专注力计算

concentration/algorithm/calculate_focus.py

@@ -8,6 +8,7 @@ import os
 # import logging
 import base64
 import io
+import math
 
 # logger = logging.getLogger(__name__)
 #
@@ -22,7 +23,7 @@ import io
 
 
 class Calculate():
-    def __init__(self, Threshold_value_low, Threshold_value_high, fs=250, win_len=10):
+    def __init__(self, Threshold_value_low, Threshold_value_high, fs=250, win_len=10, config=None):
         self.Threshold_value_low = Threshold_value_low
         self.Threshold_value_high = Threshold_value_high
         self.fs = fs
@@ -31,47 +32,73 @@ class Calculate():
         self.EVI_result = []
         self.eegQueue = deque(maxlen=win_len)
 
-        # # 存储历史数据用于绘图
-        # self.beta_history = []
-        # self.alpha_history = []
-        # self.theta_history = []
-        # self.focus_history = []
-        # self.timestamp_history = []
-        #
-        # # 记录开始时间
-        # self.start_time = None
-        # self.recording = False
-        #
-        # # 图表保存路径
-        # self.chart_dir = "reports"
-        # if not os.path.exists(self.chart_dir):
-        #     os.makedirs(self.chart_dir)
-        #     print(f"[调试] 创建目录: {self.chart_dir}")
-        
         # 初始化滤波器
         self.b_notch, self.a_notch = signal.iirnotch(50 / (self.fs/2), 30)
         self.b_design = signal.firwin(65, [2 / (self.fs/2), 40 / (self.fs/2)], pass_zero=False)
 
+        self.last_focus = None
+        # 异步滤波系数配置（核心手感控制纽）
+        self.alpha_up = 1  # 上升系数：较小，保证分数平滑爬升，过滤偶发的瞬时高能量
+        # alpha_down / shrink_factor 从 config.ini 读取，方便上位机调参
+        if config:
+            self.alpha_down = float(config.get('alpha_down', 0.8))
+            self.shrink_factor = float(config.get('shrink_factor', 0.5))
+        else:
+            self.alpha_down = 0.8
+            self.shrink_factor = 0.5
         print("[调试] Calculate 类初始化完成")
 
     def calculate_focus(self, beta, alpha, theta):
         """
-        专注度计算 - 固定映射版本
+        专注度计算 - 三区间门限异步滤波版本
         """
+        # 0. 频带特征预处理
+        theta_mod = theta ** 0.7
+
         # 原始比值
-        raw = beta / (alpha + theta + 1e-10)
+        raw = beta / (alpha + theta_mod + 1e-10)
 
-        # Sigmoid 映射：让 raw 在 0.3-1.5 区间敏感
-        # 参数可调：
-        #   k = 12  (斜率，越大越陡)
-        #   x0 = 0.6 (中心点，raw=0.6时focus≈50)
-        k = 12.0
-        x0 = 0.6
-        focus = 100.0 / (1.0 + np.exp(-k * (raw - x0)))
+        exponent = 2.0
 
-        # 可选：添加滑动平均平滑
+        # 1. 防止脑电比值出现负数异常值
+        raw_input = max(raw, 0.0)
+
+        # 2. 2次幂纵轴压缩映射 (shrink_factor 从 config.ini 读取)
+        focus_raw = 100 * self.shrink_factor * (raw_input ** exponent)
+
+        # 3. 计算当前帧的瞬时分数 (基准量级 0-120)
+        instant_focus = 120 * (1.0 - np.exp(-focus_raw / 100.0))
+
+        # 4. 核心修改：三区间门限时域滤波
+        if self.last_focus is None:
+            # 冷启动：首帧直接赋值
+            focus = instant_focus
+        else:
+            # 判断当前瞬时分数是否处于【极端区】(80以上 或 60以下)
+            if instant_focus > 85.0 or instant_focus < 60.0:
+                # 执行异步低通时域滤波
+                if instant_focus >= self.last_focus:
+                    # 趋势上升：慢爬升
+                    focus = self.alpha_up * instant_focus + (1 - self.alpha_up) * self.last_focus
+                else:
+                    # 趋势下降：快跌落
+                    focus = self.alpha_down * instant_focus + (1 - self.alpha_down) * self.last_focus
+            else:
+                # 【高灵敏自由区】(60 <= instant_focus <= 80)
+                # 不执行异步滤波，分数直接跟随瞬时值，保证中间状态绝对跟手
+                focus = instant_focus
+
+        # 5. 更新历史状态缓存
+        self.last_focus = focus
+
+        # 打印在线调试日志，方便观察区间切换
+        zone_tag = "极端区(滤波)" if (instant_focus > 80 or instant_focus < 60) else "自由区(直通)"
+        print(f"原始特征比值 raw: {raw:.4f} | 瞬时分数: {instant_focus:.1f} | 滤波后分数: {focus:.1f}")
+
+        # 最终返回整型
         return int(focus)
 
+
     def calculate_all(self, data, fs, nperseg=1000):
         mean_x = np.mean(data, axis=-1, keepdims=True)
         data = data - mean_x
@@ -319,14 +346,16 @@ class Calculate():
             if eegData.size == 0:
                 return None
             eegData -= np.mean(eegData, axis=-1, keepdims=True)
-            eegData = signal.lfilter(self.b_notch, self.a_notch, eegData)
-            eegData = signal.lfilter(self.b_design, 1, eegData)
-            focus_score, CLI_score, beta, alpha, theta = self.calculate_all(eegData, fs=self.fs, nperseg=1000)
+            # eegData = signal.lfilter(self.b_notch, self.a_notch, eegData)    # 陷波
+            # eegData = signal.lfilter(self.b_design, 1, eegData)              # 滤波
+            focus_score, CLI_score, beta_psd, alpha_psd, theta_psd = self.calculate_all(eegData, fs=self.fs, nperseg=1000)
 
-            # self.add_data_point(focus_score, beta, alpha, theta)
+            # self.add_data_point(focus_score, beta_psd, alpha_psd, theta_psd)  # 已注释（方法已移除）
+
+            # return (focus_score)
+            return (focus_score, beta_psd)
+        # return None
 
-            return focus_score
-        return None
 
 
 class Calculate2():