diff --git a/signal_method/rule_base_event.py b/signal_method/rule_base_event.py
index ddeeaf3..e9ad3df 100644
--- a/signal_method/rule_base_event.py
+++ b/signal_method/rule_base_event.py
@@ -2,6 +2,7 @@ from utils.operation_tools import timing_decorator
 import numpy as np
 from utils.operation_tools import merge_short_gaps, remove_short_durations
 
+
 @timing_decorator()
 def detect_low_amplitude_signal(signal_data, sampling_rate, window_size_sec=1, stride_sec=None,
                                 amplitude_threshold=50, merge_gap_sec=10, min_duration_sec=5):
@@ -110,8 +111,9 @@ def get_typical_segment_for_continues_signal(signal_data, sampling_rate=100, win
 # 仅对比相邻片段的幅值指标，如果存在显著差异，则认为存在睡姿变化，即每个体动相邻的30秒内存在睡姿变化，如果片段不足30秒，则按实际长度对比
 
 @timing_decorator()
-def position_based_sleep_recognition(signal_data, movement_mask, sampling_rate=100, window_size_sec=30,
-                                     interval_to_movement=10):
+def position_based_sleep_recognition_v1(signal_data, movement_mask, sampling_rate=100, window_size_sec=30,
+                                        interval_to_movement=10):
+    mav_calc_window_sec = 2  # 计算mav的窗口大小，单位秒
     # 获取有效片段起止位置
     valid_mask = 1 - movement_mask
     valid_starts = np.where(np.diff(np.concatenate([[0], valid_mask])) == 1)[0]
@@ -150,17 +152,21 @@ def position_based_sleep_recognition(signal_data, movement_mask, sampling_rate=1
             right_end = end
 
         # 新的end - start确保为200的整数倍
-        if (left_end - left_start) % (2 * sampling_rate) != 0:
-            left_end = left_start + ((left_end - left_start) // (2 * sampling_rate)) * (2 * sampling_rate)
-        if (right_end - right_start) % (2 * sampling_rate) != 0:
-            right_end = right_start + ((right_end - right_start) // (2 * sampling_rate)) * (2 * sampling_rate)
+        if (left_end - left_start) % (mav_calc_window_sec * sampling_rate) != 0:
+            left_end = left_start + ((left_end - left_start) // (mav_calc_window_sec * sampling_rate)) * (
+                        mav_calc_window_sec * sampling_rate)
+        if (right_end - right_start) % (mav_calc_window_sec * sampling_rate) != 0:
+            right_end = right_start + ((right_end - right_start) // (mav_calc_window_sec * sampling_rate)) * (
+                        mav_calc_window_sec * sampling_rate)
 
         # 计算每个片段的幅值指标
-        left_mav = np.mean(np.max(signal_data[left_start:left_end].reshape(-1, 2 * sampling_rate), axis=0)) - np.mean(
-            np.min(signal_data[left_start:left_end].reshape(-1, 2 * sampling_rate), axis=0))
+        left_mav = np.mean(np.max(signal_data[left_start:left_end].reshape(-1, mav_calc_window_sec * sampling_rate),
+                                  axis=0)) - np.mean(
+            np.min(signal_data[left_start:left_end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
         right_mav = np.mean(
-            np.max(signal_data[right_start:right_end].reshape(-1, 2 * sampling_rate), axis=0)) - np.mean(
-            np.min(signal_data[right_start:right_end].reshape(-1, 2 * sampling_rate), axis=0))
+            np.max(signal_data[right_start:right_end].reshape(-1, mav_calc_window_sec * sampling_rate),
+                   axis=0)) - np.mean(
+            np.min(signal_data[right_start:right_end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
         segment_left_average_amplitude.append(left_mav)
         segment_right_average_amplitude.append(right_mav)
 
@@ -171,6 +177,10 @@ def position_based_sleep_recognition(signal_data, movement_mask, sampling_rate=1
 
     position_changes = []
     position_change_times = []
+    # 判断是否存在显著变化 (可根据实际情况调整阈值)
+    threshold_amplitude = 0.1  # 幅值变化阈值
+    threshold_energy = 0.1  # 能量变化阈值
+
     for i in range(1, len(segment_left_average_amplitude)):
         # 计算幅值指标的变化率
         left_amplitude_change = abs(segment_left_average_amplitude[i] - segment_left_average_amplitude[i - 1]) / max(
@@ -184,15 +194,11 @@ def position_based_sleep_recognition(signal_data, movement_mask, sampling_rate=1
         right_energy_change = abs(segment_right_average_energy[i] - segment_right_average_energy[i - 1]) / max(
             segment_right_average_energy[i - 1], 1e-6)
 
-        # 判断是否存在显著变化 (可根据实际情况调整阈值)
-        threshold_amplitude = 0.1  # 幅值变化阈值
-        threshold_energy = 0.1  # 能量变化阈值
-
         # 如果左右通道中的任一通道同时满足幅值和能量的变化阈值，则认为存在姿势变化
         left_significant_change = (left_amplitude_change > threshold_amplitude) and (
-                    left_energy_change > threshold_energy)
+                left_energy_change > threshold_energy)
         right_significant_change = (right_amplitude_change > threshold_amplitude) and (
-                    right_energy_change > threshold_energy)
+                right_energy_change > threshold_energy)
 
         if left_significant_change or right_significant_change:
             # 记录姿势变化发生的时间点 用当前分割的体动的起始位置和结束位置表示
@@ -203,3 +209,73 @@ def position_based_sleep_recognition(signal_data, movement_mask, sampling_rate=1
 
     return position_changes, position_change_times
 
+
+def position_based_sleep_recognition_v2(signal_data, movement_mask, sampling_rate=100, window_size_sec=30):
+    """
+
+    :param signal_data:
+    :param movement_mask:  mask的采样率为1Hz
+    :param sampling_rate:
+    :param window_size_sec:
+    :return:
+    """
+    mav_calc_window_sec = 2  # 计算mav的窗口大小，单位秒
+    # 获取有效片段起止位置
+    valid_mask = 1 - movement_mask
+    valid_starts = np.where(np.diff(np.concatenate([[0], valid_mask])) == 1)[0]
+    valid_ends = np.where(np.diff(np.concatenate([valid_mask, [0]])) == -1)[0]
+
+    # 对于有效区间大于12分钟的，拆成多个5分钟
+
+    movement_start = np.where(np.diff(np.concatenate([[0], movement_mask])) == 1)[0]
+    movement_end = np.where(np.diff(np.concatenate([movement_mask, [0]])) == -1)[0]
+
+    segment_average_amplitude = []
+    segment_average_energy = []
+
+    for start, end in zip(valid_starts, valid_ends):
+        start *= sampling_rate
+        end *= sampling_rate
+        # 避免过短的片段
+        if end - start <= sampling_rate:  # 小于1秒的片段不考虑
+            continue
+
+        # 新的end - start确保为200的整数倍
+        if (end - start) % (mav_calc_window_sec * sampling_rate) != 0:
+            end = start + ((end - start) // (mav_calc_window_sec * sampling_rate)) * (
+                        mav_calc_window_sec * sampling_rate)
+
+        # 计算每个片段的幅值指标
+        mav = np.mean(
+            np.max(signal_data[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0)) - np.mean(
+            np.min(signal_data[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
+        segment_average_amplitude.append(mav)
+
+        energy = np.sum(np.abs(signal_data[start:end] ** 2))
+        segment_average_energy.append(energy)
+
+    position_changes = []
+    position_change_times = []
+    # 判断是否存在显著变化 (可根据实际情况调整阈值)
+    threshold_amplitude = 0.1  # 幅值变化阈值
+    threshold_energy = 0.1  # 能量变化阈值
+
+    for i in range(1, len(segment_average_amplitude)):
+        # 计算幅值指标的变化率
+        amplitude_change = abs(segment_average_amplitude[i] - segment_average_amplitude[i - 1]) / max(
+            segment_average_amplitude[i - 1], 1e-6)
+
+        # 计算能量指标的变化率
+        energy_change = abs(segment_average_energy[i] - segment_average_energy[i - 1]) / max(
+            segment_average_energy[i - 1], 1e-6)
+
+        significant_change = (amplitude_change > threshold_amplitude) and (energy_change > threshold_energy)
+
+        if significant_change:
+            # 记录姿势变化发生的时间点 用当前分割的体动的起始位置和结束位置表示
+            position_changes.append(1)
+            position_change_times.append((movement_start[i - 1], movement_end[i - 1]))
+        else:
+            position_changes.append(0)  # 0表示不存在姿势变化
+
+    return position_changes, position_change_times
diff --git a/utils/operation_tools.py b/utils/operation_tools.py
index a63b2ad..c83c82e 100644
--- a/utils/operation_tools.py
+++ b/utils/operation_tools.py
@@ -318,5 +318,3 @@ def slide_window_segment(signal_second: int, window_second, step_second, event_m
 
         yield start_second, end_second, window_event, window_score, window_disable
 
-
-