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完善耐久采集

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gitea
2025-04-07 10:38:59 +08:00
parent 671db5b1db
commit 6e547ca6a3
16 changed files with 341 additions and 387 deletions
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99
codes/durable/factory_test.py
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@ -1,4 +1,5 @@
import json
import os.path
import threading
import time
import pandas
@ -22,7 +23,6 @@ class DoFactoryTest(QThread):
self.procs = procs
self.idx = 6
self.curves = []
self.logger = clibs.logger
def initialization(self, data_dirs, data_files):
def check_files():
@ -31,13 +31,13 @@ class DoFactoryTest(QThread):
self.curves.extend(self.curve_map[proc_name])
if len(self.curves) == 0:
self.logger("ERROR", "factory", "未查询到需要记录数据的曲线，至少选择一个！", "red")
clibs.logger("ERROR", "factory", "未查询到需要记录数据的曲线，至少选择一个！", "red")
if len(data_dirs) != 0 or len(data_files) != 1:
self.logger("ERROR", "factory", "初始路径下不允许有文件夹，且初始路径下只能存在一个工程文件 —— *.zip，确认后重新运行！", "red")
clibs.logger("ERROR", "factory", "初始路径下不允许有文件夹，且初始路径下只能存在一个工程文件 —— *.zip，确认后重新运行！", "red")
if not data_files[0].endswith(".zip"):
self.logger("ERROR", "factory", f"{data_files[0]} 不是一个有效的工程文件，需确认！", "red")
clibs.logger("ERROR", "factory", f"{data_files[0]} 不是一个有效的工程文件，需确认！", "red")
return data_files[0]
@ -55,17 +55,17 @@ class DoFactoryTest(QThread):
try:
with open(local_file, mode="r", encoding="utf-8") as f_config:
configs = json.load(f_config)
except Exception as Err:
self.logger("ERROR", "factory", f"无法打开 {local_file}<br>{Err}", "red")
except Exception as err:
clibs.logger("ERROR", "factory", f"无法打开 {local_file}<br>{err}", "red")
# 最大角速度，额定电流，减速比，额定转速
version = configs["VERSION"]
m_avs = configs["MOTION"]["JOINT_MAX_SPEED"]
self.logger("INFO", "factory", f"get_configs: 机型文件版本 {robot_type}_{version}")
self.logger("INFO", "factory", f"get_configs: 各关节角速度 {m_avs}")
clibs.logger("INFO", "factory", f"get_configs: 机型文件版本 {robot_type}_{version}")
clibs.logger("INFO", "factory", f"get_configs: 各关节角速度 {m_avs}")
return m_avs
self.logger("INFO", "factory", "正在做初始化校验和配置，这可能需要一点时间......", "green")
clibs.logger("INFO", "factory", "正在做初始化校验和配置，这可能需要一点时间......", "green")
prj_file = check_files()
if prj_file is None:
return
@ -75,7 +75,7 @@ class DoFactoryTest(QThread):
"interval": self.interval,
"avs": avs,
}
self.logger("INFO", "factory", "数据目录合规性检查结束，未发现问题......", "green")
clibs.logger("INFO", "factory", "数据目录合规性检查结束，未发现问题......", "green")
return params
def change_curve_state(self, stat):
@ -103,7 +103,7 @@ class DoFactoryTest(QThread):
clibs.c_hr.execution("state.switch_motor_on")
# 3. 开始运行程序
self.logger("INFO", "factory", f"正在采集场景工程的周期，大概1min左右......", "blue")
clibs.logger("INFO", "factory", f"正在采集场景工程的周期，根据周期长短，大约需要2~5分钟左右......", "blue")
clibs.c_hr.execution("rl_task.set_run_params", loop_mode=True, override=1.0)
clibs.c_hr.execution("rl_task.run", tasks=["factory"])
t_start = time.time()
@ -113,7 +113,7 @@ class DoFactoryTest(QThread):
break
else:
if (time.time() - t_start) > 15:
self.logger("ERROR", "factory", "15s 内未收到机器人的运行信号，需要确认RL程序编写正确并正常执行...", "red")
clibs.logger("ERROR", "factory", "15s 内未收到机器人的运行信号，需要确认RL程序编写正确并正常执行...", "red")
else:
time.sleep(clibs.INTERVAL)
@ -123,12 +123,12 @@ class DoFactoryTest(QThread):
while True:
scenario_time = float(f"{float(clibs.c_md.read_scenario_time()):.2f}")
if scenario_time != 0:
self.logger("INFO", "factory", f"耐久工程的周期时间：{scenario_time}s | 单轮次执行时间：{scenario_time+interval}~{scenario_time*2+interval}")
clibs.logger("INFO", "factory", f"耐久工程的周期时间：{scenario_time}s | 单轮次执行时间：{scenario_time+interval}~{scenario_time*2+interval}")
break
else:
time.sleep(clibs.INTERVAL)
if (time.time() - t_start) > 900:
self.logger("ERROR", "factory", f"900s内未收到耐久工程的周期时间，需要确认RL程序和工具通信交互是否正常执行（支持最长工程周期时间为300s）......", "red")
clibs.logger("ERROR", "factory", f"900s内未收到耐久工程的周期时间，需要确认RL程序和工具通信交互是否正常执行（支持最长工程周期时间为300s）......", "red")
# 6. 准备数据保存文件
for proc_name, is_enabled in self.procs.items():
@ -148,7 +148,7 @@ class DoFactoryTest(QThread):
this_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
next_time_1 = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()+scenario_time+interval+1))
next_time_2 = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()+scenario_time+interval+1+scenario_time))
self.logger("INFO", "factory", f"[{this_time}] 当前次数：{count:09d} | 预计下次数据更新时间：{next_time_1}~{next_time_2}", "#008B8B")
clibs.logger("INFO", "factory", f"[{this_time}] 当前次数：{count:09d} | 预计下次数据更新时间：{next_time_1}~{next_time_2}", "#008B8B")
count += 1
# 固定间隔，更新一次数据，打开曲线，获取周期内电流，关闭曲线
time.sleep(interval)
@ -166,9 +166,9 @@ class DoFactoryTest(QThread):
self.change_curve_state(False)
# 保留数据并处理输出
self.gen_results(params, start_time, end_time)
else:
clibs.stop_flag = False
self.logger("INFO", "factory", "后台数据清零完成，现在可以重新运行其他程序。", "green")
clibs.stop_flag = False
clibs.logger("INFO", "factory", "后台数据清零完成，现在可以重新运行其他程序。", "green")
def gen_results(self, params, start_time, end_time):
s_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(start_time))
@ -199,80 +199,69 @@ class DoFactoryTest(QThread):
t.start()
def get_avg_trq(self, records, params, proc_name):
d_trq, results = [[], [], [], [], [], []], [time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))]
t = time.strftime("%Y%m%d%H%M%S", time.localtime(time.time()))
d_trq = {f"device_servo_trq_feedback_{axis}": [] for axis in range(6)}
result_data, result_file = [t, ], f"device_servo_trq_feedback_{t}"
for record in records:
data = eval(record[0])["data"]
for item in data:
d_item = reversed(item["value"])
for axis in range(6):
if item.get("channel", None) == axis and item.get("name", None) == "device_servo_trq_feedback":
d_trq[axis].extend(d_item)
d_trq[f"device_servo_trq_feedback_{axis}"].extend(reversed(item["value"]))
for axis in range(6):
df = pandas.DataFrame.from_dict({"device_servo_trq_feedback": d_trq[axis]})
df = pandas.DataFrame.from_dict({"device_servo_trq_feedback": d_trq[f"device_servo_trq_feedback_{axis}"]})
_ = math.sqrt(numpy.square(df[df.columns[0]].values * 1.27 / 1000).sum() / len(df))
results.append(_)
result_data.append(_)
path = "/".join(params["prj_file"].split("/")[:-1])
with open(f"{path}/{proc_name}.csv", mode="a+", newline="") as f_csv:
csv_writer = csv.writer(f_csv)
csv_writer.writerow(results)
csv_writer.writerow(result_data)
if not os.path.exists(f"{path}/{proc_name}/"):
os.mkdir(f"{path}/{proc_name}/")
df = pandas.DataFrame.from_dict(d_trq)
df.to_csv(f"{path}/{proc_name}/{result_file}.csv", index=False)
def get_joint_max_vel(self, records, params, proc_name):
d_trq, results = [[], [], [], [], [], []], [time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))]
t = time.strftime("%Y%m%d%H%M%S", time.localtime(time.time()))
d_vel = {f"hw_joint_vel_feedback_{axis}": [] for axis in range(6)}
result_data, result_file = [t, ], f"hw_joint_vel_feedback_{t}"
for record in records:
data = eval(record[0])["data"]
for item in data:
d_item = reversed(item["value"])
for axis in range(6):
if item.get("channel", None) == axis and item.get("name", None) == "hw_joint_vel_feedback":
d_trq[axis].extend(d_item)
d_vel[f"hw_joint_vel_feedback_{axis}"].extend(reversed(item["value"]))
for axis in range(6):
df = pandas.DataFrame.from_dict({"hw_joint_vel_feedback": d_trq[axis]})
df = pandas.DataFrame.from_dict({"hw_joint_vel_feedback": d_vel[f"hw_joint_vel_feedback_{axis}"]})
_ = df.max().iloc[0]
results.append(_)
result_data.append(_)
path = "/".join(params["prj_file"].split("/")[:-1])
with open(f"{path}/{proc_name}.csv", mode="a+", newline="") as f_csv:
csv_writer = csv.writer(f_csv)
csv_writer.writerow(results)
csv_writer.writerow(result_data)
@staticmethod
def detect_db_size():
@clibs.db_lock
def release_memory():
line_number = 20000
leftover = 4000 # 200s
clibs.cursor.execute("SELECT COUNT(id) FROM logs")
len_records = clibs.cursor.fetchone()[0]
if len_records > line_number:
del_num = len_records - leftover + 1
clibs.cursor.execute(f"DELETE FROM logs WHERE id < {del_num}")
clibs.cursor.execute(f"UPDATE logs SET id=(id-{del_num - 1}) WHERE id > {del_num - 1}")
clibs.cursor.execute(f"UPDATE sqlite_sequence SET seq = {leftover + 1} WHERE name = 'logs' ")
clibs.cursor.execute("VACUUM")
while True:
release_memory()
time.sleep(clibs.INTERVAL*10)
if not os.path.exists(f"{path}/{proc_name}/"):
os.mkdir(f"{path}/{proc_name}/")
df = pandas.DataFrame.from_dict(d_vel)
df.to_csv(f"{path}/{proc_name}/{result_file}.csv", index=False)
def processing(self):
time_start = time.time()
clibs.running[self.idx] = 1
if clibs.status["hmi"] != 1 or clibs.status["md"] != 1:
self.logger("ERROR", "factory", "processing: 需要在网络设置中连接HMI以及Modbus通信！", "red")
t = threading.Thread(target=self.detect_db_size)
t.daemon = True
t.start()
clibs.logger("ERROR", "factory", "processing: 需要在网络设置中连接HMI以及Modbus通信！", "red")
data_dirs, data_files = clibs.traversal_files(self.dir_path)
params = self.initialization(data_dirs, data_files)
clibs.c_pd.push_prj_to_server(params["prj_file"])
self.run_rl(params)
self.logger("INFO", "factory", "-"*60 + "<br>全部处理完毕<br>", "purple")
clibs.logger("INFO", "factory", "-"*60 + "<br>全部处理完毕<br>", "purple")
time_total = time.time() - time_start
msg = f"处理时间：{time_total // 3600:02.0f} h {time_total % 3600 // 60:02.0f} m {time_total % 60:02.0f} s"
self.logger("INFO", "factory", msg)
clibs.logger("INFO", "factory", msg)