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- from LIB.BACKEND import DBManager
- dbManager = DBManager.DBManager()
- import pandas as pd
- import numpy as np
- import datetime
- import seaborn as sns
- import matplotlib.pyplot as plt
- now_time=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') #type: str
- now_time=datetime.datetime.strptime(now_time,'%Y-%m-%d %H:%M:%S') #type: datetime
- start_time=now_time-datetime.timedelta(minutes=1)
- end_time=str(now_time)
- start_time=str(start_time)
- def makedataset(cellname):
- dataset = pd.DataFrame()
- for k in range(len(cellname)):
- sn = cellname[k]
- datasn = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
- datasn = datasn['bms']
- datasn['SN号']=sn
- if len(datasn)>0:
- datasn=datasn.iloc[-1]
- dataset=dataset.append(datasn)
- return dataset
- fileNamesPK504 = pd.read_excel('sn-20210903.xlsx',sheet_name='科易6060')
- fileNamesPK500 = pd.read_excel('sn-20210903.xlsx',sheet_name='科易6040')
- fileNamesPK502 = pd.read_excel('sn-20210903.xlsx',sheet_name='科易4840')
- fileNamesMGML = pd.read_excel('sn-20210903.xlsx',sheet_name='格林美-力信7255')
- fileNamesMGMC = pd.read_excel('sn-20210903.xlsx',sheet_name='格林美-CATL7255')
- fileNamesUDO = pd.read_excel('sn-20210903.xlsx',sheet_name='优旦7255')
- dataPK504=makedataset(list(fileNamesPK504['SN号']))
- dataPK500=makedataset(list(fileNamesPK500['SN号']))
- dataPK502=makedataset(list(fileNamesPK502['SN号']))
- dataMGML=makedataset(list(fileNamesMGML['SN号']))
- dataMGMC=makedataset(list(fileNamesMGMC['SN号']))
- dataUDO=makedataset(list(fileNamesUDO['SN号']))
- dataPK504=dataPK504[['SN号','单体温度1','单体温度2','单体温度3','单体温度4','其他温度2','其他温度4','其他温度5']]
- dataPK500=dataPK500[['SN号','单体温度1','单体温度2','单体温度3','单体温度4','其他温度2','其他温度3','其他温度4','其他温度5']]
- dataPK502=dataPK502[['SN号','单体温度1','单体温度2','单体温度3','单体温度4','其他温度2','其他温度3','其他温度4','其他温度5']]
- dataMGML=dataMGML[['SN号','单体温度1','单体温度2','单体温度3','单体温度4','其他温度1','其他温度3','其他温度4','其他温度5','其他温度6']]
- dataMGMC=dataMGMC[['SN号','单体温度1','单体温度2']]
- dataUDO=dataUDO[['SN号','单体温度1','单体温度2']]
- datatotal1=pd.concat([dataPK504,dataPK500,dataPK502])
- datatotal2=pd.concat([dataMGMC,dataUDO])
- def calculavg(datacell):
- avg_temp=[]
- max_temp=[]
- min_temp=[]
- for i in range(len(datacell)):
- avg_temp.append(np.mean(datacell.iloc[i,1:]))
- max_temp.append(max(datacell.iloc[i,1:]))
- min_temp.append(min(datacell.iloc[i,1:]))
- datacell['平均温度']=avg_temp
- datacell['最高温度']=max_temp
- datacell['最低温度']=min_temp
- return datacell
- datatotal1=calculavg(datatotal1)
- datatotal2=calculavg(datatotal2)
- dataMGML=calculavg(dataMGML)
- datatotal1=datatotal1[datatotal1['最低温度']>-40]
- datatotal2=datatotal2[datatotal2['最低温度']>-40]
- dataMGML=dataMGML[dataMGML['最低温度']>-40]
- datatotal1=datatotal1.reset_index(drop=True)
- datatotal2=datatotal2.reset_index(drop=True)
- dataMGML=dataMGML.reset_index(drop=True)
- def boxplot_fill(col,a):
- # 计算iqr：数据四分之三分位值与四分之一分位值的差
- iqr = col.quantile(0.75)-col.quantile(0.25)
- # 根据iqr计算异常值判断阈值
- u_th = col.quantile(0.75) + a*iqr # 上界
- l_th = col.quantile(0.25) - a*iqr # 下界
- # 定义转换函数：如果数字大于上界则用上界值填充，小于下界则用下界值填充。
- return l_th,u_th
- uptemp_out1=list(boxplot_fill(datatotal1['最高温度'],2.5))[1]
- uptemp_out2=list(boxplot_fill(datatotal2['最高温度'],5))[1]
- uptemp_out3=list(boxplot_fill(dataMGML['最高温度'],3.5))[1]
- anomalies1 = datatotal1[(datatotal1['最高温度']>uptemp_out1) & (datatotal1['最高温度']<120) & (datatotal1['最高温度']!=datatotal1['单体温度4']) & (datatotal1['单体温度4']<50) & (datatotal1['最高温度']!=datatotal1['其他温度2']) & (datatotal1['其他温度2']<50)]
- anomalies2 = datatotal2[(datatotal2['最高温度']>uptemp_out2) & (datatotal2['最高温度']<120)]
- anomalies3 = dataMGML[(dataMGML['最高温度']>uptemp_out3) & (dataMGML['最高温度']<120)]
- anomalies=pd.concat([anomalies1,anomalies2,anomalies3])
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