激光测量v2.5，叶片桨距角计算时修正俯仰分量，调整为计算水平投影的桨距角。利用轮毂中心和叶尖数据计算叶片净空距离。

data_analyse_origin.py

@@ -112,10 +112,11 @@ def data_analyse(path: List[str]):
     # 基础配置参数
     locate_file = path[0]
     measure_file = path[1]
-    noise_reduction = 0.000001  # 如果一个距离值的所有样本量小于总样本量的noise_reduction，则被去掉
-    min_difference = 1.5  # 如果相邻2个点的距离差大于min_difference，则被注意是否是周期节点
     angle_cone = float(path[2])  # 锥角
     axial_inclination = float(path[3])  # 轴向倾角
+    noise_reduction = 0.000001  # 如果一个距离值的所有样本量小于总样本量的noise_reduction，则被去掉
+    min_difference = 1.5  # 如果相邻2个点的距离差大于min_difference，则被注意是否是周期节点
+    group_length = [10000, 30000, 5000]  # 计算叶片轮廓时每个小切片的长度，三个数分别为叶中、叶根、叶尖切片长度
     return_list = []
 
     # 读取文件信息，包括风场名、风机编号、时间、采样频率、2个通道俯仰角
@@ -155,11 +156,11 @@ def data_analyse(path: List[str]):
 
     # 根据起始结束点，对叶根、对叶片数据进行归一化处理，计算每个叶片的散点表、线表、边界点表、标准循环周期长度、每个叶片平均最小值
     result_line_tip, result_scatter_tip, border_rows_tip, cycle_len_tip, min_tip \
-        = data_normalize(filtered_data_tip, start_tip, end_tip)
+        = data_normalize(filtered_data_tip, start_tip, end_tip, group_length[0])
     result_line_root, result_scatter_root, border_rows_root, cycle_len_root, min_root \
-        = data_normalize(filtered_data_root, start_root, end_root)
+        = data_normalize(filtered_data_root, start_root, end_root, group_length[1])
     result_line_nan, result_scatter_nan, border_rows_nan, cycle_len_nan, min_nan \
-        = data_normalize(filtered_data_nan, start_nan, end_nan)
+        = data_normalize(filtered_data_nan, start_nan, end_nan, group_length[2])
 
     # 计算3个叶片的平均轮廓，3个叶片的形状差
     result_avg_tip, result_diff_tip = blade_shape(result_line_tip)
@@ -446,8 +447,8 @@ def data_analyse(path: List[str]):
         json.dump(json_output, json_file, indent=4)
 
     print('csv文件路径' + str(csv_file_path))
-    print(result_line_tip[0].iloc[:, 0])
-    print(result_line_root[0].iloc[:, 0])
+    # print(result_line_tip[0].iloc[:, 0])
+    # print(result_line_root[0].iloc[:, 0])
     print('振动主频' + str(tower_freq))
     print('振动幅值' + str(tower_max))
     print('最小值', min_values)
@@ -462,9 +463,9 @@ def data_analyse(path: List[str]):
     # plot_data(result_line_tip, 'line', 'data1')
     # plot_data(result_diff_tip, 'line', 'data_diff_1')
     # plot_data(result_scatter_tip, 'scatter', 'data1')
-    plot_data(result_line_root, 'line', 'data2')
+    # plot_data(result_line_root, 'line', 'data2')
     # plot_data(result_diff_root, 'line', 'data_diff_2')
-    plot_data(result_scatter_root, 'scatter', 'data2')
+    # plot_data(result_scatter_root, 'scatter', 'data2')
     # plot_data(dist_distribute, 'scatter', 'dist_distribute')
 
     return json_output
@@ -649,7 +650,7 @@ def cycle_calculate(data_group: pd.DataFrame, noise_threshold: float, min_distan
     return start_points, end_points, filtered_data
 
 
-def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_points: pd.DataFrame) \
+def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_points: pd.DataFrame, group_len: int) \
         -> Tuple[List[pd.DataFrame], List[pd.DataFrame], List[pd.DataFrame], int, list]:
 
     """
@@ -657,6 +658,7 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
     :param data_group: cycle_calculate计算完成后的数据。
     :param start_points: 所有每个周期开始点，叶片前缘突变点。
     :param end_points: 叶片后缘突变点。
+    :param group_len: 每个分组的长度。
     :return: turbines_processed: 每个叶片的拟合数据，
              turbines_scattered: 每个叶片的散点数据，
              border_rows: 每个叶片的2个边缘数据，
@@ -710,8 +712,10 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
     turbines_processed = []
     turbines_scattered = []
     min_list = []
+    plot_points = []
+    diff_line = []
     sd_time = [-1, -1]
-    time_list = list(range(0, normalize_cycle, 9000))
+    time_list = list(range(0, normalize_cycle, group_len))
     # time_list = [(i + 1) * normalize_cycle / fs * 100 for i in range(fs * 100)]  # 生成时间序列
 
     for turbine in turbines:
@@ -722,7 +726,7 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
         first_time = turbine_sorted['time'].iloc[0]
 
         # 分组，时间列每1000为一组（每40个时间点一组）
-        bins = list(range(int(first_time), int(turbine_sorted['time'].max()), 9000))
+        bins = list(range(int(first_time), int(turbine_sorted['time'].max()), group_len))
         # 原始代码
         # bins = list(range(int(first_time), int(turbine_sorted['time'].max()) + len(start_times), int(fs / 50)))
         grouped = turbine_sorted.groupby(pd.cut(turbine_sorted['time'], bins=bins, right=False))
@@ -759,23 +763,8 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
         first_index = np.where(diff_points < sdr_diff)[0][0]
         last_index = np.where(diff_points < sdr_diff)[0][-1]
 
-        '''
-        # 创建一个总图中有3个分图的形式
-        fig, axs = plt.subplots(1, 1, figsize=(15, 9))
-        plt.subplots_adjust(hspace=2)
-
-        # 绘制 diff_points 的折线图
-        axs.plot(diff_points, label='Diff Points', color='red', marker='x', markersize=5)
-        axs.axhline(y=sdr_diff, color='red', linestyle='--')
-        axs.legend()
-        axs.set_title('Diff Points')
-        axs.set_xlabel('Index')
-        axs.set_ylabel('Value')
-
-        # 显示图形
-        plt.tight_layout()
-        plt.show()
-        '''
+        plot_points.append(diff_points)
+        diff_line.append(sdr_diff)
 
         for index, (bin, group) in enumerate(grouped):
 
@@ -811,6 +800,22 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
         turbines_processed.append(processed_df)
         turbines_scattered.append(scattered_df)
 
+    """# 创建一个总图中有3个分图的形式
+    fig, axs = plt.subplots(1, 1, figsize=(15, 9))
+    plt.subplots_adjust(hspace=2)
+
+    # 绘制 diff_points 的折线图
+    axs.plot(plot_points[1], label='Diff Points', color='blue', marker='x', markersize=5)
+    axs.axhline(y=diff_line[1], color='red', linestyle='--')
+    axs.legend()
+    axs.set_title('Diff Points')
+    axs.set_xlabel('Index')
+    axs.set_ylabel('Value')
+
+    # 显示图形
+    plt.tight_layout()
+    plt.show()"""
+
     # 把三组叶片数据按sd_time进行筛选，并把每个的边界数据保存
     border_rows = []
     for i, turbine in enumerate(turbines_processed):
@@ -955,7 +960,7 @@ def blade_angle_aero_dist(border_rows: List[pd.DataFrame], radius: float, full_c
 
         diff_time = turbine.iloc[1, 0] - turbine.iloc[0, 0]
 
-        diff_len = turbine.iloc[1, 1] - turbine.iloc[0, 1]
+        diff_len = (turbine.iloc[1, 1] - turbine.iloc[0, 1]) * np.cos(np.deg2rad(v_angle))
         mean_col2 = (turbine.iloc[1, 1] + turbine.iloc[0, 1]) / 2
         aero_dist = abs(mean_col2 - tower_dist) * np.cos(np.deg2rad(v_angle))
 
@@ -1116,8 +1121,8 @@ def blade_dist_distribute_cal(data_group: pd.DataFrame, start_points: pd.DataFra
 # locate_path = "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-20_20250630223600_20_13.03_23.32.csv"
 # measure_path= "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-20_20250630223849_20_17.89_21.07.csv"
 
-locate_path = "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-50_20250630223358_50_13.03_23.32.csv"
-measure_path= "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-50_20250630224408_50_17.89_21.07.csv"
+# locate_path = "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-50_20250630223358_50_13.03_23.32.csv"
+# measure_path= "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-50_20250630224408_50_17.89_21.07.csv"
 
 # locate_path = "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-100_20250630222752_100_13.03_23.32.csv"
 # measure_path= "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tf-100_20250630225119_100_17.89_21.07.csv"
@@ -1131,6 +1136,9 @@ measure_path= "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/
 # locate_path = "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tff-100_20250630231610_100_12.51_20.06.csv"
 # measure_path= "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/20250728/gy_10-tff-100_20250630234012_100_15.35_18.16.csv"
 
+locate_path = "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/gytest/测试数据/gy_18-RF-1_20250701154647_50_23.70_40.01.csv"
+measure_path= "C:/Users/laiwe/Desktop/风电/激光测量/测试数据/gytest/测试数据/gy_18-RF-2_20250701155057_50_29.30_36.78.csv"
+
 start_t = time.time()  # 记录开始时间
 data_path = [locate_path, measure_path, 5, 6]
 list_1 = data_analyse(data_path)

data_clean.py

@@ -102,6 +102,7 @@ def data_analyse(path: List[str]):
 
     start_tip, end_tip, filtered_data_tip = cycle_calculate(data_tip, noise_reduction, min_difference)
     start_root, end_root, filtered_data_root = cycle_calculate(data_root, noise_reduction, min_difference)
+    start_nan, end_nan, filtered_data_nan = cycle_calculate(data_nan, noise_reduction, min_difference)
     filtered_data_cen = tower_filter(data_cen, noise_reduction)
     dist_cen = np.mean(filtered_data_cen.iloc[:, 1].tolist())
     filtered_data_cen.iloc[:, 1] = filtered_data_cen.iloc[:, 1] * np.cos(np.deg2rad(angle_cen))
@@ -116,36 +117,48 @@ def data_analyse(path: List[str]):
 
     tower_dist_tip = ff.tower_cal(filtered_data_tip, start_tip, end_tip, sampling_fq_1)
     tower_dist_root = ff.tower_cal(filtered_data_root, start_root, end_root, sampling_fq_1)
+    tower_dist_nan = ff.tower_cal(filtered_data_nan, start_nan, end_nan, sampling_fq)
     lowpass_data, fft_x, fft_y, tower_freq, tower_max = ff.process_fft(filtered_data_cen, sampling_fq)
 
     result_line_tip, result_scatter_tip, border_rows_tip, cycle_len_tip, min_tip \
         = data_normalize(filtered_data_tip, start_tip, end_tip)
     result_line_root, result_scatter_root, border_rows_root, cycle_len_root, min_root \
         = data_normalize(filtered_data_root, start_root, end_root)
+    result_line_nan, result_scatter_nan, border_rows_nan, cycle_len_nan, min_nan \
+        = data_normalize(filtered_data_nan, start_nan, end_nan)
 
     result_avg_tip, result_diff_tip = blade_shape(result_line_tip)
     result_avg_root, result_diff_root = blade_shape(result_line_root)
 
     border_rows_tip_new, angle_tip_new = coordinate_normalize(border_rows_tip, angle_tip)
+    border_rows_nan_new, angle_nan_new = coordinate_normalize(border_rows_nan, angle_nan)
 
     tip_r = radius_cal(border_rows_tip_new, angle_tip_new, dist_cen, angle_cen, axial_inclination, angle_cone)
     root_r = radius_cal(border_rows_root, angle_root, dist_cen, angle_cen, axial_inclination, angle_cone)
+    nan_r = radius_cal(border_rows_nan_new, angle_nan_new, dist_cen, angle_cen, axial_inclination, angle_cone)
 
     pitch_angle_tip, aero_dist_tip, v_speed_tip, cen_blade_tip = (
         blade_angle_aero_dist(border_rows_tip, tip_r, cycle_len_tip, tower_dist_tip, angle_tip_new))
     pitch_angle_root, aero_dist_root, v_speed_root, cen_blade_root = (
         blade_angle_aero_dist(border_rows_root, root_r, cycle_len_root, tower_dist_root, angle_root))
+    pitch_angle_nan, aero_dist_nan, v_speed_nan, cen_blade_nan = (
+        blade_angle_aero_dist(border_rows_nan_new, nan_r, cycle_len_nan, tower_dist_nan, angle_nan_new))
 
     
     cen_blade_tip_array = np.array(cen_blade_tip)
+    cen_blade_nan_array = np.array(cen_blade_nan)
     min_tip_array = np.array(min_tip)
-    abs_diff = np.abs(cen_blade_tip_array - min_tip_array)  
+    min_nan_array = np.array(min_nan)
+    abs_diff = np.abs(cen_blade_tip_array - min_tip_array)
+    abs_diff_nan = np.abs(cen_blade_nan_array - min_nan_array)
     blade_dist_tip = abs_diff * np.cos(np.deg2rad(angle_tip_new))
-    blade_dist_tip.tolist()  
+    blade_dist_nan = abs_diff_nan * np.cos(np.deg2rad(angle_nan_new))
+    blade_dist_tip.tolist()
+    blade_dist_nan.tolist()
 
-    dist_distribute = blade_dist_distribute_cal(filtered_data_tip, start_tip, end_tip,
-                                                tower_dist_tip, angle_tip_new, blade_dist_tip)
-    dist_distribute = [df.round(5) for df in dist_distribute]
+    dist_distribute_nan = blade_dist_distribute_cal(filtered_data_nan, start_nan, end_nan,
+                                                    tower_dist_nan, angle_nan_new, blade_dist_nan)
+    dist_distribute = [df.round(5) for df in dist_distribute_nan]
 
     
     min_values = []
@@ -444,7 +457,7 @@ def cycle_calculate(data_group: pd.DataFrame, noise_threshold: float, min_distan
     
     top_5_distances = distance_counts.head(5).index
     mean_values = data_group[data_group['distance'].isin(top_5_distances)]['distance'].mean()
-    data_group.loc[(data_group['distance'] < mean_values - 30) | (
+    data_group.loc[(data_group['distance'] < mean_values - 31) | (
             data_group['distance'] > mean_values * 1.1), 'distance'] = np.nan
 
 
@@ -466,7 +479,7 @@ def cycle_calculate(data_group: pd.DataFrame, noise_threshold: float, min_distan
         
         current_distance = filtered_data.loc[idx, 'distance']
 
-        next_rows_large = filtered_data.loc[idx - 200: idx - 1]
+        next_rows_large = filtered_data.loc[idx - 201: idx - 1]
 
         
         if next_rows_large['distance'].le(current_distance - min_distance).all():
@@ -477,7 +490,7 @@ def cycle_calculate(data_group: pd.DataFrame, noise_threshold: float, min_distan
         
         current_distance = filtered_data.loc[idx - 1, 'distance']
 
-        next_rows_small = filtered_data.iloc[idx: idx + 200]
+        next_rows_small = filtered_data.iloc[idx: idx + 201]
 
         
         if next_rows_small['distance'].le(current_distance - min_distance).all():
@@ -538,7 +551,7 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
     turbines_scattered = []
     min_list = []
     sd_time = [-1, -1]
-    time_list = list(range(0, normalize_cycle, 9000))
+    time_list = list(range(0, normalize_cycle, 9001))
 
     for turbine in turbines:
         
@@ -548,7 +561,7 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
         first_time = turbine_sorted['time'].iloc[0]
 
         
-        bins = list(range(int(first_time), int(turbine_sorted['time'].max()), 9000))
+        bins = list(range(int(first_time), int(turbine_sorted['time'].max()), 9001))
         
         
         grouped = turbine_sorted.groupby(pd.cut(turbine_sorted['time'], bins=bins, right=False))
@@ -717,7 +730,7 @@ def blade_angle_aero_dist(border_rows: List[pd.DataFrame], radius: float, full_c
     for turbine in border_rows:
         diff_time = turbine.iloc[1, 0] - turbine.iloc[0, 0]
 
-        diff_len = turbine.iloc[1, 1] - turbine.iloc[0, 1]
+        diff_len = (turbine.iloc[1, 1] - turbine.iloc[0, 1]) * np.cos(np.deg2rad(v_angle))
         mean_col2 = (turbine.iloc[1, 1] + turbine.iloc[0, 1]) / 2
         aero_dist = abs(mean_col2 - tower_dist) * np.cos(np.deg2rad(v_angle))