feat(chat): 优化提及检测并精简兴趣度评分日志

增强了机器人提及检测逻辑，使其能够识别配置文件中设置的所有别名（alias_names），而不仅仅是主昵称。这提高了交互的灵活性和准确性。

此外，还对兴趣度评分和匹配系统的日志输出进行了大幅重构：
- 将多条评分计算日志合并为一条包含核心指标的摘要日志，使输出更简洁。
- 调整了部分日志级别，将非关键信息移至 DEBUG 级别，以减少日志噪音。
- 在关键日志中增加了消息内容预览，以便于快速上下文定位和调试。

src/chat/affinity_flow/interest_scoring.py

@@ -49,9 +49,10 @@ class InterestScoringSystem:
         self, messages: List[DatabaseMessages], bot_nickname: str
     ) -> List[InterestScore]:
         """计算消息的兴趣度评分"""
-        logger.info(f"开始为 {len(messages)} 条消息计算兴趣度...")
         user_messages = [msg for msg in messages if str(msg.user_info.user_id) != str(global_config.bot.qq_account)]
-        logger.info(f"正在处理 {len(user_messages)} 条用户消息。")
+        if not user_messages:
+            return []
+        logger.info(f"正在为 {len(user_messages)} 条用户消息计算兴趣度...")
 
         scores = []
         for i, msg in enumerate(user_messages, 1):
@@ -59,25 +60,18 @@ class InterestScoringSystem:
             score = await self._calculate_single_message_score(msg, bot_nickname)
             scores.append(score)
 
-        logger.info(f"兴趣度计算完成，共生成 {len(scores)} 个评分。")
+        logger.info(f"为 {len(scores)} 条消息生成了兴趣度评分。")
         return scores
 
     async def _calculate_single_message_score(self, message: DatabaseMessages, bot_nickname: str) -> InterestScore:
         """计算单条消息的兴趣度评分"""
-        logger.info(f"计算消息 {message.message_id} 的分数...")
-        logger.debug(f"消息长度: {len(message.processed_plain_text)} 字符")
+        message_preview = f"\033[96m{message.processed_plain_text[:30].replace('\n', ' ')}...\033[0m"
+        logger.info(f"计算消息 {message.message_id} 的分数 | 内容: {message_preview}")
 
         keywords = self._extract_keywords_from_database(message)
-        logger.debug(f"提取到 {len(keywords)} 个关键词。")
-
         interest_match_score = await self._calculate_interest_match_score(message.processed_plain_text, keywords)
-        logger.debug(f"兴趣匹配度: {interest_match_score:.3f}")
-
         relationship_score = self._calculate_relationship_score(message.user_info.user_id)
-        logger.debug(f"关系分数: {relationship_score:.3f}")
-
         mentioned_score = self._calculate_mentioned_score(message, bot_nickname)
-        logger.debug(f"提及分数: {mentioned_score:.3f}")
 
         total_score = (
             interest_match_score * self.score_weights["interest_match"]
@@ -91,9 +85,10 @@ class InterestScoringSystem:
             "mentioned": f"提及: {mentioned_score:.3f}",
         }
 
-        logger.info(f"消息 {message.message_id} 最终得分: {total_score:.3f}")
-        logger.debug(f"Score weights: {self.score_weights}")
-        logger.debug(f"Score details: {details}")
+        logger.info(
+            f"消息 {message.message_id} 得分: {total_score:.3f} "
+            f"(匹配: {interest_match_score:.2f}, 关系: {relationship_score:.2f}, 提及: {mentioned_score:.2f})"
+        )
 
         return InterestScore(
             message_id=message.message_id,
@@ -255,18 +250,19 @@ class InterestScoringSystem:
             return 0.0
 
         # 检查是否被提及
-        is_mentioned = msg.is_mentioned or (bot_nickname and bot_nickname in msg.processed_plain_text)
+        bot_aliases = [bot_nickname] + global_config.bot.alias_names
+        is_mentioned = msg.is_mentioned or any(alias in msg.processed_plain_text for alias in bot_aliases if alias)
 
         # 如果被提及或是私聊，都视为提及了bot
-        
         if is_mentioned or not hasattr(msg, "chat_info_group_id"):
             return global_config.affinity_flow.mention_bot_interest_score
 
         return 0.0
 
-    def should_reply(self, score: InterestScore) -> bool:
+    def should_reply(self, score: InterestScore, message: "DatabaseMessages") -> bool:
         """判断是否应该回复"""
-        logger.info(f"评估消息 {score.message_id} (得分: {score.total_score:.3f}) 是否回复...")
+        message_preview = f"\033[96m{(message.processed_plain_text or 'N/A')[:50].replace('\n', ' ')}\033[0m"
+        logger.info(f"评估消息 {score.message_id} (得分: {score.total_score:.3f}) | 内容: '{message_preview}...'")
         base_threshold = self.reply_threshold
 
         # 如果被提及，降低阈值

src/chat/interest_system/bot_interest_manager.py

@@ -404,8 +404,7 @@ class BotInterestManager:
         if not self.current_interests or not self._initialized:
             raise RuntimeError("❌ 兴趣标签系统未初始化")
 
-        logger.info("开始计算兴趣匹配度...")
-        logger.debug(f"消息长度: {len(message_text)}, 关键词: {len(keywords) if keywords else 0}")
+        logger.debug(f"开始计算兴趣匹配度: 消息长度={len(message_text)}, 关键词数={len(keywords) if keywords else 0}")
 
         message_id = f"msg_{datetime.now().timestamp()}"
         result = InterestMatchResult(message_id=message_id)
@@ -415,7 +414,7 @@ class BotInterestManager:
         if not active_tags:
             raise RuntimeError("没有检测到活跃的兴趣标签")
 
-        logger.info(f"正在与 {len(active_tags)} 个兴趣标签进行匹配...")
+        logger.debug(f"正在与 {len(active_tags)} 个兴趣标签进行匹配...")
 
         # 生成消息的embedding
         logger.debug("正在生成消息 embedding...")
@@ -450,9 +449,6 @@ class BotInterestManager:
                     match_count += 1
                     high_similarity_count += 1
                     result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
-                    logger.debug(
-                        f"   🏷️  '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 基础分数={weighted_score:.3f}, 增强分数={enhanced_score:.3f} [高匹配]"
-                    )
 
                 elif similarity > medium_threshold:
                     # 中相似度：中等加成
@@ -460,9 +456,6 @@ class BotInterestManager:
                     match_count += 1
                     medium_similarity_count += 1
                     result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
-                    logger.debug(
-                        f"   🏷️  '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 基础分数={weighted_score:.3f}, 增强分数={enhanced_score:.3f} [中匹配]"
-                    )
 
                 elif similarity > low_threshold:
                     # 低相似度：轻微加成
@@ -470,9 +463,6 @@ class BotInterestManager:
                     match_count += 1
                     low_similarity_count += 1
                     result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
-                    logger.debug(
-                        f"   🏷️  '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 基础分数={weighted_score:.3f}, 增强分数={enhanced_score:.3f} [低匹配]"
-                    )
 
         logger.info(
             f"匹配统计: {match_count}/{len(active_tags)} 个标签命中 | "

src/chat/planner_actions/planner.py

@@ -124,7 +124,10 @@ class ActionPlanner:
                 # 3. 根据兴趣度调整可用动作
                 if interest_scores:
                     latest_score = max(interest_scores, key=lambda s: s.total_score)
-                    should_reply, score = self.interest_scoring.should_reply(latest_score)
+                    latest_message = next(
+                        (msg for msg in unread_messages if msg.message_id == latest_score.message_id), None
+                    )
+                    should_reply, score = self.interest_scoring.should_reply(latest_score, latest_message)
 
                     reply_not_available = False
                     if not should_reply and "reply" in initial_plan.available_actions: