Interference spectrum identification method and device based on morphological characteristics, electronic equipment and storage medium

Abstract

The invention discloses an interference atlas recognition method and device based on morphological characteristics, electronic equipment and a storage medium, the method and device are applied to the electronic equipment, and specifically, binarization processing is performed on an original PRPD atlas to be recognized to obtain a binarized PRPD atlas; calculating an amplitude threshold value of a maximum bottom noise signal in the binarized PRPD atlas; calculating a distribution curve of the partial discharge pixel points greater than the maximum bottom noise signal based on the amplitude threshold; calculating based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix; and based on the constructed phase distribution judgment threshold, the phase correlation matrix and the partial discharge phase center matrix, judging the original PRPD atlas to obtain a recognition result. According to the scheme, preliminary screening of the interference atlas in the acquired PRPD atlas can be realized, so that the workload of subsequent expert analysis of partial discharge conditions based on the PRPD atlas is greatly reduced.

Description

Technical Field

[0001] The present application relates to the field of ultra-high frequency partial discharge monitoring technology, and more specifically, to a method, device, electronic device and storage medium for identifying interference patterns based on morphological features. Background Art

[0002] Ultra-high frequency (UHF) partial discharge (PD) monitoring technology is currently the primary method for identifying the types of partial discharge (PD) defects within GIS (Gas Insulated Switcher) equipment. The accumulated PRPD (Phase-Resolved Partial Discharge) patterns generated during UHF testing can capture statistical information about the PD signal's phase, discharge amplitude, and number of discharges. These information is widely used in identifying GIS PD types.

[0003] The PRPD spectrum is a two-dimensional spectrum, in which the horizontal axis is the phase information of the alternating current, and the vertical axis is the amplitude information of the received ultra-high frequency signal. The color rendering under the fixed horizontal and vertical axes (phase, amplitude) represents the frequency information of the ultra-high frequency pulse signal within the statistical time of the PRPD spectrum. The ultra-high frequency pulse signals excited by local discharges of different physical factors have different morphological characteristics within the statistical time. Based on the morphological characteristics of the PRPD spectrum, four local discharge modes can be identified, namely metal particle discharge, burr tip discharge, suspended electrode discharge and insulating air gap discharge.

[0004] However, the inventors of this application discovered that during actual partial discharge detection, the signals acquired by UHF sensors include not only the partial discharge signal but also ambient noise and interference signals within their response frequency band. Therefore, in practical applications, based on the PRPD pattern formation mechanism, the resulting PRPD pattern morphology features, in addition to the four typical partial discharge types, also include interference noise patterns. Failure to identify the interference noise patterns would make it impossible to remove their interference, increasing the workload for subsequent experts analyzing partial discharge conditions based on the PRPD patterns. Summary of the Invention

[0005] In view of this, the present application provides a method, device, electronic device and storage medium for identifying interference patterns based on morphological features, which are used to identify whether the collected PRPD pattern is an interference pattern, so as to avoid failure in partial discharge identification.

[0006] In order to achieve the above objectives, the following solutions are proposed:

[0007] A method for identifying interference patterns based on morphological features, applied to electronic equipment, comprises the following steps:

[0008] Binarizing the original PRPD spectrum to be identified to obtain a binary PRPD spectrum;

[0009] Calculating the amplitude threshold of the maximum background noise signal in the binary PRPD spectrum;

[0010] Calculating a distribution curve of partial discharge pixel points having a signal greater than the maximum background noise signal based on the amplitude threshold;

[0011] Calculating based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix;

[0012] Based on the constructed phase distribution decision threshold, the phase correlation matrix and the partial discharge phase center matrix, the original PRPD spectrum is judged to obtain a recognition result.

[0013] Optionally, the process of binarizing the original PRPD spectrum to be identified to obtain a binarized PRPD spectrum comprises the following steps:

[0014] Converting the original PRPD spectrum into a two-dimensional grayscale image;

[0015] Calculating the two-dimensional grayscale image based on a preset grayscale threshold coefficient to obtain a grayscale threshold;

[0016] Each pixel of the two-dimensional grayscale value is calibrated based on the grayscale threshold, thereby obtaining the binary PRPD map.

[0017] Optionally, the calculating the amplitude threshold of the maximum background noise signal in the binary PRPD spectrum comprises the steps of:

[0018] Based on the binary PRPD spectrum, the pixel corresponding to the minimum amplitude and the minimum phase in the original PRPD spectrum is defined as a basic pixel;

[0019] Counting the phase distribution characteristics of each pixel in the original PRPD spectrum at a fixed amplitude based on the basic pixel points;

[0020] The amplitude threshold is determined by judging whether each of the pixel points has phase correlation with the phase distribution feature.

[0021] Optionally, calculating the distribution curve of partial discharge pixels having a value greater than the maximum background noise signal based on the amplitude threshold comprises the steps of:

[0022] Determining the signal amplitude of each pixel in the original PRPD spectrum based on the amplitude threshold, and defining the set of all pixels whose signal amplitudes are less than the amplitude threshold as a noise set;

[0023] Assigning a value of 0 to the pixel points corresponding to the noise set in the binary PRPD spectrum to obtain a denoised pixel set;

[0024] The 0 and 1 values ​​in the denoised pixel set are accumulated according to a phase of 0-360° to obtain the distribution curve.

[0025] Optionally, the calculating based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix comprises the steps of:

[0026] Using 0 as a threshold, sequentially searching for multiple partial discharge pixel distribution values ​​at phase coordinates of 0 to 360 degrees for the distribution curve, and determining as a set definition point a partial discharge pixel point where both the previous partial discharge pixel distribution value and the next partial discharge pixel distribution value are simultaneously 0 or are not simultaneously greater than 0;

[0027] Calculation is performed based on all the set definition points to obtain the phase correlation matrix and the partial discharge behavior center matrix.

[0028] Optionally, judging the original PRPD spectrum based on the constructed phase distribution judgment threshold, the phase correlation matrix and the partial discharge phase center matrix to obtain an identification result includes the steps of:

[0029] Obtaining a modified phase correlation matrix and a modified partial discharge phase center matrix based on the phase correlation matrix and the partial discharge phase center matrix based on the distribution criterion threshold;

[0030] The original PRPD spectrum is identified based on the corrected phase correlation matrix and the corrected partial discharge phase center matrix to obtain the identification result.

[0031] Optionally, identifying the original PRPD spectrum based on the corrected phase correlation matrix and the corrected partial discharge phase center matrix to obtain the identification result includes the steps of:

[0032] Calculating the corresponding data in the corrected phase correlation matrix and the corrected partial discharge phase center matrix to obtain data to be identified;

[0033] The data to be identified is judged based on a threshold to be identified, and the original PRPD spectrum of the data to be identified that meets the threshold to be identified is identified as an interference spectrum.

[0034] A device for identifying interference patterns based on morphological features is applied to electronic equipment. The interference pattern identification method includes:

[0035] A binarization processing module is configured to perform binarization processing on the original PRPD spectrum to be identified to obtain a binarized PRPD spectrum;

[0036] a threshold calculation module, configured to calculate an amplitude threshold of a maximum background noise signal in the binary PRPD spectrum;

[0037] a curve calculation module, configured to calculate a distribution curve of partial discharge pixels having a value greater than the maximum background noise signal based on the amplitude threshold;

[0038] a matrix calculation module, configured to perform calculations based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix;

[0039] The identification execution module is configured to judge the original PRPD spectrum based on the constructed phase distribution decision threshold, the phase correlation matrix and the partial discharge phase center matrix to obtain an identification result.

[0040] An electronic device comprising at least one processor and a memory connected to the processor, wherein:

[0041] The memory is used to store computer programs or instructions;

[0042] The processor is used to execute the computer program or instruction to enable the electronic device to implement the interference pattern recognition method as described above.

[0043] A computer-readable storage medium is applied to an electronic device, wherein the storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, thereby enabling the electronic device to implement the interference spectrum recognition method as described above.

[0044] As can be seen from the above technical solution, the present application discloses a method, device, electronic device and storage medium for interference pattern recognition based on morphological features. The method and device are applied to electronic devices, specifically, binarizing the original PRPD pattern to be identified to obtain a binarized PRPD pattern; calculating the amplitude threshold of the maximum background noise signal in the binarized PRPD pattern; calculating the distribution curve of the partial discharge pixel points greater than the maximum background noise signal based on the amplitude threshold; calculating based on the distribution curve to obtain the phase correlation matrix and the partial discharge phase center matrix; judging the original PRPD pattern based on the constructed phase distribution judgment threshold, phase correlation matrix and partial discharge phase center matrix to obtain the recognition result. This solution can realize the preliminary screening of the interference pattern in the collected PRPD pattern, thereby greatly reducing the workload of subsequent experts in analyzing the partial discharge condition based on the PRPD pattern.

[0045] In addition, this application identifies interference patterns based on morphological features. The process does not require the use of machine learning and deep learning methods, but only uses expert knowledge-driven methods, which greatly saves the cost of interference pattern data collection and labeling in the data set production process; finally, the algorithm uses the statistical information of a simple PRPD two-dimensional image matrix, the algorithm complexity is low, and it does not need to occupy the hardware and software resources of the partial discharge equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 This is a flow chart of an interference pattern recognition method according to an embodiment of the present application;

[0048] Figure 2a A schematic diagram of a PRPD spectrum of a burr tip;

[0049] Figure 2b is a schematic diagram of a PRPD spectrum of a metal particle;

[0050] Figure 2c A schematic diagram of a PRPD spectrum of an insulation defect;

[0051] Figure 2d A schematic diagram of a PRPD spectrum of a suspended electrode;

[0052] Figure 2e is a schematic diagram of an interference pattern;

[0053] Figure 3a This is a schematic diagram of the distribution curve of partial discharge pixels after removing the background noise of the PRPD spectrum at the tip of a burr;

[0054] Figure 3b This is a schematic diagram of the partial discharge pixel distribution curve of a metal particle PRPD spectrum after background noise removal;

[0055] Figure 3c This is a schematic diagram of the partial discharge pixel distribution curve after removing the background noise of the PRPD spectrum of an insulation defect;

[0056] Figure 3d This is a schematic diagram of the partial discharge pixel distribution curve of a suspended electrode PRPD spectrum after background noise removal;

[0057] Figure 3eThis is a schematic diagram of a partial discharge pixel distribution curve after removing the background noise of an interference map;

[0058] Figure 4 This is a block diagram of an interference pattern recognition device according to an embodiment of the present application;

[0059] Figure 5 This is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0061] The inventors of this application discovered through searches that there are usually two means for existing ultra-high frequency partial discharge equipment to deal with environmental noise interference during detection. The first is to use signal processing methods to eliminate noise interference signals at the physical layer, so that the interference spectrum is not included in the formed PRPD spectrum; the second is to construct a rich interference spectrum data set and train the feature extractor through machine learning methods to directly identify the PRPD interference spectrum. However, the signal processing method requires manual intervention to completely eliminate noise signals, and the machine learning method is difficult to construct the diverse interference spectrum data set required for spectrum recognition. In order to solve the above difficulties, the present invention is based on the iconographic features of the PRPD spectrum, and discovers that four types of typical partial discharge defects have more specific morphological distribution law priors than the interference spectrum. By summarizing the difference laws between the interference spectrum and the four types of typical partial discharge defects, summarizing the expert knowledge judgment method, a PRPD interference spectrum recognition method based on morphological features that does not require data driving is designed. Based on the above ideas, this application specifically proposes the following specific implementation methods.

[0062] Figure 1 This is a flowchart of an interference pattern recognition method according to an embodiment of the present application.

[0063] like Figure 1 As shown, the interference pattern recognition method provided in this embodiment is applied to an electronic device to identify the PRPD pattern collected by a UHF sensor from a GIS device, thereby identifying an interference pattern. The electronic device can be understood as a computer, server, cloud platform, or embedded device with data computing and information processing capabilities. The interference pattern recognition method specifically includes the following steps:

[0064] S1. Binarize the original PRPD spectrum to obtain a binary PRPD spectrum.

[0065] The original PRPD spectrum is binarized to obtain a binary PRPD spectrum. The original PRPD spectrum here can be represented as a three-dimensional matrix h×w×3, where h is the total number of pixels corresponding to the amplitude of the spectrum, w is the total number of pixels corresponding to the phase in the spectrum, and 3 represents the three RGB channels. The original PRPD spectrum is converted from the RGB three channels into a two-dimensional grayscale image G_PRPD by binarization, which is represented as a two-dimensional matrix h×w. By inputting the grayscale threshold coefficient α, the grayscale threshold G of the original PRPD spectrum is calculated. th , and by adding the values ​​in the two-dimensional grayscale image that are smaller than G th The pixels of are all defined as 0, otherwise they are defined as 1, thus achieving the binarization of the spectrum. The binarized PRPD spectrum can be expressed as B_PRPD.

[0066] The present invention converts the original PRPD spectrum into a grayscale image. Since the background grayscale of the spectrum is consistent, the grayscale value of the signal pixel is significantly different from the background grayscale due to the different discharge frequencies of the pulse signal at different phase amplitude positions. Therefore, by setting the grayscale threshold coefficient and calculating the grayscale threshold, the pixels in the original PRPD spectrum are divided into background and signal pixels using the grayscale value information, which can facilitate the subsequent calculation and statistics of the morphological characteristics of the signal pixels.

[0067] In a specific embodiment of the present application, the binarization process is implemented by the following steps:

[0068] First, obtain the three-dimensional matrix I_PRPD∈h×w×3 of the original PRPD spectrum, and use the averaging method to obtain the grayscale matrix G_PRPD of the spectrum. The specific calculation method is shown in the following formula:

[0069]

[0070] Among them, the value range of x is [1, w], the value range of y is [1, h], I_PRPD_R, ​​I_PRPD_G and I_PRPD_B represent the grayscale values ​​of the RGB channels respectively.

[0071] Then, based on the input grayscale threshold coefficient α, if the grayscale value of the background pixel of the original PRPD map is G b , then the grayscale threshold of the atlas can be expressed as: G th =G b *α. In this application, the specific value of α is defined as 0.1, G b The specific value of is 200. The grayscale value range of the pixels in the entire original PRPD spectrum is [0, 255].

[0072] Finally, based on the calculated graph threshold G th , define the graph binarization interval as [G b-G th , G b +G th ], the pixel grayscale interval in the original PRPD spectrum is [G b -G th , G b +G th ] is defined as background and marked as 0. Pixels in the remaining positions are marked as 1, representing PD signals, background noise, or interference pixels, thus obtaining the binary PRPD spectrum B_PRPD. This spectrum is a two-dimensional matrix with r rows and c columns. The matrix size is expressed as r*c, and the values ​​within the matrix are either 0 or 1.

[0073] S2. Calculate the amplitude threshold of the maximum background noise signal in the binary PRPD spectrum.

[0074] The maximum noise floor amplitude threshold n is calculated based on the binary PRPD spectrum B_PRPD. Specifically, based on B_PRPD, the pixel coordinates corresponding to the minimum amplitude and minimum phase in the original PRPD spectrum are defined as (0, 0). The phase distribution characteristics of the PRPD partial discharge pixels in the original PRPD spectrum at a fixed amplitude are statistically analyzed. The maximum noise floor amplitude threshold n is determined by determining whether the partial discharge pixels at a fixed amplitude have phase correlation.

[0075] In four typical PRPD partial discharge patterns and PRPD interference patterns, the noise floor signal's phase distribution is uncorrelated, meaning the signal's presence is independent of phase. This expert prior informed the design of a noise floor pixel identification method. Using the physical prior that noise floor pixels are uniformly distributed from 0 to 360° within the pattern, the amplitude at which the signal pixel distribution exhibits phase uniformity after a fixed signal amplitude is calculated, defining it as the noise floor amplitude. The maximum noise floor amplitude is determined as the amplitude threshold n used in this application.

[0076] In a specific embodiment of the present application, the amplitude threshold n is calculated using the following steps:

[0077] First, the value of the binary PRPD map at the coordinate (i, j) is directly located as g. When (i, j) is a PD pixel, g = 1, otherwise g = 0.

[0078] Then, the pixel amplitude coordinate values ​​j that satisfy the following conditions are recorded as a set N = [n1, n2, n3, ..., n N ]. g(i,j)=1,j=1,2,3,...,w.

[0079] Finally, the maximum value in the set N is defined as the amplitude threshold n.

[0080] S3. Calculate a distribution curve of partial discharge pixels whose signal is greater than the maximum background noise signal based on the amplitude threshold.

[0081] Based on the amplitude threshold n of the maximum background noise signal, calculate the distribution curve I of the partial discharge pixels that are greater than the amplitude threshold fb Specifically, the position set of the partial discharge pixel points whose signal amplitude in the original PRPD spectrum is less than the amplitude threshold n is defined as N_noise. Then, all the values ​​of the position information in the N_noise set corresponding to the binary PRPD spectrum are assigned to 0 to obtain the B_PRPD_qz matrix. At this time, the position with a value of 1 in the B_PRPD_qz matrix is ​​the partial discharge signal after removing the background noise. The 0 and 1 values ​​in B_PRPD_qz are accumulated according to the phase of 0 to 360 degrees to obtain the partial discharge pixel point distribution curve I after removing the background noise. fb , its array dimension can be expressed as w×1.

[0082] Since both the PRPD partial discharge spectrum and the interference spectrum contain background noise information, the background noise pixels in the spectrum are not effective morphological features in the recognition. Therefore, based on the amplitude threshold n solved in step S2, this patent sets all the background noises that do not have phase correlation in the spectrum as background pixels of the PRPD spectrum. The remaining pixels are accumulated according to the phase coordinates. Under the specific phase coordinate value, the distribution curve I fb The larger the value is, the richer the amplitude of the pulse signal gathered at this phase is.

[0083] The above distribution curve I fb It is calculated by the following formula:

[0084]

[0085] Among them, j represents the jth phase coordinate, i is the i-th signal amplitude coordinate, and g(i, j) is the specific value of the position coordinate (i, j) in the B_PRPD_qz matrix.

[0086] S4. Calculate based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix.

[0087] That is, the distribution curve I based on the partial discharge pixel point fb Calculate the phase correlation coefficient matrix A and the partial discharge phase center matrix C of the original PRPD spectrum. The partial discharge pixel distribution curve I after removing the background noise of the original PRPD spectrum fb The value is greater than or equal to 0, and 0 is used as the threshold to search for I under the phase coordinates of 0-360°. fb The distribution value of the w partial discharge pixels in the curve is, for the i-th phase coordinate, when the previous I fb The pixel value and the next I fbIf the pixel values ​​are not simultaneously 0 or greater than 0, the i-th phase coordinate is recorded. The set of all phase coordinates counted is defined as P. Based on P, the phase correlation matrix A and the partial discharge phase center matrix C are calculated.

[0088] This application uses the phase correlation matrix A to count the phase distribution information of the signal pixels in the original PRPD spectrum and calculate the information of the continuous phase distribution in the spectrum. Specifically, this application divides the phase in the spectrum into multiple continuous phase segments, and the phase segment information is stored in the phase correlation matrix A. The phase center value corresponding to each phase segment is stored in the partial discharge phase center matrix C. A signal pulse is detected at each phase coordinate in the selected phase segment. The phases of the typical four types of partial discharge PRPD spectra are unipolar or bipolar. If it is bipolar, the centers of the two continuous phase segments differ by 180°. Therefore, it is possible to use the calculation matrix A and matrix C to determine whether the spectrum is a PRPD partial discharge spectrum. If it does not conform to the law of the partial discharge spectrum, the PRPD spectrum can be determined to be an interference spectrum.

[0089] In a specific embodiment of the present application, the following steps are used to calculate the phase correlation coefficient matrix A and the partial discharge phase center matrix C:

[0090] First, based on the pixel distribution curve I after removing the background noise in the original PRPD map fb , according to the phase coordinates [1,2,...,w], the corresponding I fb The phase coordinate set matrix that satisfies the following conditions is defined as P.

[0091] 1) Input I fb The value I at the first phase coordinate fb (1), judge I fb (1) Is it greater than 0? If it is greater than 0, the phase coordinate is the first element in the matrix P; if I fb (1) is equal to 0, then continue to judge I fb (2) Until the first I greater than 0 is found fb (m) value, the phase coordinate m is the first element in P.

[0092] 2) Determine the I under the phase coordinates [m+1, m+2, ...w] in sequence fb Does the value satisfy the following conditions, that is, the previous I of i fb The pixel value and the next I fb The pixel values ​​are not all 0 at the same time, or are not all greater than 0 at the same time. The phase coordinates that meet this condition are stored in P in sequence, and the phase coordinate matrix P can be obtained.

[0093] if I fb (i-1)=0and I fb(i+1)>0i∈[m+1,....,w-1]

[0094] if I fb (i-1)>0and I fb (i+1)=0i∈[m+1,....,w-1]

[0095] Then, based on the calculated phase coordinate matrix P and I fb The calculation method of the phase correlation coefficient matrix A and the partial discharge phase center matrix C is as follows.

[0096] 1) Define the phase coordinate matrix P with a total of Np phase coordinate samples. Determine the phase coordinate value I between the i-th phase coordinate P(i) and P(i+1) fb Whether the value satisfies the condition of being greater than 0. As shown in the following formula, where i∈[1,Np-1].

[0097] if I fb (j)>0j∈[P(i),P(i+1)]

[0098] 2) Combine the phase coordinates a that satisfy the above i ={P(i), P(i+1)} and record it. If there are m combinations that meet the conditions, the combination matrix can be expressed as [a1, a2, ..., a m ].

[0099] 3) Based on the calculated combination matrix [a1, a2, ..., a m ], the correlation coefficient matrix A and the signal phase center matrix C are calculated based on the elements in the combination matrix, and the i-th element A in A i The calculation method is based on the i-th coordinate combination a in the combination matrix i ={P(i), P(i+1)}. The specific calculation method is as follows:

[0100] A i =(P(i+1)-P(i)) / w

[0101] The i-th element C in the partial discharge phase center matrix C i The calculation method is based on the i-th coordinate combination a in the combination matrix i ={P(i), P(i+1)}. The specific calculation method is as follows:

[0102] C i =(P(i+1)-P(i)) / 2

[0103] S5. The original PRPD spectrum is judged based on the phase correlation matrix and the partial discharge phase center matrix to obtain an identification result.

[0104] A threshold phase distribution judgment threshold for the PRPD interference spectrum is constructed to determine whether the spectrum is an interference spectrum based on the phase correlation matrix A and the partial discharge phase center matrix C. The scattered phases in the phase correlation matrix A and the partial discharge phase center matrix C are eliminated to obtain the corrected phase correlation matrix A' and the corrected phase center matrix C'. The total number of correlation phase distributions in A' is calculated as N_A', and the difference matrix between the phase centers in C' is calculated as D_C'. When N_A' and D_C' meet the set threshold conditions, the spectrum is considered an interference spectrum.

[0105] Since the phase correlation matrix A above contains all continuous phase segments of the signal pixels in the PRPD spectrum, when the signal in the spectrum has both aggregated forms and discrete features, phase segments with extremely small phase differences will inevitably exist in the phase correlation matrix A. To avoid the influence of discrete features on phase statistical information, this application uses a threshold to filter out the extremely small phase differences in the phase correlation matrix A, and obtains the corrected phase correlation matrix A' and the corrected phase center matrix C'. By determining the number of phase segments in the A' matrix and the phase difference of the center of each phase segment in the C' matrix, PRPD interference spectrum recognition can be achieved.

[0106] In a specific embodiment of the present application, the calculation process of the two matrices is implemented as follows:

[0107] First, given that there are m elements in the phase correlation coefficient matrix A and the partial discharge phase center matrix C. Each element in the matrix A represents the degree of phase correlation aggregation. The larger the value of the i-th element in the matrix A, the higher the corresponding coordinate combination a. i The larger the phase span, the greater the threshold value A. th = 0.1, and the matrix A is smaller than A th The elements of are eliminated, and similarly the corresponding elements in the corresponding signal phase center matrix C are eliminated synchronously.

[0108] After phase correction, the elements of the same search position in the matrix A' and the matrix C' correspond to each other one by one, and physically represent the length of the searched signal pixel cluster phase segment and the center pixel position respectively.

[0109] The interference map recognition process is as follows:

[0110] First, we obtain matrices A' and C'. Since they have the same number of elements, we denote them as N_A'. Since the four typical PD spectra are phase-clustered into unipolar and bipolar distributions, N_A' takes on a value of 1 or 2.

[0111] Then, considering the phase cyclicity, the physical meaning of 0° phase and 360° phase is the same. Therefore, when the phase range of the signal pixel aggregation is near 0°, the signal phase cluster may be divided into two clusters, half of which is distributed in the first half of the spectrum, and the other half of the phase is distributed in the second half of the spectrum. In this special case, the value of N_A' in the unipolar partial discharge spectrum is equal to 2, and the value of N_A' in the bipolar spectrum is equal to 3.

[0112] For the PRPD spectrum to be identified, calculate its N_A'. When N_A' is greater than 3, the spectrum is determined to be an interference spectrum. When N_A' is less than or equal to 3, the interference spectrum judgment is broken down as follows:

[0113] 1) When N_A' is equal to 1, the PRPD spectrum is a partial discharge spectrum, otherwise it is an interference spectrum;

[0114] 2) When N_A' is equal to 2, calculate the difference D_C' between the two elements in the phase center matrix C'. There is only one difference number. When the phase difference number is approximately equal to w / 2 or w, the PRPD spectrum is a partial discharge spectrum, otherwise it is an interference spectrum.

[0115] 3) When N_A' is equal to 3, calculate the difference D_C' between each pair of elements in the phase center matrix C'. When the difference between C1 and C2 is approximately equal to w / 2, and the difference between C1 and C3 is approximately equal to w, the PRPD spectrum is a partial discharge spectrum; otherwise, it is an interference spectrum.

[0116] As can be seen from the above technical solution, this embodiment provides a method for identifying interference patterns based on morphological features. This method is applied to electronic devices and specifically involves binarizing the original PRPD pattern to be identified to obtain a binarized PRPD pattern; calculating the amplitude threshold of the maximum background noise signal in the binarized PRPD pattern; calculating the distribution curve of partial discharge pixels greater than the maximum background noise signal based on the amplitude threshold; calculating based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix; and judging the original PRPD pattern based on the constructed phase distribution judgment threshold, phase correlation matrix, and partial discharge phase center matrix to obtain an identification result. This solution can achieve preliminary screening of interference patterns within the collected PRPD pattern, thereby greatly reducing the workload of subsequent experts in analyzing partial discharge conditions based on the PRPD pattern.

[0117] In addition, this application identifies interference patterns based on morphological features. The process does not require the use of machine learning and deep learning methods, but only uses expert knowledge-driven methods, which greatly saves the cost of interference pattern data collection and labeling in the data set production process; finally, the algorithm uses the statistical information of a simple PRPD two-dimensional image matrix, the algorithm complexity is low, and it does not need to occupy the hardware and software resources of the partial discharge equipment.

[0118] The inventors of this application carried out simulation calculations based on the above technical solution.

[0119] Figure 2a to Figure 2e is the grayscale image of the five original PRPD spectra to be identified in this application. Figure 2a This is the PRPD spectrum of the burr tip. Figure 2b is the PRPD spectrum of metal particles, Figure 2c From the PRPD map of insulation defects, Figure 2d is the PRPD spectrum of the suspended electrode, Figure 2e The interference spectrum is shown in Figure 1. As can be seen from the figure, the signal phase distribution of the partial discharge PRPD spectrum has a unipolar or bipolar aggregation feature, while the interference spectrum does not have this feature.

[0120] Figure 3a to Figure 3e For the respective Figure 2a to Figure 2e The PD pixel distribution curve Ifb after removing the background noise corresponding to the original PRPD spectrum is shown in the figure. As shown in the figure, after removing the background noise, the corresponding value of the Ifb curve at the signal pixel distribution is greater than 0.

[0121] The following table shows the phase correlation matrix A' and the corrected phase center matrix C' calculated for the five original PRPD spectra after correction.

[0122] Modified phase correlation matrix A' Corrected partial discharge phase center matrix C' (a) [0.16]

[36] (b) [0.968]

[60] (c) [0.272,0.312] [19,83] (d) [0.344,0.504] [36,94] (e) null null

[0123] If N_A' in (a) and (b) is equal to 1, the spectrum to be identified is directly judged to be a PRPD partial discharge spectrum; if N_A' in (c) and (d) is equal to 2, the difference D_C' between the two elements in the phase center matrix C' in (c) is equal to 64, which is approximately equal to 125 / 2, and the PRPD spectrum to be identified is a partial discharge spectrum; similarly, if the difference D_C' between the two elements in the phase center matrix C' in (d) is equal to 58, which is approximately equal to 125 / 2, and the PRPD spectrum to be identified is a partial discharge spectrum; after correction, all phases of the PRPD spectrum to be identified (e) do not meet the threshold conditions, so the corrected correlation matrix is ​​empty, and it is directly judged to be an interference spectrum.

[0124] Although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0125] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0126] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0127] Figure 4 This is a flowchart of an interference pattern recognition method according to an embodiment of the present application.

[0128] like Figure 4 As shown, the interference pattern recognition device provided in this embodiment is applied to an electronic device to identify interference patterns from PRPD patterns collected by a UHF sensor from a GIS device. This electronic device can be understood as a computer, server, cloud platform, or embedded device with data computing and information processing capabilities. The interference pattern recognition device specifically includes a binarization processing module 10, a threshold calculation module 20, a curve calculation module 30, a matrix calculation module 40, and an identification execution module 50.

[0129] The binarization processing module is used to perform binarization processing on the original PRPD spectrum to obtain a binarized PRPD spectrum.

[0130] The original PRPD spectrum is binarized to obtain a binary PRPD spectrum. The original PRPD spectrum here can be represented as a three-dimensional matrix h×w×3, where h is the total number of pixels corresponding to the amplitude of the spectrum, w is the total number of pixels corresponding to the phase in the spectrum, and 3 represents the three RGB channels. The original PRPD spectrum is converted from the RGB three channels into a two-dimensional grayscale image G_PRPD by binarization, which is represented as a two-dimensional matrix h×w. By inputting the grayscale threshold coefficient α, the grayscale threshold G of the original PRPD spectrum is calculated. th , and by adding the values ​​in the two-dimensional grayscale image that are smaller than G th The pixels of are all defined as 0, otherwise they are defined as 1, thus achieving the binarization of the spectrum. The binarized PRPD spectrum can be expressed as B_PRPD.

[0131] The present invention converts the original PRPD spectrum into a grayscale image. Since the background grayscale of the spectrum is consistent, the grayscale value of the signal pixel is significantly different from the background grayscale due to the different discharge frequencies of the pulse signal at different phase amplitude positions. Therefore, by setting the grayscale threshold coefficient and calculating the grayscale threshold, the pixels in the original PRPD spectrum are divided into background and signal pixels using the grayscale value information, which can facilitate the subsequent calculation and statistics of the morphological characteristics of the signal pixels.

[0132] The threshold calculation module is used to calculate the amplitude threshold of the maximum background noise signal in the binary PRPD spectrum.

[0133] The maximum noise floor amplitude threshold n is calculated based on the binary PRPD spectrum B_PRPD. Specifically, based on B_PRPD, the pixel coordinates corresponding to the minimum amplitude and minimum phase in the original PRPD spectrum are defined as (0, 0). The phase distribution characteristics of the PRPD partial discharge pixels in the original PRPD spectrum at a fixed amplitude are statistically analyzed. The maximum noise floor amplitude threshold n is determined by determining whether the partial discharge pixels at a fixed amplitude have phase correlation.

[0134] In four typical PRPD partial discharge patterns and PRPD interference patterns, the noise floor signal's phase distribution is uncorrelated, meaning the signal's presence is independent of phase. This expert prior informed the design of a noise floor pixel identification method. Using the physical prior that noise floor pixels are uniformly distributed from 0 to 360° within the pattern, the amplitude at which the signal pixel distribution exhibits phase uniformity after a fixed signal amplitude is calculated, defining it as the noise floor amplitude. The maximum noise floor amplitude is determined as the amplitude threshold n used in this application.

[0135] The curve calculation module is used to calculate the distribution curve of partial discharge pixel points greater than the maximum background noise signal based on the amplitude threshold.

[0136] Based on the amplitude threshold n of the maximum background noise signal, calculate the distribution curve I of the partial discharge pixels that are greater than the amplitude threshold fb Specifically, the position set of the partial discharge pixel points whose signal amplitude in the original PRPD spectrum is less than the amplitude threshold n is defined as N_noise. Then, all the values ​​of the position information in the N_noise set corresponding to the binary PRPD spectrum are assigned to 0 to obtain the B_PRPD_qz matrix. At this time, the position with a value of 1 in the B_PRPD_qz matrix is ​​the partial discharge signal after removing the background noise. The 0 and 1 values ​​in B_PRPD_qz are accumulated according to the phase of 0 to 360 degrees to obtain the partial discharge pixel point distribution curve I after removing the background noise. fb , its array dimension can be expressed as w×1.

[0137] Since both the PRPD partial discharge spectrum and the interference spectrum contain background noise information, the background noise pixels in the spectrum are not effective morphological features in the recognition. Therefore, based on the amplitude threshold n solved in step S2, this patent sets all the background noises that do not have phase correlation in the spectrum as background pixels of the PRPD spectrum. The remaining pixels are accumulated according to the phase coordinates. Under the specific phase coordinate value, the distribution curve I fb The larger the value is, the richer the amplitude of the pulse signal gathered at this phase is.

[0138] The matrix calculation module is used to perform calculations based on the distribution curve to obtain the phase correlation matrix and the partial discharge phase center matrix.

[0139] That is, the distribution curve I based on the partial discharge pixel point fb Calculate the phase correlation coefficient matrix A and the partial discharge phase center matrix C of the original PRPD spectrum. The partial discharge pixel distribution curve I after removing the background noise of the original PRPD spectrum fb The value is greater than or equal to 0, and 0 is used as the threshold to search for I under the phase coordinates of 0-360°. fb The distribution value of the w partial discharge pixels in the curve is, for the i-th phase coordinate, when the previous I fb The pixel value and the next I fb If the pixel values ​​are not simultaneously 0 or greater than 0, the i-th phase coordinate is recorded. The set of all phase coordinates counted is defined as P. Based on P, the phase correlation matrix A and the partial discharge phase center matrix C are calculated.

[0140] This application uses the phase correlation matrix A to count the phase distribution information of the signal pixels in the original PRPD spectrum and calculate the information of the continuous phase distribution in the spectrum. Specifically, this application divides the phase in the spectrum into multiple continuous phase segments, and the phase segment information is stored in the phase correlation matrix A. The phase center value corresponding to each phase segment is stored in the partial discharge phase center matrix C. A signal pulse is detected at each phase coordinate in the selected phase segment. The phases of the typical four types of partial discharge PRPD spectra are unipolar or bipolar. If it is bipolar, the centers of the two continuous phase segments differ by 180°. Therefore, it is possible to use the calculation matrix A and matrix C to determine whether the spectrum is a PRPD partial discharge spectrum. If it does not conform to the law of the partial discharge spectrum, the PRPD spectrum can be determined to be an interference spectrum.

[0141] The identification execution module is used to judge the original PRPD spectrum based on the phase correlation matrix and the partial discharge phase center matrix to obtain the identification result.

[0142] A threshold phase distribution judgment threshold for the PRPD interference spectrum is constructed to determine whether the spectrum is an interference spectrum based on the phase correlation matrix A and the partial discharge phase center matrix C. The scattered phases in the phase correlation matrix A and the partial discharge phase center matrix C are eliminated to obtain the corrected phase correlation matrix A' and the corrected phase center matrix C'. The total number of correlation phase distributions in A' is calculated as N_A', and the difference matrix between the phase centers in C' is calculated as D_C'. When N_A' and D_C' meet the set threshold conditions, the spectrum is considered an interference spectrum.

[0143] Since the phase correlation matrix A above contains all continuous phase segments of the signal pixels in the PRPD spectrum, when the signal in the spectrum has both aggregated forms and discrete features, phase segments with extremely small phase differences will inevitably exist in the phase correlation matrix A. To avoid the influence of discrete features on phase statistical information, this application uses a threshold to filter out the extremely small phase differences in the phase correlation matrix A, and obtains the corrected phase correlation matrix A' and the corrected phase center matrix C'. By determining the number of phase segments in the A' matrix and the phase difference of the center of each phase segment in the C' matrix, PRPD interference spectrum recognition can be achieved.

[0144] As can be seen from the above technical solution, this embodiment provides an interference pattern recognition device based on morphological features, which is applied to electronic devices. Specifically, the original PRPD pattern to be identified is binarized to obtain a binary PRPD pattern; the amplitude threshold of the maximum background noise signal in the binary PRPD pattern is calculated; the distribution curve of partial discharge pixels greater than the maximum background noise signal is calculated based on the amplitude threshold; the phase correlation matrix and partial discharge phase center matrix are calculated based on the distribution curve; and the original PRPD pattern is judged based on the constructed phase distribution judgment threshold, phase correlation matrix, and partial discharge phase center matrix to obtain the recognition result. This solution can achieve preliminary screening of interference patterns within the collected PRPD pattern, thereby greatly reducing the workload of subsequent experts in analyzing partial discharge conditions based on the PRPD pattern.

[0145] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0146] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0147] Figure 5 This is a block diagram of an electronic device according to an embodiment of the present application.

[0148] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This electronic device is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0149] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 502 or a program loaded from an input device 506 into a random access memory RAM 503. Various programs and data required for the operation of the electronic device are also stored in the RAM. The processing device, ROM, and RAM are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0150] Typically, the following devices may be connected to the I / O interface: input devices such as a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 508 such as a magnetic tape, hard disk, etc.; and communication devices 509. Communication devices 509 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figures illustrate electronic devices with various devices, it should be understood that not all of the devices shown are required to be implemented or present. More or fewer devices may be implemented or present instead.

[0151] The present application also provides a computer-readable storage medium embodiment.

[0152] The computer-readable storage medium is applied to an electronic device and carries one or more programs. When the one or more computer programs are executed by the electronic device, the electronic device binarizes the original PRPD spectrum to be identified to obtain a binarized PRPD spectrum; calculates the amplitude threshold of the maximum background noise signal in the binarized PRPD spectrum; calculates the distribution curve of partial discharge pixels greater than the maximum background noise signal based on the amplitude threshold; calculates based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix; and judges the original PRPD spectrum based on the constructed phase distribution judgment threshold, phase correlation matrix, and partial discharge phase center matrix to obtain an identification result. This solution can achieve preliminary screening of interference patterns within the collected PRPD spectrum, thereby greatly reducing the workload of subsequent experts in analyzing partial discharge conditions based on the PRPD spectrum.

[0153] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0154] In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0155] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0156] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0157] The technical solution provided by the present invention is introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for identifying interference patterns based on morphological features, applied to electronic equipment, characterized in that: The interference pattern recognition method comprises the steps of: Binarizing the original PRPD spectrum to be identified to obtain a binary PRPD spectrum; Calculating the amplitude threshold of the maximum background noise signal in the binary PRPD spectrum; Calculating a distribution curve of partial discharge pixel points having a signal greater than the maximum background noise signal based on the amplitude threshold; Calculating based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix; Based on the constructed phase distribution decision threshold, the phase correlation matrix and the partial discharge phase center matrix, the original PRPD spectrum is judged to obtain a recognition result.

2. The interference pattern recognition method according to claim 1, wherein: The process of binarizing the original PRPD spectrum to be identified to obtain a binarized PRPD spectrum comprises the following steps: Converting the original PRPD spectrum into a two-dimensional grayscale image; Calculating the two-dimensional grayscale image based on a preset grayscale threshold coefficient to obtain a grayscale threshold; Each pixel of the two-dimensional grayscale value is calibrated based on the grayscale threshold, thereby obtaining the binary PRPD map.

3. The interference pattern recognition method according to claim 1, wherein: The step of calculating the amplitude threshold of the maximum background noise signal in the binary PRPD spectrum comprises the following steps: Based on the binary PRPD spectrum, the pixel corresponding to the minimum amplitude and the minimum phase in the original PRPD spectrum is defined as a basic pixel; Counting the phase distribution characteristics of each pixel in the original PRPD spectrum at a fixed amplitude based on the basic pixel points; The amplitude threshold is determined by judging whether each pixel point has phase correlation with the phase distribution feature.

4. The interference pattern recognition method according to claim 1, wherein: The method of calculating a distribution curve of partial discharge pixels having a signal greater than the maximum background noise signal based on the amplitude threshold comprises the following steps: Determining the signal amplitude of each pixel in the original PRPD spectrum based on the amplitude threshold, and defining the set of all pixels whose signal amplitudes are less than the amplitude threshold as a noise set; Assigning a value of 0 to the pixel points corresponding to the noise set in the binary PRPD spectrum to obtain a denoised pixel set; The 0 and 1 values ​​in the denoised pixel set are accumulated according to a phase of 0-360° to obtain the distribution curve.

5. The interference pattern recognition method according to claim 1, wherein: The calculation based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix includes the steps of: Using 0 as a threshold, sequentially searching for multiple partial discharge pixel distribution values ​​at phase coordinates of 0 to 360 degrees for the distribution curve, and determining as a set definition point a partial discharge pixel point where both the previous partial discharge pixel distribution value and the next partial discharge pixel distribution value are simultaneously 0 or are not simultaneously greater than 0; Calculation is performed based on all the set definition points to obtain the phase correlation matrix and the partial discharge behavior center matrix.

6. The interference pattern recognition method according to claim 1, wherein: Based on the constructed phase distribution judgment threshold, the phase correlation matrix and the partial discharge phase center matrix, the original PRPD spectrum is judged to obtain a recognition result, including the steps of: Obtaining a modified phase correlation matrix and a modified partial discharge phase center matrix based on the phase correlation matrix and the partial discharge phase center matrix based on the distribution criterion threshold; The original PRPD spectrum is identified based on the corrected phase correlation matrix and the corrected partial discharge phase center matrix to obtain the identification result.

7. The interference pattern recognition method according to claim 6, wherein: The identifying of the original PRPD spectrum based on the corrected phase correlation matrix and the corrected partial discharge phase center matrix to obtain the identification result comprises the steps of: Calculating the corresponding data in the corrected phase correlation matrix and the corrected partial discharge phase center matrix to obtain data to be identified; The data to be identified is judged based on a threshold to be identified, and the original PRPD spectrum of the data to be identified that meets the threshold to be identified is identified as an interference spectrum.

8. An interference pattern recognition device based on morphological features, applied to electronic equipment, characterized in that: The interference pattern recognition method comprises: A binarization processing module is configured to perform binarization processing on the original PRPD spectrum to be identified to obtain a binarized PRPD spectrum; a threshold calculation module, configured to calculate an amplitude threshold of a maximum background noise signal in the binary PRPD spectrum; a curve calculation module, configured to calculate a distribution curve of partial discharge pixels having a value greater than the maximum background noise signal based on the amplitude threshold; a matrix calculation module, configured to perform calculations based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix; The identification execution module is configured to judge the original PRPD spectrum based on the constructed phase distribution decision threshold, the phase correlation matrix and the partial discharge phase center matrix to obtain an identification result.

9. An electronic device, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is configured to execute the computer program or instruction so that the electronic device implements the interference pattern recognition method according to any one of claims 1 to 7.

10. A computer-readable storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device can implement the interference pattern recognition method according to any one of claims 1 to 7.

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