minor fix

aio/README.md

@@ -8,6 +8,7 @@
 4. wavelogger 波形处理，几乎不花费时间
 5. 制动自动化测试
 6. 电机电流自动化测试
+7. 耐久工程曲线指标采集（仅适用于六轴）
 
 
 ---
@@ -154,6 +155,12 @@ pyinstaller --noconfirm --onedir --windowed --add-data "C:/Users/Administrator/A
 
 基本同第五点
 
+#### 7) 耐久工程曲线指标采集
+
+可实现固定周期指定曲线指标的采集，用于长时间观察指标的变化情况，输入文件：
+- configs.xlsx，执行之前需要手动修改好configs.xlsx中的参数，以及间隔时间
+- target.zip，需要确认工程点位和动作无问题后，保存导出
+
 #### 其他
 customtkinter的tabview组件不支持修改字体大小，可以参考 [Changing Font of a Tabview](https://github.com/TomSchimansky/CustomTkinter/issues/2296) 进行手动修改源码实现：
 	a. 运行 `pip show customtkinter`，获取到库的路径

aio/code/durable_action/factory_test.py

@@ -3,12 +3,13 @@ from os.path import exists, dirname
 from os import scandir
 from paramiko import SSHClient, AutoAddPolicy
 from json import loads
-from time import sleep, time
+from time import sleep, time, strftime, localtime
 import pandas as pd
 from openpyxl import load_workbook
 from math import sqrt
 
 tab_name = 'Durable Action'
+count = 0
 durable_data_current_xlsx = f'{dirname(__file__)}/../../assets/templates/durable/durable_data_current.xlsx'
 durable_data_velocity_xlsx = f'{dirname(__file__)}/../../assets/templates/durable/durable_data_velocity.xlsx'
 display_pdo_params = [
@@ -170,26 +171,32 @@ def run_rl(path, config_file, hr, md, w2t):
     # 6. 关闭诊断曲线，停止程序运行，下电并且换成手动模式
     _response = execution('diagnosis.open', hr, w2t, open=False, display_open=False)
     _response = execution('diagnosis.set_params', hr, w2t, display_pdo_params=[])
-    _response = execution('rl_task.stop', hr, w2t, tasks=['current'])
     sleep(1)  # 保证所有数据均已返回
     # 7. 保留数据并处理输出
-    get_durable_data(path, config_file, data_all, scenario_time, hr, w2t)
+    _wb = load_workbook(config_file, read_only=True)
+    _ws = _wb['Target']
+    wait_time = float(_ws.cell(row=2, column=10).value)
+    rcs = []
+    for i in range(6):
+        rcs.append(float(_ws.cell(row=6, column=i + 2).value))
+
+    get_durable_data(path, data_all, scenario_time, wait_time, rcs, hr, w2t)
 
     # 8. 继续运行
-    _response = execution('rl_task.run', hr, w2t, tasks=['current'])
     while True:
         # 每3分钟，更新一次数据，打开曲线，获取周期内电流，关闭曲线
-        sleep(180)
+        sleep(wait_time)
         _response = execution('diagnosis.open', hr, w2t, open=True, display_open=True)
         _response = execution('diagnosis.set_params', hr, w2t, display_pdo_params=display_pdo_params)
-        sleep(scenario_time + 10)
+        sleep(scenario_time+5)
         _response = execution('diagnosis.open', hr, w2t, open=False, display_open=False)
         _response = execution('diagnosis.set_params', hr, w2t, display_pdo_params=[])
+        sleep(2)
         # 7. 保留数据并处理输出
-        get_durable_data(path, config_file, data_all, scenario_time, hr, w2t)
+        get_durable_data(path, data_all, scenario_time, wait_time, rcs, hr, w2t)
 
 
-def get_durable_data(path, config_file, data, scenario_time, hr, w2t):
+def get_durable_data(path, data, scenario_time, wait_time, rcs, hr, w2t):
     _data_list = []
     for _msg in hr.c_msg:
         if 'diagnosis.result' in _msg:
@@ -207,12 +214,6 @@ def get_durable_data(path, config_file, data, scenario_time, hr, w2t):
     #     for _ in _data_list:
     #         f_obj.write(f"{_}\n")
 
-    _wb = load_workbook(config_file, read_only=True)
-    _ws = _wb['Target']
-    rcs = []
-    for i in range(6):
-        rcs.append(float(_ws.cell(row=6, column=i + 2).value))
-
     _d2d_trq = {
         'device_servo_trq_feedback_0': [], 'device_servo_trq_feedback_1': [], 'device_servo_trq_feedback_2': [],
         'device_servo_trq_feedback_3': [], 'device_servo_trq_feedback_4': [], 'device_servo_trq_feedback_5': [],
@@ -231,35 +232,35 @@ def get_durable_data(path, config_file, data, scenario_time, hr, w2t):
                     _d2d_vel[f'hw_joint_vel_feedback_{i}'].extend(item['value'])
 
         if len(_d2d_trq['device_servo_trq_feedback_0']) / 1000 > scenario_time + 1:
-            _df = pd.DataFrame(_d2d_trq)
+            # ========= for trq =========
+            _df_1 = pd.DataFrame(_d2d_trq)
             for i in range(6):
-                _ = sqrt(100*_df[f'device_servo_trq_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'device_servo_trq_feedback_{i}']))
+                _ = sqrt(100*_df_1[f'device_servo_trq_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'device_servo_trq_feedback_{i}']))
                 del data[0][f"axis{i + 1}"][0]
                 data[0][f"axis{i + 1}"].append(_)
-            _df = pd.DataFrame(data[0])
+            _df_1 = pd.DataFrame(data[0])
-            while True:
+            # ========= for vel =========
-                if not hr.durable_lock:
+            _df_2 = pd.DataFrame(_d2d_vel)
-                    hr.durable_lock = 1
-                    _df.to_excel(durable_data_current_xlsx, index=False)
-                    hr.durable_lock = 0
-                    break
-                else:
-                    sleep(1)
-
-            _df = pd.DataFrame(_d2d_vel)
             for i in range(6):
-                _ = sqrt(100*_df[f'hw_joint_vel_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'hw_joint_vel_feedback_{i}']))
+                _ = sqrt(100*_df_2[f'hw_joint_vel_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'hw_joint_vel_feedback_{i}']))
                 del data[1][f"axis{i + 1}"][0]
                 data[1][f"axis{i + 1}"].append(_)
-            _df = pd.DataFrame(data[1])
+            _df_2 = pd.DataFrame(data[1])
+
             while True:
                 if not hr.durable_lock:
                     hr.durable_lock = 1
-                    _df.to_excel(durable_data_velocity_xlsx, index=False)
+                    _df_1.to_excel(durable_data_current_xlsx, index=False)
+                    _df_2.to_excel(durable_data_velocity_xlsx, index=False)
                     hr.durable_lock = 0
                     break
                 else:
                     sleep(1)
+            global count
+            count += 1
+            this_time = strftime("%Y-%m-%d %H:%M:%S", localtime(time()))
+            next_time = strftime("%Y-%m-%d %H:%M:%S", localtime(time()+wait_time+10+scenario_time)).split()[-1]
+            w2t(f"[{this_time}] 当前次数：{count:09d}  |  预计下次数据更新时间：{next_time}", 0, 0, '#008B8B', tab_name)
             break
     else:
         with open(f'{path}\\device_servo_trq_feedback_0.txt', 'w', encoding='utf-8') as f_obj: