# from datetime import datetime from logging import basicConfig, info from os import getcwd from time import strftime, localtime from matplotlib import pyplot as plt from numpy import array, around, append as np_append, transpose from scipy.signal import butter, lfilter from Config import ConfigInfo basicConfig(filename='Weight_info.log', level='DEBUG') conf = ConfigInfo() # 获取采样频率，对于不同采集频率设定不同的时间间隔 o_f_frequency = int(conf.get_optical_fiber_frequency()) # 获取左右车轮和轴的标准重量 # weight_left=0.667t=2/3t # weight_right=0.667t=2/3t # weight_axle=0.766t=23/30t car_weight_data = conf.weight_data() standard_left_weight = float(car_weight_data[0]) standard_right_weight = float(car_weight_data[1]) standard_axle_weight = float(car_weight_data[2]) # 获取校准补偿系数 # adjust_data = conf.adjust_data() # adjust_data_0 = adjust_data[0] # adjust_data_1 = adjust_data[1] # adjust_data_2 = adjust_data[2]
# picture_generation.py 图像生成
from os import path, listdir
from matplotlib import pyplot as plt
from Config import ConfigInfo
from Algorithm.data_splitting_integration import optical_data_splitting, optical_data_to_wheel
from Function.func_collection import read_txt
conf = ConfigInfo()
o_f_frequency = conf.get_optical_fiber_frequency(
) # 频率取不到，因为picture.py的路径不在main.py的路径上
def txt_to_list(ttp_path):
txt_set = []
txt_name = listdir(ttp_path)
for txt_signal_name in txt_name:
xy_list = []
txt_path = ttp_path + '\\' + txt_signal_name
if path.exists(txt_path):
x_list = []
y_list = []
txt = read_txt(txt_path)
for t in txt:
xy_t = t.strip().split()
x_t = float(xy_t[0])
y_t = float(xy_t[1])
x_list.append(x_t)
y_list.append(y_t)
xy_list.append(x_list)
xy_list.append(y_list)
def main_exe():
x_wheel_data = []
all_wheel_data = []
try:
# 主程序路径读取
main_path = getcwd()
# main_path = path.realpath(getcwd())
# main_path = path.dirname(path.realpath(argv[0]))
# 配置文件确认
# config_dir = main_path + '\\Config\\config.ini'
config_dir = 'D:\\Config\\config.ini'
cfp = path.exists(config_dir)
if cfp:
# 配置文件的信息读取
conf = ConfigInfo()
o_f_frequency = int(conf.get_optical_fiber_frequency())
print('当前采样频率:%sHz' % o_f_frequency)
speed_json_path = conf.speed_json_path() # 获取json文件储存路径
remove_json(speed_json_path) # 删除已有的json文件
# 数据预处理
format_conversion(main_path) # 进行数据预处理，并保存在Original_DB文件夹下
# 算法程序开始
info('---------------------------------------')
info(ctime())
print('数据正在处理，请稍等……')
a = time()
# 检测数据库中txt和AEI文件并读取，folders中为12个传感器的数据
json_file_name, folders, all_car_aei = database_creation(main_path)
# 数据库中有txt文件后，对folders读取，并做车轮数据提取整合，然后保存成json文件
# optical_fiber_data的输出格式：三维列表[12个传感器×32个车轮车×600个数据][12×32×600]的矩阵
optical_fiber_data = optical_data_splitting(folders, o_f_frequency)
# all_wheel_data的输出格式:三维列表[32个车轮×2个车轮×3600个数据][32×2×3600]的矩阵
x_wheel_data, all_wheel_data = optical_data_to_wheel(
optical_fiber_data, o_f_frequency)
# 计算车辆相关参数的重量，是否超偏载
all_weight, is_unbalanced_loads, every_wheel_speed, test_date_time = al_main_weight(
all_wheel_data, all_car_aei)
# 车辆故障检测
# is_non_circularity = fault_detection(x_wheel_data, all_wheel_data)
# 将车轮数据保存成json文件
if len(json_file_name) != 0:
all_car_set_json = car_json_integration(
json_file_name, x_wheel_data, all_wheel_data, all_weight,
all_car_aei, is_unbalanced_loads, every_wheel_speed,
test_date_time)
write_json(json_file_name, all_car_set_json)
# 算法程序结束
b = time()
print('数据处理耗时:%.4fs，程序结束！' % (b - a))
sleep(1)
info('本次数据处理已完成，共耗时%.4fs:' % (b - a))
info('---------------------------------------')
else:
print('配置文件缺失，请再次确认！')
info('配置文件缺失，请再次确认！')
sleep(1)
except Exception as e:
info('main_error:', e)
return x_wheel_data, all_wheel_data