
+        for _, row in top10.iterrows():
+            print(f"{row['rank']:2d}. {row['name']:8s} | "
+                  f"热度:{row['heat_score']:5.1f}({row['heat_level']}) | "
+                  f"收益:{row['total_return']:+6.2f}% | "
+                  f"换手:{row['avg_turnover']:5.2f}% | "
+                  f"量比:{row['volume_ratio']:4.2f} | "
+                  f"动量:{row['momentum']:.0%}")
+        
+        # 保存数据部分
+        if save:
+            today = datetime.now().strftime('%Y-%m-%d')
+            
+            # # 1. 保存JSON（可选，用于调试）
+            # result_file = f"{self.data_dir}/heat_report_{today}_{self.lookback_days}d.json"
+            # heat_df.to_json(result_file, orient='records', force_ascii=False, indent=2)
+            # print(f"\n💾 JSON报告已保存: {result_file}")
+            
+            # 2. 保存CSV（可选，便于Excel查看）
+            csv_file = f"{self.data_dir}/heat_ranking_{today}_{self.lookback_days}d.csv"
+            heat_df.to_csv(csv_file, index=False, encoding='utf-8-sig')
+            csv_file = f"{self.data_dir}/heat_ranking_newest_{self.lookback_days}d.csv"
+            heat_df.to_csv(csv_file, index=False, encoding='utf-8-sig')
+            print(f"💾 CSV排名已保存: {csv_file}")
+            
+            # 3. 存入MySQL（新增）
+            if to_mysql and self.db_config:
+                self.save_to_mysql(heat_df, calc_date)
+        
+        return heat_df
+
+
+# 使用示例
+if __name__ == "__main__":
+    # 数据库配置
+    # config = {
+    #     'host': 'localhost',
+    #     'port': 3306,
+    #     'user': 'your_username',
+    #     'password': 'your_password',
+    #     'database': 'your_database',
+    #     'charset': 'utf8mb4'
+    # }
+    config = {
+        'host': '10.127.2.207',
+        'user': 'financial_prod',
+        'password': 'mmTFncqmDal5HLRGY0BV',
+        'database': 'reference',
+        'port': 3306,
+        'charset': 'utf8mb4'
+    }
+    
+    # 创建追踪器，传入数据库配置
+    tracker = SWIndustryHeatTracker(
+        lookback_days=5,
+        db_config=config  # 传入数据库配置
+    )
+    
+    # 生成报告并自动存入MySQL
+    result = tracker.generate_report(save=True, to_mysql=True)

mqreceive.py

@@ -24,19 +24,13 @@ with open("media_score.txt", "r", encoding="utf-8") as f:
 processed_ids = set()
 
 def message_callback(ch, method, properties, body):
-    """消息处理回调函数"""
+    """消息处理回调函数"""    
-
-    
     try:
         start_time = time.time()
         data = json.loads(body)
         id_str = str(data["id"])
         input_date = data["input_date"]
         print(id_str, input_date)
-
-        # ch.basic_ack(delivery_tag=method.delivery_tag)
-        # print(f"接收到消息: {id_str}")
-        # return
         
         # 幂等性检查：如果消息已处理过，直接确认并跳过
         if id_str in processed_ids:
@@ -116,7 +110,7 @@ def start_consumer():
             connection = create_connection()
             channel = connection.channel()
             
-            # 设置QoS，限制每次只取一条消息
+            # 设置QoS，限制每次只取50条消息
             channel.basic_qos(prefetch_count=50)
             
             channel.exchange_declare(

mqreceive_multithread.py

@@ -3,11 +3,24 @@ import json
 import logging
 import time
 import os
+import re
 import threading
+from datetime import datetime
 from concurrent.futures import ThreadPoolExecutor
 from queue import Queue
 from config import *
-from llm_process import send_mq, get_label
+import jieba
+from llm_process import send_mq, get_label, get_translation, is_mostly_chinese, get_tdx_map, get_stock_map
+from label_check import load_label_mapping, validate_tags
+from check_sensitivity import SensitiveWordFilter
+from news_clustering import NewsClusterer
+from cal_etf_labels import *
+import pandas as pd
+
+import threading
+import time
+from datetime import datetime
+from apscheduler.schedulers.background import BackgroundScheduler
 
 # 声明一个全局变量，存媒体的权威度打分
 media_score = {}
@@ -23,6 +36,70 @@ with open("media_score.txt", "r", encoding="utf-8") as f:
             print(f"解析错误: {e}，行内容: {line}")
             continue
 
+# 行业及概念的一二级标签强制检查
+concept_mapping = load_label_mapping("concept_mapping.txt")
+industry_mapping = load_label_mapping("industry_mapping.txt")
+
+# 敏感词检查
+sensitive_filter = SensitiveWordFilter("sensitive_words.txt")
+
+# 聚类打分
+logging.getLogger("pika").setLevel(logging.WARNING)
+JIEBA_DICT_PATH = '/root/zzck/news_distinct_task/dict.txt.big'  # jieba分词字典路径
+if os.path.exists(JIEBA_DICT_PATH):
+    jieba.set_dictionary(JIEBA_DICT_PATH)
+news_cluster = NewsClusterer()
+news_cluster.load_clusters()
+
+# 关键词加分
+words_bonus = {}
+with open("bonus_words.txt", "r", encoding="utf-8") as f:
+    for line in f:
+        line = line.strip()
+        if not line:
+            continue
+        try:
+            word, bonus = line.split(",")
+            words_bonus[word.strip()] = int(bonus)
+        except ValueError as e:
+            print(f"关键词加分项解析错误: {e}，行内容: {line}")
+            continue
+print(words_bonus)
+
+# 申万一级行业热度分, 从csv读入dataframe后直接转dict
+# --- 1. 初始化 ---
+sw_heat_file = "/root/zzck/industry_heat_task/sw_data/heat_ranking_newest_5d.csv"
+def load_sw_map():
+    """封装读取逻辑，增加异常处理防止程序因为文件错误崩溃"""
+    try:
+        df = pd.read_csv(sw_heat_file)
+        # 使用你最优雅的一行逻辑
+        return df.set_index('name')['heat_score'].to_dict()
+    except Exception as e:
+        print(f"读取CSV失败: {e}")
+        return None
+
+# 全局变量初始化
+sw_heat_map = load_sw_map()
+
+# --- 2. 定义更新任务 ---
+def update_sw_heat_job():
+    global sw_heat_map
+    print(f"[{datetime.now()}] 开始执行每日热度数据更新...")
+    new_map = load_sw_map()
+    if new_map:
+        # 原子替换：这是线程安全的，正在运行的进程会自动切换到新引用
+        sw_heat_map = new_map
+        print("sw_heat_map 更新成功")
+        print(f"当前sw_heat_map示例: {list(sw_heat_map.items())[:5]}")  # 打印前5项检查
+
+# --- 3. 启动定时调度器 ---
+scheduler = BackgroundScheduler()
+# 每天 18:10 执行更新
+scheduler.add_job(update_sw_heat_job, 'cron', hour=18, minute=10)
+scheduler.start()
+
+
 # 幂等性存储 - 记录已处理消息ID (使用线程安全的集合)
 processed_ids = set()
 processed_ids_lock = threading.Lock()  # 用于同步对processed_ids的访问
@@ -64,31 +141,246 @@ def process_single_message(data):
                     source = d_data[0].get('sourcename', "其他")
         source_impact = media_score.get(source, 5)
         tagged_news = get_label(content, source)
-        public_opinion_score = tagged_news.get("public_opinion_score", 30) #资讯质量分
+        # deal with the label problems
-        China_factor = tagged_news.get("China_factor", 0.2) #中国股市相关度
+        tagged_news = validate_tags(tagged_news, "industry_label", "industry_confidence", industry_mapping)
-        news_score = source_impact * 0.04 + public_opinion_score * 0.25 + China_factor * 35
+        tagged_news = validate_tags(tagged_news, "concept_label", "concept_confidence", concept_mapping)
-        news_score = round(news_score, 2)
         
         industry_confidence = tagged_news.get("industry_confidence", [])
-        industry_score = list(map(lambda x: round(x * news_score, 2), industry_confidence))
         concept_confidence = tagged_news.get("concept_confidence", [])
-        concept_score = list(map(lambda x: round(x * news_score, 2), concept_confidence))
 
-        # 确保最终展示的分数是两位小数
         industry_confidence = list(map(lambda x: round(x, 2), industry_confidence))
         concept_confidence = list(map(lambda x: round(x, 2), concept_confidence))
+
+        industry_label = tagged_news.get("industry_label", [])
+        sw_heat = 10  # 默认热度分，如果没有标签或热度数据缺失则使用默认值
+        if industry_label and industry_confidence:
+            first_industry = industry_label[0].split("-")[0]
+            first_industry_confidence = industry_confidence[0]
+            if first_industry_confidence >= 0.75:
+                sw_heat = sw_heat_map.get(first_industry, 0)
+
+        public_opinion_score = tagged_news.get("public_opinion_score", 30) #资讯质量分
+        China_factor = tagged_news.get("China_factor", 0.2) #中国股市相关度
+        news_score = source_impact * 0.03 + public_opinion_score * 0.3 + China_factor * 35 + sw_heat * 0.05
         
+        
+        industry_score = list(map(lambda x: round(x * news_score, 2), industry_confidence))
+        concept_score = list(map(lambda x: round(x * news_score, 2), concept_confidence))
+        
+        # 增加一个关键词加分, 例如原文标题命中 Google 加 5 分, 不区分大小写
+        ori_title = data.get('title_EN', '')
+        hits = []
+        for word, bonus in words_bonus.items():
+            if re.search(rf'\b{re.escape(word)}\b', ori_title, re.IGNORECASE):
+                hits.append((word, bonus))
+        if hits:
+            # 按分数降序排序，取最高分项
+            hits_sorted = sorted(hits, key=lambda x: x[1], reverse=True)
+            print(f"原文标题{ori_title} 命中关键词及分数:", hits_sorted)
+            add_score = hits_sorted[0][-1] * 0.05
+            print(f"新闻{id_str} 当前分数news_score变动: {news_score} -> {news_score + add_score} ... 如果超出上限后续会调整...")
+            news_score = min(99.0, news_score + add_score)
+        else:
+            pass
+            
+        # 增加一个聚类打分，目前仅作试验
+        if datetime.now().minute == 12:
+            if not news_cluster.load_clusters():
+                print(f"聚类文件加载失败 {datetime.now()}")
+        CN_title = data.get('title_txt', '')
+        cluster_evaluate = news_cluster.evaluate_news(title=CN_title, content=content)
+        cluster_score = (cluster_evaluate['weighted_score'] ** 0.5) * 10  if cluster_evaluate else 20.0
+        center_news_id = cluster_evaluate['center_news_id'] if cluster_evaluate else None
+        news_score_exp = source_impact * 0.04 + cluster_score * 0.25 + China_factor * 35
+        
+        # if it's Ads, decrease the score
+        if all(keyword in content for keyword in ["邮箱", "电话", "网站"]):
+            print("新闻判别为广告软文，分数强制8折")
+            news_score *= 0.79
+            news_score_exp *= 0.79
+        
+        # if there is no labels at all, decrease the score
+        if news_score >= 80 and len(tagged_news.get("industry_label", [])) == 0 and len(tagged_news.get("concept_label", [])) == 0:
+            print("新闻没有打上任何有效标签，分数强制8折")
+            news_score *= 0.79
+            news_score_exp *= 0.79
+        
+        # if there are sensitive words in title and content, decrease the score
+        if news_score >= 80:
+            title = data.get('title_txt', '') 
+            news_text = f"{title} {content}"
+            result = sensitive_filter.detect(news_text)
+            if result:
+                print("新闻命中敏感词: ", ", ".join(list(result)), "; 分数强制8折")
+                news_score *= 0.79
+                news_score_exp *= 0.79
+
+        # make sure the selected news label confidence is higher than 0.7
+        if news_score >= 80:
+            industry_high_confidence = industry_confidence[0] if len(industry_confidence) > 0 else 0
+            concept_high_confidence = concept_confidence[0] if len(concept_confidence) > 0 else 0
+            if max(industry_high_confidence, concept_high_confidence) < 0.7:
+                news_score *= 0.79
+                news_score_exp *= 0.79
+                print(f"行业标签置信度为{industry_high_confidence}, 概念标签置信度为{concept_high_confidence}, 新闻得分强制8折.")
+        news_score = round(news_score, 2)
+        news_score_exp = round(news_score_exp, 2)
+        cluster_score = round(cluster_score, 2)
+
+        # if score >= 80, add translation by llm
+        if news_score >= 80:
+            ori_content = data.get('EN_content', '')
+            ori_title = data.get('title_EN', '')
+            for _ in range(3):
+                # 翻译功能存在不稳定, 至多重试三次
+                translation = get_translation(ori_content, ori_title)
+                llm_content = translation.get("llm_content", "") if translation else ""
+                llm_title = translation.get("llm_title", "") if translation else ""
+                if not is_mostly_chinese(llm_title, threshold=0.4):
+                    print("新闻标题没有被翻译成中文, 会被舍弃, LLM标题为: ", llm_title)
+                    llm_title = ""
+                    continue
+                if not is_mostly_chinese(llm_content, threshold=0.5):
+                    print("新闻正文没有被翻译成中文, 会被舍弃, LLM正文为: ", llm_content[:30] + "...")
+                    llm_content = ""
+                    continue
+                # 20250922新增 海外事件 海外宏观 中国宏观 行业新闻 公司新闻 转载来源 公司名称 等精细处理字段
+                overseas_event = translation.get("overseas_event", None) if translation else None
+                overseas_macro = translation.get("overseas_macro", None) if translation else None
+                china_macro = translation.get("china_macro", None) if translation else None
+                industry_news = translation.get("industry_news", None) if translation else None
+                company_news = translation.get("company_news", None) if translation else None
+                reprint_source = translation.get("reprint_source", None) if translation else None
+                if reprint_source == "":
+                    reprint_source = None
+                company_name = translation.get("company_name", None) if translation else None
+                if company_name:
+                    # 打个补丁, 让公司析出数强制不超过3 2025-12-19
+                    company_name_list = company_name.strip().split(",")
+                    if len(company_name_list) > 3:
+                        company_name = ",".join(company_name_list[:3])
+                if company_name == "":
+                    company_name = None
+                ## 行业和之前的标签冗余,暂时去掉
+                # industry_name = translation.get("industry_name", None) if translation else None
+                
+                # 20251217新增 修改了翻译的一些规则; 新增通达信行业标签及置信度
+                tdx_industry_ori = translation.get("tdx_industry", None) if translation else None
+                tdx_industry_confidence_ori = translation.get("tdx_industry_confidence", None) if translation else None
+                tdx_map = get_tdx_map()
+                tdx_industry = []
+                tdx_industry_confidence = []
+                if tdx_industry_ori and tdx_industry_confidence_ori and len(tdx_industry_confidence_ori) == len(tdx_industry_ori):
+                    for industry, confidence in zip(tdx_industry_ori, tdx_industry_confidence_ori):
+                        if industry in tdx_map:
+                            tdx_industry.append(industry)
+                            tdx_industry_confidence.append(float(confidence))
+                            if len(tdx_industry) == 3:
+                                break
+                # 20251226 新增 增加了一项析出A股代码的操作, 列表形式返回A股上市公司6位代码，至多5个
+                stock_codes_ori = translation.get("stock_codes", None) if translation else None
+                if stock_codes_ori:
+                    stock_codes_ori = list(set(stock_codes_ori))
+                stock_codes = []
+                stock_names = []
+                stock_map = get_stock_map()
+                if stock_codes_ori and isinstance(stock_codes_ori, list):
+                    for stock_code in stock_codes_ori:
+                        if stock_code in stock_map:
+                            stock_name = stock_map.get(stock_code)
+                            stock_codes.append(stock_code)
+                            stock_names.append(stock_name)
+                
+                # 20260128 新增审核, 返回政治敏感度、出口转内销两项打分
+                political_sensitivity = translation.get("political_sensitivity", None) if translation else None
+                export_domestic = translation.get("export_domestic", None) if translation else None
+                political_notes = translation.get("political_notes", None) if translation else None
+                additional_notes = translation.get("additional_notes", None) if translation else None         
+
+                # 20260226 新增大模型摘要
+                llm_abstract = translation.get("llm_abstract", None) if translation else None
+                if llm_content and llm_title and len(llm_content) > 30 and len(llm_title) > 3 and overseas_event is not None and overseas_macro is not None:
+                    break
+            
+            # 精选新闻添加etf
+            etf_labels = get_etf_labels(title=llm_title, industry_label=tagged_news.get("industry_label", []), industry_confidence=tagged_news.get("industry_confidence", []), concept_label=tagged_news.get("concept_label", []), concept_confidence=tagged_news.get("concept_confidence", []))
+            etf_names = [etf_name[label] for label in etf_labels] 
+            tagged_news["etf_labels"] = etf_labels
+            tagged_news["etf_names"] = etf_names            
+ 
+            tagged_news["title"] = llm_title
+            tagged_news["rewrite_content"] = llm_content
+            tagged_news["overseas_event"] = overseas_event
+            tagged_news["overseas_macro"] = overseas_macro
+            tagged_news["china_macro"] = china_macro
+            tagged_news["industry_news"] = industry_news
+            tagged_news["company_news"] = company_news
+            tagged_news["reprint_source"] = reprint_source
+            tagged_news["company_name"] = company_name
+
+            tagged_news["tdx_industry"] = tdx_industry if tdx_industry else None
+            tagged_news["tdx_industry_confidence"] = tdx_industry_confidence if tdx_industry_confidence else None
+
+            tagged_news["stock_codes"] = stock_codes if stock_codes else None
+            tagged_news["stock_names"] = stock_names if stock_names else None
+
+            tagged_news["political_sensitivity"] = political_sensitivity if political_sensitivity else None
+            tagged_news["export_domestic"] = export_domestic if export_domestic else None
+            tagged_news["political_notes"] = political_notes if political_notes else None
+            tagged_news["additional_notes"] = additional_notes if additional_notes else None
+
+            tagged_news["llm_abstract"] = llm_abstract if llm_abstract else None
+            
         tagged_news["source"] = source
         tagged_news["source_impact"] = source_impact
         tagged_news["industry_score"] = industry_score
         tagged_news["concept_score"] = concept_score
         tagged_news["news_score"] = news_score
+        tagged_news["news_score_exp"] = news_score_exp
+        tagged_news["center_news_id"] = center_news_id
         tagged_news["id"] = id_str
+        
+        tagged_news["cluster_score"] = cluster_score
         tagged_news["industry_confidence"] = industry_confidence
         tagged_news["concept_confidence"] = concept_confidence
+        
+        if news_score >= 80:
+            # 保险起见对大模型翻译内容再做一遍敏感词筛查 20260128
+            news_title_llm = tagged_news.get("title", "")
+            news_content_llm = tagged_news.get("rewrite_content", "")
+            news_text_llm = f"{news_title_llm} {news_content_llm}"
+            result = sensitive_filter.detect(news_text_llm)
+            if result:
+                print("大模型翻译内容命中敏感词: ", ", ".join(list(result)), "; 分数强制8折")
+                tagged_news["news_score"] = news_score * 0.79
+             
+            # export_domestic >= 80 或者 political_sensitivity >= 70 也直接移除精选池
+            if tagged_news.get("export_domestic", 0) >= 80 or tagged_news.get("political_sensitivity", 0) >= 70:
+                print(f'本条资讯的出口转内销得分为{tagged_news.get("export_domestic", 0)}, 政治敏感得分为{tagged_news.get("political_sensitivity", 0)}, political_notes信息为【{tagged_news.get("political_notes", "")}】, 分数8折')
+                tagged_news["news_score"] = news_score * 0.79
 
         #print(json.dumps(tagged_news, ensure_ascii=False))
-        print(tagged_news["id"], tagged_news["title"], tagged_news["news_score"], tagged_news["industry_label"], input_date)
+        print(tagged_news["id"], tagged_news["news_score"], tagged_news["news_score_exp"], tagged_news["industry_label"], tagged_news["concept_label"], public_opinion_score, tagged_news["cluster_score"], tagged_news["center_news_id"], input_date)
+        if news_score >= 80:
+            print("LLM translation:  ", tagged_news.get("rewrite_content", "")[:100])
+            print("LLM overseas_event score: ", tagged_news.get("overseas_event", None))
+            print("LLM overseas_macro score: ", tagged_news.get("overseas_macro", None))
+            print("LLM china_macro score: ", tagged_news.get("china_macro", None))
+            if tagged_news.get("reprint_source", None):
+                print("LLM reprint_source: ", tagged_news.get("reprint_source", None))
+            if tagged_news.get("company_name", None):
+                print("LLM company_name: ", tagged_news.get("company_name", None), tagged_news.get("company_news", None))
+            print("ETF NAMES: ", tagged_news.get("etf_names", []))
+            if tagged_news.get("tdx_industry", None):
+                print("LLM_max tdx_industry: ", tagged_news.get("tdx_industry", None))
+            if tagged_news.get("tdx_industry_confidence", None):
+                print("LLM_max tdx_industry_confidence: ", tagged_news.get("tdx_industry_confidence", None))
+            if tagged_news.get("stock_names", None):
+                print("LLM_max stock codes & names: ", tagged_news.get("stock_codes", None), tagged_news.get("stock_names", None))
+            if tagged_news.get("political_sensitivity", None):
+                print("LLM_max political_sensitivity: ", tagged_news.get("political_sensitivity", None))
+            if tagged_news.get("llm_abstract", None):
+                print("LLM_max llm_abstract: ", tagged_news.get("llm_abstract", None)[:30])
         return tagged_news, True
     
     except Exception as e:

mqreceivefromllm.py

@@ -24,7 +24,6 @@ def message_callback(ch, method, properties, body):
         # 数据写入资讯精选表
         write_to_news(data)
 
-
         
         # 手动确认消息
         ch.basic_ack(delivery_tag=method.delivery_tag)
@@ -33,11 +32,20 @@ def message_callback(ch, method, properties, body):
         # 拒绝消息并重新入队
         ch.basic_nack(delivery_tag=method.delivery_tag, requeue=False)
 
+def prepare_db_value(value):
+    """准备数据库值：空列表和None都转为None，其他情况JSON序列化"""
+    if value is None:
+        return None
+    if isinstance(value, list):
+        return json.dumps(value) if value else None
+    # 如果不是列表，保持原样（或者根据需求处理）
+    return value
+
 def write_to_news(data):
     news_score = data.get('news_score', 0.0)
     if float(news_score) < 80:  # 过滤掉news_score小于80的消息
         return
-
+    
     # 获取返回数据里面的 新闻id
     news_id = data.get('id', "")
     adr = jx_adr.replace("news_id", news_id)
@@ -48,7 +56,67 @@ def write_to_news(data):
         return
     print(f"新闻id:{news_id} 得分:{news_score}, 调用精选接口成功")
     
-    
+    # 某些自建字段由python脚本自行更新 by 朱思南
+    conn = pymysql.connect(
+        host=MYSQL_HOST_APP,
+        port=MYSQL_PORT_APP,
+        user=MYSQL_USER_APP,
+        password=MYSQL_PASSWORD_APP,
+        db=MYSQL_DB_APP,
+        charset='utf8mb4'
+    )
+    try:
+        with conn.cursor() as cursor: 
+            sql = '''UPDATE news
+                     set overseas_event = %s,
+                         overseas_macro = %s,
+                         china_macro = %s,
+                         industry_news = %s,
+                         company_news = %s,
+                         reprint_source = %s,
+                         company_name = %s,
+                         etf_labels = %s,
+                         etf_names = %s,
+                         tdx_industry = %s,
+                         tdx_industry_confidence = %s,
+                         stock_codes = %s,
+                         stock_names = %s,
+                         political_sensitivity = %s,
+                         export_domestic = %s,
+                         political_notes = %s,
+                         additional_notes = %s,
+                         llm_abstract = %s
+                     WHERE newsinfo_id = %s '''
+            values = (data.get("overseas_event", None),
+                data.get("overseas_macro", None),
+                data.get("china_macro", None),
+                data.get("industry_news", None),
+                data.get("company_news", None),
+                data.get("reprint_source", None),
+                data.get("company_name", None),
+                prepare_db_value(data.get("etf_labels", [])),
+                prepare_db_value(data.get("etf_names", [])),
+                prepare_db_value(data.get("tdx_industry", [])),
+                prepare_db_value(data.get("tdx_industry_confidence", [])),
+                prepare_db_value(data.get("stock_codes", [])),
+                prepare_db_value(data.get("stock_names", [])),
+                data.get("political_sensitivity", None),
+                data.get("export_domestic", None),
+                data.get("political_notes", None),
+                data.get("additional_notes", None),
+                data.get("llm_abstract", None),
+                news_id)
+            cursor.execute(sql, values)
+            if cursor.rowcount == 0:
+                print(f'warning: newsinfo_id={news_id} 不存在，更新 0 行')
+        conn.commit()
+        print(f"新闻id:{news_id} 海外事件置信度得分:{data.get('overseas_event', None)}, 关联ETF名称列表:{data.get('etf_names', None)}, 审核政治敏感度:{data.get('political_sensitivity', None)}. 自建字段写入精选news表成功!")
+    except Exception as e:
+        print(f"自建字段写入精选news表失败: {str(e)}")
+    finally:
+        if conn:
+            conn.close()
+
 
 def write_to_es(data):
     """写入ES"""
@@ -67,9 +135,9 @@ def write_to_es(data):
                     "abstract": data.get('abstract', ""),
                     "title": data.get('title', ""),
                     "rewrite_content": data.get('rewrite_content', ""),
-                    "industry_label": json.dumps(data.get('industry_label', [])),
+                    "industry_label": data.get('industry_label', []),
                     "industry_confidence": data.get('industry_confidence', []),
-                    "industry_score": data.get('industry_score', ""),
+                    "industry_score": data.get('industry_score', []),
                     "concept_label": data.get('concept_label', []),
                     "concept_confidence": data.get('concept_confidence', []),
                     "concept_score": data.get('concept_score', []),
@@ -104,29 +172,35 @@ def write_to_mysql(data):
             # 新增JSON结构解析逻辑
             # 修改后的SQL语句
             sql = """INSERT INTO news_tags 
-                    (abstract, title, rewrite_content, industry_label, industry_confidence, industry_score, concept_label, concept_confidence, concept_score, public_opinion_score, China_factor, source, source_impact, news_score, news_id)
+                    (abstract, title, rewrite_content, industry_label, industry_confidence, industry_score, concept_label, concept_confidence, concept_score, public_opinion_score, China_factor, source, source_impact, news_score, news_id, news_score_exp, cluster_score, center_news_id)
-                    VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) """
+                    VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) """
             
             values = (data.get('abstract', ""),
                 data.get('title', ""),
                 data.get('rewrite_content', ""),
-                json.dumps(data.get('industry_label', [])),
+                prepare_db_value(data.get('industry_label', [])),
-                json.dumps(data.get('industry_confidence', [])),
+                prepare_db_value(data.get('industry_confidence', [])),
-                json.dumps(data.get('industry_score', [])),
+                prepare_db_value(data.get('industry_score', [])),
-                json.dumps(data.get('concept_label', [])),
+                prepare_db_value(data.get('concept_label', [])),
-                json.dumps(data.get('concept_confidence', [])),
+                prepare_db_value(data.get('concept_confidence', [])),
-                json.dumps(data.get('concept_score', [])),
+                prepare_db_value(data.get('concept_score', [])),
                 data.get('public_opinion_score', 10),
                 data.get('China_factor', 0.1),
                 data.get('source', "其他"),
                 data.get('source_impact', 5),
                 data.get('news_score', 0.0),
-                data.get('id', "")
+                data.get('id', ""),
+                data.get('news_score_exp', 5.0),
+                data.get('cluster_score', 5.0),
+                data.get('center_news_id', None)
             )
             cursor.execute(sql, values)
         conn.commit()
-        abstract = data.get('abstract', "")
+        id = data.get('id', "")
-        print(f"{abstract}, 写入news_tags 表成功")
+        industry_label = data.get('industry_label', [])
+        concept_label = data.get('concept_label', [])
+        overseas_event = data.get('overseas_event', None)
+        print(f"{id} {industry_label} {concept_label} {data.get('news_score', 0.0)} {data.get('news_score_exp', 5.0)} 写入news_tags 表成功")
         
     except Exception as e:
         print(f"写入news_tags失败: {str(e)}")