CN112363400B - Cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal codes - Google Patents
Cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal codes Download PDFInfo
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Abstract
The invention relates to a cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal codes. The regression prediction model based on abnormal condition codes instead of specific numerical values can be obtained by utilizing the method, the problem that the conventional prediction method cannot stably run for a long time due to the binding of the conventional prediction method and specific experimental numerical values can be solved, and the intrusion condition of the cable tunnel can be effectively monitored in a longer time range.
Description
The technical field is as follows:
the invention relates to a cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal codes, which is used for monitoring cable tunnel intrusion conditions and belongs to the technical field of cable tunnel state safety monitoring.
Background art:
the power cable is an important carrier of power transmission, and the performance of construction efficiency of a power grid is influenced by the failure of a cable tunnel. The invasion condition of criminals often happens to the cable tunnel, internal cross interconnection wires, grounding copper bars and the like in the tunnel are often stolen, so that huge economic loss is caused, and the safe and effective operation of a power system is seriously influenced by the problems, so that the invasion condition of the cable tunnel is very necessary to be monitored.
The current mode of monitoring cable tunnel invasion condition has two kinds: firstly, a specially-assigned person directly checks the tunnel condition at fixed intervals, which not only needs to consume huge labor cost, but also causes untimely detection or missed detection for possible invasion conditions due to long cable distance; secondly, the situation in the tunnel is reflected by the signals of the optical fiber sensor, and a corresponding artificial intelligence model is established to predict the possible invasion situation, wherein the models are usually established based on laboratory data or data obtained at the initial stage of cable tunnel construction, and are characterized in that the precision of binding with a specific numerical value at the initial stage of system operation is very high; however, since the tunnel with large regional distribution inevitably sinks and deforms in the long-term use process, the numerical value-based models are increasingly inaccurate, and the false alarm rate continuously rises after the tunnel runs for a period of time.
Therefore, a method is needed to be provided, which can avoid the problem of binding the traditional method with a specific experimental value, and monitor the intrusion condition of the cable tunnel in a longer time range.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal code description. The method can code the abnormal condition of the input optical fiber sensor signal, further obtain a regression model based on the code, and further realize the monitoring of the cable tunnel invasion condition.
The invention relates to a cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal codes, which comprises the following steps:
s1, inputting a cable tunnel optical fiber sensor signal data list HistoryList in a normal state, inputting a data list ErrorList in the case of intrusion of a cable tunnel, obtaining the number HistoryNum of HistoryList data entries, and establishing a normal state statistical array HARray; acquiring the number ErrorNum of ErrorList data entries, establishing an intrusion state statistical array EArray, and acquiring the maximum value DistanceMax of an intrusion position;
s101, inputting a cable tunnel optical fiber sensor signal data list HistoryList in a normal state, wherein the fields of the HistoryList comprise:
HSData: and the data type of the normal state optical fiber sensor signal array is a floating point type array with 100 elements.
HRQ, obtaining the time corresponding to the HSData data by the optical fiber sensor;
s102, inputting a data list errorlst of the cable tunnel intrusion, wherein fields of the errorlst include:
ESData: the data type of the signal array of the optical fiber sensor with the intrusion condition is a floating point type array with 100 elements;
EDistance: the distance between the position where the intrusion condition occurs in the tunnel and the optical fiber sensor, wherein the data type of the distance is floating point type data;
ERQ: the optical fiber sensor acquires time corresponding to the ESData data;
s103, HistoryNum = number of data entries of HistoryList, and ErrorNum = number of data entries of ErrorList; HArray = a floating point array of 100 elements; EArray = floating point array of 100 elements; DistanceMax = = elistance field maximum for ErrorList;
s104, setting the values of all elements of the HARRAY to be 0; setting all elements of EArray to 0; sorting the HistoryList according to the HRQ value from small to large; sorting ErrorList according to the value of ERQ from small to large;
s105, historical data counter HistoryNumCounter = 1;
S106，HArray=HArray+(HistoryList[HistoryNumCounter].HSData)×(1+ (HistoryNumCounter/HistoryNum-0.5) /100)；
S107, HistoryNumCounter=HistoryNumCounter+1；
s108, if HistoryNumCount > HistoryNum, then go to S109, otherwise go to S106;
S109，HArray=HArray/HistoryNum；
s110, an intrusion condition counter ErrorNumCount = 1;
S111，EArray=EArray+(ErrorList [ErrorNumCounter].ESData)；
S112，ErrorNumCounter=ErrorNumCounter+1；
s113, if the ErrorNumCount is greater than the ErrorNum, then the S114 is turned, otherwise, the S111 is turned;
S114，EArray=EArray/ErrorNum；
s2, establishing an optical fiber signal abnormal coding description operator OFeature, wherein the input variable of OFeature is OFeatureInput, the type of OFeatureInput is a floating-point array with 100 elements, the output variable of OFeature is OFeatureOutput, and OFeatureOutput is a floating-point array with 400 elements;
s201, establishing an optical fiber signal abnormal coding description operator OFeature, wherein the input variable of the OFeature is OFeatureInput, and the type of the OFeatureInput is a floating point type array with 100 elements;
s202, establishing a floating-point array of OFeatureOutput =400 elements of an output variable OFeature, and setting all element values of the array to be 0;
s203, the operator counter ofearturecounter = 1;
s204, if ofatutureinput [ ofatuturecounter ] > harrray [ ofatuturecounter ], then ofatutureoutput [ ofatuturecounter ] = 1;
s205, if ofatutureinput [ ofatuturecounter ] > earrray [ ofatuturecounter ], then ofatutureoutput [ ofatuturecounter +100] = 1;
s206, the operator first temporary storage variable ofaturetemp 1= 0; the operator second temporary storage variable ofatuturetemp 1= 0;
s207, ofatuturetemp 1= abs (ofatutureinput [ ofatuturecounter ] -harrray [ ofatuturecounter ]), where abs is the calculated absolute value;
s208, ofatuturetemp 2= abs (ofatutureinput [ ofatuturecounter ] -earrray [ ofatuturecounter ]), where abs is the calculated absolute value;
s209, if OFeatureTemp1> OFeatureTemp2,
this ofeartureoutput [ ofearturecounter +200] = 1;
s210, if abs (ofaturetemp 1-ofaturetemp 2) >0.5, ofatureoutput [ ofaturecounter +300] =1, where abs is the calculated absolute value;
S211, OFeatureCounter=OFeatureCounter+1；
s212, go to S213 if OFeatureCounter >100, otherwise go to S204;
s213, using the OFeatureOutput as the output of the OFeature;
s3, constructing a decision information table decisionTable by using an optical fiber signal feature description operator OFeature, and training a regression model RegModel by using the decisionTable;
s301, establishing a decision information table decisionTable, wherein the field structure of the decisionTable is as follows:
DecisionInput: decision input, which is a floating-point array of 400 elements;
DecisionDistance, the distance of the invasion situation, and the data type of the distance is floating point type data;
s302, the decision counter first variable CreateCounter1= 1;
s303, the decision temporary storage array variable ctemparay = is calculated by ofeacture, and the operator inputs ofeactureinput = HistoryList [ CreateCounter1]. HSData;
s304, newly creating 1 record in the decisionTable; the value of the DecisionInput field in the newly added record is CTempray, and the value of DecisionDistance is-1;
S305，CreateCounter1=CreateCounter1+1；
s306, if the CreateCounter1> HistoryNum, then go to S307, otherwise go to S303;
s307, the decision counter second variable CreateCounter2= 1;
s308, ctemaprray = calculated by ofeacture, operator input ofeactureinput = errorlst [ CreateCounter2]. ESData;
s309, newly building 1 record in the decisionTable; in the newly added record, the value of the decisionInput field is CTempRay, and the value of the decisionDistance is ErrorList [ CreateCounter2]. EDistance/DistanceMax;
S310,CreateCounter2=CreateCounter2+1；
s311, if CreateCounter2> ErrorNum, go to S312, otherwise go to S308;
s312, establishing a neural network regression analysis model RegModel;
s313, taking a decisionInput field of decisionTable as the input of the RegModel model, and taking a decisionDistance field of decisionTable as the output of the RegModel model; inputting all data of decisionTable to train the RegModel;
s4, inputting data CurrentArray acquired by the optical fiber sensor signal in the cable tunnel, and predicting the invasion condition;
s401, inputting data CurrentArray acquired by an optical fiber sensor signal in a cable tunnel, wherein the CurrentArray is a floating-point array with 100 elements;
s402, predicting a temporary storage Feature variable Feature = calculating through OFeature, and inputting OFeatureInput = CurrentArray by an operator;
s403, taking Feature as an input of the RegModel and obtaining an output of the RegModel according to the prediction result variable PResult =;
s404, if PResult is less than 0, outputting 'no intrusion condition' and turning to S408, otherwise, turning to S405;
s405, finally predicting the distance ResultDis = preesult × detancemax;
s406, outputting the intrusion condition and outputting ResultDis as the intrusion position;
s407, go to S409;
S408，HArray=(CurrentArray-HArray)/( HistoryNum×10)+HArray；
and S409, finishing the prediction process.
The invention has the beneficial effects that:
a cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal code description is provided. The method can code the abnormal condition of the input optical fiber sensor signal, further obtain a regression model based on the code, and further realize the monitoring of the cable tunnel invasion condition. The regression prediction model based on abnormal condition codes instead of specific numerical values can be obtained by utilizing the method, the problem that the conventional prediction method cannot stably run for a long time due to the binding of the conventional prediction method and specific experimental numerical values can be solved, and the intrusion condition of the cable tunnel can be effectively monitored in a longer time range.
Detailed Description
The present invention is further illustrated by the following examples, which do not limit the present invention in any way, and any modifications or changes that can be easily made by a person skilled in the art to the present invention will fall within the scope of the claims of the present invention without departing from the technical solution of the present invention.
Example 1
S1, inputting a cable tunnel optical fiber sensor signal data list HistoryList in a normal state, inputting a data list ErrorList in the case of intrusion of a cable tunnel, obtaining the number HistoryNum of HistoryList data entries, and establishing a normal state statistical array HARray; and obtaining the number ErrorNum of ErrorList data entries, establishing an intrusion state statistical array EArray, and obtaining the maximum value DistanceMax of an intrusion position.
S101, inputting a normal cable tunnel optical fiber sensor signal data list HistoryList, where the fields of the HistoryList include:
HSData: the data type of the normal state optical fiber sensor signal array is a floating point type array with 100 elements.
HRQ is the time corresponding to HSData data acquired by the optical fiber sensor.
S102, inputting a data list errorlst of a cable tunnel intrusion condition, where fields of the errorlst include:
ESData: and the data type of the fiber sensor signal array with the intrusion condition is a floating point type array with 100 elements.
EDistance: the data type of the distance between the position with the intrusion condition in the tunnel and the optical fiber sensor is floating point type data.
ERQ: and the optical fiber sensor acquires time corresponding to the ESData data.
S103, HistoryNum = number of data entries of HistoryList, and ErrorNum = number of data entries of ErrorList; HArray = a floating point array of 100 elements; EArray = floating point array of 100 elements; DistanceMax = = elistance field maximum for ErrorList;
s104, setting the values of all elements of the HARRAY to be 0; setting all elements of EArray to 0; sorting the HistoryList according to the HRQ value from small to large; sorting ErrorList according to the value of ERQ from small to large;
s105, historical data counter HistoryNumCounter = 1;
S106，HArray=HArray+(HistoryList[HistoryNumCounter].HSData)×(1+ (HistoryNumCounter/HistoryNum-0.5) /100)；
S107, HistoryNumCounter=HistoryNumCounter+1；
s108, if HistoryNumCount > HistoryNum, then go to S109, otherwise go to S106;
S109，HArray=HArray/HistoryNum；
s110, an intrusion condition counter ErrorNumCount = 1;
S111，EArray=EArray+(ErrorList [ErrorNumCounter].ESData)；
S112，ErrorNumCounter=ErrorNumCounter+1；
s113, if the ErrorNumCount is greater than the ErrorNum, then the S114 is turned, otherwise, the S111 is turned;
S114，EArray=EArray/ErrorNum。
take data of cable tunnels 2018-2019 in place of Jilin province as an example
Inputting a cable tunnel optical fiber sensor signal data list HistoryList in a normal state, wherein the HistoryList comprises 100 pieces of data and comprises the following contents:
the data list errorlst for the case of an intrusion into the cable tunnel, which contains 100 pieces of data, is as follows:
number HistoryNum =100 to obtain HistoryList data entry
Establishing a normal state statistical array harrray = [0.21, 1.67, 1.23, 0.99, 1.38, 0.48, 1.11, 1.01, 1.18, 1.62,......... 0.28, 0.28, 0.44, 0.00, 0.42, 0.25, 0.25, 0.39, 0.00, 2.40 ];
obtaining the number ErrorNum =100 of ErrorList data entries;
establishing an intrusion status statistics array EArray = [0.11, 1.49, 1.29, 1.25, 1.49, 0.48, 1.61, 0.69, 1.25, 1.62,.... 0.28, 0.28, 0.44, 0.00, 0.42, 0.25, 0.25, 0.39, 0.00, 1.18 ];
obtaining the maximum value DistanCEMax =2349 of the intrusion position;
s2, establishing an optical fiber signal abnormal coding description operator OFeature, wherein the input variable of OFeature is OFeatureInput, the type of OFeatureInput is a floating-point array with 100 elements, the output variable of OFeature is OFeatureOutput, and OFeatureOutput is a floating-point array with 400 elements.
S201, establishing an optical fiber signal abnormal coding description operator OFeature, wherein the input variable of the OFeature is OFeatureInput, and the type of the OFeatureInput is a floating point type array with 100 elements;
s202, establishing a floating-point array of OFeatureOutput =400 elements of an output variable OFeature, and setting all element values of the array to be 0;
s203, the operator counter ofearturecounter = 1;
s204, if ofatutureinput [ ofatuturecounter ] > harrray [ ofatuturecounter ], then ofatutureoutput [ ofatuturecounter ] = 1;
s205, if ofatutureinput [ ofatuturecounter ] > earrray [ ofatuturecounter ], then ofatutureoutput [ ofatuturecounter +100] = 1;
s206, operator first temporary storage variable ofearturetemp 1= 0; the operator second temporary storage variable ofatuturetemp 1= 0;
s207, OFeatureTemp1= abs (OFeatureInput [ OFeatureCounter ] -harrray [ OFeatureCounter ]), where abs is the calculated absolute value;
s208, ofatuturetemp 2= abs (ofatutureinput [ ofatuturecounter ] -earrray [ ofatuturecounter ]), where abs is the calculated absolute value;
s209, if the ofaturetemp 1> ofaturetemp 2, the ofatureoutput [ ofaturecounter +200] = 1;
s210, if abs (ofaturetemp 1-ofaturetemp 2) >0.5, ofatureoutput [ ofaturecounter +300] =1, where abs is the calculated absolute value;
S211, OFeatureCounter=OFeatureCounter+1；
s212, go to S213 if OFeatureCounter >100, otherwise go to S204;
s213, using the OFeatureOutput as the output of the OFeature;
s3, constructing a decision information table DecisionTable by using an optical fiber signal feature description operator OFeature, and training a regression model RegModel by using the DecisionTable;
s301, establishing a decision information table decisionTable, wherein the field structure of the decisionTable is as follows:
DecisionInput: decision input, which is a floating-point array of 400 elements;
DecisionDistance, the distance of the invasion situation, and the data type of the distance is floating point type data;
s302, the decision counter first variable CreateCounter1= 1;
s303, the decision temporary storage array variable ctemparay = is calculated by ofeacture, and the operator inputs ofeactureinput = HistoryList [ CreateCounter1]. HSData;
s304, newly creating 1 record in the decisionTable; the value of the DecisionInput field in the newly added record is CTempray, and the value of DecisionDistance is-1;
S305，CreateCounter1=CreateCounter1+1；
s306, if the CreateCounter1> HistoryNum, then go to S307, otherwise go to S303;
s307, the decision counter second variable CreateCounter2= 1;
s308, ctemaprray = calculated by ofeacture, operator input ofeactureinput = errorlst [ CreateCounter2]. ESData;
s309, newly building 1 record in the decisionTable; in the newly added record, the value of the decisionInput field is CTempRay, and the value of the decisionDistance is ErrorList [ CreateCounter2]. EDistance/DistanceMax;
S310,CreateCounter2=CreateCounter2+1；
s311, if CreateCounter2> ErrorNum, go to S312, otherwise go to S308;
s312, establishing a neural network regression analysis model RegModel;
s313, taking a DesionInput field of a DesionTable as the input of the RegModel model, and taking a DesionDistance field of the DesionTable as the output of the RegModel model; inputting all data of decisionTable to train the RegModel;
s4, inputting data CurrentArray acquired by the optical fiber sensor signal in the cable tunnel, and predicting the invasion condition;
s401, inputting data CurrentArray acquired by an optical fiber sensor signal in a cable tunnel, wherein the CurrentArray is a floating-point array with 100 elements;
s402, predicting a temporary storage Feature variable Feature = calculating through OFeature, and inputting OFeatureInput = CurrentArray by an operator;
s403, taking Feature as an input of the RegModel and obtaining an output of the RegModel according to the prediction result variable PResult =;
s404, if PResult <0, outputting 'no intrusion condition exists' and turning to S408, otherwise, turning to S405;
s405, finally predicting the distance ResultDis = preesult × detancemax;
s406, outputting the intrusion condition, and outputting ResultDis as the intrusion position;
s407, go to S409;
S408，HArray=(CurrentArray-HArray)/( HistoryNum×10)+HArray；
s409, ending the prediction process;
when the collected data CurrentArray = [0.37, 1.68, 0.76, 0.83, 1.62, 0.16, 1.63, 1.13, 0.70, 1.48,....... 0.28, 0.28, 0.44, 0.00, 0.42, 0.25, 0.25, 0.39, 0.00, 0.41 ];
obtaining a PResult = -0.3 output of 'no intrusion condition';
when the collected data CurrentArray = [0.26, 1.30, 0.88, 0.74, 1.27, 0.49, 1.40, 1.22, 0.73, 1.29,.... 0.34, 0.54, 0.48, 0.24, 0.65, 0.28, 0.43, 0.62, 0.00, 2.98 ];
get prosult =0.834 output "intrusion present condition", output ResultDis =0.834 × 2349=1959.1 as the intrusion location.
Example 2:
the method provided by the invention is used for carrying out actual operation test on the cable tunnel in a certain area of Jilin province, the tunnel is put into use in 2016, actual operation data in 2016-2019 are verified, and compared with a traditional neural network regression model and an SVM regression model, the monitoring effect of the method provided by the invention is as follows:
and (4) conclusion: from the above table, it can be seen that the monitoring precision of the conventional method is continuously reduced along with the passage of time, and the false alarm rate is continuously improved, the detection precision of the method of the present invention is the highest among the three methods, and the false alarm rate is also lower.
Claims (1)
1. A cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal codes comprises the following steps:
s1, inputting a cable tunnel optical fiber sensor signal data list HistoryList in a normal state, inputting a data list ErrorList in the case of intrusion of a cable tunnel, obtaining the number HistoryNum of HistoryList data entries, and establishing a normal state statistical array HARray; acquiring the number ErrorNum of ErrorList data entries, establishing an intrusion state statistical array EArray, and acquiring the maximum value DistanceMax of an intrusion position;
s101, inputting a cable tunnel optical fiber sensor signal data list HistoryList in a normal state, wherein the fields of the HistoryList comprise:
HSData: the data type of the normal state optical fiber sensor signal array is a floating point type array with 100 elements;
HRQ: the optical fiber sensor acquires time corresponding to the HSData data;
s102, inputting a data list errorlst of a cable tunnel intrusion condition, where fields of the errorlst include:
ESData: the data type of the signal array of the optical fiber sensor with the intrusion condition is a floating point type array with 100 elements;
EDistance: the distance between the position where the intrusion condition occurs in the tunnel and the optical fiber sensor, wherein the data type of the distance is floating point type data;
ERQ: the optical fiber sensor acquires time corresponding to the ESData data;
s103, the number of the data items of HistoryNum is equal to that of HistoryList, and the number of the data items of ErrorNum is equal to that of ErrorList; HArray is equal to a floating point type array of 100 elements; EArray is equal to a floating point type array of 100 elements; DistanceMax equals the maximum value of the EDistance field of ErrorList;
s104, setting the values of all elements of the HARRAY to be 0; setting all elements of EArray to 0; sorting the HistoryList according to the HRQ value from small to large; sorting ErrorList according to the value of ERQ from small to large;
s105, historical data counter HistoryNumCounter = 1;
S106，HArray=HArray+(HistoryList[HistoryNumCounter].HSData)×(1+ (HistoryNumCounter/HistoryNum-0.5) /100)；
S107， HistoryNumCounter=HistoryNumCounter+1；
s108, if HistoryNumCount > HistoryNum, then go to S109, otherwise go to S106;
S109，HArray=HArray/HistoryNum；
s110, an intrusion condition counter ErrorNumCount = 1;
S111，EArray=EArray+(ErrorList [ErrorNumCounter].ESData)；
S112，ErrorNumCounter=ErrorNumCounter+1；
s113, if the ErrorNumCount is greater than the ErrorNum, then the S114 is turned, otherwise, the S111 is turned;
S114，EArray=EArray/ErrorNum；
s2, establishing an optical fiber signal abnormal coding description operator OFeature, wherein the input variable of OFeature is OFeatureInput, the type of OFeatureInput is a floating-point array with 100 elements, the output variable of OFeature is OFeatureOutput, and OFeatureOutput is a floating-point array with 400 elements;
s201, establishing an optical fiber signal abnormal coding description operator OFeature, wherein the input variable of the OFeature is OFeatureInput, and the type of the OFeatureInput is a floating point type array with 100 elements;
s202, establishing an output variable OFeatureOutput of OFeature, wherein the type of the OFeatureOutput is a floating-point type array of 400 elements, and setting all element values of the array to be 0;
s203, the operator counter ofearturecounter = 1;
s204, if ofeactueinput [ ofeactuecounter ] > harrray [ ofeactuecounter ], ofeactueoutput [ ofeactuecounter ] = 1;
s205, if ofatutureinput [ ofatuturecounter ] > earrray [ ofatuturecounter ], then ofatutureoutput [ ofatuturecounter +100] = 1;
s206, the operator first temporary storage variable ofaturetemp 1= 0; the operator second temporary storage variable ofatuturetemp 1= 0;
s207, ofatuturetemp 1= abs (ofatutureinput [ ofatuturecounter ] -harrray [ ofatuturecounter ]), where abs is the calculated absolute value;
s208, ofatuturetemp 2= abs (ofatutureinput [ ofatuturecounter ] -earrray [ ofatuturecounter ]), where abs is the calculated absolute value;
s209, if OFeatureTemp1> OFeatureTemp2,
this ofeartureoutput [ ofearturecounter +200] = 1;
s210, if abs (ofaturetemp 1-ofaturetemp 2) >0.5, ofatureoutput [ ofaturecounter +300] =1, where abs is the calculated absolute value;
S211，OFeatureCounter=OFeatureCounter+1；
s212, go to S213 if OFeatureCounter >100, otherwise go to S204;
s213, using the OFeatureOutput as the output of the OFeature;
s3, constructing a decision information table DecisionTable by using an optical fiber signal anomaly code descriptor OFeature, and training a neural network regression analysis model RegModel by using the DecisionTable;
s301, establishing a decision information table decisionTable, wherein the field structure of the decisionTable is as follows:
DecisionInput: decision input, which is a floating-point array of 400 elements;
DecisionDistance: the distance of the intrusion condition occurs, and the data type of the distance is floating point type data;
s302, the decision counter first variable CreateCounter1= 1;
s303, calculating a decision temporary storage array variable ctemparay through ofearture, where an operator inputs ofeartureinput = HistoryList [ CreateCounter1]. HSData;
s304, newly creating 1 record in the decisionTable; in the newly added record, the value of the DecisionInput field is CTempower, and the value of the DecisionDistance is-1;
S305，CreateCounter1=CreateCounter1+1；
s306, if the CreateCounter1> HistoryNum, then go to S307, otherwise go to S303;
s307, the decision counter second variable CreateCounter2= 1;
s308, ctemaparay is calculated by ofature, operator input ofatureinput = errorlst [ CreateCounter2]. ESData;
s309, newly building 1 record in the decisionTable; in the newly added record, the value of the DecisionInput field is CTempray, and the value of the DecisionDistance is ErrorList [ CreateCounter2]. EDistance/DistanceMax;
S310，CreateCounter2=CreateCounter2+1；
s311, if CreateCounter2> ErrorNum, go to S312, otherwise go to S308;
s312, establishing a neural network regression analysis model RegModel;
s313, taking a decisionInput field of decisionTable as the input of the RegModel model, and taking a decisionDistance field of decisionTable as the output of the RegModel model; inputting all data of decisionTable to train a RegModel model;
s4, inputting data CurrentArray acquired by the optical fiber sensor signal in the cable tunnel, and predicting the invasion condition;
s401, inputting data CurrentArray acquired by optical fiber sensor signals in a cable tunnel, wherein the CurrentArray is a floating point type array with 100 elements;
s402, calculating a predicted temporary storage characteristic variable Feature through OFeature, and inputting OFeatureInput = CurrentArray by an operator;
s403, using Feature as input of the RegModel model and obtaining output of the RegModel model, and using the output of the RegModel model as a prediction result variable PResult;
s404, if PResult <0, outputting 'no intrusion condition exists' and turning to S408, otherwise, turning to S405;
s405, finally predicting the distance ResultDis = PResult × DistanceMax;
s406, outputting the intrusion condition and outputting ResultDis as the intrusion position;
s407, go to S409;
S408，HArray=(CurrentArray-HArray)/( HistoryNum×10)+HArray；
and S409, finishing the prediction process.
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