Lightning activity information analysis method and device

Abstract

The present application relates to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for analyzing lightning activity information, which can be used in the field of power technology. The method comprises: acquiring lightning leader images under various operating conditions using an image acquisition device; grayscale processing the lightning leader images to obtain grayscale images; denoising the grayscale images to obtain denoised images of the grayscale images; edge detection processing on the denoised images to obtain edge contour images of the denoised images; contour restoration and contour correction processing on the edge contour images to obtain leader contour information of the edge contour images; geometric feature recognition processing on the leader contour information to obtain geometric feature information; constructing a database of lightning leader characteristic parameters based on the geometric feature information; and analyzing lightning discharge behavior information to obtain analysis results of the lightning discharge behavior information. This method can improve the efficiency of lightning activity information analysis.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for analyzing lightning activity information. Background Art

[0002] With the development of meteorological monitoring technology, lightning observation has attracted widespread attention as an important field of meteorological safety research. How to efficiently analyze lightning activity information has become an important research direction.

[0003] Traditional technology usually analyzes lightning activity information through manual observation and analysis; however, this method requires a lot of manual processing time, resulting in low efficiency of lightning activity information analysis. Summary of the Invention

[0004] Based on this, it is necessary to provide a lightning activity information analysis method, device, computer equipment, computer-readable storage medium and computer program product that can improve the efficiency of lightning activity information analysis in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for analyzing lightning activity information. The method comprises:

[0006] Collect lightning leader images under various working conditions through image acquisition equipment;

[0007] Performing grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image;

[0008] Performing denoising processing on the grayscale image to obtain a denoised image of the grayscale image;

[0009] Performing edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image;

[0010] Performing contour restoration and contour correction on the edge contour image to obtain leading contour information of the edge contour image;

[0011] Performing geometric feature recognition processing on the leading profile information to obtain geometric feature information of the leading profile information;

[0012] Constructing a database of lightning leader characteristic parameters based on the geometric characteristic information;

[0013] The lightning discharge behavior information to be analyzed is analyzed according to the database of lightning leader characteristic parameters to obtain an analysis result of the lightning discharge behavior information.

[0014] In one embodiment, performing grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image includes:

[0015] Performing grayscale space conversion processing on the lightning leader image to obtain an initial grayscale image of the lightning leader image;

[0016] Performing brightness update processing on the initial grayscale image to obtain the grayscale image.

[0017] In one embodiment, performing denoising on the grayscale image to obtain a denoised image of the grayscale image includes:

[0018] Processing the grayscale image using a Gaussian filter processing model to obtain a preliminary smoothed image of the grayscale image;

[0019] The preliminary smoothed image is processed by using an erosion processing model to obtain the denoised image.

[0020] In one embodiment, performing edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image includes:

[0021] Performing preliminary edge information extraction processing on the denoised image according to the grayscale gradient, and calculating the gradient magnitude and direction of each pixel in the denoised image to obtain a gradient map of the denoised image;

[0022] According to the preset threshold, binary segmentation processing is performed on the gradient image to obtain the edge contour image.

[0023] In one embodiment, performing contour restoration and contour correction on the edge contour image to obtain the leading contour information of the edge contour image includes:

[0024] According to geometric characteristic information of adjacent edge points in the edge contour image, a topological reconstruction model is used to connect the breakpoints in the edge contour image and restore the missing contour segments to obtain complete contour structure information of the edge contour image;

[0025] According to the physical field characteristic information, the complete contour structure information is subjected to multi-scale correction processing to obtain the leading contour information; the multi-scale correction processing includes correcting the abnormal contour offset information in the complete contour structure information through a mechanical model.

[0026] In one embodiment, constructing a database of lightning leader characteristic parameters based on the geometric characteristic information includes:

[0027] Associating the geometric feature information with the condition information of each working condition to obtain a multi-dimensional data structure model of the lightning leader characteristic parameters;

[0028] Classifying the lightning leader characteristic parameters according to the multidimensional data structure model to obtain classification results of the lightning leader characteristic parameters;

[0029] According to the classification result, a database of the lightning leader characteristic parameters is constructed.

[0030] In a second aspect, the present application further provides a lightning activity information analysis device. The device comprises:

[0031] An image acquisition module is used to acquire images of lightning leaders under various working conditions through an image acquisition device;

[0032] An image processing module is used to perform grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image;

[0033] An image denoising module, configured to perform denoising processing on the grayscale image to obtain a denoised image of the grayscale image;

[0034] An image detection module is used to perform edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold value to obtain an edge contour image of the denoised image;

[0035] An image restoration module is used to perform contour restoration and contour correction processing on the edge contour image to obtain leading contour information of the edge contour image;

[0036] A feature recognition module is used to perform geometric feature recognition processing on the leading profile information to obtain geometric feature information of the leading profile information;

[0037] A data construction module, configured to construct a database of lightning leader characteristic parameters based on the geometric characteristic information;

[0038] The discharge analysis module is used to analyze the lightning discharge behavior information to be analyzed based on the database of lightning leader characteristic parameters to obtain analysis results of the lightning discharge behavior information.

[0039] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0040] Collect lightning leader images under various working conditions through image acquisition equipment;

[0041] Performing grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image;

[0042] Performing denoising processing on the grayscale image to obtain a denoised image of the grayscale image;

[0043] Performing edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image;

[0044] Performing contour restoration and contour correction on the edge contour image to obtain leading contour information of the edge contour image;

[0045] Performing geometric feature recognition processing on the leading profile information to obtain geometric feature information of the leading profile information;

[0046] Constructing a database of lightning leader characteristic parameters based on the geometric characteristic information;

[0047] The lightning discharge behavior information to be analyzed is analyzed according to the database of lightning leader characteristic parameters to obtain an analysis result of the lightning discharge behavior information.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0049] Collect lightning leader images under various working conditions through image acquisition equipment;

[0050] Performing grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image;

[0051] Performing denoising processing on the grayscale image to obtain a denoised image of the grayscale image;

[0052] Performing edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image;

[0053] Performing contour restoration and contour correction on the edge contour image to obtain leading contour information of the edge contour image;

[0054] Performing geometric feature recognition processing on the leading profile information to obtain geometric feature information of the leading profile information;

[0055] Constructing a database of lightning leader characteristic parameters based on the geometric characteristic information;

[0056] The lightning discharge behavior information to be analyzed is analyzed according to the database of lightning leader characteristic parameters to obtain an analysis result of the lightning discharge behavior information.

[0057] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0058] Collect lightning leader images under various working conditions through image acquisition equipment;

[0059] Performing grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image;

[0060] Performing denoising processing on the grayscale image to obtain a denoised image of the grayscale image;

[0061] Performing edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image;

[0062] Performing contour restoration and contour correction on the edge contour image to obtain leading contour information of the edge contour image;

[0063] Performing geometric feature recognition processing on the leading profile information to obtain geometric feature information of the leading profile information;

[0064] Constructing a database of lightning leader characteristic parameters based on the geometric characteristic information;

[0065] The lightning discharge behavior information to be analyzed is analyzed according to the database of lightning leader characteristic parameters to obtain an analysis result of the lightning discharge behavior information.

[0066] The above-mentioned lightning activity information analysis method, device, computer equipment, computer-readable storage medium and computer program product collect lightning leader images under various working conditions through image acquisition equipment; perform grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image; perform denoising processing on the grayscale image to obtain a denoised image of the grayscale image; perform edge detection processing on the denoised image based on the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image; perform contour restoration processing and contour correction processing on the edge contour image to obtain leader contour information of the edge contour image; perform geometric feature recognition processing on the leader contour information to obtain geometric feature information of the leader contour information; construct a database of lightning leader characteristic parameters based on the geometric feature information; and analyze the lightning discharge behavior information to be analyzed based on the database of lightning leader characteristic parameters to obtain analysis results of the lightning discharge behavior information. This solution improves image quality by performing grayscale processing and denoising on lightning leader images; achieves accurate extraction of lightning leader features through edge detection, contour restoration, and contour correction; converts leader contour information into quantifiable geometric feature information through geometric feature recognition; achieves systematic storage and rapid recall of lightning leader features by constructing a database of lightning leader feature parameters; and achieves rapid processing of lightning activity information by automatically analyzing lightning discharge behavior information based on the database of lightning leader feature parameters, avoiding repeated manual observation and analysis processes and improving the efficiency of lightning activity information analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. 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 paying any creative work.

[0068] Figure 1 1 is a flow chart of a method for analyzing lightning activity information in one embodiment;

[0069] Figure 2 1 is a flow chart of grayscale processing steps in one embodiment;

[0070] Figure 3 Schematic diagram of the marking of characteristic parameters of a lightning leader in one embodiment;

[0071] Figure 4 is a structural block diagram of a lightning activity information analysis device in one embodiment;

[0072] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0074] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0075] In an exemplary embodiment, Figure 1 As shown, a method for analyzing lightning activity information is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, etc. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0076] Step S101: collecting lightning leader images under various working conditions by using an image acquisition device.

[0077] Step S102 : performing grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image.

[0078] Step S103 , performing denoising processing on the grayscale image to obtain a denoised image of the grayscale image.

[0079] Step S104 : performing edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold value to obtain an edge contour image of the denoised image.

[0080] Step S105 , performing contour restoration processing and contour correction processing on the edge contour image to obtain leading contour information of the edge contour image.

[0081] Step S106 , performing geometric feature recognition processing on the leading contour information to obtain geometric feature information of the leading contour information.

[0082] Step S107: constructing a database of lightning leader characteristic parameters based on the geometric characteristic information.

[0083] Step S108 : analyzing the lightning discharge behavior information to be analyzed based on the database of lightning leader characteristic parameters to obtain analysis results of the lightning discharge behavior information.

[0084] Among them, lightning activity can be the discharge phenomenon in which thunderstorm clouds release their internal electrical energy.

[0085] The image acquisition device may be an optical device for acquiring lightning leader images, for example, the image acquisition device may be a camera.

[0086] The working conditions may be combinations of different conditions in a lightning discharge experiment. For example, the working conditions may include experimental condition parameters such as voltage amplitude, ambient humidity, air pressure, temperature, and electrode spacing.

[0087] The lightning leader image may be image data recording the initial stage of lightning discharge. For example, the lightning leader image may be an image recording the concentrated enhancement of electric field intensity in a local area and the formation of a discharge channel.

[0088] Grayscale processing may be a process of converting an image into grayscale values. For example, grayscale processing may be a process of converting an RGB (red, green, blue) color space into a grayscale space and optimizing the grayscale distribution through a histogram equalization method.

[0089] The grayscale image may be an image that has been grayscale processed. For example, the grayscale image may be a single-channel image with pixel values ​​between 0 and 255.

[0090] The denoising process may be a process of eliminating image noise, for example, the denoising process may be an image processing process including Gaussian filtering and corrosion processing steps.

[0091] The denoised image may be an image that has been subjected to denoising processing. For example, the denoised image may be a clear image obtained after Gaussian filtering and corrosion processing.

[0092] The grayscale gradient may be the spatial rate of change of the grayscale value of a pixel in the image. For example, the grayscale gradient may be the degree of difference in grayscale values ​​between adjacent pixels in the denoised image.

[0093] The preset threshold may be a grayscale gradient critical value used to determine whether a pixel point is an edge point. For example, the preset threshold may be a segmentation critical value dynamically adjusted according to the mean and standard deviation of pixel values ​​in a local area.

[0094] The edge detection process may be a process of extracting edge information from an image. For example, the edge detection process may be a process of extracting edge information from an image based on a grayscale gradient and a preset threshold.

[0095] The edge contour image may be an image obtained after edge detection processing. For example, the edge contour image may be an image containing edge information obtained after binary segmentation processing.

[0096] The contour restoration process may be a process of restoring the integrity of the edge contour image. For example, the contour restoration process may be a process of connecting the breakpoints in the edge contour image through a topological reconstruction method to restore the missing contour segments.

[0097] The contour correction process may be a process of optimizing and correcting the complete contour structure information. For example, the contour correction process may be a process of correcting abnormal contour deviation through a mechanical model.

[0098] The leader profile information may be information describing the morphological characteristics of the lightning leader. For example, the leader profile information may be complete profile structure information including the leader channel.

[0099] The geometric feature recognition process may be a process of measuring geometric feature parameters of the leader profile. For example, the geometric feature recognition process may be a process of measuring the length, direction, curvature distribution and bifurcation features of the leader.

[0100] The geometric feature information may be data describing the geometric features of the leader profile, for example, the geometric feature information may be data including parameters such as leader length, direction, upper and lower leader ratio, curvature distribution and bifurcation features.

[0101] The database of lightning leader characteristic parameters may be a data set storing lightning leader characteristic parameters. For example, the database of lightning leader characteristic parameters may be a database formed by associating leader geometric characteristic parameters with experimental working conditions.

[0102] The lightning discharge behavior information belongs to lightning activity information, and the lightning discharge behavior information may be information describing characteristics of a lightning discharge process, for example, the lightning discharge behavior information may include information such as a discharge path, electric field distribution, and characteristic parameters.

[0103] Among them, the analysis results of lightning discharge behavior information can be quantitative evaluation and prediction results of the lightning discharge process based on the database of lightning leader characteristic parameters. For example, the analysis results of lightning discharge behavior information can be a comprehensive analysis report that includes discharge path prediction, electric field distribution characteristics, leader development trend, discharge energy intensity assessment, comparative analysis of leader characteristics under various working conditions, lightning protection recommendations, etc., which can also include characteristic classification and risk level assessment of lightning discharge behavior based on geometric characteristic parameters such as leader length, direction, curvature distribution, number of bifurcations and upper and lower leader ratios, combined with working conditions such as voltage amplitude, ambient humidity, air pressure, temperature and electrode spacing.

[0104] Optionally, the terminal (system) collects lightning leader images under different working conditions through image acquisition equipment such as a high-speed camera or a SLR camera, performs color space conversion on the lightning leader image, converts the RGB (red, green, and blue) color space into a grayscale space to obtain an initial grayscale image, and optimizes the grayscale distribution through a histogram equalization method to obtain a grayscale image; the terminal uses a Gaussian filtering method to smooth the grayscale image to generate a preliminary smoothed image, and then uses a corrosion processing method to perform morphological processing on the preliminary smoothed image to obtain a denoised image; the terminal performs preliminary extraction of edge information on the denoised image based on the grayscale gradient, calculates the gradient amplitude and direction of each pixel in the denoised image to obtain a gradient map, and performs binary segmentation processing on the gradient map through a preset threshold to obtain an edge contour image; the terminal uses a topological reconstruction method to connect the breakpoints based on the geometric characteristics of adjacent edge points in the edge contour image, And restore the missing contour segments to form a complete contour structure, and perform multi-scale correction and optimization on the complete contour structure in combination with the physical field characteristics to obtain the leader contour information; the terminal converts the pixel distance of the leader contour information into the actual physical scale based on the image calibration parameters, measures the length, direction and upper and lower leader ratio of the leader, and identifies the bending shape of the leader contour based on the curvature analysis method, measures the curvature distribution and bifurcation characteristics of the leader contour, and obtains geometric feature information; the terminal associates the geometric feature information with the experimental conditions of the corresponding working conditions, constructs a multidimensional data structure model of the lightning leader characteristic parameters, and uses the cluster analysis method to classify the lightning leader characteristic parameters under different working conditions, establishes a database of lightning leader characteristic parameters, and finally analyzes the lightning discharge behavior information to be analyzed based on the database of lightning leader characteristic parameters to obtain the analysis results of the lightning discharge behavior information.

[0105] For example, the terminal uses image acquisition equipment to collect lightning leader images under different working conditions; grayscale processing is performed on the lightning leader image, and the image is converted into grayscale values ​​using pixel information in the image to obtain a grayscale image; the grayscale image is denoised to obtain a denoised image, and the denoising process includes Gaussian filtering and corrosion processing steps to remove image noise; an edge detection algorithm is used to perform edge detection on the denoised image based on grayscale gradient and threshold to obtain an edge contour image; the edge contour image is restored to a complete image contour through a visualization method, and the complete image contour is described by lines to form a leader contour; the geometric feature parameters of the leader contour are determined according to the actual size ratio, and the geometric feature parameters include leader length, direction, curvature, number of bifurcations, and upper and lower leader ratios; the geometric feature parameters are classified and summarized to establish a database of lightning leader feature parameters for different working conditions. Specifically, this embodiment obtains lightning leader images through image acquisition equipment, and combines grayscale processing, Gaussian filtering and corrosion processing denoising, edge detection algorithm, contour restoration and geometric feature parameter extraction to achieve high-precision observation and analysis of the leader discharge characteristics of tower head impact discharge. At the same time, combined with multidimensional data models and cluster analysis methods, a lightning leader characteristic parameter database for different working conditions is established, which improves the observation and statistical capabilities of lightning leader discharge characteristic parameters.

[0106] For example, the terminal determines the geometric feature parameters of the leader contour according to the actual size ratio, specifically including: based on the image calibration parameters, converting the pixel distance of the leader contour into the actual physical scale, and measuring the length, direction and upper and lower leader ratio of the leader.

[0107] For example, based on the image calibration parameters, the pixel distance of the leader outline is converted into the actual physical scale, and the length, direction and upper and lower leader ratio of the leader are determined, specifically including:

[0108] Use the calibration scale factor of the image acquisition device to match the pixel scale with the actual physical scale;

[0109] For the length measurement of lightning leaders, the cumulative segment length of the leader profile is calculated along the main direction of the leader profile to obtain the overall leader length;

[0110] For the direction determination of the lightning leader, the direction angle of the line connecting the two end points of the leader profile main axis is calculated, and the correction is made based on the local direction characteristics of each branch.

[0111] For the upper and lower leader ratio measurement, the physical characteristics of the lightning discharge direction are combined to distinguish the distribution of multiple leader branches and calculate the length ratio of the upper and lower branches respectively;

[0112] The bending morphology of the leader profile is identified based on the curvature analysis method, and the curvature distribution and bifurcation characteristics of the leader profile are measured.

[0113] Exemplarily, identifying the bending shape of the leader profile based on the curvature analysis method and determining the curvature distribution and bifurcation characteristics of the leader profile specifically include:

[0114] Using the curvature calculation formula, the discrete curvature of the leading contour is estimated to obtain the local curvature distribution of each point of the contour;

[0115] In bifurcation feature recognition, based on the location of the curvature mutation point in the leading contour, combined with the Euclidean distance and local direction angle between consecutive bifurcation points, different bifurcation points are identified and their bifurcation directions are recorded;

[0116] Bifurcation characteristics include: the number of bifurcation points, the distance between adjacent bifurcation points, the bifurcation angle, and the proportion of branch morphology. The characteristic parameters are further matched with the lightning discharge characteristics.

[0117] Specifically, by converting the pixel distance of the leader contour into the actual physical scale based on image calibration parameters, the geometric characteristic parameters such as the length, direction, and upper and lower leader ratio of the lightning leader are accurately measured, and the curvature analysis method is combined to identify the bending shape and bifurcation characteristics of the leader contour.

[0118] Through steps such as cumulative segment length calculation, azimuth angle correction, and bifurcation point identification, the key geometric features of lightning leaders can be comprehensively and accurately extracted. Combined with a detailed analysis of bifurcation features, such as the number of bifurcation points, bifurcation angles, and the proportion of branch morphology, it provides high-precision geometric parameter support for the study of lightning discharge characteristics, thereby improving the measurement accuracy and applicability of the geometric characteristic parameters of lightning leaders.

[0119] In the above-mentioned lightning activity information analysis method, lightning leader images under various working conditions are collected by image acquisition equipment; grayscale processing is performed on the lightning leader image to obtain a grayscale image of the lightning leader image; denoising is performed on the grayscale image to obtain a denoised image of the grayscale image; edge detection processing is performed on the denoised image based on the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image; contour restoration processing and contour correction processing are performed on the edge contour image to obtain leader contour information of the edge contour image; geometric feature recognition processing is performed on the leader contour information to obtain geometric feature information of the leader contour information; based on the geometric feature information, a database of lightning leader characteristic parameters is constructed; based on the database of lightning leader characteristic parameters, the lightning discharge behavior information to be analyzed is analyzed to obtain analysis results of the lightning discharge behavior information. This solution improves image quality by performing grayscale processing and denoising on lightning leader images; achieves accurate extraction of lightning leader features through edge detection, contour restoration, and contour correction; converts leader contour information into quantifiable geometric feature information through geometric feature recognition; achieves systematic storage and rapid recall of lightning leader features by constructing a database of lightning leader feature parameters; and achieves rapid processing of lightning activity information by automatically analyzing lightning discharge behavior information based on the database of lightning leader feature parameters, avoiding repeated manual observation and analysis processes and improving the efficiency of lightning activity information analysis.

[0120] In an exemplary embodiment, referring to Figure 2 , grayscale processing is performed on the lightning leader image to obtain a grayscale image of the lightning leader image, which specifically includes the following contents:

[0121] Step S201, performing grayscale space conversion processing on the lightning leader image to obtain an initial grayscale image of the lightning leader image;

[0122] Step S202 : performing brightness update processing on the initial grayscale image to obtain a grayscale image.

[0123] The grayscale space conversion process may be a process of converting an image from a color space to a grayscale space. For example, the grayscale space conversion process may be a process of converting an RGB color space to a grayscale space.

[0124] The initial grayscale image may be a preliminary grayscale image obtained after grayscale space conversion processing. For example, the initial grayscale image may be an image with pixel values ​​between 0 and 255 obtained by converting RGB color space into grayscale space.

[0125] The brightness update process may be a process of adjusting the brightness and optimizing the grayscale distribution of the initial grayscale image. For example, the brightness update process may be a process of optimizing the grayscale distribution of the initial grayscale image through a histogram equalization method.

[0126] Optionally, the terminal performs color space conversion on the lightning leader image, converts the lightning leader image from RGB color space to grayscale space, and uses a weighted average method to calculate the grayscale value of each pixel in the lightning leader image to obtain an initial grayscale image of the lightning leader image; the terminal adjusts the brightness of the initial grayscale image of the lightning leader image, calculates the grayscale histogram of the initial grayscale image of the lightning leader image, counts the number of pixels of each grayscale level in the initial grayscale image of the lightning leader image, and calculates the cumulative distribution function based on the grayscale histogram of the initial grayscale image of the lightning leader image, remaps the grayscale based on the cumulative distribution function, optimizes the grayscale distribution through a histogram equalization method, achieves uniform grayscale distribution, and obtains the grayscale image of the lightning leader image.

[0127] For example, the terminal performs color space conversion on the lightning leader image, converts the RGB color space into a grayscale space, and obtains an initial grayscale image; adjusts the brightness of the initial grayscale image, and optimizes the grayscale distribution through a histogram equalization method to obtain a grayscale image.

[0128] For example, the color space conversion uses a weighted averaging method.

[0129] For example, the histogram equalization method includes the following steps: calculating the grayscale histogram of the initial grayscale image and counting the number of pixels at each grayscale level; calculating the cumulative distribution function based on the grayscale histogram, and remapping the grayscale levels based on the cumulative distribution function to achieve a uniform grayscale distribution. Specifically, this embodiment converts the RGB color space into a grayscale space by performing a color space conversion on the lightning leader image, and uses a weighted average method to accurately calculate the grayscale value. It combines brightness adjustment and histogram equalization methods to optimize the grayscale distribution, thereby improving the contrast and detail expression of the grayscale image. By calculating the grayscale histogram, the cumulative distribution function, and remapping the grayscale levels, a uniform grayscale distribution is achieved.

[0130] The technical solution provided in this embodiment simplifies the image data structure by performing grayscale space conversion processing on the lightning leader image, converting complex color image information into single-channel information in grayscale space; and optimizes the grayscale distribution of the image by performing brightness update processing on the initial grayscale image. This is beneficial to reducing the computational complexity of subsequent image processing and at the same time is beneficial to improving the recognition accuracy of lightning leader features.

[0131] In an exemplary embodiment, a grayscale image is denoised to obtain a denoised image of the grayscale image, specifically including the following contents: the grayscale image is processed through a Gaussian filtering processing model to obtain a preliminary smoothed image of the grayscale image; the preliminary smoothed image is processed through an corrosion processing model to obtain a denoised image.

[0132] The Gaussian filter processing model (ie, the Gaussian filter method) may be a mathematical model for smoothing an image.

[0133] The preliminary smoothed image may be an image obtained after being processed by a Gaussian filter processing model. For example, the preliminary smoothed image may be an image with reduced random noise obtained after smoothing a grayscale image using a Gaussian filter method.

[0134] The corrosion processing model (ie, the corrosion processing method) may be a mathematical model for performing morphological processing on an image.

[0135] Optionally, the terminal processes the grayscale image through a Gaussian filtering processing model, wherein the Gaussian filtering processing model calculates the grayscale mean and grayscale value standard deviation (where x represents the horizontal coordinate and y represents the vertical coordinate) within a local window centered on (x, y), and smoothes the grayscale image in combination with a Gaussian distribution weight function to obtain a preliminary smoothed image of the grayscale image; the terminal processes the preliminary smoothed image through an corrosion processing model, wherein the corrosion processing model calculates the pixel value mean and pixel value standard deviation of the preliminary smoothed image in a local area centered on (x, y) and within the range of the structural element matrix, and performs morphological processing on the preliminary smoothed image in combination with a local adaptive adjustment coefficient to obtain a denoised image, thereby effectively eliminating noise in the grayscale image and improving image quality.

[0136] For example, the terminal uses a Gaussian filtering method to smooth the grayscale image and generate a preliminary smoothed image;

[0137] The calculation formula of the Gaussian filtering method (Gaussian filtering processing model) is:

[0138] ;

[0139] in, For the initial smoothed image at coordinates The pixel value at For grayscale images at coordinates The pixel value at For grayscale images at coordinates The pixel value at is the weight function of the Gaussian distribution, i and j are the offsets on the x-axis and y-axis respectively, is the standard deviation of the Gaussian function, k is half the size of the filter window, is the adjustment coefficient, For The grayscale mean in the local window centered at For The standard deviation of the grayscale value within the local window centered at ;

[0140] The morphological processing of the preliminary smoothed image is performed using the corrosion processing method to eliminate image noise and obtain a denoised image;

[0141] The calculation formula of the corrosion treatment method (corrosion treatment model) is:

[0142] ;

[0143] in, The denoised image is at coordinates The pixel value at , E is the structural element matrix of the corrosion process, For the initial smoothed image at coordinates The pixel value at is the local adaptive adjustment coefficient, For is the mean pixel value of the preliminary smoothed image in the local area within the center and the range of the structural element matrix E, For The standard deviation of the pixel values ​​of the preliminary smoothed image in the local area within the center and the range of the structure element matrix E.

[0144] Specifically, this embodiment introduces an adjustment coefficient By coordinates Centered on the local mean and local standard deviation , dynamically adjust the noise smoothing degree and adjust the coefficient It can reflect the changes in brightness and contrast in different areas of the image, so that the filter can perform more rigorous smoothing in strong noise areas, while appropriately retaining edge information in detail areas, ensuring that the filtering effect can be adaptively adjusted under different working conditions and different local characteristics, effectively reducing the impact of random noise and maintaining the effective detail information of the image to the greatest extent.

[0145] To solve the problem that fixed parameters cannot take into account the brightness differences of different regions, this embodiment improves the corrosion processing formula by introducing a local adaptive adjustment coefficient so that the corrosion processing calculation for each pixel no longer simply takes the minimum value of the neighborhood, but comprehensively considers the local mean. and local standard deviation The improved formula makes the corrosion process more strongly suppress small noise in darker or noisy areas, while relatively weakening the corrosion intensity in edge and structure-rich areas, thereby better preserving important details and edge features of the image.

[0146] By introducing the local adaptive adjustment coefficient into the Gaussian filter formula, more effective smoothing processing is achieved when regional noise is large, while also performing better in preserving image edge details.

[0147] The local mean and standard deviation dynamic adjustment parameters are introduced into the corrosion processing, so that the image can automatically adjust the processing intensity according to the different local characteristics while removing noise, achieving more accurate and adaptive noise suppression.

[0148] The improved Gaussian filtering and corrosion processing formula effectively reduces the local information loss problem that may be caused by the fixed coefficients of the traditional method, thereby obtaining a purer image with clearer details, providing great support for steps such as edge detection and contour restoration, and improving the robustness of the entire tower head impact discharge leader discharge characteristic parameter observation and statistical system.

[0149] This embodiment uses a Gaussian filtering method to smooth the grayscale image, effectively reducing the random noise in the image. At the same time, it combines the corrosion processing method to perform morphological processing on the smoothed image, further eliminating small noise points and false edges in the image, improving the quality of the denoised image and the accuracy of the edge information, and enhancing the anti-interference ability and processing accuracy.

[0150] The technical solution provided in this embodiment smoothes the grayscale image through a Gaussian filtering processing model, effectively reducing the random noise in the image; performs morphological processing on the preliminary smoothed image through an corrosion processing model, further eliminating small noise points in the image; it is beneficial to remove different types of noise interference while retaining important image features, thereby improving the quality of the denoised image and the accuracy of subsequent edge detection.

[0151] In an exemplary embodiment, edge detection processing is performed on the denoised image based on the grayscale gradient and a preset threshold of the denoised image to obtain an edge contour image of the denoised image, which specifically includes the following contents: preliminary extraction processing of edge information is performed on the denoised image based on the grayscale gradient, and the gradient amplitude and direction of each pixel in the denoised image are calculated to obtain a gradient map of the denoised image; and binary segmentation processing is performed on the gradient map based on the preset threshold to obtain an edge contour image.

[0152] The preliminary extraction of edge information may be a process of preliminarily identifying and extracting edge features in an image. For example, the preliminary extraction of edge information may be a process of identifying edges by calculating the grayscale gradient of pixels in a local area.

[0153] The gradient amplitude may be the intensity of the grayscale value change at the pixel point. For example, the gradient amplitude may be a value obtained by calculating the square root of the sum of the squares of the grayscale value changes of the pixel point in the horizontal and vertical directions.

[0154] The gradient map may be a grayscale image that records the gradient amplitudes of all pixels in the image. For example, the gradient map may be an image that contains information about the intensity of grayscale value changes at each pixel.

[0155] Among them, the binary segmentation processing can be a processing process of dividing the pixel points in the gradient image into edge points and non-edge points according to a preset threshold. For example, the binary segmentation processing can be a processing process of marking the pixel points whose gradient amplitude is greater than the preset threshold as edge points (value is 1), and marking the pixel points whose gradient amplitude is less than the preset threshold as non-edge points (value is 0).

[0156] Optionally, the terminal performs preliminary extraction processing of edge information on the denoised image based on grayscale gradient, and obtains the gradient amplitude and direction of each pixel in the denoised image by calculating the grayscale value change of each pixel in the denoised image in the horizontal and vertical directions. The terminal introduces a neighborhood dynamic correction adjustment factor when calculating the gradient amplitude to adapt to the brightness and noise characteristics of different regions, so that the gradient calculation can effectively identify edges in areas with large noise or weak grayscale changes, thereby obtaining a gradient map of the denoised image; the terminal performs binary segmentation processing on the gradient map of the denoised image through a preset threshold, wherein the terminal introduces local statistical information and a dynamic correction adjustment coefficient of brightness distribution to automatically adjust the segmentation threshold according to local brightness fluctuations, thereby obtaining an edge contour image.

[0157] For example, the terminal performs preliminary edge information extraction on the denoised image based on the grayscale gradient, calculates the gradient magnitude and direction of each pixel in the denoised image, and obtains a gradient map;

[0158] The calculation formula of the gradient amplitude is:

[0159] ;

[0160] in, For coordinates The gradient amplitude at , The denoised image is at coordinates The pixel value at is the neighborhood dynamic correction adjustment factor, a and b are the neighborhood offsets on the x-axis and y-axis respectively;

[0161] Perform binary segmentation on the gradient image using a preset threshold to obtain an edge contour image;

[0162] The calculation formula for binary segmentation is:

[0163] ;

[0164] in, The edge contour image at coordinates The pixel value at , T is the preset threshold, For The pixel value of the local area centered is For The mean pixel value of the local area centered at For The standard deviation of the pixel values ​​in the local area centered is Dynamically correct the adjustment coefficient for brightness distribution.

[0165] Specifically, this embodiment introduces a neighborhood dynamic correction adjustment factor into the gradient amplitude. , which is used to adapt to the brightness and noise characteristics of different areas, so that the gradient calculation can effectively identify edges in areas with large noise or slight grayscale changes. This ensures that even if the image noise is not completely removed, the true edge gradient can still be accurately calculated, avoiding gradient errors caused by local noise interference and helping to extract subtle edge information in the image.

[0166] This embodiment incorporates local statistical information and a dynamic correction coefficient for brightness distribution into the binary segmentation formula. This automatically adjusts the segmentation threshold based on local brightness fluctuations. This allows for dynamic adjustment of the threshold when the overall or local brightness of the image is uneven, effectively preventing edge over-segmentation or missed detection issues caused by fixed thresholds. This adaptive adjustment allows for accurate extraction of true edge information even in complex image processing conditions, improving the accuracy of contour reconstruction and feature parameter extraction.

[0167] This embodiment calculates the gradient amplitude and direction of each pixel in the denoised image based on the grayscale gradient to generate a gradient map, and performs binary segmentation on the gradient map in combination with a preset threshold. It accurately extracts the edge information in the image and generates an edge contour image, thereby enhancing the sensitivity of edge detection and accurately capturing the edge features of lightning leader discharges. At the same time, through the dynamic correction adjustment factor and the optimization of the brightness distribution, the robustness of the edge detection algorithm under complex lighting conditions and noise interference is improved.

[0168] The technical solution provided in this embodiment calculates the grayscale gradient of the denoised image to obtain the gradient amplitude and direction information of each pixel point, effectively identifying areas in the image where the grayscale value changes significantly, thereby accurately locating the edge position; performs binary segmentation processing on the gradient map through a preset threshold, and clearly divides the pixels in the image into edge points and non-edge points, which is conducive to generating a clear edge contour image; thereby, it is conducive to improving the accuracy of edge detection and the clarity of edge contours.

[0169] In an exemplary embodiment, the edge contour image is subjected to contour restoration processing and contour correction processing to obtain the leading contour information of the edge contour image, which specifically includes the following contents: according to the geometric characteristic information of adjacent edge points in the edge contour image, the breakpoints in the edge contour image are connected by using a topological reconstruction model, and the missing contour segments are restored to obtain the complete contour structure information of the edge contour image; according to the physical field characteristic information, the complete contour structure information is subjected to multi-scale correction processing to obtain the leading contour information; the multi-scale correction processing includes the correction processing of the abnormal contour offset information in the complete contour structure information through the mechanical model.

[0170] Among them, the geometric characteristic information of adjacent edge points can be data information describing the spatial relationship between adjacent edge points in the edge contour image. For example, the geometric characteristic information of adjacent edge points can be data information including parameters such as Euclidean distance, curvature change rate and directional discontinuity.

[0171] The topology reconstruction model (ie, the topology reconstruction method) may be a mathematical model for connecting breakpoints and restoring contours.

[0172] The complete contour structure information may be complete contour data information obtained after contour restoration processing. For example, the complete contour structure information may be continuous contour data without breakpoints obtained after topological reconstruction model processing.

[0173] The physical field characteristic information may be data information describing the physical field distribution during the lightning leader discharge process. For example, the physical field characteristic information may be data information including electric field distribution and air flow characteristics.

[0174] The multi-scale correction process may be a process of correcting the contour at different scales. For example, the multi-scale correction process may be a process including micro-scale local optimization and macro-scale overall curvature adjustment.

[0175] Among them, the mechanical model can be a mathematical model that describes the deformation characteristics of the leader profile under the action of the physical field. For example, the mechanical model can be a mathematical model established by the finite element method to calculate the natural shape of the leader profile line under the action of the electric field and air flow.

[0176] The abnormal contour offset information may be offset data information describing a complete contour structure that does not conform to physical laws. For example, the abnormal contour offset information may be contour offset data that is inconsistent with the physical direction of discharge.

[0177] Optionally, the terminal models the edge breakpoints in the edge contour image as a topological graph according to the adjacent relationship, wherein the edge breakpoints serve as nodes of the topological graph and the potential connection paths serve as edges of the topological graph. The terminal calculates the connection weights between nodes based on geometric characteristic information such as Euclidean distance, curvature change rate and directional discontinuity, and optimizes the topological graph using the minimum spanning tree algorithm to screen out the optimal connection path that meets the contour smoothness, thereby obtaining the complete contour structure information of the edge contour image; the terminal uses the finite element method to establish a physical field characteristic model of the leader discharge, regards the leader contour line in the complete contour structure information as a force boundary, and calculates the natural morphological distribution of the leader contour line under the action of the electric field and air flow, and physically corrects the abnormal contour offset information in the complete contour structure information to obtain the leader contour information.

[0178] For example, based on the geometric characteristics of adjacent edge points in the edge contour image, the terminal uses a topological reconstruction method to connect the breakpoints, restore the missing contour segments, and form a complete contour structure;

[0179] Exemplarily, connecting the breakpoints using the topology reconstruction method includes:

[0180] The edge breakpoints in the edge contour image are modeled as a topological graph according to the adjacent relationship, where the breakpoints are the nodes of the graph and the potential connection paths are the edges;

[0181] The connection weights between nodes are calculated based on geometric characteristics such as Euclidean distance, curvature change rate, and directional discontinuity. The smaller the weight value, the higher the connection possibility.

[0182] Use the minimum spanning tree algorithm or the shortest path algorithm to optimize the topology graph and select the optimal connection path that meets the contour smoothness;

[0183] After completing the global contour connection once, the local connection is optimized twice to make the contour meet the global smoothness by improving the point positions of the connected nodes.

[0184] Combined with the physical field characteristics, the complete contour structure is corrected and optimized at multiple scales, including correcting abnormal contour deviations of the complete contour structure through mechanical models to obtain a more accurate pilot contour.

[0185] Exemplarily, performing multi-scale correction and optimization on the complete contour structure in combination with physical field characteristics includes:

[0186] The finite element method is used to establish the physical field characteristic model of the leader discharge. The leader contour line is regarded as the force boundary. The natural shape distribution of the leader contour line under the action of the electric field and air flow is calculated to perform physical correction on the leader contour line.

[0187] At the microscale, the noise interference and artificial connection errors of local contour points are considered and the contour is locally optimized through smoothing filtering operations;

[0188] On a macro scale, the overall curvature of the contour is adjusted to ensure that the leading contour line is consistent with the physical direction of discharge. The correction step introduces a direction alignment constraint, and the global curvature optimal solution is achieved by solving the variational problem.

[0189] Specifically, this embodiment connects the breakpoints based on the geometric characteristics of the edge contour image and utilizes a topological reconstruction method to restore the missing contour segments. It also optimizes the connection path by combining parameters such as Euclidean distance, curvature change rate, and directional discontinuity to form a complete contour structure.

[0190] Global contour connection is achieved through a minimum spanning tree algorithm or a shortest path algorithm, combined with local optimization and smoothing to ensure contour fluidity and global consistency. Furthermore, the complete contour is corrected and optimized at multiple scales based on physical field characteristics. The finite element method is used to establish a physical field characteristic model of the pilot discharge, further correcting abnormal contour offsets and ensuring the contour matches the physical direction of the discharge, thereby improving the accuracy and reliability of contour restoration.

[0191] The technical solution provided in this embodiment effectively restores the missing contour segments in the edge contour image by connecting the breakpoints based on the geometric characteristic information of adjacent edge points using a topological reconstruction model; corrects the abnormal contour offset information through multi-scale correction processing based on physical field characteristic information and a mechanical model, thereby optimizing the complete contour structure information; it is conducive to obtaining complete and physically compliant leader contour information, thereby improving the accuracy of lightning leader contour restoration.

[0192] In an exemplary embodiment, a database of lightning leader characteristic parameters is constructed based on geometric feature information, which specifically includes the following contents: correlating the geometric feature information with the condition information of each working condition to obtain a multidimensional data structure model of the lightning leader characteristic parameters; classifying the lightning leader characteristic parameters based on the multidimensional data structure model to obtain classification results of the lightning leader characteristic parameters; and constructing a database of lightning leader characteristic parameters based on the classification results.

[0193] The working condition information may be parameter information describing the experimental environment and settings. For example, the working condition information may be information including experimental condition parameters such as voltage amplitude, ambient humidity, air pressure, temperature, and electrode spacing.

[0194] The association processing may be a process of establishing a correspondence between geometric feature information and condition information of working conditions. For example, the association processing may be a process of pairing and associating the condition parameters of each set of experimental working conditions with the corresponding geometric feature parameters.

[0195] Among them, the multidimensional data structure model can be a data model that describes the relationship between the characteristic parameters of the lightning leader and the experimental conditions. For example, the multidimensional data structure model can be a simplified data model formed by extracting the characteristic parameters and the main influencing factors of the experimental conditions through the principal component analysis method.

[0196] The classification process may be a process of classifying and arranging lightning leader characteristic parameters. For example, the classification process may be a process of classifying lightning leader characteristics into different categories using a K-means clustering algorithm.

[0197] The classification result may be category information obtained after classification of lightning leader characteristic parameters. For example, the classification result may be a feature data subset and classification boundary conditions for different working conditions.

[0198] Among them, the database of lightning leader characteristic parameters can be a data system that stores and manages lightning leader characteristic parameters. For example, the database of lightning leader characteristic parameters can be a data system that indexes and stores according to different working conditions and characteristic parameters and supports multi-dimensional parameter cross-query.

[0199] Optionally, the terminal associates the geometric feature information of the leader profile with the condition information of the corresponding working condition, where the condition information of the working condition includes experimental condition parameters such as voltage amplitude, ambient humidity, air pressure, temperature and electrode spacing. The terminal simplifies the dimension of the associated data through the principal component analysis method, extracts the main influencing factors of the feature parameters and experimental conditions, and forms a multidimensional data structure model of the lightning leader feature parameters; based on the multidimensional data structure model of the lightning leader feature parameters, the terminal uses the K-means clustering algorithm to classify the lightning leader feature parameters under different working conditions, and performs a posteriori verification and boundary condition adjustment on the classification results in combination with the experimental working condition parameters to obtain the classification results of the lightning leader feature parameters; based on the classification results of the lightning leader feature parameters, the terminal establishes feature data subsets for different working conditions, and indexes and stores them according to the lightning leader feature parameters, and constructs a database of lightning leader feature parameters that supports multi-dimensional parameter cross-query.

[0200] For example, the terminal associates the geometric characteristic parameters of the leader profile with the experimental conditions of the corresponding working conditions to construct a multi-dimensional data structure model of the lightning leader characteristic parameters.

[0201] Exemplarily, the steps of constructing the multidimensional data structure model include:

[0202] For each set of experimental conditions, record the experimental condition parameters, including voltage amplitude, ambient humidity, air pressure, temperature, and electrode spacing;

[0203] Correlate the recorded experimental condition parameters with the corresponding geometric characteristic parameters (leader length, direction, curvature distribution, number of bifurcations, and upper and lower leader ratio);

[0204] Based on the principal component analysis method, the dimensions are simplified, the main influencing factors of characteristic parameters and experimental conditions are extracted, and a simplified multidimensional data structure model is formed;

[0205] The multidimensional data structure model data is stored in the feature parameter database to facilitate subsequent classification and query;

[0206] Based on the multidimensional data structure model, the cluster analysis method is used to classify the lightning leader characteristic parameters under different working conditions, and a lightning leader characteristic parameter database is established according to the classification results.

[0207] For example, based on the multidimensional data structure model, a cluster analysis method is used to classify the lightning leader characteristic parameters under different working conditions, and a lightning leader characteristic parameter database is established based on the classification results, specifically including:

[0208] The leader geometric feature parameters in the multidimensional data structure model are extracted, and the K-means clustering algorithm is used to classify the lightning leader features into different categories based on the similarity of the extracted features.

[0209] Conduct a posteriori verification on the classification results and further adjust the classification boundary conditions based on the experimental working condition parameters to ensure classification accuracy;

[0210] Based on the classification results, feature data subsets for different working conditions are established and indexed and stored according to lightning leader characteristic parameters, while allowing cross-query of parameters of different dimensions;

[0211] The lightning leader characteristic parameter database can output typical leader profile characteristic graphics under various working conditions to guide the prediction and evaluation of actual working conditions.

[0212] Specifically, this embodiment establishes an association between the geometric characteristic parameters of the leader profile and the experimental working conditions, constructs a multidimensional data structure model, and combines principal component analysis and cluster analysis methods to achieve the classification and induction of lightning leader characteristic parameters.

[0213] This embodiment effectively extracts and simplifies the key factors influencing lightning leader characteristics, generating characteristic data subsets and classification results tailored to different operating conditions, significantly improving the efficiency of organizing and storing lightning leader characteristic parameters. Furthermore, by establishing a lightning leader characteristic parameter database, it supports cross-querying of multi-dimensional parameters and output of typical characteristic graphs, providing efficient technical support for the research, prediction, and actual operating condition assessment of lightning leader characteristics.

[0214] The technical solution provided in this embodiment establishes a correspondence between characteristic parameters and operating conditions by associating the geometric characteristic information of the lightning leader with the condition information of each working condition, which is conducive to forming a structured multi-dimensional data structure model; by classifying the characteristic parameters of the lightning leader, the systematic management and organization of the characteristic parameters under different working conditions are achieved, which is conducive to building a complete lightning leader characteristic parameter database, providing a reliable data basis for subsequent data analysis and feature comparison.

[0215] The following is an application example to illustrate the lightning activity information analysis method provided by this application. This application example uses the method applied to a terminal as an example.

[0216] Lightning, a phenomenon in which thunderstorm clouds release their internal electrical energy over extremely long distances and on an extremely large scale, is characterized by high frequency, high current intensity, and strong destructive power, posing a serious threat to the safety of power transmission lines. Cloud-to-ground lightning primarily consists of the streamer-leader process, the connection process, the first return stroke, and the subsequent return stroke. The streamer-leader discharge, as the initial stage of lightning occurrence, continuously develops under the influence of the electric field and plays a decisive role in the formation of subsequent lightning return stroke channels. In particular, the charge stored in the leader channel directly supplies the return stroke current, directly determining the discharge energy and destructive power of lightning. Tower head impulse discharge leader technology is a key area of ​​research in lightning discharge processes, primarily used to simulate the formation and development mechanisms of leader discharges during lightning discharges. Leader discharge is the initial stage of lightning discharge, characterized by a concentrated increase in electric field intensity in a localized area, leading to the breakdown of the air medium and the formation of a discharge channel. The tower head impulse discharge experiment simulates the leading process of lightning discharge by applying impulse voltage to the high-voltage tower head, and studies the electric field distribution, discharge path and its characteristic parameters.

[0217] The characteristic parameters of lightning leaders are mainly obtained by high-speed cameras and spectral acquisition. High-speed cameras mainly collect discharge images and discharge development processes, which can reflect the morphology and development process of lightning leader discharges; spectral acquisition can obtain the particle state during the lightning discharge process, such as the electron density in the plasma channel, channel conductivity and other characteristic parameters. However, in the above parameter approaches, there is a lack of means to quantify and statistically analyze the leader morphology.

[0218] Fractal dimension can be used to quantify the complexity of discharge morphology, but quantitative statistical methods for lightning leader discharge morphological parameters are still relatively scarce. They mainly focus on the development process of the leader, with less research on leader morphology. The lightning leader characteristics under different working conditions have not been classified and summarized, and it is impossible to form a systematic lightning leader characteristic database, making it difficult to support large-scale data analysis and feature comparison under different working conditions.

[0219] The main steps of this application example include:

[0220] (1) Lightning leader image acquisition:

[0221] Using optical acquisition equipment such as high-speed cameras or SLR cameras, the lightning leader process is captured under different working conditions to obtain high-resolution images of the lightning leader's development. The captured images contain the morphological characteristics and development process of the lightning leader, providing a data foundation for subsequent processing.

[0222] (2) Grayscale processing:

[0223] The captured lightning leader image is imported into MATLAB (mathematical software) and converted into a grayscale image using a grayscale processing algorithm, so that the grayscale value of each pixel in the image is between 0 and 255. This effectively highlights the brightness characteristics of the lightning leader and lays the foundation for subsequent denoising and edge detection.

[0224] (3) Image denoising and edge detection:

[0225] The grayscale processed image is then subjected to Gaussian filtering to smooth the image and reduce the interference of random noise. Subsequently, an erosion operation is used to further remove small noise points and enhance the edge clarity of the image.

[0226] The Canny (edge ​​detection) operator is used to detect edges in the denoised image. By selecting appropriate dual thresholds, the edge contour of the lightning leader is extracted, resulting in a clear leader shape and a complete presentation of the overall outline of the lightning leader.

[0227] (4) Contour tracing and feature parameter extraction:

[0228] The image after edge detection is imported into CAD (computer-aided design) software, and the edge contour of the lightning leader is manually or automatically traced to generate a complete leader channel contour map.

[0229] According to the scale of the image, use the annotation function in CAD to measure and extract the geometric feature parameters of the pilot contour, including but not limited to the following parameters:

[0230] Pilot Length: Measure the total length of the pilot channel.

[0231] Leading direction: Calculates the overall development direction of the leading channel.

[0232] Curvature: Analyzes the curvature of the pilot channel.

[0233] Number of forks: Count the number of fork points in the leading channel.

[0234] Upper and Lower Pilot Ratio: Calculates the length ratio of the upper and lower parts of the pilot channel.

[0235] Extracted feature parameter reference Figure 3 (Annotated diagram of lightning leader characteristic parameters), which includes the annotated values ​​24.39, 21.51, 10.72, 31.43, 90°, 129° and 110°, forming a complete lightning leader characteristic data.

[0236] (5) Feature data statistics and database construction:

[0237] The lightning leader characteristic parameters extracted under different operating conditions were classified and statistically analyzed to construct a lightning leader characteristic parameter database. This database, which includes multi-dimensional data such as leader length, direction, curvature, and number of bifurcations, provides important data support for lightning discharge characteristics research and transmission line protection design. This generated lightning leader characteristic parameter database provides comprehensive data support and technical assurance for lightning discharge characteristic research, prediction, and protection design under different operating conditions, effectively improving the efficiency and accuracy of lightning discharge behavior analysis.

[0238] The technical solution provided in this application example realizes: (1) obtaining lightning leader images through image acquisition equipment, and combining grayscale processing, Gaussian filtering and corrosion processing denoising, edge detection algorithm, contour restoration and geometric feature parameter extraction to achieve high-precision observation and analysis of the tower head impact discharge leader discharge characteristics. At the same time, combined with multidimensional data models and cluster analysis methods, a lightning leader characteristic parameter database for different working conditions is established, which improves the observation and statistical ability of lightning leader discharge characteristic parameters; (2) by using Gaussian filtering method to smooth the grayscale image, the random noise in the image is effectively reduced, and the corrosion processing method is combined to the smoothed image. Morphological processing is performed on the image to further eliminate the small noise points and pseudo edges in the image, improve the quality of the denoised image and the accuracy of the edge information, and enhance the anti-interference ability and processing accuracy; (3) The gradient amplitude and direction of each pixel in the denoised image are calculated based on the grayscale gradient to generate a gradient map, and the gradient map is binary segmented with a preset threshold to accurately extract the edge information in the image and generate an edge contour image, thereby enhancing the sensitivity of edge detection and being able to accurately capture the edge features of the lightning leader discharge. At the same time, the robustness of the edge detection algorithm under complex lighting conditions and noise interference is improved by dynamically correcting the adjustment factor and optimizing the brightness distribution.

[0239] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0240] Based on the same inventive concept, embodiments of the present application also provide a lightning activity information analysis device for implementing the aforementioned lightning activity information analysis method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the lightning activity information analysis device provided below can be found in the above-described limitations of the lightning activity information analysis method and will not be further elaborated here.

[0241] In an exemplary embodiment, Figure 4 As shown, a lightning activity information analysis device is provided, and the lightning activity information analysis device 400 may include:

[0242] An image acquisition module 401 is configured to acquire images of lightning leaders under various working conditions through an image acquisition device;

[0243] An image processing module 402 is configured to perform grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image;

[0244] An image denoising module 403 is used to perform denoising on the grayscale image to obtain a denoised image of the grayscale image;

[0245] An image detection module 404 is configured to perform edge detection on the denoised image based on the grayscale gradient of the denoised image and a preset threshold value to obtain an edge contour image of the denoised image;

[0246] The image restoration module 405 is used to perform contour restoration and contour correction processing on the edge contour image to obtain the leading contour information of the edge contour image;

[0247] A feature recognition module 406 is used to perform geometric feature recognition processing on the leading contour information to obtain geometric feature information of the leading contour information;

[0248] The data construction module 407 is used to construct a database of lightning leader characteristic parameters based on the geometric characteristic information;

[0249] The discharge analysis module 408 is configured to analyze the lightning discharge behavior information to be analyzed based on the database of lightning leader characteristic parameters to obtain analysis results of the lightning discharge behavior information.

[0250] In an exemplary embodiment, the image processing module 402 is further configured to perform grayscale space conversion processing on the lightning leader image to obtain an initial grayscale image of the lightning leader image; and perform brightness update processing on the initial grayscale image to obtain a grayscale image.

[0251] In an exemplary embodiment, the image denoising module 403 is further configured to process the grayscale image using a Gaussian filter processing model to obtain a preliminary smoothed image of the grayscale image; and process the preliminary smoothed image using an erosion processing model to obtain a denoised image.

[0252] In an exemplary embodiment, the image detection module 404 is further used to perform preliminary extraction processing of edge information on the denoised image based on the grayscale gradient, and calculate the gradient amplitude and direction of each pixel point in the denoised image to obtain a gradient map of the denoised image; and perform binary segmentation processing on the gradient map according to a preset threshold to obtain an edge contour image.

[0253] In an exemplary embodiment, the image restoration module 405 is also used to connect the breakpoints in the edge contour image and restore the missing contour segments using a topological reconstruction model based on the geometric characteristic information of adjacent edge points in the edge contour image, so as to obtain the complete contour structure information of the edge contour image; perform multi-scale correction processing on the complete contour structure information based on the physical field characteristic information to obtain the leading contour information; the multi-scale correction processing includes correcting the abnormal contour offset information in the complete contour structure information through a mechanical model.

[0254] In an exemplary embodiment, the data construction module 407 is also used to associate the geometric feature information with the condition information of each working condition to obtain a multidimensional data structure model of the lightning leader feature parameters; classify the lightning leader feature parameters according to the multidimensional data structure model to obtain the classification results of the lightning leader feature parameters; and construct a database of the lightning leader feature parameters based on the classification results.

[0255] Each module in the lightning activity information analysis device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0256] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, while the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means may be implemented via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for analyzing lightning activity information. The display unit of the computer device is used to produce a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0257] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0258] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0259] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0260] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0261] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0262] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0263] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for analyzing lightning activity information, characterized in that: The method comprises: Collect lightning leader images under various working conditions through image acquisition equipment; Performing grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image; Performing denoising processing on the grayscale image to obtain a denoised image of the grayscale image; Performing edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image; Performing contour restoration and contour correction on the edge contour image to obtain leading contour information of the edge contour image; Performing geometric feature recognition processing on the leading profile information to obtain geometric feature information of the leading profile information; Constructing a database of lightning leader characteristic parameters based on the geometric characteristic information; Analyzing the lightning discharge behavior information to be analyzed according to the database of lightning leader characteristic parameters to obtain an analysis result of the lightning discharge behavior information; The contour correction processing of the edge contour image includes: using the finite element method to establish a physical field characteristic model of the leader discharge, treating the leader contour line in the complete contour structure information as a force boundary, calculating the natural morphological distribution of the leader contour line under the action of the electric field and air flow, and performing physical correction on the abnormal contour offset information in the complete contour structure information to obtain the leader contour information; The leader profile information is subjected to geometric feature recognition processing to obtain the geometric feature information of the leader profile information, including: for the length measurement of the lightning leader, the cumulative segment length of the profile is calculated along the main direction of the leader profile to obtain the overall leader length; for the direction measurement of the lightning leader, the direction angle of the line connecting the two end points of the leader profile main axis is calculated, and the correction is performed in combination with the local direction characteristics of each branch; for the upper and lower leader ratio measurement, the distribution of multiple leader branches is distinguished in combination with the physical characteristics of the lightning discharge direction, and the length ratio of the upper and lower branches is calculated respectively; based on the curvature analysis method, the bending shape of the leader profile is identified, and the curvature distribution and bifurcation characteristics of the leader profile are measured.

2. The method according to claim 1, characterized in that The grayscale processing of the lightning leader image to obtain the grayscale image of the lightning leader image includes: Performing grayscale space conversion processing on the lightning leader image to obtain an initial grayscale image of the lightning leader image; Performing brightness update processing on the initial grayscale image to obtain the grayscale image.

3. The method according to claim 1, characterized in that The performing denoising on the grayscale image to obtain a denoised image of the grayscale image includes: Processing the grayscale image using a Gaussian filter processing model to obtain a preliminary smoothed image of the grayscale image; The preliminary smoothed image is processed by using an erosion processing model to obtain the denoised image.

4. The method according to claim 1, wherein The step of performing edge detection on the denoised image according to the grayscale gradient of the denoised image and a preset threshold to obtain an edge contour image of the denoised image comprises: Performing preliminary edge information extraction processing on the denoised image according to the grayscale gradient, and calculating the gradient magnitude and direction of each pixel in the denoised image to obtain a gradient map of the denoised image; According to the preset threshold, binary segmentation processing is performed on the gradient image to obtain the edge contour image.

5. The method according to claim 1, wherein The performing of contour restoration and contour correction on the edge contour image to obtain leading contour information of the edge contour image includes: According to geometric characteristic information of adjacent edge points in the edge contour image, a topological reconstruction model is used to connect the breakpoints in the edge contour image and restore the missing contour segments to obtain complete contour structure information of the edge contour image; According to the physical field characteristic information, the complete contour structure information is subjected to multi-scale correction processing to obtain the leading contour information; the multi-scale correction processing includes correcting the abnormal contour offset information in the complete contour structure information through a mechanical model.

6. The method according to any one of claims 1 to 5, characterized in that The step of constructing a database of lightning leader characteristic parameters based on the geometric characteristic information includes: Associating the geometric feature information with the condition information of each working condition to obtain a multi-dimensional data structure model of the lightning leader characteristic parameters; Classifying the lightning leader characteristic parameters according to the multidimensional data structure model to obtain classification results of the lightning leader characteristic parameters; According to the classification result, a database of the lightning leader characteristic parameters is constructed.

7. A lightning activity information analysis device, characterized in that: The device comprises: An image acquisition module is used to acquire images of lightning leaders under various working conditions through an image acquisition device; An image processing module is used to perform grayscale processing on the lightning leader image to obtain a grayscale image of the lightning leader image; An image denoising module, configured to perform denoising processing on the grayscale image to obtain a denoised image of the grayscale image; An image detection module is used to perform edge detection processing on the denoised image according to the grayscale gradient of the denoised image and a preset threshold value to obtain an edge contour image of the denoised image; An image restoration module is used to perform contour restoration and contour correction processing on the edge contour image to obtain leading contour information of the edge contour image; A feature recognition module is used to perform geometric feature recognition processing on the leading profile information to obtain geometric feature information of the leading profile information; A data construction module, configured to construct a database of lightning leader characteristic parameters based on the geometric characteristic information; a discharge analysis module, configured to analyze the lightning discharge behavior information to be analyzed based on the database of lightning leader characteristic parameters to obtain an analysis result of the lightning discharge behavior information; The contour correction processing of the edge contour image includes: using the finite element method to establish a physical field characteristic model of the leader discharge, treating the leader contour line in the complete contour structure information as a force boundary, calculating the natural morphological distribution of the leader contour line under the action of the electric field and air flow, and performing physical correction on the abnormal contour offset information in the complete contour structure information to obtain the leader contour information; The leader profile information is subjected to geometric feature recognition processing to obtain the geometric feature information of the leader profile information, including: for the length measurement of the lightning leader, the cumulative segment length of the profile is calculated along the main direction of the leader profile to obtain the overall leader length; for the direction measurement of the lightning leader, the direction angle of the line connecting the two end points of the leader profile main axis is calculated, and the correction is performed in combination with the local direction characteristics of each branch; for the upper and lower leader ratio measurement, the distribution of multiple leader branches is distinguished in combination with the physical characteristics of the lightning discharge direction, and the length ratio of the upper and lower branches is calculated respectively; based on the curvature analysis method, the bending shape of the leader profile is identified, and the curvature distribution and bifurcation characteristics of the leader profile are measured.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images