Charged non-contact insulator degradation detection method and device

Abstract

The invention provides an electrified non-contact insulator degradation detection method and device. The method comprises the following steps: receiving an infrared image, an ultraviolet image and environment humidity of a to-be-detected insulator string; preprocessing the infrared image to obtain a target grayscale image; all gray level image pixel points are obtained, and a target threshold value is determined through a maximum between-class variance method; determining a steel foot area of each insulator and a characteristic temperature of each insulator according to the target threshold value; and constructing a gray temperature relation curve and then exporting an axial temperature data file. Determining a temperature difference threshold according to all the axial temperature data files so as to judge the degraded insulator; and estimating the pollution grade of the degraded insulator according to the ultraviolet image and the environment humidity, and generating and outputting a detection report. According to the method, the segmentation effect is effectively enhanced and the image segmentation accuracy is improved by shooting the infrared image and the ultraviolet image and establishing the pollution grade corresponding relation and using the Otsu method, the degraded insulator is judged based on statistical analysis, the workload is remarkably reduced, and the detection accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of insulator detection, and in particular to a method and a device for detecting degradation of an energized non-contact insulator. Background Art

[0002] In high-voltage transmission lines, insulators not only play the role of fixing and suspending, but also assume the important function of electrical insulation, which is crucial to the transmission and distribution of electricity. Since insulators are in high-voltage electric fields for a long time, they bear mechanical loads and face harsh environments such as high temperature, strong sunlight, high humidity and dirt, their degradation is inevitable. When the insulator deteriorates to a certain extent, its insulation performance will decrease, which may cause accidents such as flashover, string drop and conductor falling, thus affecting the reliability of the transmission line. Therefore, it is particularly important to detect deteriorated insulators in a timely manner.

[0003] However, the number of line insulators is extremely large, and the deterioration location and distribution are random and irregular, resulting in an extremely heavy workload for deteriorated insulator detection. Reducing the detection workload can not only reduce misjudgments caused by fatigue of operation and maintenance personnel, but also reduce the time cost so that more detection frequencies can be achieved, thereby discovering potential insulator failures more timely and reducing line power outages and equipment damage caused by insulator failures.

[0004] Therefore, how to optimize the detection judgment process and logic to reduce the detection workload and improve the detection accuracy is a problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for detecting degradation of an energized non-contact insulator to solve the problems of low detection efficiency and unreliability.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] The first aspect of the present invention discloses a method for detecting degradation of a live non-contact insulator, the method comprising:

[0008] receiving multiple infrared images, multiple ultraviolet images and environmental humidity of the insulator string to be inspected sent by the inspection equipment;

[0009] For each of the infrared images, preprocessing the infrared image to obtain a target grayscale image;

[0010] Based on all grayscale image pixels of the target grayscale image, calculate the inter-class variance corresponding to each threshold within the image grayscale threshold range, and mark the threshold corresponding to the largest inter-class variance among all inter-class variances as the target threshold;

[0011] Convert the target grayscale image into a binary image according to the target threshold value to determine the steel foot area of ​​each insulator in the infrared image and the average temperature of each steel foot area, and determine the average temperature as the characteristic temperature of each insulator;

[0012] Constructing a gray-temperature relationship curve based on the maximum temperature in the target gray-scale image and the maximum gray in the binary image;

[0013] Based on the characteristic temperature of each insulator and the gray-scale temperature relationship curve, derive an axial temperature data file of each insulator in the infrared image;

[0014] Calculate the temperature difference between each insulator and its adjacent insulator in the insulator string to be tested according to all axial temperature data files;

[0015] Determine a temperature difference threshold value based on the temperature difference value, and determine the insulator whose temperature difference value is greater than the temperature difference threshold value as a degraded insulator in the insulator string to be detected;

[0016] The contamination level of the degraded insulator is estimated according to the ultraviolet image corresponding to the degraded insulator and the environmental humidity, and a detection report of the degraded insulator is generated and output.

[0017] Preferably, for each infrared image, preprocessing the infrared image to obtain a target grayscale image includes:

[0018] For each infrared image, obtaining the RGB value of each pixel of the infrared image, and converting the infrared image into a grayscale image based on the RGB value;

[0019] Performing median filtering on the grayscale image to reduce noise, to obtain a grayscale image after noise reduction;

[0020] The de-noised grayscale image is subjected to tilt correction processing based on a preset edge detection operator and a Hough transform operator to obtain a target grayscale image.

[0021] Preferably, the step of calculating the inter-class variance corresponding to each threshold value within the image grayscale threshold range based on all grayscale image pixels of the target grayscale image, and marking the threshold value corresponding to the largest inter-class variance among all inter-class variances as the target threshold value, includes:

[0022] Obtain the number of all grayscale image pixels of the target grayscale image;

[0023] Calculate the grayscale mean of the target grayscale image according to the quantity;

[0024] For each threshold value in the grayscale threshold range of the image, calculate a first number of all the grayscale image pixel numbers that are less than or equal to the threshold value, and a second number of all the grayscale image pixel numbers that are greater than the threshold value;

[0025] Calculating a first between-class variance corresponding to the first quantity, a first class weight corresponding to the first quantity, a second between-class variance corresponding to the second quantity, and a second class weight corresponding to the second quantity;

[0026] Calculate the inter-class variance corresponding to the threshold based on the first inter-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second inter-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity, and obtain the inter-class variance corresponding to each threshold in the image grayscale threshold range;

[0027] The largest inter-class variance is extracted from all inter-class variances, and the threshold corresponding to the largest inter-class variance is marked as the target threshold.

[0028] Preferably, converting the target grayscale image into a binary image according to the target threshold value to determine the steel foot area of ​​each insulator in the infrared image and the average temperature of each steel foot area, and determining the average temperature as the characteristic temperature of each insulator, comprises:

[0029] Converting the target grayscale image into a binary image according to the target threshold;

[0030] Find the first n connected regions in the binary image from large to small in area, and mark the connected regions as the steel foot regions of each insulator in the infrared image;

[0031] Extracting the image grayscale value corresponding to each of the steel foot regions according to the target grayscale image;

[0032] For each of the steel foot regions, calculating an average grayscale value of the steel foot region based on the area of ​​the steel foot region and the image grayscale value;

[0033] The average temperature corresponding to each of the steel leg regions is determined based on the average gray value, and the average temperature is determined as the characteristic temperature of each insulator.

[0034] Preferably, determining the temperature difference threshold based on the temperature difference includes:

[0035] The temperature difference mean and standard deviation corresponding to all axial temperature data files are calculated based on the temperature difference, and the temperature difference threshold is determined according to the temperature difference mean and the standard deviation.

[0036] A second aspect of the present invention discloses a live non-contact insulator degradation detection device, the device comprising:

[0037] A receiving unit, used for receiving a plurality of infrared images, a plurality of ultraviolet images and environmental humidity of the insulator string to be inspected sent by the inspection equipment;

[0038] A preprocessing unit, used for preprocessing each infrared image to obtain a target grayscale image;

[0039] A first calculation unit is used to calculate the inter-class variance corresponding to each threshold value within the image grayscale threshold range based on all grayscale image pixels of the target grayscale image, and mark the threshold corresponding to the largest inter-class variance among all inter-class variances as the target threshold value;

[0040] A first determination unit is used to convert the target grayscale image into a binary image according to the target threshold value, so as to determine the steel foot area of ​​each insulator in the infrared image and the average temperature of each of the steel foot areas, and determine the average temperature as the characteristic temperature of each insulator;

[0041] A construction unit, configured to construct a gray-temperature relationship curve based on the maximum temperature in the target gray-scale image and the maximum gray in the binary image;

[0042] An export unit, used for exporting an axial temperature data file of each insulator in the infrared image based on the characteristic temperature of each insulator and the gray-scale temperature relationship curve;

[0043] A second calculation unit is used to calculate the temperature difference between each insulator in the insulator string to be detected and its adjacent insulator according to all axial temperature data files;

[0044] A second determining unit is used to determine a temperature difference threshold value based on the temperature difference value, and determine the insulator whose temperature difference value is greater than the temperature difference threshold value as a degraded insulator in the insulator string to be detected;

[0045] The output unit is used to estimate the contamination level of the degraded insulator according to the ultraviolet image corresponding to the degraded insulator and the environmental humidity, and generate and output a detection report of the degraded insulator.

[0046] Preferably, the pre-processing unit comprises:

[0047] An acquisition module, used for acquiring, for each infrared image, an RGB value of each pixel of the infrared image, and converting the infrared image into a grayscale image based on the RGB value;

[0048] A denoising module, used for performing median filtering on the grayscale image to reduce noise, so as to obtain a denoised grayscale image;

[0049] The correction module is used to perform tilt correction processing on the denoised grayscale image based on a preset edge detection operator and a Hough transform operator to obtain a target grayscale image.

[0050] Preferably, the first computing unit comprises:

[0051] A quantity acquisition module is used to acquire the quantity of all grayscale image pixels of the target grayscale image;

[0052] A first calculation module, used for calculating the grayscale mean of the target grayscale image according to the quantity;

[0053] A second calculation module is used to calculate, for each threshold value in the grayscale threshold range of the image, a first number of all the numbers of grayscale image pixels that are less than or equal to the threshold value, and a second number of all the numbers of grayscale image pixels that are greater than the threshold value;

[0054] A third calculation module, used for calculating a first between-class variance corresponding to the first quantity, a first category weight corresponding to the first quantity, a second between-class variance corresponding to the second quantity, and a second category weight corresponding to the second quantity;

[0055] A fourth calculation module, configured to calculate the inter-class variance corresponding to the threshold based on the first inter-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second inter-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity, to obtain the inter-class variance corresponding to each threshold in the image grayscale threshold range;

[0056] The marking module is used to extract the maximum inter-class variance from all inter-class variances, and mark the threshold corresponding to the maximum inter-class variance as the target threshold.

[0057] Preferably, the first determining unit includes:

[0058] A conversion module, used for converting the target grayscale image into a binary image according to the target threshold;

[0059] A search module, used for searching the first n connected regions in the binary image from large to small in area, and marking the connected regions as the steel foot regions of each insulator in the infrared image;

[0060] An extraction module, used for extracting the image grayscale value corresponding to each steel foot area according to the target grayscale image;

[0061] A fifth calculation module, for calculating, for each of the steel foot regions, an average gray value of the steel foot region based on the area of ​​the steel foot region and the image gray value;

[0062] A determination module is used to determine an average temperature corresponding to each of the steel foot regions based on the average gray value and determine the average temperature as a characteristic temperature of each insulator.

[0063] Preferably, the second determining unit is specifically used to: calculate the temperature difference mean and standard deviation corresponding to all axial temperature data files based on the temperature difference, and determine the temperature difference threshold according to the temperature difference mean and the standard deviation.

[0064] Based on the above-mentioned embodiment of the present invention, a method and device for detecting deterioration of live non-contact insulators are provided, receiving infrared images, ultraviolet images and environmental humidity of the insulator string to be detected; preprocessing the infrared image to obtain a target grayscale image; obtaining all grayscale image pixels therefrom, and determining the target threshold by the maximum inter-class variance method; determining the steel foot area of ​​each insulator and the characteristic temperature of each insulator according to the target threshold; constructing a grayscale temperature relationship curve and then exporting the axial temperature data file. Determine the temperature difference threshold according to all axial temperature data files to determine the deteriorated insulator; estimate the pollution level of the deteriorated insulator according to the ultraviolet image and the environmental humidity, and generate and output a detection report. The present invention shoots infrared images, ultraviolet images and establishes a corresponding relationship between pollution levels, uses the maximum inter-class variance method to effectively enhance the segmentation effect, improve the accuracy of image segmentation, and discriminates deteriorated insulators based on statistical analysis, significantly reducing the workload and improving the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0066] Figure 1 A flowchart of a live non-contact insulator degradation detection method provided by an embodiment of the present invention;

[0067] Figure 2 A flowchart of preprocessing an infrared image provided by an embodiment of the present invention;

[0068] Figure 3 A flow chart of calculating the inter-class variance based on the target grayscale image to determine the target threshold value provided by an embodiment of the present invention;

[0069] Figure 4 A flow chart for determining the average temperature of a steel foot area and each steel foot area according to a target threshold value provided in an embodiment of the present invention;

[0070] Figure 5A structural block diagram of a live non-contact insulator degradation detection device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0073] As can be seen from the background technology, the number of live non-contact insulators is very large, and the deterioration position and distribution are random and irregular, which makes the detection of deteriorated insulators a huge task. With the continuous expansion of the scale of the power grid and the gradual construction of ultra-high voltage lines, the requirements for insulator performance have become more stringent, and the detection work is also facing greater challenges. The current tower detection method is inefficient, and the operation and maintenance personnel may misjudge due to fatigue, resulting in unreliable detection results.

[0074] Since a considerable proportion of degraded insulators cannot be identified through visual images, the characteristics of local overheating, local discharge and physical damage to the dielectric surface of degraded transmission line insulators make it possible to measure and evaluate the degradation through ultraviolet imagers, infrared imagers or ultrasonic transmitting and receiving devices. In addition, degraded insulators cause changes in the spatial electric field distribution, and degraded insulators can be detected by non-contact electric field measurement. However, ultrasonic and ultraviolet detection are not effective in determining whether an insulator is degraded, and are only effective in determining the degree of contamination. The electric field measurement method is too labor-intensive to measure the electric field distribution piece by piece along the insulator string. The infrared detection method of degraded insulators can achieve rapid detection, but the differences in the heating characteristics of degraded insulators under different contamination conditions and the evaluation accuracy of the current infrared detection technology itself may lead to unreliable detection results.

[0075] Therefore, the embodiment of the present invention provides a method and device for detecting deterioration of live non-contact insulators, which receives infrared images, ultraviolet images and environmental humidity of an insulator string to be detected; pre-processes each infrared image to obtain a target grayscale image; determines the target threshold value by the maximum inter-class variance method based on all grayscale image pixels of the target grayscale image; determines the steel foot area of ​​each insulator in the infrared image and the average temperature of each steel foot area according to the target threshold value, thereby determining the characteristic temperature of each insulator; constructs a grayscale temperature relationship curve and then exports the axial temperature data file of each insulator in the infrared image. Determine the temperature difference threshold value according to all axial temperature data files, thereby determining the deteriorated insulator in the insulator string to be detected; further estimate the pollution level of the deteriorated insulator according to the ultraviolet image and environmental humidity corresponding to the deteriorated insulator, and generate and output the detection report of the deteriorated insulator. The present invention uses a drone to shoot characteristic parameters such as infrared spectra and ultraviolet images, establishes a corresponding relationship between pollution levels, and performs data processing and statistical analysis to determine the degradation of insulators. The use of the maximum between-class variance method can effectively enhance the segmentation effect, improve the clarity and accuracy of image segmentation, and reduce the probability of misclassification. The present invention significantly improves the performance and accuracy of detection.

[0076] See also Figure 1 , shows a flow chart of a method for detecting degradation of a live non-contact insulator provided by an embodiment of the present invention. The method comprises:

[0077] It is understandable that the insulator consists of three parts: steel cap, steel foot and porcelain plate, which are axially symmetrical. By shooting its side view, the axial thermal field distribution of the insulator can be effectively captured and analyzed.

[0078] In practical applications, the method is applicable to a live non-contact insulator degradation detection system, which includes but is not limited to a flight module, a detection module, a ground analysis and processing module, and a wireless communication module.

[0079] The flight module includes but is not limited to a drone that can carry equipment. The detection module includes but is not limited to detection equipment such as an infrared thermal imager, an ultraviolet imager, and a hygrometer, which are mounted on the flight module. In other words, the detection module is mounted on the flight module.

[0080] It should be noted that the multiple infrared images, multiple ultraviolet images and ambient humidity taken by the detection module are transmitted to the ground analysis and processing module through the wireless communication module; the ground analysis and processing module is used to execute a live non-contact insulator degradation detection method provided in an embodiment of the present invention, analyze and process the received multiple infrared images, multiple ultraviolet images and ambient humidity, and thus output a detection report on the degraded insulator.

[0081] Step S101: receiving a plurality of infrared images, a plurality of ultraviolet images and environmental humidity of an insulator string to be inspected sent by a detection device.

[0082] In practical applications, by mounting infrared imagers, ultraviolet imagers, hygrometers and other detection equipment on a mountable drone, the shooting angle is continuously adjusted, the corresponding images of insulator strings along the transmission line are captured, their positions are marked and the humidity of the environment is detected, thus obtaining multiple infrared images, multiple ultraviolet images and environmental humidity of the insulator strings to be detected.

[0083] In the specific implementation process of step S101, multiple infrared images, multiple ultraviolet images and environmental humidity of the insulator string to be inspected sent by the inspection device are received, and all infrared images are stored in the insulator infrared library.

[0084] For example, the infrared images are stored in the insulator infrared library, and each infrared image is named Infrare(t,S i ,X j ,L k ,P m ,I n ).jpg. Among them, t is the shooting time; S is the line file number, i is the number; X is the side where the insulator string is located, the power transmission side (small side) is 1, the load side (large side) is 2, j is the number; L is the circuit number, k is the number; P is the phase number, m is the number; I is the string number, n is the number.

[0085] Step S102: for each infrared image, preprocess the infrared image to obtain a target grayscale image.

[0086] In the specific implementation of step S102, each received infrared image is preprocessed, including but not limited to converting the infrared image into a grayscale image, performing median filtering noise reduction and angle correction, and obtaining a target grayscale image.

[0087] It is understandable that converting infrared images into grayscale images, performing median filtering to reduce noise and angle correction can effectively improve image quality, reduce noise interference, and ensure image consistency and accuracy, which are important foundations for subsequent analysis and processing.

[0088] See the embodiment of the present invention. Figure 2 ,The process of preprocessing the infrared image includes:

[0089] Step S201: for each infrared image, obtain the RGB value of each pixel of the infrared image, and convert the infrared image into a grayscale image based on the RGB value.

[0090] In the specific implementation of step S201, for each infrared image, the RGB value of each pixel of the infrared image is obtained, and the infrared image is converted into a grayscale image according to formula (1).

[0091] gray=0.2989×R+0.5870×G+0.1140×B (1)

[0092] Among them, gray is the grayscale image; R is the R value of the pixel in the infrared image; G is the G value of the pixel in the infrared image; B is the B value of the pixel in the infrared image.

[0093] It should be noted that, in the RGB color space, RGB stands for red, green, and blue, respectively.

[0094] It is understandable that, since the grayscale image only contains brightness information, the amount of data is reduced, making subsequent image processing and analysis more efficient. Furthermore, in infrared imaging, the temperature differences of objects will be reflected in the brightness of the image, and the grayscale image can intuitively display these differences, making it easier to observe and analyze.

[0095] Step S202: performing median filtering to reduce noise on the grayscale image to obtain a grayscale image after noise reduction.

[0096] In the specific implementation process of step S202, the grayscale image is loaded, the filter window size is determined, and then the grayscale image is traversed. For each pixel in the grayscale image, the selected filter window is used to collect the values ​​of the surrounding pixels, and the median of all pixel values ​​in the filter window is calculated. Finally, the calculated median replaces the value of the current pixel to obtain the denoised grayscale image.

[0097] It can be understood that, compared with mean filtering, median filtering for grayscale images can better remove outliers while retaining the edge features of the image, and is not easily affected by extreme values. Furthermore, median filtering can adapt to different noise distributions and has a certain degree of robustness.

[0098] Step S203: performing tilt correction processing on the denoised grayscale image based on a preset edge detection operator and a Hough transform operator to obtain a target grayscale image.

[0099] In the specific implementation of step S203, the grayscale image after noise reduction is subjected to tilt correction processing by using a preset edge detection operator and a Hough transform operator to obtain a target grayscale image.

[0100] Among them, the preset edge detection operator is the Canny edge detection operator; the Hough transform operator is the Hough transform operator.

[0101] Specifically, the Canny edge detection operator is used to extract the image edge features in the denoised grayscale image, and then the straight line features in the denoised grayscale image are detected by the Hough transform operator, the longest straight line and its inclination angle are calculated, and finally the affine transformation matrix is ​​determined according to the calculated inclination angle. The denoised grayscale image is rotated based on the affine transformation matrix to perform tilt correction processing on it to obtain the target grayscale image.

[0102] Here, the affine transformation matrix is ​​such as formula (2).

[0103]

[0104] Here, θ is the tilt angle.

[0105] Combination Figure 2 As shown in the figure, after converting the infrared image into a grayscale image, the complexity of the data can be reduced, making subsequent image processing (such as filtering and edge detection) more efficient. In addition, the median filter can effectively remove dirty spots such as salt noise, while retaining edge information, improving image quality, and enhancing the accuracy of subsequent analysis. Images processed by the median filter usually show clearer features, which is helpful for object recognition and other image analysis tasks. At the same time, through angle correction, the distortion caused by different shooting angles can be eliminated, making the image more consistent with the actual scene, which is convenient for further analysis. Overall, the denoised and corrected images can significantly improve the performance and accuracy of subsequent detection.

[0106] Step S103: Based on all grayscale image pixels of the target grayscale image, the inter-class variance corresponding to each threshold within the image grayscale threshold range is calculated, and the threshold corresponding to the largest inter-class variance among all inter-class variances is marked as the target threshold.

[0107] In the specific implementation of step S103, all grayscale image pixels are obtained from the target grayscale image, and the image grayscale threshold range (0-255) is obtained. All grayscale image pixels and each threshold within the image grayscale threshold range are combined to calculate the inter-class variance, thereby determining the target threshold.

[0108] It can be understood that using the maximum inter-class variance method (Otsu method) to calculate the inter-class variance of the target grayscale image and thus determine the target threshold can effectively improve the quality of image segmentation and has many advantages such as strong adaptability, reduced mis-segmentation, and a solid theoretical basis.

[0109] See the embodiment of the present invention. Figure 3 , the process of calculating the inter-class variance based on the target grayscale image to determine the target threshold includes:

[0110] Step S301: Obtain the number of all grayscale pixel points of the target grayscale image.

[0111] In the specific implementation of step S301, the number of all grayscale pixel points in the target grayscale image is counted.

[0112] Step S302: Calculate the grayscale mean of the target grayscale image according to the quantity.

[0113] In the specific implementation of step S302, the grayscale mean of the target grayscale image is calculated according to the number of all grayscale image pixels, which can be calculated specifically according to formula (3).

[0114]

[0115] Among them, α av is the grayscale mean; n is the total number of pixels in the grayscale image; α(i) is the grayscale value of the i-th pixel in the target grayscale image.

[0116] It can be understood that by calculating the grayscale mean of the target grayscale image, it can help to quickly evaluate the overall brightness distribution of the image, which is helpful for subsequently determining the appropriate target threshold.

[0117] Step S303: for each threshold in the image grayscale threshold range, calculate a first number of all grayscale image pixel numbers that are less than or equal to the threshold, and a second number of all grayscale image pixel numbers that are greater than the threshold.

[0118] It should be noted that, in the image grayscale threshold range, the grayscale value ranges from 0 to 255.

[0119] In the process of implementing step S303, the image grayscale threshold range is obtained, and for each threshold in the range (i.e., 0 to 255), the number of grayscale pixel points that are less than or equal to the threshold among all grayscale pixel points is counted, marked as a first number, and the number of grayscale pixel points that are greater than the threshold among all grayscale pixel points is counted, marked as a second number.

[0120] Step S304: Calculate the first between-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second between-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity.

[0121] In the specific implementation of step S304, the first between-class variance corresponding to the first quantity and the second between-class variance corresponding to the second quantity are calculated, specifically according to formula (4). The first category weight corresponding to the first quantity and the second category weight corresponding to the second quantity are calculated, specifically according to formula (5).

[0122] Formula (4) is as follows:

[0123]

[0124] Among them, t is any threshold in the grayscale threshold range of the image, α av1 is the first between-class variance corresponding to the first quantity; α av2 is the second between-class variance corresponding to the second quantity; α(i) is the grayscale value of the i-th pixel in the target grayscale image; N1 is the first quantity less than or equal to t; N2 is the second quantity greater than t.

[0125] Formula (5) is as follows:

[0126]

[0127] Among them, t is any threshold in the grayscale threshold range of the image, N1 is a first number less than or equal to t; N2 is a second number greater than t; N is the number of all grayscale image pixels; ω1(t) is the first category weight corresponding to the first number; ω2(t) is the second category weight corresponding to the second number.

[0128] Step S305: Calculate the inter-class variance corresponding to the threshold based on the first inter-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second inter-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity to obtain the inter-class variance corresponding to each threshold in the image grayscale threshold range.

[0129] In the specific implementation of step S305, the inter-class variance corresponding to the threshold is calculated based on the first inter-class variance, the first category weight, the second inter-class variance and the second category weight to obtain the inter-class variance corresponding to each threshold in the image grayscale threshold range.

[0130] Specifically, the calculation is performed according to formula (6).

[0131]

[0132] in, is the inter-class variance corresponding to the threshold t; α av is the grayscale mean; ω1(t) is the first category weight corresponding to the first quantity; ω2(t) is the second category weight corresponding to the second quantity; α av1 is the first between-class variance corresponding to the first quantity; α av2 is the second between-class variance corresponding to the second quantity.

[0133] Step S306: extract the largest inter-class variance from all inter-class variances, and mark the threshold corresponding to the largest inter-class variance as the target threshold.

[0134] In the specific implementation of step S306 , the inter-class variance with the largest value is extracted from all inter-class variances, and the threshold corresponding to the largest inter-class variance is marked as the target threshold.

[0135] As shown in formula (7).

[0136]

[0137] Where t′ is the target threshold.

[0138] Combination Figure 3 The results show that the maximum inter-class variance method (Otsu method) can effectively enhance the segmentation effect, improve the clarity and accuracy of image segmentation, and reduce the probability of misclassification. Furthermore, this method does not rely on prior knowledge of image content, can automatically find the optimal target threshold, and is applicable to images with various characteristics.

[0139] Step S104: converting the target grayscale image into a binary image according to the target threshold value to determine the steel foot area of ​​each insulator in the infrared image and the average temperature of each steel foot area, and determining the average temperature as the characteristic temperature of each insulator.

[0140] In the process of implementing step S104, the target grayscale image is first converted into a binary image according to the target threshold value, the steel foot area of ​​each insulator in the infrared image is determined based on the binary image, and then the average temperature corresponding to each steel foot area is determined based on the target grayscale image. Finally, the average temperature corresponding to each steel foot area is determined as the characteristic temperature of each insulator corresponding to each steel foot area.

[0141] It can be understood that binarization of the target grayscale image can clearly segment the steel foot area, reduce background interference, and improve the accuracy of subsequent temperature measurement. Clear area division makes it more efficient to calculate the average temperature of each steel foot area, saving time for manual intervention. In addition, the advantages of image processing and temperature measurement are combined to improve the efficiency and accuracy of detection.

[0142] See the embodiment of the present invention. Figure 4 , the process of determining the average temperature of the steel foot area and each steel foot area according to the target threshold, and determining the average temperature as the characteristic temperature of each insulator includes:

[0143] Step S401: converting a target grayscale image into a binary image according to a target threshold.

[0144] In the specific implementation of step S401 , the target grayscale image is converted based on the target threshold value to obtain a binary image.

[0145] Step S402: searching for the first n connected regions in the binary image from large to small in area, and marking the connected regions as the steel foot regions of each insulator in the infrared image.

[0146] It should be noted that, since the temperature rise in the steel leg area of ​​the insulator is relatively high, it often appears as a connected area in the binary image.

[0147] In the specific implementation process of step S402, all connected regions in the binary image are searched and the areas of these connected regions are calculated; all connected regions are sorted in order from large to small in area, and the first n (for example, 37) connected regions are extracted therefrom, and the extracted connected regions are marked as the steel foot regions of each insulator in the infrared image.

[0148] It can be understood that after the target grayscale image is binarized, the steel foot area can be clearly segmented, thereby improving the accuracy of subsequent temperature measurement.

[0149] Step S403: extracting the image grayscale value corresponding to each steel foot area according to the target grayscale image.

[0150] In the specific implementation of step S403, for each steel foot region, the image grayscale value corresponding to the steel foot region is extracted according to the target grayscale image.

[0151] It can be understood that each steel foot area corresponds to a plurality of image grayscale values.

[0152] Step S404: For each steel foot region, the average grayscale value of the steel foot region is calculated based on the area of ​​the steel foot region and the image grayscale value.

[0153] In the specific implementation of step S404, for each steel foot region, the sum of the image grayscale values ​​corresponding to the steel foot region is calculated, and the quotient of the area of ​​the steel foot region and the sum is calculated to obtain the average grayscale value of the steel foot region.

[0154] Step S405: determining the average temperature corresponding to each steel foot region based on the average gray value, and determining the average temperature as the characteristic temperature of each insulator.

[0155] It is understandable that the grayscale value is usually associated with the temperature of the object. Infrared imaging technology acquires images by detecting infrared radiation radiated by objects. Different objects radiate infrared radiation of different intensities at different temperatures, so their grayscale values ​​can reflect temperature changes.

[0156] In the process of specifically implementing step S405, the temperature corresponding to the average grayscale value is the average temperature corresponding to the steel foot area. Therefore, based on the average grayscale value of each steel foot area, the average temperature corresponding to each steel foot area is determined, and each steel foot area corresponds to each insulator. The average temperature corresponding to each steel foot area is determined as the characteristic temperature of each insulator.

[0157] Combination Figure 4 As shown in the figure, binarization of the target grayscale image can clearly segment the steel foot area and reduce background interference, thereby improving the accuracy of subsequent temperature measurement. Clear area division makes it more efficient to calculate the average temperature of each steel foot area. In addition, by comparing the average temperatures of different areas, steel feet with abnormal temperatures can be quickly identified, so that maintenance measures can be taken in time to ensure the safety of the equipment.

[0158] Step S105: constructing a gray-temperature relationship curve based on the maximum temperature in the target gray-scale image and the maximum gray in the binary image.

[0159] In the specific implementation of step S105, a gray-temperature relationship curve is constructed by linear interpolation based on the maximum temperature and minimum temperature of all temperatures in the target grayscale image and the maximum grayscale value and minimum grayscale value in the binary image, specifically according to formula (8).

[0160]

[0161] Where T(α) represents the temperature corresponding to the gray value α; α min is the minimum gray value in the image; α max is the maximum gray value in the image; T min is the minimum temperature in the target grayscale image; T max is the maximum temperature in the target grayscale image.

[0162] It should be noted that formula (8) uses linear interpolation to calculate the grayscale value range (from α min to α max ) and the corresponding temperature range (from T min to T max ) to calculate the temperature value corresponding to each gray value, and then establish an approximate linear relationship between gray and temperature based on the temperature values ​​corresponding to all gray values.

[0163] It is understandable that by establishing the relationship between grayscale value and temperature, quantitative measurement of temperature can be achieved, so that image processing is not limited to visual analysis, but also specific temperature data can be obtained, which improves detection accuracy and reduces errors. In addition, insulators of different materials or coatings may have different grayscale temperature characteristics. By establishing corresponding relationship curves, it can better adapt to diverse application scenarios. By establishing grayscale temperature relationship curves, deteriorated insulators can be discovered in time, enhancing the scientificity and effectiveness of detection and management.

[0164] Step S106: deriving an axial temperature data file of each insulator in the infrared image based on the characteristic temperature of each insulator and the gray-scale temperature relationship curve.

[0165] In the specific implementation of step S106, the axial temperature data file of each insulator in the infrared image is derived based on the characteristic temperature of each insulator and the gray-scale temperature relationship curve, for example: Infrare(t, Si, Xj, Lk, Pm, In).dat. Among them, t is the shooting time; S is the line file sequence number, i is the sequence number; X is the side where the insulator string is located, the power transmission side (small side) is 1, the load side (large side) is 2, j is the sequence number; L is the circuit sequence number, k is the sequence number; P is the phase sequence number, m is the sequence number; I is the string sequence number, n is the sequence number.

[0166] It should be noted that the insulator axial temperature data file derived from the infrared image and grayscale temperature relationship curve has significant advantages. First, it can provide accurate temperature assessment to help quickly identify deteriorated insulators. In addition, the formed data file is convenient for subsequent analysis, supports long-term monitoring and evaluation of insulator performance, and provides a scientific basis for equipment management and maintenance decisions. It helps to reduce the risk of major failures of insulators, and also provides intuitive information to maintenance personnel through visual display, further optimizing maintenance plans.

[0167] Step S107: Calculate the temperature difference between each insulator and its adjacent insulator in the insulator string to be inspected according to all axial temperature data files.

[0168] It is understood that the thermal characteristic of a deteriorated insulator is that the temperature difference between it and adjacent insulators is significantly greater than the temperature difference between intact insulators. In addition, the number of deteriorated insulators in a line is also much less than the number of intact insulators.

[0169] Therefore, based on all axial temperature data files, the temperature difference between each insulator in the insulator string to be tested and its adjacent insulator is calculated, as shown in formula (9).

[0170]

[0171] Wherein, p is the total number of insulators in the insulator string to be tested, i is the sequence number of each insulator in the insulator string to be tested, and i is 1 to p from the high voltage side to the ground side; T i is the characteristic temperature of the i-th insulator.

[0172] Step S108: determining a temperature difference threshold based on the temperature difference, and determining insulators with a temperature difference greater than the temperature difference threshold as degraded insulators in the insulator string to be detected.

[0173] In the specific implementation of step S108, firstly, the mean and standard deviation of the temperature difference corresponding to all axial temperature data files are calculated based on the temperature difference, and the temperature difference threshold is determined according to the mean and standard deviation of the temperature difference; then, the insulators whose temperature difference is greater than the temperature difference threshold are determined as degraded insulators in the insulator string to be detected. The specific process of calculating the mean and standard deviation of the temperature difference corresponding to all axial temperature data files is shown in formula (10) and formula (11).

[0174]

[0175] Among them, ΔTav is the mean temperature difference corresponding to all axial temperature data files; ΔTstd is the standard deviation corresponding to all axial temperature data files; G is the number of insulator strings to be tested; T i is the temperature of the i-th insulator of the insulator string to be tested; P g is the total number of insulators in the gth string.

[0176] It should be noted that the method of determining the temperature difference threshold according to the temperature difference mean and standard deviation corresponding to all axial temperature data files is shown in formula (12).

[0177] ΔT thresh =ΔT av +3·ΔT std (12)

[0178] That is to say, the sum of the temperature difference mean and three times the standard deviation corresponding to all axial temperature data files is used as the temperature difference threshold.

[0179] It is understandable that the degraded insulator can be determined according to the temperature difference threshold. For example, the temperature difference threshold t is 1.2°C, and the data file Infrare (2024.05.27.18.24.30, S 110 ,X2,L2,P3,I1).dat, the temperature difference of the 30th element is 2°C. That is to say, the 30th piece of the first string of the second phase c of the large side of the 110th file photographed at 2024.05.27.18:24:30 may be a deteriorated insulator.

[0180] Step S109: Estimate the contamination level of the degraded insulator according to the ultraviolet image corresponding to the degraded insulator and the ambient humidity, and generate and output a detection report of the degraded insulator.

[0181] It is understandable that the temperature rise of insulators with different degrees of contamination is different, especially in a wet state. The higher the degree of contamination, the more obvious the temperature rise. Therefore, it is very important to record the UV image and measure the ambient humidity while taking the infrared image. By analyzing the number of photons measured in the UV image and the ambient humidity data provided by the hygrometer, the degree of contamination of the deteriorated insulator can be roughly assessed, so that the insulator strings can be classified according to the degree of contamination, avoiding the difference in temperature rise of insulators with different degrees of contamination affecting the accuracy of the test results.

[0182] In the specific implementation of step S109, the number of photons in the ultraviolet image corresponding to the degraded insulator is obtained, the contamination level of the degraded insulator is determined by looking up the table based on the number of photons and the ambient humidity, and a detection report of the degraded insulator is generated and output.

[0183] It should be noted that, as shown in Table 1, the levels of environmental humidity are divided into: dry, low humidity, medium humidity and high humidity.

[0184] Table 1

[0185] Ambient humidity <0.7 0.7～0.8 0.8～0.9 0.9～1.0 Relative humidity dry Low humidity Medium wet High humidity

[0186] Table 2 shows the classification of the number of photons in ultraviolet images, where the number of photons is classified into five levels: Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅴ.

[0187] Table 2

[0188] Number of photons <100 100～1000 1000～10000 10000～100000 >100000 Photon Magnitude Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ

[0189] Table 3 shows the classification of pollution levels, where the pollution levels are classified into: clean, mild, moderate, severe and serious.

[0190] Table 3

[0191]

[0192]

[0193] It should be noted that when generating a detection report of a degraded insulator, the required information includes, but is not limited to, the contamination level of the degraded insulator, its relationship with the temperature difference threshold, and the location information of the degraded insulator.

[0194] In specific applications, the pollution levels of all insulators in the insulator string to be inspected can also be classified based on all UV images and ambient humidity. The temperature difference threshold is determined for all insulator strings with different pollution levels, and then the deteriorated insulators in each insulator string are detected based on the temperature difference threshold, which helps to avoid eliminating the temperature rise difference caused by pollution and improve the accuracy of detection.

[0195] In an embodiment of the present invention, the infrared image is preprocessed, including grayscale, noise reduction filtering, angle correction, etc.; in view of the characteristics of infrared images with large noise and low contrast, the median filter is applied to remove the high noise in the infrared image; the steel foot area is identified by image threshold segmentation; the average temperature of the steel foot area is used as the characteristic temperature of the insulator to construct a gray-scale temperature relationship curve; the temperature difference between adjacent insulators is used to determine whether the insulator is a degraded insulator, and a detection report of the degraded insulator is generated in combination with the pollution level, so as to notify the operation and maintenance personnel to perform maintenance. The present invention takes infrared images, ultraviolet images, and establishes a corresponding relationship between pollution levels, uses the maximum inter-class variance method to effectively enhance the segmentation effect, improve the accuracy of image segmentation, and determines the degraded insulator based on statistical analysis, which significantly improves the detection accuracy.

[0196] The above-mentioned embodiment of the present invention Figure 1 A live non-contact insulator degradation detection method is shown in FIG. Figure 5 , shows a structural block diagram of a live non-contact insulator degradation detection device shown in an embodiment of the present invention, the detection device includes a receiving unit 501, a preprocessing unit 502, a first calculation unit 503, a first determination unit 504, a construction unit 505, a derivation unit 506, a second calculation unit 507, a second determination unit 508 and an output unit 509.

[0197] The receiving unit 501 is used to receive a plurality of infrared images, a plurality of ultraviolet images and environmental humidity of the insulator string to be inspected sent by the inspection device.

[0198] The preprocessing unit 502 is used to preprocess each infrared image to obtain a target grayscale image.

[0199] The first calculation unit 503 is used to calculate the inter-class variance corresponding to each threshold within the image grayscale threshold range based on all grayscale image pixels of the target grayscale image, and mark the threshold corresponding to the largest inter-class variance among all inter-class variances as the target threshold.

[0200] The first determination unit 504 is used to convert the target grayscale image into a binary image according to the target threshold value to determine the steel foot area of ​​each insulator in the infrared image and the average temperature of each steel foot area, and determine the average temperature as the characteristic temperature of each insulator.

[0201] The construction unit 505 is used to construct a gray-temperature relationship curve based on the maximum temperature in the target gray-scale image and the maximum gray in the binary image.

[0202] The export unit 506 is used to export the axial temperature data file of each insulator in the infrared image based on the characteristic temperature of each insulator and the gray-scale temperature relationship curve.

[0203] The second calculation unit 507 is used to calculate the temperature difference between each insulator in the insulator string to be detected and its adjacent insulator according to all axial temperature data files.

[0204] The second determining unit 508 is configured to determine a temperature difference threshold value based on the temperature difference value, and determine an insulator having a temperature difference value greater than the temperature difference threshold value as a degraded insulator in the insulator string to be detected.

[0205] The second determining unit 508 is specifically configured to calculate the temperature difference mean and standard deviation corresponding to all axial temperature data files based on the temperature difference, and determine the temperature difference threshold according to the temperature difference mean and standard deviation.

[0206] The output unit 509 is used to estimate the contamination level of the degraded insulator according to the ultraviolet image corresponding to the degraded insulator and the ambient humidity, and generate and output a detection report of the degraded insulator.

[0207] In an embodiment of the present invention, the infrared image is preprocessed, including grayscale, noise reduction filtering, angle correction, etc.; in view of the characteristics of infrared images with large noise and low contrast, the median filter is applied to remove the high noise in the infrared image; the steel foot area is identified by image threshold segmentation; the average temperature of the steel foot area is used as the characteristic temperature of the insulator to construct a gray-scale temperature relationship curve; the temperature difference between adjacent insulators is used to determine whether the insulator is a degraded insulator, and a detection report of the degraded insulator is generated in combination with the pollution level, so as to notify the operation and maintenance personnel to perform maintenance. The present invention takes infrared images, ultraviolet images, and establishes a corresponding relationship between pollution levels, uses the maximum inter-class variance method to effectively enhance the segmentation effect, improve the accuracy of image segmentation, and determines the degraded insulator based on statistical analysis, which significantly improves the detection accuracy.

[0208] Combination Figure 5 The content shown, the pre-processing unit 502, includes: an acquisition module, a noise reduction module and a correction module.

[0209] The acquisition module is used to obtain the RGB value of each pixel of each infrared image and convert the infrared image into a grayscale image based on the RGB value.

[0210] The denoising module is used to perform median filtering on the grayscale image to reduce the noise and obtain a denoised grayscale image.

[0211] The correction module is used to perform tilt correction processing on the denoised grayscale image based on a preset edge detection operator and a Hough transform operator to obtain a target grayscale image.

[0212] Combination Figure 5 The content shown is that the first calculation unit 503 includes: a quantity acquisition module, a first calculation module, a second calculation module, a third calculation module, a fourth calculation module and a marking module.

[0213] The quantity acquisition module is used to obtain the quantity of all grayscale pixels of the target grayscale image.

[0214] The first calculation module is used to calculate the grayscale mean of the target grayscale image according to the quantity.

[0215] The second calculation module is used to calculate, for each threshold in the image grayscale threshold range, a first number of all grayscale image pixel numbers that are less than or equal to the threshold, and a second number of all grayscale image pixel numbers that are greater than the threshold.

[0216] The third calculation module is used to calculate the first between-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second between-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity.

[0217] The fourth calculation module is used to calculate the inter-class variance corresponding to the threshold based on the first between-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second between-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity, so as to obtain the inter-class variance corresponding to each threshold in the image grayscale threshold range.

[0218] The marking module is used to extract the largest inter-class variance from all inter-class variances and mark the threshold corresponding to the largest inter-class variance as the target threshold.

[0219] Combination Figure 5 As shown in the content, the first determination unit 504 includes: a conversion module, a search module, an extraction module, a fifth calculation module and a determination module.

[0220] The conversion module is used to convert the target grayscale image into a binary image according to a target threshold.

[0221] The search module is used to search for the first n connected regions with areas from large to small in the binary image, and mark the connected regions as the steel foot regions of each insulator in the infrared image.

[0222] The extraction module is used to extract the image grayscale value corresponding to each steel foot area according to the target grayscale image.

[0223] The fifth calculation module is used to calculate the average gray value of each steel foot region based on the area of ​​the steel foot region and the image gray value, and determine the average temperature as the characteristic temperature of each insulator.

[0224] The determination module is used to determine the average temperature corresponding to each steel foot area based on the average gray value.

[0225] In summary, the present invention provides a method and device for detecting degradation of live non-contact insulators. By taking infrared images and ultraviolet images and establishing corresponding relationships between pollution levels, the maximum between-class variance method is used to effectively enhance the segmentation effect and improve the accuracy of image segmentation. Degraded insulators are identified based on statistical analysis, thereby significantly improving detection accuracy.

[0226] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0227] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0228] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting deterioration of an energized non-contact insulator, characterized in that: The method comprises: receiving multiple infrared images, multiple ultraviolet images and environmental humidity of the insulator string to be inspected sent by the inspection equipment; For each of the infrared images, preprocessing the infrared image to obtain a target grayscale image; Based on all grayscale image pixels of the target grayscale image, calculate the inter-class variance corresponding to each threshold within the image grayscale threshold range, and mark the threshold corresponding to the largest inter-class variance among all inter-class variances as the target threshold; Convert the target grayscale image into a binary image according to the target threshold value, so as to determine the steel foot area of ​​each insulator in the infrared image and the average temperature of each steel foot area, and determine the average temperature as the characteristic temperature of each insulator; Constructing a gray-temperature relationship curve based on the maximum temperature in the target gray-scale image and the maximum gray in the binary image; Based on the characteristic temperature of each insulator and the gray-scale temperature relationship curve, derive an axial temperature data file of each insulator in the infrared image; Calculate the temperature difference between each insulator and its adjacent insulator in the insulator string to be tested according to all axial temperature data files; Determine a temperature difference threshold value based on the temperature difference value, and determine the insulator whose temperature difference value is greater than the temperature difference threshold value as a degraded insulator in the insulator string to be detected; The contamination level of the degraded insulator is estimated according to the ultraviolet image corresponding to the degraded insulator and the environmental humidity, and a detection report of the degraded insulator is generated and output.

2. The method according to claim 1, characterized in that The step of preprocessing each infrared image to obtain a target grayscale image includes: For each infrared image, obtaining the RGB value of each pixel of the infrared image, and converting the infrared image into a grayscale image based on the RGB value; Performing median filtering on the grayscale image to reduce noise, to obtain a grayscale image after noise reduction; The de-noised grayscale image is subjected to tilt correction processing based on a preset edge detection operator and a Hough transform operator to obtain a target grayscale image.

3. The method according to claim 1, characterized in that The step of calculating the inter-class variance corresponding to each threshold value within the image grayscale threshold range based on all grayscale image pixels of the target grayscale image, and marking the threshold value corresponding to the largest inter-class variance among all inter-class variances as the target threshold value, comprises: Obtain the number of all grayscale image pixels of the target grayscale image; Calculate the grayscale mean of the target grayscale image according to the quantity; For each threshold value in the grayscale threshold range of the image, calculate a first number of all the grayscale image pixel numbers that are less than or equal to the threshold value, and a second number of all the grayscale image pixel numbers that are greater than the threshold value; Calculating a first between-class variance corresponding to the first quantity, a first class weight corresponding to the first quantity, a second between-class variance corresponding to the second quantity, and a second class weight corresponding to the second quantity; Calculate the inter-class variance corresponding to the threshold based on the first inter-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second inter-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity, and obtain the inter-class variance corresponding to each threshold in the image grayscale threshold range; The largest inter-class variance is extracted from all inter-class variances, and the threshold corresponding to the largest inter-class variance is marked as the target threshold.

4. The method according to claim 1, characterized in that: The step of converting the target grayscale image into a binary image according to the target threshold value to determine the steel foot region of each insulator in the infrared image and the average temperature of each of the steel foot regions, and determining the average temperature as the characteristic temperature of each insulator, comprises: Converting the target grayscale image into a binary image according to the target threshold; Find the first n connected regions in the binary image from large to small in area, and mark the connected regions as the steel foot regions of each insulator in the infrared image; Extracting the image grayscale value corresponding to each of the steel foot regions according to the target grayscale image; For each of the steel foot regions, calculating an average grayscale value of the steel foot region based on the area of ​​the steel foot region and the image grayscale value; The average temperature corresponding to each of the steel leg regions is determined based on the average gray value, and the average temperature is determined as the characteristic temperature of each insulator.

5. The method according to claim 1, characterized in that The determining of the temperature difference threshold based on the temperature difference value comprises: The temperature difference mean and standard deviation corresponding to all axial temperature data files are calculated based on the temperature difference, and the temperature difference threshold is determined according to the temperature difference mean and the standard deviation.

6. A live non-contact insulator degradation detection device, characterized in that: The device comprises: A receiving unit, used for receiving a plurality of infrared images, a plurality of ultraviolet images and environmental humidity of the insulator string to be inspected sent by the inspection equipment; A preprocessing unit, used for preprocessing each infrared image to obtain a target grayscale image; A first calculation unit is used to calculate the inter-class variance corresponding to each threshold value within the image grayscale threshold range based on all grayscale image pixels of the target grayscale image, and mark the threshold value corresponding to the largest inter-class variance among all inter-class variances as the target threshold value; A first determining unit is used to convert the target grayscale image into a binary image according to the target threshold value, so as to determine the steel foot area of ​​each insulator in the infrared image and the average temperature of each of the steel foot areas, and determine the average temperature as the characteristic temperature of each insulator; A construction unit, configured to construct a gray-temperature relationship curve based on the maximum temperature in the target gray-scale image and the maximum gray in the binary image; An export unit, used for exporting an axial temperature data file of each insulator in the infrared image based on the characteristic temperature of each insulator and the gray-scale temperature relationship curve; A second calculation unit is used to calculate the temperature difference between each insulator in the insulator string to be detected and its adjacent insulator according to all axial temperature data files; A second determining unit is configured to determine a temperature difference threshold value based on the temperature difference value, and determine the insulator whose temperature difference value is greater than the temperature difference threshold value as a degraded insulator in the insulator string to be detected; The output unit is used to estimate the contamination level of the degraded insulator according to the ultraviolet image corresponding to the degraded insulator and the environmental humidity, and generate and output a detection report of the degraded insulator.

7. The device according to claim 6, characterized in that The pre-processing unit comprises: An acquisition module, used for acquiring, for each infrared image, an RGB value of each pixel of the infrared image, and converting the infrared image into a grayscale image based on the RGB value; A denoising module, used for performing median filtering on the grayscale image to reduce noise, so as to obtain a denoised grayscale image; The correction module is used to perform tilt correction processing on the denoised grayscale image based on a preset edge detection operator and a Hough transform operator to obtain a target grayscale image.

8. The device according to claim 6, characterized in that The first computing unit comprises: A quantity acquisition module is used to acquire the quantity of all grayscale image pixels of the target grayscale image; A first calculation module, used for calculating the grayscale mean of the target grayscale image according to the quantity; A second calculation module is used to calculate, for each threshold value in the grayscale threshold range of the image, a first number of all the numbers of grayscale image pixels that are less than or equal to the threshold value, and a second number of all the numbers of grayscale image pixels that are greater than the threshold value; A third calculation module, used for calculating a first between-class variance corresponding to the first quantity, a first category weight corresponding to the first quantity, a second between-class variance corresponding to the second quantity, and a second category weight corresponding to the second quantity; A fourth calculation module, configured to calculate the inter-class variance corresponding to the threshold based on the first inter-class variance corresponding to the first quantity, the first category weight corresponding to the first quantity, the second inter-class variance corresponding to the second quantity, and the second category weight corresponding to the second quantity, to obtain the inter-class variance corresponding to each threshold in the image grayscale threshold range; The marking module is used to extract the maximum inter-class variance from all inter-class variances, and mark the threshold corresponding to the maximum inter-class variance as the target threshold.

9. The device according to claim 6, characterized in that The first determining unit includes: A conversion module, used for converting the target grayscale image into a binary image according to the target threshold; A search module, used for searching the first n connected regions in the binary image from large to small in area, and marking the connected regions as the steel foot regions of each insulator in the infrared image; An extraction module, used for extracting the image grayscale value corresponding to each steel foot area according to the target grayscale image; A fifth calculation module, for calculating, for each of the steel foot regions, an average gray value of the steel foot region based on the area of ​​the steel foot region and the image gray value; A determination module is used to determine an average temperature corresponding to each of the steel foot regions based on the average gray value and determine the average temperature as a characteristic temperature of each insulator.

10. The device according to claim 6, characterized in that The second determination unit is specifically used to: calculate the temperature difference mean and standard deviation corresponding to all axial temperature data files based on the temperature difference, and determine the temperature difference threshold according to the temperature difference mean and the standard deviation.

Images