fix: 修复表情动作模型调用并完善视频分析结果存储

- 表情动作: 将模型调用从 `planner` 切换到 `utils`，以使用更合适的模型进行表情推荐。
- 视频分析: 增加检查逻辑，仅当分析成功且结果不为错误提示时，才将结果存入数据库，防止存储无效记录。

src/chat/utils/utils_video.py

@@ -233,34 +233,28 @@ class VideoAnalyzer:
                     return {"summary": summary}
                 finally:
                     if os.path.exists(temp_path):
-                        try:
-                            os.remove(temp_path)
-                        except Exception:  # pragma: no cover
-                            pass
-            except Exception as e:  # pragma: no cover
-                return {"summary": f"❌ 处理失败: {e}"}
+                        os.unlink(temp_path)
 
-    # ---- 缓存辅助 ----
-    async def _get_cached(self, video_hash: str) -> Optional[str]:
-        try:
-            async with get_db_session() as session:  # type: ignore
-                result = await session.execute(select(Videos).where(Videos.video_hash == video_hash))  # type: ignore
-                obj: Optional[Videos] = result.scalar_one_or_none()  # type: ignore
-                if obj and obj.vlm_processed and obj.description:
-                    # 更新使用次数
-                    try:
-                        await session.execute(
-                            update(Videos)
-                            .where(Videos.id == obj.id)  # type: ignore
-                            .values(count=obj.count + 1 if obj.count is not None else 1)
-                        )
-                        await session.commit()
-                    except Exception:  # pragma: no cover
-                        await session.rollback()
-                    return obj.description
-        except Exception:  # pragma: no cover
-            pass
-        return None
+                # 保存分析结果到数据库（仅保存成功的结果）
+                if success and not result.startswith("❌"):
+                    metadata = {"filename": filename, "file_size": len(video_bytes), "analysis_timestamp": time.time()}
+                    self._store_video_result(video_hash=video_hash, description=result, metadata=metadata)
+                    logger.info("✅ 分析结果已保存到数据库")
+                else:
+                    logger.warning("⚠️ 分析失败，不保存到数据库以便后续重试")
+
+                # 处理完成，通知等待者并清理资源
+                video_event.set()
+                async with video_lock_manager:
+                    # 清理资源
+                    video_locks.pop(video_hash, None)
+                    video_events.pop(video_hash, None)
+
+                return {"summary": result}
+
+        except Exception as e:
+            error_msg = f"❌ 从字节数据分析视频失败: {str(e)}"
+            logger.error(error_msg)
 
     async def _save_cache(self, video_hash: str, summary: str, file_size: int) -> None:
         try:

src/plugins/built_in/emoji_plugin/emoji.py

@@ -152,10 +152,10 @@ class EmojiAction(BaseAction):
 
                     # 调用LLM
                     models = llm_api.get_available_models()
-                    chat_model_config = models.get("planner")
+                    chat_model_config = models.get("utils")
                     if not chat_model_config:
-                        logger.error(f"{self.log_prefix} 未找到'planner'模型配置，无法调用LLM")
-                        return False, "未找到'planner'模型配置"
+                        logger.error(f"{self.log_prefix} 未找到'utils'模型配置，无法调用LLM")
+                        return False, "未找到'utils'模型配置"
 
                     success, chosen_emotion, _, _ = await llm_api.generate_with_model(
                         prompt, model_config=chat_model_config, request_type="emoji"
@@ -212,10 +212,10 @@ class EmojiAction(BaseAction):
 
                 # 调用LLM
                 models = llm_api.get_available_models()
-                chat_model_config = models.get("planner")
+                chat_model_config = models.get("utils")
                 if not chat_model_config:
-                    logger.error(f"{self.log_prefix} 未找到'planner'模型配置，无法调用LLM")
-                    return False, "未找到'planner'模型配置"
+                    logger.error(f"{self.log_prefix} 未找到'utils'模型配置，无法调用LLM")
+                    return False, "未找到'utils'模型配置"
 
                 success, chosen_description, _, _ = await llm_api.generate_with_model(
                     prompt, model_config=chat_model_config, request_type="emoji"