修复1在WebSocket回调线程内执行stop/start竞争条件，'socket already closed'循环出现，2陈旧结果5秒复用窗口旧识别结果污染新请求，意图混乱

config/voice.yaml

@@ -1,11 +1,5 @@
 # ROS 语音包配置文件
 
-asr:
-  mode: 'cloud'  # 'cloud' | 'local' - ASR模式选择
-  local:
-    server_url: "ws://127.0.0.1:10095"  # 本地FunASR服务地址
-  # 云端模式配置在dashscope中
-
 dashscope:
   api_key: "sk-7215a5ab7a00469db4072e1672a0661e"
   asr:
@@ -56,11 +50,11 @@ system:
   shutup_keywords: "bi zui"  # 闭嘴指令关键词（拼音，逗号分隔）
   interrupt_command_queue_depth: 10  # 中断命令订阅的队列深度（QoS）
   sv_enabled: false  # 是否启用声纹识别
-  sv_model_path: "~/hivecore_robot_os1/voice_model" # 声纹模型路径
-  # sv_model_path: "~/ros_learn/speech_campplus_sv_zh-cn_16k-common" # 声纹模型路径
+  # sv_model_path: "~/hivecore_robot_os1/voice_model" # 声纹模型路径
+  sv_model_path: "~/ros_learn/speech_campplus_sv_zh-cn_16k-common" # 声纹模型路径
   sv_threshold: 0.65  # 声纹识别阈值（0.0-1.0，值越小越宽松，值越大越严格）
-  sv_speaker_db_path: "~/hivecore_robot_os1/config/speakers.json"  # 声纹数据库保存路径（JSON格式，相对于ROS2包share目录）
-  # sv_speaker_db_path: "~/ros_learn/hivecore_robot_voice/config/speakers.json"  # 声纹数据库保存路径（JSON格式，相对于ROS2包share目录）
+  # sv_speaker_db_path: "~/hivecore_robot_os1/config/speakers.json"  # 声纹数据库保存路径（JSON格式，相对于ROS2包share目录）
+  sv_speaker_db_path: "~/ros_learn/hivecore_robot_voice/config/speakers.json"  # 声纹数据库保存路径（JSON格式，相对于ROS2包share目录）
   sv_buffer_size: 96000  # 声纹验证录音缓冲区大小（样本数，48kHz下2秒=96000）
   continue_without_image: true  # 多模态意图（skill_sequence/chat_camera）未获取到图片时是否继续推理
   
@@ -69,5 +63,5 @@ camera:
     jpeg_quality: 85  # JPEG压缩质量（0-100，85是质量和大小平衡点）
 
 interfaces:
-  root_path: "~/hivecore_robot_os1/hivecore_robot_interfaces/src"  # 接口文件根目录，支持 ~ 展开和相对路径
-  # root_path: "~/ros_learn/hivecore_robot_interfaces/src"  # 接口文件根目录，支持 ~ 展开和相对路径
+  # root_path: "~/hivecore_robot_os1/hivecore_robot_interfaces/src"  # 接口文件根目录，支持 ~ 展开和相对路径
+  root_path: "~/ros_learn/hivecore_robot_interfaces/src"  # 接口文件根目录，支持 ~ 展开和相对路径

robot_speaker/services/asr_audio_node.py

@@ -199,7 +199,7 @@ class DashScopeASR:
                 enable_turn_detection=True,
                 turn_detection_type='server_vad',
                 prefix_padding_ms=1000,
-                turn_detection_threshold=0.2,
+                turn_detection_threshold=0.3,
                 turn_detection_silence_duration_ms=800,
             )
             
@@ -395,7 +395,7 @@ class ASRAudioNode(Node):
         # ========== 异常识别检测 ==========
         self._abnormal_results = ["嗯。", "。", "啊。", "哦。"]  # 异常识别结果列表
         self._consecutive_abnormal_count = 0  # 连续异常识别次数
-        self.MAX_CONSECUTIVE_ABNORMAL = 2  # 最大连续异常次数
+        self.MAX_CONSECUTIVE_ABNORMAL = 5  # 最大连续异常次数
         
         self.recording_thread = threading.Thread(
             target=self.audio_recorder.record, name="RecordingThread", daemon=True
@@ -450,7 +450,7 @@ class ASRAudioNode(Node):
         
         self.asr_client.on_sentence_end = self._on_asr_result
         self.asr_client.on_speech_started = lambda: self._put_vad_event("speech_started")
-        self.asr_client.on_speech_stopped = lambda: self._put_vad_event("speech_stopped")
+        self.asr_client.on_speech_stopped = lambda: (self._clear_result(), self._put_vad_event("speech_stopped"))
         self.asr_client.start()
     
     def _on_asr_result(self, text: str):
@@ -467,15 +467,9 @@ class ASRAudioNode(Node):
             self.get_logger().warn(f"[ASR] 检测到异常识别结果: '{self._last_result}' | 连续异常:{self._consecutive_abnormal_count}次")
             # 如果连续多次异常，强制重置 ASR 连接
             if self._consecutive_abnormal_count >= self.MAX_CONSECUTIVE_ABNORMAL:
-                self.get_logger().error(f"[ASR] 连续{self._consecutive_abnormal_count}次异常识别，强制重置连接")
-                try:
-                    self.asr_client.stop()
-                    time.sleep(0.5)
-                    self.asr_client.start()
-                    self._consecutive_abnormal_count = 0
-                    self.get_logger().info("[ASR] 连接已重置")
-                except Exception as e:
-                    self.get_logger().error(f"[ASR] 重置连接失败: {e}")
+                self.get_logger().error(f"[ASR] 连续{self._consecutive_abnormal_count}次异常识别，标记需要重连")
+                self.asr_client._consecutive_send_failures = self.asr_client.MAX_CONSECUTIVE_FAILURES
+                self._consecutive_abnormal_count = 0
         else:
             # 正常识别，重置异常计数
             self._consecutive_abnormal_count = 0
@@ -601,7 +595,7 @@ class ASRAudioNode(Node):
         if self.asr_client.running:
             current_time = time.time()
             if (self._last_result and self._last_result_time and 
-                (current_time - self._last_result_time) < 5.0) or (self._result_event.is_set() and self._last_result):
+                (current_time - self._last_result_time) < 0.3) or (self._result_event.is_set() and self._last_result):
                 return self._return_result(response, self._last_result, "返回最近识别结果")
             if self._result_event.wait(timeout=2.0) and self._last_result:
                 return self._return_result(response, self._last_result, "识别成功（等待中）")