fix: 分析器和消息处理

src/application/services/analysis_application_service.py

@@ -63,25 +63,34 @@ class AnalysisApplicationService:
         days = self.config_manager.get_analysis_days()
         max_count = self.config_manager.get_max_messages()
 
-        unified_messages = await adapter.fetch_messages(
+        raw_messages = await adapter.fetch_messages(
             group_id=group_id, days=days, max_count=max_count
         )
 
-        if not unified_messages:
+        if not raw_messages:
             logger.warning(f"群 {group_id} 在最近 {days} 天内无消息或无法获取")
             return {"success": False, "reason": "no_messages"}
 
-        # 检查最小消息阈值
-        if (
-            len(unified_messages) < self.config_manager.get_min_messages_threshold()
-            and not manual
-        ):
+        # 3. 清理消息 (Filter commands, bot messages, noise)
+        from ...domain.services.message_cleaner_service import MessageCleanerService
+
+        cleaner = MessageCleanerService()
+        bot_self_ids = self.config_manager.get_bot_self_ids()
+
+        # 对于自动任务，强制过滤指令；对于手动任务，也建议过滤以保持报告纯净
+        unified_messages = cleaner.clean_messages(
+            raw_messages, bot_self_ids=bot_self_ids, filter_commands=True
+        )
+
+        # 4. 检查最小消息阈值 (在清理后进行)
+        threshold = self.config_manager.get_min_messages_threshold()
+        if len(unified_messages) < threshold and not manual:
             logger.info(
-                f"群 {group_id} 消息数 ({len(unified_messages)}) 未达到自动分析阈值"
+                f"群 {group_id} 有效消息数 ({len(unified_messages)}) 未达到自动分析阈值 ({threshold})"
             )
             return {"success": False, "reason": "below_threshold"}
 
-        # 3. 基础统计 (Domain Service)
+        # 5. 基础统计 (Domain Service)
         statistics = await asyncio.to_thread(
             self.statistics_service.calculate_group_statistics, unified_messages
         )
@@ -120,7 +129,7 @@ class AnalysisApplicationService:
             f"{platform_id}:GroupMessage:{group_id}" if platform_id else group_id
         )
 
-        if topic_enabled and user_title_enabled and golden_quote_enabled:
+        if topic_enabled or user_title_enabled or golden_quote_enabled:
             (
                 topics,
                 user_titles,
@@ -131,10 +140,10 @@ class AnalysisApplicationService:
                 user_activity,
                 umo=unified_msg_origin,
                 top_users=top_users,
+                topic_enabled=topic_enabled,
+                user_title_enabled=user_title_enabled,
+                golden_quote_enabled=golden_quote_enabled,
             )
-        else:
-            # 按需串行执行 (略，实际实现可补全或合并)
-            pass
 
         # 回填结果
         statistics.golden_quotes = golden_quotes

src/domain/services/message_cleaner_service.py

@@ -0,0 +1,109 @@
+"""
+消息清理服务 - 领域层
+负责过滤掉机器人消息、指令、技术性内容（如原始表情代码）及敏感内容。
+"""
+
+import re
+from dataclasses import replace
+
+from ..value_objects.unified_message import (
+    MessageContent,
+    MessageContentType,
+    UnifiedMessage,
+)
+
+
+class MessageCleanerService:
+    """消息清理服务"""
+
+    # Discord 自定义表情正则 <:name:id> 或 <a:name:id>
+    DISCORD_CUSTOM_EMOJI_PATTERN = re.compile(r"<a?:.+?:\d+>")
+
+    # 指令匹配正则：匹配以 / 开头，或者以 @某人 / 开头的消息
+    # 比如: "/group_analysis", "@bot /help", " /test"
+    COMMAND_PATTERN = re.compile(r"^\s*(?:<@\d+>\s+)?/")
+
+    def clean_messages(
+        self,
+        messages: list[UnifiedMessage],
+        bot_self_ids: list[str] = None,
+        filter_commands: bool = True,
+    ) -> list[UnifiedMessage]:
+        """
+        清理并过滤消息列表。
+
+        Args:
+            messages: 原始统一格式消息列表
+            bot_self_ids: 机器人自身的 ID 列表
+            filter_commands: 是否过滤指令消息
+
+        Returns:
+            清理后的消息列表
+        """
+        bot_ids = set(bot_self_ids or [])
+        cleaned_list = []
+
+        for msg in messages:
+            # 1. 过滤机器人发送的消息
+            if msg.sender_id in bot_ids:
+                continue
+
+            # 2. 预检指令消息（首个内容块通常是文本）
+            is_command = False
+            first_text = msg.text_content
+            if (
+                filter_commands
+                and first_text
+                and self.COMMAND_PATTERN.match(first_text)
+            ):
+                is_command = True
+
+            if is_command:
+                continue
+
+            # 3. 清理消息内容中的技术性噪音
+            cleaned_contents = []
+            has_meaningful_content = False
+
+            for content in msg.contents:
+                if content.type == MessageContentType.TEXT:
+                    text = content.text or ""
+
+                    # 移除 Discord 原始表情代码
+                    text = self.DISCORD_CUSTOM_EMOJI_PATTERN.sub("", text)
+
+                    # 移除 @mentions 文本 (e.g. <@123456>)
+                    text = re.sub(r"<@\d+>", "", text)
+
+                    # 清理多余空格
+                    text = text.strip()
+
+                    if text:
+                        cleaned_contents.append(
+                            MessageContent(type=MessageContentType.TEXT, text=text)
+                        )
+                        has_meaningful_content = True
+                else:
+                    # 其他类型（图片、回复等）暂时保留，但由后续分析器决定是否使用
+                    cleaned_contents.append(content)
+                    if content.type != MessageContentType.REPLY:
+                        has_meaningful_content = True
+
+            # 4. 如果清理后仍有内容，则保留消息
+            if has_meaningful_content:
+                # 重新合成 text_content 用于 LLM 分析
+                new_text_content = "".join(
+                    [
+                        c.text
+                        for c in cleaned_contents
+                        if c.type == MessageContentType.TEXT
+                    ]
+                ).strip()
+
+                # 使用 replace 创建新实例（Frozen dataclass 必须如此）
+                new_msg = replace(
+                    msg, contents=tuple(cleaned_contents), text_content=new_text_content
+                )
+                cleaned_list.append(new_msg)
+
+        return cleaned_list

src/domain/services/statistics_service.py

@@ -97,7 +97,11 @@ class StatisticsService:
             legacy_list.append(
                 {
                     "time": msg.timestamp,
-                    "sender": {"user_id": msg.sender_id},
+                    "sender": {
+                        "user_id": msg.sender_id,
+                        "nickname": msg.sender_name,
+                        "card": msg.sender_card or "",
+                    },
                     "message": [
                         {"type": "text", "data": {"text": msg.text_content or ""}}
                     ],

src/infrastructure/analysis/analyzers/golden_quote_analyzer.py

@@ -170,40 +170,34 @@ class GoldenQuoteAnalyzer(BaseAnalyzer):
 
     def extract_interesting_messages(self, messages: list[dict]) -> list[dict]:
         """
-        提取圣经的文本消息
+        根据清理后的消息提取可能有意义的消息片段用于金句分析。
 
         Args:
-            messages: 群聊消息列表
+            messages: 已由 MessageCleaner 处理过的 legacy 消息列表
 
         Returns:
-            圣经的文本消息列表
+            提取的文本消息列表
         """
-        try:
-            interesting_messages = []
+        interesting_messages = []
 
-            for msg in messages:
-                sender = msg.get("sender", {})
-                nickname = InfoUtils.get_user_nickname(self.config_manager, sender)
-                msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M")
+        for msg in messages:
+            # 获取发送者显示名
+            sender = msg.get("sender", {})
+            nickname = InfoUtils.get_user_nickname(self.config_manager, sender)
+            msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M")
 
-                for content in msg.get("message", []):
-                    if content.get("type") == "text":
-                        text = content.get("data", {}).get("text", "").strip()
-                        # 过滤长度适中、可能圣经的消息
-                        if 5 <= len(text) <= 100 and not text.startswith(
-                            ("http", "www", "/")
-                        ):
-                            interesting_messages.append(
-                                {
-                                    "sender": nickname,
-                                    "time": msg_time,
-                                    "content": text,
-                                    "user_id": str(sender.get("user_id", "")),
-                                }
-                            )
+            for content in msg.get("message", []):
+                if content.get("type") == "text":
+                    text = content.get("data", {}).get("text", "").strip()
+                    # 过滤掉过短或过长的噪音（已经在 cleaner 处理过一遍基本垃圾）
+                    if 2 <= len(text) <= 500:
+                        interesting_messages.append(
+                            {
+                                "sender": nickname,
+                                "time": msg_time,
+                                "content": text,
+                                "user_id": str(sender.get("user_id", "")),
+                            }
+                        )
 
-            return interesting_messages
-
-        except Exception as e:
-            logger.error(f"提取圣经消息失败: {e}")
-            return []
+        return interesting_messages

src/infrastructure/analysis/analyzers/topic_analyzer.py

@@ -257,79 +257,38 @@ class TopicAnalyzer(BaseAnalyzer):
 
     def extract_text_messages(self, messages: list[dict]) -> list[dict]:
         """
-        从群聊消息中提取文本消息
+        从已清理的消息中提取文本消息用于话题分析。
 
         Args:
-            messages: 群聊消息列表
+            messages: 已由 MessageCleaner 处理过的 legacy 消息列表
 
         Returns:
             提取的文本消息列表
         """
-        logger.debug(
-            f"extract_text_messages 开始处理，输入消息数量: {len(messages) if messages else 0}"
-        )
-        logger.debug(f"extract_text_messages 输入消息类型: {type(messages)}")
-
-        if not messages:
-            logger.warning("extract_text_messages 收到空消息列表")
-            return []
-
         text_messages = []
 
-        for i, msg in enumerate(messages):
-            logger.debug(f"处理第 {i + 1} 条消息，类型: {type(msg)}")
-            # 确保msg是字典类型，避免'str' object has no attribute 'get'错误
-            if not isinstance(msg, dict):
-                logger.warning(f"跳过非字典类型的消息: {type(msg)} - {msg}")
-                continue
+        for msg in messages:
+            # 获取发送者显示名
+            sender = msg.get("sender", {})
+            nickname = InfoUtils.get_user_nickname(self.config_manager, sender)
+            msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M")
 
-            try:
-                sender = msg.get("sender", {})
-                # 确保sender是字典类型，避免'str' object has no attribute 'get'错误
-                if not isinstance(sender, dict):
-                    logger.warning(
-                        f"extract_text_messages 跳过sender非字典类型的消息: {type(sender)} - {sender}"
-                    )
-                    continue
+            for content in msg.get("message", []):
+                if content.get("type") == "text":
+                    text = content.get("data", {}).get("text", "").strip()
+                    # 已经在 MessageCleaner 中处理过基本的垃圾内容
+                    if text:
+                        # 简单的额外清理
+                        cleaned_text = text.replace("\n", " ").replace("\r", " ")
+                        cleaned_text = re.sub(r"[\x00-\x1f\x7f-\x9f]", "", cleaned_text)
 
-                # 获取发送者ID并过滤机器人消息
-                user_id = str(sender.get("user_id", ""))
-                bot_self_ids = self.config_manager.get_bot_self_ids()
-
-                # 跳过机器人自己的消息
-                if bot_self_ids and user_id in [str(uid) for uid in bot_self_ids]:
-                    logger.debug(f"extract_text_messages 过滤掉机器人QQ号: {user_id}")
-                    continue
-
-                nickname = InfoUtils.get_user_nickname(self.config_manager, sender)
-                msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M")
-
-                for content in msg.get("message", []):
-                    if content.get("type") == "text":
-                        text = content.get("data", {}).get("text", "").strip()
-                        if text and len(text) > 2 and not text.startswith("/"):
-                            # 清理消息内容
-                            text = text.replace('""', '"').replace('""', '"')
-                            text = text.replace(""", "'").replace(""", "'")
-                            text = text.replace("\n", " ").replace("\r", " ")
-                            text = text.replace("\t", " ")
-                            text = re.sub(r"[\x00-\x1f\x7f-\x9f]", "", text)
-                            text_messages.append(
-                                {
-                                    "sender": nickname,
-                                    "time": msg_time,
-                                    "content": text.strip(),
-                                }
-                            )
-            except Exception as e:
-                logger.error(f"处理第 {i + 1} 条消息时出错: {e}", exc_info=True)
-                continue
-
-        logger.debug(
-            f"extract_text_messages 完成，提取到 {len(text_messages)} 条文本消息"
-        )
-        if text_messages:
-            logger.debug(f"extract_text_messages 第一条文本消息: {text_messages[0]}")
+                        text_messages.append(
+                            {
+                                "sender": nickname,
+                                "time": msg_time,
+                                "content": cleaned_text.strip(),
+                            }
+                        )
         return text_messages
 
     async def analyze_topics(

src/infrastructure/analysis/analyzers/user_title_analyzer.py

@@ -178,16 +178,15 @@ class UserTitleAnalyzer(BaseAnalyzer):
 
             for user_id, stats in user_analysis.items():
                 user_id_str = str(user_id)
-                # 过滤机器人自己的消息
+                # 过滤机器人由 MessageCleaner 已处理，此处仅作为二级防御
                 if bot_self_ids and user_id_str in [str(uid) for uid in bot_self_ids]:
-                    logger.debug(f"过滤掉机器人ID: {user_id}")
                     continue
 
-                # 只处理活跃用户
+                # 只处理活跃用户 (top_users 或 消息数>=5)
                 if user_id_str not in target_user_ids:
                     continue
 
-                # 分析用户特征
+                # 分析用户特征 (此处已基于已清理的 stats)
                 night_messages = sum(stats["hours"][h] for h in range(6))
                 avg_chars = (
                     stats["char_count"] / stats["message_count"]
@@ -198,7 +197,7 @@ class UserTitleAnalyzer(BaseAnalyzer):
                 user_summaries.append(
                     {
                         "name": stats["nickname"],
-                        "user_id": user_id_str,  # 使用 user_id
+                        "user_id": user_id_str,
                         "message_count": stats["message_count"],
                         "avg_chars": round(avg_chars, 1),
                         "emoji_ratio": round(

src/infrastructure/analysis/llm_analyzer.py

@@ -156,15 +156,21 @@ class LLMAnalyzer:
         user_analysis: dict,
         umo: str = None,
         top_users: list[dict] = None,
+        topic_enabled: bool = True,
+        user_title_enabled: bool = True,
+        golden_quote_enabled: bool = True,
     ) -> tuple[list[SummaryTopic], list[UserTitle], list[GoldenQuote], TokenUsage]:
         """
-        并发执行所有分析任务（话题、用户称号、金句）
+        并发执行所有分析任务（话题、用户称号、金句），支持按需启用。
 
         Args:
             messages: 群聊消息列表
             user_analysis: 用户分析统计
             umo: 模型唯一标识符
             top_users: 活跃用户列表(可选)
+            topic_enabled: 是否启用话题分析
+            user_title_enabled: 是否启用用户称号分析
+            golden_quote_enabled: 是否启用金句分析
 
         Returns:
             (话题列表, 用户称号列表, 金句列表, 总Token使用统计)
@@ -179,10 +185,13 @@ class LLMAnalyzer:
             else:
                 session_id = timestamp
 
-            logger.info(f"开始并发执行所有分析任务，会话ID: {session_id}")
+            logger.info(
+                f"开始并发执行分析任务 (话题:{topic_enabled}, 称号:{user_title_enabled}, 金句:{golden_quote_enabled})，会话ID: {session_id}"
+            )
 
             # 保存原始消息数据 (Debug Mode)
             if self.config_manager.get_debug_mode():
+                # ... (保持原有的调试保存代码)
                 try:
                     import json
                     from pathlib import Path
@@ -202,44 +211,57 @@ class LLMAnalyzer:
                     msg_file_path = debug_dir / f"{session_id}_messages.json"
                     with open(msg_file_path, "w", encoding="utf-8") as f:
                         json.dump(messages, f, ensure_ascii=False, indent=2)
-                    logger.info(f"已保存原始消息数据到: {msg_file_path}")
-                except Exception as e:
-                    logger.error(f"保存原始消息数据失败: {e}", exc_info=True)
+                except Exception:
+                    pass
 
-            # 并发执行三个分析任务
-            results = await asyncio.gather(
-                self.topic_analyzer.analyze_topics(messages, umo, session_id),
-                self.user_title_analyzer.analyze_user_titles(
-                    messages, user_analysis, umo, top_users, session_id
-                ),
-                self.golden_quote_analyzer.analyze_golden_quotes(
-                    messages, umo, session_id
-                ),
-                return_exceptions=True,
-            )
+            # 构建并发任务列表
+            tasks = []
+            task_names = []
+
+            if topic_enabled:
+                tasks.append(
+                    self.topic_analyzer.analyze_topics(messages, umo, session_id)
+                )
+                task_names.append("topic")
+
+            if user_title_enabled:
+                tasks.append(
+                    self.user_title_analyzer.analyze_user_titles(
+                        messages, user_analysis, umo, top_users, session_id
+                    )
+                )
+                task_names.append("user_title")
+
+            if golden_quote_enabled:
+                tasks.append(
+                    self.golden_quote_analyzer.analyze_golden_quotes(
+                        messages, umo, session_id
+                    )
+                )
+                task_names.append("golden_quote")
+
+            if not tasks:
+                return [], [], [], TokenUsage()
+
+            results = await asyncio.gather(*tasks, return_exceptions=True)
 
             # 处理结果
             topics, topic_usage = [], TokenUsage()
             user_titles, title_usage = [], TokenUsage()
             golden_quotes, quote_usage = [], TokenUsage()
 
-            # 话题分析结果
-            if isinstance(results[0], Exception):
-                logger.error(f"话题分析失败: {results[0]}")
-            else:
-                topics, topic_usage = results[0]
+            for i, result in enumerate(results):
+                name = task_names[i]
+                if isinstance(result, Exception):
+                    logger.error(f"分析任务 {name} 失败: {result}")
+                    continue
 
-            # 用户称号分析结果
-            if isinstance(results[1], Exception):
-                logger.error(f"用户称号分析失败: {results[1]}")
-            else:
-                user_titles, title_usage = results[1]
-
-            # 金句分析结果
-            if isinstance(results[2], Exception):
-                logger.error(f"金句分析失败: {results[2]}")
-            else:
-                golden_quotes, quote_usage = results[2]
+                if name == "topic":
+                    topics, topic_usage = result
+                elif name == "user_title":
+                    user_titles, title_usage = result
+                elif name == "golden_quote":
+                    golden_quotes, quote_usage = result
 
             # 合并Token使用统计
             total_usage = TokenUsage(

src/infrastructure/analysis/utils/info_utils.py

@@ -8,6 +8,14 @@ class InfoUtils:
         """
         enable_user_card = config_manager.get_enable_user_card()
         if enable_user_card:
-            return sender.get("card", "") or sender.get("nickname", "")
+            return (
+                sender.get("card", "")
+                or sender.get("nickname", "")
+                or str(sender.get("user_id", ""))
+            )
         else:
-            return sender.get("nickname", "") or sender.get("card", "")
+            return (
+                sender.get("nickname", "")
+                or sender.get("card", "")
+                or str(sender.get("user_id", ""))
+            )

src/infrastructure/analysis/utils/json_utils.py

@@ -6,7 +6,7 @@ JSON处理工具模块
 import json
 import re
 
-from ...utils.logger import logger
+from ....utils.logger import logger
 
 
 def fix_json(text: str) -> str:

src/infrastructure/platform/bot_manager.py

@@ -105,9 +105,6 @@ class BotManager:
     def _refresh_from_stored_platforms(self):
         """尝试从已存储的平台对象中刷新 bot 实例 (Lazy Load)"""
         for platform_id, platform in self._platforms.items():
-            if platform_id in self._bot_instances:
-                continue
-
             bot_client = None
             # 优先尝试 get_client()
             if hasattr(platform, "get_client"):
@@ -121,6 +118,13 @@ class BotManager:
                 bot_client = platform.client
 
             if bot_client:
+                # 检查是否已存在且是否发生变化（防止重复创建适配器）
+                old_client = self._bot_instances.get(platform_id)
+
+                # 如果 client 对象没变且已经有适配器，跳过
+                if bot_client is old_client and platform_id in self._adapters:
+                    continue
+
                 platform_name = None
                 if hasattr(platform, "metadata"):
                     # 优先使用 type
@@ -129,6 +133,16 @@ class BotManager:
                     elif hasattr(platform.metadata, "name"):
                         platform_name = platform.metadata.name
 
+                # 兼容不同版本的元数据获取
+                if not platform_name:
+                    meta = getattr(platform, "meta", None)
+                    if callable(meta):
+                        try:
+                            metadata = meta()
+                            platform_name = getattr(metadata, "name", None)
+                        except Exception:
+                            pass
+
                 # 后备检测：如果不支持名称
                 if not platform_name or not PlatformAdapterFactory.is_supported(
                     str(platform_name)
@@ -138,7 +152,7 @@ class BotManager:
                         platform_name = detected
 
                 self.set_bot_instance(bot_client, platform_id, platform_name)
-                logger.info(f"懒加载发现平台 {platform_id} 的 bot 实例")
+                logger.info(f"已刷新/发现平台 {platform_id} 的 bot 实例 (变动或懒加载)")
 
     def get_all_bot_instances(self) -> dict:
         """获取所有已加载的bot实例 {platform_id: bot_instance}"""
@@ -213,17 +227,29 @@ class BotManager:
         这是 DDD 架构操作的主要方法。
         """
         if platform_id:
+            # 无论是否存在适配器，都尝试检测一次 client 是否有变（如重启后 session 变化）
+            if platform_id in self._platforms:
+                self._refresh_from_stored_platforms()
+
             return self._adapters.get(platform_id)
 
         if self._adapters:
             if len(self._adapters) == 1:
                 return list(self._adapters.values())[0]
 
-            logger.error(
+            logger.warning(
                 f"存在多个适配器 {list(self._adapters.keys())}，但未指定 platform_id。"
             )
             return None
 
+        # 如果没有任何适配器，尝试全局刷新一次
+        self._refresh_from_stored_platforms()
+        if self._adapters:
+            if platform_id:
+                return self._adapters.get(platform_id)
+            if len(self._adapters) == 1:
+                return list(self._adapters.values())[0]
+
         return None
 
     def get_all_adapters(self) -> dict:
@@ -373,17 +399,22 @@ class BotManager:
 
     def update_from_event(self, event):
         """从事件更新bot实例（用于手动命令）"""
-        if hasattr(event, "bot") and event.bot:
+        # 兼容不同平台的 bot 实例属性名 (OneBot 使用 bot, Discord 使用 client)
+        bot_instance = getattr(event, "bot", None) or getattr(event, "client", None)
+
+        if bot_instance:
             # 从事件中获取平台ID
             platform_id = None
-            if hasattr(event, "platform") and isinstance(event.platform, str):
+            if hasattr(event, "get_platform_id"):
+                platform_id = event.get_platform_id()
+            elif hasattr(event, "platform_meta") and hasattr(event.platform_meta, "id"):
+                platform_id = event.platform_meta.id
+            elif hasattr(event, "platform") and isinstance(event.platform, str):
                 platform_id = event.platform
-            elif hasattr(event, "metadata") and hasattr(event.metadata, "id"):
-                platform_id = event.metadata.id
 
-            self.set_bot_instance(event.bot, platform_id)
+            self.set_bot_instance(bot_instance, platform_id)
             # 每次都尝试从bot实例提取ID
-            bot_self_id = self._extract_bot_self_id(event.bot)
+            bot_self_id = self._extract_bot_self_id(bot_instance)
             if bot_self_id:
                 # 将单个ID转换为列表，保持统一处理
                 self.set_bot_self_ids([bot_self_id])