CN117093941A - Vibration wave filtering method, system, equipment and medium for eliminating environmental noise - Google Patents
Vibration wave filtering method, system, equipment and medium for eliminating environmental noise Download PDFInfo
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Abstract
The invention discloses a vibration wave filtering method, a system, equipment and a medium for eliminating environmental noise, which comprise the steps of inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in target vibration waveform data into a preset fault judgment model, and outputting a fault value; determining a fault result of the fault value based on a preset fault threshold; calculating the true value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data respectively; judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of a fault result according to the judging result. The technical problem of the lower accuracy of distinguishing of prior art has been solved. The invention improves the judgment authenticity, and has various and complex installation and distribution environments aiming at the medium-voltage distribution switch, multiple interference waves and self-adaptive specific application requirements on the filtering of the sampling vibration waves.
Description
Technical Field
The invention relates to the technical field of vibration wave filtering, in particular to a vibration wave filtering method, a system, equipment and a medium for eliminating environmental noise.
Background
With the construction of distribution networks, the problem of mechanical performance quality of distribution switches is increasingly prominent, and many electrical faults are usually evolved from mechanical faults. The main mechanical faults of the distribution switch comprise problems of contact wear, ring jamming, loose static contact, contact jamming, support breakage and the like, and as a plurality of switch manufacturers continuously update the switching mode and the switching-off technology and singly rely on an electric characteristic to diagnose, the current form requirements cannot be met increasingly, so that the monitoring of equipment by adopting mechanical vibration waves becomes an important monitoring means.
However, at present, some mechanical abnormality problems can be effectively found through vibration wave monitoring under the switching-on impact, but when the mode is adopted, as the distribution switch is all installed outdoors, the interference factors of daily vibration wave monitoring are more, and the monitoring reliability of switching mechanical problems through vibration wave characteristics is restricted to a certain extent.
Therefore, in the prior art, the vibration data of the optical fiber gyroscope is generally filtered, but the vibration noise suppression method of the method has defects, and cannot be used for outdoor noisy environments such as bird sounds, car sounds and human sounds, so that relatively pure vibration waves in a mechanical state of a switch cannot be given, and the identification accuracy is low.
Disclosure of Invention
The invention provides a vibration wave filtering method, a system, equipment and a medium for eliminating environmental noise, which solve the technical problem that the prior art cannot adopt a filtering method aiming at outdoor noisy environments such as bird sounds, car sounds and human sounds, cannot give relatively pure vibration waves in a mechanical state of a switch, and has low identification accuracy.
The vibration wave filtering method for eliminating environmental noise provided by the first aspect of the invention comprises the following steps:
responding to a vibration wave filtering instruction request, and acquiring target vibration waveform data of a medium-voltage distribution switch corresponding to the vibration wave filtering instruction request;
inputting the target vibration waveform data into a preset target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data;
inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value;
determining a fault result of the fault value based on a preset fault threshold;
calculating the true value of the fault result by adopting intensity peak value data corresponding to second vibration waveform data and vibration wave real-time waveform data in the target vibration waveform data respectively;
Judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of the fault result according to a judging result.
Optionally, the step of responding to the vibration wave filtering instruction request and acquiring the target vibration waveform data of the medium voltage distribution switch corresponding to the vibration wave filtering instruction request includes:
responding to a vibration wave filtering instruction request, and determining a medium-voltage distribution switch corresponding to the vibration wave filtering instruction request;
respectively acquiring first vibration wave data and vibration wave real-time waveform data of a medium-voltage distribution switch in a noise-free environment and in an installation environment by using vibration wave data acquisition equipment;
acquiring bird sound wave form data, car sound wave form data and man sound wave form data in a preset area of a medium-voltage distribution switch by adopting a wave form acquisition device;
generating second vibration waveform data by adopting the bird sound wave waveform data, the car sound wave waveform data and the man sound wave waveform data;
and generating target vibration waveform data by adopting the first vibration waveform data, the vibration wave real-time waveform data and the second vibration waveform data.
Optionally, the step of inputting the target vibration waveform data into a preset target detection model and extracting intensity peak data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data includes:
Dividing peaks of each waveform data in the target vibration waveform data at different time points according to a preset duty ratio coefficient to generate a training set and a testing set;
inputting waveform data corresponding to the training set into a preset initial detection model for training, and generating an updated detection model;
inputting waveform data corresponding to the test set into the updated detection model for testing, and generating a target detection model;
inputting the real-time waveform data of the vibration wave into the target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the real-time waveform data of the vibration wave;
and inputting the second vibration waveform data into the target detection model, and extracting intensity peak value data corresponding to the second vibration waveform data.
Optionally, the step of inputting the intensity peak value data, the preset waveform time data and the vibration wave frequency corresponding to the vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value includes:
inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model; the calculation formula of the preset fault judgment model is as follows:
Wherein F is a fault value, a 1 A is a time duty ratio coefficient 2 For peak duty cycle, a 3 Is the frequency duty ratio coefficient, t max -t min For the set frequency wave time safety range, t m Is closest to t in the time safety range of vibration wave 1 Value of X max -X min For the set vibration wave intensity safety range value, X m Is closest to X in the safe range of vibration wave intensity fz The value of K max -K min For a set vibration frequency safety range, K m For the frequency of vibration to be closest to K in the safety range fz Value of 1=a 1 +a 2 +a 3 ；
And calculating the fault value of the vibration wave real-time waveform data through the preset fault judging model.
Optionally, the step of determining the fault result of the fault value based on a preset fault threshold includes:
judging whether the fault value is larger than or equal to a preset fault threshold value or not;
if yes, determining that a fault occurs, and generating a fault result;
if not, the failure does not occur, and a failure-free result is generated.
Optionally, the step of calculating the true value of the fault result using intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data includes:
acquiring second vibration waveform data and vibration wave real-time waveform data in the target vibration waveform data;
Calculating the total number corresponding to the bird sound wave waveform data, the car sound wave waveform data and the human sound wave waveform data in the second vibration waveform data, and respectively generating the total number of bird sounds, the total number of car sounds and the total number of human sounds;
inputting preset true value models by adopting intensity peak value data, the total number of bird sounds, the total number of car sounds and the total number of human sounds, which correspond to the bird sound wave data, the car sound wave data, the human sound wave data and the vibration wave real-time wave data respectively;
and calculating the true value of the fault result through the preset true value model.
Optionally, the step of determining whether the real value is greater than or equal to a preset real threshold value, and determining a fault signal of the fault result according to the determination result includes:
judging whether the true value is larger than or equal to a preset true threshold value;
if yes, determining the fault result as a false fault signal without alarming;
if not, determining the fault result as a fault signal and generating alarm information.
A second aspect of the present invention provides a vibration wave filtering system for removing environmental noise, including:
The target vibration waveform data module is used for responding to a vibration wave filtering instruction request and acquiring target vibration waveform data of the medium-voltage distribution switch corresponding to the vibration wave filtering instruction request;
the vibration wave frequency module is used for inputting the target vibration waveform data into a preset target detection model and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data;
the fault value module is used for inputting the intensity peak value data, the preset waveform time data and the vibration wave frequency corresponding to the vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model and outputting a fault value;
the fault result module is used for determining a fault result of the fault value based on a preset fault threshold value;
the real value module is used for calculating the real value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data respectively;
and the fault signal module is used for judging whether the true value is larger than or equal to a preset true threshold value or not, and determining a fault signal of the fault result according to a judging result.
An electronic device according to a third aspect of the present invention includes a memory and a processor, where the memory stores a computer program, and the computer program when executed by the processor causes the processor to execute the steps of the method for filtering a vibration wave for eliminating environmental noise as described in any one of the above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed, implements the vibration wave filtering method of removing ambient noise as set forth in any one of the above.
From the above technical scheme, the invention has the following advantages:
according to the method, the target vibration waveform data of the medium-voltage distribution switch corresponding to the vibration wave filtering instruction request is obtained by responding to the vibration wave filtering instruction request; inputting the target vibration waveform data into a preset target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data; inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value; determining a fault result of the fault value based on a preset fault threshold; calculating the true value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data respectively; judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of a fault result according to the judging result. The method solves the technical problems that the prior art cannot adopt a filtering method aiming at outdoor noisy environments such as bird sounds, car sounds and human sounds, cannot give relatively pure vibration waves of a switch mechanical state, and has low distinguishing accuracy.
The method substitutes the collected real-time waveform data into the constructed fault filtering model of the medium-voltage distribution switch, monitors vibration wave data in real time, identifies whether faults occur, substitutes the judgment result and the real-time waveform data into a true value identification strategy, carries out the authenticity identification of the judgment result, outputs the fault type and the authenticity of the judgment, compares the true value with a true threshold value, judges whether to give an alarm or not, improves the authenticity of the judgment, and has various and complex installation and distribution environments aiming at the medium-voltage distribution switch, multiple interference waves and self-adaptive specific application requirements on the filtering of the sampling vibration waves.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flowchart showing steps of a method for filtering vibration waves to eliminate environmental noise according to a first embodiment of the present invention;
FIG. 2 is a flowchart showing steps of a method for filtering vibration waves to eliminate environmental noise according to a second embodiment of the present invention;
fig. 3 is a block diagram of a vibration wave filtering system for eliminating environmental noise according to a third embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a vibration wave filtering method, a system, equipment and a medium for eliminating environmental noise, which are used for solving the technical problem that the existing technology cannot adopt the filtering method aiming at outdoor noisy environments such as bird sounds, car sounds and human sounds, cannot give relatively pure vibration waves in a mechanical state of a switch, and the identification accuracy is low.
In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for filtering vibration waves to eliminate environmental noise according to an embodiment of the present invention.
The invention provides a vibration wave filtering method for eliminating environmental noise, which comprises the following steps:
and step 101, responding to a Yu Zhendong wave filtering instruction request, and acquiring target vibration waveform data of the medium-voltage distribution switch corresponding to the vibration wave filtering instruction request.
The vibration wave filtering instruction request refers to performing vibration wave filtering processing on vibration waves of the medium-voltage distribution switch and noise near the medium-voltage distribution switch, so as to determine whether the medium-voltage distribution switch has a fault.
The target vibration waveform data refers to vibration wave data of a medium-voltage distribution switch in a noise-free environment, and meanwhile, vibration wave real-time waveform data of the medium-voltage distribution switch in an installation environment at the same time, and further comprises noise data such as bird sound wave waveform data, car sound wave waveform data, man sound wave waveform data and the like near the medium-voltage distribution switch.
In the implementation, when responding to a vibration wave filtering instruction request, vibration wave data of the medium-voltage distribution switch under a noise environment corresponding to the vibration wave filtering instruction request, and vibration wave real-time waveform data of the medium-voltage distribution switch under an installation environment at the same time are obtained, and noise data such as bird sound wave waveform data, car sound wave waveform data, human sound wave waveform data and the like near the medium-voltage distribution switch are also included.
Step 102, inputting the target vibration waveform data into a preset target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data.
It should be noted that the target detection model refers to a regression detection model obtained after training and testing by the initial regression model. The regression detection model network is one of linear regression, a decision tree, a support vector machine or a random forest model.
The intensity peak data refers to the intensity peak point size of the vibration waveform data extracted.
The preset waveform time refers to the time during which the waveform accounting for eighty percent of the intensity peak exists.
In the specific implementation, the target vibration waveform data are respectively input into a target detection model, and the intensity peak point size, the time of eighty percent of the intensity peak and the vibration wave frequency of the waveform of the target vibration waveform data are extracted through the target detection model.
And step 103, inputting the intensity peak value data, the preset waveform time data and the vibration wave frequency corresponding to the vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value.
It should be noted that the preset failure determination model refers to a model generated by a failure determination calculation formula.
In specific implementation, intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in target vibration waveform data are input into a preset fault judgment model, and substituted into a fault judgment calculation formula, wherein the calculation formula is as follows:
wherein F is a fault value, a 1 A is a time duty ratio coefficient 2 For peak duty cycle, a 3 Is the frequency duty ratio coefficient, t max -t min For the set frequency wave time safety range, t m Is closest to t in the time safety range of vibration wave 1 Value of X max -X min For the set vibration wave intensity safety range value, X m Is closest to X in the safe range of vibration wave intensity fz The value of K max -K min For a set vibration frequency safety range, K m For the frequency of vibration to be closest to K in the safety range fz Value of 1=a 1 +a 2 +a 3 。
And calculating the fault value of the medium-voltage distribution switch corresponding to the current vibration wave real-time waveform data by adopting the calculation formula.
Step 104, determining a fault result of the fault value based on a preset fault threshold.
The preset fault threshold is set according to the actual situation, and is not limited herein.
In specific implementation, comparing the fault value with a preset fault threshold, if the fault value is greater than or equal to the preset fault threshold, indicating that a fault occurs, otherwise, indicating that no fault occurs.
And 105, calculating the true value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data.
The second vibration waveform data refers to bird sound wave waveform data, car sound wave waveform data, and man sound wave waveform data.
In the specific implementation, intensity peak value data corresponding to bird sound wave form data, car sound wave form data, man sound wave form data and vibration wave real-time wave form data are input into a real value calculation formula, and a real value of a fault result is calculated.
And 106, judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of a fault result according to the judging result.
The preset true threshold is set according to actual conditions, and is not limited herein.
In specific implementation, comparing the true value with a preset true threshold, if the true value is greater than or equal to the preset true threshold, indicating that the fault result is a false fault signal, otherwise, indicating that the fault result is a fault signal, and generating alarm information to alarm.
According to the method, the target vibration waveform data of the medium-voltage distribution switch corresponding to the vibration wave filtering instruction request is obtained by responding to the vibration wave filtering instruction request; inputting the target vibration waveform data into a preset target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data; inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value; determining a fault result of the fault value based on a preset fault threshold; calculating the true value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data respectively; judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of a fault result according to the judging result. The method solves the technical problems that the prior art cannot adopt a filtering method aiming at outdoor noisy environments such as bird sounds, car sounds and human sounds, cannot give relatively pure vibration waves of a switch mechanical state, and has low distinguishing accuracy.
The method substitutes the collected real-time waveform data into the constructed fault filtering model of the medium-voltage distribution switch, monitors vibration wave data in real time, identifies whether faults occur, substitutes the judgment result and the real-time waveform data into a true value identification strategy, carries out the authenticity identification of the judgment result, outputs the fault type and the authenticity of the judgment, compares the true value with a true threshold value, judges whether to give an alarm or not, improves the authenticity of the judgment, and has various and complex installation and distribution environments aiming at the medium-voltage distribution switch, multiple interference waves and self-adaptive specific application requirements on the filtering of the sampling vibration waves.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for filtering vibration waves to eliminate environmental noise according to a second embodiment of the present invention.
The invention provides a vibration wave filtering method for eliminating environmental noise, which comprises the following steps:
and step 201, responding to the Yu Zhendong wave filtering instruction request, and determining a medium-voltage distribution switch corresponding to the vibration wave filtering instruction request.
In the embodiment of the present invention, the implementation process of step 201 is similar to that of step 101, and will not be repeated here.
Step 202, adopting vibration wave data acquisition equipment to acquire first vibration wave data and vibration wave real-time waveform data of a medium-voltage distribution switch in a noise-free environment and in an installation environment respectively.
The first vibration wave data refers to vibration wave data of a medium voltage distribution switch in a noise-free environment.
The vibration wave real-time waveform data refers to vibration wave data of a medium-voltage distribution switch which is collected in real time in an installation environment.
In specific implementation, vibration wave data of the medium-voltage distribution switch in a noise-free environment and vibration wave data of the medium-voltage distribution switch in an installation environment at the same time are collected by vibration wave data collection equipment.
And 203, acquiring bird sound wave form data, car sound wave form data and man sound wave form data in a preset area of the medium-voltage distribution switch by adopting a wave form acquisition device.
The preset area of the medium voltage distribution switch refers to the vicinity/periphery of the medium voltage distribution switch, and the specific range is planned according to actual situations, which is not limited herein.
In the specific implementation, the waveform acquisition equipment is used for respectively acquiring bird sound wave waveform data, car sound wave waveform data and man sound wave waveform data of the medium-voltage distribution switch accessory.
And 204, generating second vibration waveform data by adopting bird sound wave waveform data, car sound wave waveform data and man sound wave waveform data.
In the specific implementation, the bird sound wave form data, the car sound wave form data and the man sound wave form data are all classified into the second vibration wave form data.
And 205, generating target vibration waveform data by using the first vibration waveform data, the vibration wave real-time waveform data and the second vibration waveform data.
In the implementation, vibration wave data, vibration wave real-time waveform data, bird sound wave waveform data, car sound wave waveform data and man sound wave waveform data of a medium-voltage power switch in a noise-free environment are combined to obtain target vibration wave waveform data.
And 206, inputting the target vibration waveform data into a preset target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data.
Optionally, step 206 includes the following steps S11-S15:
s11, dividing peaks of each waveform data in the target vibration waveform data at different time points according to a preset duty ratio coefficient, and generating a training set and a testing set;
s12, inputting waveform data corresponding to the training set into a preset initial detection model for training, and generating an updated detection model;
s13, inputting waveform data corresponding to the test set into the update detection model for testing, and generating a target detection model;
S14, inputting the real-time waveform data of the vibration wave into a target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the real-time waveform data of the vibration wave;
s15, inputting the second vibration waveform data into the target detection model, and extracting intensity peak value data corresponding to the second vibration waveform data.
The preset duty ratio is divided into a duty ratio of 70% and a duty ratio of 30%. And taking peaks of waveform data of the medium-voltage distribution switch in the noiseless environment, the medium-voltage distribution switch in the installation environment, the bird sound wave waveform data, the car sound wave waveform data and the man sound wave waveform data at different time points as 70% duty ratio coefficients as training sets, and 30% duty ratio coefficients as test sets.
The preset initial detection model is a regression model network.
Updating the detection model refers to a regression model network after training and optimizing the initial detection model.
In specific implementation, a training set with a duty ratio coefficient of 70% is input into an initial detection model, training is carried out to obtain an optimized updated detection model, the updated detection model is tested by using a test set with a duty ratio coefficient of 30%, and an optimal detection model meeting the preset waveform test accuracy is output as a target detection model.
In specific implementation, waveform data of vibration wave real-time waveform data, bird sound wave waveform data, car sound wave waveform data and man sound wave waveform data collected in a medium-voltage distribution switch in an installation environment are input into a target detection model, the input waveform data is analyzed by the target detection model, and the intensity peak point X of the vibration wave real-time waveform data is extracted through the target detection model fz Time t when a waveform having eighty percent of the intensity peak exists 1 And vibration wave frequency K fz 。
And respectively extracting the intensity peak values of the bird sound wave form data, the car sound wave form data and the man sound wave form data through the target detection model.
And 207, inputting the intensity peak value data, the preset waveform time data and the vibration wave frequency corresponding to the vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value.
Optionally, step 207 includes the following steps S21-S22:
s21, inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model; the calculation formula of the preset fault judgment model is as follows:
Wherein F is a fault value, a 1 A is a time duty ratio coefficient 2 For peak duty cycle, a 3 Is the frequency duty ratio coefficient, t max -t min For the set frequency wave time safety range, t m Is closest to t in the time safety range of vibration wave 1 Value of X max -X min For the set vibration wave intensity safety range value, X m Is closest to X in the safe range of vibration wave intensity fz The value of K max -K min For a set vibration frequency safety range, K m For the frequency of vibration to be closest to K in the safety range fz Value of 1=a 1 +a 2 +a 3 ；
S22, calculating a fault value of the vibration wave real-time waveform data through a preset fault judgment model.
In specific implementation, the intensity peak value X in the vibration wave real-time waveform data is respectively extracted through the target detection model fz Time t when a waveform having eighty percent of the intensity peak exists 1 And vibration wave frequency K fz 。
Inputting an intensity peak value, the time of eighty percent of the intensity peak value in the vibration wave real-time waveform data and the vibration wave frequency into a preset fault judgment model, wherein the calculation formula of the preset fault judgment model is as follows:
wherein F is a fault value, a 1 A is a time duty ratio coefficient 2 For peak duty cycle, a 3 Is the frequency duty ratio coefficient, t max -t min For the set frequency wave time safety range, t m Is closest to t in the time safety range of vibration wave 1 Value of X max -X min For the set vibration wave intensity safety range value, X m Is closest to X in the safe range of vibration wave intensity fz The value of K max -K min For a set vibration frequency safety range, K m For the frequency of vibration to be closest to K in the safety range fz Value of 1=a 1 +a 2 +a 3 ；
And calculating the fault value of the medium-voltage distribution switch corresponding to the vibration wave real-time waveform data according to a calculation formula of a preset fault judgment model.
Step 208, determining a fault result of the fault value based on a preset fault threshold.
Optionally, step 208 includes the following steps S31-S33:
s31, judging whether the fault value is larger than or equal to a preset fault threshold value;
s32, if yes, determining that a fault occurs, and generating a fault result;
if not, the failure does not occur, and a failure-free result is generated.
In specific implementation, the fault value is compared with a preset fault threshold, if the true value is greater than or equal to the preset true threshold, the fault judgment is accurate, a fault sending result is generated, if the true value is smaller than the preset fault threshold, the fault judgment is inaccurate, a fault result which does not occur is generated or the fault result needs to be judged again, and therefore the true value judgment of the fault result needs to be further carried out.
And 209, calculating the true value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data.
Optionally, step 209 includes the following steps S41-S44:
s41, acquiring second vibration waveform data and vibration wave real-time waveform data in the target vibration waveform data;
s42, calculating total numbers corresponding to the bird sound wave waveform data, the car sound wave waveform data and the man sound wave waveform data in the second vibration waveform data, and respectively generating the total number of bird sounds, the total number of car sounds and the total number of man sounds;
s43, inputting intensity peak value data, total bird sound number, total car sound number and total man sound number corresponding to bird sound wave data, car sound wave data, man sound wave data and vibration wave real-time wave data respectively into a preset true value model;
s44, calculating the true value of the fault result through a preset true value model.
The intensity peak points in the bird sound wave waveform data extracted by the target detection model are (m 1 ,m 2 ,m 3 ,...m n1 ) Strong in acoustic waveform data of car soundThe degree peak points are (k) 1 ,k 2 ,k 3 ,...k n2 ) The intensity peak points in the human voice acoustic waveform data are respectively (P 1 ,P 2 ,P 3 ,...P n3 ) And intensity peak value X in vibration wave real-time waveform data fz Respectively calculating the total number of the bird sound wave form data, the car sound wave form data and the man sound wave form data, wherein n1 is the total number of bird sounds, and m i I are intensity peaks of i bird voice waveforms, i epsilon (1, n 1), n2 are total number of car voices, and k j Is the intensity peak value of j car sound waveforms, j epsilon (1, n 2), n3 is the total number of human voice, p z For the intensity peaks of z individual waveforms, z e (1, n 3).
In specific implementation, bird sound wave form data, car sound wave form data, man sound wave form data and vibration wave real-time wave form data in target vibration wave form data are obtained, and intensity peak points in the bird sound wave form data are respectively (m) 1 ,m 2 ,m 3 ,...m n1 ) The intensity peak points in the car sound acoustic waveform data are (k) 1 ,k 2 ,k 3 ,...k n2 ) The intensity peak points in the human voice acoustic waveform data are respectively (P 1 ,P 2 ,P 3 ,...P n3 ) And intensity peak value X in vibration wave real-time waveform data fz And the total number of bird sounds, the total number of car sounds and the total number of human sounds are substituted into a calculation formula of a preset true value model, wherein the calculation formula is as follows:
wherein T is a true value, m i Peak intensity, k for i bird's voice waveforms j Intensity peak value, p, of j car sound waveforms z Intensity peak value, X of z voice waveforms fz The intensity peak value in the vibration wave real-time waveform data is represented by n1, n2 and n3, wherein n1 is the total number of bird sounds, n2 is the total number of car sounds and n3 is the total number of human sounds.
Step 210, judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of a fault result according to the judging result.
Optionally, step 210 includes the following steps S51-S53:
s51, judging whether the true value is larger than or equal to a preset true threshold value;
s52, if yes, determining that the fault result is a false fault signal, and not needing to be alarmed;
and S53, if not, determining that the fault result is a fault signal, and generating alarm information.
The alarm information is information for alarming a fault signal of the medium-voltage distribution switch.
In a specific implementation, the actual value calculated in step 209 is compared with a preset actual threshold, if the actual value is greater than or equal to the preset actual threshold, the actual value is indicated as a false fault signal, alarm processing is not required, if the actual value is less than the preset actual threshold, the actual value is indicated as a fault signal, alarm processing is required, and the generated alarm information is sent to a manager or a maintainer. The judgment authenticity is improved, and meanwhile, the processing is reported in time.
According to the method, the target vibration waveform data of the medium-voltage distribution switch corresponding to the vibration wave filtering instruction request is obtained by responding to the vibration wave filtering instruction request; inputting the target vibration waveform data into a preset target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data; inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value; determining a fault result of the fault value based on a preset fault threshold; calculating the true value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data respectively; judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of a fault result according to the judging result. The method solves the technical problems that the prior art cannot adopt a filtering method aiming at outdoor noisy environments such as bird sounds, car sounds and human sounds, cannot give relatively pure vibration waves of a switch mechanical state, and has low distinguishing accuracy.
The method substitutes the collected real-time waveform data into the constructed fault filtering model of the medium-voltage distribution switch, monitors vibration wave data in real time, identifies whether faults occur, substitutes the judgment result and the real-time waveform data into a true value identification strategy, carries out the authenticity identification of the judgment result, outputs the fault type and the authenticity of the judgment, compares the true value with a true threshold value, judges whether to give an alarm or not, improves the authenticity of the judgment, and has various and complex installation and distribution environments aiming at the medium-voltage distribution switch, multiple interference waves and self-adaptive specific application requirements on the filtering of the sampling vibration waves.
Referring to fig. 3, fig. 3 is a block diagram illustrating a vibration wave filtering system for eliminating environmental noise according to a third embodiment of the present invention.
The invention provides a vibration wave filtering system for eliminating environmental noise, which comprises:
the target vibration waveform data module 301 is configured to respond to a vibration wave filtering instruction request, and obtain target vibration waveform data of the medium-voltage power distribution switch corresponding to the vibration wave filtering instruction request;
the vibration wave frequency module 302 is configured to input the target vibration waveform data into a preset target detection model, and extract intensity peak data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data;
The fault value module 303 is configured to input intensity peak value data, preset waveform time data, and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and output a fault value;
a fault result module 304, configured to determine a fault result of the fault value based on a preset fault threshold;
a true value module 305, configured to calculate a true value of the fault result by using intensity peak data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data, respectively;
the fault signal module 306 is configured to determine whether the true value is greater than or equal to a preset true threshold, and determine a fault signal of the fault result according to the determination result.
Optionally, the target vibration waveform data module 301 includes:
the medium-voltage distribution switch sub-module is used for responding to the vibration wave filtering instruction request and determining a medium-voltage distribution switch corresponding to the vibration wave filtering instruction request;
the vibration wave real-time waveform data submodule is used for respectively acquiring first vibration wave data and vibration wave real-time waveform data of the medium-voltage distribution switch in a noise-free environment and in an installation environment by adopting vibration wave data acquisition equipment;
The voice sound wave data sub-module is used for acquiring bird voice sound wave data, car voice sound wave data and voice sound wave data in a preset area of the medium-voltage distribution switch by adopting the wave acquisition equipment;
the second vibration waveform data submodule is used for generating second vibration waveform data by adopting bird sound wave waveform data, car sound wave waveform data and man sound wave waveform data;
and the target vibration waveform data submodule is used for generating target vibration waveform data by adopting the first vibration waveform data, the vibration wave real-time waveform data and the second vibration waveform data.
Optionally, the vibration wave frequency module 302 includes:
the test set submodule is used for dividing peak values of each waveform data in the target vibration waveform data at different time points according to a preset duty ratio coefficient to generate a training set and a test set;
the updating detection mold module is used for inputting waveform data corresponding to the training set into a preset initial detection model for training, and generating an updating detection model;
the target detection model submodule is used for inputting waveform data corresponding to the test set into the update detection model for testing, and generating a target detection model;
the vibration wave frequency submodule is used for inputting vibration wave real-time waveform data into the target detection model and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the vibration wave real-time waveform data;
And the intensity peak value data sub-module is used for inputting the second vibration waveform data into the target detection model and extracting intensity peak value data corresponding to the second vibration waveform data.
Optionally, the fault value module 303 includes:
the fault judgment model submodule is used for inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model; the calculation formula of the preset fault judgment model is as follows:
wherein F is a fault value, a 1 A is a time duty ratio coefficient 2 For peak duty cycle, a 3 Is the frequency duty ratio coefficient, t max -t min For the set frequency wave time safety range, t m Is closest to t in the time safety range of vibration wave 1 Value of X max -X min For the set vibration wave intensity safety range value, X m Is closest to X in the safe range of vibration wave intensity fz The value of K max -K min For a set vibration frequency safety range, K m For the frequency of vibration to be closest to K in the safety range fz Value of 1=a 1 +a 2 +a 3 ；
And the fault value sub-module is used for calculating the fault value of the vibration wave real-time waveform data through a preset fault judgment model.
Optionally, the fault result module 304 includes:
the first judging submodule is used for judging whether the fault value is larger than or equal to a preset fault threshold value or not;
The failure occurrence sub-module is used for determining that a failure occurs if yes, and generating a failure occurrence result;
and the failure-free sub-module is used for generating a failure-free result if the failure-free sub-module does not generate a failure.
Optionally, the real value module 305 includes:
the acquisition sub-module is used for acquiring second vibration waveform data and vibration wave real-time waveform data in the target vibration waveform data;
the calculating submodule is used for calculating the total number corresponding to the bird sound wave waveform data, the car sound wave waveform data and the man sound wave waveform data in the second vibration waveform data respectively and generating the total number of bird sounds, the total number of car sounds and the total number of man sounds respectively;
the preset real value model submodule is used for inputting intensity peak value data, total bird sound number, total car sound number and total human sound number corresponding to bird sound wave data, car sound wave data, human sound wave data and vibration wave real-time wave data respectively into a preset real value model;
and the true value sub-module is used for calculating the true value of the fault result through a preset true value model.
Optionally, the fault signal module 306 includes:
the second judging submodule is used for judging whether the true value is larger than or equal to a preset true threshold value;
The false fault signal sub-module is used for determining that the fault result is a false fault signal if yes, and no alarm is needed;
and the fault signal sub-module is used for determining that the fault result is a fault signal and generating alarm information if the fault signal sub-module is not used for determining that the fault result is a fault signal.
An electronic device according to a fourth embodiment of the present application includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for filtering a vibration wave for eliminating environmental noise according to any one of the above embodiments.
A fifth embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, where the computer program when executed implements the vibration wave filtering method for removing environmental noise according to any one of the above embodiments.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A vibration wave filtering method for eliminating environmental noise, comprising:
responding to a vibration wave filtering instruction request, and acquiring target vibration waveform data of a medium-voltage distribution switch corresponding to the vibration wave filtering instruction request;
inputting the target vibration waveform data into a preset target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data;
inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model, and outputting a fault value;
Determining a fault result of the fault value based on a preset fault threshold;
calculating the true value of the fault result by adopting intensity peak value data corresponding to second vibration waveform data and vibration wave real-time waveform data in the target vibration waveform data respectively;
judging whether the true value is larger than or equal to a preset true threshold value, and determining a fault signal of the fault result according to a judging result.
2. The method according to claim 1, wherein the step of acquiring the target vibration waveform data of the medium voltage distribution switch corresponding to the vibration wave filtering instruction request in response to the vibration wave filtering instruction request comprises:
responding to a vibration wave filtering instruction request, and determining a medium-voltage distribution switch corresponding to the vibration wave filtering instruction request;
respectively acquiring first vibration wave data and vibration wave real-time waveform data of a medium-voltage distribution switch in a noise-free environment and in an installation environment by using vibration wave data acquisition equipment;
acquiring bird sound wave form data, car sound wave form data and man sound wave form data in a preset area of a medium-voltage distribution switch by adopting a wave form acquisition device;
Generating second vibration waveform data by adopting the bird sound wave waveform data, the car sound wave waveform data and the man sound wave waveform data;
and generating target vibration waveform data by adopting the first vibration waveform data, the vibration wave real-time waveform data and the second vibration waveform data.
3. The method for filtering vibration waves to eliminate environmental noise according to claim 2, wherein the step of inputting the target vibration waveform data into a preset target detection model and extracting intensity peak data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data comprises the steps of:
dividing peaks of each waveform data in the target vibration waveform data at different time points according to a preset duty ratio coefficient to generate a training set and a testing set;
inputting waveform data corresponding to the training set into a preset initial detection model for training, and generating an updated detection model;
inputting waveform data corresponding to the test set into the updated detection model for testing, and generating a target detection model;
inputting the real-time waveform data of the vibration wave into the target detection model, and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the real-time waveform data of the vibration wave;
And inputting the second vibration waveform data into the target detection model, and extracting intensity peak value data corresponding to the second vibration waveform data.
4. The method for filtering vibration waves to eliminate environmental noise according to claim 1, wherein the step of inputting intensity peak data, preset waveform time data, and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset failure judgment model, and outputting a failure value comprises:
inputting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model; the calculation formula of the preset fault judgment model is as follows:
wherein F is a fault value, a 1 A is a time duty ratio coefficient 2 For peak duty cycle, a 3 Is the frequency duty ratio coefficient, t max -t min For the set frequency wave time safety range, t m Is closest to t in the time safety range of vibration wave 1 Value of X max -X min For the set vibration wave intensity safety range value, X m Is closest to X in the safe range of vibration wave intensity fz The value of K max -K min For a set vibration frequency safety range, K m For the frequency of vibration to be closest to K in the safety range fz Value of 1=a 1 +a 2 +a 3 ；
And calculating the fault value of the vibration wave real-time waveform data through the preset fault judging model.
5. The method of vibration wave filtering for eliminating environmental noise according to claim 1, wherein the step of determining a failure result of the failure value based on a preset failure threshold value comprises:
judging whether the fault value is larger than or equal to a preset fault threshold value or not;
if yes, determining that a fault occurs, and generating a fault result;
if not, the failure does not occur, and a failure-free result is generated.
6. The method of filtering vibration waves for eliminating environmental noise according to claim 2, wherein the step of calculating a true value of the fault result using intensity peak data corresponding to the second vibration waveform data and the vibration wave real-time waveform data, respectively, of the target vibration waveform data includes:
acquiring second vibration waveform data and vibration wave real-time waveform data in the target vibration waveform data;
calculating the total number corresponding to the bird sound wave waveform data, the car sound wave waveform data and the human sound wave waveform data in the second vibration waveform data, and respectively generating the total number of bird sounds, the total number of car sounds and the total number of human sounds;
Inputting preset true value models by adopting intensity peak value data, the total number of bird sounds, the total number of car sounds and the total number of human sounds, which correspond to the bird sound wave data, the car sound wave data, the human sound wave data and the vibration wave real-time wave data respectively;
and calculating the true value of the fault result through the preset true value model.
7. The method according to claim 1, wherein the step of determining whether the true value is greater than or equal to a preset true threshold value, and determining a fault signal of the fault result according to the determination result, comprises:
judging whether the true value is larger than or equal to a preset true threshold value;
if yes, determining the fault result as a false fault signal without alarming;
if not, determining the fault result as a fault signal and generating alarm information.
8. A vibration wave filtering system for removing ambient noise, comprising:
the target vibration waveform data module is used for responding to a vibration wave filtering instruction request and acquiring target vibration waveform data of the medium-voltage distribution switch corresponding to the vibration wave filtering instruction request;
The vibration wave frequency module is used for inputting the target vibration waveform data into a preset target detection model and extracting intensity peak value data, preset waveform time data and vibration wave frequency corresponding to the target vibration waveform data;
the fault value module is used for inputting the intensity peak value data, the preset waveform time data and the vibration wave frequency corresponding to the vibration wave real-time waveform data in the target vibration waveform data into a preset fault judgment model and outputting a fault value;
the fault result module is used for determining a fault result of the fault value based on a preset fault threshold value;
the real value module is used for calculating the real value of the fault result by adopting intensity peak value data corresponding to the second vibration waveform data and the vibration wave real-time waveform data in the target vibration waveform data respectively;
and the fault signal module is used for judging whether the true value is larger than or equal to a preset true threshold value or not, and determining a fault signal of the fault result according to a judging result.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method of vibration wave filtering for removing ambient noise as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed, implements the method of vibration wave filtering for removing environmental noise according to any one of claims 1-7.
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| CN117310353A (en) * | 2023-11-30 | 2023-12-29 | 淮安苏达电气有限公司 | Method and system for testing through-flow pressurization faults of primary and secondary circuits of transformer substation |
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| CN117310353A (en) * | 2023-11-30 | 2023-12-29 | 淮安苏达电气有限公司 | Method and system for testing through-flow pressurization faults of primary and secondary circuits of transformer substation |
| CN117310353B (en) * | 2023-11-30 | 2024-02-09 | 淮安苏达电气有限公司 | Method and system for testing through-flow pressurization faults of primary and secondary circuits of transformer substation |
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