A multi-source data monitoring and processing method and system for GIS equipment

Abstract

The present invention discloses a multi-source data monitoring and processing method and system for GIS equipment. The method includes real-time acquisition of environmental impact data corresponding to the insulation characteristics of a target GIS device, as well as density and pressure data of the internal gas of the target GIS device during operation; inputting the environmental impact data, density data, and pressure data into a pre-built insulation monitoring model to obtain an insulation prediction result for the target GIS device, and generating a corresponding fault processing signal based on the insulation prediction result; in response to the fault processing signal, executing a contact fault monitoring mode and a high-temperature fault monitoring mode; in the contact fault monitoring mode, obtaining a contact fault result for the target GIS device; in the high-temperature fault monitoring mode, determining a high-temperature region fault result for the target GIS device; and executing a corresponding GIS device fault processing strategy based on the obtained contact fault result and / or high-temperature region fault result. The method of the present invention improves the accuracy of GIS device fault identification.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a multi-source data monitoring and processing method and system for GIS equipment. Background Art

[0002] As power systems continue to expand, gas-insulated switchgear (GIS) has become a core component of modern substations due to its compact structure and high reliability. However, GIS equipment operates in complex environments such as high voltage and strong electromagnetic fields over long periods of time, making it susceptible to multiple faults such as mechanical wear, insulation degradation, and partial discharge.

[0003] Traditional monitoring methods only analyze the density data of detected GIS equipment to determine whether a fault has occurred. This monitoring method is difficult to accurately determine the fault location of the GIS equipment and still requires maintenance personnel to perform further analysis, resulting in poor overall monitoring results and greatly reduced maintenance efficiency.

[0004] Therefore, how to accurately monitor the fault location of GIS equipment to improve maintenance efficiency has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] The present invention provides a multi-source data monitoring and processing method and system for GIS equipment to solve the technical problem of how to accurately monitor the fault location of GIS equipment, thereby improving the maintenance efficiency of GIS equipment.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a multi-source data monitoring and processing method for a GIS device, comprising:

[0007] Real-time acquisition of environmental impact data corresponding to insulation characteristics of target GIS equipment, as well as density data and pressure data of internal gas of the target GIS equipment during operation;

[0008] Inputting the environmental impact data, the density data, and the pressure data into a pre-built insulation monitoring model to obtain an insulation prediction result of the target GIS equipment, and generating a fault processing signal according to the insulation prediction result;

[0009] executing a contact fault monitoring mode and a high temperature fault monitoring mode in response to the fault processing signal;

[0010] In the contact fault monitoring mode, the contact feature points of the circuit breaker of the target GIS device in the acquired image data set are located according to the corner detection algorithm, the trajectory information of the contact feature points under the selected typical working conditions is determined based on the optical flow method, the trajectory information is intelligently identified, and the contact fault result of the target GIS device is obtained;

[0011] In the high-temperature fault monitoring mode, an abnormally high-temperature area of ​​the GIS equipment is identified based on the infrared image data set acquired in real time, the abnormally high-temperature area is compared with the partial discharge signal detection data in the abnormally high-temperature area, and a high-temperature area fault result of the target GIS equipment is determined based on the comparison result;

[0012] Based on the obtained contact fault result and / or the high temperature area fault result, a corresponding GIS equipment fault handling strategy is executed.

[0013] As one preferred solution, the environmental impact data includes: temperature data and humidity data;

[0014] Before inputting the environmental impact data, the density data, and the pressure data into a pre-built insulation monitoring model, the method further includes:

[0015] Acquire historical environmental impact data of the GIS equipment, as well as historical density data and historical pressure data of the gas inside the target GIS equipment;

[0016] Performing time alignment processing on the historical environmental impact data, historical density data, and historical pressure data based on an interpolation processing algorithm to obtain multi-source monitoring data;

[0017] An initial insulation monitoring model is constructed based on a random forest algorithm, and the multi-source monitoring data is input into the initial insulation monitoring model for training to obtain a trained insulation monitoring model.

[0018] As one preferred solution, the insulation prediction result includes the material aging rate and internal gas insulation strength of the target GIS equipment;

[0019] Generating a fault processing signal according to the insulation prediction result includes:

[0020] Comparing the material aging rate with a preset aging rate threshold, and generating the fault processing signal if the material aging rate is greater than the aging rate threshold;

[0021] Alternatively, the internal gas insulation strength is compared with a preset insulation strength threshold, and if the internal gas insulation strength is less than the insulation strength threshold, a fault processing signal is generated.

[0022] As one preferred solution, the contact feature points of the circuit breaker of the target GIS device in the image data set acquired by locating the contact feature points according to the corner detection algorithm include:

[0023] Performing grayscale conversion, filtering, and edge detection on the image data set in sequence to obtain an image data set to be extracted, wherein the image data set to be extracted includes a plurality of consecutive frames of images to be extracted;

[0024] Calculating the autocorrelation matrix of each pixel in the image to be extracted, and calculating the corner point response function value according to the eigenvalue of the autocorrelation matrix;

[0025] Pixel points whose corner point response function values ​​are greater than a preset threshold are taken as corner points to construct a corner point dataset. Based on the acquired positional features and shape features of the circuit breaker contacts, the corner points belonging to the circuit breaker contacts in the corner point dataset are screened out as the contact feature points.

[0026] As one preferred solution, the trajectory information includes moving speed and moving time;

[0027] The determining of the trajectory information of the contact feature point under the selected typical working condition based on the optical flow method includes:

[0028] Obtaining the coordinate position of the contact feature point in each frame of the image to be extracted in the image data set;

[0029] Calculating the displacement data of the feature points between adjacent frames based on the coordinate positions of the images to be extracted in adjacent frames;

[0030] The time interval data between adjacent frames is acquired, and the moving speed and moving time of the contact feature point are calculated according to the displacement data and the time interval data.

[0031] As one preferred solution, the intelligent identification of the trajectory information to obtain the contact fault result of the target GIS device includes:

[0032] If the moving time is greater than a preset time threshold, it is determined that the circuit breaker of the target GIS device is faulty;

[0033] Alternatively, if the moving speed is less than a preset speed threshold, it is determined that the circuit breaker of the target GIS device is faulty.

[0034] As one preferred solution, the step of identifying abnormally high temperature areas of the GIS equipment based on the infrared image data set acquired in real time includes:

[0035] Performing image enhancement and noise removal processing on the infrared image data set in sequence to obtain an image to be recognized;

[0036] The image to be identified is binarized based on a pre-selected binarization threshold to obtain a binarized image, and the abnormally high temperature area of ​​the target GIS device is identified based on the binarized image.

[0037] As one preferred solution, comparing the abnormally high temperature area based on the partial discharge signal detection data in the abnormally high temperature area, and determining the high temperature area fault result of the target GIS equipment based on the comparison result, includes:

[0038] Acquire the partial discharge signal of the target GIS device based on the partial discharge acquisition device, and perform filtering processing on the partial discharge signal;

[0039] Time-synchronizing the filtered partial discharge signal with infrared image data generated based on the high-temperature area;

[0040] The infrared image data after time synchronization is correlated with the partial discharge signal, the fault type is determined according to the analysis result, and a high temperature area fault result corresponding to the fault type is generated.

[0041] As one preferred solution, executing the corresponding GIS equipment fault handling strategy also includes:

[0042] Establish a knowledge base of fault type-handling measures mapping;

[0043] Performing a fuzzy logic comprehensive evaluation on the contact fault result and the high temperature area fault result according to the fault type-handling measure mapping knowledge base to calculate the fault confidence;

[0044] When the fault confidence exceeds a preset first confidence threshold, a GIS equipment fault handling strategy generated based on the fault type-handling measure mapping knowledge base is executed.

[0045] Another embodiment of the present invention provides a multi-source data monitoring and processing system for GIS equipment, comprising:

[0046] An acquisition module is used to acquire, in real time, environmental impact data corresponding to insulation characteristics of a target GIS device, as well as density data and pressure data of internal gas of the target GIS device during operation;

[0047] An insulation monitoring module, configured to input the environmental impact data, the density data, and the pressure data into a pre-built insulation monitoring model to obtain an insulation prediction result of the target GIS device, and generate a fault handling signal according to the insulation prediction result;

[0048] a response module, configured to execute a contact fault monitoring mode and a high temperature fault monitoring mode in response to the fault processing signal;

[0049] A first identification module is configured to locate, in the contact fault monitoring mode, contact feature points of the circuit breaker of the target GIS device in the acquired image data set according to a corner point detection algorithm, determine trajectory information of the contact feature points under selected typical working conditions based on an optical flow method, and intelligently identify the trajectory information to obtain a contact fault result of the target GIS device;

[0050] a second identification module configured to, in the high-temperature fault monitoring mode, identify an abnormally high-temperature area of ​​the GIS device based on the infrared image data set acquired in real time, compare the abnormally high-temperature area with the partial discharge signal detection data within the abnormally high-temperature area, and determine a high-temperature area fault result of the target GIS device based on the comparison result;

[0051] The execution module is used to execute the corresponding GIS equipment fault processing strategy based on the obtained contact fault result and / or the high temperature area fault result.

[0052] As one preferred solution, the environmental impact data includes: temperature data and humidity data;

[0053] The insulation monitoring module is further used for:

[0054] Acquire historical environmental impact data of the GIS equipment, as well as historical density data and historical pressure data of the gas inside the target GIS equipment;

[0055] Performing time alignment processing on the historical environmental impact data, historical density data, and historical pressure data based on an interpolation processing algorithm to obtain multi-source monitoring data;

[0056] An initial insulation monitoring model is constructed based on a random forest algorithm, and the multi-source monitoring data is input into the initial insulation monitoring model for training to obtain a trained insulation monitoring model.

[0057] As one preferred solution, the insulation prediction result includes the material aging rate and internal gas insulation strength of the target GIS equipment;

[0058] The insulation monitoring module is further used for:

[0059] Comparing the material aging rate with a preset aging rate threshold, and generating the fault processing signal if the material aging rate is greater than the aging rate threshold;

[0060] Alternatively, the internal gas insulation strength is compared with a preset insulation strength threshold, and if the internal gas insulation strength is less than the insulation strength threshold, a fault processing signal is generated.

[0061] As one preferred solution, the first identification module is further configured to:

[0062] Performing grayscale conversion, filtering, and edge detection on the image data set in sequence to obtain an image data set to be extracted, wherein the image data set to be extracted includes a plurality of consecutive frames of images to be extracted;

[0063] Calculating the autocorrelation matrix of each pixel in the image to be extracted, and calculating the corner point response function value according to the eigenvalue of the autocorrelation matrix;

[0064] Pixel points whose corner point response function values ​​are greater than a preset threshold are taken as corner points to construct a corner point dataset. Based on the acquired positional features and shape features of the circuit breaker contacts, the corner points belonging to the circuit breaker contacts in the corner point dataset are screened out as the contact feature points.

[0065] As one preferred solution, the trajectory information includes moving speed and moving time;

[0066] The first identification module is further configured to:

[0067] Obtaining the coordinate position of the contact feature point in each frame of the image to be extracted in the image data set;

[0068] Calculating the displacement data of the feature points between adjacent frames based on the coordinate positions of the images to be extracted in adjacent frames;

[0069] The time interval data between adjacent frames is acquired, and the moving speed and moving time of the contact feature point are calculated according to the displacement data and the time interval data.

[0070] As one preferred solution, the first identification module is further configured to:

[0071] If the moving time is greater than a preset time threshold, it is determined that the circuit breaker of the target GIS device is faulty;

[0072] Alternatively, if the moving speed is less than a preset speed threshold, it is determined that the circuit breaker of the target GIS device is faulty.

[0073] As one preferred solution, the second identification module is further configured to:

[0074] Performing image enhancement and noise removal processing on the infrared image data set in sequence to obtain an image to be recognized;

[0075] The image to be identified is binarized based on a pre-selected binarization threshold to obtain a binarized image, and the abnormally high temperature area of ​​the target GIS device is identified based on the binarized image.

[0076] As one preferred solution, the second identification module is further configured to:

[0077] Acquire the partial discharge signal of the target GIS device based on the partial discharge acquisition device, and perform filtering processing on the partial discharge signal;

[0078] Time-synchronizing the filtered partial discharge signal with infrared image data generated based on the high-temperature area;

[0079] The infrared image data after time synchronization is correlated with the partial discharge signal, the fault type is determined according to the analysis result, and a high temperature area fault result corresponding to the fault type is generated.

[0080] As one preferred solution, the execution module is further configured to:

[0081] Establish a knowledge base of fault type-handling measures mapping;

[0082] Performing a fuzzy logic comprehensive evaluation on the contact fault result and the high temperature area fault result according to the fault type-handling measure mapping knowledge base to calculate the fault confidence;

[0083] When the fault confidence exceeds a preset first confidence threshold, a GIS equipment fault handling strategy generated based on the fault type-handling measure mapping knowledge base is executed.

[0084] Another embodiment of the present invention provides a multi-source data monitoring processing device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the multi-source data monitoring processing method as described above.

[0085] Yet another embodiment of the present invention provides a computer-readable storage medium storing a computer program. When a device containing the computer-readable storage medium executes the computer program, the multi-source data monitoring and processing method described above is implemented.

[0086] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0087] (1) The present invention processes and analyzes multi-source data based on a pre-built insulation monitoring model, generates insulation prediction results and fault processing signals based on the data, and then executes corresponding fault monitoring modes and processing strategies, avoiding the subjectivity and uncertainty of human judgment, making fault diagnosis and processing more scientific and accurate.

[0088] (2) The present invention provides operation and maintenance personnel with accurate fault information and processing basis, helping them to formulate and implement operation and maintenance plans in a targeted manner, reasonably arrange maintenance resources and time, avoid blind inspections and excessive maintenance, improve the efficiency and quality of operation and maintenance work, and realize the transformation from traditional regular maintenance to state-based intelligent maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 1 is a flow chart of a multi-source data monitoring and processing method for a GIS device in one embodiment of the present invention;

[0090] Figure 2 Schematic diagram of a multi-source data monitoring and processing system for GIS equipment in one embodiment of the present invention;

[0091] Figure 3 It is a schematic diagram of a multi-source data monitoring and processing device of a GIS device in one embodiment of the present invention. DETAILED DESCRIPTION

[0092] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0093] In the description of this application, the terms "first," "second," "third," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0094] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0095] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by those skilled in the art in specific circumstances.

[0096] One embodiment of the present invention provides a multi-source data monitoring and processing method for GIS equipment. For details, see Figure 1 , Figure 1 The figure shows a flow chart of a multi-source data monitoring and processing method for a GIS device in one embodiment of the present invention, which includes steps S1-S6:

[0097] S1: Real-time acquisition of environmental impact data corresponding to the insulation characteristics of the target GIS equipment, as well as density data and pressure data of the internal gas of the target GIS equipment during operation;

[0098] Real-time collection of various data related to the insulation characteristics of target GIS (Gas-Insulated Switchgear) equipment. Environmental impact data, for example, plays a significant role in insulating equipment performance. For example, changes in temperature and humidity can affect the performance of insulation materials. Furthermore, real-time data on the density and pressure of the gas inside the equipment is collected during operation. GIS equipment typically utilizes insulating gases (such as sulfur hexafluoride) for electrical insulation and arc extinguishing. The density and pressure of these gases are directly related to their insulation performance. For example, a drop in gas pressure below a certain level can lead to a decrease in insulation performance, increasing the risk of equipment failure.

[0099] Preferably, in one embodiment of the present invention, the environmental impact data includes: temperature data and humidity data;

[0100] Before environmental impact data, density data, and pressure data are entered into the pre-built insulation monitoring model, this also includes:

[0101] Obtain historical environmental impact data of GIS equipment, as well as historical density data and historical pressure data of the gas inside the target GIS equipment;

[0102] Based on the interpolation processing algorithm, the historical environmental impact data, historical density data and historical pressure data are time-aligned to obtain multi-source monitoring data;

[0103] Based on the interpolation processing algorithm, the historical environmental impact data, historical density data and historical pressure data are time-aligned to obtain multi-source monitoring data;

[0104] An initial insulation monitoring model is constructed based on the random forest algorithm, and multi-source monitoring data are input into the initial insulation monitoring model for training to obtain a trained insulation monitoring model.

[0105] Because the historical environmental impact data, historical density data, and historical pressure data may originate from different monitoring devices or systems, their collection intervals and start times may be inconsistent. Using this data directly can lead to temporal confusion and an inability to accurately reflect the relationships between the data, which in turn affects the accuracy of the insulation monitoring model. Therefore, time alignment is necessary. An interpolation algorithm is used to time-align these three types of historical data. The basic principle of an interpolation algorithm is to construct a function between known data points to estimate the value of an unknown point.

[0106] In one embodiment of the present invention, the data in the temperature sensor data set and the humidity sensor data set are preprocessed to form a first parameter set. ,in Indicates that during the preset time period Neidi A first parameter group corresponding to a first sensor group, Indicates the The temperature sensors in the first sensor group are The temperature data set within Indicates the The humidity sensors in the first sensor group are Humidity dataset within.

[0107] Using a sliding window and Select multiple temperature data and humidity data to form temperature time series and humidity time series; move the sliding window one to obtain multiple temperature time series and humidity time series. Extract multiple temperature characteristic values ​​and humidity characteristic values ​​from each temperature data series and humidity data series to form a temperature feature set and a humidity feature set. Secondly, the gas density and pressure of the insulating gas SF6 inside the GIS equipment are also key factors that determine its insulation performance. The monitoring model predicts the current insulation characteristics of the equipment based on the real-time density and pressure data of the SF6 gas inside the GIS equipment and the temperature data, humidity data and dust concentration data of the external environment. Obtain the aging parameters of the insulation materials used in the GIS from the database, and use the temperature feature set, humidity feature set, SF6 volume density data, pressure data and insulation material aging parameters to form a training sample set; use the training sample set to train the initial insulation monitoring model, and deploy it after meeting the performance requirements.

[0108] That is, the environmental impact data and the internal gas pressure and density data are combined into multi-source data, thereby improving the accuracy of the insulation prediction of GIS equipment.

[0109] S2: Input the environmental impact data, density data, and pressure data into the pre-built insulation monitoring model to obtain the insulation prediction results of the target GIS equipment, and generate a fault processing signal according to the insulation prediction results;

[0110] Specifically, real-time environmental impact data (such as temperature and humidity) and internal gas density and pressure data from the target GIS equipment during operation are input into a pre-built insulation monitoring model. The model's computational analysis yields insulation predictions for the target GIS equipment, including two key indicators: material aging rate and internal gas insulation strength. The material aging rate reflects the rate at which the insulation materials in the GIS equipment age over time and under changing operating conditions. For example, in harsh environments such as high temperature and high humidity, insulation material aging may accelerate. The internal gas insulation strength directly reflects the current insulation capacity of the insulation gas (such as sulfur hexafluoride) within the equipment. These two indicators reflect the insulation performance of the GIS equipment from different perspectives, providing a basis for subsequent identification of potential equipment failures.

[0111] Preferably, in one embodiment of the present invention, the insulation prediction result includes the material aging rate and internal gas insulation strength of the target GIS equipment;

[0112] Generate fault processing signals based on insulation prediction results, including:

[0113] Comparing the material aging rate with a preset aging rate threshold, and generating a fault processing signal if the material aging rate is greater than the aging rate threshold;

[0114] Alternatively, the internal gas insulation strength is compared with a preset insulation strength threshold, and if the internal gas insulation strength is less than the insulation strength threshold, a fault processing signal is generated.

[0115] S3: In response to the fault processing signal, executing the contact fault monitoring mode and the high temperature fault monitoring mode;

[0116] When the system receives a fault handling signal, it indicates that the insulation prediction results for the GIS equipment indicate a potential risk, potentially impacting normal operation. At this point, the system quickly triggers a pre-set response program, allocating appropriate computing resources and data channels to ensure rapid and stable activation of the contact fault monitoring mode and the high-temperature fault monitoring mode. Simultaneously, the system transmits key information related to the fault handling signal, such as the trigger signal's insulation prediction indicators (material aging rate, internal gas insulation strength, etc.), to subsequent monitoring modules for comprehensive analysis during the monitoring process.

[0117] S4: In the contact fault monitoring mode, the contact feature points of the circuit breaker of the target GIS device in the acquired image data set are located using the corner detection algorithm, the trajectory information of the contact feature points under the selected typical working conditions is determined based on the optical flow method, and the trajectory information is intelligently identified to obtain the contact fault result of the target GIS device;

[0118] Preferably, in one embodiment of the present invention, locating contact feature points of a circuit breaker of a target GIS device in an acquired image data set according to a corner detection algorithm includes:

[0119] The image data set is sequentially subjected to grayscale conversion, filtering, and edge detection processing to obtain an image data set to be extracted, wherein the image data set to be extracted includes images to be extracted of several consecutive frames;

[0120] Calculate the autocorrelation matrix of each pixel in the image to be extracted, and calculate the corner point response function value according to the eigenvalue of the autocorrelation matrix;

[0121] Pixel points whose corner point response function values ​​are greater than a preset threshold are taken as corner points to construct a corner point dataset. Based on the acquired position features and shape features of the circuit breaker contacts, the corner points belonging to the circuit breaker contacts in the corner point dataset are screened out as contact feature points.

[0122] Specifically, the original image dataset may be a color image, rich in color information. However, color information is not essential for corner detection and can actually increase computational complexity. Therefore, the color image is first converted to grayscale. In a grayscale image, each pixel has a single brightness value, simplifying the image data representation. However, the converted grayscale image may contain noise, which can interfere with the accuracy of subsequent corner detection. Therefore, filtering is required on the grayscale image.

[0123] After filtering, the image is then subjected to edge detection. The goal of edge detection is to identify areas in the image where grayscale values ​​vary dramatically; these areas often correspond to the edges of objects. For example, the Canny edge detection algorithm smoothes the image using a Gaussian filter, then calculates the image's gradient magnitude and direction. It then performs non-maximum suppression to remove possible false edges. Finally, it uses dual-threshold detection and edge connection to determine the edges in the image.

[0124] For each pixel in the image to be extracted, calculate its autocorrelation matrix. The autocorrelation matrix describes the grayscale variation of a pixel in different directions. Assume that a small neighborhood window is centered on a pixel. Within this window, the autocorrelation matrix is ​​constructed by calculating the grayscale variation of the pixel in the horizontal and vertical directions. For example, for a two-dimensional image, the autocorrelation matrix is ​​typically a 2x2 matrix whose elements are related to the horizontal and vertical gradients of the pixels within the window.

[0125] After obtaining the autocorrelation matrix, the eigenvalues ​​of the matrix are calculated. The eigenvalues ​​reflect the degree of grayscale change of the pixel in different directions. Then, the corner response function value is calculated based on these eigenvalues. Common corner response functions (such as the response function in the Harris corner detection algorithm) will comprehensively consider the size relationship of the two eigenvalues. For example, when both eigenvalues ​​are large and close, it means that the grayscale change of the pixel in all directions is very obvious, and it is likely to be a corner point; when one eigenvalue is large and the other eigenvalue is very small, it means that the grayscale change of the pixel in a certain direction is drastic, and it may be an edge point; when both eigenvalues ​​are very small, it means that the grayscale change of the pixel is not obvious, and it is a point in a flat area. In this way, a corner response function value is calculated for each pixel to measure the possibility of whether the pixel is a corner point.

[0126] Pixels whose corner response function values ​​exceed a preset threshold are considered corners, and these corners form the corner dataset. The threshold setting needs to be adjusted based on the actual situation; it determines the sensitivity of corner detection. If the threshold is set too high, some true corners may be missed; if the threshold is set too low, too many false corners may be detected.

[0127] Based on the acquired positional and shape features of the circuit breaker contacts, corner points belonging to the circuit breaker contacts are selected from the corner point dataset as contact feature points. Circuit breaker contacts typically have specific positions and shapes. For example, their positions in the image are relatively fixed, and their shapes may be regular geometric shapes (such as circles or rectangles). By analyzing and matching these features, we can accurately select corner points related to the contacts from a large number of corner points, laying the foundation for subsequent analysis of the contact's motion trajectory based on these feature points.

[0128] Preferably, in one embodiment of the present invention, the trajectory information includes movement speed and movement time;

[0129] Based on the optical flow method, the trajectory information of the contact feature points under the selected typical working conditions is determined, including:

[0130] Obtaining the coordinate position of the contact feature point in each frame of the image to be extracted in the image data set;

[0131] Calculate the displacement data of feature points between adjacent frames based on the coordinate positions of the images to be extracted in adjacent frames;

[0132] The time interval data between adjacent frames is obtained, and the moving speed and moving time of the contact feature point are calculated based on the displacement data and the time interval data.

[0133] Specifically, for each frame of the image to be extracted in the image dataset, the coordinates of the contact feature points, previously located, are obtained within that frame. The coordinate system in an image typically uses the upper left corner as the origin, the horizontal x-axis as the horizontal axis, and the vertical y-axis as the vertical axis. By recording the (x, y) coordinates of each contact feature point in each frame, we provide the foundational data for calculating its trajectory.

[0134] Based on the coordinate positions of the contact feature points in the images to be extracted from adjacent frames, the displacement data of the feature points between adjacent frames is calculated. Assuming that the coordinates of a contact feature point in the nth frame image are (x1, y1) and the coordinates of the feature point in the n+1th frame image are (x2, y2), then the displacement of this feature point between these two frames is Δx = x2 - x1 in the x-direction and Δy = y2 - y1 in the y-direction. By calculating the displacement of all contact feature points between adjacent frames, displacement data is obtained, which reflects the movement changes of the contact feature points between adjacent frames.

[0135] In addition to image data, the time interval between adjacent frames is also required. This typically depends on the frame rate of the image acquisition device. For example, if the frame rate is 25 frames per second, the time interval between adjacent frames is 1 / 25 second. The movement speed and movement time of the contact feature point are calculated based on the displacement and time interval data. The movement speed can be calculated by the ratio of displacement to time interval. For example, the movement speed in the x-direction (x) = Δx / Δt, and the movement speed in the y-direction (y) = Δy / Δt. Combining the speeds in both directions yields the actual movement speed of the feature point. The movement time is the time elapsed from the start of monitoring to the current frame. It can be calculated by multiplying the number of frames by the time interval. For example, if N frames have passed, and the time interval between each frame is Δt, the movement time T = N * Δt. Through these calculations, the trajectory information of the contact feature point under selected typical operating conditions is obtained, including the movement speed and movement time, thereby determining the closing time and speed of the circuit breaker contacts.

[0136] Preferably, in one embodiment of the present invention, intelligent identification of trajectory information is performed to obtain a contact failure result of the target GIS device, including:

[0137] If the moving time is greater than the preset time threshold, it is determined that the circuit breaker of the target GIS device is faulty;

[0138] Alternatively, if the moving speed is less than a preset speed threshold, it is determined that the circuit breaker of the target GIS device is faulty.

[0139] The calculated contact feature point movement time is compared with a preset time threshold. This threshold is set based on the normal operating characteristics of GIS device circuit breakers and empirical experience. It represents the maximum time allowed for a contact to complete a single action (e.g., close or open) under normal circumstances. If the contact feature point movement time exceeds this threshold, it indicates that the contact movement time is too long, possibly due to mechanical failure or poor contact, preventing the contact from completing normal movement in a timely manner. In this case, the target GIS device circuit breaker is considered faulty. The contact feature point movement speed is compared with a preset speed threshold. This threshold is also set based on the device's normal operating parameters and reflects the speed range of the contact during normal operation. If the contact feature point movement speed is less than the threshold, it indicates that the contact movement speed is too slow, possibly due to contact wear or sticking, which can affect the normal operation of the circuit breaker. In this case, the target GIS device circuit breaker is considered faulty. By evaluating these two key parameters, movement speed and movement time, it is possible to intelligently identify whether a contact fault exists, thereby determining contact fault status for the target GIS device.

[0140] S5: In the high-temperature fault monitoring mode, the abnormally high-temperature area of ​​the GIS equipment is identified based on the infrared image data set acquired in real time, the abnormally high-temperature area is compared based on the partial discharge signal detection data in the abnormally high-temperature area, and the high-temperature area fault result of the target GIS equipment is determined based on the comparison result;

[0141] Preferably, in one embodiment of the present invention, identifying abnormally high temperature areas of GIS equipment based on infrared image datasets acquired in real time includes:

[0142] Perform image enhancement and noise removal on the infrared image dataset in sequence to obtain the image to be identified;

[0143] The image to be identified is binarized based on a pre-selected binarization threshold to obtain a binarized image, and the abnormally high temperature area of ​​the target GIS equipment is identified based on the binarized image.

[0144] Infrared image datasets acquired in real time may suffer from low contrast and unclear details due to environmental factors, equipment performance, and other factors. The goal of image enhancement is to improve the visual quality of images and highlight useful information through specific algorithms. For example, histogram equalization can be used. This method redistributes the number of pixels at each grayscale level in the image, making the grayscale distribution more uniform and thus enhancing the image contrast. For infrared images, this helps to more clearly display the differences between different temperature regions. Alternatively, image enhancement algorithms based on wavelet transforms can be used. These can analyze images at different scales, enhance image details, and make the boundaries of high-temperature areas more distinct.

[0145] The pre-processed image to be identified is binarized based on a pre-selected binarization threshold. The purpose of binarization is to divide the pixels in the image into two categories, usually represented by 0 and 1, corresponding to different areas in the image. In the scenario of identifying abnormally high temperature areas, pixels above the binarization threshold can be considered to belong to abnormally high temperature areas, while pixels below the threshold belong to normal temperature areas. The selection of the binarization threshold is very critical and needs to be reasonably set based on the temperature range of the GIS equipment during normal operation and historical data. For example, if it is known that the maximum temperature of the GIS equipment during normal operation is T1, considering a certain safety margin, the binarization threshold is set to T2 (T2>T1). In this way, after the binarization process, the area with a temperature higher than T2 will be marked as 1, forming a binary image.

[0146] Abnormally high temperature areas of the target GIS device can be identified based on areas with pixel values ​​of 1 in the binary image. In the binary image, these areas with pixel values ​​of 1 form continuous blocks or irregular shapes. Using image analysis algorithms, such as the connected component labeling algorithm, these abnormally high temperature areas can be marked and extracted, thereby determining information such as their location, shape, and size.

[0147] Preferably, in one embodiment of the present invention, the abnormally high temperature area is compared based on the partial discharge signal detection data in the abnormally high temperature area, and the high temperature area fault result of the target GIS equipment is determined based on the comparison result, including:

[0148] Obtain the partial discharge signal of the target GIS equipment based on the partial discharge acquisition device and perform filtering on the partial discharge signal;

[0149] Time synchronization of the filtered partial discharge signal with the infrared image data generated based on the high temperature area;

[0150] The infrared image data after time synchronization is correlated with the partial discharge signal, the fault type is determined based on the analysis results, and the high-temperature area fault result corresponding to the fault type is generated.

[0151] Specifically, a partial discharge acquisition device is used to acquire partial discharge signals from target GIS equipment. Partial discharge (PD) refers to the discharge that occurs in a localized area within the GIS equipment due to insulation defects and other factors. This discharge generates various physical signals, including electrical, acoustic, and optical signals. The PD acquisition device can detect these signals to obtain relevant information about the PD, such as discharge intensity and frequency.

[0152] The filtered partial discharge signal is time-synchronized with the infrared image data generated based on the high-temperature area. In actual monitoring, the acquisition of infrared images and partial discharge signals may deviate in time due to equipment asynchrony, data transmission delays, and other factors. The purpose of time synchronization is to ensure that the partial discharge signal and the corresponding infrared image data are accurately matched, enabling effective correlation analysis. This can be achieved by setting a synchronized clock signal in the acquisition system or by utilizing the timestamp information in the data for time calibration. For example, when acquiring infrared images and partial discharge signals, the acquisition time of each data point is recorded simultaneously. Then, an algorithm is used to match and adjust the time information of the two data sets so that the partial discharge signal at the same moment corresponds to the corresponding infrared image data.

[0153] Correlation analysis is performed on the time-synchronized infrared image data and the partial discharge signal. This step primarily seeks to identify the inherent connection between the two to determine the fault type. For example, if a strong partial discharge signal is detected during a time period corresponding to an abnormally high temperature area, and the characteristics of the partial discharge signal (such as discharge amplitude and frequency) match those of partial discharge caused by insulation aging, it can be preliminarily determined that the fault in this high-temperature area is likely caused by insulation aging. By establishing a correlation model between the partial discharge signal and infrared image features corresponding to different fault types, the collected data is analyzed and matched to determine the fault type.

[0154] Based on the results of the correlation analysis, the fault type is determined and a high-temperature area fault result corresponding to the fault type is generated. The fault result should not only clearly define the fault type but also include an assessment of the severity of the fault.

[0155] S6: Based on the obtained contact fault results and / or high temperature area fault results, execute the corresponding GIS equipment fault handling strategy.

[0156] Among them, the consequences of contact failure include but are not limited to contact overheating, contact wear and contact welding, and the consequences of high-temperature area failure include but are not limited to insulation performance degradation, component damage and partial discharge.

[0157] In one embodiment of the present invention, once contact overheating is detected, the first step is to reduce the equipment's load current to prevent further overheating. Specifically, this can be achieved by adjusting the grid's operating mode to shift some of the load to other equipment. At the same time, professional technicians should promptly perform a power outage and overhaul of the equipment to check the contact condition, such as whether the contact pressure spring is functioning properly and whether there is dirt or burns on the contact surface. If contact wear is detected, different measures should be taken depending on the extent of the wear. If the wear is minor, the contact surface can be polished to remove burrs and oxides, improving contact performance. Furthermore, the contact pressure and travel should be adjusted to ensure good contact during opening and closing. If contact wear is severe, such as when the remaining thickness is less than half of its original thickness, new contacts must be replaced. If contact welding occurs, the equipment will be unable to properly interrupt the circuit, and an immediate power outage is required. First, remove the welded contacts and analyze the cause of the welding, such as improper contact capacity, excessive operating frequency, or damaged contact springs. Take appropriate measures for different reasons, such as replacing contacts with appropriate capacity, reducing the operating frequency or replacing the contact spring.

[0158] When insulation degradation due to high temperatures is detected, the first step is to lower the equipment's operating temperature. Measures such as increased ventilation and heat dissipation and the installation of cooling devices can be implemented. Furthermore, comprehensive insulation testing of the equipment should be conducted, such as insulation resistance testing and dielectric loss testing, to assess the extent of the degradation. Component damage due to high temperatures, such as aging and deformation of seals or loose connections, should be promptly replaced. When replacing seals, select high-quality, heat-resistant and aging-resistant sealing materials, and ensure proper installation to prevent gas leakage. When partial discharge is detected in high-temperature areas, the first step is to monitor and analyze the partial discharge signal to determine the location and severity of the discharge. Appropriate treatment measures should then be implemented based on the discharge situation.

[0159] Preferably, in one embodiment of the present invention, executing the corresponding GIS equipment fault handling strategy further includes:

[0160] Establish a knowledge base of fault type-handling measures mapping;

[0161] Based on the fault type-treatment measure mapping knowledge base, fuzzy logic comprehensive evaluation is performed on the contact fault results and high temperature area fault results to calculate the fault confidence level.

[0162] When the fault confidence exceeds a preset first confidence threshold, a GIS equipment fault handling strategy generated based on a fault type-handling measure mapping knowledge base is executed.

[0163] Fuzzy logic comprehensive evaluation is an effective method for handling uncertainty. First, for contact fault results and high-temperature area fault results, relevant knowledge and rules are extracted from the fault type-treatment action mapping knowledge base. These rules typically take the form of fuzzy conditional statements, such as "If the contact wear is severe and the temperature in the high-temperature area exceeds a certain threshold, then the probability of a fault is high." Then, based on these rules and combined with actual fault data, such as the specific degree of contact wear and the temperature values ​​in the high-temperature area, a fuzzy reasoning algorithm is used to perform a comprehensive evaluation. The evaluation process considers the influence of multiple factors, including the frequency and severity of the fault, and ultimately calculates a fault confidence level. The fault confidence level is a value between 0 and 1 that indicates the confidence level of the fault judgment. For example, a fault confidence level of 0.8 indicates an 80% confidence that the device has the corresponding fault.

[0164] The preset first confidence threshold is a key decision-making basis, determined based on factors such as the importance of the equipment, the operating environment, and the acceptable level of risk. When the calculated fault confidence exceeds this first confidence threshold, it indicates a high probability of a device failure. At this point, the system immediately executes the GIS equipment fault handling strategy generated based on the fault type-handling measure mapping knowledge base. For example, if the fault type is determined to be contact adhesion and the fault confidence reaches the preset threshold, the system will issue instructions according to the handling measures for contact adhesion faults in the knowledge base, arranging operations and maintenance personnel to perform operations such as contact replacement or repair, in order to restore normal operation of the equipment as soon as possible and reduce the impact of the fault on the entire system.

[0165] Another embodiment of the present invention provides a multi-source data monitoring and processing method for GIS equipment. For details, see Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a multi-source data monitoring and processing system for a GIS device in one embodiment of the present invention, which includes:

[0166] An acquisition module 11 is used to acquire, in real time, environmental impact data corresponding to the insulation characteristics of the target GIS equipment, as well as density data and pressure data of the internal gas of the target GIS equipment during operation;

[0167] The insulation monitoring module 12 is used to input environmental impact data, density data, and pressure data into a pre-built insulation monitoring model to obtain insulation prediction results of the target GIS equipment and generate a fault processing signal according to the insulation prediction results;

[0168] a response module 13, configured to execute a contact fault monitoring mode and a high temperature fault monitoring mode in response to the fault processing signal;

[0169] The first recognition module 14 is configured to, in the contact fault monitoring mode, locate the contact feature points of the circuit breaker of the target GIS device in the acquired image data set using a corner point detection algorithm, determine the trajectory information of the contact feature points under selected typical working conditions based on an optical flow method, perform intelligent recognition on the trajectory information, and obtain the contact fault result of the target GIS device;

[0170] The second identification module 15 is used to identify abnormally high temperature areas of the GIS equipment based on the infrared image data set acquired in real time in the high temperature fault monitoring mode, compare the abnormally high temperature areas with the partial discharge signal detection data within the abnormally high temperature areas, and determine the high temperature area fault result of the target GIS equipment based on the comparison results;

[0171] The execution module 16 is used to execute the corresponding GIS equipment fault processing strategy based on the obtained contact fault result and / or high temperature area fault result.

[0172] Preferably, in one embodiment of the present invention, the environmental impact data includes: temperature data and humidity data;

[0173] The insulation monitoring module is further used for:

[0174] Acquire historical environmental impact data of the GIS equipment, as well as historical density data and historical pressure data of the gas inside the target GIS equipment;

[0175] Performing time alignment processing on the historical environmental impact data, historical density data, and historical pressure data based on an interpolation processing algorithm to obtain multi-source monitoring data;

[0176] An initial insulation monitoring model is constructed based on a random forest algorithm, and the multi-source monitoring data is input into the initial insulation monitoring model for training to obtain a trained insulation monitoring model.

[0177] Preferably, in one embodiment of the present invention, the insulation prediction result includes the material aging rate and internal gas insulation strength of the target GIS equipment;

[0178] The insulation monitoring module is further used for:

[0179] Comparing the material aging rate with a preset aging rate threshold, and generating the fault processing signal if the material aging rate is greater than the aging rate threshold;

[0180] Alternatively, the internal gas insulation strength is compared with a preset insulation strength threshold, and if the internal gas insulation strength is less than the insulation strength threshold, a fault processing signal is generated.

[0181] Preferably, in one embodiment of the present invention, the first identification module is further configured to:

[0182] Performing grayscale conversion, filtering, and edge detection on the image data set in sequence to obtain an image data set to be extracted, wherein the image data set to be extracted includes a plurality of consecutive frames of images to be extracted;

[0183] Calculating the autocorrelation matrix of each pixel in the image to be extracted, and calculating the corner point response function value according to the eigenvalue of the autocorrelation matrix;

[0184] Pixel points whose corner point response function values ​​are greater than a preset threshold are taken as corner points to construct a corner point dataset. Based on the acquired positional features and shape features of the circuit breaker contacts, the corner points belonging to the circuit breaker contacts in the corner point dataset are screened out as the contact feature points.

[0185] Preferably, in one embodiment of the present invention, the trajectory information includes movement speed and movement time;

[0186] The first identification module is further configured to:

[0187] Obtaining the coordinate position of the contact feature point in each frame of the image to be extracted in the image data set;

[0188] Calculating the displacement data of the feature points between adjacent frames based on the coordinate positions of the images to be extracted in adjacent frames;

[0189] The time interval data between adjacent frames is acquired, and the moving speed and moving time of the contact feature point are calculated according to the displacement data and the time interval data.

[0190] Preferably, in one embodiment of the present invention, the first identification module is further configured to:

[0191] If the moving time is greater than a preset time threshold, it is determined that the circuit breaker of the target GIS device is faulty;

[0192] Alternatively, if the moving speed is less than a preset speed threshold, it is determined that the circuit breaker of the target GIS device is faulty.

[0193] Preferably, in one embodiment of the present invention, the second identification module is further configured to:

[0194] Performing image enhancement and noise removal processing on the infrared image data set in sequence to obtain an image to be recognized;

[0195] The image to be identified is binarized based on a pre-selected binarization threshold to obtain a binarized image, and the abnormally high temperature area of ​​the target GIS device is identified based on the binarized image.

[0196] Preferably, in one embodiment of the present invention, the second identification module is further configured to:

[0197] Acquire the partial discharge signal of the target GIS device based on the partial discharge acquisition device, and perform filtering processing on the partial discharge signal;

[0198] Time-synchronizing the filtered partial discharge signal with infrared image data generated based on the high-temperature area;

[0199] The infrared image data after time synchronization is correlated with the partial discharge signal, the fault type is determined according to the analysis result, and a high temperature area fault result corresponding to the fault type is generated.

[0200] Preferably, in one embodiment of the present invention, the execution module is further configured to:

[0201] Establish a knowledge base of fault type-handling measures mapping;

[0202] Performing a fuzzy logic comprehensive evaluation on the contact fault result and the high temperature area fault result according to the fault type-handling measure mapping knowledge base to calculate the fault confidence;

[0203] When the fault confidence exceeds a preset first confidence threshold, a GIS equipment fault handling strategy generated based on the fault type-handling measure mapping knowledge base is executed.

[0204] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0205] (1) The present invention processes and analyzes multi-source data based on a pre-built insulation monitoring model, generates insulation prediction results and fault processing signals based on the data, and then executes corresponding fault monitoring modes and processing strategies, avoiding the subjectivity and uncertainty of human judgment, making fault diagnosis and processing more scientific and accurate.

[0206] (2) The present invention provides operation and maintenance personnel with accurate fault information and processing basis, helping them to formulate and implement operation and maintenance plans in a targeted manner, reasonably arrange maintenance resources and time, avoid blind inspections and excessive maintenance, improve the efficiency and quality of operation and maintenance work, and realize the transformation from traditional regular maintenance to state-based intelligent maintenance.

[0207] Another embodiment of the present invention provides a multi-source data monitoring and processing device for a GIS device, see Figure 3 , which is a structural block diagram of a multi-source data monitoring and processing device provided by an embodiment of the present invention. The multi-source data monitoring and processing device provided by an embodiment of the present invention includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, the steps in the embodiment of the multi-source data monitoring and processing method for the GIS device are implemented, for example Figure 1 or, when the processor 21 executes the computer program, the functions of the modules in the above-mentioned device embodiments are realized, such as the acquisition module 11.

[0208] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the multi-source data monitoring and processing device. For example, the computer program may be divided into an acquisition module 11, an insulation monitoring module 12, a response module 13, and so on.

[0209] The multi-source data monitoring and processing device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of a multi-source data monitoring and processing device and does not limit the multi-source data monitoring and processing device. The device may include more or fewer components than shown, or a combination of certain components, or different components. For example, the multi-source data monitoring and processing device may also include input and output devices, network access devices, buses, and the like.

[0210] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor 21 is the control center of the multi-source data monitoring and processing device, and utilizes various interfaces and lines to connect various parts of the multi-source data monitoring and processing device.

[0211] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements the various functions of the multi-source data monitoring and processing device by running or executing the computer programs and / or modules stored in the memory 22 and accessing the data stored in the memory 22. The memory 22 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory 22 may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0212] If the integrated modules of the multi-source data monitoring and processing device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0213] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0214] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the steps of the multi-source data monitoring processing method for GIS devices in the above embodiment, for example Figure 1 Steps S1 to S6 described in .

[0215] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A multi-source data monitoring and processing method for GIS equipment, characterized in that: include: Real-time acquisition of environmental impact data corresponding to insulation characteristics of target GIS equipment, as well as density data and pressure data of internal gas of the target GIS equipment during operation; Inputting the environmental impact data, the density data, and the pressure data into a pre-built insulation monitoring model to obtain an insulation prediction result of the target GIS equipment, and generating a fault processing signal according to the insulation prediction result; executing a contact fault monitoring mode and a high temperature fault monitoring mode in response to the fault processing signal; In the contact fault monitoring mode, the contact feature points of the circuit breaker of the target GIS device in the acquired image data set are located according to the corner detection algorithm, the trajectory information of the contact feature points under the selected typical working conditions is determined based on the optical flow method, the trajectory information is intelligently identified, and the contact fault result of the target GIS device is obtained; In the high-temperature fault monitoring mode, an abnormally high-temperature area of ​​the target GIS device is identified based on the infrared image data set acquired in real time, the abnormally high-temperature area is compared with the partial discharge signal detection data in the abnormally high-temperature area, and a high-temperature area fault result of the target GIS device is determined based on the comparison result; Based on the obtained contact fault result and / or the high temperature area fault result, executing the corresponding target GIS equipment fault handling strategy; The insulation prediction results include the material aging rate and internal gas insulation strength of the target GIS equipment; Generating a fault processing signal according to the insulation prediction result includes: Comparing the material aging rate with a preset aging rate threshold, and generating the fault processing signal if the material aging rate is greater than the aging rate threshold; Alternatively, the internal gas insulation strength is compared with a preset insulation strength threshold, and if the internal gas insulation strength is less than the insulation strength threshold, the fault processing signal is generated.

2. The multi-source data monitoring and processing method for GIS equipment according to claim 1, characterized in that: The environmental impact data includes: temperature data and humidity data; Before inputting the environmental impact data, the density data, and the pressure data into a pre-built insulation monitoring model, the method further includes: Acquire historical environmental impact data of the target GIS device, as well as historical density data and historical pressure data of gas inside the target GIS device; Performing time alignment processing on the historical environmental impact data, historical density data, and historical pressure data based on an interpolation processing algorithm to obtain multi-source monitoring data; An initial insulation monitoring model is constructed based on a random forest algorithm, and the multi-source monitoring data is input into the initial insulation monitoring model for training to obtain a trained insulation monitoring model.

3. The multi-source data monitoring and processing method for GIS equipment according to claim 1, characterized in that: The contact feature points of the circuit breaker of the target GIS device in the image data set obtained by locating the contact feature points according to the corner point detection algorithm include: Performing grayscale conversion, filtering, and edge detection on the image data set in sequence to obtain an image data set to be extracted, wherein the image data set to be extracted includes a plurality of consecutive frames of images to be extracted; Calculating the autocorrelation matrix of each pixel in the image to be extracted, and calculating the corner point response function value according to the eigenvalue of the autocorrelation matrix; Pixel points whose corner point response function values ​​are greater than a preset threshold are taken as corner points to construct a corner point dataset. Based on the acquired positional features and shape features of the circuit breaker contacts, the corner points belonging to the circuit breaker contacts in the corner point dataset are screened out as the contact feature points.

4. The multi-source data monitoring and processing method for GIS equipment according to claim 1, characterized in that: The trajectory information includes movement speed and movement time; The determining of the trajectory information of the contact feature point under the selected typical working condition based on the optical flow method includes: Obtaining the coordinate position of the contact feature point in each frame of the image to be extracted in the image data set; Calculating the displacement data of the feature points between adjacent frames based on the coordinate positions of the images to be extracted in adjacent frames; The time interval data between adjacent frames is acquired, and the moving speed and moving time of the contact feature point are calculated according to the displacement data and the time interval data.

5. The multi-source data monitoring and processing method for GIS equipment according to claim 4, characterized in that: The intelligently identifying the trajectory information to obtain the contact fault result of the target GIS device includes: If the moving time is greater than a preset time threshold, it is determined that the circuit breaker of the target GIS device is faulty; Alternatively, if the moving speed is less than a preset speed threshold, it is determined that the circuit breaker of the target GIS device is faulty.

6. The multi-source data monitoring and processing method for GIS equipment according to claim 1, characterized in that: The identifying of the abnormally high temperature area of ​​the target GIS device based on the infrared image data set acquired in real time includes: Performing image enhancement and noise removal processing on the infrared image data set in sequence to obtain an image to be recognized; The image to be identified is binarized based on a pre-selected binarization threshold to obtain a binarized image, and the abnormally high temperature area of ​​the target GIS device is identified based on the binarized image.

7. The multi-source data monitoring and processing method for GIS equipment according to claim 6, characterized in that: The comparing the abnormally high temperature area based on the partial discharge signal detection data in the abnormally high temperature area, and determining the high temperature area fault result of the target GIS device based on the comparison result, includes: Acquire the partial discharge signal of the target GIS device based on the partial discharge acquisition device, and perform filtering processing on the partial discharge signal; Time-synchronizing the filtered partial discharge signal with infrared image data generated based on the high-temperature area; The infrared image data after time synchronization is correlated with the partial discharge signal, the fault type is determined according to the analysis result, and a high temperature area fault result corresponding to the fault type is generated.

8. The multi-source data monitoring and processing method for GIS equipment according to claim 1, characterized in that: The executing of the corresponding target GIS device fault handling strategy also includes: Establish a knowledge base of fault type-handling measures mapping; Performing a fuzzy logic comprehensive evaluation on the contact fault result and the high temperature area fault result according to the fault type-handling measure mapping knowledge base to calculate the fault confidence; When the fault confidence exceeds a preset first confidence threshold, a target GIS equipment fault handling strategy generated based on the fault type-handling measure mapping knowledge base is executed.

9. A multi-source data monitoring and processing system for GIS equipment, characterized in that: include: An acquisition module is used to acquire, in real time, environmental impact data corresponding to insulation characteristics of a target GIS device, as well as density data and pressure data of internal gas of the target GIS device during operation; An insulation monitoring module, configured to input the environmental impact data, the density data, and the pressure data into a pre-built insulation monitoring model to obtain an insulation prediction result of the target GIS device, and generate a fault handling signal according to the insulation prediction result; a response module, configured to execute a contact fault monitoring mode and a high temperature fault monitoring mode in response to the fault processing signal; A first identification module is configured to locate, in the contact fault monitoring mode, contact feature points of the circuit breaker of the target GIS device in the acquired image data set according to a corner point detection algorithm, determine trajectory information of the contact feature points under selected typical working conditions based on an optical flow method, and intelligently identify the trajectory information to obtain a contact fault result of the target GIS device; a second identification module configured to, in the high-temperature fault monitoring mode, identify an abnormally high-temperature area of ​​the target GIS device based on the infrared image data set acquired in real time, compare the abnormally high-temperature area with the partial discharge signal detection data within the abnormally high-temperature area, and determine a high-temperature area fault result of the target GIS device based on the comparison result; An execution module, configured to execute a corresponding target GIS equipment fault handling strategy based on the obtained contact fault result and / or the high temperature area fault result; The insulation prediction results include the material aging rate and internal gas insulation strength of the target GIS equipment; The insulation monitoring module is further used for: Comparing the material aging rate with a preset aging rate threshold, and generating the fault processing signal if the material aging rate is greater than the aging rate threshold; Alternatively, the internal gas insulation strength is compared with a preset insulation strength threshold, and if the internal gas insulation strength is less than the insulation strength threshold, the fault processing signal is generated.

10. The multi-source data monitoring and processing system for GIS equipment according to claim 9, characterized in that: The environmental impact data includes: temperature data and humidity data; The insulation monitoring module is further used for: Acquire historical environmental impact data of the target GIS device, as well as historical density data and historical pressure data of gas inside the target GIS device; Performing time alignment processing on the historical environmental impact data, historical density data, and historical pressure data based on an interpolation processing algorithm to obtain multi-source monitoring data; An initial insulation monitoring model is constructed based on a random forest algorithm, and the multi-source monitoring data is input into the initial insulation monitoring model for training to obtain a trained insulation monitoring model.

11. The multi-source data monitoring and processing system for GIS equipment according to claim 9, characterized in that: The first identification module is further configured to: Performing grayscale conversion, filtering, and edge detection on the image data set in sequence to obtain an image data set to be extracted, wherein the image data set to be extracted includes a plurality of consecutive frames of images to be extracted; Calculating the autocorrelation matrix of each pixel in the image to be extracted, and calculating the corner point response function value according to the eigenvalue of the autocorrelation matrix; Pixel points whose corner point response function values ​​are greater than a preset threshold are taken as corner points to construct a corner point dataset. Based on the acquired positional features and shape features of the circuit breaker contacts, the corner points belonging to the circuit breaker contacts in the corner point dataset are screened out as the contact feature points.

12. The multi-source data monitoring and processing system for GIS equipment according to claim 9, characterized in that: The trajectory information includes movement speed and movement time; The first identification module is further configured to: Obtaining the coordinate position of the contact feature point in each frame of the image to be extracted in the image data set; Calculating the displacement data of the feature points between adjacent frames based on the coordinate positions of the images to be extracted in adjacent frames; The time interval data between adjacent frames is acquired, and the moving speed and moving time of the contact feature point are calculated according to the displacement data and the time interval data.

13. The multi-source data monitoring and processing system for GIS equipment according to claim 12, wherein: The first identification module is further configured to: If the moving time is greater than a preset time threshold, it is determined that the circuit breaker of the target GIS device is faulty; Alternatively, if the moving speed is less than a preset speed threshold, it is determined that the circuit breaker of the target GIS device is faulty.

14. The multi-source data monitoring and processing system for GIS equipment according to claim 9, wherein: The second identification module is further configured to: Performing image enhancement and noise removal processing on the infrared image data set in sequence to obtain an image to be recognized; The image to be identified is binarized based on a pre-selected binarization threshold to obtain a binarized image, and the abnormally high temperature area of ​​the target GIS device is identified based on the binarized image.

15. The multi-source data monitoring and processing system for GIS equipment according to claim 14, characterized in that: The second identification module is further configured to: Acquire the partial discharge signal of the target GIS device based on the partial discharge acquisition device, and perform filtering processing on the partial discharge signal; Time-synchronizing the filtered partial discharge signal with infrared image data generated based on the high-temperature area; The infrared image data after time synchronization is correlated with the partial discharge signal, the fault type is determined according to the analysis result, and a high temperature area fault result corresponding to the fault type is generated.

16. The multi-source data monitoring and processing system for GIS equipment according to claim 9, characterized in that: The execution module is further configured to: Establish a knowledge base of fault type-handling measures mapping; Performing a fuzzy logic comprehensive evaluation on the contact fault result and the high temperature area fault result according to the fault type-handling measure mapping knowledge base to calculate the fault confidence; When the fault confidence exceeds a preset first confidence threshold, a target GIS equipment fault handling strategy generated based on the fault type-handling measure mapping knowledge base is executed.

17. A multi-source data monitoring and processing device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the multi-source data monitoring processing method according to any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the multi-source data monitoring and processing method according to any one of claims 1 to 8 is implemented.

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