PRPD atlas processing method and related equipment

Abstract

The invention provides a PRPD atlas processing method and related equipment, and the method comprises the steps: obtaining an original atlas corresponding to an atlas processing instruction in response to the atlas processing instruction; performing content extraction on the original atlas to obtain atlas content in the original atlas; adjusting the size of the map content of the original map to obtain a standard size map; background information in the standard size map is eliminated, and a target map is obtained; rGB data of all target pixel points in the target map are converted into gray values, a pseudo-color map is generated according to the gray values obtained through conversion by means of a preset pseudo-color mapping formula, and the target pixel points are pixel points not smaller than a preset gray threshold value. By applying the method provided by the embodiment of the invention, the background information in the atlas can be efficiently eliminated.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method for processing PRPD maps and related devices. Background Art

[0002] With the increasing number of GIS (Gas Insulated Switchgear) in operation year by year and the accumulation of the operating time of these devices, ensuring the safe operation of GIS devices has become crucial. Partial discharge monitoring and partial discharge type identification, as one of the important means to ensure the safety of GIS devices, play a key role in this process. Ultra-high frequency signal monitoring, as an effective detection method, can obtain the statistical feature map of the phase-discharge amplitude-discharge times of partial discharge signals, that is, the PRPD (Phase Resolved Partial Discharge) map, and is widely used in the process of GIS discharge identification. However, due to the differences in the technologies and standards adopted by partial discharge monitoring devices produced by different manufacturers, the formats of the accumulated PRPD maps of partial discharge defects are not unified, which poses challenges to subsequent map analysis.

[0003] Existing PRPD map processing methods usually rely on manually selecting or setting the color range thresholds of the background and grid lines to detect and extract the background and grid line pixels in the map according to the color thresholds. This method is not only time-consuming and laborious, but also due to the differences in the drawing colors, background settings and grid lines of the PRPD maps generated by partial discharge monitoring devices of different manufacturers, it further increases the complexity and resource consumption of manual image preprocessing. Summary of the Invention

[0004] The technical problem to be solved by this application is to provide a method for processing PRPD maps and related devices, which can efficiently eliminate the background information in the maps. The specific solutions are as follows:

[0005] A method for processing PRPD maps, the method includes:

[0006] In response to a map processing instruction, obtain the original map corresponding to the map processing instruction;

[0007] Extract the content of the original map to obtain the map content in the original map;

[0008] Adjust the size of the map content of the original map to obtain a standard size map;

[0009] Eliminate the background information in the standard size map to obtain a target map;

[0010] Convert the RGB data of each target pixel point in the target map into a grayscale value, and generate a pseudo-color map according to the converted grayscale value by using a preset pseudo-color mapping formula, where the target pixel point is a pixel point not less than a preset grayscale threshold.

[0011] For the above method, optionally, the extracting the content of the original map to obtain the map content in the original map includes:

[0012] Perform edge detection on the original map to obtain the target area of the original map;

[0013] Detect each line segment in the target area;

[0014] Determine a plurality of target line segments among the line segments, and determine the effective content area of the original map according to the plurality of target line segments, where the target line segment is a line segment whose length meets a preset length condition and is closest to any boundary of the target area;

[0015] Extract the content of the effective content area to obtain the map content of the original map.

[0016] For the above method, optionally, the eliminating the background information in the standard-size map to obtain a target map includes:

[0017] Transform the standard-size map from the two-dimensional spatial domain to the frequency domain;

[0018] Filter the standard-size map transformed to the frequency domain by using a preset grid filter, and transform the filtered standard-size map to the two-dimensional spatial domain to obtain an initial map;

[0019] Eliminate the curves in the initial map to obtain a target map.

[0020] For the above method, optionally, the transforming the standard-size map from the two-dimensional spatial domain to the frequency domain includes:

[0021] Perform grayscale analysis on the standard-size map to obtain the grayscale distribution of the standard-size map;

[0022] Determine the background pixel positions of the standard-size map according to the grayscale distribution of the standard-size map;

[0023] Eliminate the background pixels of the standard-size map according to the background pixel positions of the standard-size map;

[0024] Transform the standard-size map with background pixels eliminated from the two-dimensional spatial domain to the frequency domain.

[0025] The above method, optionally, the eliminating the curve in the initial map to obtain a target map includes:

[0026] Determining the curve position in the initial map according to the curve characteristics of the initial map;

[0027] Eliminating the curve in the initial map according to the curve position in the initial map to obtain a target map.

[0028] A PRPD map processing device includes:

[0029] An acquisition unit, configured to acquire the original map corresponding to the map processing instruction in response to the map processing instruction;

[0030] An extraction unit, configured to extract the map content in the original map to obtain the map content in the original map;

[0031] An adjustment unit, configured to adjust the size of the map content in the original map to obtain a standard size map;

[0032] A background elimination unit, configured to eliminate the background information in the standard size map to obtain a target map;

[0033] An execution unit, configured to convert the RGB data of each target pixel point in the target map into a gray value, and generate a pseudo-color map according to the converted gray value by using a preset pseudo-color mapping formula, where the target pixel point is a pixel point not less than a preset gray threshold.

[0034] The above device, optionally, the extraction unit includes:

[0035] An edge detection subunit, configured to perform edge detection on the original map to obtain the target area of the original map;

[0036] A line segment detection subunit, configured to detect each line segment in the target area;

[0037] A determination subunit, configured to determine a plurality of target line segments from each of the line segments, and determine the effective content area of the original map according to the plurality of target line segments, where the target line segment is a line segment whose length meets a preset length condition and is closest to any boundary of the target area;

[0038] An extraction subunit, configured to extract the content of the effective content area to obtain the map content of the original map.

[0039] The above device, optionally, the background elimination unit includes:

[0040] A transform sub-unit for transforming the standard-size atlas from the two-dimensional spatial domain to the frequency domain;

[0041] A filtering sub-unit for filtering the standard-size atlas transformed to the frequency domain by using a preset grid filter, and transforming the filtered standard-size atlas to the two-dimensional spatial domain to obtain an initial atlas;

[0042] An execution sub-unit for eliminating the curves in the initial atlas to obtain a target atlas.

[0043] A storage medium including stored instructions, wherein when the instructions are running, the device where the storage medium is located is controlled to execute the PRPD atlas processing method as described above.

[0044] An electronic device includes a memory, and one or more instructions, wherein one or more instructions are stored in the memory and are configured to be executed by one or more processors to execute the PRPD atlas processing method as described above.

[0045] Based on the PRPD atlas processing method and related devices provided by the embodiments of the present application, the method includes: in response to an atlas processing instruction, obtaining an original atlas corresponding to the atlas processing instruction; extracting the content of the original atlas to obtain the atlas content in the original atlas; adjusting the size of the atlas content of the original atlas to obtain a standard-size atlas; eliminating the background information in the standard-size atlas to obtain a target atlas; converting the RGB data of each target pixel point in the target atlas into a gray value, and generating a pseudo-color map by using a preset pseudo-color mapping formula according to the converted gray value, where the target pixel point is a pixel point not less than a preset gray threshold. By applying the method provided by the embodiments of the present application, the background information in the atlas can be efficiently eliminated, and resource consumption can be reduced. Description of the Drawings

[0046] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0047] Figure 1 It is a flowchart of a method for a PRPD atlas processing method provided by the present application;

[0048] Figure 2 It is a flowchart of a process for obtaining the atlas content in the original atlas provided by the present application;

[0049] Figure 3 A flowchart of a process for obtaining a target spectrum provided by this application;

[0050] Figure 4 A flowchart of a preprocessing process for a PRPD two-dimensional spectrum provided by this application;

[0051] Figure 5 An example diagram of an original spectrum provided by this application;

[0052] Figure 6 An example diagram of the spectrum features after effective content positioning provided by this application;

[0053] Figure 7 An example diagram of a spectrum transformed to the frequency domain provided by this application;

[0054] Figure 8 An example diagram of a frequency-domain spectrum filtered by a grid filter provided by this application;

[0055] Figure 9 An example diagram of a target spectrum provided by this application;

[0056] Figure 10 A schematic structural diagram of a PRPD spectrum processing device provided by this application;

[0057] Figure 11 A schematic structural diagram of an electronic device provided by this application. Detailed implementation manners

[0058] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0059] In this application, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, the element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0060] Existing PRPD pattern processing methods usually rely on manual selection or setting of the background and color range thresholds of grid lines to detect and extract the background and grid line pixels in the pattern according to the color thresholds. This method is not only time-consuming and laborious, but also increases the complexity and resource consumption of manual image preprocessing due to the differences in the drawing colors, background settings, and grid lines of the partial discharge monitoring devices of different manufacturers.

[0061] In addition, traditional general-purpose preprocessing methods use image processing operations such as erosion-dilation to eliminate the grid lines and coordinate ranges in the pattern. Although they can achieve the purpose of eliminating the background and extracting the discharge characteristics of the pattern to a certain extent, these operations often lead to the degradation of the original discharge characteristics. Especially for those characteristics with low discharge frequencies and scattered phase-amplitude distributions, they may completely disappear. Therefore, existing methods are difficult to meet the requirements of universality and minimizing discharge characteristic loss when facing the measured raw data of PRPD from multiple manufacturers.

[0062] Based on this, the embodiments of the present application provide a PRPD pattern processing method, which can be applied to an electronic device. The method flow chart of the method is as Figure 1 shown, and specifically includes:

[0063] S101: In response to a pattern processing instruction, obtain the original pattern corresponding to the pattern processing instruction.

[0064] In this embodiment, the original pattern can be an original statistical PRPD (Phase Resolved Patterns of Partial Discharge) feature pattern of the ultra-high frequency signal monitoring that can obtain the phase-discharge amplitude-discharge times of the partial discharge signal.

[0065] Optionally, the original PRPD two-dimensional pattern usually contains redundant information such as coordinate information and border information, and the background colors and sizes are not uniform.

[0066] S102: Perform content extraction on the original pattern to obtain the pattern content in the original pattern.

[0067] In this embodiment, the pattern content in the original pattern can be the content within the effective content border, and the effective content border is composed of target line segments in multiple directions.

[0068] In an embodiment provided by the present application, based on the above solution, optionally, the performing content extraction on the original pattern to obtain the pattern content in the original pattern, as Figure 2 shown, includes:

[0069] S201: Perform edge detection on the original map to obtain the target area of the original map.

[0070] In this embodiment, Gaussian filtering can be used to perform convolution denoising on the original map, and then the gradient values and gradient directions of each pixel in the convolved and denoised original map are calculated through the Sobel operator; the non-maximum suppression method is used to eliminate edge false information. Specifically, the gradient direction of each pixel point can be approximated to the up, down, left, right, and 45° directions, which are 0, 45, 90, 135, 180, 225, 270, and 315 respectively. Compare the gradient intensity of the pixel point with the pixel points in each surrounding gradient direction. If the gradient of this pixel point is the largest, it is retained; otherwise, it is suppressed. Finally, the double-threshold method is used to determine potential edges. Specifically, the upper and lower threshold values can be set. Pixel points smaller than the lower threshold value are discarded, and pixel points larger than the upper threshold value are retained as strong edges. Pixel points between the upper and lower threshold values are retained if they are connected to the strong edges; otherwise, they are deleted, obtaining the target area I of the original map. c 。

[0071] S202: Detect each line segment in the target area.

[0072] In this embodiment, each line segment in the target area can be detected through a line segment detection method. For example, the line segments in the target area Ic can be detected based on the Hough transform.

[0073] S203: Determine multiple target line segments among each of the line segments, and determine the effective content area of the original map according to the multiple target line segments. The target line segment is a line segment whose length satisfies a preset length condition and is closest to any boundary of the target area.

[0074] In this embodiment, the target line segments can be determined according to the coordinate information of the endpoints of each detected line segment.

[0075] Optionally, define the length li of the detected ith line segment and the endpoint coordinates of the position where the line segment is located as {di, (x 1i , y 1i ), (x 2i , y 2i )}, determine the type of the line segment. The type of the line segment can be one of the horizontal line type and the vertical line type. If the length of the line segment is greater than the length threshold corresponding to the type, it is determined that the line segment satisfies the length condition. The length threshold corresponding to the type of the line segment can be set according to actual needs. For example, the length threshold corresponding to the horizontal line type can be 2 / 3 of the length of the target area, and the length threshold corresponding to the vertical line type can be 2 / 3 of the target length.

[0076] Optionally, the distance between the target line segment and the boundary of the target area can be the distance between the center point of the target line segment and the boundary.

[0077] S204: Extract the content from the effective content area to obtain the atlas content of the original atlas.

[0078] In some embodiments, lines in the PRPD two-dimensional atlas can be detected based on the Hough transform. All the detected lines in the atlas are analyzed. Define the length li of the detected ith line segment and the endpoint coordinates of the position where the line segment is located as {di, (x 1i , y 1i ), (x 2i , y 2i )}. The horizontal line length is greater than 2 / 3 of the length of the two-dimensional atlas, the vertical line length is greater than 2 / 3 of the width of the two-dimensional atlas, and the four lines closest to the atlas boundary are used as the target line segments. The effective content border of the original atlas is composed of the four target line segments. The content within this border is defined as the effective content pixels (i.e., the atlas content), that is, the abscissa of the pixel position coordinates satisfies x ∈ [x min , x max , and the ordinate satisfies y ∈ [y min , y max . The content outside the border is useless redundant information.

[0079] S103: Adjust the size of the atlas content of the original atlas to obtain a standard-size atlas.

[0080] In this embodiment, the effective pixels of the atlas with an indefinite size are uniformly standardized to a size of row * col.

[0081] S104: Eliminate the background information in the standard-size atlas to obtain the target atlas.

[0082] In this embodiment, at least one of the curves and gray-scale background pixels in the standard-size atlas can be eliminated to obtain the target atlas.

[0083] In an embodiment provided by the present application, based on the above solution, optionally, the process of eliminating the background information in the standard-size atlas to obtain the target atlas is as Figure 3 shown and includes:

[0084] S301: Transform the standard-size atlas from the two-dimensional spatial domain to the frequency domain.

[0085] In an embodiment provided by the present application, based on the above solution, optionally, the transformation of the standard-size atlas from the two-dimensional spatial domain to the frequency domain includes:

[0086] Perform grayscale analysis on the standard size atlas to obtain the grayscale distribution of the standard size atlas;

[0087] Determine the background pixel positions of the standard size atlas according to the grayscale distribution of the standard size atlas;

[0088] Eliminate the background pixels of the standard size atlas according to the background pixel positions of the standard size atlas;

[0089] Transform the standard size atlas with background pixels eliminated from the two-dimensional spatial domain to the frequency domain.

[0090] In this embodiment, the standard size atlas can be denoted as I, and the grayscale histogram H of the standard size atlas is drawn i , and analyze the grayscale distribution characteristics of the grayscale histogram H i , define the area in H i that is large and has a small grayscale value span as the grayscale range [G min , G max corresponding to the atlas background area. Determine the pixel positions that satisfy the grayscale range [G min , G max as the background pixel positions. The background pixels at the background pixel positions in the standard size atlas can be eliminated, and then transformed from the two-dimensional spatial domain to the frequency domain to perform background elimination on the standard size atlas transformed to the frequency domain through a grid elimination filter.

[0091] S302: Filter the standard size atlas transformed to the frequency domain using a preset grid filter, and transform the filtered standard size atlas to the two-dimensional spatial domain to obtain an initial atlas.

[0092] In this embodiment, in this embodiment, the standard size atlas I can be transformed into the frequency domain characteristics F(u, v) of the image, analyze the corresponding components in the frequency domain of the uniformly distributed grid lines in the background of the atlas I, design a grid elimination filter H(u, v) based on the analysis results, eliminate the grid components in the frequency domain of the effective content, and then restore from the frequency domain to the two-dimensional spatial domain. The spatial atlas after spectral transformation filtering is denoted as , that is, the initial atlas.

[0093] S303: Eliminate the curves in the initial atlas to obtain a target atlas.

[0094] In an embodiment provided by the present application, based on the above solution, optionally, the eliminating the curves in the initial atlas to obtain a target atlas includes:

[0095] Determine the curve positions in the initial atlas according to the curve characteristics of the initial atlas;

[0096] Eliminate the curves in the initial map according to the curve positions in the initial map to obtain a target map.

[0097] Optionally, the size of the standard-size map is row*col. The period T of the sine curve in the effective content background is T = col, the amplitude A is A = row / 2, and the angular velocity w of the sine curve is w = 2*π / T. Then the expression of the sine curve in the effective content of the standard-size map is: B = A*sin(w*t), where the value range of t is [1:col]. Use the fitted sine curve to process the initial map for background elimination. The spatial map after background elimination is denoted as , that is, the target map.

[0098] S105: Convert the RGB data of each target pixel point in the target map into a gray value. According to the converted gray value, generate a pseudo-color map using a preset pseudo-color mapping formula. The target pixel points are pixel points not less than a preset gray threshold.

[0099] In this embodiment, for the PRPD map partial discharge feature localization and the realization of the jet color mapping I_unified: Input , after passing through the grid elimination filter, the gray values corresponding to the grid pixels are greatly reduced. Define a gray threshold σ, and define all feature pixels less than the gray threshold as the background. The set of spatial positions of the remaining pixel points is denoted as Index. Based on the coordinates within Index, convert the RGB three-channel data at the corresponding coordinates into gray values, and use the jet mapping formula to convert the gray values into a pseudo-color map.

[0100] See Figure 4 , which is a preprocessing process of a PRPD two-dimensional map provided by this application, including the following steps:

[0101] A1: Locate the effective content area [x min , x max , y min , y max of the PRPD two-dimensional map.

[0102] In this embodiment, as Figure 5 shown, the original PRPD two-dimensional maps collected by 4 partial discharge monitoring devices. The background colors, sizes, and corresponding positions of the coordinate axes of these four maps are different and vary greatly. First, edge detection can be performed on the original map to generate an edge detection map I c , and then use the Hough transform to detect the line segments in I c , and analyze all detected line segments. Define the length l i of the i-th line segment and its endpoint coordinates as {di, (x 1i , y1i ), (x 2i , y 2i )}, by screening, retain the line segments whose horizontal line length is greater than 2 / 3 of the two-dimensional spectrum length and the vertical line length is greater than 2 / 3 of the two-dimensional spectrum width, and define the four straight lines closest to the spectrum boundary as the effective content border of the image. The area within the border is defined as the effective content pixels, whose horizontal and vertical coordinates respectively satisfy x ∈ [x min , x max and y ∈ [y min , y max , while the content outside the border is regarded as useless redundant information.

[0103] In this embodiment, the Hough transform is used to detect the border information and grid lines in the image to identify the effective content area of the PRPD spectrum. In view of the fact that the characteristic effective content of the PRPD spectrum is usually contained within the coordinate axis border and the grid lines are also within this border, this embodiment locates the position of the coordinate axis by determining the four straight lines closest to the image boundary in the original image. Thus, the pixel interval enclosed by the coordinate axis is regarded as the effective content area of the effective PRPD spectrum features.

[0104] As Figure 6 shown, from the spectrum features after effective content positioning, it can be seen from the positioning result that the spectra of four different detection devices can achieve precise positioning of the four-week coordinates, realize effective content extraction, and extract the invalid redundant features outside the coordinate axis through the method provided by the embodiment of the present application. The redundant pixel points of the spectrum features are reduced, which improves the quality of the data set for data set construction. For the target to be recognized, the data dimension input to the recognition network is reduced through this operation.

[0105] In this embodiment, the spectrum edge detection process in step A1 includes the following steps:

[0106] A11: Denoise the PRPD spectrum, specifically using Gaussian filter convolution for denoising, and the expression of the convolution kernel is as follows.

[0107]

[0108] A12: Calculate the gradient value and gradient direction of each pixel based on the Sobel operator.

[0109] Among them, the convolution templates in the x and y directions can be respectively expressed as:

[0110] ,

[0111] The convolution results in the x and y directions are expressed as cx and cy.

[0112] Finally, the gradient value and direction of each pixel can be respectively expressed as:

[0113] ,

[0114] A13: Use the non-maximum suppression method to refine the edges and eliminate false edge information. In this step, the gradient direction of each pixel is approximately classified into eight standard directions (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°), and it is determined whether to retain the pixel as part of the edge by comparing the gradient intensity of the pixel and its adjacent pixels along the gradient direction.

[0115] A14: Use the double-threshold technique to determine potential edges. Set two thresholds: an upper bound and a lower bound. Pixels below the lower bound are directly discarded as non-edge points, and pixels above the upper bound are identified as strong edge points and retained. For pixels between the upper and lower bounds, if they are connected to strong edge points, they are also considered edge points and retained, otherwise they are deleted. This method effectively identifies the true edges in the image while excluding irrelevant noise and pseudo-edges.

[0116] A2: PRPD effective content size normalization and atlas background pixel gray level interval estimation.

[0117] In this embodiment, in order to standardize the effective pixels of atlases of different sizes, the atlas can be adjusted to a unified size of row*col, and this standardized two-dimensional image is denoted as I. Based on this standardized atlas, its gray histogram Hi is drawn to analyze the gray distribution characteristics. According to the histogram Hi, a region with a larger area and a smaller range of gray values is defined as the gray range [G min , G max corresponding to the background region. Pixel points that conform to this gray range are regarded as the background positions of the atlas.

[0118] Considering that the PRPD atlases obtained from different partial discharge monitoring devices may have different background colors, but the variation range of these background colors is usually small, while the characteristic color range for characterizing the number of events is wider. Using this prior knowledge, this application adjusts the effective feature intervals of different sizes to a unified specification through scale transformation, and further analyzes the gray histogram to identify pixel points that conform to the color distribution characteristics of the background pixels. Finally, the gray value ranges corresponding to each channel of the atlas background in the RGB color space are defined. This method can not only effectively remove background noise, but also more accurately extract and analyze the key feature information in the PRPD atlas.

[0119] In this embodiment, first, the effective content matrix I of the PRPD spectrum is input, and then the pixel matrices I1, I2, I3, and I4 on the periphery of the spectrum are selected and the grayscale histogram H is drawn; then the grayscale level G with the highest grayscale frequency is recorded, and the frequency attenuation amount of the grayscale interval [G−σ,G+σ] is calculated with G as the center; if the frequency attenuation amount of this interval reaches half of the maximum frequency, then G is selected as the background grayscale value, otherwise the second highest peak value is reselected as the new G, and the above process is repeated until the background grayscale value that meets the conditions is found (in this embodiment, σ = 30); finally, the grayscale interval that meets the conditions is recorded as: [G min , G max .

[0120] A3: Design of the frequency domain filter for removing grid lines in the background of the PRPD two-dimensional spectrum.

[0121] In this embodiment, the normalized effective two-dimensional spectrum I is converted to the frequency domain feature F(u, v) through Fourier transform, and the corresponding components of the uniformly distributed grid lines in the background in the frequency domain are analyzed, and it is found that they are manifested as horizontal and vertical straight lines passing through the center point of the frequency domain. Based on this characteristic, the grid elimination filter H(u, v) is designed to accurately remove the frequency components corresponding to the grid lines in the frequency domain. Subsequently, the processed frequency domain feature is restored to the spatial domain through inverse Fourier transform to obtain the spatial spectrum I' after removing the grid lines.

[0122] This application eliminates the background grid in the two-dimensional image of the PRPD spectrum monitored by different devices by setting a grid filter in the frequency domain. As Figure 7 shown, based on the spatial-frequency analysis, it is found that the vertical lines in the spatial domain image are manifested as horizontal line distributions in the frequency domain, while the horizontal lines are manifested as vertical line distributions. Therefore, the grid lines in the spatial domain are manifested as two horizontal and vertical straight lines passing through the center point of the frequency domain in the frequency domain. According to this characteristic, a special filter is designed to accurately remove these frequency components representing the grid lines in the frequency domain, so as to effectively purify the features of the PRPD spectrum, eliminate the interference of the background grid, as Figure 8 shown, which shows the result after multiplying the frequency domain image by the grid line removal filter.

[0123] In this embodiment, the process of removing the grid lines in the background of the spectrum described in step A3 includes the following steps:

[0124] A31: Convert the input effective content image I(i, j) from the spatial domain to the frequency domain F(u, v), where i and j represent the spatial pixel positions of the effective content of the spectrum, and u and v represent the positions of the effective content of the spectrum in the frequency domain. The spatial-frequency transformation formula is:

[0125]

[0126] Among them, M and N represent the number of pixels in the length and width of the valid content, then M = row and N = col;

[0127] A32: Move the zero-frequency point of the spatial-frequency transformation result F(u, v) to the center position of the spectrogram. Since most of the information of the image is distributed in the low-frequency part of the spectrum, when zero-frequency shifting is not performed, the energy of the spectrogram is mainly concentrated in the four corners, which is not conducive to observation. The centering transformation formula is:

[0128]

[0129] A33: Calculate the amplitude of the spectrogram |F’(u, v)|:

[0130]

[0131] Among them, R and Q are the real and imaginary parts of the frequency-domain function F’(u, v) after centering.

[0132] A34: Transform the amplitude of the frequency-domain image into the logarithmic interval Fl’(u, v), which is more convenient for observing the low-frequency part:

[0133]

[0134] A35: Define the frequency-domain grid removal filter D(u, v) = D1(u, v) * D2(u, v) * D3(u, v) * D4(u, v):

[0135]

[0136]

[0137]

[0138]

[0139] A36: Perform grid removal in the frequency domain. Multiply the filter D(u, v) by F’(u, v), and the formula is as follows:

[0140]

[0141] A37: Perform inverse transformation on the filtered spectrogram.

[0142]

[0143] A4: Eliminate the sinusoidal curve of the PRPD pattern features.

[0144] In this embodiment, the input PRPD map is an image after size normalization, with a size of row*col. In the background of the valid content, the period T of the sine curve is set to col, the amplitude A is row / 2, and the angular velocity w is calculated as 2π / T. Based on these parameters, the mathematical expression of the sine curve in the valid content of the PRPD map can be expressed as: B = A*sin(w*t), where the value range of t is [1, col]. By fitting this sine curve, the periodic interference in the background can be simulated and removed. The spatial map obtained after removing the background is denoted as I''.

[0145] This application fits the sine curve features in the background and uses a specific curve function expression to accurately locate the position of the sine line in the valid content. Based on this method, the pixel points that conform to the sine curve pattern can be identified and defined as background feature pixels, rather than part of the partial discharge (PD) features. This method effectively distinguishes background noise from actual PD events, helps to more accurately analyze and extract key feature information in the PRPD map, thereby improving the accuracy of subsequent processing and interpretation. The map after removing these background features can more clearly show the real situation of PD activities.

[0146] A5: Localization of PD features in the PRPD map and jet color mapping I_unified.

[0147] In this embodiment, by setting the gray threshold σ, the pixels with gray values less than σ are defined as the background and removed. The set of spatial positions of the remaining pixel points is denoted as Index. Based on the coordinates in Index, the RGB three-channel data at the corresponding positions are extracted and converted into gray values, and then the gray values are converted into a pseudo-color map using the jet mapping formula, as Figure 9 shown. This process effectively removes background interference, highlights PD features, and enhances visual recognition through pseudo-color mapping, facilitating the further analysis and interpretation of key information in the PRPD map.

[0148] In short, a general PRPD two-dimensional map preprocessing method provided by this application can efficiently preprocess PRPD maps generated by different PD monitoring devices, replace the traditional manual monitoring method, and greatly reduce labor costs. In addition, this method can accurately locate the valid content and remove redundant features in the map, greatly facilitating the construction of diverse and high-quality PRPD datasets and laying a foundation for future PRPD map recognition. This method not only improves work efficiency but also provides strong support for further research and application.

[0149] The present invention discloses a general PRPD two-dimensional map preprocessing scheme, which can uniformly normalize the original PRPD maps obtained during the partial discharge monitoring of GIS (Gas Insulated Switchgear) by different manufacturers, laying a foundation for the subsequent construction of diverse PRPD datasets. This scheme sequentially realizes the following steps: First, the Hough transform is used to detect the positions of long straight lines in the map to locate the effective content, and a method for judging whether there is redundant information outside the phase-amplitude interval is designed; Second, the gray threshold of the background pixels is estimated based on the gray histogram to remove the background pixels; Then, a grid removal filter is designed to remove the grid lines in the map through transformation in the frequency domain; Finally, the discharge characteristics after removing the grid lines and the background are mapped to the jet color space. This method can batch process PRPD maps from different sources, overcomes the limitations of traditional methods that require different preprocessing means due to equipment differences, replaces the time-consuming and inefficient manual processing process with a small amount of computing resources, and at the same time converts the map discharge characteristics to a unified color space, providing a solid foundation for the establishment of future PRPD map databases.

[0150] Corresponding to Figure 1 the method described above, an embodiment of the present application also provides a PRPD map processing device, which is applied to an electronic device and is used for Figure 1 the specific implementation of the method in Figure 10 as shown, including:

[0151] An acquisition unit 1001, configured to acquire the original map corresponding to the map processing instruction in response to the map processing instruction;

[0152] An extraction unit 1002, configured to extract the content of the original map to obtain the map content in the original map;

[0153] An adjustment unit 1003, configured to adjust the size of the map content of the original map to obtain a standard size map;

[0154] A background elimination unit 1004, configured to eliminate the background information in the standard size map to obtain a target map;

[0155] An execution unit 1005, configured to convert the RGB data of each target pixel point in the target map into a gray value, and generate a pseudo-color map according to the converted gray value by using a preset pseudo-color mapping formula, where the target pixel point is a pixel point not less than a preset gray threshold.

[0156] In an embodiment provided by the present application, based on the above scheme, optionally, the extraction unit 1002 includes:

[0157] An edge detection subunit for performing edge detection on the original atlas to obtain a target area of the original atlas;

[0158] A line segment detection subunit for detecting each line segment within the target area;

[0159] A determination subunit for determining a plurality of target line segments from among the respective line segments, and determining an effective content area of the original atlas based on the plurality of target line segments, where the target line segment is a line segment whose length satisfies a preset length condition and is closest to any boundary of the target area;

[0160] An extraction subunit for extracting content from the effective content area to obtain the atlas content of the original atlas.

[0161] In an embodiment provided by the present application, based on the above solution, optionally, the background elimination unit 804 includes:

[0162] A transformation subunit for transforming the standard-size atlas from the two-dimensional spatial domain to the frequency domain;

[0163] A filtering subunit for filtering the standard-size atlas transformed to the frequency domain using a preset grid filter, and transforming the filtered standard-size atlas back to the two-dimensional spatial domain to obtain an initial atlas;

[0164] An execution subunit for eliminating curves in the initial atlas to obtain a target atlas.

[0165] In an embodiment provided by the present application, based on the above solution, optionally, the transformation subunit includes:

[0166] A grayscale analysis module for performing grayscale analysis on the standard-size atlas to obtain the grayscale distribution of the standard-size atlas;

[0167] A determination module for determining the background pixel positions of the standard-size atlas based on the grayscale distribution of the standard-size atlas;

[0168] A background elimination module for eliminating the background pixels of the standard-size atlas based on the background pixel positions of the standard-size atlas;

[0169] A transformation module for transforming the standard-size atlas with background pixels eliminated from the two-dimensional spatial domain to the frequency domain.

[0170] In an embodiment provided by the present application, based on the above solution, optionally, the background elimination unit includes:

[0171] A first processing subunit for determining the curve positions in the initial atlas based on the curve features of the initial atlas;

[0172] A second processing subunit, configured to eliminate the curves in the initial map according to the curve positions in the initial map, so as to obtain a target map.

[0173] For the specific principles and execution processes of each unit and module in the PRPD map processing device disclosed in the embodiments of the present application above, they are the same as those of the PRPD map processing method disclosed in the embodiments of the present application above. For the corresponding parts in the PRPD map processing method provided in the embodiments of the present application above, reference can be made, and details will not be elaborated here.

[0174] The embodiments of the present application further provide a storage medium, which includes stored instructions. When the instructions run, the device where the storage medium is located is controlled to execute the above-mentioned PRPD map processing method.

[0175] The embodiments of the present application further provide an electronic device, and its structural schematic diagram is as Figure 11 shown, specifically including a memory 1101, and one or more instructions 1102. One or more instructions 1102 are stored in the memory 1101 and are configured to be executed by one or more processors 1103 to execute the above-mentioned PRPD map processing method.

[0176] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For device embodiments, since they are basically similar to method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0177] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0178] For the convenience of description, when describing the above device, various units are described separately according to their functions. Of course, when implementing the present application, the functions of each unit can be realized in one or more software and / or hardware.

[0179] From the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0180] The above has introduced in detail a method for processing PRPD spectra provided by this application. Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for processing a PRPD spectrum, characterized in that, including: In response to a map processing instruction, obtaining the original map corresponding to the map processing instruction; Performing content extraction on the original map to obtain the map content in the original map; Adjusting the size of the map content of the original map to obtain a standard-size map; Eliminating the background information in the standard-size map to obtain a target map; Converting the RGB data of each target pixel point in the target map into a grayscale value, and generating a pseudo-color map according to the converted grayscale value by using a preset pseudo-color mapping formula, where the target pixel point is a pixel point not less than a preset grayscale threshold.

2. The method according to claim 1, wherein The performing content extraction on the original map to obtain the map content in the original map includes: Performing edge detection on the original map to obtain the target area of the original map; Detecting each line segment in the target area; Determining a plurality of target line segments among each of the line segments, and determining the effective content area of the original map according to the plurality of target line segments, where the target line segment is a line segment whose length meets a preset length condition and is closest to any boundary of the target area; Performing content extraction on the effective content area to obtain the map content of the original map.

3. The method according to claim 1, wherein The eliminating the background information in the standard-size map to obtain a target map includes: Transforming the standard-size map from a two-dimensional spatial domain to a frequency domain; Filtering the standard-size map transformed to the frequency domain by using a preset grid filter, and transforming the filtered standard-size map to the two-dimensional spatial domain to obtain an initial map; Eliminating the curves in the initial map to obtain a target map.

4. The method according to claim 3, wherein The transforming the standard-size map from a two-dimensional spatial domain to a frequency domain includes: Performing grayscale analysis on the standard-size map to obtain the grayscale distribution of the standard-size map; Determining the background pixel positions of the standard-size map according to the grayscale distribution of the standard-size map; Eliminating the background pixels of the standard-size map according to the background pixel positions of the standard-size map; Transforming the standard-size map with background pixels eliminated from the two-dimensional spatial domain to the frequency domain.

5. The method according to claim 3, wherein The eliminating the curves in the initial map to obtain a target map includes: Determining the curve positions in the initial map according to the curve characteristics of the initial map; Eliminating the curves in the initial map according to the curve positions in the initial map to obtain a target map.

6. A PRPD spectrum processing device, characterized in that including: An obtaining unit, configured to obtain the original map corresponding to the map processing instruction in response to the map processing instruction; An extracting unit, configured to perform content extraction on the original map to obtain the map content in the original map; An adjusting unit, configured to adjust the size of the map content of the original map to obtain a standard-size map; A background eliminating unit, configured to eliminate the background information in the standard-size map to obtain a target map; An executing unit, configured to convert the RGB data of each target pixel point in the target map into a grayscale value, and generate a pseudo-color map according to the converted grayscale value by using a preset pseudo-color mapping formula, where the target pixel point is a pixel point not less than a preset grayscale threshold.

7. The device according to claim 6, characterized in that, The extraction unit includes: An edge detection subunit, configured to perform edge detection on the original map to obtain a target area of the original map; A line segment detection subunit, configured to detect each line segment within the target area; A determination subunit, configured to determine a plurality of target line segments from the respective line segments, and determine an effective content area of the original map according to the plurality of target line segments, where the target line segment is a line segment whose length satisfies a preset length condition and is closest to any boundary of the target area; An extraction subunit, configured to extract content from the effective content area to obtain the map content of the original map.

8. The device according to claim 6, characterized in that, The background elimination unit includes: A transformation subunit, configured to transform the standard size map from a two-dimensional spatial domain to a frequency domain; A filtering subunit, configured to filter the standard size map transformed to the frequency domain by using a preset grid filter, and transform the filtered standard size map to the two-dimensional spatial domain to obtain an initial map; An execution subunit, configured to eliminate curves in the initial map to obtain a target map.

9. A storage medium, characterized in that, The storage medium includes stored instructions, wherein when the instructions are running, the device where the storage medium is located is controlled to execute the PRPD map processing method according to any one of claims 1 to 5.

10. An electronic device, characterized in that, It includes a memory, and one or more instructions, where one or more instructions are stored in the memory and are configured to be executed by one or more processors to execute the PRPD map processing method according to any one of claims 1 to 5.

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