del train

Decoder.py

@@ -96,7 +96,7 @@ class Decoder_main(threading.Thread):
         elif decoder_class == 'ssmvep':
             self.zmqServer.interval_init(decoder_class)
             self.n_chan = 8
-            self.interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))
+            self.interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))  # [0.2, 2.2]
             self.sample_length = round(self.interval_epoch[1] - self.interval_epoch[0], 6)  # 解码数据长度2s,# 精确到小数点后6位
             self.single_train = 10  # 单类别数量
             self.num_target = 2  # 分类目标数目
@@ -268,26 +268,29 @@ class Decoder_main(threading.Thread):
 
         '''训练阶段采集数据'''
         if self.zmqServer.state_mode == 'train':  # 训练状态
-            if self.zmqServer.StartTrain:
+
+            
+            if self.zmqServer.epoch_finished  and self.zmqServer.paradigmBuffer.GetDataLenCount() >= \
+                    self.train_epoch[1] + self.zmqServer.event_inner_idx:
+                
                 self.currentLabel = self.zmqServer.currentLabel
-                self.zmqServer.StartTrain = False
+                
-            if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
+                print('训练队列数据：', self.zmqServer.paradigmBuffer.GetDataLenCount())
-                    self.train_epoch[1] \
+                trainTrial = self.zmqServer.paradigmBuffer.get_SSMVEPData()  # 取出所有数据
-                    + self.zmqServer.event_inner_idx:
+                
+                print('取出的： ', trainTrial.shape, 'event: ', trainTrial[-2, self.zmqServer.event_inner_idx])
+                trainTrial = self.preprocess(trainTrial[:self.n_chan, :])  # 预处理
+                trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.train_epoch[
+                    0]:self.zmqServer.event_inner_idx + self.train_epoch[1]]
+                print('trial: ', self.zmqServer.event_inner_idx, self.train_epoch[0], self.train_epoch[1])
+                if trainTrial.shape[1] == (self.train_epoch[1] - self.train_epoch[0]) and isinstance(
+                        self.trainLabel, list) \
+                        and self.trainLabel.count(self.currentLabel) < self.single_train:
+                    self.trainData.append(trainTrial)
+                    self.trainLabel.append(self.currentLabel)
+            else:
                 time.sleep(0.0001)
                 return
-            print('训练队列数据：', self.zmqServer.paradigmBuffer.GetDataLenCount())
-            trainTrial = self.zmqServer.paradigmBuffer.get_SSMVEPData()  # 取出所有数据
-            print('取出的： ', trainTrial.shape, 'event: ', trainTrial[-2, self.zmqServer.event_inner_idx])
-            trainTrial = self.preprocess(trainTrial[:self.n_chan, :])  # 预处理
-            trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.train_epoch[
-                0]:self.zmqServer.event_inner_idx + self.train_epoch[1]]
-            print('trial: ', self.zmqServer.event_inner_idx, self.train_epoch[0], self.train_epoch[1])
-            if trainTrial.shape[1] == (self.train_epoch[1] - self.train_epoch[0]) and isinstance(
-                    self.trainLabel, list) \
-                    and self.trainLabel.count(self.currentLabel) < self.single_train:
-                self.trainData.append(trainTrial)
-                self.trainLabel.append(self.currentLabel)
 
         elif self.zmqServer.state_mode == 'predict':  # 测试状态
             if self.load_model == False:  # 模型尚未训练完成

README.md

@@ -19,3 +19,4 @@ source activate 3in1Py310
 python runDecoder.py
 python datamock.py
 python ZeroMQClient_mock.py
+python system_test.py

Zmq/zmqServer.py

@@ -21,6 +21,10 @@ class zmqServer(threading.Thread):
         self.device_info = device_info
         
         self.host = host
+        
+        test_host = "10.200.27.140"
+        self.host = test_host
+        
         self.cmd_port = cmd_port    # 命令交互端口：收JSON命令 + 返JSON结果
         self.data_port = data_port  # 数据交互端口：收二进制原始脑电 + 返二进制滤波结果
         self.running = False
@@ -105,14 +109,14 @@ class zmqServer(threading.Thread):
 
     def interval_init(self, decoder_class):
         if decoder_class == 'ssmvep':
-            interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))
+            interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))  # [0.2, 2.2]
-            self.interval_epoch = [int(i * self.device_info['sample_rate']) for i in interval_epoch]
+            self.interval_epoch = [int(i * self.device_info['sample_rate']) for i in interval_epoch]  # [50, 550]
             self.train_epoch = [
                 int(self.interval_epoch[0]),
                 int(self.interval_epoch[1] + 0.1 * self.device_info['sample_rate'])
-            ]
+            ] # [50, 575]
-            self.latency = (self.interval_epoch[1] + 0.1 * self.device_info['sample_rate']) // 5
+            self.latency = (self.interval_epoch[1] + 0.1 * self.device_info['sample_rate']) // 5       #115包, 575个点
-            self.train_latency = (self.train_epoch[1] + 0.1 * self.device_info['sample_rate']) // 5
+            self.train_latency = (self.train_epoch[1] + 0.1 * self.device_info['sample_rate']) // 5    #120包  600个点
 
         elif decoder_class == 'mi':
             interval_epoch = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))
@@ -246,8 +250,6 @@ class zmqServer(threading.Thread):
                 self.decoder_switch = True
         elif method == "train":
             self.state_mode = 'train'
-            self.StartTrain = True
-            self.currentLabel = params
         elif method == "predict":
             self.state_mode = 'predict'
             if params == 1: #开始解码
@@ -322,22 +324,24 @@ class zmqServer(threading.Thread):
     def detect_event(self, samples):
         self.pack_contain_event = False
         # 第65通道为事件通道
-        events = samples[-2].tolist()
+        event = int(samples[-2][0])
-        for idx, event in enumerate(events):
+        # for idx, event in enumerate(events):
-            if int(event) in self.events:
+        if event in self.events:
-                new_key = "".join(
+            new_key = "".join(
-                    [
+                [
-                        str(event),
+                    str(event),
-                        datetime.datetime.now().strftime("%Y-%m-%d \
+                    datetime.datetime.now().strftime("%Y-%m-%d \
-                                                -%H-%M-%S"),
+                                            -%H-%M-%S"),
-                    ]
+                ]
-                )
+            )
-                if event == self.predict_event:
+            self.currentLabel = event
-                    self.count_events[new_key] = self.latency + 1
+            if event == self.predict_event:
-                else:
+                self.count_events[new_key] = self.latency + 1
-                    self.count_events[new_key] = self.train_latency + 1
+            else:
-                self.event_inner_idx = idx
+                self.count_events[new_key] = self.train_latency + 1
-                self.pack_contain_event = True
+            self.event_inner_idx = self.device_info['frame_points'] - 1
+            # algo_log(f"事件检测到: {event}，索引: {idx}", level="DEBUG")
+            self.pack_contain_event = True   
                 
         # 倒计时并清理过期事件
         drop_items = []

datamock.py

@@ -11,6 +11,7 @@ N_CHAN = 66           # 通道数: 64 EEG + 1 标签值 + 1 标签序号
 EEG_FREQ = 10         # EEG 正弦波频率 Hz
 EEG_AMP = 100.0      # EEG 幅值 100μV
 LABEL_INTERVAL = 5    # 标签间隔秒数
+# SERVER_ADDR = 'tcp://127.0.0.1:8100'
 SERVER_ADDR = 'tcp://127.0.0.1:8100'
 
 # 发送间隔: 每包 5 采样点 / 250Hz = 20ms

logs/log.py

@@ -1,24 +1,54 @@
 import os
-from datetime import datetime
+from datetime import datetime, timedelta
 import logging
 from logging.handlers import RotatingFileHandler
-import inspect  # 新增导入
+import inspect
 from PubLibrary.InifileHelper import IniRead
 
-
+# 全局配置
 console_output = IniRead('system', 'console_output', '1')
 log_level = IniRead('system', 'algo_log_level', 'INFO')
 log_once_cache = set()
-
-# 缓存已经创建过的logger，避免重复创建handler
 logger_cache = {}
+LOG_RETENTION_DAYS = 3
+LOG_DIR = './logs/'
+LOG_FILE_PREFIX = 'algo_log_'
+
+# 日志格式：时间 - 日志器名 - 级别 - 文件名:行号 - 函数名 - 日志内容
+LOG_FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+DATE_FORMAT = '%Y-%m-%d %H:%M:%S'
+
+
+def clean_old_logs():
+    """清理超过指定天数的旧日志文件"""
+    try:
+        if not os.path.exists(LOG_DIR):
+            return
+        expire_date = datetime.now() - timedelta(days=LOG_RETENTION_DAYS)
+        for filename in os.listdir(LOG_DIR):
+            if not filename.startswith(LOG_FILE_PREFIX) or not filename.endswith('.log'):
+                continue
+            date_str = filename[len(LOG_FILE_PREFIX):-4]
+            try:
+                file_date = datetime.strptime(date_str, '%Y-%m-%d')
+                if file_date < expire_date:
+                    file_path = os.path.join(LOG_DIR, filename)
+                    os.remove(file_path)
+                    print(f"清理过期日志: {file_path}")
+            except ValueError:
+                continue
+    except Exception as e:
+        print(f"清理旧日志异常: {str(e)}")
+
 
 def init_module_logger(logger_name):
-    log_dir = './logs/'
+    """初始化日志器 + 清理旧日志"""
-    os.makedirs(log_dir, exist_ok=True)
+    os.makedirs(LOG_DIR, exist_ok=True)
-    log_file = os.path.join(log_dir, f'algo_log_{datetime.now().strftime("%Y-%m-%d")}.log')
+    clean_old_logs()
+
+    current_date = datetime.now().strftime("%Y-%m-%d")
+    log_file = os.path.join(LOG_DIR, f"{LOG_FILE_PREFIX}{current_date}.log")
 
-    # 已创建直接返回
     if logger_name in logger_cache:
         return logger_cache[logger_name]
 
@@ -28,19 +58,18 @@ def init_module_logger(logger_name):
         logger_cache[logger_name] = logger
         return logger
 
+    # 文件输出处理器
     file_handler = RotatingFileHandler(
         log_file,
-        maxBytes=10*1024*1024,
+        maxBytes=10 * 1024 * 1024,
         backupCount=10,
         encoding='utf-8'
     )
-    formatter = logging.Formatter(
+    formatter = logging.Formatter(LOG_FORMAT, datefmt=DATE_FORMAT)
-        '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S'
-    )
     file_handler.setFormatter(formatter)
     logger.addHandler(file_handler)
 
+    # 控制台输出
     if console_output:
         console_handler = logging.StreamHandler()
         console_handler.setFormatter(formatter)
@@ -51,29 +80,35 @@ def init_module_logger(logger_name):
 
 
 def algo_log(content, level="INFO", record_once=False):
-    # 向上回溯1层栈，拿到调用algo_log的代码文件信息
+    """
-    frame = inspect.currentframe().f_back
+    日志入口函数
-    file_path = frame.f_code.co_filename
+    自动记录：调用文件名、代码行号、所在函数
-    # 提取py文件名（不带后缀/带后缀自选）
+    """
-    file_name = os.path.basename(file_path)  # 例：zmqServer.py
+    # 回溯栈帧，获取真正调用 algo_log 的代码位置
-    # file_name = os.path.splitext(os.path.basename(file_path))[0] # 例：zmqServer
+    # f_back(1) -> algo_log 自身，f_back(2) -> 业务调用处
+    frame = inspect.currentframe().f_back.f_back
+    if not frame:
+        file_name = "unknown"
+    else:
+        file_name = os.path.basename(frame.f_code.co_filename)
 
     logger = init_module_logger(file_name)
 
+    # 单次日志去重
     if record_once:
         log_key = f"{level.upper()}_{content}"
         if log_key in log_once_cache:
             return
         log_once_cache.add(log_key)
 
+    # 日志级别分发
     level_upper = level.upper()
-    if level_upper == "DEBUG":
+    log_map = {
-        logger.debug(content)
+        "DEBUG": logger.debug,
-    elif level_upper == "WARNING":
+        "WARNING": logger.warning,
-        logger.warning(content)
+        "ERROR": logger.error,
-    elif level_upper == "ERROR":
+        "FATAL": logger.fatal,
-        logger.error(content)
+        "INFO": logger.info
-    elif level_upper == "FATAL":
+    }
-        logger.fatal(content)
+    log_func = log_map.get(level_upper, logger.info)
-    else:
+    log_func(content)
-        logger.info(content)

system_test.py

@@ -0,0 +1,422 @@
+# -*- coding: utf-8 -*-
+"""
+ZMQ 脑电数据测试工具【语法错误修复版】
+修复点：
+1. dataclass 可变列表默认值报错
+2. threading.Thread daemon 参数语法错误
+适配：Python3.10、全链路 float64、ZMQ DEALER<->ROUTER
+端口：8099(命令) / 8100(数据)
+"""
+import zmq
+import time
+import threading
+import numpy as np
+import matplotlib.pyplot as plt
+import json
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union, Tuple
+from matplotlib.animation import FuncAnimation
+
+# ===================== 1. 配置管理 =====================
+@dataclass(frozen=True)  # 冻结配置类
+class TestConfig:
+    # 网络配置
+    SERVER_IP: str = "127.0.0.1"
+    CMD_PORT: int = 8099
+    DATA_PORT: int = 8100
+
+    # 硬件与时序
+    SAMPLE_RATE: int = 250
+    FRAME_INTERVAL_MS: int = 20
+    SEND_INTERVAL: float = FRAME_INTERVAL_MS / 1000
+    CHANNEL_NUMS: int = 66
+    FRAME_POINTS: int = 5
+    FILTER_OUT_CHAN: int = 64
+    FILTER_FRAME_POINTS: int = 50
+
+    # 数据类型 & 字节数 (float64 8字节)
+    DATA_DTYPE: np.dtype = np.float64
+    RAW_FRAME_BYTES: int = CHANNEL_NUMS * FRAME_POINTS * 8    # 66*5*8 = 2640
+    FILTER_FRAME_BYTES: int = FILTER_OUT_CHAN * FILTER_FRAME_POINTS * 8  # 25600
+
+    # 事件通道索引
+    EVENT_CHANNEL_IDX: int = -2
+
+    # 列表类型 使用 default_factory 规避可变默认值报错
+    EVENT_TAGS: List[int] = field(default_factory=lambda: [1, 2, 99])
+    SIM_SIGNAL_FREQ: List[float] = field(default_factory=lambda: [8.0, 9.0])
+
+    # 仿真噪声
+    NOISE_STD: float = 0.25
+
+    # 可视化配置
+    PLOT_TARGET_CHAN: int = 0
+    PLOT_WINDOW_LEN: int = 400
+    PLOT_REFRESH_INTERVAL: int = 50
+
+    # 日志限流
+    FRAME_ERR_INTERVAL: float = 3.0
+
+    # ZMQ 配置
+    SEND_RETRY_MAX: int = 3
+    SEND_RETRY_SLEEP: float = 0.01
+    ZMQ_HWM: int = 1000
+
+# 初始化全局配置
+CONFIG = TestConfig()
+
+# ===================== 2. 全局状态管理 =====================
+class GlobalState:
+    def __init__(self):
+        self.run_flag: bool = True
+        self.last_frame_err_time: float = 0.0
+
+GLOBAL_STATE = GlobalState()
+
+# ===================== 3. Matplotlib 中文初始化 =====================
+def init_matplotlib():
+    # Windows 黑体，Linux/Mac 自行替换字体
+    plt.rcParams['font.sans-serif'] = ['SimHei']
+    plt.rcParams['axes.unicode_minus'] = False  # 修复负号乱码
+
+init_matplotlib()
+
+# ===================== 4. ZMQ DEALER 客户端 =====================
+class ZmqDealerClient:
+    """适配 ROUTER 的 DEALER 客户端，高频流式数据专用"""
+    def __init__(self, server_ip: str, port: int):
+        self.ctx: zmq.Context = zmq.Context()
+        self.socket: zmq.Socket = self.ctx.socket(zmq.DEALER)
+        self._configure_socket()
+        self.socket.connect(f"tcp://{server_ip}:{port}")
+
+    def _configure_socket(self):
+        """套接字参数配置"""
+        self.socket.setsockopt(zmq.RCVHWM, CONFIG.ZMQ_HWM)
+        self.socket.setsockopt(zmq.SNDHWM, CONFIG.ZMQ_HWM)
+        self.socket.setsockopt(zmq.RCVTIMEO, 0)
+        self.socket.setsockopt(zmq.SNDTIMEO, 0)
+
+    def send_json(self, data: Dict) -> bool:
+        """发送JSON命令，带重试机制"""
+        try:
+            payload = json.dumps(data, ensure_ascii=False).encode("utf-8")
+        except Exception as e:
+            print(f"[JSON序列化失败] {e}")
+            return False
+
+        for _ in range(CONFIG.SEND_RETRY_MAX):
+            try:
+                self.socket.send_multipart([b"", payload])
+                return True
+            except zmq.Again:
+                time.sleep(CONFIG.SEND_RETRY_SLEEP)
+            except Exception as e:
+                print(f"[JSON发送异常] {e}")
+                time.sleep(CONFIG.SEND_RETRY_SLEEP)
+        print(f"[JSON发送重试失败]")
+        return False
+
+    def send_bytes(self, data: bytes) -> bool:
+        """发送二进制脑电数据，带重试"""
+        for _ in range(CONFIG.SEND_RETRY_MAX):
+            try:
+                self.socket.send_multipart([b"", data])
+                return True
+            except zmq.Again:
+                time.sleep(CONFIG.SEND_RETRY_SLEEP)
+            except Exception as e:
+                print(f"[二进制发送异常] {e}")
+                time.sleep(CONFIG.SEND_RETRY_SLEEP)
+        print(f"[二进制发送重试失败]")
+        return False
+
+    def recv_json(self) -> Optional[Dict]:
+        """接收JSON命令响应（标准3帧）"""
+        try:
+            frames = self.socket.recv_multipart()
+            if len(frames) < 3:
+                self._log_frame_err(f"帧数异常: {len(frames)}")
+                return None
+            payload = frames[2].decode("utf-8")
+            return json.loads(payload)
+        except json.JSONDecodeError:
+            self._log_frame_err("JSON解析失败")
+            return None
+        except Exception as e:
+            self._log_frame_err(f"接收异常: {e}")
+            return None
+
+    def recv_bytes(self) -> Optional[bytes]:
+        """接收滤波数据，兼容3/4帧格式"""
+        try:
+            frames = self.socket.recv_multipart()
+            frame_len = len(frames)
+            if frame_len == 3:
+                payload = frames[2]
+            elif frame_len == 4:
+                payload = frames[3]
+            else:
+                self._log_frame_err(f"帧数异常: {frame_len}")
+                return None
+
+            if len(payload) != CONFIG.FILTER_FRAME_BYTES:
+                self._log_frame_err(f"字节不匹配: 期望{CONFIG.FILTER_FRAME_BYTES}, 实际{len(payload)}")
+                return None
+            return payload
+        except Exception as e:
+            self._log_frame_err(f"数据接收异常: {e}")
+            return None
+
+    def _log_frame_err(self, msg: str):
+        """日志限流，防止刷屏"""
+        now = time.time()
+        if now - GLOBAL_STATE.last_frame_err_time > CONFIG.FRAME_ERR_INTERVAL:
+            print(f"[帧异常] {msg}")
+            GLOBAL_STATE.last_frame_err_time = now
+
+    def close(self):
+        """优雅释放ZMQ资源"""
+        try:
+            self.socket.close(linger=0)
+            self.ctx.term()
+        except Exception as e:
+            print(f"[资源释放异常] {e}")
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+# ===================== 5. 仿真脑电数据生成 =====================
+def generate_raw_eeg_frame(add_event: bool = False) -> np.ndarray:
+    """生成单帧float64仿真脑电数据"""
+    t = np.linspace(
+        0, CONFIG.FRAME_POINTS / CONFIG.SAMPLE_RATE,
+        CONFIG.FRAME_POINTS, endpoint=False
+    )
+    eeg_frame = np.zeros(
+        (CONFIG.CHANNEL_NUMS, CONFIG.FRAME_POINTS),
+        dtype=CONFIG.DATA_DTYPE
+    )
+
+    # 模拟脑电信号 + 高斯噪声
+    for freq in CONFIG.SIM_SIGNAL_FREQ:
+        eeg_frame[:CONFIG.FILTER_OUT_CHAN] += np.sin(2 * np.pi * freq * t)
+    eeg_frame[:CONFIG.FILTER_OUT_CHAN] += np.random.normal(
+        0, CONFIG.NOISE_STD,
+        size=(CONFIG.FILTER_OUT_CHAN, CONFIG.FRAME_POINTS)
+    )
+
+    # 事件通道处理
+    eeg_frame[CONFIG.EVENT_CHANNEL_IDX] = 0.0
+    if add_event:
+        event_pos = np.random.randint(0, CONFIG.FRAME_POINTS)
+        eeg_frame[CONFIG.EVENT_CHANNEL_IDX, event_pos] = np.random.choice(CONFIG.EVENT_TAGS)
+
+    # 预留通道置0
+    eeg_frame[-1] = 0.0
+    return eeg_frame
+
+# ===================== 6. 后台工作线程 =====================
+def start_cmd_response_thread(cmd_client: ZmqDealerClient):
+    """命令响应接收线程"""
+    print("[线程-命令接收] 已启动")
+    while GLOBAL_STATE.run_flag:
+        msg = cmd_client.recv_json()
+        if msg:
+            print(f"\n【命令响应】{json.dumps(msg, ensure_ascii=False, indent=2)}")
+        time.sleep(0.01)
+    print("[线程-命令接收] 已退出")
+
+def start_raw_eeg_send_thread(data_client: ZmqDealerClient):
+    """原始脑电发送线程（20ms/帧）"""
+    print(f"[线程-原始数据发送] 20ms/帧 | 单帧{CONFIG.RAW_FRAME_BYTES}字节 | float64")
+    frame_count = 0
+    while GLOBAL_STATE.run_flag:
+        insert_event = (frame_count % 20 == 0)
+        eeg_frame = generate_raw_eeg_frame(add_event=insert_event)
+        frame_bytes = eeg_frame.tobytes()
+
+        # 字节校验
+        if len(frame_bytes) != CONFIG.RAW_FRAME_BYTES:
+            print(f"[字节警告] 期望{CONFIG.RAW_FRAME_BYTES}, 实际{len(frame_bytes)}")
+            time.sleep(CONFIG.SEND_INTERVAL)
+            frame_count += 1
+            continue
+
+        data_client.send_bytes(frame_bytes)
+        frame_count += 1
+        time.sleep(CONFIG.SEND_INTERVAL)
+    print("[线程-原始数据发送] 已退出")
+
+def start_filter_data_recv_thread(data_client: ZmqDealerClient, plot_queue: List[np.ndarray]):
+    """滤波数据接收线程"""
+    print(f"[线程-滤波数据接收] 单包{CONFIG.FILTER_FRAME_BYTES}字节 | float64")
+    while GLOBAL_STATE.run_flag:
+        raw_bytes = data_client.recv_bytes()
+        if not raw_bytes:
+            time.sleep(0.01)
+            continue
+
+        try:
+            filter_arr = np.frombuffer(raw_bytes, dtype=CONFIG.DATA_DTYPE)
+            filter_arr = filter_arr.reshape(CONFIG.FILTER_FRAME_POINTS, CONFIG.FILTER_OUT_CHAN)
+            plot_queue.append(filter_arr[:, CONFIG.PLOT_TARGET_CHAN])
+        except Exception as e:
+            print(f"[滤波数据解析异常] {e}")
+            continue
+    print("[线程-滤波数据接收] 已退出")
+
+# ===================== 7. 实时波形可视化 =====================
+def start_wave_visualization(plot_queue: List[np.ndarray]):
+    """启动实时滤波波形绘图"""
+    fig, ax = plt.subplots(figsize=(14, 4))
+    x_axis = np.arange(0, CONFIG.PLOT_WINDOW_LEN)
+    wave_data = np.zeros(CONFIG.PLOT_WINDOW_LEN, dtype=CONFIG.DATA_DTYPE)
+    line, = ax.plot(x_axis, wave_data, color="#2E86AB", linewidth=1.2)
+
+    ax.set_title(
+        f"实时滤波脑电波形 | 通道 {CONFIG.PLOT_TARGET_CHAN} | {CONFIG.SAMPLE_RATE}Hz | float64",
+        fontsize=12
+    )
+    ax.set_ylim(-3.0, 3.0)
+    ax.grid(True, alpha=0.3, linestyle="--")
+    plt.tight_layout()
+
+    def update_plot(_):
+        nonlocal wave_data
+        if plot_queue:
+            new_wave = plot_queue.pop(0)
+            wave_data = np.roll(wave_data, -len(new_wave))
+            wave_data[-len(new_wave)] = new_wave
+        line.set_ydata(wave_data)
+        return (line,)
+
+    ani = FuncAnimation(
+        fig, update_plot,
+        interval=CONFIG.PLOT_REFRESH_INTERVAL,
+        blit=True,
+        cache_frame_data=False
+    )
+    plt.show()
+
+# ===================== 8. 全量业务测试用例 =====================
+def run_full_test_cases(cmd_client: ZmqDealerClient):
+    """全覆盖 zmqServer 所有命令：sync/targetFreqs/decoderClass/impedance/train/predict/rest"""
+    print("\n" + "="*60)
+    print("开始执行全量命令测试用例")
+    print("="*60)
+    time.sleep(2)
+
+    # 1. 同步命令
+    print("\n[用例 1] 发送 sync 命令")
+    cmd_client.send_json({"method": "sync", "params": {}})
+    time.sleep(1)
+
+    # 2. 设置目标频率
+    print("\n[用例 2] 发送 targetFreqs = [8.0, 9.0]")
+    cmd_client.send_json({"method": "targetFreqs", "params": [8.0, 9.0]})
+    time.sleep(1)
+
+    # 3. 切换解码器
+    print("\n[用例 3] 切换解码器为 ssmvep")
+    cmd_client.send_json({"method": "decoderClass", "params": "ssmvep"})
+    time.sleep(2)
+    print("\n[用例 3-2] 切换解码器为 mi")
+    cmd_client.send_json({"method": "decoderClass", "params": "mi"})
+    time.sleep(2)
+
+    # 4. 阻抗检测开关
+    print("\n[用例 4] 开启阻抗检测 impedance=1")
+    cmd_client.send_json({"method": "impedance", "params": 1})
+    time.sleep(1)
+    print("\n[用例 4-2] 关闭阻抗检测 impedance=2")
+    cmd_client.send_json({"method": "impedance", "params": 2})
+    time.sleep(1)
+
+    # 5. 训练模式
+    print("\n[用例 5] 启动训练 train，标签=1")
+    cmd_client.send_json({"method": "train", "params": 1})
+    time.sleep(3)
+
+    # # 6. 休息模式
+    # print("\n[用例 6] 切换 rest 休息模式")
+    # cmd_client.send_json({"method": "rest", "params": {}})
+    # time.sleep(1)
+
+    # 7. 启动解码
+    print("\n[用例 7] 启动解码 predict=1")
+    cmd_client.send_json({"method": "predict", "params": 1})
+    time.sleep(4)
+
+    # # 8. 非法命令（异常测试）
+    # print("\n[用例 8] 发送非法命令 test_cmd_illegal")
+    # cmd_client.send_json({"method": "test_cmd_illegal", "params": {}})
+    # time.sleep(1)
+
+    # # 9. 停止解码
+    # print("\n[用例 9] 停止解码 predict=2")
+    # cmd_client.send_json({"method": "predict", "params": 2})
+    # time.sleep(2)
+
+    print("\n" + "="*60)
+    print("所有测试用例执行完毕")
+    print("="*60)
+
+# ===================== 主程序入口（修复线程语法） =====================
+if __name__ == "__main__":
+    print("="*60)
+    print("ZMQ 脑电仿真测试工具 启动")
+    print(f"命令端口: {CONFIG.CMD_PORT} | 数据端口: {CONFIG.DATA_PORT}")
+    print(f"原始帧{CONFIG.RAW_FRAME_BYTES}字节 | 滤波帧{CONFIG.FILTER_FRAME_BYTES}字节 | float64")
+    print("="*60)
+
+    try:
+        with ZmqDealerClient(CONFIG.SERVER_IP, CONFIG.CMD_PORT) as cmd_client, \
+             ZmqDealerClient(CONFIG.SERVER_IP, CONFIG.DATA_PORT) as data_client:
+
+            plot_queue = []
+
+            # ========== 重点修复：线程语法，daemon 移出 args ==========
+            # 命令接收线程
+            t_cmd = threading.Thread(
+                target=start_cmd_response_thread,
+                args=(cmd_client,),  # 单元素元组保留逗号
+                daemon=True
+            )
+            # 原始数据发送线程
+            t_eeg = threading.Thread(
+                target=start_raw_eeg_send_thread,
+                args=(data_client,),
+                daemon=True
+            )
+            # 滤波数据接收线程
+            t_filter = threading.Thread(
+                target=start_filter_data_recv_thread,
+                args=(data_client, plot_queue),
+                daemon=True
+            )
+
+            # 启动线程
+            t_cmd.start()
+            t_eeg.start()
+            t_filter.start()
+
+            # 执行测试用例
+            run_full_test_cases(cmd_client)
+
+            # 启动可视化（阻塞主线程）
+            print("\n[提示] 波形窗口已启动，关闭窗口 / Ctrl+C 退出程序")
+            start_wave_visualization(plot_queue)
+
+    except KeyboardInterrupt:
+        print("\n\n[用户中断] 接收到 Ctrl+C，准备退出...")
+    except Exception as e:
+        print(f"\n[程序异常] {e}")
+    finally:
+        # 停止所有后台线程
+        GLOBAL_STATE.run_flag = False
+        time.sleep(0.2)
+        print("程序已安全退出")