Deteriorated insulator diagnosis method and system and storage medium

Abstract

According to the deteriorated insulator diagnosis method and system and the storage medium provided by the invention, the to-be-detected insulator is subjected to double diagnosis through the electric field distribution curve and the infrared image, so that the identification accuracy of the deteriorated insulator is ensured; the degradation degree of the degraded insulator is judged and classified through the infrared image; the method comprises the following steps: collecting image and spectral data of a degraded insulator, and classifying the pollution degree of the degraded insulator according to a constructed pollution diagnosis model; and finally, through the constructed knowledge graph model, analyzing each marked degraded insulator to obtain a degradation reason and a corresponding solution of the degraded insulator. According to the method, the accuracy and efficiency of diagnosis of the degraded insulator are greatly improved, the cost and labor intensity of manual diagnosis are reduced, stable operation and power supply reliability of a power system are ensured, and the method has great significance in preventing power accidents and reducing economic losses.

Description

Technical Field

[0001] The invention belongs to the technical field of electric power engineering and relates to a diagnostic method, system, equipment and storage medium for an insulator. Background Art

[0002] Insulators are common devices on power transmission lines in power systems. They are installed between conductors of different potentials or between conductors and grounding components. They can withstand voltage and mechanical stress. Insulators are exposed to harsh environments for a long time. There are many types of insulators with different shapes. Although the structures and appearances of different types of insulators are quite different, they are all composed of two major parts: insulating parts and connecting fittings. Degraded insulators refer to insulators with reduced overall insulation performance. This is because the insulators are exposed to ultraviolet radiation, electrothermal stress, mechanical damage, chemical corrosion, environmental pollution, and temperature difference changes during long-term use, which leads to irreversible changes in the performance of the insulation material, reduced overall insulation performance, and even zero-value insulators, thereby increasing the risk of flashover and failure of transmission lines. For degraded insulators, traditional diagnostic methods rely on manual climbing or ground diagnosis, which is not only inefficient, but also has safety hazards. At the same time, it is impossible to achieve intuitive visualization of the degree of degradation of degraded insulators and the causes of degradation of degraded insulators. The diagnostic results are inaccurate, and it is difficult for power operation and maintenance personnel to use them as a basis for formulating maintenance plans. Summary of the invention

[0003] In order to solve the problems of low diagnostic efficiency of deteriorated insulators, inability to intuitively judge the degree of degradation and the cause of degradation, and low accuracy of diagnostic results described in the background technology, the present invention provides a diagnostic method, system and storage medium for deteriorated insulators.

[0004] The method of the present invention comprises:

[0005] Conduct live deterioration diagnosis on the insulators to be tested, and identify the deteriorated insulators in the insulator string based on the electric field distribution curve obtained by diagnosis;

[0006] Acquire an infrared image of the insulator to be tested, compare the infrared image with an infrared image of a standard insulator, and identify the deteriorated insulator;

[0007] When the deteriorated insulator identified based on the electric field distribution curve and the deteriorated insulator identified based on the infrared image are the same deteriorated insulator, the deteriorated insulator is marked;

[0008] comparing the infrared image of the marked deteriorated insulator with the infrared image of the standard insulator to determine the degree of deterioration of the marked deteriorated insulator;

[0009] The images and spectral data of insulators with different degrees of contamination are input into the convolutional neural network model for training to build a contamination diagnosis model;

[0010] Using the image and spectrum data of the marked deteriorated insulator, the contamination degree of the marked deteriorated insulator is determined according to the contamination diagnosis model;

[0011] A knowledge graph model is constructed based on the degree of degradation of degraded insulators, the degree of contamination of degraded insulators, the ledger data of degraded insulators, the regional environmental data of degraded insulators, all degradation causes of degraded insulators, and the solutions corresponding to each degradation cause;

[0012] Each marked deteriorated insulator is analyzed based on the knowledge graph model to obtain the degradation cause of the deteriorated insulator and the corresponding solution.

[0013] Furthermore, the method for diagnosing the deterioration of the insulator under test under live conditions is as follows: an electric field sensor is carried by a drone and flies to the outside of the edge of the shed of the insulator under test, and a high-potential to low-potential deterioration diagnosis is performed on the insulator under test under live conditions, and an electric field distribution curve is obtained, and the location of the deteriorated insulator is determined by the area where the electric field is distorted in the electric field distribution curve.

[0014] Furthermore, the method for acquiring the infrared image of the insulator to be tested is as follows: an infrared thermal imager is carried by a drone and photographs the insulator to be tested to obtain an infrared image, and the degraded insulator is identified based on the difference in surface temperature between the photographed infrared image and the infrared image of a normal insulator; the method for determining the degree of degradation of the marked degraded insulator is as follows: the temperature of the infrared image of the marked degraded insulator is compared with the temperature of the infrared image of the normal insulator, when the temperature of the marked degraded insulator is higher than the temperature of the normal insulator, the marked degraded insulator is a low-value insulator, and when the temperature of the marked degraded insulator is lower than the temperature of the normal insulator, the marked degraded insulator is a zero-value insulator.

[0015] Furthermore, the construction process of the contamination diagnosis model includes:

[0016] Collect insulators of different pollution levels on the transmission line, and collect images and spectral data of the insulators through a hyperspectral imager;

[0017] Perform image preprocessing on the image and spectral data, including denoising, black and white correction, and multivariate scattering correction, to obtain preprocessed data;

[0018] The pre-processed data were analyzed using segmented principal component analysis to extract pollution characteristics;

[0019] The contamination characteristics and insulator data of different contamination levels are combined to train and verify the convolutional neural network mathematical model to identify insulators of different contamination levels.

[0020] Furthermore, the calculation formula for the black and white correction is:

[0021]

[0022] In the formula, R corrected Represents the corrected reflectivity, R raw Represents the original reflectivity, R max Represents the maximum value of the pixel value, that is, the average reflectance of the all-black calibration hyperspectral image, R min It represents the minimum pixel value, that is, the average reflectance of the all-white calibrated hyperspectral image;

[0023] The specific steps of the multivariate scatter correction are as follows:

[0024] Assume that the average of all spectral data is an "ideal spectrum":

[0025]

[0026] In the formula, h i is the spectral vector of a single sample, n is the number of selected sample spectral lines, and m is the number of spectral bands; The average spectral vector obtained by averaging the spectral data of all samples at each band is also called the "ideal spectrum";

[0027] Then the spectral data of each group h i By performing a linear regression calculation with the ideal spectrum, the relative offset a of each spectrum can be obtained. i and the baseline shift b i :

[0028]

[0029] Finally, after obtaining the baseline shift and offset, the baseline shift is subtracted from each set of sample spectral data, and the relative baseline tilt of the spectral data is corrected by dividing it by the offset, so that the baseline shift and offset of each spectrum are corrected with reference to the standard spectrum.

[0030]

[0031] In the formula, h i(MSC) is the spectral vector of a single sample after multivariate scattering correction.

[0032] Furthermore, the method for determining the degree of contamination of the marked deteriorated insulator is as follows: a hyperspectral imager is carried by a drone to perform aerial scanning of the deteriorated insulator to collect images and spectral data;

[0033] The image and spectrum data are input into a contamination diagnosis model to diagnose the contamination degree of the marked deteriorated insulators, and the deteriorated insulators are classified into insulators with contamination and insulators without contamination according to the contamination degree.

[0034] Furthermore, the construction process of the knowledge graph model includes:

[0035] Collect the inventory data of deteriorated insulators, obtain the installation time, production date, and service life of the deteriorated insulators, and use named entity recognition technology to construct a deteriorated insulator map;

[0036] Obtain the regional environmental data of the deteriorated insulator, obtain the environmental climate and air humidity of the deteriorated insulator, and use named entity recognition technology to construct a regional environmental map;

[0037] Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, combined with the degraded insulator atlas, the time extraction algorithm is used to identify the time information of degraded insulators, obtain the service time and degradation trend of degraded insulators, and construct a time series atlas;

[0038] Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, a degradation cause map is constructed by combining the degraded insulator map, the regional environment map, the time series map and all degradation causes;

[0039] Build a solution map based on the solutions corresponding to each degradation cause;

[0040] The knowledge graph is obtained by performing graph fusion, graph completion and graph reasoning on the degraded insulator graph, regional environment graph, degradation cause graph and solution graph based on the event extraction algorithm and TranSE algorithm.

[0041] The present invention also proposes a diagnostic system for degraded insulators, including a degraded insulator electric field identification module, a degraded insulator infrared identification module, a degraded insulator marking module, a degradation degree determination module, a contamination diagnosis model construction module, a contamination degree determination module, a knowledge graph model construction module, and a degraded insulator analysis module.

[0042] The degraded insulator electric field identification module is used to perform live degradation diagnosis on the insulator to be tested, and identify the degraded insulators in the insulator string according to the electric field distribution curve obtained by diagnosis.

[0043] The degraded insulator infrared recognition module is used to obtain an infrared image of the insulator to be tested, compare the infrared image with the infrared image of a standard insulator, and identify the degraded insulator.

[0044] The degraded insulator marking module is used to mark the degraded insulator when the degraded insulator identified based on the electric field distribution curve and the degraded insulator identified based on the infrared image are the same degraded insulator.

[0045] The degradation degree determination module is used to compare the infrared image of the marked degraded insulator with the infrared image of the standard insulator to determine the degradation degree of the marked degraded insulator.

[0046] The pollution diagnosis model building module is used to input images and spectral data of insulators with different pollution levels into a convolutional neural network model for training to build a pollution diagnosis model.

[0047] The contamination degree determination module is used to determine the contamination degree of the marked deteriorated insulator according to the contamination diagnosis model by using the image and spectrum data of the marked deteriorated insulator.

[0048] The knowledge graph model construction module is used to construct a knowledge graph model based on the degradation degree of the degraded insulator, the contamination degree of the degraded insulator, the ledger data of the degraded insulator, the regional environmental data of the degraded insulator, all degradation causes of the degraded insulator and the solutions corresponding to each degradation cause.

[0049] The degraded insulator analysis module is used to analyze each marked degraded insulator according to the knowledge graph model to obtain the degradation cause of the degraded insulator and the corresponding solution.

[0050] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for diagnosing a deteriorated insulator as described above is implemented.

[0051] Compared with the prior art, the present invention ensures the accuracy of identifying degraded insulators by performing dual diagnosis on the insulators to be tested through electric field distribution curves and infrared images; and judges and classifies the degree of deterioration of degraded insulators through infrared images; classifies the degree of contamination of degraded insulators according to the constructed contamination diagnosis model by collecting images and spectral data of degraded insulators; and finally analyzes each marked degraded insulator through the constructed knowledge graph model to obtain the deterioration cause and corresponding solution of the degraded insulator. The present invention can quickly cover a wide range of insulator strings on transmission lines, and realizes the rapid and timely discovery and positioning of degraded insulators during the operation and inspection of transmission lines through the combination of electric field, infrared image, image and spectral data, and can intuitively judge the degree of deterioration and the cause of deterioration of degraded insulators, greatly improving the accuracy and efficiency of degraded insulator diagnosis, reducing the cost and labor intensity of manual diagnosis, ensuring the stable operation and power supply reliability of the power system, and having great significance for preventing power accidents and reducing economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The present invention is a flow chart of the method.

[0053] Figure 2 It is a system architecture diagram of the present invention.

[0054] Figure 3 This is an example diagram of the knowledge graph model of the present invention. DETAILED DESCRIPTION

[0055] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with 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.

[0056] Example 1

[0057] A method for diagnosing deteriorated insulators, the flow chart is as follows Figure 1 As shown, the details are as follows.

[0058] Firstly, the insulator to be tested is diagnosed for deterioration under power by using an electric field sensor, and the deteriorated insulators in the insulator string are identified according to the electric field distribution curve obtained by the diagnosis.

[0059] Specifically, the electric field sensor can be an electro-optical sensor based on the Pockels effect; the electric field sensor is carried by a drone and flies to the outside of the edge of the shed of the insulator to be tested, and performs a live degradation diagnosis from high potential to low potential on the insulator to be tested, obtains the electric field distribution curve, and determines the location of the degraded insulator through the area where the electric field is distorted in the electric field distribution curve.

[0060] Then, the insulator to be tested is photographed by an infrared thermal imager to obtain an infrared image, and the photographed infrared image is compared with the infrared image of a normal insulator to identify the deteriorated insulator.

[0061] Specifically, the infrared thermal imager is carried by a drone to take an infrared image of the insulator to be tested, and the deteriorated insulator is identified based on the difference in surface temperature between the infrared image taken and the infrared image of a normal insulator.

[0062] Then, the degraded insulators identified by the electric field sensor are compared and verified with the degraded insulators identified by the infrared thermal imager. If the comparison and verification are inconsistent, the insulator string to be tested is re-identified by the electric field sensor and the infrared thermal imager. If the comparison and verification are consistent, the degraded insulator is marked.

[0063] The insulators to be tested are double diagnosed by electric field sensors and infrared thermal imagers to ensure the accuracy of identification of deteriorated insulators.

[0064] Next, the infrared image of the marked deteriorated insulator is compared with the infrared image of the normal insulator to judge the degree of deterioration of the deteriorated insulator, and the deteriorated insulator is classified into low-value insulators and zero-value insulators according to the degree of deterioration.

[0065] Specifically, the temperature of the infrared image of the marked deteriorated insulator is compared with that of the infrared image of the normal insulator. When the temperature of the marked deteriorated insulator is higher than the temperature of the normal insulator, the marked deteriorated insulator is a low-value insulator. When the temperature of the marked deteriorated insulator is lower than the temperature of the normal insulator, the marked deteriorated insulator is a zero-value insulator.

[0066] Next, the images and spectral data of insulators with different degrees of contamination are collected through a hyperspectral imager and input into the convolutional neural network mathematical model for training and verification, insulators with different degrees of contamination are identified, and a contamination diagnosis model is constructed.

[0067] Specifically, the construction process of the pollution diagnosis model includes:

[0068] Collect insulators of different pollution levels on the transmission line, and collect images and spectral data of the insulators through a hyperspectral imager;

[0069] Perform image preprocessing on the image and spectral data, including denoising, black and white correction, and multivariate scattering correction, to obtain preprocessed data;

[0070] The calculation formula for black and white correction is:

[0071]

[0072] In the formula, Rcorrected Represents the corrected reflectivity, R raw Represents the original reflectivity, R max Represents the maximum value of the pixel value, that is, the average reflectance of the all-black calibration hyperspectral image, R min It represents the minimum pixel value, that is, the average reflectance of the all-white calibrated hyperspectral image;

[0073] The specific steps of multivariate scatter correction are as follows:

[0074] Assume that the average of all spectral data is an "ideal spectrum":

[0075]

[0076] In the formula, h i is the spectral vector of a single sample, n is the number of selected sample spectral lines, and m is the number of spectral bands; The average spectral vector obtained by averaging the spectral data of all samples at each band is also called the "ideal spectrum";

[0077] Then the spectral data of each group h i By performing a linear regression calculation with the ideal spectrum, the relative offset a of each spectrum can be obtained. i and the baseline shift b i :

[0078]

[0079] Finally, after obtaining the baseline shift and offset, the baseline shift is subtracted from each set of sample spectral data, and the relative baseline tilt of the spectral data is corrected by dividing it by the offset, so that the baseline shift and offset of each spectrum are corrected with reference to the standard spectrum.

[0080]

[0081] In the formula, h i(MSC) is the spectral vector of a single sample after multivariate scattering correction;

[0082] The pre-processed data were analyzed using segmented principal component analysis to extract pollution characteristics;

[0083] The contamination characteristics and insulator data of different contamination levels are combined to train and verify the convolutional neural network mathematical model to identify insulators of different contamination levels.

[0084] Next, the images and spectral data of the degraded insulators are collected by a hyperspectral imager, and the contamination degree of the degraded insulators is diagnosed according to the contamination diagnosis model. The degraded insulators are classified into insulators with contamination and insulators without contamination according to the contamination degree.

[0085] Specifically, the hyperspectral imager is carried by a drone to perform aerial scanning of degraded insulators to collect images and spectral data.

[0086] Next, based on the degree of degradation of the degraded insulators, the degree of contamination of the degraded insulators, the inventory data of the degraded insulators, the regional environmental data of the degraded insulators, all degradation causes of the degraded insulators and the solutions corresponding to each degradation cause, the degradation causes of the degraded insulators are identified, and the corresponding solutions are output to construct a knowledge graph model.

[0087] Specifically, the construction process of the knowledge graph model includes:

[0088] Collect the inventory data of deteriorated insulators, obtain the installation time, production date, and service life of the deteriorated insulators, and use named entity recognition technology to construct a deteriorated insulator map;

[0089] Obtain the regional environmental data of the deteriorated insulator, obtain the environmental climate and air humidity of the deteriorated insulator, and use named entity recognition technology to construct a regional environmental map;

[0090] Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, combined with the degraded insulator atlas, the time extraction algorithm is used to identify the time information of degraded insulators, obtain the service time and degradation trend of degraded insulators, and construct a time series atlas;

[0091] Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, a degradation cause map is constructed by combining the degraded insulator map, the regional environment map, the time series map and all degradation causes;

[0092] Build a solution map based on the solutions corresponding to each degradation cause;

[0093] The knowledge graph is obtained by performing graph fusion, graph completion and graph reasoning on the degraded insulator graph, regional environment graph, degradation cause graph and solution graph based on the event extraction algorithm and TranSE algorithm.

[0094] An example of a knowledge graph model is shown below: Figure 3 shown.

[0095] Finally, each marked deteriorated insulator is analyzed based on the knowledge graph model to obtain the degradation cause of the deteriorated insulator and the corresponding solution.

[0096] Example 2

[0097] A diagnostic system for deteriorated insulators, the structure of which is shown in the figure Figure 2As shown, it consists of a degraded insulator electric field recognition module, a degraded insulator infrared recognition module, a degraded insulator marking module, a degraded degree determination module, a contamination diagnosis model construction module, a contamination degree determination module, a knowledge graph model construction module, and a degraded insulator analysis module.

[0098] The degraded insulator electric field identification module uses electric field sensors to perform live degradation diagnosis on the insulator to be tested, and identifies the degraded insulators in the insulator string based on the electric field distribution curve obtained by diagnosis.

[0099] Specifically, the electric field sensor can be an electro-optical sensor based on the Pockels effect; the electric field sensor is carried by a drone and flies to the outside of the edge of the shed of the insulator to be tested, and performs a live degradation diagnosis from high potential to low potential on the insulator to be tested, obtains the electric field distribution curve, and determines the location of the degraded insulator through the area where the electric field is distorted in the electric field distribution curve.

[0100] The infrared recognition module for deteriorated insulators uses an infrared thermal imager to photograph the insulator to be tested to obtain an infrared image, and compares the photographed infrared image with the infrared image of a normal insulator to identify the deteriorated insulator.

[0101] Specifically, the infrared thermal imager is carried by a drone to take an infrared image of the insulator to be tested, and the deteriorated insulator is identified based on the difference in surface temperature between the infrared image taken and the infrared image of a normal insulator.

[0102] The degraded insulator marking module compares and verifies the degraded insulators identified by the electric field sensor with the degraded insulators identified by the infrared thermal imager. If the comparison and verification are inconsistent, the insulator string to be tested is re-identified by the electric field sensor and the infrared thermal imager. If the comparison and verification are consistent, the degraded insulator is marked.

[0103] The insulators to be tested are double diagnosed by electric field sensors and infrared thermal imagers to ensure the accuracy of identification of deteriorated insulators.

[0104] The degradation degree determination module compares the infrared image of the marked degraded insulator with the infrared image of the normal insulator, judges the degradation degree of the degraded insulator, and classifies the degraded insulator into low-value insulators and zero-value insulators according to the degradation degree.

[0105] Specifically, the temperature of the infrared image of the marked deteriorated insulator is compared with that of the infrared image of the normal insulator. When the temperature of the marked deteriorated insulator is higher than the temperature of the normal insulator, the marked deteriorated insulator is a low-value insulator. When the temperature of the marked deteriorated insulator is lower than the temperature of the normal insulator, the marked deteriorated insulator is a zero-value insulator.

[0106] The pollution diagnosis model construction module collects images and spectral data of insulators with different pollution levels through a hyperspectral imager, and inputs them into the convolutional neural network mathematical model for training and verification, so as to identify insulators with different pollution levels and build a pollution diagnosis model.

[0107] Specifically, the construction process of the pollution diagnosis model includes:

[0108] Collect insulators of different pollution levels on the transmission line, and collect images and spectral data of the insulators through a hyperspectral imager;

[0109] Perform image preprocessing on the image and spectral data, including denoising, black and white correction, and multivariate scattering correction, to obtain preprocessed data;

[0110] The calculation formula for black and white correction is:

[0111]

[0112] In the formula, R corrected Represents the corrected reflectivity, R raw Represents the original reflectivity, R max Represents the maximum value of the pixel value, that is, the average reflectance of the all-black calibration hyperspectral image, R min It represents the minimum pixel value, that is, the average reflectance of the all-white calibrated hyperspectral image;

[0113] The specific steps of multivariate scatter correction are as follows:

[0114] Assume that the average of all spectral data is an "ideal spectrum":

[0115]

[0116] In the formula, h i is the spectral vector of a single sample, n is the number of selected sample spectral lines, and m is the number of spectral bands; The average spectral vector obtained by averaging the spectral data of all samples at each band is also called the "ideal spectrum";

[0117] Then the spectral data of each group h i By performing a linear regression calculation with the ideal spectrum, the relative offset a of each spectrum can be obtained. i and the baseline shift b i :

[0118]

[0119] Finally, after obtaining the baseline shift and offset, the baseline shift is subtracted from each set of sample spectral data, and the relative baseline tilt of the spectral data is corrected by dividing it by the offset, so that the baseline shift and offset of each spectrum are corrected with reference to the standard spectrum.

[0120]

[0121] In the formula, h i(MSC) is the spectral vector of a single sample after multivariate scattering correction;

[0122] The pre-processed data were analyzed using segmented principal component analysis to extract pollution characteristics;

[0123] The contamination characteristics and insulator data of different contamination levels are combined to train and verify the convolutional neural network mathematical model to identify insulators of different contamination levels.

[0124] The contamination degree determination module collects images and spectral data of degraded insulators through a hyperspectral imager, diagnoses the contamination degree of the degraded insulators based on the contamination diagnosis model, and classifies the degraded insulators into insulators with contamination and insulators without contamination according to the contamination degree.

[0125] Specifically, the hyperspectral imager is carried by a drone to perform aerial scanning of degraded insulators to collect images and spectral data.

[0126] The knowledge graph model construction module identifies the degradation causes of degraded insulators based on the degradation degree of degraded insulators, the degree of contamination of degraded insulators, the ledger data of degraded insulators, the regional environmental data of degraded insulators, all degradation causes of degraded insulators and the solutions corresponding to each degradation cause, and outputs the corresponding solutions to build a knowledge graph model.

[0127] Specifically, the construction process of the knowledge graph model includes:

[0128] Collect the inventory data of deteriorated insulators, obtain the installation time, production date, and service life of the deteriorated insulators, and use named entity recognition technology to construct a deteriorated insulator map;

[0129] Obtain the regional environmental data of the deteriorated insulator, obtain the environmental climate and air humidity of the deteriorated insulator, and use named entity recognition technology to construct a regional environmental map;

[0130] Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, combined with the degraded insulator atlas, the time extraction algorithm is used to identify the time information of degraded insulators, obtain the service time and degradation trend of degraded insulators, and construct a time series atlas;

[0131] Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, a degradation cause map is constructed by combining the degraded insulator map, the regional environment map, the time series map and all degradation causes;

[0132] Build a solution map based on the solutions corresponding to each degradation cause;

[0133] The knowledge graph is obtained by performing graph fusion, graph completion and graph reasoning on the degraded insulator graph, regional environment graph, degradation cause graph and solution graph based on the event extraction algorithm and TranSE algorithm.

[0134] An example of a knowledge graph model is shown below: Figure 3 shown.

[0135] The degraded insulator analysis module analyzes each marked degraded insulator based on the knowledge graph model to obtain the degradation cause of the degraded insulator and the corresponding solution.

[0136] Example 3

[0137] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for diagnosing a degraded insulator as described in Example 1 and a system for diagnosing a degraded insulator as described in Example 2.

[0138] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiment, complete software embodiment, or the embodiment in combination with software and hardware. Moreover, the application can adopt the form of the computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java, C++, Python and literal scripting language JavaScript, etc.

[0139] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0140] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0142] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0143] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for diagnosing a deteriorated insulator, characterized in that: include: Conduct live deterioration diagnosis on the insulators to be tested, and identify the deteriorated insulators in the insulator string based on the electric field distribution curve obtained by diagnosis; Acquire an infrared image of the insulator to be tested, compare the infrared image with an infrared image of a standard insulator, and identify the deteriorated insulator; When the deteriorated insulator identified based on the electric field distribution curve and the deteriorated insulator identified based on the infrared image are the same deteriorated insulator, the deteriorated insulator is marked; comparing the infrared image of the marked deteriorated insulator with the infrared image of the standard insulator to determine the degree of deterioration of the marked deteriorated insulator; The images and spectral data of insulators with different degrees of contamination are input into the convolutional neural network model for training to build a contamination diagnosis model; Using the image and spectrum data of the marked deteriorated insulator, the contamination degree of the marked deteriorated insulator is determined according to the contamination diagnosis model; A knowledge graph model is constructed based on the degree of degradation of degraded insulators, the degree of contamination of degraded insulators, the ledger data of degraded insulators, the regional environmental data of degraded insulators, all degradation causes of degraded insulators, and the solutions corresponding to each degradation cause; Each marked degraded insulator is analyzed based on the knowledge graph model to obtain the degradation cause and corresponding solution of each marked degraded insulator.

2. A method for diagnosing deteriorated insulators according to claim 1, characterized in that: The method for diagnosing the deterioration of the insulator under test on an electric condition is as follows: an electric field sensor is carried by a drone and flies to the outside of the edge of the shed of the insulator under test, and a high-potential to low-potential deterioration diagnosis is performed on the insulator under test on an electric condition, an electric field distribution curve is obtained, and the position of the deteriorated insulator is determined by the area where the electric field is distorted in the electric field distribution curve.

3. A method for diagnosing deteriorated insulators according to claim 2, characterized in that: The method for obtaining the infrared image of the insulator to be tested is as follows: an infrared thermal imager is carried by a drone and photographs the insulator to be tested to obtain an infrared image, and a deteriorated insulator is identified based on the difference in surface temperature between the infrared image obtained and the infrared image of a normal insulator; The method for determining the degree of degradation of the marked degraded insulator is as follows: comparing the temperature of the infrared image of the marked degraded insulator with that of the infrared image of the normal insulator; when the temperature of the marked degraded insulator is higher than the temperature of the normal insulator, the marked degraded insulator is a low-value insulator; and when the temperature of the marked degraded insulator is lower than the temperature of the normal insulator, the marked degraded insulator is a zero-value insulator.

4. A method for diagnosing deteriorated insulators according to claim 3, characterized in that: The construction process of the pollution diagnosis model includes: Collect insulators of different pollution levels on the transmission line, and collect images and spectral data of the insulators through a hyperspectral imager; Perform image preprocessing on the image and spectral data, including denoising, black and white correction, and multivariate scattering correction, to obtain preprocessed data; The pre-processed data were analyzed using segmented principal component analysis to extract pollution characteristics; The contamination characteristics and insulator data of different contamination levels are combined to train and verify the convolutional neural network mathematical model, and insulators of different contamination levels are identified; The calculation formula for the black and white correction is: In the formula, R corrected represents the corrected reflectivity, R raw Represents the original reflectivity, R max Represents the maximum value of the pixel value, that is, the average reflectance of the all-black calibration hyperspectral image, R min It represents the minimum pixel value, that is, the average reflectance of the all-white calibrated hyperspectral image; The specific steps of the multivariate scatter correction are as follows: Assume that the average value of all spectral data is an "ideal spectrum": In the formula, h i is the spectral vector of a single sample, n is the number of selected sample spectral lines, and m is the number of spectral bands; The average spectral vector obtained by averaging the spectral data of all samples at each band, which is also the "ideal spectrum"; Then the spectral data of each group h i By performing a linear regression calculation with the ideal spectrum, the relative offset a of each spectrum can be obtained. i and the baseline shift b i : Finally, after obtaining the baseline shift and offset, the baseline shift is subtracted from each set of sample spectral data, and the relative baseline tilt of the spectral data is corrected by dividing it by the offset, so that the baseline shift and offset of each spectrum are corrected with reference to the standard spectrum. In the formula, h i(MSC) is the spectral vector of a single sample after multivariate scattering correction.

5. A method for diagnosing deteriorated insulators according to claim 4, characterized in that: The method for determining the degree of contamination of the marked deteriorated insulator is as follows: a hyperspectral imager is carried by an unmanned aerial vehicle to perform an aerial scan of the deteriorated insulator to collect images and spectral data; The image and spectrum data are input into a contamination diagnosis model to diagnose the contamination degree of the marked deteriorated insulators, and the deteriorated insulators are classified into insulators with contamination and insulators without contamination according to the contamination degree.

6. A method for diagnosing deteriorated insulators according to claim 5, characterized in that: The construction process of the knowledge graph model includes: Collect the inventory data of deteriorated insulators, obtain the installation time, production date, and service life of the deteriorated insulators, and use named entity recognition technology to construct a deteriorated insulator map; Obtain the regional environmental data of the deteriorated insulator, obtain the environmental climate and air humidity of the deteriorated insulator, and use named entity recognition technology to construct a regional environmental map; Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, combined with the degraded insulator atlas, the time extraction algorithm is used to identify the time information of degraded insulators, obtain the service time and degradation trend of degraded insulators, and construct a time series atlas; Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, a degradation cause map is constructed by combining the degraded insulator map, the regional environment map, the time series map and all degradation causes; Build a solution map based on the solutions corresponding to each degradation cause; The knowledge graph is obtained by performing graph fusion, graph completion and graph reasoning on the degraded insulator graph, regional environment graph, degradation cause graph and solution graph based on the event extraction algorithm and TranSE algorithm.

7. A diagnostic system for deteriorated insulators, characterized in that: It includes a degraded insulator electric field identification module, a degraded insulator infrared identification module, a degraded insulator marking module, a degraded degree determination module, a contamination diagnosis model construction module, a contamination degree determination module, a knowledge graph model construction module, and a degraded insulator analysis module; The degraded insulator electric field identification module is used to perform live degradation diagnosis on the insulator to be tested, and identify the degraded insulators in the insulator string according to the electric field distribution curve obtained by diagnosis; The degraded insulator infrared recognition module is used to obtain an infrared image of the insulator to be tested, compare the infrared image with the infrared image of a standard insulator, and identify the degraded insulator; The degraded insulator marking module is used to mark the degraded insulator when the degraded insulator identified based on the electric field distribution curve and the degraded insulator identified based on the infrared image are the same degraded insulator; The degradation degree determination module is used to compare the infrared image of the marked degraded insulator with the infrared image of the standard insulator to determine the degradation degree of the marked degraded insulator; The pollution diagnosis model building module is used to input the images and spectral data of insulators with different pollution levels into the convolutional neural network model for training to build a pollution diagnosis model; The contamination degree determination module is used to determine the contamination degree of the marked deteriorated insulator according to the contamination diagnosis model using the image and spectral data of the marked deteriorated insulator; The knowledge graph model building module is used to build a knowledge graph model based on the degree of degradation of the degraded insulator, the degree of contamination of the degraded insulator, the ledger data of the degraded insulator, the regional environmental data of the degraded insulator, all degradation causes of the degraded insulator and the solutions corresponding to each degradation cause; The degraded insulator analysis module is used to analyze each marked degraded insulator according to the knowledge graph model to obtain the degradation cause of the degraded insulator and the corresponding solution.

8. A diagnostic system for deteriorated insulators according to claim 7, characterized in that: In the degraded insulator electric field identification module, the electric field sensor is carried by a drone and flies to the outside of the edge of the shed of the insulator to be tested, and a live degradation diagnosis from high potential to low potential is performed on the insulator to be tested, and an electric field distribution curve is obtained. The location of the degraded insulator is determined by the area where the electric field is distorted in the electric field distribution curve.

9. A diagnostic system for deteriorated insulators according to claim 8, characterized in that: In the degraded insulator infrared recognition module, the infrared thermal imager is carried by a drone and photographs the insulator to be tested to obtain an infrared image, and the degraded insulator is identified based on the difference in surface temperature between the infrared image obtained and the infrared image of a normal insulator; In the degradation degree confirmation module, the temperature of the infrared image of the marked degraded insulator is compared with the temperature of the infrared image of the normal insulator. When the temperature of the marked degraded insulator is higher than the temperature of the normal insulator, the marked degraded insulator is a low-value insulator. When the temperature of the marked degraded insulator is lower than the temperature of the normal insulator, the marked degraded insulator is a zero-value insulator.

10. A diagnostic system for deteriorated insulators according to claim 9, characterized in that: In the contamination diagnosis model construction module, the construction process of the contamination diagnosis model includes: Collect insulators of different pollution levels on the transmission line, and collect images and spectral data of the insulators through a hyperspectral imager; Perform image preprocessing on the image and spectral data, including denoising, black and white correction, and multivariate scattering correction, to obtain preprocessed data; The pre-processed data were analyzed using segmented principal component analysis to extract pollution characteristics; The contamination characteristics and insulator data of different contamination levels are combined to train and verify the convolutional neural network mathematical model, and insulators of different contamination levels are identified; The calculation formula for the black and white correction is: In the formula, R corrected Represents the corrected reflectivity, R raw Represents the original reflectivity, R max Represents the maximum value of the pixel value, that is, the average reflectance of the all-black calibration hyperspectral image, R min It represents the minimum pixel value, that is, the average reflectance of the all-white calibrated hyperspectral image; The specific steps of the multivariate scatter correction are as follows: Assume that the average value of all spectral data is an "ideal spectrum": In the formula, h i is the spectral vector of a single sample, n is the number of selected sample spectral lines, and m is the number of spectral bands; The average spectral vector obtained by averaging the spectral data of all samples at each band, which is also the "ideal spectrum"; Then the spectral data of each group h i By performing a linear regression calculation with the ideal spectrum, the relative offset a of each spectrum can be obtained. i and the baseline shift b i : Finally, after obtaining the baseline shift and offset, the baseline shift is subtracted from each set of sample spectral data, and the relative baseline tilt of the spectral data is corrected by dividing it by the offset, so that the baseline shift and offset of each spectrum are corrected with reference to the standard spectrum. In the formula, h i(MSC) is the spectral vector of a single sample after multivariate scattering correction.

11. A diagnostic system for deteriorated insulators according to claim 10, characterized in that: In the pollution degree determination module, a hyperspectral imager is carried by a drone to perform aerial scanning of deteriorated insulators to collect images and spectral data; The image and spectrum data are input into a contamination diagnosis model to diagnose the contamination degree of the marked deteriorated insulators, and the deteriorated insulators are classified into insulators with contamination and insulators without contamination according to the contamination degree.

12. A diagnostic system for deteriorated insulators according to claim 11, characterized in that: In the knowledge graph model construction module, the ledger data of deteriorated insulators are collected to obtain the installation time, production date, and service life of the deteriorated insulators, and the deteriorated insulator graph is constructed using named entity recognition technology; Obtain the regional environmental data of the deteriorated insulator, obtain the environmental climate and air humidity of the deteriorated insulator, and use named entity recognition technology to construct a regional environmental map; Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, combined with the degraded insulator atlas, the time extraction algorithm is used to identify the time information of degraded insulators, obtain the service time and degradation trend of degraded insulators, and construct a time series atlas; Based on the degradation degree of degraded insulators and the degree of contamination of degraded insulators, a degradation cause map is constructed by combining the degraded insulator map, the regional environment map, the time series map and all degradation causes; Build a solution map based on the solutions corresponding to each degradation cause; The knowledge graph is obtained by performing graph fusion, graph completion and graph reasoning on the degraded insulator graph, regional environment graph, degradation cause graph and solution graph based on the event extraction algorithm and TranSE algorithm.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for diagnosing a deteriorated insulator according to any one of claims 1 to 6 is implemented.

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