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204 lines
11 KiB
204 lines
11 KiB
2 weeks ago
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import numpy as np
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from numba import jit
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import config
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import json
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import sys
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import requests
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import datetime
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import jenkspy
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import xlrd
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import AANN_Fit
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import traceback
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@jit(nopython=True, cache=True)
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def AANN_Fit(testdata, v1, v2, w1, w2):
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"""
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:param testdata:
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:param model:
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:return:
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"""
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y1 = 1/(1+np.exp(-np.dot(testdata, v1)))
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out1 = np.dot(y1, w1)
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y2 = 1/(1+np.exp(-np.dot(out1, v2)))
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output = np.dot(y2, w2)
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return output
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def get_history_value(points,time,interval):
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#url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys"
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url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
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headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token()
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point_array = points.split(",")
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time_span = time.split(";")
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value_array = []
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for item in point_array:
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value_group = []
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for time_piece in time_span:
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st = time_piece.split(",")[0]
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et = time_piece.split(",")[1]
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para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval}
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response = requests.get(url, headers=headers, params=para)
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content = response.text.replace('"[','[').replace(']"',']')
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value = json.loads(content)
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if not isinstance(value, list):
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print("aaa")
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for row in value:
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value_group.append(row[1])
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value_array.append(value_group)
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return np.transpose(np.array(value_array))
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#return values
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def isnumber(limits):
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flag=True
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for item in limits:
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item=item.replace("-","")
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if(item.isdigit()==False):
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flag=False
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break
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return flag
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def AANN_Test(model,Data):
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v1 = np.array(model["v1"])
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v2 = np.array(model["v2"])
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w1 = np.array(model["w1"])
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w2 = np.array(model["w2"])
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maxdata = np.array(model["maxdata"])
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mindata = np.array(model["mindata"])
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mm, nn = Data.shape
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# 预处理 数据归一化
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mdata = (2 * Data - (maxdata + mindata + np.zeros((mm, nn)))) / (maxdata - mindata + np.zeros((mm, nn)))
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reconData = AANN_Fit(mdata, v1, v2, w1, w2)
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# 预处理 数据反归一化
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a = maxdata - mindata + np.zeros((mm, nn))
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b = maxdata + mindata + np.zeros((mm, nn))
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reconData = (np.multiply(reconData, a) + b) / 2
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res = {}
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paraState = np.zeros([np.array(Data).shape[0], np.array(Data).shape[1]])
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SPE_list = []
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R = 0
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errorData = Data - reconData # 偏差值
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res["sampleData"]=np.transpose(np.array(Data)).tolist()
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res["reconData"]=np.around(np.transpose(np.array(reconData)), decimals=3).tolist()
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res["errorData"]=np.around(np.transpose(np.array(errorData)), decimals=3).tolist()
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res["R"]=np.around(R, decimals=3).tolist()
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res["SPE"]=np.around(np.transpose(np.array(SPE_list)), decimals=3).tolist()
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res["paraState"]=np.transpose(np.array(paraState)).tolist()
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return res
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def clean_main(info):
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try:
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# datatype = info['type']
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condition = info["condition"].replace("=", "==").replace(">=", ">").replace("<=", "<")
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times = info["time"].split(';')
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points = info["point"].split(',')
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interval = 300000
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dead = info["dead"].split(',')
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limit = info["limit"].split(',')
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uplower = info["uplow"].split(';')
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res = json.loads(info["model"])
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filename = res["Model_type"]
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count = 0
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ItemsInfo, SamplingTimePeriods = [], []
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Constraint = ""
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for i in range(len(points)):
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iteminfo = {}
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iteminfo["ItemName"] = points[i] # 加点
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if (dead[i] == "1"): # 判断是否参与死区清洗
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iteminfo["ClearDeadZone"] = "true"
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else:
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iteminfo["ClearDeadZone"] = "false"
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if (limit[i] == "1"): # 参与上下限清洗
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limits = uplower[i].split(',')
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if (isnumber(limits) == True): # 输入上下限正确
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count += 1
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Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[
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1] + " and "
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ItemsInfo.append(iteminfo)
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if (count != 0):
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Constraint = Constraint[:len(Constraint) - 4:]
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else:
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Constraint = "1==1" # 没有上下限清洗
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Constraint += " and (" + condition + ")"
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for i in range(len(times)):
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Eachsampletime = {}
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timess = times[i].split(',')
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Eachsampletime["StartingTime"] = timess[0]
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Eachsampletime["TerminalTime"] = timess[1]
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SamplingTimePeriods.append(Eachsampletime)
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Constraint = Constraint.replace("\n", " ")
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url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % (
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ItemsInfo, SamplingTimePeriods, Constraint, interval)
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response = requests.get(url)
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content = json.loads(response.text)
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origndata = np.array([item for item in content["ClearData"]])
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result = AANN_Test(res["Model_info"],origndata.T)
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result["sampleData"] = origndata.tolist()
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result["CleanOrNot"] = True
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except Exception as e:
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points = info['point']
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time1 = info["time"]
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interval = info['interval']
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data_x, origndata = get_history_value(points, time1, interval)
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res = json.loads(info["model"])
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filename = res["Model_type"]
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origndata = origndata.tolist()
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result = AANN_Test(res["Model_info"],origndata.T)
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result["sampleData"] = origndata
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result["CleanOrNot"] = False
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return result
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if __name__ == '__main__':
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# info = {"point":"DH4_40MAG20CT362,DH4_40MAG20AN002GT,DH4_40MAG20CE102,DH4_40MAG20CT312,DH4_40MAG20CT322,DH4_40MAG20CT332","dead":"1,1,1,1,1,1","condition":"[DH4_40MAG20CE102]>20","limit":"0,0,0,0,0,0","uplow":"null,null;null,null;null,null;null,null;null,null;null,null","time":"2021-06-02 17:37:17,2021-06-03 17:37:17","model":"{\"Model_info\":{\"Train_X_min\":[22.848,14.865,45.127,24.787,24.875,24.699],\"Train_X_max\":[49.215,54.841,185.917,80.635,81.084,80.097],\"Train_X_std\":[7.014,12.071,37.054,15.602,15.881,15.695],\"Train_X_mean\":[37.569,36.848,113.843,53.193,53.611,53.304],\"Train_X_bais_max\":[[0,0,0,0,0,0]],\"Train_X_bais_min\":[[0,0,0,0,0,0]],\"Train_X_bais_mean\":[[0,0,0,0,0,0]],\"QCUL_95_line\":[0,0,0,0,0,0],\"QCUL_99_line\":[0,0,0,0,0,0],\"count\":1957,\"maxdata\":[49.21523,54.841,185.917023,80.63535,81.08379,80.0973053],\"mindata\":[22.84761,14.864501,45.1272545,24.7868519,24.8750286,24.698679],\"sigma\":0.017395612757076084,\"v1\":[[0.05396457607128241,-1.1037970788364901,-0.9023458013688983,0.8166076266011788,-0.17679687427234053],[0.5200933810436045,0.007280858992126217,-0.5349408644184176,0.2845470690775108,-0.6855420074109138],[0.36869788692168753,0.022827023461346962,0.5064698648194299,-0.4703408850462729,-0.36541089496833906],[0.12892337610198143,0.8200230541433281,0.0447757887183383,0.1670333002651392,0.8993457054279524],[-0.2971657506583043,0.485522778322426,0.7944907999619857,-0.7713468714572788,-0.3803782724966992],[-0.24463129252472893,-0.8040155541706245,-0.4787131792272823,-0.6808617963702621,0.8823560072658708]],\"v2\":[[-0.624183704662755,-1.0451052181462246,-0.2814222311475065,-0.3354562640454462,2.000215994798745],[1.304013807858937,-1.2949290118426027,-1.274288771981608,1.3783276766624326,0.5918960339827718]],\"w1\":[[-1.804815936738639,-1.177820457498696],[-0.26858615380810363,0.942696914999828],[0.045393854822685514,1.0175755864672742],[0.9657639036369005,0.8851033322191303],[0.9882029320129792,-1.731780185362415]],\"w2\":[[-1.2783993770868123,0.12079935279430898,0.5404098532155389,-0.7614378857555226,-0.8806415399208807,-1.1425373462620356],[1.1519307867425934,1.7851341509876484,1.4232979025735768,1.9335508578138167,1.2388319720461638,1.2688802766586058],[1.0836842481718094,0.24511103285668004,0.19049263794782964,0.26731984115471025,1.0669397542018493,1.067090986764354],[-1.1907723532803773,0.19092261395624277,-0.17818889703150334,-1.2393336475691328,-0.9319272349811759,-0.7091295825768392],[0.2667338682884674,-2.3674951959521846,-2.19524658584935,-0.3717940912566747,-0.6570853336446724,-0.6287452290848574]]},\"Model_type\":\"AANN\",\"BeforeCleanSamNum\":2143,\"AfterCleanSamNum\":2067,\"CleanOrNot\":true}","interval":300000}
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# result = clean_main(info)
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# print('aaa')
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model={
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"Train_X_min": [ 20.821, 14.869, 43.163, 21.262, 21.35, 21.262 ],
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"Train_X_max": [ 49.126, 49.925, 169.502, 77.588, 78.394, 77.856 ],
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"Train_X_std": [ 7.125, 10.472, 31.807, 14.634, 14.847, 14.718 ],
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"Train_X_mean": [ 36.205, 36.429, 111.547, 50.583, 50.955, 50.672 ],
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"Train_X_bais_max": [ [ 0, 0, 0, 0, 0, 0 ] ],
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"Train_X_bais_min": [ [ 0, 0, 0, 0, 0, 0 ] ],
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"Train_X_bais_mean": [ [ 0, 0, 0, 0, 0, 0 ] ],
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"QCUL_95_line": [ 0, 0, 0, 0, 0, 0 ],
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"QCUL_99_line": [ 0, 0, 0, 0, 0, 0 ],
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"r2": [ 0.9868677166895696, 0.9972168348807384, 0.994155641760367, 0.997452401678795, 0.9972480237895449, 0.9973549357761101 ],
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"count": 1837,
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"maxdata": [ 49.1263924, 49.92462, 169.501648, 77.58756, 78.39406, 77.85637 ],
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"mindata": [ 20.8214855, 14.86908, 43.1634521, 21.261837, 21.3499146, 21.261837 ],
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"sigma": 0.01806189580260563,
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"v1": [
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[ -0.23316133948572393, -0.9217914025920456, -0.07554897165584737, 0.5760860199806274, -1.0764354048741178 ],
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[ 0.6269288587688561, 0.2873108121988404, -0.4522541335965181, 0.1976888146807624, -0.06952149372511203 ],
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[ 0.1787240198568913, 0.19306067329316998, -0.6544931571469833, -0.7615224968711883, 0.34011752017824165 ],
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[ 0.018938620699273264, 0.730637377526106, 0.4933076792373918, 0.6451509410317868, 0.053819869986792335 ],
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[ -0.19609562960367105, 0.11172696177830604, -0.1584273023561399, 0.331300527227796, 0.5888365193680527 ],
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[ 0.06189724560442225, -0.8971329436091366, 0.5277390828634656, -0.5983529844040149, 0.7979948968923328 ]
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],
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"v2": [
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[ 1.2232098033189849, -1.6743334472355316, 0.801596559892827, 1.1379819603344574, 0.6490732883102263 ],
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[ 1.0900875026570802, -0.9667042438318552, -0.6416000023658467, -0.506978913609041, 1.6559592831593986 ]
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],
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"w1": [
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[ -1.330531486686979, 0.2236374553075324 ],
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[ 0.7317719867861042, 2.0240829271774823 ],
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[ 1.9002236694443064, -1.1102667989668475 ],
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[ -0.18577976434243573, -0.5562627341260915 ],
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[ -1.0437074026078084, -0.7558997859191849 ]
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],
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"w2": [
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[ -1.3748396962059533, -0.4858035766258788, -0.8013045354908804, -0.6155000036590996, -0.48891412875006274, -1.0627377754183602 ],
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[ 1.4595863114619005, 2.68853721134863, 2.238122625694176, 1.9127830289008108, 1.9551336244607311, 1.8698425061744566 ],
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[ 0.5758092226574661, -0.4237580862942265, -1.4401060479517787, 0.28527357176477214, 0.1858972429240083, 0.054877840090252594 ],
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[ 0.7197290283109676, -1.73044863841078, -0.4906062165572168, -0.19012584014090636, -0.18883049934775636, 0.22915594950041754 ],
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[ -1.4023393826788018, 0.16500469061488093, 0.4718410186558856, -1.5843596374153093, -1.6622678974964407, -1.2898135647821567 ]
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]
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}
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Data=np.array([[24.25785,22.9852276,71.4623947,26.1979523,26.2861671,26.2861671]])
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result = AANN_Test(model, Data)
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print('aaa')
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