"""
高级数据分析工具
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。
"""
import re
from collections import Counter, defaultdict
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from difflib import SequenceMatcher
from ..services.data_service import DataService
from ..utils.validators import (
validate_platforms,
validate_limit,
validate_keyword,
validate_top_n,
validate_date_range
)
from ..utils.errors import MCPError, InvalidParameterError, DataNotFoundError
def calculate_news_weight(news_data: Dict, rank_threshold: int = 5) -> float:
"""
计算新闻权重（用于排序）
- 排名权重 (60%)：新闻在榜单中的排名
- 频次权重 (30%)：新闻出现的次数
- 热度权重 (10%)：高排名出现的比例
Args:
news_data: 新闻数据字典，包含 ranks 和 count 字段
rank_threshold: 高排名阈值，默认5
Returns:
权重分数（0-100之间的浮点数）
"""
ranks = news_data.get("ranks", [])
if not ranks:
return 0.0
count = news_data.get("count", len(ranks))
# 权重配置（与 config.yaml 保持一致）
RANK_WEIGHT = 0.6
FREQUENCY_WEIGHT = 0.3
HOTNESS_WEIGHT = 0.1
# 1. 排名权重：Σ(11 - min(rank, 10)) / 出现次数
rank_scores = []
for rank in ranks:
score = 11 - min(rank, 10)
rank_scores.append(score)
rank_weight = sum(rank_scores) / len(ranks) if ranks else 0
# 2. 频次权重：min(出现次数, 10) × 10
frequency_weight = min(count, 10) * 10
# 3. 热度加成：高排名次数 / 总出现次数 × 100
high_rank_count = sum(1 for rank in ranks if rank <= rank_threshold)
hotness_ratio = high_rank_count / len(ranks) if ranks else 0
hotness_weight = hotness_ratio * 100
# 综合权重
total_weight = (
rank_weight * RANK_WEIGHT
+ frequency_weight * FREQUENCY_WEIGHT
+ hotness_weight * HOTNESS_WEIGHT
)
return total_weight
class AnalyticsTools:
"""高级数据分析工具类"""
def __init__(self, project_root: str = None):
"""
初始化分析工具
Args:
project_root: 项目根目录
"""
self.data_service = DataService(project_root)
def analyze_data_insights_unified(
self,
insight_type: str = "platform_compare",
topic: Optional[str] = None,
date_range: Optional[Dict[str, str]] = None,
min_frequency: int = 3,
top_n: int = 20
) -> Dict:
"""
统一数据洞察分析工具 - 整合多种数据分析模式
Args:
insight_type: 洞察类型，可选值：
- "platform_compare": 平台对比分析（对比不同平台对话题的关注度）
- "platform_activity": 平台活跃度统计（统计各平台发布频率和活跃时间）
- "keyword_cooccur": 关键词共现分析（分析关键词同时出现的模式）
topic: 话题关键词（可选，platform_compare模式适用）
date_range: 日期范围，格式: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
min_frequency: 最小共现频次（keyword_cooccur模式），默认3
top_n: 返回TOP N结果（keyword_cooccur模式），默认20
Returns:
数据洞察分析结果字典
Examples:
- analyze_data_insights_unified(insight_type="platform_compare", topic="人工智能")
- analyze_data_insights_unified(insight_type="platform_activity", date_range={...})
- analyze_data_insights_unified(insight_type="keyword_cooccur", min_frequency=5)
"""
try:
# 参数验证
if insight_type not in ["platform_compare", "platform_activity", "keyword_cooccur"]:
raise InvalidParameterError(
f"无效的洞察类型: {insight_type}",
suggestion="支持的类型: platform_compare, platform_activity, keyword_cooccur"
)
# 根据洞察类型调用相应方法
if insight_type == "platform_compare":
return self.compare_platforms(
topic=topic,
date_range=date_range
)
elif insight_type == "platform_activity":
return self.get_platform_activity_stats(
date_range=date_range
)
else: # keyword_cooccur
return self.analyze_keyword_cooccurrence(
min_frequency=min_frequency,
top_n=top_n
)
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_trend_unified(
self,
topic: str,
analysis_type: str = "trend",
date_range: Optional[Dict[str, str]] = None,
granularity: str = "day",
threshold: float = 3.0,
time_window: int = 24,
lookahead_hours: int = 6,
confidence_threshold: float = 0.7
) -> Dict:
"""
统一话题趋势分析工具 - 整合多种趋势分析模式
Args:
topic: 话题关键词（必需）
analysis_type: 分析类型，可选值：
- "trend": 热度趋势分析（追踪话题的热度变化）
- "lifecycle": 生命周期分析（从出现到消失的完整周期）
- "viral": 异常热度检测（识别突然爆火的话题）
- "predict": 话题预测（预测未来可能的热点）
date_range: 日期范围（trend和lifecycle模式），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
granularity: 时间粒度（trend模式），默认"day"（hour/day）
threshold: 热度突增倍数阈值（viral模式），默认3.0
time_window: 检测时间窗口小时数（viral模式），默认24
lookahead_hours: 预测未来小时数（predict模式），默认6
confidence_threshold: 置信度阈值（predict模式），默认0.7
Returns:
趋势分析结果字典
Examples (假设今天是 2025-11-17):
- 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"})
- 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"})
- analyze_topic_trend_unified(topic="比特币", analysis_type="viral", threshold=3.0)
- analyze_topic_trend_unified(topic="ChatGPT", analysis_type="predict", lookahead_hours=6)
"""
try:
# 参数验证
topic = validate_keyword(topic)
if analysis_type not in ["trend", "lifecycle", "viral", "predict"]:
raise InvalidParameterError(
f"无效的分析类型: {analysis_type}",
suggestion="支持的类型: trend, lifecycle, viral, predict"
)
# 根据分析类型调用相应方法
if analysis_type == "trend":
return self.get_topic_trend_analysis(
topic=topic,
date_range=date_range,
granularity=granularity
)
elif analysis_type == "lifecycle":
return self.analyze_topic_lifecycle(
topic=topic,
date_range=date_range
)
elif analysis_type == "viral":
# viral模式不需要topic参数，使用通用检测
return self.detect_viral_topics(
threshold=threshold,
time_window=time_window
)
else: # predict
# predict模式不需要topic参数，使用通用预测
return self.predict_trending_topics(
lookahead_hours=lookahead_hours,
confidence_threshold=confidence_threshold
)
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_topic_trend_analysis(
self,
topic: str,
date_range: Optional[Dict[str, str]] = None,
granularity: str = "day"
) -> Dict:
"""
热度趋势分析 - 追踪特定话题的热度变化趋势
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
granularity: 时间粒度，仅支持 day（天）
Returns:
趋势分析结果字典
Examples:
用户询问示例：
- "帮我分析一下'人工智能'这个话题最近一周的热度趋势"
- "查看'比特币'过去一周的热度变化"
- "看看'iPhone'最近7天的趋势如何"
- "分析'特斯拉'最近一个月的热度趋势"
- "查看'ChatGPT'2024年12月的趋势变化"
代码调用示例：
>>> tools = AnalyticsTools()
>>> # 分析7天趋势（假设今天是 2025-11-17）
>>> result = tools.get_topic_trend_analysis(
... topic="人工智能",
... date_range={"start": "2025-11-11", "end": "2025-11-17"},
... granularity="day"
... )
>>> # 分析历史月份趋势
>>> result = tools.get_topic_trend_analysis(
... topic="特斯拉",
... date_range={"start": "2024-12-01", "end": "2024-12-31"},
... granularity="day"
... )
>>> print(result['trend_data'])
"""
try:
# 验证参数
topic = validate_keyword(topic)
# 验证粒度参数（只支持day）
if granularity != "day":
from ..utils.errors import InvalidParameterError
raise InvalidParameterError(
f"不支持的粒度参数: {granularity}",
suggestion="当前仅支持 'day' 粒度，因为底层数据按天聚合"
)
# 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)
# 收集趋势数据
trend_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
)
# 统计该时间点的话题出现次数
count = 0
matched_titles = []
for _, titles in all_titles.items():
for title in titles.keys():
if topic.lower() in title.lower():
count += 1
matched_titles.append(title)
trend_data.append({
"date": current_date.strftime("%Y-%m-%d"),
"count": count,
"sample_titles": matched_titles[:3] # 只保留前3个样本
})
except DataNotFoundError:
trend_data.append({
"date": current_date.strftime("%Y-%m-%d"),
"count": 0,
"sample_titles": []
})
# 按天增加时间
current_date += timedelta(days=1)
# 计算趋势指标
counts = [item["count"] for item in trend_data]
total_days = (end_date - start_date).days + 1
if len(counts) >= 2:
# 计算涨跌幅度
first_non_zero = next((c for c in counts if c > 0), 0)
last_count = counts[-1]
if first_non_zero > 0:
change_rate = ((last_count - first_non_zero) / first_non_zero) * 100
else:
change_rate = 0
# 找到峰值时间
max_count = max(counts)
peak_index = counts.index(max_count)
peak_time = trend_data[peak_index]["date"]
else:
change_rate = 0
peak_time = None
max_count = 0
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
},
"granularity": granularity,
"trend_data": trend_data,
"statistics": {
"total_mentions": sum(counts),
"average_mentions": round(sum(counts) / len(counts), 2) if counts else 0,
"peak_count": max_count,
"peak_time": peak_time,
"change_rate": round(change_rate, 2)
},
"trend_direction": "上升" if change_rate > 10 else "下降" if change_rate < -10 else "稳定"
}
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 compare_platforms(
self,
topic: Optional[str] = None,
date_range: Optional[Dict[str, str]] = None
) -> Dict:
"""
平台对比分析 - 对比不同平台对同一话题的关注度
Args:
topic: 话题关键词（可选，不指定则对比整体活跃度）
date_range: 日期范围，格式: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
Returns:
平台对比分析结果
Examples:
用户询问示例：
- "对比一下各个平台对'人工智能'话题的关注度"
- "看看知乎和微博哪个平台更关注科技新闻"
- "分析各平台今天的热点分布"
代码调用示例：
>>> # 对比各平台（假设今天是 2025-11-17）
>>> result = tools.compare_platforms(
... topic="人工智能",
... date_range={"start": "2025-11-08", "end": "2025-11-17"}
... )
>>> print(result['platform_stats'])
"""
try:
# 参数验证
if topic:
topic = validate_keyword(topic)
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()
# 收集各平台数据
platform_stats = defaultdict(lambda: {
"total_news": 0,
"topic_mentions": 0,
"unique_titles": set(),
"top_keywords": Counter()
})
# 遍历日期范围
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)
for title in titles.keys():
platform_stats[platform_name]["total_news"] += 1
platform_stats[platform_name]["unique_titles"].add(title)
# 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
