from ctypes import Structure import pandas as pd import numpy as np import datetime import time, datetime import matplotlib.pyplot as plt from pylab import* import math import os import log path = r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\02析锂分析\各项目析锂情况_211130\增加波峰高度与峰谷比计算结果' mylog=log.Mylog('log_diag.txt','error') mylog.logcfg() def get_file(): #创建一个空列表 files =os.listdir(path) files.sort() #排序 file_list= [] for file in files: if not os.path.isdir(path +file): #判断该文件是否是一个文件夹 f_name = str(file) # print(f_name) tr = '\\' #多增加一个斜杠 filename = path + tr + f_name file_list.append(filename) return file_list file_list = get_file() for file in file_list: df_lipltd_level = pd.DataFrame(columns=['sn','total_num','total_amount','liplt_num','liplated_amount','liplated_level','warnning_level']) data = pd.read_csv(file,encoding='GB18030') #----------------------------------------筛选充电后静置数据------------------------------------------------------------ # MGMCLN_name = list(filter(lambda x: 'MGMCLN' in x, data['sn'])) # df_MGMCLN7255 = data.loc[data['sn'].isin(MGMCLN_name)] df_MGMCLN7255_del = data.drop_duplicates(subset = ['sn','time'], keep = 'first', inplace = False)#去除重复值 MGMCLN_list = np.unique(df_MGMCLN7255_del['sn'], return_index = False, return_inverse = False, return_counts = False)# temperature = [15, 20, 22, 21, 17, 6, 4, -0.5]#5~11月平均气温 temperatuer = list(map(lambda x: x + 273.15, temperature)) for item in MGMCLN_list: df_liamount = pd.DataFrame() df_amount_temp = pd.DataFrame() df_num = pd.DataFrame() df_Num_temp = pd.DataFrame() df_data_temp = df_MGMCLN7255_del.loc[df_MGMCLN7255_del['sn'] == item] df_data_temp.reset_index(drop = True,inplace=True) df_month_temp = pd.to_datetime(df_data_temp['time']) df_month = df_month_temp.dt.month df_month = list(map(lambda x: x - 5, df_month))#月份对应的温度位置 for i in range(0,len(df_data_temp)):#len(df_data_temp) df_Num_temp = pd.DataFrame(eval(df_data_temp.iloc[i]['liplated']))#统计析锂次数 df_num = pd.concat([df_num,df_Num_temp], axis = 1)#统计总次数 df_lipltamount_temp = eval(df_data_temp.iloc[i]['liplated_amount'])#解析原数据列表 df_lipltamount_corrt = 4e-17*math.exp(0.1289*temperatuer[df_month[i]])#析锂量的温度系数 df_amount_temp = pd.DataFrame(list(map(lambda x: x*df_lipltamount_corrt, df_lipltamount_temp))) df_liamount = pd.concat([df_liamount,df_amount_temp], axis = 1)#按列拼接各个该sn不同时刻的析锂量，每一行为一个电芯 df_sn_liamount = np.sum(df_liamount, axis = 1)#对该sn中每个电芯的析锂量求和 df_total_amount = np.sum(df_sn_liamount)#所有的析锂量 df_sn_num = np.sum(df_num, axis = 1)#对该sn中每个电芯析锂次数的求和 df_total_num = np.sum(df_sn_num)#所有的析锂次数 df_level_temp = df_sn_liamount.tolist()[:] df_temp_level = np.array(df_level_temp) df_temp_level[df_temp_level < 30] = 0 df_temp_level[(df_temp_level >= 30) & (df_temp_level < 60)] = 1 df_temp_level[(df_temp_level >= 60) & (df_temp_level < 90)] = 2 df_temp_level[(df_temp_level >= 90) & (df_temp_level < 120)] = 3 df_temp_level[(df_temp_level >= 120) & (df_temp_level < 180)] = 4 df_temp_level[(df_temp_level >= 180) & (df_temp_level <240)] = 5 df_temp_level[df_temp_level >= 240] = 6 df_level = np.sum(df_temp_level)#电池包析锂量评级 df_liplt_num = df_sn_num.tolist() df_liamount_list = df_sn_liamount.tolist() df_liplated_level = df_temp_level.tolist() df_warnning_level = df_level.tolist() df_lipltd_confir_level = pd.DataFrame({"sn":[item],"total_num":[df_total_num],"total_amount":[df_total_amount],"liplt_num":[str(df_liplt_num)], "liplated_amount":[str(df_liamount_list)], "liplated_level":[str(df_liplated_level)],"warnning_level":[str(df_warnning_level)]}) df_lipltd_level = df_lipltd_level.append(df_lipltd_confir_level) df_lipltd_level = df_lipltd_level.reset_index(drop = True) df_lipltd_level.sort_values(by = ['total_amount'], ascending=False,inplace=True)#对故障信息按照时间进行排序 temp = file.split('\\') sn_name = temp[-1].split('.')[0] title = sn_name df_lipltd_level.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\02析锂分析\各项目析锂情况_211130\增加波峰高度与峰谷比计算结果\\'+ sn_name + '析锂排序.csv',index=False,encoding='GB18030') # df_li_amount = df_data_temp['liplated_amount'] # #--------------------------------------------------------确认是否析锂---------------------------------------------------------------------------- # # df_lipltd_data = pd.DataFrame(columns=['sn','date','liplated']) # for item in range(0,k): # lipltd_confirm = [] # lipltd_amount = [] # df_check_liplated_temp = df_rest_volt_diffdt.loc[df_rest_volt_diffdt['chrgr_rest'] == item].reset_index(drop = True) # df_lipltd_volt_temp = df_check_liplated_temp[cellvolt_list] # df_lipltd_volt_len = len(df_lipltd_volt_temp) # df_data_temp_add = df_lipltd_volt_temp.iloc[df_lipltd_volt_len-4:df_lipltd_volt_len-1] # df_lipltd_volt_temp_add = df_lipltd_volt_temp.append(df_data_temp_add) # df_lipltd_volt_temp_dif = np.diff(df_lipltd_volt_temp_add,axis=0)#电压一次微分，计算dv/dt # df_lipltd_volt_temp_dif = pd.DataFrame(df_lipltd_volt_temp_dif) # df_lipltd_volt_temp_dif.columns = cellvolt_list # df_lipltd_volt_temp_difdif = np.diff(df_lipltd_volt_temp_dif,axis=0)#电压二次微分，判断升降 # df_lipltd_volt_temp_difdif = pd.DataFrame(df_lipltd_volt_temp_difdif) # df_lipltd_volt_temp_difdif.columns = cellvolt_list # df_lipltd_volt_temp_difdif_temp = df_lipltd_volt_temp_difdif # df_lipltd_volt_temp_difdif_temp[df_lipltd_volt_temp_difdif_temp >= 0] = 1 # df_lipltd_volt_temp_difdif_temp[df_lipltd_volt_temp_difdif_temp < 0] = -1 # df_lipltd_volt_temp_difdifdif = np.diff(df_lipltd_volt_temp_difdif_temp,axis=0)#三次微分，利用-2，2判断波分和波谷 # df_lipltd_volt_difdifdif = pd.DataFrame(df_lipltd_volt_temp_difdifdif) # df_lipltd_volt_difdifdif.columns = cellvolt_list # df_lipltd_volt_difdifdif['chrgr_rest'] = k # df_lipltd_volt_difdifdif['时间戳'] = list(df_check_liplated_temp['时间戳']) # df_lipltd_volt_difdifdif = df_lipltd_volt_difdifdif.reset_index(drop = True) # df_lipltd_data_temp = df_lipltd_volt_difdifdif.loc[df_lipltd_volt_difdifdif['时间戳'] < (df_check_liplated_temp['时间戳'][0] + datetime.timedelta(minutes=90))] # for cell_name in cellvolt_list:#对每个电芯判断 # df_check_plated_data = df_lipltd_data_temp[cell_name] # peak_pos = np.where(df_check_plated_data == -2) # bot_pos = np.where(df_check_plated_data == 2) # if len(peak_pos[0]) & len(bot_pos[0]): # if (peak_pos[0][0] > bot_pos[0][0]) & (df_lipltd_volt_temp_dif[cell_name][peak_pos[0][0] + 1] < 0): # lipltd_confirm.append(1)#1为析锂，0为非析锂 # lipltd_amount.append((df_check_liplated_temp['时间戳'][bot_pos[0][0] + 2] - df_check_liplated_temp['时间戳'][0])/pd.Timedelta(1, 'min')) # else: # lipltd_confirm.append(0) # lipltd_amount.append(0) # else: # lipltd_confirm.append(0) # lipltd_amount.append(0) # if any(lipltd_confirm) & (max(lipltd_amount) > 5): # df_lipltd_confir_temp = pd.DataFrame({"sn":['sn'], "time":[df_check_liplated_temp['时间戳'][0]], "liplated":[str(lipltd_confirm)], "liplated_amount":[str(lipltd_amount)]}) # df_lipltd_data = df_lipltd_data.append(df_lipltd_confir_temp) # df_lipltd_data = df_lipltd_data.reset_index(drop = True) # df_lipltd_data.sort_values(by = ['time'], axis = 0, ascending=True,inplace=True)#对故障信息按照时间进行排序 # df_lipltd_data.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\01下载数据\MGMCLN750N215N049\析锂检测.csv',index=False,encoding='GB18030') # # for i in range(1,k): # # # fig = plt.figure(figsize=(20,10)) # # df_fig = df_rest_volt_diffdt.loc[df_rest_volt_diffdt['chrgr_rest'] == i] # # df_fig = df_fig.reset_index(drop=True) # # length = len(df_fig) # # for volt_num in cellvolt_name[5:6]: # # # plt.scatter([x for x in range(0, length)], df_fig[volt_num][0:length], label=volt_num) # # plt.plot(df_fig['时间戳'], df_fig[volt_num],linewidth = 2, linestyle = '-', marker = 's',label=volt_num) # # plt.rcParams['font.family']=['SimHei'] # # plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 # # plt.legend() # # temp = file.split('\\') # # sn_name = temp[9].split('.')[0] # # title = sn_name + '-' + df_fig['时间戳'][0].strftime('%y-%m-%d')#df_fig['device_id'][0] + '-' + # # print(type(title)) # # plt.xlabel('时间', fontsize=14) # # plt.ylabel('电压的时间微分(mv/min)', fontsize=14) # # plt.xlim(df_fig['时间戳'][0]-datetime.timedelta(minutes=5),df_fig['时间戳'][0] + datetime.timedelta(hours=3)) # # # plt.ylim(-2.5,0.5) # # plt.title(title) # # plt.savefig(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\01下载数据\MGMCLN750N215N049\\'+title+'.png', dpi=300) # # # plt.show() # fig = plt.figure(figsize=(20,10)) # for i in range(3,4):#0,k # # fig = plt.figure(figsize=(20,10)) # df_fig = df_rest_volt_diffdt.loc[df_rest_volt_diffdt['chrgr_rest'] == i] # df_fig = df_fig.reset_index(drop=True) # length = len(df_fig) # temp = file.split('\\') # sn_name = temp[9].split('.')[0] # title = sn_name + '-' + df_fig['时间戳'][0].strftime('%y-%m-%d')#df_fig['device_id'][0] + '-' + # plt_data_len = len(df_fig) # # for volt_num in cellvolt_name[5:6]: # # plt.scatter([x for x in range(0, length)], df_fig[volt_num][0:length], label=volt_num) # # plt.plot([x for x in range(0, plt_data_len)], df_fig[cellvolt_name[5]],linewidth = 2, linestyle = '-', marker = 's',label = title) # # plt.plot([x for x in range(0, plt_data_len)], df_fig[cellvolt_name[2]],linewidth = 2, linestyle = '-', marker = 's',label = title) # plt.plot(df_fig['时间戳'], df_fig[cellvolt_name[5]],linewidth = 2, linestyle = '-', marker = 's',label = cellvolt_name[5]) # mpl.rcParams['font.sans-serif']=['SimHei'] # mpl.rcParams['axes.unicode_minus']=False #用来正常显示负号 # plt.legend() # plt.xlabel('采样点', fontsize=14) # plt.ylabel('电压的时间微分(mv/min)', fontsize=14) # plt.xlim(df_fig['时间戳'][0]-datetime.timedelta(minutes=5),df_fig['时间戳'][0] + datetime.timedelta(hours=5)) # # plt.xlim(0, 60) # # plt.ylim(-2.5,0.5) # plt.title('6#电芯不同时间电压微分曲线') # plt.savefig(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\01下载数据\MGMCLN750N215N049\\'+'6#电芯析锂'+'.png', dpi=300) # # plt.show()