ExcessTemp V1_0_0

LIB/MIDDLE/ExcessTemp/V1_0_0/ExcessTemp.py

@@ -0,0 +1,90 @@
+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])