mirror of
https://gitee.com/houhuan/TrendRadar.git
synced 2025-12-21 16:07:15 +08:00
1997 lines
73 KiB
Python
1997 lines
73 KiB
Python
"""
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高级数据分析工具
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提供热度趋势分析、平台对比、关键词共现、情感分析等高级分析功能。
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"""
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import re
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from collections import Counter, defaultdict
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional
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from difflib import SequenceMatcher
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from ..services.data_service import DataService
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from ..utils.validators import (
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validate_platforms,
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validate_limit,
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validate_keyword,
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validate_top_n,
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validate_date_range
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)
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from ..utils.errors import MCPError, InvalidParameterError, DataNotFoundError
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def calculate_news_weight(news_data: Dict, rank_threshold: int = 5) -> float:
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"""
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计算新闻权重（用于排序）
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基于 main.py 的权重算法实现，综合考虑：
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- 排名权重 (60%)：新闻在榜单中的排名
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- 频次权重 (30%)：新闻出现的次数
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- 热度权重 (10%)：高排名出现的比例
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Args:
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news_data: 新闻数据字典，包含 ranks 和 count 字段
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rank_threshold: 高排名阈值，默认5
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Returns:
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权重分数（0-100之间的浮点数）
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"""
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ranks = news_data.get("ranks", [])
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if not ranks:
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return 0.0
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count = news_data.get("count", len(ranks))
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# 权重配置（与 config.yaml 保持一致）
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RANK_WEIGHT = 0.6
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FREQUENCY_WEIGHT = 0.3
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HOTNESS_WEIGHT = 0.1
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# 1. 排名权重：Σ(11 - min(rank, 10)) / 出现次数
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rank_scores = []
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for rank in ranks:
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score = 11 - min(rank, 10)
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rank_scores.append(score)
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rank_weight = sum(rank_scores) / len(ranks) if ranks else 0
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# 2. 频次权重：min(出现次数, 10) × 10
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frequency_weight = min(count, 10) * 10
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# 3. 热度加成：高排名次数 / 总出现次数 × 100
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high_rank_count = sum(1 for rank in ranks if rank <= rank_threshold)
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hotness_ratio = high_rank_count / len(ranks) if ranks else 0
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hotness_weight = hotness_ratio * 100
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# 综合权重
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total_weight = (
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rank_weight * RANK_WEIGHT
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+ frequency_weight * FREQUENCY_WEIGHT
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+ hotness_weight * HOTNESS_WEIGHT
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)
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return total_weight
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class AnalyticsTools:
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"""高级数据分析工具类"""
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def __init__(self, project_root: str = None):
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"""
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初始化分析工具
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Args:
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project_root: 项目根目录
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"""
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self.data_service = DataService(project_root)
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def analyze_data_insights_unified(
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self,
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insight_type: str = "platform_compare",
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topic: Optional[str] = None,
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date_range: Optional[Dict[str, str]] = None,
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min_frequency: int = 3,
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top_n: int = 20
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) -> Dict:
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"""
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统一数据洞察分析工具 - 整合多种数据分析模式
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Args:
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insight_type: 洞察类型，可选值：
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- "platform_compare": 平台对比分析（对比不同平台对话题的关注度）
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- "platform_activity": 平台活跃度统计（统计各平台发布频率和活跃时间）
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- "keyword_cooccur": 关键词共现分析（分析关键词同时出现的模式）
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topic: 话题关键词（可选，platform_compare模式适用）
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date_range: 日期范围，格式: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
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min_frequency: 最小共现频次（keyword_cooccur模式），默认3
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top_n: 返回TOP N结果（keyword_cooccur模式），默认20
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Returns:
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数据洞察分析结果字典
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Examples:
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- analyze_data_insights_unified(insight_type="platform_compare", topic="人工智能")
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- analyze_data_insights_unified(insight_type="platform_activity", date_range={...})
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- analyze_data_insights_unified(insight_type="keyword_cooccur", min_frequency=5)
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"""
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try:
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# 参数验证
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if insight_type not in ["platform_compare", "platform_activity", "keyword_cooccur"]:
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raise InvalidParameterError(
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f"无效的洞察类型: {insight_type}",
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suggestion="支持的类型: platform_compare, platform_activity, keyword_cooccur"
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)
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# 根据洞察类型调用相应方法
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if insight_type == "platform_compare":
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return self.compare_platforms(
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topic=topic,
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date_range=date_range
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)
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elif insight_type == "platform_activity":
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return self.get_platform_activity_stats(
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date_range=date_range
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)
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else: # keyword_cooccur
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return self.analyze_keyword_cooccurrence(
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min_frequency=min_frequency,
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top_n=top_n
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)
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except MCPError as e:
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return {
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"success": False,
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"error": e.to_dict()
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}
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except Exception as e:
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return {
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"success": False,
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"error": {
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"code": "INTERNAL_ERROR",
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"message": str(e)
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}
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}
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def analyze_topic_trend_unified(
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self,
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topic: str,
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analysis_type: str = "trend",
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date_range: Optional[Dict[str, str]] = None,
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granularity: str = "day",
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threshold: float = 3.0,
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time_window: int = 24,
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lookahead_hours: int = 6,
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confidence_threshold: float = 0.7
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) -> Dict:
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"""
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统一话题趋势分析工具 - 整合多种趋势分析模式
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Args:
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topic: 话题关键词（必需）
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analysis_type: 分析类型，可选值：
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- "trend": 热度趋势分析（追踪话题的热度变化）
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- "lifecycle": 生命周期分析（从出现到消失的完整周期）
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- "viral": 异常热度检测（识别突然爆火的话题）
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- "predict": 话题预测（预测未来可能的热点）
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date_range: 日期范围（trend和lifecycle模式），可选
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- **格式**: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
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- **默认**: 不指定时默认分析最近7天
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granularity: 时间粒度（trend模式），默认"day"（hour/day）
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threshold: 热度突增倍数阈值（viral模式），默认3.0
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time_window: 检测时间窗口小时数（viral模式），默认24
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lookahead_hours: 预测未来小时数（predict模式），默认6
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confidence_threshold: 置信度阈值（predict模式），默认0.7
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Returns:
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趋势分析结果字典
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Examples (假设今天是 2025-11-17):
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- 用户："分析AI最近7天的趋势" → analyze_topic_trend_unified(topic="人工智能", analysis_type="trend", date_range={"start": "2025-11-11", "end": "2025-11-17"})
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- 用户："看看特斯拉本月的热度" → analyze_topic_trend_unified(topic="特斯拉", analysis_type="lifecycle", date_range={"start": "2025-11-01", "end": "2025-11-17"})
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- analyze_topic_trend_unified(topic="比特币", analysis_type="viral", threshold=3.0)
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- analyze_topic_trend_unified(topic="ChatGPT", analysis_type="predict", lookahead_hours=6)
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"""
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try:
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# 参数验证
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topic = validate_keyword(topic)
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if analysis_type not in ["trend", "lifecycle", "viral", "predict"]:
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raise InvalidParameterError(
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f"无效的分析类型: {analysis_type}",
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suggestion="支持的类型: trend, lifecycle, viral, predict"
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)
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# 根据分析类型调用相应方法
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if analysis_type == "trend":
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return self.get_topic_trend_analysis(
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topic=topic,
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date_range=date_range,
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granularity=granularity
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)
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elif analysis_type == "lifecycle":
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return self.analyze_topic_lifecycle(
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topic=topic,
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date_range=date_range
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)
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elif analysis_type == "viral":
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# viral模式不需要topic参数，使用通用检测
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return self.detect_viral_topics(
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threshold=threshold,
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time_window=time_window
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)
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else: # predict
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# predict模式不需要topic参数，使用通用预测
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return self.predict_trending_topics(
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lookahead_hours=lookahead_hours,
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confidence_threshold=confidence_threshold
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)
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except MCPError as e:
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return {
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"success": False,
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"error": e.to_dict()
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}
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except Exception as e:
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return {
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"success": False,
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"error": {
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"code": "INTERNAL_ERROR",
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"message": str(e)
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}
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}
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def get_topic_trend_analysis(
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self,
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topic: str,
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date_range: Optional[Dict[str, str]] = None,
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granularity: str = "day"
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) -> Dict:
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"""
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热度趋势分析 - 追踪特定话题的热度变化趋势
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Args:
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topic: 话题关键词
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date_range: 日期范围（可选）
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- **格式**: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
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- **默认**: 不指定时默认分析最近7天
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granularity: 时间粒度，仅支持 day（天）
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Returns:
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趋势分析结果字典
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Examples:
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用户询问示例：
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- "帮我分析一下'人工智能'这个话题最近一周的热度趋势"
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- "查看'比特币'过去一周的热度变化"
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- "看看'iPhone'最近7天的趋势如何"
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- "分析'特斯拉'最近一个月的热度趋势"
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- "查看'ChatGPT'2024年12月的趋势变化"
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代码调用示例：
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>>> tools = AnalyticsTools()
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>>> # 分析7天趋势（假设今天是 2025-11-17）
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>>> result = tools.get_topic_trend_analysis(
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... topic="人工智能",
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... date_range={"start": "2025-11-11", "end": "2025-11-17"},
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... granularity="day"
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... )
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>>> # 分析历史月份趋势
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>>> result = tools.get_topic_trend_analysis(
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... topic="特斯拉",
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... date_range={"start": "2024-12-01", "end": "2024-12-31"},
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... granularity="day"
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... )
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>>> print(result['trend_data'])
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"""
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try:
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# 验证参数
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topic = validate_keyword(topic)
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# 验证粒度参数（只支持day）
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if granularity != "day":
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from ..utils.errors import InvalidParameterError
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raise InvalidParameterError(
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f"不支持的粒度参数: {granularity}",
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suggestion="当前仅支持 'day' 粒度，因为底层数据按天聚合"
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)
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# 处理日期范围（不指定时默认最近7天）
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if date_range:
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from ..utils.validators import validate_date_range
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date_range_tuple = validate_date_range(date_range)
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start_date, end_date = date_range_tuple
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else:
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# 默认最近7天
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end_date = datetime.now()
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start_date = end_date - timedelta(days=6)
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# 收集趋势数据
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trend_data = []
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current_date = start_date
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while current_date <= end_date:
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try:
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all_titles, _, _ = self.data_service.parser.read_all_titles_for_date(
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date=current_date
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)
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# 统计该时间点的话题出现次数
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count = 0
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matched_titles = []
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for _, titles in all_titles.items():
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for title in titles.keys():
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if topic.lower() in title.lower():
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count += 1
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matched_titles.append(title)
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trend_data.append({
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"date": current_date.strftime("%Y-%m-%d"),
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"count": count,
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"sample_titles": matched_titles[:3] # 只保留前3个样本
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})
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except DataNotFoundError:
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trend_data.append({
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"date": current_date.strftime("%Y-%m-%d"),
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"count": 0,
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"sample_titles": []
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})
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# 按天增加时间
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current_date += timedelta(days=1)
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# 计算趋势指标
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counts = [item["count"] for item in trend_data]
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total_days = (end_date - start_date).days + 1
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if len(counts) >= 2:
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# 计算涨跌幅度
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first_non_zero = next((c for c in counts if c > 0), 0)
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last_count = counts[-1]
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if first_non_zero > 0:
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change_rate = ((last_count - first_non_zero) / first_non_zero) * 100
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else:
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change_rate = 0
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# 找到峰值时间
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max_count = max(counts)
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peak_index = counts.index(max_count)
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peak_time = trend_data[peak_index]["date"]
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else:
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change_rate = 0
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peak_time = None
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max_count = 0
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return {
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"success": True,
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"topic": topic,
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"date_range": {
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"start": start_date.strftime("%Y-%m-%d"),
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"end": end_date.strftime("%Y-%m-%d"),
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"total_days": total_days
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},
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"granularity": granularity,
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"trend_data": trend_data,
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"statistics": {
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"total_mentions": sum(counts),
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"average_mentions": round(sum(counts) / len(counts), 2) if counts else 0,
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"peak_count": max_count,
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"peak_time": peak_time,
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"change_rate": round(change_rate, 2)
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},
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"trend_direction": "上升" if change_rate > 10 else "下降" if change_rate < -10 else "稳定"
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}
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except MCPError as e:
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return {
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"success": False,
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"error": e.to_dict()
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}
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except Exception as e:
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return {
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"success": False,
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"error": {
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"code": "INTERNAL_ERROR",
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"message": str(e)
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}
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}
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def compare_platforms(
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self,
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topic: Optional[str] = None,
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date_range: Optional[Dict[str, str]] = None
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) -> Dict:
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"""
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平台对比分析 - 对比不同平台对同一话题的关注度
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Args:
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topic: 话题关键词（可选，不指定则对比整体活跃度）
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date_range: 日期范围，格式: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
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Returns:
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平台对比分析结果
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Examples:
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用户询问示例：
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- "对比一下各个平台对'人工智能'话题的关注度"
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- "看看知乎和微博哪个平台更关注科技新闻"
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- "分析各平台今天的热点分布"
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代码调用示例：
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>>> # 对比各平台（假设今天是 2025-11-17）
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>>> result = tools.compare_platforms(
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... topic="人工智能",
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... date_range={"start": "2025-11-08", "end": "2025-11-17"}
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... )
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>>> print(result['platform_stats'])
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"""
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try:
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# 参数验证
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if topic:
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topic = validate_keyword(topic)
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date_range_tuple = validate_date_range(date_range)
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# 确定日期范围
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if date_range_tuple:
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start_date, end_date = date_range_tuple
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else:
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start_date = end_date = datetime.now()
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# 收集各平台数据
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platform_stats = defaultdict(lambda: {
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"total_news": 0,
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"topic_mentions": 0,
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"unique_titles": set(),
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"top_keywords": Counter()
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})
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# 遍历日期范围
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current_date = start_date
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while current_date <= end_date:
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try:
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all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date(
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date=current_date
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)
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for platform_id, titles in all_titles.items():
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platform_name = id_to_name.get(platform_id, platform_id)
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for title in titles.keys():
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platform_stats[platform_name]["total_news"] += 1
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platform_stats[platform_name]["unique_titles"].add(title)
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|
||
# 如果指定了话题，统计包含话题的新闻
|
||
if topic and topic.lower() in title.lower():
|
||
platform_stats[platform_name]["topic_mentions"] += 1
|
||
|
||
# 提取关键词（简单分词）
|
||
keywords = self._extract_keywords(title)
|
||
platform_stats[platform_name]["top_keywords"].update(keywords)
|
||
|
||
except DataNotFoundError:
|
||
pass
|
||
|
||
current_date += timedelta(days=1)
|
||
|
||
# 转换为可序列化的格式
|
||
result_stats = {}
|
||
for platform, stats in platform_stats.items():
|
||
coverage_rate = 0
|
||
if stats["total_news"] > 0:
|
||
coverage_rate = (stats["topic_mentions"] / stats["total_news"]) * 100
|
||
|
||
result_stats[platform] = {
|
||
"total_news": stats["total_news"],
|
||
"topic_mentions": stats["topic_mentions"],
|
||
"unique_titles": len(stats["unique_titles"]),
|
||
"coverage_rate": round(coverage_rate, 2),
|
||
"top_keywords": [
|
||
{"keyword": k, "count": v}
|
||
for k, v in stats["top_keywords"].most_common(5)
|
||
]
|
||
}
|
||
|
||
# 找出各平台独有的热点
|
||
unique_topics = self._find_unique_topics(platform_stats)
|
||
|
||
return {
|
||
"success": True,
|
||
"topic": topic,
|
||
"date_range": {
|
||
"start": start_date.strftime("%Y-%m-%d"),
|
||
"end": end_date.strftime("%Y-%m-%d")
|
||
},
|
||
"platform_stats": result_stats,
|
||
"unique_topics": unique_topics,
|
||
"total_platforms": len(result_stats)
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def analyze_keyword_cooccurrence(
|
||
self,
|
||
min_frequency: int = 3,
|
||
top_n: int = 20
|
||
) -> Dict:
|
||
"""
|
||
关键词共现分析 - 分析哪些关键词经常同时出现
|
||
|
||
Args:
|
||
min_frequency: 最小共现频次
|
||
top_n: 返回TOP N关键词对
|
||
|
||
Returns:
|
||
关键词共现分析结果
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "分析一下哪些关键词经常一起出现"
|
||
- "看看'人工智能'经常和哪些词一起出现"
|
||
- "找出今天新闻中的关键词关联"
|
||
|
||
代码调用示例：
|
||
>>> tools = AnalyticsTools()
|
||
>>> result = tools.analyze_keyword_cooccurrence(
|
||
... min_frequency=5,
|
||
... top_n=15
|
||
... )
|
||
>>> print(result['cooccurrence_pairs'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
min_frequency = validate_limit(min_frequency, default=3, max_limit=100)
|
||
top_n = validate_top_n(top_n, default=20)
|
||
|
||
# 读取今天的数据
|
||
all_titles, _, _ = self.data_service.parser.read_all_titles_for_date()
|
||
|
||
# 关键词共现统计
|
||
cooccurrence = Counter()
|
||
keyword_titles = defaultdict(list)
|
||
|
||
for platform_id, titles in all_titles.items():
|
||
for title in titles.keys():
|
||
# 提取关键词
|
||
keywords = self._extract_keywords(title)
|
||
|
||
# 记录每个关键词出现的标题
|
||
for kw in keywords:
|
||
keyword_titles[kw].append(title)
|
||
|
||
# 计算两两共现
|
||
if len(keywords) >= 2:
|
||
for i, kw1 in enumerate(keywords):
|
||
for kw2 in keywords[i+1:]:
|
||
# 统一排序，避免重复
|
||
pair = tuple(sorted([kw1, kw2]))
|
||
cooccurrence[pair] += 1
|
||
|
||
# 过滤低频共现
|
||
filtered_pairs = [
|
||
(pair, count) for pair, count in cooccurrence.items()
|
||
if count >= min_frequency
|
||
]
|
||
|
||
# 排序并取TOP N
|
||
top_pairs = sorted(filtered_pairs, key=lambda x: x[1], reverse=True)[:top_n]
|
||
|
||
# 构建结果
|
||
result_pairs = []
|
||
for (kw1, kw2), count in top_pairs:
|
||
# 找出同时包含两个关键词的标题样本
|
||
titles_with_both = [
|
||
title for title in keyword_titles[kw1]
|
||
if kw2 in self._extract_keywords(title)
|
||
]
|
||
|
||
result_pairs.append({
|
||
"keyword1": kw1,
|
||
"keyword2": kw2,
|
||
"cooccurrence_count": count,
|
||
"sample_titles": titles_with_both[:3]
|
||
})
|
||
|
||
return {
|
||
"success": True,
|
||
"cooccurrence_pairs": result_pairs,
|
||
"total_pairs": len(result_pairs),
|
||
"min_frequency": min_frequency,
|
||
"generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def analyze_sentiment(
|
||
self,
|
||
topic: Optional[str] = None,
|
||
platforms: Optional[List[str]] = None,
|
||
date_range: Optional[Dict[str, str]] = None,
|
||
limit: int = 50,
|
||
sort_by_weight: bool = True,
|
||
include_url: bool = False
|
||
) -> Dict:
|
||
"""
|
||
情感倾向分析 - 生成用于 AI 情感分析的结构化提示词
|
||
|
||
本工具收集新闻数据并生成优化的 AI 提示词，你可以将其发送给 AI 进行深度情感分析。
|
||
|
||
Args:
|
||
topic: 话题关键词（可选），只分析包含该关键词的新闻
|
||
platforms: 平台过滤列表（可选），如 ['zhihu', 'weibo']
|
||
date_range: 日期范围（可选），格式: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
|
||
不指定则默认查询今天的数据
|
||
limit: 返回新闻数量限制，默认50，最大100
|
||
sort_by_weight: 是否按权重排序，默认True（推荐）
|
||
include_url: 是否包含URL链接，默认False（节省token）
|
||
|
||
Returns:
|
||
包含 AI 提示词和新闻数据的结构化结果
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "分析一下今天新闻的情感倾向"
|
||
- "看看'特斯拉'相关新闻是正面还是负面的"
|
||
- "分析各平台对'人工智能'的情感态度"
|
||
- "看看'特斯拉'相关新闻是正面还是负面的，请选择一周内的前10条新闻来分析"
|
||
|
||
代码调用示例：
|
||
>>> tools = AnalyticsTools()
|
||
>>> # 分析今天的特斯拉新闻，返回前10条
|
||
>>> result = tools.analyze_sentiment(
|
||
... topic="特斯拉",
|
||
... limit=10
|
||
... )
|
||
>>> # 分析一周内的特斯拉新闻（假设今天是 2025-11-17）
|
||
>>> result = tools.analyze_sentiment(
|
||
... topic="特斯拉",
|
||
... date_range={"start": "2025-11-11", "end": "2025-11-17"},
|
||
... limit=10
|
||
... )
|
||
>>> print(result['ai_prompt']) # 获取生成的提示词
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
if topic:
|
||
topic = validate_keyword(topic)
|
||
platforms = validate_platforms(platforms)
|
||
limit = validate_limit(limit, default=50)
|
||
|
||
# 处理日期范围
|
||
if date_range:
|
||
date_range_tuple = validate_date_range(date_range)
|
||
start_date, end_date = date_range_tuple
|
||
else:
|
||
# 默认今天
|
||
start_date = end_date = datetime.now()
|
||
|
||
# 收集新闻数据（支持多天）
|
||
all_news_items = []
|
||
current_date = start_date
|
||
|
||
while current_date <= end_date:
|
||
try:
|
||
all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date(
|
||
date=current_date,
|
||
platform_ids=platforms
|
||
)
|
||
|
||
# 收集该日期的新闻
|
||
for platform_id, titles in all_titles.items():
|
||
platform_name = id_to_name.get(platform_id, platform_id)
|
||
for title, info in titles.items():
|
||
# 如果指定了话题，只收集包含话题的标题
|
||
if topic and topic.lower() not in title.lower():
|
||
continue
|
||
|
||
news_item = {
|
||
"platform": platform_name,
|
||
"title": title,
|
||
"ranks": info.get("ranks", []),
|
||
"count": len(info.get("ranks", [])),
|
||
"date": current_date.strftime("%Y-%m-%d")
|
||
}
|
||
|
||
# 条件性添加 URL 字段
|
||
if include_url:
|
||
news_item["url"] = info.get("url", "")
|
||
news_item["mobileUrl"] = info.get("mobileUrl", "")
|
||
|
||
all_news_items.append(news_item)
|
||
|
||
except DataNotFoundError:
|
||
# 该日期没有数据，继续下一天
|
||
pass
|
||
|
||
# 下一天
|
||
current_date += timedelta(days=1)
|
||
|
||
if not all_news_items:
|
||
time_desc = "今天" if start_date == end_date else f"{start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}"
|
||
raise DataNotFoundError(
|
||
f"未找到相关新闻（{time_desc}）",
|
||
suggestion="请尝试其他话题、日期范围或平台"
|
||
)
|
||
|
||
# 去重（同一标题只保留一次）
|
||
unique_news = {}
|
||
for item in all_news_items:
|
||
key = f"{item['platform']}::{item['title']}"
|
||
if key not in unique_news:
|
||
unique_news[key] = item
|
||
else:
|
||
# 合并 ranks（如果同一新闻在多天出现）
|
||
existing = unique_news[key]
|
||
existing["ranks"].extend(item["ranks"])
|
||
existing["count"] = len(existing["ranks"])
|
||
|
||
deduplicated_news = list(unique_news.values())
|
||
|
||
# 按权重排序（如果启用）
|
||
if sort_by_weight:
|
||
deduplicated_news.sort(
|
||
key=lambda x: calculate_news_weight(x),
|
||
reverse=True
|
||
)
|
||
|
||
# 限制返回数量
|
||
selected_news = deduplicated_news[:limit]
|
||
|
||
# 生成 AI 提示词
|
||
ai_prompt = self._create_sentiment_analysis_prompt(
|
||
news_data=selected_news,
|
||
topic=topic
|
||
)
|
||
|
||
# 构建时间范围描述
|
||
if start_date == end_date:
|
||
time_range_desc = start_date.strftime("%Y-%m-%d")
|
||
else:
|
||
time_range_desc = f"{start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}"
|
||
|
||
result = {
|
||
"success": True,
|
||
"method": "ai_prompt_generation",
|
||
"summary": {
|
||
"total_found": len(deduplicated_news),
|
||
"returned_count": len(selected_news),
|
||
"requested_limit": limit,
|
||
"duplicates_removed": len(all_news_items) - len(deduplicated_news),
|
||
"topic": topic,
|
||
"time_range": time_range_desc,
|
||
"platforms": list(set(item["platform"] for item in selected_news)),
|
||
"sorted_by_weight": sort_by_weight
|
||
},
|
||
"ai_prompt": ai_prompt,
|
||
"news_sample": selected_news,
|
||
"usage_note": "请将 ai_prompt 字段的内容发送给 AI 进行情感分析"
|
||
}
|
||
|
||
# 如果返回数量少于请求数量，增加提示
|
||
if len(selected_news) < limit and len(deduplicated_news) >= limit:
|
||
result["note"] = "返回数量少于请求数量是因为去重逻辑（同一标题在不同平台只保留一次）"
|
||
elif len(deduplicated_news) < limit:
|
||
result["note"] = f"在指定时间范围内仅找到 {len(deduplicated_news)} 条匹配的新闻"
|
||
|
||
return result
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def _create_sentiment_analysis_prompt(
|
||
self,
|
||
news_data: List[Dict],
|
||
topic: Optional[str]
|
||
) -> str:
|
||
"""
|
||
创建情感分析的 AI 提示词
|
||
|
||
Args:
|
||
news_data: 新闻数据列表（已排序和限制数量）
|
||
topic: 话题关键词
|
||
|
||
Returns:
|
||
格式化的 AI 提示词
|
||
"""
|
||
# 按平台分组
|
||
platform_news = defaultdict(list)
|
||
for item in news_data:
|
||
platform_news[item["platform"]].append({
|
||
"title": item["title"],
|
||
"date": item.get("date", "")
|
||
})
|
||
|
||
# 构建提示词
|
||
prompt_parts = []
|
||
|
||
# 1. 任务说明
|
||
if topic:
|
||
prompt_parts.append(f"请分析以下关于「{topic}」的新闻标题的情感倾向。")
|
||
else:
|
||
prompt_parts.append("请分析以下新闻标题的情感倾向。")
|
||
|
||
prompt_parts.append("")
|
||
prompt_parts.append("分析要求：")
|
||
prompt_parts.append("1. 识别每条新闻的情感倾向（正面/负面/中性）")
|
||
prompt_parts.append("2. 统计各情感类别的数量和百分比")
|
||
prompt_parts.append("3. 分析不同平台的情感差异")
|
||
prompt_parts.append("4. 总结整体情感趋势")
|
||
prompt_parts.append("5. 列举典型的正面和负面新闻样本")
|
||
prompt_parts.append("")
|
||
|
||
# 2. 数据概览
|
||
prompt_parts.append(f"数据概览：")
|
||
prompt_parts.append(f"- 总新闻数：{len(news_data)}")
|
||
prompt_parts.append(f"- 覆盖平台：{len(platform_news)}")
|
||
|
||
# 时间范围
|
||
dates = set(item.get("date", "") for item in news_data if item.get("date"))
|
||
if dates:
|
||
date_list = sorted(dates)
|
||
if len(date_list) == 1:
|
||
prompt_parts.append(f"- 时间范围：{date_list[0]}")
|
||
else:
|
||
prompt_parts.append(f"- 时间范围：{date_list[0]} 至 {date_list[-1]}")
|
||
|
||
prompt_parts.append("")
|
||
|
||
# 3. 按平台展示新闻
|
||
prompt_parts.append("新闻列表（按平台分类，已按重要性排序）：")
|
||
prompt_parts.append("")
|
||
|
||
for platform, items in sorted(platform_news.items()):
|
||
prompt_parts.append(f"【{platform}】({len(items)} 条)")
|
||
for i, item in enumerate(items, 1):
|
||
title = item["title"]
|
||
date_str = f" [{item['date']}]" if item.get("date") else ""
|
||
prompt_parts.append(f"{i}. {title}{date_str}")
|
||
prompt_parts.append("")
|
||
|
||
# 4. 输出格式说明
|
||
prompt_parts.append("请按以下格式输出分析结果：")
|
||
prompt_parts.append("")
|
||
prompt_parts.append("## 情感分布统计")
|
||
prompt_parts.append("- 正面：XX条 (XX%)")
|
||
prompt_parts.append("- 负面：XX条 (XX%)")
|
||
prompt_parts.append("- 中性：XX条 (XX%)")
|
||
prompt_parts.append("")
|
||
prompt_parts.append("## 平台情感对比")
|
||
prompt_parts.append("[各平台的情感倾向差异]")
|
||
prompt_parts.append("")
|
||
prompt_parts.append("## 整体情感趋势")
|
||
prompt_parts.append("[总体分析和关键发现]")
|
||
prompt_parts.append("")
|
||
prompt_parts.append("## 典型样本")
|
||
prompt_parts.append("正面新闻样本：")
|
||
prompt_parts.append("[列举3-5条]")
|
||
prompt_parts.append("")
|
||
prompt_parts.append("负面新闻样本：")
|
||
prompt_parts.append("[列举3-5条]")
|
||
|
||
return "\n".join(prompt_parts)
|
||
|
||
def find_similar_news(
|
||
self,
|
||
reference_title: str,
|
||
threshold: float = 0.6,
|
||
limit: int = 50,
|
||
include_url: bool = False
|
||
) -> Dict:
|
||
"""
|
||
相似新闻查找 - 基于标题相似度查找相关新闻
|
||
|
||
Args:
|
||
reference_title: 参考标题
|
||
threshold: 相似度阈值（0-1之间）
|
||
limit: 返回条数限制，默认50
|
||
include_url: 是否包含URL链接，默认False（节省token）
|
||
|
||
Returns:
|
||
相似新闻列表
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "找出和'特斯拉降价'相似的新闻"
|
||
- "查找关于iPhone发布的类似报道"
|
||
- "看看有没有和这条新闻相似的报道"
|
||
|
||
代码调用示例：
|
||
>>> tools = AnalyticsTools()
|
||
>>> result = tools.find_similar_news(
|
||
... reference_title="特斯拉宣布降价",
|
||
... threshold=0.6,
|
||
... limit=10
|
||
... )
|
||
>>> print(result['similar_news'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
reference_title = validate_keyword(reference_title)
|
||
|
||
if not 0 <= threshold <= 1:
|
||
raise InvalidParameterError(
|
||
"threshold 必须在 0 到 1 之间",
|
||
suggestion="推荐值：0.5-0.8"
|
||
)
|
||
|
||
limit = validate_limit(limit, default=50)
|
||
|
||
# 读取数据
|
||
all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date()
|
||
|
||
# 计算相似度
|
||
similar_items = []
|
||
|
||
for platform_id, titles in all_titles.items():
|
||
platform_name = id_to_name.get(platform_id, platform_id)
|
||
|
||
for title, info in titles.items():
|
||
if title == reference_title:
|
||
continue
|
||
|
||
# 计算相似度
|
||
similarity = self._calculate_similarity(reference_title, title)
|
||
|
||
if similarity >= threshold:
|
||
news_item = {
|
||
"title": title,
|
||
"platform": platform_id,
|
||
"platform_name": platform_name,
|
||
"similarity": round(similarity, 3),
|
||
"rank": info["ranks"][0] if info["ranks"] else 0
|
||
}
|
||
|
||
# 条件性添加 URL 字段
|
||
if include_url:
|
||
news_item["url"] = info.get("url", "")
|
||
|
||
similar_items.append(news_item)
|
||
|
||
# 按相似度排序
|
||
similar_items.sort(key=lambda x: x["similarity"], reverse=True)
|
||
|
||
# 限制数量
|
||
result_items = similar_items[:limit]
|
||
|
||
if not result_items:
|
||
raise DataNotFoundError(
|
||
f"未找到相似度超过 {threshold} 的新闻",
|
||
suggestion="请降低相似度阈值或尝试其他标题"
|
||
)
|
||
|
||
result = {
|
||
"success": True,
|
||
"summary": {
|
||
"total_found": len(similar_items),
|
||
"returned_count": len(result_items),
|
||
"requested_limit": limit,
|
||
"threshold": threshold,
|
||
"reference_title": reference_title
|
||
},
|
||
"similar_news": result_items
|
||
}
|
||
|
||
if len(similar_items) < limit:
|
||
result["note"] = f"相似度阈值 {threshold} 下仅找到 {len(similar_items)} 条相似新闻"
|
||
|
||
return result
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def search_by_entity(
|
||
self,
|
||
entity: str,
|
||
entity_type: Optional[str] = None,
|
||
limit: int = 50,
|
||
sort_by_weight: bool = True
|
||
) -> Dict:
|
||
"""
|
||
实体识别搜索 - 搜索包含特定人物/地点/机构的新闻
|
||
|
||
Args:
|
||
entity: 实体名称
|
||
entity_type: 实体类型（person/location/organization），可选
|
||
limit: 返回条数限制，默认50，最大200
|
||
sort_by_weight: 是否按权重排序，默认True
|
||
|
||
Returns:
|
||
实体相关新闻列表
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "搜索马斯克相关的新闻"
|
||
- "查找关于特斯拉公司的报道，返回前20条"
|
||
- "看看北京有什么新闻"
|
||
|
||
代码调用示例：
|
||
>>> tools = AnalyticsTools()
|
||
>>> result = tools.search_by_entity(
|
||
... entity="马斯克",
|
||
... entity_type="person",
|
||
... limit=20
|
||
... )
|
||
>>> print(result['related_news'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
entity = validate_keyword(entity)
|
||
limit = validate_limit(limit, default=50)
|
||
|
||
if entity_type and entity_type not in ["person", "location", "organization"]:
|
||
raise InvalidParameterError(
|
||
f"无效的实体类型: {entity_type}",
|
||
suggestion="支持的类型: person, location, organization"
|
||
)
|
||
|
||
# 读取数据
|
||
all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date()
|
||
|
||
# 搜索包含实体的新闻
|
||
related_news = []
|
||
entity_context = Counter() # 统计实体周边的词
|
||
|
||
for platform_id, titles in all_titles.items():
|
||
platform_name = id_to_name.get(platform_id, platform_id)
|
||
|
||
for title, info in titles.items():
|
||
if entity in title:
|
||
url = info.get("url", "")
|
||
mobile_url = info.get("mobileUrl", "")
|
||
ranks = info.get("ranks", [])
|
||
count = len(ranks)
|
||
|
||
related_news.append({
|
||
"title": title,
|
||
"platform": platform_id,
|
||
"platform_name": platform_name,
|
||
"url": url,
|
||
"mobileUrl": mobile_url,
|
||
"ranks": ranks,
|
||
"count": count,
|
||
"rank": ranks[0] if ranks else 999
|
||
})
|
||
|
||
# 提取实体周边的关键词
|
||
keywords = self._extract_keywords(title)
|
||
entity_context.update(keywords)
|
||
|
||
if not related_news:
|
||
raise DataNotFoundError(
|
||
f"未找到包含实体 '{entity}' 的新闻",
|
||
suggestion="请尝试其他实体名称"
|
||
)
|
||
|
||
# 移除实体本身
|
||
if entity in entity_context:
|
||
del entity_context[entity]
|
||
|
||
# 按权重排序（如果启用）
|
||
if sort_by_weight:
|
||
related_news.sort(
|
||
key=lambda x: calculate_news_weight(x),
|
||
reverse=True
|
||
)
|
||
else:
|
||
# 按排名排序
|
||
related_news.sort(key=lambda x: x["rank"])
|
||
|
||
# 限制返回数量
|
||
result_news = related_news[:limit]
|
||
|
||
return {
|
||
"success": True,
|
||
"entity": entity,
|
||
"entity_type": entity_type or "auto",
|
||
"related_news": result_news,
|
||
"total_found": len(related_news),
|
||
"returned_count": len(result_news),
|
||
"sorted_by_weight": sort_by_weight,
|
||
"related_keywords": [
|
||
{"keyword": k, "count": v}
|
||
for k, v in entity_context.most_common(10)
|
||
]
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def generate_summary_report(
|
||
self,
|
||
report_type: str = "daily",
|
||
date_range: Optional[Dict[str, str]] = None
|
||
) -> Dict:
|
||
"""
|
||
每日/每周摘要生成器 - 自动生成热点摘要报告
|
||
|
||
Args:
|
||
report_type: 报告类型（daily/weekly）
|
||
date_range: 自定义日期范围（可选）
|
||
|
||
Returns:
|
||
Markdown格式的摘要报告
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "生成今天的新闻摘要报告"
|
||
- "给我一份本周的热点总结"
|
||
- "生成过去7天的新闻分析报告"
|
||
|
||
代码调用示例：
|
||
>>> tools = AnalyticsTools()
|
||
>>> result = tools.generate_summary_report(
|
||
... report_type="daily"
|
||
... )
|
||
>>> print(result['markdown_report'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
if report_type not in ["daily", "weekly"]:
|
||
raise InvalidParameterError(
|
||
f"无效的报告类型: {report_type}",
|
||
suggestion="支持的类型: daily, weekly"
|
||
)
|
||
|
||
# 确定日期范围
|
||
if date_range:
|
||
date_range_tuple = validate_date_range(date_range)
|
||
start_date, end_date = date_range_tuple
|
||
else:
|
||
if report_type == "daily":
|
||
start_date = end_date = datetime.now()
|
||
else: # weekly
|
||
end_date = datetime.now()
|
||
start_date = end_date - timedelta(days=6)
|
||
|
||
# 收集数据
|
||
all_keywords = Counter()
|
||
all_platforms_news = defaultdict(int)
|
||
all_titles_list = []
|
||
|
||
current_date = start_date
|
||
while current_date <= end_date:
|
||
try:
|
||
all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date(
|
||
date=current_date
|
||
)
|
||
|
||
for platform_id, titles in all_titles.items():
|
||
platform_name = id_to_name.get(platform_id, platform_id)
|
||
all_platforms_news[platform_name] += len(titles)
|
||
|
||
for title in titles.keys():
|
||
all_titles_list.append({
|
||
"title": title,
|
||
"platform": platform_name,
|
||
"date": current_date.strftime("%Y-%m-%d")
|
||
})
|
||
|
||
# 提取关键词
|
||
keywords = self._extract_keywords(title)
|
||
all_keywords.update(keywords)
|
||
|
||
except DataNotFoundError:
|
||
pass
|
||
|
||
current_date += timedelta(days=1)
|
||
|
||
# 生成报告
|
||
report_title = f"{'每日' if report_type == 'daily' else '每周'}新闻热点摘要"
|
||
date_str = f"{start_date.strftime('%Y-%m-%d')}" if report_type == "daily" else f"{start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}"
|
||
|
||
# 构建Markdown报告
|
||
markdown = f"""# {report_title}
|
||
|
||
**报告日期**: {date_str}
|
||
**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
|
||
|
||
---
|
||
|
||
## 📊 数据概览
|
||
|
||
- **总新闻数**: {len(all_titles_list)}
|
||
- **覆盖平台**: {len(all_platforms_news)}
|
||
- **热门关键词数**: {len(all_keywords)}
|
||
|
||
## 🔥 TOP 10 热门话题
|
||
|
||
"""
|
||
|
||
# 添加TOP 10关键词
|
||
for i, (keyword, count) in enumerate(all_keywords.most_common(10), 1):
|
||
markdown += f"{i}. **{keyword}** - 出现 {count} 次\n"
|
||
|
||
# 平台分析
|
||
markdown += "\n## 📱 平台活跃度\n\n"
|
||
sorted_platforms = sorted(all_platforms_news.items(), key=lambda x: x[1], reverse=True)
|
||
|
||
for platform, count in sorted_platforms:
|
||
markdown += f"- **{platform}**: {count} 条新闻\n"
|
||
|
||
# 趋势变化（如果是周报）
|
||
if report_type == "weekly":
|
||
markdown += "\n## 📈 趋势分析\n\n"
|
||
markdown += "本周热度持续的话题（样本数据）：\n\n"
|
||
|
||
# 简单的趋势分析
|
||
top_keywords = [kw for kw, _ in all_keywords.most_common(5)]
|
||
for keyword in top_keywords:
|
||
markdown += f"- **{keyword}**: 持续热门\n"
|
||
|
||
# 添加样本新闻（按权重选择，确保确定性）
|
||
markdown += "\n## 📰 精选新闻样本\n\n"
|
||
|
||
# 确定性选取：按标题的权重排序，取前5条
|
||
# 这样相同输入总是返回相同结果
|
||
if all_titles_list:
|
||
# 计算每条新闻的权重分数（基于关键词出现次数）
|
||
news_with_scores = []
|
||
for news in all_titles_list:
|
||
# 简单权重：统计包含TOP关键词的次数
|
||
score = 0
|
||
title_lower = news['title'].lower()
|
||
for keyword, count in all_keywords.most_common(10):
|
||
if keyword.lower() in title_lower:
|
||
score += count
|
||
news_with_scores.append((news, score))
|
||
|
||
# 按权重降序排序，权重相同则按标题字母顺序（确保确定性）
|
||
news_with_scores.sort(key=lambda x: (-x[1], x[0]['title']))
|
||
|
||
# 取前5条
|
||
sample_news = [item[0] for item in news_with_scores[:5]]
|
||
|
||
for news in sample_news:
|
||
markdown += f"- [{news['platform']}] {news['title']}\n"
|
||
|
||
markdown += "\n---\n\n*本报告由 TrendRadar MCP 自动生成*\n"
|
||
|
||
return {
|
||
"success": True,
|
||
"report_type": report_type,
|
||
"date_range": {
|
||
"start": start_date.strftime("%Y-%m-%d"),
|
||
"end": end_date.strftime("%Y-%m-%d")
|
||
},
|
||
"markdown_report": markdown,
|
||
"statistics": {
|
||
"total_news": len(all_titles_list),
|
||
"platforms_count": len(all_platforms_news),
|
||
"keywords_count": len(all_keywords),
|
||
"top_keyword": all_keywords.most_common(1)[0] if all_keywords else None
|
||
}
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def get_platform_activity_stats(
|
||
self,
|
||
date_range: Optional[Dict[str, str]] = None
|
||
) -> Dict:
|
||
"""
|
||
平台活跃度统计 - 统计各平台的发布频率和活跃时间段
|
||
|
||
Args:
|
||
date_range: 日期范围（可选）
|
||
|
||
Returns:
|
||
平台活跃度统计结果
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "统计各平台今天的活跃度"
|
||
- "看看哪个平台更新最频繁"
|
||
- "分析各平台的发布时间规律"
|
||
|
||
代码调用示例：
|
||
>>> # 查看各平台活跃度（假设今天是 2025-11-17）
|
||
>>> result = tools.get_platform_activity_stats(
|
||
... date_range={"start": "2025-11-08", "end": "2025-11-17"}
|
||
... )
|
||
>>> print(result['platform_activity'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
date_range_tuple = validate_date_range(date_range)
|
||
|
||
# 确定日期范围
|
||
if date_range_tuple:
|
||
start_date, end_date = date_range_tuple
|
||
else:
|
||
start_date = end_date = datetime.now()
|
||
|
||
# 统计各平台活跃度
|
||
platform_activity = defaultdict(lambda: {
|
||
"total_updates": 0,
|
||
"days_active": set(),
|
||
"news_count": 0,
|
||
"hourly_distribution": Counter()
|
||
})
|
||
|
||
# 遍历日期范围
|
||
current_date = start_date
|
||
while current_date <= end_date:
|
||
try:
|
||
all_titles, id_to_name, timestamps = self.data_service.parser.read_all_titles_for_date(
|
||
date=current_date
|
||
)
|
||
|
||
for platform_id, titles in all_titles.items():
|
||
platform_name = id_to_name.get(platform_id, platform_id)
|
||
|
||
platform_activity[platform_name]["news_count"] += len(titles)
|
||
platform_activity[platform_name]["days_active"].add(current_date.strftime("%Y-%m-%d"))
|
||
|
||
# 统计更新次数（基于文件数量）
|
||
platform_activity[platform_name]["total_updates"] += len(timestamps)
|
||
|
||
# 统计时间分布（基于文件名中的时间）
|
||
for filename in timestamps.keys():
|
||
# 解析文件名中的小时（格式：HHMM.txt）
|
||
match = re.match(r'(\d{2})(\d{2})\.txt', filename)
|
||
if match:
|
||
hour = int(match.group(1))
|
||
platform_activity[platform_name]["hourly_distribution"][hour] += 1
|
||
|
||
except DataNotFoundError:
|
||
pass
|
||
|
||
current_date += timedelta(days=1)
|
||
|
||
# 转换为可序列化的格式
|
||
result_activity = {}
|
||
for platform, stats in platform_activity.items():
|
||
days_count = len(stats["days_active"])
|
||
avg_news_per_day = stats["news_count"] / days_count if days_count > 0 else 0
|
||
|
||
# 找出最活跃的时间段
|
||
most_active_hours = stats["hourly_distribution"].most_common(3)
|
||
|
||
result_activity[platform] = {
|
||
"total_updates": stats["total_updates"],
|
||
"news_count": stats["news_count"],
|
||
"days_active": days_count,
|
||
"avg_news_per_day": round(avg_news_per_day, 2),
|
||
"most_active_hours": [
|
||
{"hour": f"{hour:02d}:00", "count": count}
|
||
for hour, count in most_active_hours
|
||
],
|
||
"activity_score": round(stats["news_count"] / max(days_count, 1), 2)
|
||
}
|
||
|
||
# 按活跃度排序
|
||
sorted_platforms = sorted(
|
||
result_activity.items(),
|
||
key=lambda x: x[1]["activity_score"],
|
||
reverse=True
|
||
)
|
||
|
||
return {
|
||
"success": True,
|
||
"date_range": {
|
||
"start": start_date.strftime("%Y-%m-%d"),
|
||
"end": end_date.strftime("%Y-%m-%d")
|
||
},
|
||
"platform_activity": dict(sorted_platforms),
|
||
"most_active_platform": sorted_platforms[0][0] if sorted_platforms else None,
|
||
"total_platforms": len(result_activity)
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def analyze_topic_lifecycle(
|
||
self,
|
||
topic: str,
|
||
date_range: Optional[Dict[str, str]] = None
|
||
) -> Dict:
|
||
"""
|
||
话题生命周期分析 - 追踪话题从出现到消失的完整周期
|
||
|
||
Args:
|
||
topic: 话题关键词
|
||
date_range: 日期范围（可选）
|
||
- **格式**: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
|
||
- **默认**: 不指定时默认分析最近7天
|
||
|
||
Returns:
|
||
话题生命周期分析结果
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "分析'人工智能'这个话题的生命周期"
|
||
- "看看'iPhone'话题是昙花一现还是持续热点"
|
||
- "追踪'比特币'话题的热度变化"
|
||
|
||
代码调用示例：
|
||
>>> # 分析话题生命周期（假设今天是 2025-11-17）
|
||
>>> result = tools.analyze_topic_lifecycle(
|
||
... topic="人工智能",
|
||
... date_range={"start": "2025-10-19", "end": "2025-11-17"}
|
||
... )
|
||
>>> print(result['lifecycle_stage'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
topic = validate_keyword(topic)
|
||
|
||
# 处理日期范围（不指定时默认最近7天）
|
||
if date_range:
|
||
from ..utils.validators import validate_date_range
|
||
date_range_tuple = validate_date_range(date_range)
|
||
start_date, end_date = date_range_tuple
|
||
else:
|
||
# 默认最近7天
|
||
end_date = datetime.now()
|
||
start_date = end_date - timedelta(days=6)
|
||
|
||
# 收集话题历史数据
|
||
lifecycle_data = []
|
||
current_date = start_date
|
||
while current_date <= end_date:
|
||
try:
|
||
all_titles, _, _ = self.data_service.parser.read_all_titles_for_date(
|
||
date=current_date
|
||
)
|
||
|
||
# 统计该日的话题出现次数
|
||
count = 0
|
||
for _, titles in all_titles.items():
|
||
for title in titles.keys():
|
||
if topic.lower() in title.lower():
|
||
count += 1
|
||
|
||
lifecycle_data.append({
|
||
"date": current_date.strftime("%Y-%m-%d"),
|
||
"count": count
|
||
})
|
||
|
||
except DataNotFoundError:
|
||
lifecycle_data.append({
|
||
"date": current_date.strftime("%Y-%m-%d"),
|
||
"count": 0
|
||
})
|
||
|
||
current_date += timedelta(days=1)
|
||
|
||
# 计算分析天数
|
||
total_days = (end_date - start_date).days + 1
|
||
|
||
# 分析生命周期阶段
|
||
counts = [item["count"] for item in lifecycle_data]
|
||
|
||
if not any(counts):
|
||
time_desc = f"{start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}"
|
||
raise DataNotFoundError(
|
||
f"在 {time_desc} 内未找到话题 '{topic}'",
|
||
suggestion="请尝试其他话题或扩大时间范围"
|
||
)
|
||
|
||
# 找到首次出现和最后出现
|
||
first_appearance = next((item["date"] for item in lifecycle_data if item["count"] > 0), None)
|
||
last_appearance = next((item["date"] for item in reversed(lifecycle_data) if item["count"] > 0), None)
|
||
|
||
# 计算峰值
|
||
max_count = max(counts)
|
||
peak_index = counts.index(max_count)
|
||
peak_date = lifecycle_data[peak_index]["date"]
|
||
|
||
# 计算平均值和标准差（简单实现）
|
||
non_zero_counts = [c for c in counts if c > 0]
|
||
avg_count = sum(non_zero_counts) / len(non_zero_counts) if non_zero_counts else 0
|
||
|
||
# 判断生命周期阶段
|
||
recent_counts = counts[-3:] # 最近3天
|
||
early_counts = counts[:3] # 前3天
|
||
|
||
if sum(recent_counts) > sum(early_counts):
|
||
lifecycle_stage = "上升期"
|
||
elif sum(recent_counts) < sum(early_counts) * 0.5:
|
||
lifecycle_stage = "衰退期"
|
||
elif max_count in recent_counts:
|
||
lifecycle_stage = "爆发期"
|
||
else:
|
||
lifecycle_stage = "稳定期"
|
||
|
||
# 分类：昙花一现 vs 持续热点
|
||
active_days = sum(1 for c in counts if c > 0)
|
||
|
||
if active_days <= 2 and max_count > avg_count * 2:
|
||
topic_type = "昙花一现"
|
||
elif active_days >= total_days * 0.6:
|
||
topic_type = "持续热点"
|
||
else:
|
||
topic_type = "周期性热点"
|
||
|
||
return {
|
||
"success": True,
|
||
"topic": topic,
|
||
"date_range": {
|
||
"start": start_date.strftime("%Y-%m-%d"),
|
||
"end": end_date.strftime("%Y-%m-%d"),
|
||
"total_days": total_days
|
||
},
|
||
"lifecycle_data": lifecycle_data,
|
||
"analysis": {
|
||
"first_appearance": first_appearance,
|
||
"last_appearance": last_appearance,
|
||
"peak_date": peak_date,
|
||
"peak_count": max_count,
|
||
"active_days": active_days,
|
||
"avg_daily_mentions": round(avg_count, 2),
|
||
"lifecycle_stage": lifecycle_stage,
|
||
"topic_type": topic_type
|
||
}
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def detect_viral_topics(
|
||
self,
|
||
threshold: float = 3.0,
|
||
time_window: int = 24
|
||
) -> Dict:
|
||
"""
|
||
异常热度检测 - 自动识别突然爆火的话题
|
||
|
||
Args:
|
||
threshold: 热度突增倍数阈值
|
||
time_window: 检测时间窗口（小时）
|
||
|
||
Returns:
|
||
爆火话题列表
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "检测今天有哪些突然爆火的话题"
|
||
- "看看有没有热度异常的新闻"
|
||
- "预警可能的重大事件"
|
||
|
||
代码调用示例：
|
||
>>> tools = AnalyticsTools()
|
||
>>> result = tools.detect_viral_topics(
|
||
... threshold=3.0,
|
||
... time_window=24
|
||
... )
|
||
>>> print(result['viral_topics'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
if threshold < 1.0:
|
||
raise InvalidParameterError(
|
||
"threshold 必须大于等于 1.0",
|
||
suggestion="推荐值：2.0-5.0"
|
||
)
|
||
|
||
time_window = validate_limit(time_window, default=24, max_limit=72)
|
||
|
||
# 读取当前和之前的数据
|
||
current_all_titles, _, _ = self.data_service.parser.read_all_titles_for_date()
|
||
|
||
# 读取昨天的数据作为基准
|
||
yesterday = datetime.now() - timedelta(days=1)
|
||
try:
|
||
previous_all_titles, _, _ = self.data_service.parser.read_all_titles_for_date(
|
||
date=yesterday
|
||
)
|
||
except DataNotFoundError:
|
||
previous_all_titles = {}
|
||
|
||
# 统计当前的关键词频率
|
||
current_keywords = Counter()
|
||
current_keyword_titles = defaultdict(list)
|
||
|
||
for _, titles in current_all_titles.items():
|
||
for title in titles.keys():
|
||
keywords = self._extract_keywords(title)
|
||
current_keywords.update(keywords)
|
||
|
||
for kw in keywords:
|
||
current_keyword_titles[kw].append(title)
|
||
|
||
# 统计之前的关键词频率
|
||
previous_keywords = Counter()
|
||
|
||
for _, titles in previous_all_titles.items():
|
||
for title in titles.keys():
|
||
keywords = self._extract_keywords(title)
|
||
previous_keywords.update(keywords)
|
||
|
||
# 检测异常热度
|
||
viral_topics = []
|
||
|
||
for keyword, current_count in current_keywords.items():
|
||
previous_count = previous_keywords.get(keyword, 0)
|
||
|
||
# 计算增长倍数
|
||
if previous_count == 0:
|
||
# 新出现的话题
|
||
if current_count >= 5: # 至少出现5次才认为是爆火
|
||
growth_rate = float('inf')
|
||
is_viral = True
|
||
else:
|
||
continue
|
||
else:
|
||
growth_rate = current_count / previous_count
|
||
is_viral = growth_rate >= threshold
|
||
|
||
if is_viral:
|
||
viral_topics.append({
|
||
"keyword": keyword,
|
||
"current_count": current_count,
|
||
"previous_count": previous_count,
|
||
"growth_rate": round(growth_rate, 2) if growth_rate != float('inf') else "新话题",
|
||
"sample_titles": current_keyword_titles[keyword][:3],
|
||
"alert_level": "高" if growth_rate > threshold * 2 else "中"
|
||
})
|
||
|
||
# 按增长率排序
|
||
viral_topics.sort(
|
||
key=lambda x: x["current_count"] if x["growth_rate"] == "新话题" else x["growth_rate"],
|
||
reverse=True
|
||
)
|
||
|
||
if not viral_topics:
|
||
return {
|
||
"success": True,
|
||
"viral_topics": [],
|
||
"total_detected": 0,
|
||
"message": f"未检测到热度增长超过 {threshold} 倍的话题"
|
||
}
|
||
|
||
return {
|
||
"success": True,
|
||
"viral_topics": viral_topics,
|
||
"total_detected": len(viral_topics),
|
||
"threshold": threshold,
|
||
"time_window": time_window,
|
||
"detection_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
def predict_trending_topics(
|
||
self,
|
||
lookahead_hours: int = 6,
|
||
confidence_threshold: float = 0.7
|
||
) -> Dict:
|
||
"""
|
||
话题预测 - 基于历史数据预测未来可能的热点
|
||
|
||
Args:
|
||
lookahead_hours: 预测未来多少小时
|
||
confidence_threshold: 置信度阈值
|
||
|
||
Returns:
|
||
预测的潜力话题列表
|
||
|
||
Examples:
|
||
用户询问示例：
|
||
- "预测接下来6小时可能的热点话题"
|
||
- "有哪些话题可能会火起来"
|
||
- "早期发现潜力话题"
|
||
|
||
代码调用示例：
|
||
>>> tools = AnalyticsTools()
|
||
>>> result = tools.predict_trending_topics(
|
||
... lookahead_hours=6,
|
||
... confidence_threshold=0.7
|
||
... )
|
||
>>> print(result['predicted_topics'])
|
||
"""
|
||
try:
|
||
# 参数验证
|
||
lookahead_hours = validate_limit(lookahead_hours, default=6, max_limit=48)
|
||
|
||
if not 0 <= confidence_threshold <= 1:
|
||
raise InvalidParameterError(
|
||
"confidence_threshold 必须在 0 到 1 之间",
|
||
suggestion="推荐值：0.6-0.8"
|
||
)
|
||
|
||
# 收集最近3天的数据用于预测
|
||
keyword_trends = defaultdict(list)
|
||
|
||
for days_ago in range(3, 0, -1):
|
||
date = datetime.now() - timedelta(days=days_ago)
|
||
|
||
try:
|
||
all_titles, _, _ = self.data_service.parser.read_all_titles_for_date(
|
||
date=date
|
||
)
|
||
|
||
# 统计关键词
|
||
keywords_count = Counter()
|
||
for _, titles in all_titles.items():
|
||
for title in titles.keys():
|
||
keywords = self._extract_keywords(title)
|
||
keywords_count.update(keywords)
|
||
|
||
# 记录每个关键词的历史数据
|
||
for keyword, count in keywords_count.items():
|
||
keyword_trends[keyword].append(count)
|
||
|
||
except DataNotFoundError:
|
||
pass
|
||
|
||
# 添加今天的数据
|
||
try:
|
||
all_titles, _, _ = self.data_service.parser.read_all_titles_for_date()
|
||
|
||
keywords_count = Counter()
|
||
keyword_titles = defaultdict(list)
|
||
|
||
for _, titles in all_titles.items():
|
||
for title in titles.keys():
|
||
keywords = self._extract_keywords(title)
|
||
keywords_count.update(keywords)
|
||
|
||
for kw in keywords:
|
||
keyword_titles[kw].append(title)
|
||
|
||
for keyword, count in keywords_count.items():
|
||
keyword_trends[keyword].append(count)
|
||
|
||
except DataNotFoundError:
|
||
raise DataNotFoundError(
|
||
"未找到今天的数据",
|
||
suggestion="请等待爬虫任务完成"
|
||
)
|
||
|
||
# 预测潜力话题
|
||
predicted_topics = []
|
||
|
||
for keyword, trend_data in keyword_trends.items():
|
||
if len(trend_data) < 2:
|
||
continue
|
||
|
||
# 简单的线性趋势预测
|
||
# 计算增长率
|
||
recent_value = trend_data[-1]
|
||
previous_value = trend_data[-2] if len(trend_data) >= 2 else 0
|
||
|
||
if previous_value == 0:
|
||
if recent_value >= 3:
|
||
growth_rate = 1.0
|
||
else:
|
||
continue
|
||
else:
|
||
growth_rate = (recent_value - previous_value) / previous_value
|
||
|
||
# 判断是否是上升趋势
|
||
if growth_rate > 0.3: # 增长超过30%
|
||
# 计算置信度（基于趋势的稳定性）
|
||
if len(trend_data) >= 3:
|
||
# 检查是否连续增长
|
||
is_consistent = all(
|
||
trend_data[i] <= trend_data[i+1]
|
||
for i in range(len(trend_data)-1)
|
||
)
|
||
confidence = 0.9 if is_consistent else 0.7
|
||
else:
|
||
confidence = 0.6
|
||
|
||
if confidence >= confidence_threshold:
|
||
predicted_topics.append({
|
||
"keyword": keyword,
|
||
"current_count": recent_value,
|
||
"growth_rate": round(growth_rate * 100, 2),
|
||
"confidence": round(confidence, 2),
|
||
"trend_data": trend_data,
|
||
"prediction": "上升趋势，可能成为热点",
|
||
"sample_titles": keyword_titles.get(keyword, [])[:3]
|
||
})
|
||
|
||
# 按置信度和增长率排序
|
||
predicted_topics.sort(
|
||
key=lambda x: (x["confidence"], x["growth_rate"]),
|
||
reverse=True
|
||
)
|
||
|
||
return {
|
||
"success": True,
|
||
"predicted_topics": predicted_topics[:20], # 返回TOP 20
|
||
"total_predicted": len(predicted_topics),
|
||
"lookahead_hours": lookahead_hours,
|
||
"confidence_threshold": confidence_threshold,
|
||
"prediction_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
||
"note": "预测基于历史趋势，实际结果可能有偏差"
|
||
}
|
||
|
||
except MCPError as e:
|
||
return {
|
||
"success": False,
|
||
"error": e.to_dict()
|
||
}
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"error": {
|
||
"code": "INTERNAL_ERROR",
|
||
"message": str(e)
|
||
}
|
||
}
|
||
|
||
# ==================== 辅助方法 ====================
|
||
|
||
def _extract_keywords(self, title: str, min_length: int = 2) -> List[str]:
|
||
"""
|
||
从标题中提取关键词（简单实现）
|
||
|
||
Args:
|
||
title: 标题文本
|
||
min_length: 最小关键词长度
|
||
|
||
Returns:
|
||
关键词列表
|
||
"""
|
||
# 移除URL和特殊字符
|
||
title = re.sub(r'http[s]?://\S+', '', title)
|
||
title = re.sub(r'[^\w\s]', ' ', title)
|
||
|
||
# 简单分词（按空格和常见分隔符）
|
||
words = re.split(r'[\s，。！？、]+', title)
|
||
|
||
# 过滤停用词和短词
|
||
stopwords = {'的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这'}
|
||
|
||
keywords = [
|
||
word.strip() for word in words
|
||
if word.strip() and len(word.strip()) >= min_length and word.strip() not in stopwords
|
||
]
|
||
|
||
return keywords
|
||
|
||
def _calculate_similarity(self, text1: str, text2: str) -> float:
|
||
"""
|
||
计算两个文本的相似度
|
||
|
||
Args:
|
||
text1: 文本1
|
||
text2: 文本2
|
||
|
||
Returns:
|
||
相似度分数（0-1之间）
|
||
"""
|
||
# 使用 SequenceMatcher 计算相似度
|
||
return SequenceMatcher(None, text1, text2).ratio()
|
||
|
||
def _find_unique_topics(self, platform_stats: Dict) -> Dict[str, List[str]]:
|
||
"""
|
||
找出各平台独有的热点话题
|
||
|
||
Args:
|
||
platform_stats: 平台统计数据
|
||
|
||
Returns:
|
||
各平台独有话题字典
|
||
"""
|
||
unique_topics = {}
|
||
|
||
# 获取每个平台的TOP关键词
|
||
platform_keywords = {}
|
||
for platform, stats in platform_stats.items():
|
||
top_keywords = set([kw for kw, _ in stats["top_keywords"].most_common(10)])
|
||
platform_keywords[platform] = top_keywords
|
||
|
||
# 找出独有关键词
|
||
for platform, keywords in platform_keywords.items():
|
||
# 找出其他平台的所有关键词
|
||
other_keywords = set()
|
||
for other_platform, other_kws in platform_keywords.items():
|
||
if other_platform != platform:
|
||
other_keywords.update(other_kws)
|
||
|
||
# 找出独有的
|
||
unique = keywords - other_keywords
|
||
if unique:
|
||
unique_topics[platform] = list(unique)[:5] # 最多5个
|
||
|
||
return unique_topics
|