121 lines
5.1 KiB
Python
121 lines
5.1 KiB
Python
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#!/users/dsc/binp/Python3
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#-*-coding: UTF-8 -*-
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from datetime import datetime
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from tokenize import Double
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from comlib.shm.MemMap import MemMap
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import logging as d
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from comlib.shm.Serialization import Serialization
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import struct
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import pandas as pd
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import numpy as np
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from glitch.alarm_info import AlarmInfo
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from glitch.GlitchDetector import GlitchDetector
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from datetime import datetime
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class GM:
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def __init__(self):
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self.mm = MemMap('ZONE20', 16008)
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self.serializtion=Serialization("2000dii")
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self.alarm_info=AlarmInfo()
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self.limit_down=0
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self.limit_up=0
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self.current_value=0
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self.kurtosis_threshold=15#阈值 baseline_kurtosis的倍数
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self.detector = GlitchDetector(kurtosis_threshold=self.kurtosis_threshold)
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self.baseline_kurtosis=0.08#正常数据的峰度（绝对值）
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self.last_alarm_time={}
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self.now_time=datetime.now()
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self.AlarmIntervalHours=1 #最小报警时间间隔 小时
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def dispose(self,ruleid:str,rulename:str,alarm_content:str,stime,endtime,th:float):
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self.now_time=datetime.now()
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d.info("----dispose---")
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res=self.mm[ruleid]
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if res is None:
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d.error(f"MemMap not find key '{ruleid}' !")
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d.debug("|ruleid|"+res.key)
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data_value=self.serializtion.deserialize(res.value)
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d_n=data_value[-2]
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dataX=data_value[:data_value[-2]]
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self.df=pd.DataFrame(data=dataX)
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is_alarm=self.detection(dataX=dataX,th=th)
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if is_alarm:
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# alarm_content=alarm_content+" 当前值："+format(self.result["current_kurtosis"],'.3f')+" 合理区间:["+format(-self.baseline_kurtosis*self.kurtosis_threshold,'.3f')+","+format(self.baseline_kurtosis*self.kurtosis_threshold,'.3f')+"]"
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alarm_result=self.alarm_info.build_alarm(ruleid=ruleid,rulename=rulename,
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alarm_content=alarm_content,
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stime=stime/1000,etime=endtime/1000)
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d.debug(alarm_result)
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if self.check_last_alarm_time(ruleid)==False:
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d.debug(self.last_alarm_time[ruleid].strftime("%Y-%m-%d %H:%M:%S.%f")[:-3])
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return None
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d.debug("Alarm!"+self.last_alarm_time[ruleid].strftime("%Y-%m-%d %H:%M:%S.%f")[:-3])
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return alarm_result
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return None
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def detection(self,dataX,th):
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self.df=pd.DataFrame(data=dataX,columns=["data"])
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self.result = self.detector.detect_glitches(self.df,baseline_kurtosis=self.baseline_kurtosis)
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# self.limit_down=self.detector.lb
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# self.limit_up=self.detector.ub
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# self.current_value=self.detector.va
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# d.debug("self.df.kurtosis():"+str(self.df.kurtosis()))
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# d.debug("self.df.skew():"+str(self.df.skew()))
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# res=self.IQR()
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# d.debug("IQR----res:"+str(res))
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# res2=self.sigma(3)
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# d.debug("sigma---res:"+str(res2))
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# self.limit_up=th
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# self.current_value=res
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if self.result["has_glitch"]:
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self.current_value=1
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return True
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self.current_value=0
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return False
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def check_last_alarm_time(self,ruleid):
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if ruleid in self.last_alarm_time.keys():
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d.debug("ruleid:"+ruleid+" in self.last_alarm_time.keys!")
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exp_time=(self.now_time-self.last_alarm_time[ruleid]).total_seconds()
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if exp_time> self.AlarmIntervalHours*3600:
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self.last_alarm_time[ruleid]=self.now_time
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return True
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else:
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d.debug("time:"+str(exp_time)+"s <"+str(self.AlarmIntervalHours*3600)+"s")
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return False
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else:
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d.debug("ruleid:"+ruleid+" not in self.last_alarm_time.keys!")
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self.last_alarm_time[ruleid]=self.now_time
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return True
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def IQR(self):
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if self.df is not None:
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d.debug("IQR--------1")
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if self.df["data"].std()==0:
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d.debug("数据平稳")
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return 0
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self.df["data"]=(self.df["data"]-self.df["data"].mean())/self.df["data"].std()
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Q1=self.df["data"].quantile(0.25)
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Q3=self.df["data"].quantile(0.75)
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IQR=Q3-Q1
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lb=Q1-1.5*IQR
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ub=Q3+1.5*IQR
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d.debug(f"Q1:{Q1},Q3:{Q3},IQR:{IQR}")
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d.debug("lb:"+str(lb)+",ub:"+str(ub))
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d.debug("IQR--------2")
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no_outer=(self.df["data"]>=lb)&(self.df["data"]<=ub)
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d.debug("max:")
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d.debug(self.df["data"][(~no_outer)].max())
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return (~no_outer).sum()/len(self.df)
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return None
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def sigma(self,th=3):
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if self.df is not None:
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d.debug("sigma------1")
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mean=self.df["data"].mean()
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std=self.df["data"].std()
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lb=mean-th*std
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ub=mean+th*std
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d.debug(f"mean:{mean}, std:{std}")
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d.debug("lb:"+str(lb)+",ub:"+str(ub))
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d.debug("sigma------2")
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no_outer=(self.df["data"]>=lb)&(self.df["data"]<=ub)
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return (~no_outer).sum()/len(self.df)
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def IQR(self,df):
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pass
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