Transformer voiceprint denoising method and system based on edge calculation

Abstract

The invention discloses a transformer voiceprint denoising method and a transformer voiceprint denoising system based on edge computing, and aims to achieve the purpose of denoising by performing large-scale neural network processing on a server of a power system or a platform at the present stage, which is large in operation load, low in efficiency and general in data analysis accuracy of extracted voiceprint features. The method comprises the following steps: determining a complete voiceprint sampling signal after processing environmental noise characteristics in a transformer voiceprint sampling signal at an edge end, converting the complete voiceprint sampling signal into a frequency domain signal, obtaining a frequency spectrum of a corresponding segmented signal, carrying out characteristic processing, constructing a transformer voiceprint denoising processing model, and carrying out frequency spectrum data evaluation on the segmented signal. And the region-level detection background automatically adjusts the gain according to the de-noising spectrum data of each section of the transformer, so that the de-noising processing work of the transformer can be performed more quickly to obtain a more accurate noise estimation result, and subsequent fault detection and diagnosis are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer data processing, and in particular to a transformer voiceprint denoising method and system based on edge computing. Background Art

[0002] Transformer voiceprint technology is a technology that uses sound signals to monitor and diagnose the operating status of power transformers. It analyzes the acoustic and vibration signals generated during the operation of the transformer, extracts the voiceprint features, and evaluates the health of the transformer.

[0003] The vibration noise of the windings and cores in the transformer is the main component of the transformer body noise. The sound generated by the transformer is related to the applied voltage; the main spectral component of the core vibration and winding vibration sound is 100hz; in addition, there are also environmental noises such as fans and other cooling systems, load-regulating tap changers, etc.; for traditional transformer voiceprint denoising methods, most of them use the power system or platform server to perform large-scale BP neural network algorithm or CNN convolutional neural network processing to achieve the purpose of denoising, which has a large computational load, low efficiency, and the data analysis accuracy of the voiceprint features extracted is average. At the same time, traditional fault inspection and diagnosis rely on later offline analysis. Even online monitoring transmits massive data to the server for processing and analysis. However, at this stage, there is a lack of edge processing of transformer voiceprint data. For this reason, we propose a transformer voiceprint denoising method and system based on edge computing. Summary of the invention

[0004] In view of the above-mentioned problems existing in the existing transformer voiceprint data processing, the present invention is proposed.

[0005] Therefore, one of the objects of the present invention is to provide a transformer voiceprint denoising method and system based on edge computing, which constructs a transformer voiceprint denoising processing model based on lightweight edge computing at the edge by processing the transformer voiceprint data at the edge, and can realize faster transformer denoising processing to obtain more accurate noise estimation results, which is beneficial to subsequent fault detection and diagnosis.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] On the one hand, the present invention provides a transformer voiceprint denoising method based on edge computing, comprising:

[0008] S101, after the edge end obtains the transformer voiceprint sampling signal through the sensor, the filtering algorithm is used to process the environmental noise characteristics in the transformer voiceprint sampling signal, and a complete voiceprint sampling signal is determined by dividing it into several segments of the transformer voiceprint signal and determining the width of each segment of the transformer voiceprint signal;

[0009] S102, after windowing each segment of the transformer voiceprint signal, Fourier transform is performed on the transformer voiceprint signal in the window, so that each segment of the transformer voiceprint signal is converted from a time domain signal to a frequency domain signal and a spectrum of a corresponding segmented signal is obtained;

[0010] S103. The edge end performs machine learning annotation on the spectrum of the segmented signal, determines the preset core vibration and winding vibration sound characteristics and the characteristics of the environmental noise influence and the corresponding parameter information according to the annotation processing results, builds a transformer voiceprint denoising processing model and performs spectrum data evaluation of the segmented signal, generates a frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, calculates the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal according to the frequency domain signal evaluation coefficient, that is, represents the characteristic parameters of the sound signals of different transformer noises, performs transformer voiceprint denoising based on the energy value of the segmented frequency and generates transformer denoised spectrum data of each segment, and transmits the transformer denoised spectrum data of each segment to the regional detection background;

[0011] S104, after the regional detection background automatically adjusts the gain according to the transformer denoising spectrum data of each segment, it is input into the neural network and cyclically identified and verified, and then the voiceprint is identified and matched according to the transformer voiceprint feature vector to complete the determination of the transformer fault, and at the same time, feedback is provided to verify whether the transformer voiceprint denoising meets the standard and output the corresponding control information to the edge end;

[0012] S105. The edge end receives corresponding control information and performs corresponding control work based on the transformer.

[0013] As a preferred solution of the present invention, in S102, the transformer voiceprint signal in the window is subjected to Fourier transform. Given a transformer voiceprint signal x(t) in a continuous time domain, its Fourier transform is expressed as follows:

[0014]

[0015] Where x(f) is a complex-valued function with frequency f, x(t) is the transformer voiceprint signal at the tth time point in the continuous time domain, i is an imaginary unit, f is the frequency in the continuous time domain, and dt is the differential sign.

[0016] As a preferred solution of the present invention, in S103, the transformer voiceprint denoising processing model is constructed and the spectrum data of the segmented signal is evaluated to generate the frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, which is specifically as follows:

[0017]

[0018] Among them, Gi is the evaluation coefficient of the i-th frequency domain signal; They represent the weight coefficients of the three specification gradients of the edge-end transformer equipment respectively; They represent the weight coefficients of the α, β and χ indicators corresponding to the three characteristics of core vibration, winding vibration and environmental noise vibration respectively; U(xi) represents the parameter value of the spectrum data of the segmented signal.

[0019] As a preferred solution of the present invention, in S103, the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal is calculated according to the frequency domain signal evaluation coefficient, as follows:

[0020] E i =G i *∫X i (t) 2 dt;

[0021] Among them, E i is the energy value of the ith frequency domain signal and is used as the characteristic parameter of the transformer noise sound signal. i (t) is the original data value of the transformer voiceprint signal in the i-th frequency domain signal.

[0022] As a preferred solution of the present invention, it also includes analysis of the core vibration and winding vibration weights, as follows:

[0023] Assume that the load current flowing through the transformer winding is The magnitude of the electromagnetic force on the transformer winding is

[0024] Among them, I is the transformer winding load current value, I m is the load current amplitude, is the initial phase value, is the cosine of the load current, p is the electromagnetic force coefficient, and it is taken as a constant under steady-state operating conditions, and F is the electromagnetic force on the winding;

[0025] The electromagnetic force of the winding is composed of a constant and a sinusoidal quantity, the frequency is twice the power supply frequency, and the magnitude is proportional to the square of the load current;

[0026] When the current contains harmonics, the frequency components of the electromagnetic force are divided into harmonic multiples, sum frequencies and difference frequencies, and the distribution information of the axial electromagnetic force and the radial electromagnetic force is obtained;

[0027] According to the harmonic frequency doublings, sum frequencies and difference frequencies and the distribution information of the magnitude of the axial electromagnetic force and the radial electromagnetic force, a two-dimensional characteristic matrix of the corresponding harmonic category is constructed, which is a two-dimensional characteristic matrix of the frequency doublings, a two-dimensional characteristic matrix of the sum frequency, and a two-dimensional characteristic matrix of the difference frequency, that is, the two-dimensional characteristic matrix of the frequency doublings is expressed as F1(μ, γ), the two-dimensional characteristic matrix of the sum frequency is expressed as F2(μ, γ), and the two-dimensional characteristic matrix of the difference frequency is expressed as F3(μ, γ); wherein (μ, γ) are the characteristic data of the axial and radial electromagnetic forces, respectively;

[0028] A weighted analysis model of axial and radial core vibration and winding vibration is established based on the two-dimensional characteristic matrix of the harmonic category, and weight coefficients of core vibration and winding vibration are generated according to the analysis of the weighted analysis model of axial and radial core vibration and winding vibration.

[0029] As a preferred solution of the present invention, the filtering algorithm in S101 adopts a minimum mean square error algorithm.

[0030] As a preferred solution of the present invention, the voiceprint recognition and matching in S104 uses a Gaussian mixture model or a support vector machine for voiceprint recognition.

[0031] As a preferred solution of the present invention, the weighted analysis model of the axial and radial directions of the core vibration and the winding vibration is specifically as follows:

[0032]

[0033] Where n represents the i-th data of the transformer under the current harmonic category, that is, the i-th data of the two-dimensional characteristic matrix of harmonic multiple frequency, sum frequency and difference frequency, j represents the index of weight analysis, that is, the axial index of core vibration and winding vibration, k represents the radial index, y ij Indicates the axial electromagnetic force characteristic data of the core vibration and winding vibration under the current harmonic, y ik It represents the radial electromagnetic force characteristic data of the core vibration and winding vibration under the current harmonic, p ij 、p ik The weighted analysis results of the axial and radial directions of the core vibration and winding vibration respectively.

[0034] On the one hand, the present invention provides a system for a transformer voiceprint denoising method based on edge computing, comprising:

[0035] The edge acquisition module is used to obtain the transformer voiceprint sampling signal through the sensor, and then use the filtering algorithm to process the environmental noise characteristics in the transformer voiceprint sampling signal to determine a complete voiceprint sampling signal;

[0036] An edge processing module, for performing a windowing process on each segment of the transformer voiceprint signal, and then performing a Fourier transform on the transformer voiceprint signal in the window, so that each segment of the transformer voiceprint signal is converted from a time domain signal to a frequency domain signal and obtains a spectrum of a corresponding segmented signal;

[0037] The edge analysis module is used to perform machine learning annotation on the spectrum of the segmented signal, determine the preset core vibration and winding vibration sound characteristics and the characteristics of the environmental noise impact and the corresponding parameter information according to the annotation processing results, build a transformer voiceprint denoising processing model and evaluate the spectrum data of the segmented signal, generate the frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, and calculate the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal according to the frequency domain signal evaluation coefficient;

[0038] The regional detection backend is used to automatically adjust the gain based on the transformer denoising spectrum data of each section and input it into the transformer cloud service platform;

[0039] The transformer cloud service platform is used to receive the uploaded information from the regional detection background, input it into the neural network and after cyclic identification and verification, it identifies and matches the voiceprint according to the transformer voiceprint feature vector to complete the determination of the transformer fault. At the same time, it verifies whether the transformer voiceprint denoising meets the standards and outputs the corresponding control information to the edge end, and sends the corresponding control information transmission value to the transformer to make corresponding control work.

[0040] Beneficial effects of the present invention: The present invention constructs a transformer voiceprint denoising processing model based on edge lightweight edge computing through processing of transformer voiceprint data at the edge, which can realize faster transformer denoising processing to obtain more accurate noise estimation results, which is beneficial to subsequent fault detection and diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0042] Figure 1 It is a flowchart of the method of the present invention;

[0043] Figure 2 Another judgment flow chart of the method of the present invention;

[0044] Figure 3 Schematic diagram of the modular structure of the system of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0046] Example 1

[0047] Reference Figure 1 and Figure 2 , is an embodiment of the present invention, which provides a transformer voiceprint denoising method based on edge computing, including:

[0048] S101, after the edge end obtains the transformer voiceprint sampling signal through the sensor, the filtering algorithm is used to process the environmental noise characteristics in the transformer voiceprint sampling signal, and a complete voiceprint sampling signal is determined by dividing it into several segments of the transformer voiceprint signal and determining the width of each segment of the transformer voiceprint signal, wherein the filtering algorithm can use the minimum mean square error algorithm;

[0049] S102, after windowing each segment of the transformer voiceprint signal, Fourier transform is performed on the transformer voiceprint signal in the window, so that each segment of the transformer voiceprint signal is converted from a time domain signal to a frequency domain signal and a spectrum of a corresponding segmented signal is obtained;

[0050] S103. The edge end performs machine learning annotation on the spectrum of the segmented signal, determines the preset core vibration and winding vibration sound characteristics and the characteristics of the environmental noise influence and the corresponding parameter information according to the annotation processing results, builds a transformer voiceprint denoising processing model and performs spectrum data evaluation of the segmented signal, generates a frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, calculates the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal according to the frequency domain signal evaluation coefficient, that is, represents the characteristic parameters of the sound signals of different transformer noises, performs transformer voiceprint denoising based on the energy value of the segmented frequency and generates transformer denoised spectrum data of each segment, and transmits the transformer denoised spectrum data of each segment to the regional detection background;

[0051] S104, after the regional detection background automatically adjusts the gain according to the transformer denoising spectrum data of each segment, it is input into the neural network and cyclically identified and verified, and then the voiceprint is identified and matched according to the transformer voiceprint feature vector to complete the determination of the transformer fault. At the same time, feedback is provided to verify whether the transformer voiceprint denoising meets the standard and output the corresponding control information to the edge end, wherein the voiceprint recognition and matching uses a Gaussian mixture model or a support vector machine for voiceprint recognition;

[0052] S105. The edge end receives corresponding control information and performs corresponding control work based on the transformer.

[0053] Specifically described in one embodiment, in S102, the transformer voiceprint signal in the window is Fourier transformed. Given a transformer voiceprint signal x(t) in a continuous time domain, its Fourier transform is expressed as follows:

[0054]

[0055] Where x(f) is a complex-valued function with frequency f, x(t) is the transformer voiceprint signal at the tth time point in the continuous time domain, i is an imaginary unit, f is the frequency in the continuous time domain, and dt is the differential sign.

[0056] In one embodiment, it should be emphasized that in S103, the transformer voiceprint denoising processing model is constructed and the spectrum data of the segmented signal is evaluated to generate the frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, which is specifically as follows:

[0057]

[0058] Among them, G i is the evaluation coefficient of the i-th frequency domain signal; They represent the weight coefficients of the three specification gradients of the edge-end transformer equipment respectively; They represent the weight coefficients of the α, β and χ indicators corresponding to the three characteristics of core vibration, winding vibration and environmental noise vibration respectively; U(xi) represents the parameter value of the spectrum data of the segmented signal.

[0059] In addition, in S103, the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal is calculated according to the frequency domain signal evaluation coefficient, as follows:

[0060] E i =G i *∫X i (t) 2 dt;

[0061] Among them, E i is the energy value of the ith frequency domain signal and is used as the characteristic parameter of the transformer noise sound signal. i (t) is the original data value of the transformer voiceprint signal in the i-th frequency domain signal.

[0062] Example 2

[0063] It is further explained that in one embodiment, the alternating electromagnetic force not only causes axial vibration, but also radial vibration of the winding. Compared with the research on the axial electromagnetic force of the winding, there has been less research on the radial vibration electromagnetic force in the past. At the same time, there is a lack of correlation analysis of the axial vibration and radial vibration caused by the electromagnetic force at the edge end at this stage. With the increase in transformer capacity and other reasons, more and more researchers have proposed to comprehensively consider axial and radial vibrations to obtain more accurate noise estimation results. For this reason, based on the first embodiment, a second embodiment is given. In the second embodiment, it also includes an analysis of the weights of the core vibration and the winding vibration, as follows:

[0064] Assume that the load current flowing through the transformer winding is The magnitude of the electromagnetic force on the transformer winding is

[0065] Among them, I is the transformer winding load current value, I m is the load current amplitude, is the initial phase value, is the cosine of the load current, p is the electromagnetic force coefficient, and it is taken as a constant under steady-state operating conditions, and F is the electromagnetic force on the winding;

[0066] The electromagnetic force of the winding is composed of a constant and a sinusoidal quantity, the frequency is twice the power supply frequency, and the magnitude is proportional to the square of the load current;

[0067] When the current contains harmonics, the frequency components of the electromagnetic force are divided into harmonic multiples, sum frequencies and difference frequencies, and the distribution information of the axial electromagnetic force and the radial electromagnetic force is obtained;

[0068] According to the harmonic frequency doublings, sum frequencies and difference frequencies and the distribution information of the magnitude of the axial electromagnetic force and the radial electromagnetic force, a two-dimensional characteristic matrix of the corresponding harmonic category is constructed, which is a two-dimensional characteristic matrix of the frequency doublings, a two-dimensional characteristic matrix of the sum frequency, and a two-dimensional characteristic matrix of the difference frequency, that is, the two-dimensional characteristic matrix of the frequency doublings is expressed as F1(μ, γ), the two-dimensional characteristic matrix of the sum frequency is expressed as F2(μ, γ), and the two-dimensional characteristic matrix of the difference frequency is expressed as F3(μ, γ); wherein (μ, γ) are the characteristic data of the axial and radial electromagnetic forces, respectively;

[0069] A weighted analysis model of axial and radial core vibration and winding vibration is established based on the two-dimensional characteristic matrix of the harmonic category, and weight coefficients of core vibration and winding vibration are generated according to the analysis of the weighted analysis model of axial and radial core vibration and winding vibration.

[0070] According to the content of the above-mentioned embodiment 2, this embodiment preferably provides the axial and radial weight analysis models of the core vibration and the winding vibration as follows:

[0071]

[0072] Where n represents the i-th data of the transformer under the current harmonic category, that is, the i-th data of the two-dimensional characteristic matrix of harmonic multiple frequency, sum frequency and difference frequency, j represents the index of weight analysis, that is, the axial index of core vibration and winding vibration, k represents the radial index, y ij Indicates the axial electromagnetic force characteristic data of the core vibration and winding vibration under the current harmonic, y ik It represents the radial electromagnetic force characteristic data of the core vibration and winding vibration under the current harmonic, p ij 、p ik The weighted analysis results of the axial and radial directions of the core vibration and winding vibration respectively.

[0073] like Figure 2 As shown, based on the above two embodiments, it can be known that based on the processing and feature analysis of the collected voiceprint data, a transformer voiceprint denoising processing model of edge-end lightweight edge computing can be constructed, and the spectrum data of the segmented signal in the voiceprint denoising processing model is evaluated to generate a frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, and the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal is calculated according to the frequency domain signal evaluation coefficient, and the transformer voiceprint denoising work is performed according to the energy value of the segmented frequency and the transformer denoising spectrum data of each segment is generated, and then the transformer denoising spectrum data of each segment is transmitted to the regional detection background; In this way, the amount of data processed by the platform can be effectively reduced. Through the precise analysis and judgment of the energy value of the segmented frequency, the voiceprint denoising of core vibration, winding vibration and environmental noise can be performed on transformers of different specifications at three levels. After the gain is automatically adjusted by the regional-level detection background and uploaded to the platform for fault identification, feedback is provided to verify whether the transformer voiceprint denoising meets the standards and output the corresponding control information to the edge end, and corresponding control information is made. The overall process design is reasonable, and compared with traditional voiceprint denoising, the response is faster, the central calculation amount is greatly reduced, the accuracy is high, and it is conducive to subsequent fault detection and diagnosis.

[0074] At the same time Figure 3 As shown, this embodiment is also based on the above-mentioned transformer voiceprint denoising method based on edge computing, and a corresponding system is provided, including an edge computing terminal 10, a regional detection background 20 and a transformer cloud service platform 30, wherein the edge computing terminal 10 of this system is provided with an edge terminal acquisition module, an edge terminal processing module and an edge terminal analysis module, and its functions are expressed as follows:

[0075] The edge acquisition module is used to obtain the transformer voiceprint sampling signal through the sensor, and then use the filtering algorithm to process the environmental noise characteristics in the transformer voiceprint sampling signal to determine a complete voiceprint sampling signal;

[0076] An edge processing module, for performing a windowing process on each segment of the transformer voiceprint signal, and then performing a Fourier transform on the transformer voiceprint signal in the window, so that each segment of the transformer voiceprint signal is converted from a time domain signal to a frequency domain signal and obtains a spectrum of a corresponding segmented signal;

[0077] The edge analysis module is used to perform machine learning annotation on the spectrum of the segmented signal, determine the preset core vibration and winding vibration sound characteristics and the characteristics of the environmental noise impact and the corresponding parameter information according to the annotation processing results, build a transformer voiceprint denoising processing model and evaluate the spectrum data of the segmented signal, generate the frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, and calculate the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal according to the frequency domain signal evaluation coefficient;

[0078] The regional detection backend 20 is used to automatically adjust the gain according to the transformer denoising spectrum data of each section and input it to the transformer cloud service platform;

[0079] The transformer cloud service platform 30 is used to receive the uploaded information from the regional detection background, input it into the neural network and after cyclic identification and verification, identify and match the voiceprint according to the transformer voiceprint feature vector to complete the determination of the transformer fault, and at the same time feedback to verify whether the transformer voiceprint denoising meets the standard and output the corresponding control information to the edge end, and send the corresponding control information transmission value transformer to perform corresponding control work.

[0080] To sum up, the present invention constructs a transformer voiceprint denoising processing model based on lightweight edge computing at the edge through processing of transformer voiceprint data at the edge, which can realize faster transformer denoising processing to obtain more accurate noise estimation results, which is beneficial to subsequent fault detection and diagnosis.

[0081] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0083] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0084] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0085] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0086] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0087] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0088] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A transformer voiceprint denoising method based on edge computing, characterized in that: include: S101, after the edge end obtains the transformer voiceprint sampling signal through the sensor, the filtering algorithm is used to process the environmental noise characteristics in the transformer voiceprint sampling signal, and a complete voiceprint sampling signal is determined by dividing it into several segments of the transformer voiceprint signal and determining the width of each segment of the transformer voiceprint signal; S102, after windowing each segment of the transformer voiceprint signal, Fourier transform is performed on the transformer voiceprint signal in the window, so that each segment of the transformer voiceprint signal is converted from a time domain signal to a frequency domain signal and a spectrum of a corresponding segmented signal is obtained; S103. The edge end performs machine learning annotation on the spectrum of the segmented signal, determines the preset core vibration and winding vibration sound characteristics and the characteristics of the environmental noise influence and the corresponding parameter information according to the annotation processing results, builds a transformer voiceprint denoising processing model and performs spectrum data evaluation of the segmented signal, generates a frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, calculates the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal according to the frequency domain signal evaluation coefficient, that is, represents the characteristic parameters of the sound signals of different transformer noises, performs transformer voiceprint denoising based on the energy value of the segmented frequency and generates transformer denoised spectrum data of each segment, and transmits the transformer denoised spectrum data of each segment to the regional detection background; S104, after the regional detection background automatically adjusts the gain according to the transformer denoising spectrum data of each segment, it is input into the neural network and cyclically identified and verified, and then the voiceprint is identified and matched according to the transformer voiceprint feature vector to complete the determination of the transformer fault, and at the same time, feedback is provided to verify whether the transformer voiceprint denoising meets the standard and output the corresponding control information to the edge end; S105. The edge end receives corresponding control information and performs corresponding control work based on the transformer.

2. The transformer voiceprint denoising method based on edge computing according to claim 1, characterized in that: In S102, the transformer voiceprint signal in the window is Fourier transformed. Given a transformer voiceprint signal x(t) in a continuous time domain, its Fourier transform is expressed as follows: Where x(f) is a complex-valued function with frequency f, x(t) is the transformer voiceprint signal at the tth time point in the continuous time domain, i is an imaginary unit, f is the frequency in the continuous time domain, and dt is the differential sign.

3. The transformer voiceprint denoising method based on edge computing according to claim 1, characterized in that: In S103, the transformer voiceprint denoising processing model is constructed and the spectrum data of the segmented signal is evaluated to generate the frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, which is specifically as follows: Among them, G i is the evaluation coefficient of the i-th frequency domain signal; They represent the weight coefficients of the three specification gradients of the edge-end transformer equipment respectively; They represent the weight coefficients of the α, β and χ indicators corresponding to the three characteristics of core vibration, winding vibration and environmental noise vibration respectively; U(xi) represents the parameter value of the spectrum data of the segmented signal.

4. The transformer voiceprint denoising method based on edge computing according to claim 3 is characterized in that: In S103, the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal is calculated according to the frequency domain signal evaluation coefficient, as follows: E i =G i *∫X i (t) 2 dt; Among them, E i is the energy value of the ith frequency domain signal and is used as the characteristic parameter of the transformer noise sound signal. i (t) is the original data value of the transformer voiceprint signal in the i-th frequency domain signal.

5. The transformer voiceprint denoising method based on edge computing according to claim 3 is characterized in that: It also includes the analysis of the core vibration and winding vibration weights, as follows: Assume that the load current flowing through the transformer winding is The magnitude of the electromagnetic force on the transformer winding is Among them, I is the transformer winding load current value, I m is the load current amplitude, is the initial phase value, is the cosine of the load current, p is the electromagnetic force coefficient, and it is taken as a constant under steady-state operating conditions, and F is the electromagnetic force on the winding; The electromagnetic force of the winding is composed of a constant and a sinusoidal quantity, the frequency is twice the power supply frequency, and the magnitude is proportional to the square of the load current; When the current contains harmonics, the frequency components of the electromagnetic force are divided into harmonic multiples, sum frequencies and difference frequencies, and the distribution information of the axial electromagnetic force and the radial electromagnetic force is obtained; According to the harmonic frequency doublings, sum frequencies and difference frequencies and the distribution information of the magnitude of the axial electromagnetic force and the radial electromagnetic force, a two-dimensional characteristic matrix of the corresponding harmonic category is constructed, which is a two-dimensional characteristic matrix of the frequency doublings, a two-dimensional characteristic matrix of the sum frequency, and a two-dimensional characteristic matrix of the difference frequency, that is, the two-dimensional characteristic matrix of the frequency doublings is expressed as F1(μ, γ), the two-dimensional characteristic matrix of the sum frequency is expressed as F2(μ, γ), and the two-dimensional characteristic matrix of the difference frequency is expressed as F3(μ, γ); wherein (μ, γ) are the characteristic data of the axial and radial electromagnetic forces, respectively; A weighted analysis model of axial and radial core vibration and winding vibration is established based on the two-dimensional characteristic matrix of the harmonic category, and weight coefficients of core vibration and winding vibration are generated according to the analysis of the weighted analysis model of axial and radial core vibration and winding vibration.

6. The transformer voiceprint denoising method based on edge computing according to claim 1, characterized in that: The filtering algorithm in S101 adopts the minimum mean square error algorithm.

7. The transformer voiceprint denoising method based on edge computing according to claim 1, characterized in that: The voiceprint recognition and matching in S104 uses a Gaussian mixture model or a support vector machine to perform voiceprint recognition.

8. The transformer voiceprint denoising method based on edge computing according to claim 5, characterized in that: The weighted analysis model of the axial and radial directions of the core vibration and the winding vibration is as follows: Where n represents the i-th data of the transformer under the current harmonic category, that is, the i-th data of the two-dimensional characteristic matrix of harmonic multiple frequency, sum frequency and difference frequency, j represents the index of weight analysis, that is, the axial index of core vibration and winding vibration, k represents the radial index, y ij Indicates the axial electromagnetic force characteristic data of the core vibration and winding vibration under the current harmonic, y ik It represents the radial electromagnetic force characteristic data of the core vibration and winding vibration under the current harmonic, p ij 、p ik The weighted analysis results of the axial and radial directions of the core vibration and winding vibration respectively.

9. A system applied to the transformer voiceprint denoising method based on edge computing as claimed in claim 1, characterized in that: include: The edge acquisition module is used to obtain the transformer voiceprint sampling signal through the sensor, and then use the filtering algorithm to process the environmental noise characteristics in the transformer voiceprint sampling signal to determine a complete voiceprint sampling signal; An edge processing module, for performing a windowing process on each segment of the transformer voiceprint signal, and then performing a Fourier transform on the transformer voiceprint signal in the window, so that each segment of the transformer voiceprint signal is converted from a time domain signal to a frequency domain signal and obtains a spectrum of a corresponding segmented signal; The edge analysis module is used to perform machine learning annotation on the spectrum of the segmented signal, determine the preset core vibration and winding vibration sound characteristics and the characteristics of the environmental noise impact and the corresponding parameter information according to the annotation processing results, build a transformer voiceprint denoising processing model and evaluate the spectrum data of the segmented signal, generate the frequency domain signal evaluation coefficient corresponding to the spectrum data of the segmented signal, and calculate the energy value of the segmented frequency corresponding to the spectrum data of the segmented signal according to the frequency domain signal evaluation coefficient; The regional detection backend is used to automatically adjust the gain based on the transformer denoising spectrum data of each section and input it into the transformer cloud service platform; The transformer cloud service platform is used to receive the uploaded information from the regional detection background, input it into the neural network and after cyclic identification and verification, it identifies and matches the voiceprint according to the transformer voiceprint feature vector to complete the determination of the transformer fault. At the same time, it verifies whether the transformer voiceprint denoising meets the standards and outputs the corresponding control information to the edge end, and sends the corresponding control information transmission value to the transformer to make corresponding control work.

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