feat: video analysis switched to push mode (upload whole video, sync response)

Rationale: Oracle cannot reach NAS (Tailscale userspace mode on NAS, no TUN),
the old pull+webhook design requires Edge to download video from NAS and
callback to NAS - both blocked. New design is one-way NAS -> Oracle:

- FAM-Edge: new POST /api/edge/video/push endpoint accepts multipart video
  upload, reuses existing OpenCV scene-change keyframe selection, analyzes
  synchronously and returns the result payload directly in the HTTP response
  (no webhook callback). Old /api/edge/video/analyze kept for compatibility.
- FAM-Edge: VideoPreprocessor.save_upload() saves the uploaded file
- FAM-Edge: AIOrchestrator.process_push_task() runs the full pipeline
  (health check -> extract -> select -> compress -> VLM -> fusion) and
  returns callback-style payload dict
- FAM-Core: Dispatcher rewritten to push mode - reads local video file,
  uploads with task metadata (camera_name, event_start_time from file mtime,
  known_members_context), applies the result to DB via shared
  event_receiver.apply_success_event()
- FAM-Core: event_receiver success logic extracted into reusable
  apply_success_event() (used by both webhook route and dispatcher)
- config: edge_url -> /api/edge/video/push, push_timeout 1800s, gunicorn
  Edge timeout raised to 1800s for long synchronous analysis

fam-edge/src/fam_edge/ai_orchestrator/orchestrator.py

@@ -246,7 +246,7 @@ class AIOrchestrator:
             logger.error(f"[task_id={task_id}] 失败回调也失败: {e}")
 
     def process_task(self, task_data: dict):
-        """端到端处理任务"""
+        """端到端处理任务（拉取模式，webhook 回调）"""
         task_id = task_data.get('task_id')
         video_url = task_data.get('video_url')
         webhook_url = task_data.get('webhook_url')
@@ -325,3 +325,82 @@ class AIOrchestrator:
                 preprocessor.cleanup()
 
         return 200
+
+    def process_push_task(self, task_data: dict, video_path: str,
+                          preprocessor: 'VideoPreprocessor') -> dict:
+        """推送模式：同步处理上传的视频，结果直接返回（无 webhook 回调）
+
+        返回 payload 结构与原 webhook 回调一致:
+        - 成功: {task_id, status: "success", event_start_time, ..., frame_details, ...}
+        - 失败: {task_id, status: "failed", failure_stage, error_message}
+        """
+        task_id = task_data.get('task_id')
+        known_members = task_data.get('known_members_context', '')
+        event_start_time = task_data.get('event_start_time', '')
+
+        logger.info(f"[task_id={task_id}] ====== 开始处理推送任务 ======")
+        start_time = time.time()
+
+        try:
+            # 1. 健康检查
+            healthy_adapters = self.health_check_all()
+            if not healthy_adapters:
+                logger.error(f"[task_id={task_id}] 所有模型不健康")
+                return {
+                    "task_id": task_id, "status": "failed",
+                    "failure_stage": "vlm_visual",
+                    "error_message": "All models unhealthy"
+                }
+
+            # 2. 抽帧（视频已由调用方保存到本地，无需下载）
+            candidate_frames = preprocessor.extract_candidate_frames(video_path)
+            if not candidate_frames:
+                raise Exception("抽帧失败，无候选帧")
+
+            key_frames = preprocessor.select_key_frames(candidate_frames)
+            compressed_frames = preprocessor.compress_frames(key_frames)
+            if not compressed_frames:
+                raise Exception("压缩后无可用帧")
+
+            frame_timestamps = preprocessor.compute_timestamps(
+                video_path, len(compressed_frames), event_start_time
+            )
+
+            # 3. 并行视觉分析
+            model_outputs = self.run_visual_analysis(
+                healthy_adapters, compressed_frames, frame_timestamps, known_members
+            )
+            if not model_outputs:
+                raise Exception('All models failed in visual analysis')
+
+            # 4. 文本融合
+            fusion_result = self.run_text_fusion(model_outputs, known_members, task_id)
+
+            total_ms = int((time.time() - start_time) * 1000)
+            log_task(logger, task_id, 'overall', '推送任务完成', duration_ms=total_ms)
+
+            return {
+                "task_id": task_id,
+                "status": "success",
+                "event_start_time": event_start_time,
+                "event_end_time": task_data.get('event_end_time', ''),
+                "camera_name": task_data.get('camera_name', ''),
+                "global_summary": fusion_result.get('global_summary', ''),
+                "entities_json": fusion_result.get('entities_json', []),
+                "frame_details": fusion_result.get('frame_details', []),
+                "compute_provider": fusion_result.get('compute_provider', []),
+                "error_message": None
+            }
+
+        except VLMOutputInvalidError as e:
+            logger.error(f"[task_id={task_id}] VLM 输出解析失败: {e}")
+            return {
+                "task_id": task_id, "status": "failed",
+                "failure_stage": "vlm_fusion", "error_message": str(e)
+            }
+        except Exception as e:
+            logger.error(f"[task_id={task_id}] 推送任务处理失败: {e}", exc_info=True)
+            return {
+                "task_id": task_id, "status": "failed",
+                "failure_stage": "process", "error_message": str(e)
+            }