705 lines
24 KiB
Python
705 lines
24 KiB
Python
from fastapi import APIRouter, Query
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from typing import Optional
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from datetime import datetime
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import pytz
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from app.analysis.trend_analyzer import TrendAnalyzer
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from app.analysis.predictor import TrendPredictor
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from app.utils.logger import log
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from app.core import cache
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router = APIRouter()
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@router.get(
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"/trend",
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summary="热点聚合分析",
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description="分析各平台热点数据的共性和差异，提取共同关键词、跨平台热点话题等",
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response_description="返回热点分析结果",
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responses={
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200: {"description": "成功获取热点聚合分析"},
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500: {"description": "分析过程出错"}
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}
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)
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async def get_trend_analysis(
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date: Optional[str] = Query(
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default=None,
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description="指定日期，格式为 YYYY-MM-DD，默认为当天",
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example="2024-01-15"
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),
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type: str = Query(
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default="main",
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description="分析类型：main(主题分析), platform(平台对比), cross(跨平台热点), advanced(高级分析)",
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enum=["main", "platform", "cross", "advanced"]
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)
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):
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"""
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**热点聚合分析**
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对多个平台的热点数据进行聚合分析，识别共同话题和传播趋势。
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**分析类型说明：**
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- `main`: 主题分析 - 提取核心热点话题和关键词
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- `platform`: 平台对比 - 比较不同平台的特点和差异
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- `cross`: 跨平台热点 - 识别在多个平台同时出现的话题
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- `advanced`: 高级分析 - 提供更深层次的数据洞察
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**应用场景：**
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- 舆情监控
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- 热点追踪
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- 内容运营决策
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- 市场趋势分析
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"""
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try:
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if not date:
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date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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# 从缓存中获取数据
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cache_key = f"analysis:trend:{date}:{type}"
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cached_data = cache.get_cache(cache_key)
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if cached_data:
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log.info(f"Retrieved trend analysis from cache for {date}, type: {type}")
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return cached_data
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# 如果缓存中没有，则生成新的分析数据
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analyzer = TrendAnalyzer()
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result = analyzer.get_analysis(date, type)
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return result
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except Exception as e:
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log.error(f"Error in trend analysis: {e}")
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return {
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"status": "error",
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"message": str(e),
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"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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}
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@router.get(
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"/platform-comparison",
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summary="平台对比分析",
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description="分析各平台热点数据的特点、热度排行、更新频率等，比较不同平台间的异同",
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response_description="返回平台对比分析结果",
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responses={
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200: {"description": "成功获取平台对比分析"}
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}
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)
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async def get_platform_comparison(
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date: Optional[str] = Query(
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default=None,
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description="指定日期，格式为 YYYY-MM-DD，默认为当天",
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example="2024-01-15"
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)
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):
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"""
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**平台对比分析**
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对比分析不同平台的特点、热度分布和内容特征。
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**分析维度：**
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- 数据统计：各平台新闻数量、平均热度
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- 更新频率：数据更新速度
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- 热度排行：按平台热度排序
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- 特征分析：各平台的内容特点
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**适用场景：**
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- 平台选择决策
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- 投放策略制定
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- 用户画像分析
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"""
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try:
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if not date:
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date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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# 从缓存中获取数据
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cache_key = f"analysis:trend:{date}:platform_comparison"
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cached_data = cache.get_cache(cache_key)
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if cached_data:
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log.info(f"Retrieved platform comparison from cache for {date}")
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return cached_data
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# 如果缓存中没有，则生成新的分析数据
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analyzer = TrendAnalyzer()
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result = analyzer.get_platform_comparison(date)
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return result
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except Exception as e:
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log.error(f"Error in platform comparison: {e}")
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return {
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"status": "error",
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"message": str(e),
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"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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}
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@router.get(
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"/cross-platform",
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summary="跨平台热点分析",
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description="分析在多个平台上出现的热点话题，以及热点的传播路径",
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response_description="返回跨平台热点分析结果",
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responses={
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200: {
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"description": "成功获取跨平台热点分析",
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"content": {
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"application/json": {
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"example": {
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"status": "success",
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"date": "2024-01-15",
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"cross_platform_topics": [
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{
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"topic": "某明星事件",
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"platforms": ["weibo", "douyin", "baidu"],
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"first_platform": "weibo",
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"spread_path": ["weibo", "zhihu", "baidu", "douyin"],
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"heat_trend": "rising"
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}
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],
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"topic_count": 15,
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"avg_platforms_per_topic": 3.5
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}
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}
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}
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}
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}
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)
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async def get_cross_platform_analysis(
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date: Optional[str] = Query(
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default=None,
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description="指定日期，格式为 YYYY-MM-DD，默认为当天",
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example="2024-01-15"
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),
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refresh: bool = Query(
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default=False,
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description="是否强制刷新缓存"
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)
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):
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"""
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**跨平台热点分析**
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识别在多个平台同时出现的热点话题，分析传播路径和发展趋势。
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**分析内容：**
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- 跨平台话题识别
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- 首发平台判断
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- 传播路径追踪
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- 热度趋势分析
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**价值：**
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- 发现全网热点
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- 追踪舆情走向
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- 把握传播规律
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"""
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try:
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if not date:
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date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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# 从缓存中获取数据
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cache_key = f"analysis:trend:{date}:cross_platform"
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# 如果不是强制刷新，尝试从缓存获取
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if not refresh:
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cached_data = cache.get_cache(cache_key)
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if cached_data:
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log.info(f"Retrieved cross platform analysis from cache for {date}")
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return cached_data
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# 如果缓存中没有或需要刷新，则生成新的分析数据
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analyzer = TrendAnalyzer()
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result = analyzer.get_cross_platform_analysis(date, refresh)
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return result
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except Exception as e:
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log.error(f"Error in cross platform analysis: {e}")
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return {
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"status": "error",
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"message": str(e),
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"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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}
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@router.get(
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"/advanced",
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summary="高级分析",
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description="提供更深入的热点分析，包括关键词云图、情感分析、热点演变趋势等",
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response_description="返回高级分析结果",
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responses={
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200: {
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"description": "成功获取高级分析",
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"content": {
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"application/json": {
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"example": {
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"status": "success",
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"date": "2024-01-15",
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"keyword_cloud": [{"word": "AI", "weight": 95}],
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"sentiment_analysis": {
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"positive": 45,
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"neutral": 40,
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"negative": 15
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},
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"evolution_trend": [
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{"hour": "00:00", "heat": 5000},
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{"hour": "12:00", "heat": 8500}
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]
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}
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}
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}
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}
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}
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)
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async def get_advanced_analysis(
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date: Optional[str] = Query(
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default=None,
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description="指定日期，格式为 YYYY-MM-DD，默认为当天",
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example="2024-01-15"
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),
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refresh: bool = Query(
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default=False,
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description="是否强制刷新缓存"
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)
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):
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"""
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**高级分析**
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提供深度的数据分析功能，包括词云、情感分析和演变趋势。
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**分析模块：**
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- 关键词云图：可视化展示高频词汇
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- 情感分析：正/中/负向情感分布
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- 演变趋势：时间维度上的热度变化
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- 深度洞察：数据背后的规律
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**适用场景：**
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- 深度报告生成
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- 趋势研究
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- 舆情分析
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"""
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try:
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if not date:
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date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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# 从缓存中获取数据
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cache_key = f"analysis:trend:{date}:advanced_analysis"
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# 如果不是强制刷新，尝试从缓存获取
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if not refresh:
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cached_data = cache.get_cache(cache_key)
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if cached_data:
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log.info(f"Retrieved advanced analysis from cache for {date}")
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return cached_data
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# 如果缓存中没有或需要刷新，则生成新的分析数据
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analyzer = TrendAnalyzer()
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result = analyzer.get_advanced_analysis(date, refresh)
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return result
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except Exception as e:
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log.error(f"Error in advanced analysis: {e}")
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return {
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"status": "error",
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"message": str(e),
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"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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}
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@router.get(
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"/prediction",
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summary="热点趋势预测",
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description="基于历史数据预测热点话题的发展趋势，包括上升趋势、下降趋势、持续热门话题等",
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response_description="返回热点预测结果",
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responses={
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200: {
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"description": "成功获取热点预测",
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"content": {
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"application/json": {
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"example": {
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"status": "success",
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"date": "2024-01-15",
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"rising_topics": [
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{"topic": "新技术发布", "trend": "rising", "confidence": 0.85}
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],
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"declining_topics": [
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{"topic": "旧闻", "trend": "declining", "confidence": 0.75}
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],
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"sustained_hot_topics": [
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{"topic": "持续热点", "trend": "stable", "duration": "3 天"}
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]
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}
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}
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}
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}
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}
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)
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async def get_trend_prediction(
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date: Optional[str] = Query(
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default=None,
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description="指定日期，格式为 YYYY-MM-DD，默认为当天",
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example="2024-01-15"
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)
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):
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"""
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**热点趋势预测**
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基于历史数据和算法模型预测热点话题的未来发展趋势。
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**预测类型：**
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- 上升趋势：热度正在上涨的话题
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- 下降趋势：热度逐渐消退的话题
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- 持续热门：长期保持高热度的话题
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**输出指标：**
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- 趋势方向：rising/declining/stable
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- 置信度：预测的可信程度 (0-1)
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- 持续时间：话题预计持续的时间
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**应用价值：**
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- 提前布局内容
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- 把握流量机会
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- 规避过时话题
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"""
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try:
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if not date:
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date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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# 从缓存中获取数据
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cache_key = f"analysis:prediction:{date}"
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cached_data = cache.get_cache(cache_key)
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if cached_data:
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log.info(f"Retrieved trend prediction from cache for {date}")
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return cached_data
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# 如果缓存中没有，则生成新的预测数据
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predictor = TrendPredictor()
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result = predictor.get_prediction(date)
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return result
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except Exception as e:
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log.error(f"Error in trend prediction: {e}")
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return {
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"status": "error",
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"message": str(e),
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"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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}
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@router.get(
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"/keyword-cloud",
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summary="关键词云图",
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description="提取热点数据中的关键词，按不同类别（科技、娱乐、社会等）进行分类，用于生成词云",
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response_description="返回关键词云图数据",
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responses={
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200: {
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"description": "成功获取关键词云图",
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"content": {
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"application/json": {
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"example": {
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"status": "success",
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"date": "2024-01-15",
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"keyword_clouds": {
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"科技": [
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{"word": "人工智能", "weight": 95},
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{"word": "大模型", "weight": 88}
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],
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"娱乐": [
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{"word": "电影", "weight": 75},
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{"word": "明星", "weight": 70}
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]
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},
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"total_keywords": 200
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}
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}
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}
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}
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}
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)
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async def get_keyword_cloud(
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date: Optional[str] = Query(
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default=None,
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description="指定日期，格式为 YYYY-MM-DD，默认为当天",
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example="2024-01-15"
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),
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refresh: bool = Query(
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default=False,
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description="是否强制刷新缓存"
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),
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platforms: Optional[str] = Query(
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default=None,
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description="指定平台，多个平台用逗号分隔，如 baidu,weibo",
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example="baidu,weibo"
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),
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category: Optional[str] = Query(
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default=None,
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description="指定分类，如科技、娱乐等",
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example="科技"
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),
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keyword_count: int = Query(
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default=200,
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ge=50,
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le=1000,
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description="返回的关键词数量，范围 50-1000"
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)
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):
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"""
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**关键词云图**
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从热点数据中提取高频关键词，并按类别分类，可用于可视化展示。
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**参数说明：**
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- `platforms`: 限定分析的平台范围
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- `category`: 筛选特定分类的关键词
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- `keyword_count`: 返回的关键词数量
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**分类体系：**
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- 科技：互联网、数码、AI 等
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- 娱乐：影视、明星、综艺等
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- 社会：民生、时事等
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- 体育：赛事、运动员等
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- 财经：经济、金融、股市等
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**数据格式：**
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每个关键词包含：
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- `word`: 词语本身
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- `weight`: 权重值（基于词频和重要性）
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**应用场景：**
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- 数据可视化
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- 内容标签生成
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- 主题挖掘
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"""
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try:
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if not date:
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date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
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|
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# 从缓存中获取数据
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cache_key = f"analysis:keyword_cloud:{date}"
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# 如果不是强制刷新，尝试从缓存获取
|
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if not refresh:
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cached_data = cache.get_cache(cache_key)
|
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if cached_data:
|
||
log.info(f"Retrieved keyword cloud from cache for {date}")
|
||
# 如果指定了分类，过滤结果
|
||
if category and cached_data.get("status") == "success" and "keyword_clouds" in cached_data:
|
||
if category in cached_data["keyword_clouds"]:
|
||
filtered_data = cached_data.copy()
|
||
filtered_data["keyword_clouds"] = {category: cached_data["keyword_clouds"][category]}
|
||
return filtered_data
|
||
return cached_data
|
||
|
||
# 如果缓存中没有或需要刷新，则生成新的关键词云数据
|
||
analyzer = TrendAnalyzer()
|
||
result = analyzer.get_keyword_cloud(date, refresh, keyword_count)
|
||
return result
|
||
except Exception as e:
|
||
log.error(f"Error in keyword cloud analysis: {e}")
|
||
return {
|
||
"status": "error",
|
||
"message": str(e),
|
||
"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
|
||
}
|
||
|
||
@router.get(
|
||
"/data-visualization",
|
||
summary="数据可视化分析",
|
||
description="提供热点数据的可视化分析，包括主题热度分布图",
|
||
response_description="返回数据可视化分析结果",
|
||
responses={
|
||
200: {
|
||
"description": "成功获取数据可视化分析",
|
||
"content": {
|
||
"application/json": {
|
||
"example": {
|
||
"status": "success",
|
||
"date": "2024-01-15",
|
||
"theme_distribution": {
|
||
"科技": 35,
|
||
"娱乐": 25,
|
||
"社会": 20,
|
||
"财经": 15,
|
||
"体育": 5
|
||
},
|
||
"platform_heatmap": {
|
||
"weibo": {"morning": 80, "afternoon": 95, "evening": 100},
|
||
"zhihu": {"morning": 60, "afternoon": 75, "evening": 85}
|
||
},
|
||
"charts_data": {...}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
)
|
||
async def get_data_visualization(
|
||
date: Optional[str] = Query(
|
||
default=None,
|
||
description="指定日期，格式为 YYYY-MM-DD，默认为当天",
|
||
example="2024-01-15"
|
||
),
|
||
refresh: bool = Query(
|
||
default=False,
|
||
description="是否强制刷新缓存"
|
||
),
|
||
platforms: str = Query(
|
||
default=None,
|
||
description="指定要分析的平台，多个平台用逗号分隔",
|
||
example="baidu,weibo,douyin"
|
||
)
|
||
):
|
||
"""
|
||
**数据可视化分析**
|
||
|
||
提供用于前端可视化的结构化数据分析结果。
|
||
|
||
**可视化类型：**
|
||
- 主题热度分布图：饼图/柱状图数据
|
||
- 平台热力图：时间段热度对比
|
||
- 趋势线图：时间序列数据
|
||
- 排行榜：TOP N 数据
|
||
|
||
**输出格式：**
|
||
数据格式适合常见图表库（ECharts、Chart.js 等）直接使用。
|
||
|
||
**支持的平台筛选：**
|
||
可以指定部分平台进行分析，减少数据量。
|
||
|
||
**典型应用：**
|
||
- Dashboard 展示
|
||
- 数据报表
|
||
- 实时监控大屏
|
||
"""
|
||
try:
|
||
if not date:
|
||
date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
|
||
|
||
# 从缓存中获取数据
|
||
cache_key = f"analysis:data_visualization:{date}"
|
||
|
||
# 如果不是强制刷新，尝试从缓存获取
|
||
if not refresh:
|
||
cached_data = cache.get_cache(cache_key)
|
||
if cached_data:
|
||
log.info(f"Retrieved data visualization from cache for {date}")
|
||
return cached_data
|
||
|
||
# 解析平台参数
|
||
platform_list = None
|
||
if platforms:
|
||
platform_list = [p.strip() for p in platforms.split(",") if p.strip()]
|
||
|
||
# 如果缓存中没有或需要刷新，则生成新的可视化数据
|
||
analyzer = TrendAnalyzer()
|
||
result = analyzer.get_data_visualization(date, refresh, platform_list)
|
||
return result
|
||
except Exception as e:
|
||
log.error(f"Error in data visualization: {e}")
|
||
return {
|
||
"status": "error",
|
||
"message": str(e),
|
||
"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
|
||
}
|
||
|
||
@router.get(
|
||
"/trend-forecast",
|
||
summary="热点趋势预测分析",
|
||
description="分析热点话题的演变趋势，预测热点的发展方向",
|
||
response_description="返回热点趋势预测分析结果",
|
||
responses={
|
||
200: {
|
||
"description": "成功获取热点趋势预测",
|
||
"content": {
|
||
"application/json": {
|
||
"example": {
|
||
"status": "success",
|
||
"date": "2024-01-15",
|
||
"time_range": "24h",
|
||
"forecast": [
|
||
{
|
||
"topic": "热门话题 A",
|
||
"current_heat": 8500,
|
||
"predicted_heat": 9500,
|
||
"trend": "rising",
|
||
"confidence": 0.82
|
||
},
|
||
{
|
||
"topic": "热门话题 B",
|
||
"current_heat": 6000,
|
||
"predicted_heat": 4500,
|
||
"trend": "declining",
|
||
"confidence": 0.75
|
||
}
|
||
],
|
||
"key_insights": "预计未来 24 小时内，科技类话题将持续升温..."
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
)
|
||
async def get_trend_forecast(
|
||
date: Optional[str] = Query(
|
||
default=None,
|
||
description="指定日期，格式为 YYYY-MM-DD，默认为当天",
|
||
example="2024-01-15"
|
||
),
|
||
refresh: bool = Query(
|
||
default=False,
|
||
description="是否强制刷新缓存"
|
||
),
|
||
time_range: str = Query(
|
||
default="24h",
|
||
description="预测时间范围：24h(24 小时), 7d(7 天), 30d(30 天)",
|
||
enum=["24h", "7d", "30d"]
|
||
)
|
||
):
|
||
"""
|
||
**热点趋势预测分析**
|
||
|
||
基于时间序列分析预测热点话题的未来发展趋势。
|
||
|
||
**预测时间范围：**
|
||
- `24h`: 短期预测，适合快速变化的话题
|
||
- `7d`: 中期预测，适合持续性话题
|
||
- `30d`: 长期预测，适合重大事件
|
||
|
||
**输出内容：**
|
||
- 当前热度值
|
||
- 预测热度值
|
||
- 趋势方向（上升/下降/平稳）
|
||
- 置信度评分
|
||
- 关键洞察总结
|
||
|
||
**预测维度：**
|
||
- 整体热度走势
|
||
- 分话题发展趋势
|
||
- 平台表现预测
|
||
- 新兴话题发现
|
||
|
||
**业务价值：**
|
||
- 内容策划参考
|
||
- 资源投放决策
|
||
- 风险预警
|
||
"""
|
||
try:
|
||
if not date:
|
||
date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d")
|
||
|
||
# 验证时间范围参数
|
||
valid_time_ranges = ["24h", "7d", "30d"]
|
||
if time_range not in valid_time_ranges:
|
||
time_range = "24h" # 默认使用 24 小时
|
||
|
||
# 从缓存中获取数据
|
||
cache_key = f"analysis:trend_forecast:{date}:{time_range}"
|
||
|
||
# 如果不是强制刷新，尝试从缓存获取
|
||
if not refresh:
|
||
cached_data = cache.get_cache(cache_key)
|
||
if cached_data:
|
||
log.info(f"Retrieved trend forecast from cache for {date}, time_range: {time_range}")
|
||
return cached_data
|
||
|
||
# 如果缓存中没有或需要刷新，则生成新的趋势预测数据
|
||
analyzer = TrendAnalyzer()
|
||
result = analyzer.get_trend_forecast(date, refresh, time_range)
|
||
return result
|
||
except Exception as e:
|
||
log.error(f"Error in trend forecast: {e}")
|
||
return {
|
||
"status": "error",
|
||
"message": str(e),
|
||
"date": date or datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d"),
|
||
"time_range": time_range
|
||
}
|