oficcejo
1022 lines
36 KiB
Python
1022 lines
36 KiB
Python
"""
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新闻流量数据库模块
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用于存储和管理新闻流量监测数据
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包含：快照、新闻、情绪、预警、AI分析、定时任务日志
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"""
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import sqlite3
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import json
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional
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from collections import Counter
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class NewsFlowDatabase:
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"""新闻流量数据库管理类"""
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def __init__(self, db_path: str = "news_flow.db"):
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self.db_path = db_path
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self.init_database()
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def get_connection(self):
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"""获取数据库连接"""
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conn = sqlite3.connect(self.db_path)
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conn.row_factory = sqlite3.Row
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return conn
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def init_database(self):
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"""初始化数据库表"""
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conn = self.get_connection()
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cursor = conn.cursor()
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# 1. 新闻流量快照表（记录每次监测的整体情况）
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS flow_snapshots (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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fetch_time TEXT NOT NULL,
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total_platforms INTEGER NOT NULL,
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success_count INTEGER NOT NULL,
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total_score INTEGER NOT NULL,
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flow_level TEXT NOT NULL,
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social_score INTEGER,
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news_score INTEGER,
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finance_score INTEGER,
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tech_score INTEGER,
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analysis TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP
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)
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''')
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# 2. 平台新闻表（存储各平台的新闻数据）
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS platform_news (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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snapshot_id INTEGER NOT NULL,
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platform TEXT NOT NULL,
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platform_name TEXT NOT NULL,
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category TEXT NOT NULL,
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weight INTEGER NOT NULL,
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title TEXT NOT NULL,
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content TEXT,
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url TEXT,
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source TEXT,
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publish_time TEXT,
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rank INTEGER,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (snapshot_id) REFERENCES flow_snapshots(id)
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)
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''')
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# 3. 股票相关新闻表
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS stock_related_news (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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snapshot_id INTEGER NOT NULL,
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platform TEXT NOT NULL,
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platform_name TEXT NOT NULL,
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category TEXT NOT NULL,
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weight INTEGER NOT NULL,
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title TEXT NOT NULL,
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content TEXT,
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url TEXT,
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source TEXT,
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publish_time TEXT,
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matched_keywords TEXT,
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keyword_count INTEGER,
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score INTEGER,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (snapshot_id) REFERENCES flow_snapshots(id)
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)
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''')
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# 4. 热门话题表
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS hot_topics (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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snapshot_id INTEGER NOT NULL,
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topic TEXT NOT NULL,
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count INTEGER NOT NULL,
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heat INTEGER NOT NULL,
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cross_platform INTEGER,
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sources TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (snapshot_id) REFERENCES flow_snapshots(id)
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)
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''')
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# 5. 监测历史统计表（按天汇总）
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS flow_statistics (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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date TEXT NOT NULL UNIQUE,
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avg_score INTEGER,
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max_score INTEGER,
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min_score INTEGER,
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snapshot_count INTEGER,
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top_topics TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP
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)
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''')
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# 6. 情绪指标记录表【新增】
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS sentiment_records (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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snapshot_id INTEGER,
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sentiment_index INTEGER NOT NULL,
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sentiment_class TEXT NOT NULL,
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flow_stage TEXT NOT NULL,
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momentum REAL,
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viral_k REAL,
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flow_type TEXT,
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stage_analysis TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (snapshot_id) REFERENCES flow_snapshots(id)
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)
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''')
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# 7. 预警记录表【新增】
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS flow_alerts (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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alert_type TEXT NOT NULL,
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alert_level TEXT NOT NULL,
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title TEXT NOT NULL,
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content TEXT,
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related_topics TEXT,
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trigger_value TEXT,
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threshold_value TEXT,
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is_notified INTEGER DEFAULT 0,
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snapshot_id INTEGER,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (snapshot_id) REFERENCES flow_snapshots(id)
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)
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''')
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# 8. AI分析记录表【新增】
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS ai_analysis (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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snapshot_id INTEGER,
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affected_sectors TEXT,
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recommended_stocks TEXT,
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risk_level TEXT,
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risk_factors TEXT,
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advice TEXT,
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confidence INTEGER,
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summary TEXT,
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raw_response TEXT,
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model_used TEXT,
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analysis_time REAL,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (snapshot_id) REFERENCES flow_snapshots(id)
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)
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''')
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# 9. 定时任务日志表【新增】
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS scheduler_logs (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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task_name TEXT NOT NULL,
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task_type TEXT,
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status TEXT NOT NULL,
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message TEXT,
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duration REAL,
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snapshot_id INTEGER,
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executed_at TEXT DEFAULT CURRENT_TIMESTAMP
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)
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''')
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# 10. 预警配置表【新增】
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS alert_config (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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config_key TEXT NOT NULL UNIQUE,
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config_value TEXT NOT NULL,
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description TEXT,
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updated_at TEXT DEFAULT CURRENT_TIMESTAMP
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)
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''')
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# 初始化预警配置默认值
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default_configs = [
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('heat_threshold', '800', '热度飙升阈值'),
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('rank_change_threshold', '10', '排名变化阈值'),
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('sentiment_high_threshold', '90', '情绪高位阈值'),
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('sentiment_low_threshold', '20', '情绪低位阈值'),
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('viral_k_threshold', '1.5', 'K值阈值'),
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('alert_enabled', 'true', '预警开关'),
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('notification_enabled', 'true', '通知开关'),
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]
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for key, value, desc in default_configs:
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cursor.execute('''
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INSERT OR IGNORE INTO alert_config (config_key, config_value, description)
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VALUES (?, ?, ?)
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''', (key, value, desc))
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# 数据库迁移：添加缺失的列
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self._migrate_database(cursor)
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conn.commit()
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conn.close()
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logger.info("✅ 新闻流量数据库初始化完成")
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def _migrate_database(self, cursor):
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"""数据库迁移：添加缺失的列"""
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# 定义需要迁移的列
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migrations = [
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# (表名, 列名, 列定义)
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('stock_related_news', 'score', 'INTEGER DEFAULT 0'),
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('stock_related_news', 'rank', 'INTEGER'),
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('platform_news', 'rank', 'INTEGER'),
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('hot_topics', 'cross_platform', 'INTEGER'),
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('hot_topics', 'sources', 'TEXT'),
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]
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for table, column, column_def in migrations:
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try:
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# 检查列是否存在
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cursor.execute(f"PRAGMA table_info({table})")
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columns = [row[1] for row in cursor.fetchall()]
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if column not in columns:
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cursor.execute(f"ALTER TABLE {table} ADD COLUMN {column} {column_def}")
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logger.info(f"✅ 迁移: 向 {table} 添加列 {column}")
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except Exception as e:
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logger.warning(f"迁移列 {table}.{column} 时出错: {e}")
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# ==================== 快照相关方法 ====================
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def save_flow_snapshot(self, flow_data: Dict, platforms_data: List[Dict],
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stock_news: List[Dict], hot_topics: List[Dict]) -> int:
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"""
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保存完整的流量快照
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Args:
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flow_data: 流量得分数据
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platforms_data: 平台新闻数据
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stock_news: 股票相关新闻
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hot_topics: 热门话题
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Returns:
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snapshot_id: 快照ID
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"""
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conn = self.get_connection()
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cursor = conn.cursor()
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try:
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# 1. 保存快照主表
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cursor.execute('''
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INSERT INTO flow_snapshots
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(fetch_time, total_platforms, success_count, total_score, flow_level,
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social_score, news_score, finance_score, tech_score, analysis)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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len(platforms_data),
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sum(1 for p in platforms_data if p.get('success')),
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flow_data['total_score'],
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flow_data['level'],
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flow_data.get('social_score', 0),
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flow_data.get('news_score', 0),
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flow_data.get('finance_score', 0),
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flow_data.get('tech_score', 0),
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flow_data.get('analysis', '')
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))
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snapshot_id = cursor.lastrowid
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# 2. 保存平台新闻
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for platform_data in platforms_data:
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if not platform_data.get('success'):
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continue
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for news in platform_data.get('data', []):
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cursor.execute('''
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INSERT INTO platform_news
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(snapshot_id, platform, platform_name, category, weight,
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title, content, url, source, publish_time, rank)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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snapshot_id,
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platform_data['platform'],
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platform_data['platform_name'],
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platform_data['category'],
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platform_data['weight'],
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news.get('title') or '',
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news.get('content') or '',
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news.get('url') or '',
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news.get('source') or '',
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news.get('publish_time') or '',
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news.get('rank', 0)
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))
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# 3. 保存股票相关新闻
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for news in stock_news:
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cursor.execute('''
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INSERT INTO stock_related_news
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(snapshot_id, platform, platform_name, category, weight,
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title, content, url, source, publish_time, matched_keywords, keyword_count, score)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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snapshot_id,
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news['platform'],
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news['platform_name'],
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news['category'],
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news['weight'],
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news['title'],
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news.get('content') or '',
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news.get('url') or '',
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news.get('source') or '',
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news.get('publish_time') or '',
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json.dumps(news.get('matched_keywords', []), ensure_ascii=False),
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news.get('keyword_count', 0),
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news.get('score', 0)
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))
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# 4. 保存热门话题
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for topic in hot_topics:
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cursor.execute('''
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INSERT INTO hot_topics
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(snapshot_id, topic, count, heat, cross_platform, sources)
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VALUES (?, ?, ?, ?, ?, ?)
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''', (
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snapshot_id,
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topic['topic'],
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topic['count'],
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topic['heat'],
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topic.get('cross_platform', 0),
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json.dumps(topic.get('sources', []), ensure_ascii=False)
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))
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# 5. 更新每日统计
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self._update_daily_statistics(cursor, flow_data['total_score'], hot_topics)
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conn.commit()
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logger.info(f"✅ 保存流量快照成功，ID: {snapshot_id}")
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return snapshot_id
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except Exception as e:
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conn.rollback()
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logger.error(f"❌ 保存流量快照失败: {e}")
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raise
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finally:
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conn.close()
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def _update_daily_statistics(self, cursor, score: int, hot_topics: List[Dict]):
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"""更新每日统计"""
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today = datetime.now().strftime('%Y-%m-%d')
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cursor.execute('''
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SELECT avg_score, max_score, min_score, snapshot_count, top_topics
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FROM flow_statistics WHERE date = ?
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''', (today,))
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row = cursor.fetchone()
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if row:
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old_avg = row['avg_score'] or 0
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old_count = row['snapshot_count'] or 0
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new_avg = int((old_avg * old_count + score) / (old_count + 1))
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new_max = max(row['max_score'] or 0, score)
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new_min = min(row['min_score'] or 999999, score)
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old_topics = json.loads(row['top_topics']) if row['top_topics'] else []
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new_topics = old_topics + [t['topic'] for t in hot_topics[:10]]
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topic_counter = Counter(new_topics)
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top_topics = [topic for topic, _ in topic_counter.most_common(20)]
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cursor.execute('''
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UPDATE flow_statistics
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SET avg_score = ?, max_score = ?, min_score = ?,
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snapshot_count = ?, top_topics = ?
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WHERE date = ?
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''', (new_avg, new_max, new_min, old_count + 1,
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json.dumps(top_topics, ensure_ascii=False), today))
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else:
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top_topics = [t['topic'] for t in hot_topics[:20]]
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cursor.execute('''
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INSERT INTO flow_statistics
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(date, avg_score, max_score, min_score, snapshot_count, top_topics)
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VALUES (?, ?, ?, ?, ?, ?)
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''', (today, score, score, score, 1,
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json.dumps(top_topics, ensure_ascii=False)))
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def get_latest_snapshot(self) -> Optional[Dict]:
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"""获取最新的流量快照"""
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conn = self.get_connection()
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cursor = conn.cursor()
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cursor.execute('''
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SELECT * FROM flow_snapshots
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ORDER BY created_at DESC LIMIT 1
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''')
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row = cursor.fetchone()
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conn.close()
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if row:
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return dict(row)
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return None
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def get_recent_snapshots(self, limit: int = 10) -> List[Dict]:
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"""获取最近的流量快照列表"""
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conn = self.get_connection()
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cursor = conn.cursor()
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cursor.execute('''
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SELECT * FROM flow_snapshots
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ORDER BY created_at DESC LIMIT ?
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''', (limit,))
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snapshots = []
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for row in cursor.fetchall():
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snapshots.append(dict(row))
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conn.close()
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return snapshots
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def get_snapshot_detail(self, snapshot_id: int) -> Dict:
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"""获取快照详细信息"""
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conn = self.get_connection()
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cursor = conn.cursor()
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cursor.execute('SELECT * FROM flow_snapshots WHERE id = ?', (snapshot_id,))
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row = cursor.fetchone()
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if not row:
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conn.close()
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return {}
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snapshot = dict(row)
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cursor.execute('''
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SELECT * FROM stock_related_news
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WHERE snapshot_id = ?
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ORDER BY COALESCE(score, 0) DESC, weight DESC
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''', (snapshot_id,))
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stock_news = []
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for row in cursor.fetchall():
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news = dict(row)
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news['matched_keywords'] = json.loads(news['matched_keywords']) if news['matched_keywords'] else []
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stock_news.append(news)
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cursor.execute('''
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SELECT * FROM hot_topics
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WHERE snapshot_id = ?
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ORDER BY heat DESC
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''', (snapshot_id,))
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hot_topics = []
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for row in cursor.fetchall():
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topic = dict(row)
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topic['sources'] = json.loads(topic['sources']) if topic['sources'] else []
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hot_topics.append(topic)
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# 获取情绪记录
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cursor.execute('''
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SELECT * FROM sentiment_records
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WHERE snapshot_id = ?
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ORDER BY created_at DESC LIMIT 1
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''', (snapshot_id,))
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sentiment_row = cursor.fetchone()
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sentiment = dict(sentiment_row) if sentiment_row else None
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# 获取AI分析
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cursor.execute('''
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SELECT * FROM ai_analysis
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WHERE snapshot_id = ?
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ORDER BY created_at DESC LIMIT 1
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''', (snapshot_id,))
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ai_row = cursor.fetchone()
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ai_analysis = None
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if ai_row:
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ai_analysis = dict(ai_row)
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ai_analysis['affected_sectors'] = json.loads(ai_analysis['affected_sectors']) if ai_analysis['affected_sectors'] else []
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ai_analysis['recommended_stocks'] = json.loads(ai_analysis['recommended_stocks']) if ai_analysis['recommended_stocks'] else []
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ai_analysis['risk_factors'] = json.loads(ai_analysis['risk_factors']) if ai_analysis['risk_factors'] else []
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conn.close()
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return {
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'snapshot': snapshot,
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'stock_news': stock_news,
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'hot_topics': hot_topics,
|
|
'sentiment': sentiment,
|
|
'ai_analysis': ai_analysis,
|
|
}
|
|
|
|
def get_history_snapshots(self, limit: int = 50) -> List[Dict]:
|
|
"""获取历史快照列表"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT id, fetch_time, total_score, flow_level,
|
|
success_count, total_platforms, analysis
|
|
FROM flow_snapshots
|
|
ORDER BY created_at DESC
|
|
LIMIT ?
|
|
''', (limit,))
|
|
|
|
snapshots = [dict(row) for row in cursor.fetchall()]
|
|
conn.close()
|
|
|
|
return snapshots
|
|
|
|
def get_daily_statistics(self, days: int = 7) -> List[Dict]:
|
|
"""获取每日统计数据"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT * FROM flow_statistics
|
|
ORDER BY date DESC
|
|
LIMIT ?
|
|
''', (days,))
|
|
|
|
stats = []
|
|
for row in cursor.fetchall():
|
|
stat = dict(row)
|
|
stat['top_topics'] = json.loads(stat['top_topics']) if stat['top_topics'] else []
|
|
stats.append(stat)
|
|
|
|
conn.close()
|
|
return stats
|
|
|
|
def get_recent_scores(self, hours: int = 24) -> List[Dict]:
|
|
"""获取最近N小时的得分记录"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
since = (datetime.now() - timedelta(hours=hours)).strftime('%Y-%m-%d %H:%M:%S')
|
|
|
|
cursor.execute('''
|
|
SELECT id, fetch_time, total_score, flow_level
|
|
FROM flow_snapshots
|
|
WHERE fetch_time >= ?
|
|
ORDER BY fetch_time ASC
|
|
''', (since,))
|
|
|
|
scores = [dict(row) for row in cursor.fetchall()]
|
|
conn.close()
|
|
return scores
|
|
|
|
def search_stock_news(self, keyword: str, limit: int = 50) -> List[Dict]:
|
|
"""搜索股票相关新闻"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT srn.*, fs.fetch_time, fs.flow_level
|
|
FROM stock_related_news srn
|
|
JOIN flow_snapshots fs ON srn.snapshot_id = fs.id
|
|
WHERE srn.title LIKE ? OR srn.content LIKE ?
|
|
ORDER BY srn.created_at DESC
|
|
LIMIT ?
|
|
''', (f'%{keyword}%', f'%{keyword}%', limit))
|
|
|
|
results = []
|
|
for row in cursor.fetchall():
|
|
news = dict(row)
|
|
news['matched_keywords'] = json.loads(news['matched_keywords']) if news['matched_keywords'] else []
|
|
results.append(news)
|
|
|
|
conn.close()
|
|
return results
|
|
|
|
# ==================== 情绪记录相关方法 ====================
|
|
|
|
def save_sentiment_record(self, snapshot_id: int, sentiment_data: Dict) -> int:
|
|
"""保存情绪记录"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
INSERT INTO sentiment_records
|
|
(snapshot_id, sentiment_index, sentiment_class, flow_stage,
|
|
momentum, viral_k, flow_type, stage_analysis)
|
|
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
|
''', (
|
|
snapshot_id,
|
|
sentiment_data.get('sentiment_index', 50),
|
|
sentiment_data.get('sentiment_class', '中性'),
|
|
sentiment_data.get('flow_stage', '未知'),
|
|
sentiment_data.get('momentum', 0),
|
|
sentiment_data.get('viral_k', 1.0),
|
|
sentiment_data.get('flow_type', '未知'),
|
|
sentiment_data.get('stage_analysis', '')
|
|
))
|
|
|
|
record_id = cursor.lastrowid
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
return record_id
|
|
|
|
def get_sentiment_history(self, limit: int = 50) -> List[Dict]:
|
|
"""获取情绪历史记录"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT sr.*, fs.fetch_time, fs.total_score
|
|
FROM sentiment_records sr
|
|
LEFT JOIN flow_snapshots fs ON sr.snapshot_id = fs.id
|
|
ORDER BY sr.created_at DESC
|
|
LIMIT ?
|
|
''', (limit,))
|
|
|
|
records = [dict(row) for row in cursor.fetchall()]
|
|
conn.close()
|
|
return records
|
|
|
|
def get_latest_sentiment(self) -> Optional[Dict]:
|
|
"""获取最新情绪记录"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT sr.*, fs.fetch_time, fs.total_score, fs.flow_level
|
|
FROM sentiment_records sr
|
|
LEFT JOIN flow_snapshots fs ON sr.snapshot_id = fs.id
|
|
ORDER BY sr.created_at DESC
|
|
LIMIT 1
|
|
''')
|
|
|
|
row = cursor.fetchone()
|
|
conn.close()
|
|
|
|
return dict(row) if row else None
|
|
|
|
# ==================== 预警相关方法 ====================
|
|
|
|
def save_alert(self, alert_data: Dict) -> int:
|
|
"""保存预警记录"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
INSERT INTO flow_alerts
|
|
(alert_type, alert_level, title, content, related_topics,
|
|
trigger_value, threshold_value, is_notified, snapshot_id)
|
|
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
|
''', (
|
|
alert_data['alert_type'],
|
|
alert_data.get('alert_level', 'info'),
|
|
alert_data['title'],
|
|
alert_data.get('content', ''),
|
|
json.dumps(alert_data.get('related_topics', []), ensure_ascii=False),
|
|
str(alert_data.get('trigger_value', '')),
|
|
str(alert_data.get('threshold_value', '')),
|
|
1 if alert_data.get('is_notified') else 0,
|
|
alert_data.get('snapshot_id')
|
|
))
|
|
|
|
alert_id = cursor.lastrowid
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
return alert_id
|
|
|
|
def get_alerts(self, days: int = 7, alert_type: str = None) -> List[Dict]:
|
|
"""获取预警记录"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
since = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
|
|
|
|
if alert_type:
|
|
cursor.execute('''
|
|
SELECT * FROM flow_alerts
|
|
WHERE created_at >= ? AND alert_type = ?
|
|
ORDER BY created_at DESC
|
|
''', (since, alert_type))
|
|
else:
|
|
cursor.execute('''
|
|
SELECT * FROM flow_alerts
|
|
WHERE created_at >= ?
|
|
ORDER BY created_at DESC
|
|
''', (since,))
|
|
|
|
alerts = []
|
|
for row in cursor.fetchall():
|
|
alert = dict(row)
|
|
alert['related_topics'] = json.loads(alert['related_topics']) if alert['related_topics'] else []
|
|
alerts.append(alert)
|
|
|
|
conn.close()
|
|
return alerts
|
|
|
|
def get_unnotified_alerts(self) -> List[Dict]:
|
|
"""获取未通知的预警"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT * FROM flow_alerts
|
|
WHERE is_notified = 0
|
|
ORDER BY created_at DESC
|
|
''')
|
|
|
|
alerts = []
|
|
for row in cursor.fetchall():
|
|
alert = dict(row)
|
|
alert['related_topics'] = json.loads(alert['related_topics']) if alert['related_topics'] else []
|
|
alerts.append(alert)
|
|
|
|
conn.close()
|
|
return alerts
|
|
|
|
def mark_alert_notified(self, alert_id: int):
|
|
"""标记预警为已通知"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
UPDATE flow_alerts SET is_notified = 1 WHERE id = ?
|
|
''', (alert_id,))
|
|
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
# ==================== AI分析相关方法 ====================
|
|
|
|
def save_ai_analysis(self, snapshot_id: int, analysis_data: Dict) -> int:
|
|
"""保存AI分析结果"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
INSERT INTO ai_analysis
|
|
(snapshot_id, affected_sectors, recommended_stocks, risk_level,
|
|
risk_factors, advice, confidence, summary, raw_response, model_used, analysis_time)
|
|
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
|
''', (
|
|
snapshot_id,
|
|
json.dumps(analysis_data.get('affected_sectors', []), ensure_ascii=False),
|
|
json.dumps(analysis_data.get('recommended_stocks', []), ensure_ascii=False),
|
|
analysis_data.get('risk_level', '未知'),
|
|
json.dumps(analysis_data.get('risk_factors', []), ensure_ascii=False),
|
|
analysis_data.get('advice', '观望'),
|
|
analysis_data.get('confidence', 50),
|
|
analysis_data.get('summary', ''),
|
|
analysis_data.get('raw_response', ''),
|
|
analysis_data.get('model_used', 'deepseek-chat'),
|
|
analysis_data.get('analysis_time', 0)
|
|
))
|
|
|
|
analysis_id = cursor.lastrowid
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
return analysis_id
|
|
|
|
def get_latest_ai_analysis(self) -> Optional[Dict]:
|
|
"""获取最新AI分析结果"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT aa.*, fs.fetch_time, fs.total_score, fs.flow_level
|
|
FROM ai_analysis aa
|
|
LEFT JOIN flow_snapshots fs ON aa.snapshot_id = fs.id
|
|
ORDER BY aa.created_at DESC
|
|
LIMIT 1
|
|
''')
|
|
|
|
row = cursor.fetchone()
|
|
conn.close()
|
|
|
|
if row:
|
|
analysis = dict(row)
|
|
analysis['affected_sectors'] = json.loads(analysis['affected_sectors']) if analysis['affected_sectors'] else []
|
|
analysis['recommended_stocks'] = json.loads(analysis['recommended_stocks']) if analysis['recommended_stocks'] else []
|
|
analysis['risk_factors'] = json.loads(analysis['risk_factors']) if analysis['risk_factors'] else []
|
|
return analysis
|
|
return None
|
|
|
|
def get_ai_analysis_history(self, limit: int = 20) -> List[Dict]:
|
|
"""获取AI分析历史"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT aa.*, fs.fetch_time, fs.total_score, fs.flow_level
|
|
FROM ai_analysis aa
|
|
LEFT JOIN flow_snapshots fs ON aa.snapshot_id = fs.id
|
|
ORDER BY aa.created_at DESC
|
|
LIMIT ?
|
|
''', (limit,))
|
|
|
|
results = []
|
|
for row in cursor.fetchall():
|
|
analysis = dict(row)
|
|
analysis['affected_sectors'] = json.loads(analysis['affected_sectors']) if analysis['affected_sectors'] else []
|
|
analysis['recommended_stocks'] = json.loads(analysis['recommended_stocks']) if analysis['recommended_stocks'] else []
|
|
analysis['risk_factors'] = json.loads(analysis['risk_factors']) if analysis['risk_factors'] else []
|
|
results.append(analysis)
|
|
|
|
conn.close()
|
|
return results
|
|
|
|
# ==================== 定时任务日志相关方法 ====================
|
|
|
|
def save_scheduler_log(self, task_name: str, task_type: str,
|
|
status: str, message: str = '',
|
|
duration: float = 0, snapshot_id: int = None) -> int:
|
|
"""保存定时任务日志"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
INSERT INTO scheduler_logs
|
|
(task_name, task_type, status, message, duration, snapshot_id)
|
|
VALUES (?, ?, ?, ?, ?, ?)
|
|
''', (task_name, task_type, status, message, duration, snapshot_id))
|
|
|
|
log_id = cursor.lastrowid
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
return log_id
|
|
|
|
def get_scheduler_logs(self, days: int = 7, task_type: str = None) -> List[Dict]:
|
|
"""获取定时任务日志"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
since = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
|
|
|
|
if task_type:
|
|
cursor.execute('''
|
|
SELECT * FROM scheduler_logs
|
|
WHERE executed_at >= ? AND task_type = ?
|
|
ORDER BY executed_at DESC
|
|
''', (since, task_type))
|
|
else:
|
|
cursor.execute('''
|
|
SELECT * FROM scheduler_logs
|
|
WHERE executed_at >= ?
|
|
ORDER BY executed_at DESC
|
|
''', (since,))
|
|
|
|
logs = [dict(row) for row in cursor.fetchall()]
|
|
conn.close()
|
|
return logs
|
|
|
|
# ==================== 预警配置相关方法 ====================
|
|
|
|
def get_alert_config(self, key: str) -> Optional[str]:
|
|
"""获取预警配置"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
SELECT config_value FROM alert_config WHERE config_key = ?
|
|
''', (key,))
|
|
|
|
row = cursor.fetchone()
|
|
conn.close()
|
|
|
|
return row['config_value'] if row else None
|
|
|
|
def set_alert_config(self, key: str, value: str, description: str = None):
|
|
"""设置预警配置"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('''
|
|
INSERT OR REPLACE INTO alert_config (config_key, config_value, description, updated_at)
|
|
VALUES (?, ?, ?, ?)
|
|
''', (key, value, description, datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
|
|
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
def get_all_alert_configs(self) -> Dict[str, str]:
|
|
"""获取所有预警配置"""
|
|
conn = self.get_connection()
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute('SELECT config_key, config_value FROM alert_config')
|
|
|
|
configs = {row['config_key']: row['config_value'] for row in cursor.fetchall()}
|
|
conn.close()
|
|
return configs
|
|
|
|
|
|
# 全局数据库实例
|
|
news_flow_db = NewsFlowDatabase()
|
|
|
|
|
|
# 测试代码
|
|
if __name__ == "__main__":
|
|
print("=== 测试新闻流量数据库 ===")
|
|
|
|
# 测试保存快照
|
|
flow_data = {
|
|
'total_score': 650,
|
|
'social_score': 200,
|
|
'news_score': 180,
|
|
'finance_score': 220,
|
|
'tech_score': 50,
|
|
'level': '高',
|
|
'analysis': '流量较高，市场活跃'
|
|
}
|
|
|
|
platforms_data = [{
|
|
'success': True,
|
|
'platform': 'weibo',
|
|
'platform_name': '微博热搜',
|
|
'category': 'social',
|
|
'weight': 10,
|
|
'data': [
|
|
{'title': '某某股票大涨', 'content': '今日涨停', 'url': 'http://example.com',
|
|
'source': '微博', 'publish_time': '2026-01-25 10:00:00', 'rank': 1}
|
|
]
|
|
}]
|
|
|
|
stock_news = [{
|
|
'platform': 'weibo',
|
|
'platform_name': '微博热搜',
|
|
'category': 'social',
|
|
'weight': 10,
|
|
'title': '某某股票大涨',
|
|
'content': '今日涨停',
|
|
'url': 'http://example.com',
|
|
'source': '微博',
|
|
'publish_time': '2026-01-25 10:00:00',
|
|
'matched_keywords': ['股票', '涨停'],
|
|
'keyword_count': 2,
|
|
'score': 100
|
|
}]
|
|
|
|
hot_topics = [
|
|
{'topic': 'AI', 'count': 50, 'heat': 95, 'cross_platform': 5, 'sources': ['微博', '抖音']},
|
|
{'topic': '新能源', 'count': 30, 'heat': 80, 'cross_platform': 3, 'sources': ['微博']}
|
|
]
|
|
|
|
snapshot_id = news_flow_db.save_flow_snapshot(flow_data, platforms_data, stock_news, hot_topics)
|
|
print(f"✅ 保存快照成功，ID: {snapshot_id}")
|
|
|
|
# 测试保存情绪记录
|
|
sentiment_data = {
|
|
'sentiment_index': 75,
|
|
'sentiment_class': '乐观',
|
|
'flow_stage': '加速',
|
|
'momentum': 1.5,
|
|
'viral_k': 1.2,
|
|
'flow_type': '增量流量型',
|
|
'stage_analysis': '流量正在快速上升'
|
|
}
|
|
sentiment_id = news_flow_db.save_sentiment_record(snapshot_id, sentiment_data)
|
|
print(f"✅ 保存情绪记录成功，ID: {sentiment_id}")
|
|
|
|
# 测试保存预警
|
|
alert_data = {
|
|
'alert_type': 'heat_surge',
|
|
'alert_level': 'warning',
|
|
'title': '热度飙升预警',
|
|
'content': '当前流量得分650，超过阈值500',
|
|
'related_topics': ['AI', '新能源'],
|
|
'trigger_value': 650,
|
|
'threshold_value': 500,
|
|
'snapshot_id': snapshot_id
|
|
}
|
|
alert_id = news_flow_db.save_alert(alert_data)
|
|
print(f"✅ 保存预警成功，ID: {alert_id}")
|
|
|
|
# 测试保存AI分析
|
|
ai_data = {
|
|
'affected_sectors': [{'name': 'AI', 'impact': '利好', 'reason': '政策支持'}],
|
|
'recommended_stocks': [{'code': '000001', 'name': '平安银行', 'reason': '龙头'}],
|
|
'risk_level': '中等',
|
|
'risk_factors': ['追高风险', '流动性风险'],
|
|
'advice': '观望',
|
|
'confidence': 75,
|
|
'summary': '当前市场热度较高，建议观望',
|
|
'model_used': 'deepseek-chat',
|
|
'analysis_time': 2.5
|
|
}
|
|
ai_id = news_flow_db.save_ai_analysis(snapshot_id, ai_data)
|
|
print(f"✅ 保存AI分析成功，ID: {ai_id}")
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|
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# 测试保存任务日志
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log_id = news_flow_db.save_scheduler_log(
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'热点同步', 'sync_hotspots', 'success',
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'成功同步22个平台', 5.2, snapshot_id
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|
)
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|
print(f"✅ 保存任务日志成功，ID: {log_id}")
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|
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|
# 测试获取详情
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detail = news_flow_db.get_snapshot_detail(snapshot_id)
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print(f"\n快照详情:")
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|
print(f" 流量得分: {detail['snapshot']['total_score']}")
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|
print(f" 情绪指数: {detail['sentiment']['sentiment_index'] if detail['sentiment'] else 'N/A'}")
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|
print(f" AI建议: {detail['ai_analysis']['advice'] if detail['ai_analysis'] else 'N/A'}")
|