将@关系批处理业务迁移到 value_rank 插件

- 从 MessageStorageDB 移除@抽取与社交图写入逻辑，消息层仅保留归档职责

- 从系统级任务移除 process_pending_mentions，取消 message_to_db 中对应入口

- 在 value_rank 插件新增定时动作 value_rank_mentions_extract（每10分钟）

- 在插件内实现窗口化批处理（默认10~20分钟前）、@提取、幂等写入明细/边表/日汇总及 unique_interactors 回填

- 新增插件侧可配置参数 mention_batch_size / mention_window_start_minutes / mention_window_end_minutes

utils/system_jobs.py

@@ -43,14 +43,6 @@ def get_system_job_definitions(robot) -> List[Dict[str, Any]]:
             "trigger_config": {"seconds": 300},
             "handler": _build_process_pending_images_handler(robot),
         },
-        {
-            "job_key": "process_pending_mentions",
-            "name": "待抽取@关系处理",
-            "description": "每 10 分钟处理一次待抽取@消息并更新社交图",
-            "trigger_type": "every_seconds",
-            "trigger_config": {"seconds": 600},
-            "handler": _build_process_pending_mentions_handler(robot),
-        },
     ]
 
 def _build_process_pending_images_handler(robot) -> Callable[[], Awaitable[None]]:
@@ -61,18 +53,6 @@ def _build_process_pending_images_handler(robot) -> Callable[[], Awaitable[None]
     return _handler
 
 
-def _build_process_pending_mentions_handler(robot) -> Callable[[], Awaitable[None]]:
-    async def _handler():
-        if hasattr(robot, "message_storage") and robot.message_storage:
-            await robot.message_storage.process_pending_mentions(
-                batch_size=200,
-                window_start_minutes=20,
-                window_end_minutes=10,
-            )
-
-    return _handler
-
-
 class SystemJobLoader:
     """系统任务加载器：从数据库读取调度配置并注册到 async_job。"""
 

utils/wechat/message_to_db.py

@@ -358,49 +358,6 @@ class MessageStorage:
         except Exception as e:
             logger.exception(f"定时处理媒体任务出错: {e}")
 
-    async def process_pending_mentions(
-        self,
-        batch_size: int = 200,
-        window_start_minutes: int = 20,
-        window_end_minutes: int = 10,
-    ):
-        """定时任务：批量处理待抽取 @ 的消息并写入社交图。
-
-        说明：
-        1. 该任务与主消息归档链路解耦，不阻塞实时收发；
-        2. 每次只处理有限批次，避免长事务和数据库抖动；
-        3. 重复执行安全：底层按 message_id + sender + mentioned_user_id 做幂等控制。
-        4. 默认只处理 10~20 分钟前的数据，减少对热数据区间的扫描压力。
-        """
-        try:
-            started_at = datetime.now()
-            logger.info(
-                "触发定时@抽取任务: "
-                f"batch_size={batch_size}, window=[NOW-{window_start_minutes}m, NOW-{window_end_minutes}m)"
-            )
-            stats = self.message_db.process_pending_mentions(
-                batch_size=batch_size,
-                window_start_minutes=window_start_minutes,
-                window_end_minutes=window_end_minutes,
-            )
-            total = int(stats.get("total", 0))
-            if total == 0:
-                elapsed_ms = int((datetime.now() - started_at).total_seconds() * 1000)
-                logger.info(f"定时@抽取任务结束: 无待处理数据, 耗时={elapsed_ms}ms")
-                return
-
-            elapsed_ms = int((datetime.now() - started_at).total_seconds() * 1000)
-            logger.info(
-                "定时@抽取任务结束: "
-                f"读取={stats.get('total', 0)}, "
-                f"处理={stats.get('processed', 0)}, "
-                f"含@={stats.get('with_mentions', 0)}, "
-                f"失败={stats.get('failed', 0)}, "
-                f"耗时={elapsed_ms}ms"
-            )
-        except Exception as e:
-            logger.exception(f"定时处理@抽取任务出错: {e}")
-
     def _process_image_done(self, future):
         """任务完成统一回调（极轻量）"""
         try: