优化：解耦帧调度与推理队列，提升单卡实时推理稳定性

core/tensorrt_engine.py

@@ -116,7 +116,7 @@ class TensorRTEngine:
                 if self._context is not None:
                     self._release_resources()
                 
-                self._cuda_context = cuda.Device(0).make_context()
+                self._cuda_context = cuda.Device(self.config.device_id).make_context()
                 self._stream = cuda.Stream()
                 
                 TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

core/video_stream.py

@@ -102,6 +102,7 @@ class RTSPStreamReader:
         
         self._logger = get_logger("video_stream")
         self._lock = threading.Lock()
+        self._latest_frame: Optional[VideoFrame] = None
     
     @property
     def is_connected(self) -> bool:
@@ -221,6 +222,8 @@ class RTSPStreamReader:
                             pass
 
                     self._frame_buffer.put_nowait(frame_obj)
+                    with self._lock:
+                        self._latest_frame = frame_obj
 
                     if self._on_frame_callback:
                         self._on_frame_callback(frame_obj)
@@ -293,6 +296,9 @@ class RTSPStreamReader:
                 self._frame_buffer.get_nowait()
             except queue.Empty:
                 break
+
+        with self._lock:
+            self._latest_frame = None
         
         self._logger.info(f"视频流已停止: {self.camera_id}")
     
@@ -305,16 +311,11 @@ class RTSPStreamReader:
     
     def get_latest_frame(self, timeout: float = 1.0) -> Optional[VideoFrame]:
         """获取最新帧(丢弃中间帧)"""
-        try:
-            while True:
-                try:
-                    frame = self._frame_buffer.get_nowait()
-                    if self._frame_buffer.empty():
-                        return frame
-                except queue.Empty:
-                    return None
-        except Exception:
-            return None
+        del timeout
+        with self._lock:
+            frame = self._latest_frame
+            self._latest_frame = None
+        return frame
     
     def get_frame_batch(self, max_count: int = 8, 
                         timeout: float = 2.0) -> List[VideoFrame]: