Fix/bug en zh (#385)

* [fix]The log retains genuine alerts and errors, while filtering out unnecessary noise.

* [fix]Scenario English and Chinese, emotion specifications

* [fix]Change the "no data" scenario from 0.0 to None

* [fix]The emotional health indicators, emotional advice, and emotional distribution analysis are all linked together.

* [fix]The emotional health indicators, emotional advice, and emotional distribution analysis are all linked together.

* [fix]Separate expected errors from unexpected errors

api/app/controllers/emotion_controller.py

@@ -244,16 +244,46 @@ async def get_emotion_suggestions(
         )
 
         if data is None:
-            # 缓存不存在或已过期
+            # 缓存不存在或已过期，自动触发生成
             api_logger.info(
-                f"用户 {request.end_user_id} 的建议缓存不存在或已过期",
+                f"用户 {request.end_user_id} 的建议缓存不存在或已过期，自动生成新建议",
                 extra={"end_user_id": request.end_user_id}
             )
-            return fail(
-                BizCode.NOT_FOUND,
-                "建议缓存不存在或已过期，请右上角刷新生成新建议",
-                ""
-            )
+            try:
+                data = await emotion_service.generate_emotion_suggestions(
+                    end_user_id=request.end_user_id,
+                    db=db,
+                    language=language
+                )
+                # 保存到缓存
+                await emotion_service.save_suggestions_cache(
+                    end_user_id=request.end_user_id,
+                    suggestions_data=data,
+                    db=db,
+                    expires_hours=24
+                )
+            except (ValueError, KeyError) as gen_e:
+                # 预期内的业务异常：配置缺失、数据格式问题等
+                api_logger.warning(
+                    f"自动生成建议失败（业务异常）: {str(gen_e)}",
+                    extra={"end_user_id": request.end_user_id}
+                )
+                return fail(
+                    BizCode.NOT_FOUND,
+                    f"自动生成建议失败: {str(gen_e)}",
+                    ""
+                )
+            except Exception as gen_e:
+                # 非预期异常：记录完整 traceback 便于排查
+                api_logger.error(
+                    f"自动生成建议时发生未预期异常: {str(gen_e)}",
+                    extra={"end_user_id": request.end_user_id},
+                    exc_info=True
+                )
+                raise HTTPException(
+                    status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                    detail=f"生成建议时发生内部错误: {str(gen_e)}"
+                )
 
         api_logger.info(
             "个性化建议获取成功（缓存）",

api/app/repositories/neo4j/emotion_repository.py

@@ -73,11 +73,11 @@ class EmotionRepository:
             params["emotion_type"] = emotion_type
         
         if start_date:
-            where_clauses.append("s.created_at >= $start_date")
+            where_clauses.append("s.created_at IS NOT NULL AND datetime(s.created_at) >= datetime($start_date)")
             params["start_date"] = start_date
         
         if end_date:
-            where_clauses.append("s.created_at <= $end_date")
+            where_clauses.append("s.created_at IS NOT NULL AND datetime(s.created_at) <= datetime($end_date)")
             params["end_date"] = end_date
         
         where_str = " AND ".join(where_clauses)
@@ -211,17 +211,18 @@ class EmotionRepository:
         # 计算起始日期（使用字符串比较，避免时区问题）
         start_date = (datetime.now() - timedelta(days=days)).isoformat()
         
-        # 优化的 Cypher 查询：使用字符串比较避免时区问题
+        # 使用 datetime() 函数进行时间比较，与其他查询保持一致
         query = """
         MATCH (s:Statement)
         WHERE s.end_user_id = $end_user_id
           AND s.emotion_type IS NOT NULL
-          AND s.created_at >= $start_date
+          AND s.created_at IS NOT NULL
+          AND datetime(s.created_at) >= datetime($start_date)
         RETURN s.id as statement_id,
                s.emotion_type as emotion_type,
                s.emotion_intensity as emotion_intensity,
                s.created_at as created_at
-        ORDER BY s.created_at ASC
+        ORDER BY datetime(s.created_at) ASC
         """
         
         try:

api/app/services/emotion_analytics_service.py

@@ -388,6 +388,16 @@ class EmotionAnalyticsService:
                 time_range=time_range
             )
 
+            # 如果指定时间范围内没有数据，尝试更大的时间范围
+            if not emotions and time_range != "90d":
+                logger.info(f"用户 {end_user_id} 在 {time_range} 内无数据，尝试90天范围")
+                emotions = await self.emotion_repo.get_emotions_in_range(
+                    end_user_id=end_user_id,
+                    time_range="90d"
+                )
+                if emotions:
+                    time_range = "90d"
+
             # 如果没有数据，返回默认值
             if not emotions:
                 logger.warning(f"用户 {end_user_id} 在时间范围 {time_range} 内没有情绪数据")
@@ -574,6 +584,27 @@ class EmotionAnalyticsService:
                 time_range="30d"
             )
 
+            # 3.1 如果30天内没有数据，尝试获取90天的数据
+            if not emotions:
+                logger.info(f"用户 {end_user_id} 30天内无情绪数据，尝试获取90天数据")
+                emotions = await self.emotion_repo.get_emotions_in_range(
+                    end_user_id=end_user_id,
+                    time_range="90d"
+                )
+                health_data = await self.calculate_emotion_health_index(end_user_id, time_range="90d")
+
+            # 3.2 如果仍然没有时间范围内的数据，从情绪标签统计获取（无时间过滤）
+            if not emotions:
+                logger.info(f"用户 {end_user_id} 90天内也无情绪数据，从标签统计获取全量数据")
+                tags_data = await self.get_emotion_tags(end_user_id=end_user_id)
+                if tags_data.get("total_count", 0) > 0:
+                    # 用标签统计数据构建简化的 health_data
+                    health_data["emotion_distribution"] = {
+                        tag["emotion_type"]: tag["count"]
+                        for tag in tags_data.get("tags", [])
+                    }
+                    health_data["total_emotion_count"] = tags_data["total_count"]
+
             # 4. 分析情绪模式
             patterns = self._analyze_emotion_patterns(emotions)