Lightning activity information analysis method and device

Abstract

The invention relates to a lightning activity information analysis method and device, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of electric power. The method comprises the following steps: acquiring lightning pilot images under various working conditions through image acquisition equipment; performing gray processing on the lightning pilot image to obtain a gray image; performing denoising processing on the grayscale image to obtain a denoised image of the grayscale image; performing edge detection processing on the de-noised image to obtain an edge contour image of the de-noised image; performing contour reduction processing and contour correction processing on the edge contour image to obtain pilot contour information of the edge contour image; performing geometric feature recognition processing on the pilot contour information to obtain geometric feature information; constructing a database of thunder and lightning pilot characteristic parameters according to the geometric characteristic information; and analyzing the lightning discharge behavior information to obtain an analysis result of the lightning discharge behavior information. By adopting the method, the lightning activity information analysis efficiency can be improved.

Description

Technical Field

[0001] This application relates to the technical field of electric power, and particularly to a method, device, computer device, 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, as an important field of meteorological safety research, has received extensive attention. How to efficiently analyze lightning activity information has become an important research direction.

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

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

[0005] In a first aspect, this application provides a method for analyzing lightning activity information. The method includes:

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

[0007] Perform grayscale processing on the lightning leader images to obtain grayscale images of the lightning leader images;

[0008] Perform denoising processing on the grayscale images to obtain denoised images of the grayscale images;

[0009] Perform edge detection processing on the denoised images according to the grayscale gradients and preset thresholds of the denoised images to obtain edge contour images of the denoised images;

[0010] Perform contour restoration processing and contour correction processing on the edge contour images to obtain leading contour information of the edge contour images;

[0011] Perform geometric feature recognition processing on the leading contour information to obtain geometric feature information of the leading contour information;

[0012] Construct a database of lightning leader feature parameters according to the geometric feature information;

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

[0014] In one embodiment, the gray-scale processing of the lightning leader image to obtain the gray-scale image of the lightning leader image includes:

[0015] Perform gray-scale space conversion processing on the lightning leader image to obtain the initial gray-scale image of the lightning leader image;

[0016] Perform brightness update processing on the initial gray-scale image to obtain the gray-scale image.

[0017] In one embodiment, the denoising processing of the gray-scale image to obtain the denoised image of the gray-scale image includes:

[0018] Process the gray-scale image through a Gaussian filtering processing model to obtain the preliminary smoothed image of the gray-scale image;

[0019] Process the preliminary smoothed image through an erosion processing model to obtain the denoised image.

[0020] In one embodiment, the edge detection processing of the denoised image according to the gray-scale gradient and preset threshold of the denoised image to obtain the edge contour image of the denoised image includes:

[0021] According to the gray-scale gradient, perform preliminary extraction processing of edge information on the denoised image, and calculate the gradient amplitude and direction of each pixel point in the denoised image to obtain the gradient map of the denoised image;

[0022] Perform binary segmentation processing on the gradient map according to the preset threshold to obtain the edge contour image.

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

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

[0025] According to the physical field characteristic information, perform multi-scale correction processing on the complete contour structure information to obtain the leader contour information; the multi-scale correction processing includes the correction processing of the abnormal contour offset information in the complete contour structure information through a mechanical model.

[0026] In one embodiment, the construction of the database of lightning leader characteristic parameters according to the geometric feature information includes:

[0027] Perform an association process on the geometric feature information and the condition information of each working condition to obtain a multi-dimensional data structure model of the lightning leader characteristic parameters;

[0028] According to the multi-dimensional data structure model, classify the lightning leader characteristic parameters to obtain a classification result of the lightning leader characteristic parameters;

[0029] Construct a database of the lightning leader characteristic parameters according to the classification result.

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

[0031] An image acquisition module, configured to acquire lightning leader images under various working conditions through an image acquisition device;

[0032] An image processing module, configured to perform gray-scale processing on the lightning leader image to obtain a gray-scale image of the lightning leader image;

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

[0034] An image detection module, configured to perform edge detection processing on the denoised image according to the gray-scale gradient and a preset threshold of the denoised image to obtain an edge contour image of the denoised image;

[0035] An image restoration module, configured to perform contour restoration processing and contour correction processing on the edge contour image to obtain the leader contour information of the edge contour image;

[0036] A feature recognition module, configured to perform geometric feature recognition processing on the leader contour information to obtain geometric feature information of the leader contour information;

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

[0038] A discharge analysis module, configured to analyze the lightning discharge behavior information to be analyzed according to the database of the lightning leader characteristic parameters to obtain an analysis result of the lightning discharge behavior information.

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

[0040] Acquire lightning leader images under various working conditions through an image acquisition device;

[0041] Perform gray-scale processing on the lightning leader image to obtain the gray-scale image of the lightning leader image;

[0042] Perform denoising processing on the gray-scale image to obtain the denoised image of the gray-scale image;

[0043] Perform edge detection processing on the denoised image according to the gray-scale gradient and preset threshold of the denoised image to obtain the edge contour image of the denoised image;

[0044] Perform contour restoration processing and contour correction processing on the edge contour image to obtain the leader contour information of the edge contour image;

[0045] Perform geometric feature recognition processing on the leader contour information to obtain the geometric feature information of the leader contour information;

[0046] Construct a database of lightning leader feature parameters according to the geometric feature information;

[0047] Analyze the lightning discharge behavior information to be analyzed according to the database of lightning leader feature parameters to obtain the analysis result of the lightning discharge behavior information.

[0048] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the following steps are implemented:

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

[0050] Perform gray-scale processing on the lightning leader image to obtain the gray-scale image of the lightning leader image;

[0051] Perform denoising processing on the gray-scale image to obtain the denoised image of the gray-scale image;

[0052] Perform edge detection processing on the denoised image according to the gray-scale gradient and preset threshold of the denoised image to obtain the edge contour image of the denoised image;

[0053] Perform contour restoration processing and contour correction processing on the edge contour image to obtain the leader contour information of the edge contour image;

[0054] Perform geometric feature recognition processing on the leader contour information to obtain the geometric feature information of the leader contour information;

[0055] Construct a database of lightning leader feature parameters according to the geometric feature information;

[0056] Analyze the lightning discharge behavior information to be analyzed according to the database of the lightning leader characteristic parameters, and obtain the analysis result of the lightning discharge behavior information.

[0057] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

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

[0059] Perform grayscale processing on the lightning leader images to obtain grayscale images of the lightning leader images;

[0060] Perform denoising processing on the grayscale images to obtain denoised images of the grayscale images;

[0061] According to the gray gradient of the denoised image and a preset threshold, perform edge detection processing on the denoised image to obtain an edge contour image of the denoised image;

[0062] Perform contour restoration processing and contour correction processing on the edge contour image to obtain the leader contour information of the edge contour image;

[0063] Perform geometric feature recognition processing on the leader contour information to obtain geometric feature information of the leader contour information;

[0064] Construct a database of lightning leader characteristic parameters according to the geometric feature information;

[0065] Analyze the lightning discharge behavior information to be analyzed according to the database of the lightning leader characteristic parameters, and obtain the analysis result of the lightning discharge behavior information.

[0066] The above lightning activity information analysis method, device, computer device, computer-readable storage medium, and computer program product collect lightning leader images under various working conditions through an image acquisition device; perform grayscale processing on the lightning leader images to obtain grayscale images of the lightning leader images; perform denoising processing on the grayscale images to obtain denoised images of the grayscale images; perform edge detection processing on the denoised images according to the grayscale gradient and preset threshold of the denoised images to obtain edge contour images of the denoised images; perform contour restoration processing and contour correction processing on the edge contour images to obtain leading contour information of the edge contour images; perform geometric feature recognition processing on the leading contour information to obtain geometric feature information of the leading contour information; construct a database of lightning leader feature parameters according to the geometric feature information; analyze the lightning discharge behavior information to be analyzed according to the database of lightning leader feature parameters to obtain an analysis result of the lightning discharge behavior information. This solution improves the image quality by performing grayscale processing and denoising processing on the lightning leader images; realizes the accurate extraction of lightning leader features through edge detection processing, contour restoration processing, and contour correction processing; converts the leading contour information into quantifiable geometric feature information through geometric feature recognition processing; realizes the systematic storage and rapid invocation of lightning leader features by constructing a database of lightning leader feature parameters; and realizes the rapid processing of lightning activity information by automatically analyzing the lightning discharge behavior information based on the database of lightning leader feature parameters, avoiding the repetitive manual observation and analysis processes, which is beneficial to 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 will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0068] Figure 1 It is a schematic flowchart of a lightning activity information analysis method in an embodiment;

[0069] Figure 2 It is a schematic flowchart of the steps of grayscale processing in an embodiment;

[0070] Figure 3 It is a schematic diagram of the annotation of lightning leader feature parameters in an embodiment;

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

[0072] Figure 5 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0073] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present 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 for analysis, stored data, displayed data, etc.) involved in the present 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 need to comply with relevant regulations.

[0075] In an exemplary embodiment, as Figure 1 shown, a method for analyzing lightning activity information is provided. In this embodiment, the method is exemplified by being applied to a terminal; it can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, etc.; the server can be an independent physical server, can also be a server cluster or distributed system composed of multiple physical servers, and can also be a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0076] Step S101: Collect lightning leader images under various working conditions through an image acquisition device.

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

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

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

[0080] Step S105: Perform contour restoration processing and contour correction processing on the edge contour image to obtain the leader contour information of the edge contour image.

[0081] Step S106: Perform geometric feature recognition processing on the leader contour information to obtain geometric feature information of the leader contour information.

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

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

[0084] Among them, lightning activity can be the discharge phenomenon in which a thunderstorm cloud releases the electric energy inside it.

[0085] Among them, the image acquisition device can be an optical device for acquiring lightning leader images. For example, the image acquisition device can be a camera.

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

[0087] Among them, the lightning leader image can be the image data recording the initial stage of lightning discharge. For example, the lightning leader image can be the image recording that the electric field intensity intensifies locally and forms a discharge channel.

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

[0089] Among them, the grayscale image can be the image after grayscale processing. For example, the grayscale image can be a single-channel image with pixel values between 0 and 255.

[0090] Among them, denoising processing can be the processing process of eliminating image noise. For example, denoising processing can be an image processing process including Gaussian filtering and erosion processing steps.

[0091] Among them, the denoised image can be the image after denoising processing. For example, the denoised image can be a clear image obtained after Gaussian filtering and erosion processing.

[0092] Among them, the grayscale gradient can be the change rate of the grayscale value of a pixel point in the image in space. For example, the grayscale gradient can be the degree of difference in grayscale values between adjacent pixel points in the denoised image.

[0093] Among them, the preset threshold can be the grayscale gradient critical value for judging whether a pixel point is an edge point. For example, the preset threshold can be a segmentation critical value dynamically adjusted according to the mean and standard deviation of pixel values in the local area.

[0094] Among them, edge detection processing can be a process of extracting edge information in an image. For example, edge detection processing can be a process of extracting edge information from an image based on gray-scale gradient and a preset threshold.

[0095] Among them, an edge contour image can be an image obtained after edge detection processing. For example, an edge contour image can be an image containing edge information obtained through binary segmentation processing.

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

[0097] Among them, contour correction processing can be a process of optimizing and correcting complete contour structure information. For example, contour correction processing can be a process of correcting abnormal contour offsets through a mechanical model.

[0098] Among them, leading contour information can be information describing the morphological characteristics of a lightning leader. For example, leading contour information can be information containing the complete contour structure information of the leader channel.

[0099] Among them, geometric feature recognition processing can be a process of measuring geometric feature parameters of a leading contour. For example, geometric feature recognition processing can be a process of measuring the length, direction, curvature distribution, and bifurcation characteristics of a leader.

[0100] Among them, geometric feature information can be data describing the geometric features of a leading contour. For example, geometric feature information can be data containing parameters such as the length, direction, upper and lower leader ratio, curvature distribution, and bifurcation characteristics of a leader.

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

[0102] Among them, lightning discharge behavior information belongs to lightning activity information, and lightning discharge behavior information can be information describing the characteristics of the lightning discharge process. For example, lightning discharge behavior information can be information containing the discharge path, electric field distribution, and characteristic parameters, etc.

[0103] Among them, the analysis result of lightning discharge behavior information can be a quantitative evaluation and prediction result of the lightning discharge process based on the database of lightning leader characteristic parameters. For example, the analysis result of lightning discharge behavior information can be a comprehensive analysis report including lightning discharge path prediction, electric field distribution characteristics, leader development trend, discharge energy intensity evaluation, comparative analysis of leader characteristics under various working conditions, lightning protection suggestions, etc. It can also include the characteristic classification and risk level evaluation of lightning discharge behavior based on geometric characteristic parameters such as leader length, direction, curvature distribution, number of bifurcations, and ratio of upper and lower leaders, 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 devices such as high-speed cameras or single-lens reflex cameras, performs color space conversion on the lightning leader images, converts the RGB (red, green, blue) color space to the grayscale space to obtain the initial grayscale image, and optimizes the grayscale distribution through the histogram equalization method to obtain the grayscale image; the terminal uses the Gaussian filtering method to smooth the grayscale image to generate a preliminary smoothed image, and then uses the erosion processing method to perform morphological processing on the preliminary smoothed image to obtain a denoised image; the terminal preliminarily extracts the edge information of the denoised image based on the grayscale gradient, calculates the gradient amplitude and direction of each pixel point 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 connects the breakpoints using the topological reconstruction method based on the geometric characteristics of adjacent edge points in the edge contour image, and restores the missing contour segments to form a complete contour structure, and performs multi-scale correction and optimization on the complete contour structure combined 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 ratio of upper and lower leaders 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 to obtain the geometric characteristic information; the terminal associates the geometric characteristic information with the experimental conditions of the corresponding working conditions, constructs a multi-dimensional data structure model of lightning leader characteristic parameters, and uses the clustering analysis method to classify the lightning leader characteristic parameters under different working conditions to establish 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 result of the lightning discharge behavior information.

[0105] For example, the terminal acquires lightning leader images under different working conditions through an image acquisition device; performs grayscale processing on the lightning leader images, converts the images into grayscale values using the pixel information in the images to obtain grayscale images; performs denoising processing on the grayscale images to obtain denoised images, and the denoising processing includes Gaussian filtering and erosion processing steps for removing image noise; uses an edge detection algorithm and based on the grayscale gradient and threshold to perform edge detection on the denoised images to obtain edge contour images; restores the edge contour images to complete image contours through a visualization method, and performs line description on the complete image contours to form leader contours; determines the geometric feature parameters of the leader contours according to the actual size ratio, and the geometric feature parameters include leader length, direction, curvature, bifurcation number, and upper and lower leader ratio; classifies and summarizes the geometric feature parameters, and establishes a lightning leader feature parameter database for different working conditions. Specifically, in this embodiment, lightning leader images are obtained through an image acquisition device, and through grayscale processing, Gaussian filtering and erosion processing for denoising, edge detection algorithm, contour restoration, and geometric feature parameter extraction, high-precision observation and analysis of the leader discharge characteristics of tower head impulse discharge are realized. At the same time, combined with a multi-dimensional data model and a clustering analysis method, a lightning leader feature parameter database for different working conditions is established, improving the observation and statistical ability of lightning leader discharge feature 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 determining the length, direction, and upper and lower leader ratio of the leader.

[0107] Exemplarily, based on the image calibration parameters, converting the pixel distance of the leader contour into the actual physical scale, and determining the length, direction, and upper and lower leader ratio of the leader, specifically including:

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

[0109] For the determination of the length of the lightning leader, calculate the cumulative segment length of the contour along the main direction of the leader contour to obtain the overall leader length;

[0110] For the determination of the direction of the lightning leader, calculate the connection direction angle of the two endpoints of the main axis of the leader contour, and perform correction in combination with the local direction characteristics of each branch;

[0111] For the determination of the upper and lower leader ratio, in combination with the physical characteristics of the lightning discharge direction, distinguish the distribution of multiple leader branches, and calculate the length ratio of the upper and lower branches respectively;

[0112] Based on the curvature analysis method, identify the bending shape of the leader contour, and determine the curvature distribution and bifurcation characteristics of the leader contour.

[0113] Exemplarily, based on the curvature analysis method, the bending morphology of the leader contour is identified, and the curvature distribution and bifurcation characteristics of the leader contour are measured, specifically including:

[0114] Using the curvature calculation formula, discrete curvature estimation is performed on the leader contour to obtain the local curvature distribution of each point on the contour;

[0115] In the identification of bifurcation characteristics, based on the positions of the curvature mutation points in the leader 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] The bifurcation characteristics include: the number of bifurcation points, the distance between adjacent bifurcation points, the bifurcation angle, and the proportion of the branch morphology. The characteristic parameters are further matched with the lightning discharge characteristics.

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

[0118] Through steps such as cumulative segment length calculation, direction angle correction, and bifurcation point identification, the key geometric characteristics of the lightning leader can be comprehensively and accurately extracted. At the same time, combined with a detailed analysis of the bifurcation characteristics, such as the number of bifurcation points, the bifurcation angle, and the proportion of the branch morphology, it provides high-precision geometric parameter support for the study of lightning discharge characteristics, and improves the measurement accuracy and applicability of the geometric characteristic parameters of the lightning leader.

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

[0120] In an exemplary embodiment, with reference to Figure 2 , the lightning leader images are subjected to grayscale processing to obtain grayscale images of the lightning leader images, which specifically includes the following contents:

[0121] Step S201, perform grayscale space conversion processing on the lightning leader images to obtain initial grayscale images of the lightning leader images;

[0122] Step S202, perform brightness update processing on the initial grayscale images to obtain grayscale images.

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

[0124] Among them, the initial grayscale images may be preliminary grayscale images obtained after grayscale space conversion processing. For example, the initial grayscale images may be images with pixel values between 0 and 255 obtained by converting the RGB color space to a grayscale space.

[0125] Among them, the brightness update process can be a process of adjusting the brightness and optimizing the gray level distribution of the initial gray image. For example, the brightness update process can be a process of optimizing the gray level distribution of the initial gray image by using the histogram equalization method.

[0126] Optionally, the terminal performs a color space conversion on the lightning leader image, converts the lightning leader image from the RGB color space to the gray space, calculates the gray value of each pixel point in the lightning leader image by using the weighted average method, and obtains the initial gray image of the lightning leader image; the terminal adjusts the brightness of the initial gray image of the lightning leader image, calculates the gray histogram of the initial gray image of the lightning leader image, counts the number of pixels at each gray level in the initial gray image of the lightning leader image, calculates the cumulative distribution function according to the gray histogram of the initial gray image of the lightning leader image, remaps the gray levels based on the cumulative distribution function, and optimizes the gray level distribution by using the histogram equalization method to achieve the uniformization of the gray level distribution, and obtains the gray image of the lightning leader image.

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

[0128] For example, the weighted average method is used for the color space conversion.

[0129] For example, the histogram equalization method includes the following steps: calculating the gray histogram of the initial gray image and counting the number of pixels at each gray level; calculating the cumulative distribution function according to the gray histogram and remapping the gray levels based on the cumulative distribution function to achieve the uniformization of the gray level distribution. Specifically, in this embodiment, by performing a color space conversion on the lightning leader image, converting the RGB color space to the gray space, and using the weighted average method to accurately calculate the gray value, combining the brightness adjustment and the histogram equalization method to optimize the gray level distribution, the contrast and detail expressiveness of the gray image are improved. By calculating the gray histogram, the cumulative distribution function, and the gray level remapping, the uniformization of the gray level distribution is achieved.

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

[0131] In an exemplary embodiment, denoising processing is performed on a grayscale image to obtain a denoised image of the grayscale image, which specifically includes the following: 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 erosion processing model to obtain a denoised image.

[0132] Among them, the Gaussian filtering processing model (i.e., the Gaussian filtering method) can be a mathematical model for smoothing an image.

[0133] Among them, the preliminary smoothed image can be an image obtained after being processed by the Gaussian filtering processing model. For example, the preliminary smoothed image can be an image with reduced random noise obtained by smoothing the grayscale image through the Gaussian filtering method.

[0134] Among them, the erosion processing model (i.e., the erosion processing method) can be a mathematical model for morphological processing of an image.

[0135] Optionally, the terminal processes the grayscale image through the Gaussian filtering processing model. The Gaussian filtering processing model calculates the grayscale mean value and the grayscale value standard deviation within the local window centered on (x, y) (where x represents the abscissa and y represents the ordinate), and combines the weight function of the Gaussian distribution to smooth the grayscale image to obtain a preliminary smoothed image of the grayscale image; the terminal processes the preliminary smoothed image through the erosion processing model. The erosion processing model calculates the pixel value mean and the pixel value standard deviation of the preliminary smoothed image within the local area centered on (x, y) and within the range of the structural element matrix, and combines the local adaptive adjustment coefficient to perform morphological processing on the preliminary smoothed image to obtain a denoised image, thereby effectively eliminating the noise points in the grayscale image and improving the image quality.

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

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

[0138] ;

[0139] Among them, is the pixel value of the preliminary smoothed image at the coordinate , is the pixel value of the grayscale image at the coordinate , is the pixel value of the grayscale image at the coordinate , 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, and k is half of the size value of the filtering window. is an adjustment coefficient, is the average gray value within a local window centered on , is the standard deviation of the gray values within a local window centered on ;

[0140] The morphological processing is performed on the preliminarily smoothed image using an erosion processing method to eliminate image noise and obtain a denoised image;

[0141] The calculation formula of the erosion processing method (erosion processing model) is:

[0142] ;

[0143] where is the pixel value of the denoised image at the coordinate , E is the structural element matrix for erosion processing, is the pixel value of the preliminarily smoothed image at the coordinate , is the local adaptive adjustment coefficient, is the average value of the pixel values of the preliminarily smoothed image within a local area centered on and within the range of the structural element matrix E, is the standard deviation of the pixel values of the preliminarily smoothed image within a local area centered on and within the range of the structural element matrix E.

[0144] Specifically, in this embodiment, the adjustment coefficient is introduced, centered on the coordinate , depending on the local mean and the local standard deviation , the noise smoothing degree is dynamically adjusted. The adjustment coefficient can reflect the brightness and contrast changes in different regions of the image, so that the filtering can perform more rigorous smoothing processing in strong noise regions, while appropriately retaining edge information in detail regions, ensuring that the filtering effect can be adaptively adjusted under different working conditions and different local characteristics, effectively reducing the influence of random noise and maximizing the retention of the effective detail information of the image.

[0145] To solve the problem that fixed parameters cannot take into account the brightness differences in each region, this embodiment improves the erosion processing formula. The specific method is to introduce a local adaptive adjustment coefficient, so that the calculation of each pixel in the erosion processing no longer simply takes the minimum value of the neighborhood, but comprehensively considers the local mean and the local standard deviation To address the influence, the formula is improved so that the corrosion process can more strongly suppress small noise in darker or noisier areas, while relatively weakening the corrosion intensity in areas with rich edges and structures, thereby better preserving the important details and edge features of the image.

[0146] By introducing a locally adaptive adjustment coefficient into the Gaussian filtering formula, more effective smoothing processing is achieved when the regional noise is large, and it also performs well in preserving the edge details of the image.

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

[0148] The improved Gaussian filtering and corrosion processing formulas effectively reduce the problem of local information loss that may be caused by the fixed coefficients of traditional methods, thereby obtaining a cleaner image with clear details, providing great support for steps such as edge detection and contour restoration, and enhancing the robustness of the entire tower head impulse discharge leader discharge characteristic parameter observation and statistical system.

[0149] In this embodiment, the gray-scale image is smoothed by using the Gaussian filtering method, effectively reducing the random noise in the image. At the same time, the morphological processing is performed on the smoothed image in combination with the corrosion processing method, further eliminating the small noise and pseudo-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 precision.

[0150] The technical solution provided in this embodiment effectively reduces the random noise in the image through the Gaussian filtering processing model for smoothing the gray-scale image; and further eliminates the small noise in the image through the corrosion processing model for morphological processing of the preliminarily smoothed image; which is beneficial to removing different types of noise interference while preserving the important features of the image, thereby being beneficial to improving the quality of the denoised image and the accuracy of subsequent edge detection.

[0151] In an exemplary embodiment, the edge detection process is performed on the denoised image according to the gray-scale gradient of the denoised image and a preset threshold to obtain the edge contour image of the denoised image, which specifically includes the following content: According to the gray-scale gradient, the preliminary extraction process of the edge information is performed on the denoised image, and the gradient magnitude and direction of each pixel point in the denoised image are calculated to obtain the gradient map of the denoised image; according to the preset threshold, the binary segmentation process is performed on the gradient map to obtain the edge contour image.

[0152] Among them, the preliminary extraction process of the edge information can be a process of preliminary identification and extraction of the edge features in the image. For example, the preliminary extraction process of the edge information can be a process of identifying the edge by calculating the gray-scale gradient of the pixel points in the local area.

[0153] Among them, the gradient magnitude can be the intensity of the change in the gray value at a pixel point. For example, the gradient magnitude can be a value obtained by calculating the square root of the sum of the squares of the changes in the gray values of the pixel point in the horizontal and vertical directions.

[0154] Among them, the gradient map can be a grayscale image that records the gradient magnitudes of all pixel points in the image. For example, the gradient map can be an image that contains information on the intensity of the gray value change at each pixel point.

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

[0156] Optionally, the terminal performs a preliminary extraction process of edge information on the denoised image based on gray-scale gradient. By calculating the changes in the gray values of each pixel point in the denoised image in the horizontal and vertical directions, the gradient magnitude and direction of each pixel point in the denoised image are obtained. Among them, the terminal introduces a neighborhood dynamic correction adjustment factor when calculating the gradient magnitude to adapt to the brightness and noise characteristics of different regions, enabling the gradient calculation to effectively identify edges even in regions with high noise or weak gray-scale changes, and obtaining the gradient map of the denoised image; the terminal performs a binary segmentation process on the gradient map of the denoised image through a preset threshold. Among them, the terminal introduces a local statistical information and brightness distribution dynamic correction adjustment coefficient to automatically adjust the segmentation threshold according to the local brightness fluctuation, thereby obtaining an edge contour image.

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

[0158] The calculation formula for the gradient magnitude is:

[0159] ;

[0160] Among them, is the gradient magnitude at the coordinate , is the pixel value of the denoised image at the coordinate , is the neighborhood dynamic correction adjustment factor, and a and b are the neighborhood offsets on the x-axis and y-axis respectively;

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

[0162] The calculation formula for the binary segmentation process is:

[0163] ;

[0164] wherein, is the pixel value of the edge contour image at the coordinate , T is a preset threshold value, is the pixel value of the local area centered on , is the average value of the pixel values of the local area centered on , is the standard deviation of the pixel values of the local area centered on , is the dynamic correction adjustment coefficient of the brightness distribution.

[0165] Specifically, in this embodiment, a neighborhood dynamic correction adjustment factor is introduced into the gradient magnitude to adapt to the brightness and noise characteristics of different regions, so that the gradient calculation can effectively identify edges in regions with large noise or weak gray-scale changes, thereby ensuring that the true edge gradient can be accurately calculated even when the image noise is not completely removed, avoiding gradient errors caused by local noise interference, and helping to extract fine edge information in the image.

[0166] In this embodiment, local statistical information and a dynamic correction adjustment coefficient of the brightness distribution are introduced into the binary segmentation formula to automatically adjust the segmentation threshold according to local brightness fluctuations, so that when the overall or local brightness of the image is uneven, the threshold standard can be dynamically adjusted, thereby effectively suppressing the problems of over-segmentation or missed detection of edges caused by a fixed threshold. Through adaptive adjustment, the true edge information can be accurately extracted when processing images under complex working conditions, and the accuracy of contour reconstruction and feature parameter extraction can be improved.

[0167] In this embodiment, the gradient magnitude and direction of each pixel point in the denoised image are calculated based on the gray-scale gradient to generate a gradient map, and the gradient map is subjected to binary segmentation processing in combination with a preset threshold to accurately extract the edge information in the image, generate an edge contour image, enhance the sensitivity of edge detection, and be able to accurately capture the edge characteristics of lightning leader discharges. At the same time, through the optimization of the dynamic correction adjustment factor and the brightness distribution, the robustness of the edge detection algorithm under complex lighting conditions and noise interference is improved.

[0168] The technical solution provided by this embodiment calculates the gray gradient of the denoised image to obtain the gradient magnitude and direction information of each pixel point, effectively identifies the regions where the gray values in the image change significantly, and thus accurately locates the edge positions; performs binary segmentation processing on the gradient map through a preset threshold, clearly divides the pixel points in the image into edge points and non-edge points, which is beneficial to generating a clear edge contour image; thereby facilitating the improvement of the accuracy of edge detection and the clarity of the edge contour.

[0169] In an exemplary embodiment, contour restoration processing and contour correction processing are performed on the edge contour image to obtain the leading contour information of the edge contour image, which specifically includes the following content: According to the geometric characteristic information of adjacent edge points in the edge contour image, the topological reconstruction model is used to connect the breakpoints in the edge contour image and restore the missing contour segments to obtain the complete contour structure information of the edge contour image; according to the physical field characteristic information, multi-scale correction processing is performed on the complete contour structure information 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 a 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 direction discontinuity degree.

[0171] Among them, the topological reconstruction model (i.e., the topological reconstruction method) can be a mathematical model for connecting breakpoints and restoring contours.

[0172] Among them, the complete contour structure information can be the complete contour data information obtained after contour restoration processing. For example, the complete contour structure information can be the continuous contour data without breakpoints obtained through the processing of the topological reconstruction model.

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

[0174] Among them, the multi-scale correction processing can be a processing process for correcting the contour at different scales. For example, the multi-scale correction processing can be a processing process including micro-scale local optimization and macro-scale overall curvature adjustment.

[0175] Among them, the mechanical model can be a mathematical model describing the deformation characteristics of the leading contour under the action of the physical field. For example, the mechanical model can be a mathematical model established by the finite element method for calculating the natural form of the leading contour line under the action of the electric field and air flow.

[0176] Among them, the abnormal contour offset information may be the offset data information that does not conform to the physical law in the complete contour structure. For example, the abnormal contour offset information may be the 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, where the edge breakpoints are used as the nodes of the topological graph and the potential connection paths are used as the edges of the topological graph. The terminal calculates the connection weights between the nodes based on geometric characteristic information such as the Euclidean distance, the curvature change rate, and the direction discontinuity degree, and uses the minimum spanning tree algorithm to optimize the topological graph, screening out the optimal connection path that conforms to the contour smoothness, so as to obtain 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 the force boundary, and performs physical correction on the abnormal contour offset information in the complete contour structure information by calculating the natural shape distribution of the leader contour line under the action of the electric field and air flow to obtain the leader contour information.

[0178] For example, the terminal connects the breakpoints by using the topological reconstruction method based on the geometric characteristics of adjacent edge points in the edge contour image, restores the missing contour segments, and forms a complete contour structure;

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

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

[0181] Calculating the connection weights between the nodes based on geometric characteristic information such as the Euclidean distance, the curvature change rate, and the direction discontinuity degree, and the smaller the weight value, the higher the connection possibility;

[0182] Using the minimum spanning tree algorithm or the shortest path algorithm to optimize the topological graph, screening out the optimal connection path that conforms to the contour smoothness;

[0183] After completing a global contour connection, perform secondary optimization on the local connection, and make the contour satisfy the global smoothness by improving the positions of the connected nodes.

[0184] Combining the physical field characteristics to perform multi-scale correction and optimization on the complete contour structure, including correcting the abnormal contour offset of the complete contour structure through the mechanical model to obtain a more accurate leader contour;

[0185] Exemplarily, combining the physical field characteristics to perform multi-scale correction and optimization on the complete contour structure includes:

[0186] A physical field characteristic model of leader discharge is established using the finite element method. The leader contour line is regarded as the force boundary, and 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.

[0187] On the microscale, considering the noise interference and artificial connection errors of local contour points, local optimization of the contour is performed through smoothing filtering operations.

[0188] On the macroscale, the overall curvature of the contour is adjusted to ensure that the leader contour line is consistent with the physical direction of discharge. Among them, the correction step introduces a direction alignment constraint to achieve the global curvature optimal solution by solving a variational problem.

[0189] Specifically, in this embodiment, based on the geometric characteristics of the edge contour image, the topological reconstruction method is used to connect the breakpoints, restore the missing contour segments, and optimize the connection path by combining parameters such as the Euclidean distance, curvature change rate, and direction discontinuity degree to form a complete contour structure.

[0190] Global contour connection is achieved through the minimum spanning tree algorithm or the shortest path algorithm, and combined with local optimization and smoothing processing to ensure the smoothness and global consistency of the contour. In addition, multi-scale correction and optimization are performed on the complete contour in combination with the physical field characteristics. A physical field characteristic model of leader discharge is established using the finite element method to further correct the abnormal contour offset and ensure the matching of the contour with the physical direction of discharge, 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 using the topological reconstruction model to connect the breakpoints based on the geometric characteristic information of adjacent edge points; corrects the abnormal contour offset information through multi-scale correction processing based on physical field characteristic information and a mechanical model, realizing the optimization of the complete contour structure information; is conducive to obtaining complete and physically regular leader contour information, thereby being conducive to improving the accuracy of lightning leader contour restoration.

[0192] In an exemplary embodiment, a database of lightning leader characteristic parameters is constructed according to the geometric feature information, which specifically includes the following contents: associating the geometric feature information with the condition information of each working condition to obtain a multi-dimensional data structure model of lightning leader characteristic parameters; classifying the lightning leader characteristic parameters according to the multi-dimensional data structure model to obtain the classification result of lightning leader characteristic parameters; and constructing a database of lightning leader characteristic parameters according to the classification result.

[0193] Among them, the condition information of the working condition can be parameter information describing the experimental environment and settings. For example, the condition information of the working condition can be information including experimental condition parameters such as voltage amplitude, environmental humidity, air pressure, temperature, and electrode spacing.

[0194] Among them, the association process can be a process of establishing a corresponding relationship between geometric feature information and condition information of working conditions. For example, the association process can be a process of pairing and associating the condition parameters of each group of experimental working conditions with the corresponding geometric feature parameters.

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

[0196] Among them, the classification process can be a process of classifying and sorting lightning leader characteristic parameters. For example, the classification process can be a process of classifying lightning leader characteristics into different categories by using the K-means clustering algorithm.

[0197] Among them, the classification result can be the category information obtained after classifying lightning leader characteristic parameters. For example, the classification result can be the subset of characteristic data and classification boundary conditions for different working conditions.

[0198] Among them, the database of lightning leader characteristic parameters can be a data system for storing and managing lightning leader characteristic parameters. For example, the database of lightning leader characteristic parameters can be an index storage according to different working conditions and characteristic parameters, and support a data system for cross-query of multi-dimensional parameters.

[0199] Optionally, the terminal performs an association process on the geometric feature information of the leader contour and the condition information of the corresponding working condition. 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 dimensions of the associated data through the principal component analysis method, extracts the characteristic parameters and the main influencing factors of the experimental conditions, and forms a multi-dimensional data structure model of lightning leader characteristic parameters; based on the multi-dimensional data structure model of lightning leader characteristic parameters, the terminal uses the K-means clustering algorithm to classify the lightning leader characteristic parameters under different working conditions, and combines the experimental working condition parameters to perform posterior verification and boundary condition adjustment on the classification result to obtain the classification result of lightning leader characteristic parameters; according to the classification result of lightning leader characteristic parameters, the terminal respectively establishes subsets of characteristic data for different working conditions, and stores them according to the lightning leader characteristic parameters for indexing, and constructs a database of lightning leader characteristic parameters that supports cross-query of multi-dimensional parameters.

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

[0201] Exemplarily, the steps for constructing the multi-dimensional 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, etc.;

[0203] Establish an association between the recorded experimental condition parameters and the corresponding geometric feature parameters (leader length, direction, curvature distribution, number of bifurcations, and upper and lower leader ratio);

[0204] Based on the principal component analysis method, perform dimensionality reduction, extract the main influencing factors of the characteristic parameters and experimental conditions, and form a simplified multi-dimensional data structure model;

[0205] Store the multi-dimensional data structure model data in the characteristic parameter database for subsequent classification and query;

[0206] Based on the multi-dimensional data structure model, use the clustering analysis method to classify the lightning leader characteristic parameters under different working conditions, and establish a lightning leader characteristic parameter database according to the classification results.

[0207] Exemplarily, based on the multi-dimensional data structure model, use the clustering analysis method to classify the lightning leader characteristic parameters under different working conditions, and establish a lightning leader characteristic parameter database according to the classification results, specifically including:

[0208] Extract the leader geometric feature parameters in the multi-dimensional data structure model, and according to the similarity of the extracted features, use the K-means clustering algorithm to classify the lightning leader characteristics into different categories;

[0209] Perform posterior verification on the classification results, and further adjust the classification boundary conditions in combination with the experimental condition parameters to ensure classification accuracy;

[0210] Based on the classification results, establish characteristic data subsets for different working conditions respectively, and store them by indexing according to the lightning leader characteristic parameters, while allowing cross-query of different dimensional parameters;

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

[0212] Specifically, in this embodiment, by establishing an association between the geometric feature parameters of the leader profile and the experimental condition parameters, constructing a multi-dimensional data structure model, and combining the principal component analysis and clustering analysis methods, the classification and induction of the lightning leader characteristic parameters are realized.

[0213] This embodiment can effectively extract and simplify the main factors affecting the lightning leader characteristics, form characteristic data subsets and classification results for different working conditions, and significantly improve the organization and storage efficiency of lightning leader characteristic parameters. At the same time, by establishing a lightning leader characteristic parameter database, it supports cross-query of multi-dimensional parameters and output of typical characteristic graphs, providing efficient technical support for the research, prediction and actual working condition evaluation of lightning leader characteristics.

[0214] The technical solution provided by this embodiment establishes the corresponding relationship between the characteristic parameters and the working condition 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; through the classification processing of the lightning leader characteristic parameters, the systematic management and organization of the characteristic parameters under different working conditions are realized, which is conducive to constructing a complete lightning leader characteristic parameter database and providing a reliable data basis for subsequent data analysis and characteristic comparison.

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

[0216] Lightning, as an ultra-long-distance and ultra-large-scale discharge phenomenon in which a thunderstorm cloud releases its internal electrical energy, has the characteristics of high occurrence frequency, large current intensity, strong destructive ability, etc., posing a serious threat to the safety of transmission lines. For cloud-to-ground lightning, its occurrence mainly consists of processes such as the streamer-leader process, the connection process, the first return stroke, and the subsequent return strokes. The streamer-leader discharge, as the starting stage of lightning occurrence, develops continuously under the driving of the electric field and plays a decisive role in the formation of the subsequent lightning return stroke channel. In particular, the charge stored in the leader channel will be directly used to supply the return stroke current, directly determining the discharge energy and destructive ability of lightning. The tower-top impulse discharge leader technology is one of the important fields for studying the lightning discharge process, mainly used to simulate the formation and development mechanism of the leader discharge in the lightning discharge process. The leader discharge is the initial stage of the lightning discharge, manifested as the concentrated enhancement of the electric field intensity in a local area, resulting in the breakdown of the air medium and the formation of a discharge channel. The tower-top impulse discharge experiment simulates the leader process of lightning discharge by applying an impulse voltage to the high-voltage tower top, and studies the electric field distribution, discharge path and its characteristic parameters.

[0217] The acquisition of lightning leader characteristic parameters is mainly obtained by high-speed cameras and spectral acquisition. The high-speed camera mainly focuses on the acquisition of discharge images and the discharge development process, and can reflect the morphology and development process of the lightning leader discharge; spectral acquisition can obtain the particle state during the lightning discharge process, such as characteristic parameters such as the electron density and channel conductivity in the plasma channel. However, in the above parameter acquisition methods, there is a lack of means for quantitatively statistically analyzing the leader morphology.

[0218] The fractal dimension can be used to quantify the complexity of the discharge morphology. However, the quantification and statistical methods for the lightning leader discharge morphology parameters are still scarce, mainly focusing on the development process of the leader, with less research on the leader morphology. The lightning leader characteristics under different working conditions have not been classified and summarized, and a systematic lightning leader characteristic database cannot be formed, making it difficult to support large-scale data analysis and characteristic comparison under different working conditions.

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

[0220] (1) Lightning leader image acquisition:

[0221] Use optical acquisition devices such as high-speed cameras or single-lens reflex cameras to photograph the lightning discharge leader process under different working conditions to obtain high-resolution lightning leader development images. The acquired images contain the morphological characteristics and development process of the lightning leader, providing a data basis for subsequent processing.

[0222] (2) Grayscale processing:

[0223] Import the acquired lightning leader images into MATLAB (mathematical software), and use the grayscale processing algorithm to convert the color images into grayscale images so that the grayscale value of each pixel point in the image is between 0 and 255. It can effectively highlight the brightness characteristics of the lightning leader and lay a foundation for subsequent denoising and edge detection.

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

[0225] Perform Gaussian filtering on the grayscale processed image, use the Gaussian filter to smooth the image, and reduce the interference of random noise. Subsequently, combined with the erosion operation to further remove small noise points and enhance the edge clarity of the image.

[0226] On the denoised image, use the Canny (edge detection) operator for edge detection. By selecting appropriate double thresholds, extract the edge contour of the lightning leader to obtain a clear leader morphology, presenting the overall contour of the lightning leader completely.

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

[0228] Import the image after edge detection into CAD (computer-aided design) software, and manually or automatically trace the edge contour of the lightning leader 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 leader contour, including but not limited to the following parameters:

[0230] Leader length: Measure the total length of the leader channel.

[0231] Pilot direction: Calculate the overall development direction of the pilot channel.

[0232] Curvature: Analyze the degree of bending of the pilot channel.

[0233] Number of bifurcations: Count the number of bifurcation points of the pilot channel.

[0234] Ratio of upper and lower pilots: Calculate the length ratio of the upper and lower parts of the pilot channel.

[0235] Reference of extracted characteristic parameters Figure 3 (Schematic diagram of the annotation of lightning leader characteristic parameters), which includes the annotated values 24.39, 21.51, 10.72, 31.43, 90°, 129° and 110°, forming complete lightning leader characteristic data.

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

[0237] Classify and statistically analyze the lightning leader characteristic parameters extracted under different working conditions, and construct a lightning leader characteristic parameter database. The database contains multi-dimensional data such as leader length, direction, curvature, and number of bifurcations, providing important data support for the research on lightning discharge characteristics and the protection design of transmission lines. The generated lightning leader characteristic parameter database provides comprehensive data support and technical guarantee for the research, prediction, and protection design of lightning discharge characteristics under different working conditions, effectively improving the efficiency and accuracy of lightning discharge behavior analysis.

[0238] The technical solution provided by this application example realizes: (1) Obtain lightning leader images through image acquisition devices, and combine gray processing, Gaussian filtering, corrosion processing for denoising, edge detection algorithms, contour restoration, and geometric feature parameter extraction to achieve high-precision observation and analysis of the leader discharge characteristics of tower head impulse discharge. At the same time, combined with multi-dimensional data models and clustering analysis methods, a lightning leader characteristic parameter database for different working conditions is established, improving the observation and statistical ability of lightning leader discharge characteristic parameters; (2) Smooth the gray image by using the Gaussian filtering method, effectively reducing the random noise in the image. At the same time, combine the corrosion processing method to perform morphological processing on the smoothed image, further eliminating the fine noise and pseudo-edges in the image, improving the quality of the denoised image and the accuracy of edge information, and enhancing the anti-interference ability and processing precision; (3) Calculate the gradient amplitude and direction of each pixel point in the denoised image based on the gray gradient, generate a gradient map, and perform binary segmentation processing on the gradient map in combination with a preset threshold to accurately extract the edge information in the image and generate an edge contour image, enhancing the sensitivity of edge detection, being able to accurately capture the edge characteristics of lightning leader discharge. At the same time, through the dynamic correction of the 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.

[0239] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear description in this document, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of the steps or stages in other steps or other steps.

[0240] Based on the same inventive concept, an embodiment of the present application further provides a lightning activity information analysis device for implementing the lightning activity information analysis method described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the lightning activity information analysis device provided below can refer to the limitations on the lightning activity information analysis method in the foregoing, and will not be elaborated herein.

[0241] In an exemplary embodiment, as Figure 4 shown, a lightning activity information analysis device is provided. The lightning activity information analysis device 400 may include:

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

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

[0244] An image denoising module 403, configured to perform denoising processing on the grayscale images to obtain denoised images of the grayscale images;

[0245] An image detection module 404, configured to perform edge detection processing on the denoised images according to the grayscale gradient and a preset threshold of the denoised images to obtain edge contour images of the denoised images;

[0246] An image restoration module 405, configured to perform contour restoration processing and contour correction processing on the edge contour images to obtain the leader contour information of the edge contour images;

[0247] A feature recognition module 406, configured to perform geometric feature recognition processing on the leader contour information to obtain geometric feature information of the leader contour information;

[0248] A data construction module 407, configured to construct a database of lightning leader characteristic parameters according to geometric feature information;

[0249] A discharge analysis module 408, configured to analyze the lightning discharge behavior information to be analyzed according to the database of lightning leader characteristic parameters, and obtain an analysis result of the lightning discharge behavior information.

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

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

[0252] In an exemplary embodiment, the image detection module 404 is further configured to perform a preliminary extraction process of edge information on the denoised image according to the gray 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; perform a binary segmentation process 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 further configured to connect the breakpoints in the edge contour image by using a topological reconstruction model according to the geometric feature information of adjacent edge points in the edge contour image, and restore the missing contour segments to obtain the complete contour structure information of the edge contour image; perform a multi-scale correction process on the complete contour structure information according to the physical field characteristic information to obtain the leader contour information; the multi-scale correction process includes a correction process of 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 further configured to perform an association process on the geometric feature information and the condition information of each working condition to obtain a multi-dimensional data structure model of lightning leader characteristic parameters; classify the lightning leader characteristic parameters according to the multi-dimensional data structure model to obtain a classification result of the lightning leader characteristic parameters; construct a database of lightning leader characteristic parameters according to the classification result.

[0255] Each module in the above lightning activity information analysis device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in a computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above respective modules.

[0256] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structural diagram may be as shown in Figure 5 the following figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a lightning activity information analysis method. The display unit of the computer device is used to form a visually visible picture, which can 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. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

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

[0258] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[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 the processor, the steps in the above 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 the processor, the steps in the above method embodiments are implemented.

[0261] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0262] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0263] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for analyzing lightning activity information, characterized in that The method includes: Collecting lightning leader images under various working conditions through an image acquisition device; Performing gray-scale processing on the lightning leader images to obtain gray-scale images of the lightning leader images; Performing denoising processing on the gray-scale images to obtain denoised images of the gray-scale images; Performing edge detection processing on the denoised images according to the gray-scale gradient and a preset threshold of the denoised images to obtain edge contour images of the denoised images; Performing contour restoration processing and contour correction processing on the edge contour images to obtain the leader contour information of the edge contour images; Performing geometric feature recognition processing on the leader contour information to obtain geometric feature information of the leader contour information; Constructing a database of lightning leader feature parameters according to the geometric feature information; Analyzing the lightning discharge behavior information to be analyzed according to the database of the lightning leader feature parameters to obtain an analysis result of the lightning discharge behavior information.

2. The method according to claim 1, wherein The performing gray-scale processing on the lightning leader images to obtain gray-scale images of the lightning leader images includes: Performing gray-scale space conversion processing on the lightning leader images to obtain initial gray-scale images of the lightning leader images; Performing brightness update processing on the initial gray-scale images to obtain the gray-scale images.

3. The method according to claim 1, wherein The performing denoising processing on the gray-scale images to obtain denoised images of the gray-scale images includes: Processing the gray-scale images through a Gaussian filtering processing model to obtain preliminary smoothed images of the gray-scale images; Processing the preliminary smoothed images through an erosion processing model to obtain the denoised images.

4. The method according to claim 1, characterized in that, The performing edge detection processing on the denoised images according to the gray-scale gradient and a preset threshold of the denoised images to obtain edge contour images of the denoised images includes: Performing preliminary extraction processing of edge information on the denoised images according to the gray-scale gradient, and calculating the gradient amplitude and direction of each pixel point in the denoised images to obtain a gradient map of the denoised images; Performing binary segmentation processing on the gradient map according to the preset threshold to obtain the edge contour images.

5. The method according to claim 1, wherein The performing contour restoration processing and contour correction processing on the edge contour images to obtain the leader contour information of the edge contour images includes: Connecting the breakpoints in the edge contour images by using a topological reconstruction model according to the geometric characteristic information of adjacent edge points in the edge contour images, and restoring the missing contour segments to obtain complete contour structure information of the edge contour images; Performing multi-scale correction processing on the complete contour structure information according to physical field characteristic information to obtain the leader contour information; the multi-scale correction processing includes correction processing of 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 constructing a database of lightning leader feature parameters according to the geometric feature information includes: Performing association processing on the geometric feature information and the condition information of each working condition to obtain a multi-dimensional data structure model of the lightning leader feature parameters; Classify the lightning leader characteristic parameters according to the multi-dimensional data structure model to obtain the classification result of the lightning leader characteristic parameters; Construct a database of the lightning leader characteristic parameters according to the classification result.

7. A lightning activity information analysis device, characterized in that, The device includes: An image acquisition module, configured to acquire lightning leader images under various working conditions through an image acquisition device; An image processing module, configured to perform gray processing on the lightning leader images to obtain gray images of the lightning leader images; An image denoising module, configured to perform denoising processing on the gray images to obtain denoised images of the gray images; An image detection module, configured to perform edge detection processing on the denoised images according to the gray gradients and preset thresholds of the denoised images to obtain edge contour images of the denoised images; An image restoration module, configured to perform contour restoration processing and contour correction processing on the edge contour images to obtain the leader contour information of the edge contour images; A feature recognition module, configured to perform geometric feature recognition processing on the leader contour information to obtain geometric feature information of the leader contour information; A data construction module, configured to construct a database of lightning leader characteristic parameters according to the geometric feature information; A discharge analysis module, configured to analyze the lightning discharge behavior information to be analyzed according to the database of the lightning leader characteristic parameters to obtain the analysis result of the lightning discharge behavior information.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, 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 the 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 the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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