Electrified detection method and system for ultraviolet imaging of power equipment

Abstract

The invention discloses a power equipment ultraviolet imaging live detection method and system, relates to the technical field of ultraviolet imaging live detection, and aims to solve the problem of inaccurate imaging live detection data. The live detection result can be displayed from different angles through different data types, detection personnel can more comprehensively understand the state of the equipment, in the report generation process, when the report is displayed on the display terminal, abnormal early warning is synchronously carried out according to the early warning information, real-time monitoring and early warning can be achieved, and the detection efficiency is improved. According to the method, unexpected power failure and damage caused by equipment faults are prevented, early warning processing of different intensities is carried out according to different abnormal degrees, it can be ensured that maintainers know the severity of the discharge problem in time, corresponding maintenance measures are taken, damage to power equipment and occurrence of safety accidents are avoided, and the working efficiency is improved. The deep learning model is trained by using historical data, complex modes and laws of discharge characteristics can be learned, and the analysis accuracy of live recognition data is improved.

Description

Technical Field

[0001] The invention relates to the technical field of ultraviolet imaging charging detection, and in particular to an ultraviolet imaging charging detection method and system for electric power equipment. Background Art

[0002] UV imaging live testing refers to the use of UV cameras to capture UV light signals emitted from the surface of power equipment and perform image analysis to identify and evaluate potential defects and faults.

[0003] The Chinese patent with publication number CN114034995A discloses a UV imager and a method for using the same. The method mainly collects existing UV typical defect maps and establishes a typical defect fault database for external insulation equipment by setting a database management module. The original UV features are analyzed by an image intelligent processing module and matched and compared with the original features of the typical library, thereby realizing intelligent identification detection. The detection monitoring module detects, records and analyzes the detection operations of discharge detection personnel according to the standardized process and standardized operation template of UV imaging live detection operations. The detection report generation module combines the detection operation requirements of the detection monitoring module for the detection personnel and the processing results of the UV image by the image intelligent processing module to generate periodic reports of the monitoring equipment at regular intervals, which is convenient for the standardization and standardization of inspection operations. Although the above patent solves the problem of using UV imagers, the following problems still exist in actual operation:

[0004] 1. The collected data is not effectively processed and image fused, resulting in the inability to extract key features more accurately.

[0005] 2. The processed image data is not effectively confirmed for data features, and the feature data is not subjected to more accurate data analysis and abnormality judgment, resulting in the inability to judge the detection results with the highest efficiency.

[0006] 3. The final test results were not generated and presented in an inspection report, which resulted in the staff being unable to make faster abnormal decisions based on the test results. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for ultraviolet imaging live detection of electric equipment. Different data types can be used to display the results of live detection from different angles, which helps detection personnel to understand the status of the equipment more comprehensively. During the report generation process, when the report is displayed on the display terminal, an abnormal warning is synchronously performed according to the warning information, which helps to achieve real-time monitoring and warning, and prevent unexpected power outages and damage caused by equipment failures. Different intensities of warning processing are performed according to different degrees of abnormality, which can ensure that maintenance personnel are aware of the severity of the discharge problem in a timely manner, take corresponding maintenance measures, avoid damage to power equipment and the occurrence of safety accidents, and solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The method for detecting live power equipment by ultraviolet imaging includes the following steps:

[0010] S1: Equipment detection and optical signal collection: Before collecting optical signals, the collection equipment is first tested. After all the equipment is tested and qualified, optical signal collection is performed. After the collection is completed, the signal data to be processed is obtained.

[0011] S2: Image generation and processing: Generate an image of the signal data to be processed, perform image preprocessing on the generated image, and obtain a target processed image after image preprocessing;

[0012] S3: Image fusion: fusing the target processed image, which includes an ultraviolet light image and a visible light image, to obtain a target fused image;

[0013] S4: Fusion image recognition: the target fusion image is used for discharge target recognition, and the discharge target in the recognized target fusion image is marked. After marking, the discharge target is integrated to obtain the charged recognition data;

[0014] S5: Identification data analysis and early warning: Analyze the live identification data, screen abnormal data based on the analysis results, judge the degree of abnormality of the screened abnormal data, and issue an early warning based on the judgment results.

[0015] Preferably, in S1, the acquisition equipment is tested, and optical signal acquisition is performed after all the equipment is tested to be qualified, including:

[0016] The acquisition equipment includes an ultraviolet imager, which collects ultraviolet light signals and visible light signals;

[0017] Before collecting ultraviolet and visible light signals, the ultraviolet imager is first tested;

[0018] Among them, the equipment inspection of UV imagers includes appearance inspection, function inspection, performance inspection and safety inspection;

[0019] After all the equipment are tested and qualified, signal collection will be carried out; if the equipment is tested and qualified, repair or replacement will be carried out;

[0020] The ultraviolet imager converts the collected ultraviolet light signals and visible light signals into digital signals, and performs digital signal processing after the conversion is completed;

[0021] Digital signal processing includes signal amplification and filtering;

[0022] After digital signal processing, the signal data to be processed is obtained.

[0023] Preferably, in step S2, the image of the signal data to be processed is generated, and the generated image is subjected to image preprocessing, including:

[0024] Reconstructing the signal data to be processed into digital signals by using digital signal processing technology, and forming preliminary image data of the signal data to be processed after the digital signal reconstruction;

[0025] The preliminary image data of the signal data to be processed includes a preliminary ultraviolet light image and a preliminary visible light image;

[0026] Performing image preprocessing on the preliminary ultraviolet light image and the preliminary visible light image;

[0027] Image preprocessing includes denoising, enhancing, rectifying, cropping and scaling the image;

[0028] Unifying the formats of the preliminary ultraviolet light image and the preliminary visible light image after image preprocessing;

[0029] After the format is unified, the target processed image is obtained.

[0030] Preferably, performing image enhancement on the preliminary ultraviolet light image and the preliminary visible light image comprises:

[0031] Extracting grayscale values ​​of pixels contained in the preliminary ultraviolet image as first grayscale value data;

[0032] Extracting the central grayscale value of the preliminary visible light image;

[0033] Using the central grayscale value of the preliminary visible light image and the central grayscale value of the preliminary ultraviolet light image to obtain a difference between the central grayscale values ​​as an observed difference;

[0034] Obtaining a preliminary ultraviolet image enhancement coefficient using the first gray value data and the central gray value of the preliminary visible light image in combination with an observed difference;

[0035] Wherein, the preliminary ultraviolet image enhancement coefficient is obtained by the following formula:

[0036]

[0037] Among them, S z represents the preliminary ultraviolet image enhancement coefficient; n represents the number of gray values ​​contained in the first gray value data; H 01i represents the gray value corresponding to the i-th first gray value data; H 01i+1 represents the grayscale value corresponding to the i+1th first grayscale value data; H 01z Represents the central gray value of the preliminary UV image; H 02z Represents the central gray value of the preliminary visible light image; H 01b represents the standard deviation of the grayscale value corresponding to the first grayscale value data; H 02b represents the gray value standard deviation corresponding to the second gray value data;

[0038] The contrast of the preliminary ultraviolet light image and the preliminary visible light image is adjusted by using the preliminary ultraviolet light image enhancement coefficient.

[0039] Preferably, using the preliminary ultraviolet image enhancement coefficient to adjust the contrast of the preliminary ultraviolet image and the preliminary visible light image includes:

[0040] Extracting grayscale values ​​of pixels contained in the preliminary visible light image as second grayscale value data;

[0041] Extracting the central grayscale value and the observed difference value of the preliminary ultraviolet image;

[0042] Obtaining a preliminary visible light image enhancement coefficient by using the central grayscale value and the observed difference of the preliminary ultraviolet image in combination with each grayscale value data in the second grayscale value data;

[0043] The preliminary visible light image enhancement coefficient is obtained by the following formula:

[0044]

[0045] Among them, S k represents the preliminary visible light image enhancement coefficient; m represents the number of grayscale data contained in the second grayscale value data; H 02i represents the gray value corresponding to the i-th second gray value data; H 02i+1 represents the gray value corresponding to the i+1th second gray value data; H 01z Represents the central gray value of the preliminary UV image; H 02z represents the central grayscale value of the preliminary visible light image; f represents the adjustment factor, and the adjustment factor is obtained by the following formula:

[0046]

[0047] Where, f represents the adjustment factor; H 01b represents the standard deviation of the grayscale value corresponding to the first grayscale value data; H 02b represents the gray value standard deviation corresponding to the second gray value data;

[0048] The contrast between the preliminary ultraviolet light image and the preliminary visible light image is adjusted using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient.

[0049] Preferably, adjusting the contrast of the preliminary ultraviolet light image and the preliminary visible light image by using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient comprises:

[0050] Extracting preliminary visible light image enhancement coefficients and preliminary ultraviolet light image enhancement coefficients;

[0051] Obtaining a comprehensive contrast adjustment coefficient using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient;

[0052] The comprehensive contrast adjustment coefficient is obtained by the following formula:

[0053]

[0054] Among them, S x Comprehensive contrast adjustment factor; S k represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 Respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient;

[0055] Performing contrast adjustment on the preliminary visible light image using the comprehensive contrast adjustment coefficient to obtain a preliminary visible light image after contrast adjustment;

[0056] The contrast value corresponding to the preliminary visible light image after contrast adjustment is obtained by the following formula:

[0057]

[0058] Among them, D k represents the contrast value corresponding to the preliminary visible light image after contrast adjustment; D k0 Indicates the contrast value of the preliminary visible light image before adjustment; S x Comprehensive contrast adjustment factor; Sk represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 Respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient;

[0059] Using the comprehensive contrast adjustment coefficient to perform contrast adjustment on the preliminary ultraviolet image to obtain a preliminary ultraviolet image after contrast adjustment;

[0060] The contrast value corresponding to the preliminary ultraviolet image after contrast adjustment is obtained by the following formula:

[0061]

[0062] Among them, D z represents the contrast value corresponding to the preliminary ultraviolet image after contrast adjustment; D z0 Indicates the contrast value before adjustment corresponding to the preliminary ultraviolet image; S x Comprehensive contrast adjustment factor; S k represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 They respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient.

[0063] Preferably, performing image fusion on the target processed image in S3 includes:

[0064] The preliminary ultraviolet image and the preliminary visible light image in the target processing image are aligned by using an image registration technology, and in the image alignment process, a feature point matching model is used to perform alignment processing, and the feature point matching model is retrieved from a database;

[0065] After the preliminary ultraviolet image and the preliminary visible light image are aligned, the preliminary ultraviolet image and the preliminary visible light image are merged using a pixel-level fusion method;

[0066] After the images are merged, image fusion processing is performed. The image fusion processing is to calculate the weighted average value of each corresponding pixel of the preliminary ultraviolet light image and the preliminary visible light image. After the calculation is completed, the pixel value of the preliminary ultraviolet light image and the preliminary visible light image after fusion is obtained;

[0067] Performing contrast enhancement and brightness adjustment on the preliminary ultraviolet light image and the preliminary visible light image after image fusion processing;

[0068] After contrast enhancement and brightness adjustment, the target fused image is obtained.

[0069] Preferably, the target fusion image is subjected to discharge target identification in S4, and the discharge targets in the identified target fusion image are marked, and then integrated after marking, including:

[0070] The target fusion image is separated from the foreground and background by edge detection, wherein the foreground in the target fusion image is the discharge target recognition area, and the background in the target fusion image is the remaining area except the foreground;

[0071] Extract features of the foreground in the target fusion image, including shape features, texture features, spectrum features, color features, brightness features, time data, intensity data, position data and spatial relationship features;

[0072] The extracted features are used to identify discharge targets using convolutional neural networks;

[0073] Marking and integrating the identified discharge targets;

[0074] Finally, the charged identification data is obtained.

[0075] Preferably, the charged identification data is analyzed in S5, abnormal data is screened according to the analysis result, the abnormal degree of the screened abnormal data is judged, and an early warning is issued according to the judgment result, including:

[0076] The charged identification data is preprocessed, and the data preprocessing includes removing noise, processing missing data and normalizing data;

[0077] Use feature selection algorithm to extract key indicator data of discharge characteristics from charged identification data;

[0078] Using historical data to train a deep learning model, wherein the historical data and the deep learning model are retrieved from a database;

[0079] After the deep learning model training is completed, the key indicator data of the discharge characteristics in the charged identification data are analyzed;

[0080] The analysis results are confirmed as abnormal data, wherein the analysis results are compared with the standard data for data thresholds, and according to the threshold comparison results, the analysis results that are not within the standard data threshold range are marked as abnormal data;

[0081] Determine the degree of abnormality based on the abnormal threshold range of abnormal data;

[0082] The degree of abnormality is divided into mild abnormality, moderate abnormality and severe abnormality;

[0083] Carry out early warning processing of different intensities according to different abnormality levels;

[0084] Abnormal data and non-abnormal data, as well as the abnormal degree and warning information of abnormal data are uniformly marked as standard live detection data.

[0085] The UV imaging live detection system for power equipment includes:

[0086] Live detection report generation unit, used for:

[0087] Generate a report using a visualization tool for standard live-test data, wherein the report layout is designed before generating the report;

[0088] The report layout includes text data, image data, table data and chart data. The chart data includes pie charts, curve charts, heat maps and bar charts.

[0089] After the report layout design is completed, use the visualization tool to generate a report of the abnormal data and non-abnormal data in the standard live detection data;

[0090] The abnormal degree and warning information in the abnormal data are marked separately;

[0091] Finally, the generated report is transmitted to the display terminal for display. When the report is displayed, an abnormal warning is synchronously issued according to the warning information.

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] 1. The UV imaging live detection method and system for electric equipment provided by the present invention can avoid long-term downtime or damage caused by equipment failure through timely equipment detection and repair and replacement. This helps to reduce the maintenance cost of electric equipment and extend the service life of the equipment. The image fusion technology can realize rapid detection of the state of electric equipment. Through one shot and processing, a fused image containing discharge information and morphological information can be obtained, without multiple shots and separate analysis, which improves the detection efficiency.

[0094] 2. The UV imaging live detection method and system for electric power equipment provided by the present invention can perform early warning processing of different intensities according to different degrees of abnormality, which can ensure that maintenance personnel are aware of the severity of the discharge problem in a timely manner and take corresponding maintenance measures to avoid damage to power equipment and safety accidents. The deep learning model is trained using historical data to learn the complex patterns and laws of discharge characteristics, improve the analysis accuracy of live identification data, and achieve a comprehensive description and accurate identification of discharge targets by fusing multiple feature information. The convolutional neural network has a strong generalization ability and can adapt to the discharge target identification needs in different scenarios.

[0095] 3. The UV imaging live detection method and system for electric power equipment provided by the present invention can display the results of live detection from different angles with different data types, which helps the inspectors to understand the status of the equipment more comprehensively. During the report generation process, abnormal data and its abnormality degree and warning information are separately marked, which helps the inspectors to quickly identify and locate the problem, so as to take timely and effective measures. When the report is displayed on the display terminal, abnormal warning is synchronously issued according to the warning information, which helps to achieve real-time monitoring and warning, and prevent unexpected power outages and damage caused by equipment failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 It is a schematic diagram of the ultraviolet imaging charging detection steps of the present invention;

[0097] Figure 2 It is a schematic diagram of the ultraviolet imaging charging detection process of the present invention. DETAILED DESCRIPTION

[0098] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] In order to solve the problem in the prior art that after collecting ultraviolet light signals and visible light signals, the collected data are not effectively processed and image fused, resulting in the inability to extract key features more accurately, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0100] The method for detecting live power equipment by ultraviolet imaging includes the following steps:

[0101] S1: Equipment detection and optical signal collection: Before collecting optical signals, the collection equipment is first tested. After all the equipment is tested and qualified, optical signal collection is performed. After the collection is completed, the signal data to be processed is obtained.

[0102] Among them, the detection efficiency can be significantly improved through automated and standardized equipment detection and signal acquisition processes;

[0103] S2: Image generation and processing: Generate an image of the signal data to be processed, perform image preprocessing on the generated image, and obtain a target processed image after image preprocessing;

[0104] Among them, the preliminary ultraviolet image and the preliminary visible light image are processed in a unified format to ensure that they have the same resolution, color space and other parameters;

[0105] S3: Image fusion: fusing the target processed image, which includes an ultraviolet light image and a visible light image, to obtain a target fused image;

[0106] Among them, by training the machine learning model, the discharge phenomenon and morphological information in the fused image can be automatically identified and analyzed, further improving the automation and accuracy of the detection;

[0107] S4: Fusion image recognition: the target fusion image is used for discharge target recognition, and the discharge target in the recognized target fusion image is marked. After marking, the discharge target is integrated to obtain the charged recognition data;

[0108] Among them, the flexibility and robustness of image processing are further enhanced by integrating information from different sources;

[0109] S5: Identification data analysis and early warning: Analyze the live identification data, screen abnormal data according to the analysis results, judge the abnormal degree of the screened abnormal data, and issue an early warning according to the judgment results;

[0110] Among them, different intensities of early warning processing are carried out according to different degrees of abnormality, which can ensure that maintenance personnel can understand the severity of the discharge problem in time and take corresponding maintenance measures.

[0111] The acquisition equipment in S1 is tested. After all the equipment passes the test, optical signal acquisition is performed, including:

[0112] The acquisition equipment includes an ultraviolet imager, which collects ultraviolet light signals and visible light signals;

[0113] Before collecting ultraviolet and visible light signals, the ultraviolet imager is first tested;

[0114] Among them, the equipment inspection of UV imagers includes appearance inspection, function inspection, performance inspection and safety inspection;

[0115] After all the equipment are tested and qualified, signal collection will be carried out; if the equipment is tested and qualified, repair or replacement will be carried out;

[0116] The ultraviolet imager converts the collected ultraviolet light signals and visible light signals into digital signals, and performs digital signal processing after the conversion is completed;

[0117] Digital signal processing includes signal amplification and filtering;

[0118] After digital signal processing, the signal data to be processed is obtained.

[0119] Specifically, by conducting strict equipment testing (including appearance inspection, function testing, performance testing and safety testing) on ​​the UV imager, its accuracy and reliability in collecting UV and visible light signals can be ensured. This helps to improve the accuracy of subsequent digital signal processing and data analysis, so as to more accurately judge the operating status of power equipment. The equipment testing process not only includes basic functional inspection of the UV imager, but also covers performance and safety evaluation. This helps to promptly discover and solve potential equipment problems, avoid failures or errors during the acquisition process, and thus enhance the overall reliability of the equipment. The UV imager can collect UV and visible light signals at the same time, which provides rich data support for the comprehensive evaluation of the status of power equipment. In addition, through digital signal conversion and processing (including signal amplification and filtering), the signal quality can be further optimized, the readability and analyzability of the signal can be improved, and the detection efficiency can be significantly improved through automated and standardized equipment detection and signal acquisition processes. Once the equipment is qualified, signal acquisition and processing can be carried out immediately without additional waiting time. This helps shorten the detection cycle and improve the timeliness of live detection of power equipment. Through timely equipment detection and repair and replacement, long downtime or damage caused by equipment failure can be avoided. This helps reduce the maintenance cost of power equipment and extend the service life of the equipment.

[0120] In step S2, the signal data to be processed is image-generated, and the generated image is image-preprocessed, including:

[0121] Reconstructing the signal data to be processed into digital signals by using digital signal processing technology, and forming preliminary image data of the signal data to be processed after the digital signal reconstruction;

[0122] The preliminary image data of the signal data to be processed includes a preliminary ultraviolet light image and a preliminary visible light image;

[0123] Performing image preprocessing on the preliminary ultraviolet light image and the preliminary visible light image;

[0124] Image preprocessing includes denoising, enhancing, rectifying, cropping and scaling the image;

[0125] Unifying the formats of the preliminary ultraviolet light image and the preliminary visible light image after image preprocessing;

[0126] After the format is unified, the target processed image is obtained.

[0127] Specifically, the preliminary ultraviolet image and the preliminary visible light image may contain various noises, such as granular temporal and spatial random flashing bright spots. Through the denoising step in image preprocessing, the interference of these noises can be effectively reduced and the clarity of the image can be improved. The image features of the discharge area may be relatively weak and difficult to observe directly. Image enhancement technology can highlight the features of these discharge areas and make them more obvious, which is convenient for subsequent analysis and judgment. Through image correction, these distortions or distortions can be eliminated to ensure the accuracy of the image. Cropping can remove irrelevant parts of the image and highlight the discharge area. Scaling can adjust the size of the image to make it more suitable for subsequent analysis and processing. The preliminary ultraviolet image and the preliminary visible light image are processed in a unified format to ensure that they have the same parameters such as resolution and color space. This facilitates subsequent steps such as image fusion, feature extraction, and pattern recognition. The images after the unified format can be more conveniently fused, and the images of the ultraviolet imaging channel are superimposed on the images of the visible light channel. This helps to observe the discharge phenomenon more intuitively and improve the efficiency and accuracy of detection.

[0128] Specifically, performing image enhancement on the preliminary ultraviolet light image and the preliminary visible light image includes:

[0129] Extracting grayscale values ​​of pixels contained in the preliminary ultraviolet image as first grayscale value data;

[0130] Extracting the central grayscale value of the preliminary visible light image;

[0131] Using the central grayscale value of the preliminary visible light image and the central grayscale value of the preliminary ultraviolet light image to obtain a difference between the central grayscale values ​​as an observed difference;

[0132] Obtaining a preliminary ultraviolet image enhancement coefficient using the first gray value data and the central gray value of the preliminary visible light image in combination with an observed difference;

[0133] Wherein, the preliminary ultraviolet image enhancement coefficient is obtained by the following formula:

[0134]

[0135] Among them, S z represents the preliminary ultraviolet image enhancement coefficient; n represents the number of gray values ​​contained in the first gray value data; H 01i represents the gray value corresponding to the i-th first gray value data; H 01i+1 represents the grayscale value corresponding to the i+1th first grayscale value data; H 01z Represents the central gray value of the preliminary UV image; H 02z Represents the central gray value of the preliminary visible light image; H 01brepresents the standard deviation of the grayscale value corresponding to the first grayscale value data; H 02b represents the gray value standard deviation corresponding to the second gray value data;

[0136] The contrast of the preliminary ultraviolet light image and the preliminary visible light image is adjusted by using the preliminary ultraviolet light image enhancement coefficient.

[0137] The technical effect of the above technical solution is: by calculating the difference between the central grayscale values ​​of the preliminary ultraviolet image and the preliminary visible light image as the observed difference, and using the difference and grayscale value data to calculate the preliminary ultraviolet image enhancement coefficient, the contrast of the image is adjusted. This process can significantly improve the contrast of the image, making the details in the image clearer. Image enhancement not only improves the contrast, but also enriches the details of the image by adjusting the grayscale value distribution. This is of great significance for subsequent image analysis, target detection and other tasks. The technical solution can calculate the enhancement coefficient according to the actual grayscale value distribution of the preliminary ultraviolet image and the preliminary visible light image, so it has strong adaptability. Different images can get different enhancement effects, so as to meet different application requirements. By calculating statistical information such as the standard deviation of the grayscale value and adjusting it in combination with the central grayscale value, the technical solution can maintain the overall contrast of the image while taking into account the enhancement of local details. This helps to avoid the problem of local overexposure or underexposure of the image during the enhancement process. In the field of ultraviolet image detection, the technical solution can significantly improve the image quality, thereby improving the accuracy and reliability of detection. This is of great significance to the fields of environmental monitoring, material analysis, etc. In addition to ultraviolet images, this technical solution is also applicable to the enhancement and optimization of visible light images. By adjusting the contrast and enriching the details, the visualization effect and application value of visible light images can be significantly improved. This technical solution can also provide technical support for multi-spectral image fusion. By calculating the enhancement coefficients of different spectral images and fusing them, richer and more comprehensive image information can be obtained, providing better data support for subsequent image analysis, target recognition and other tasks.

[0138] In summary, the above technical solution calculates the enhancement coefficient by calculating the difference between the central grayscale values ​​of the preliminary ultraviolet image and the preliminary visible light image and the grayscale value data, and adjusts the contrast of the image, which has significant technical effects and application prospects. It can not only improve the quality and detail richness of the image, but also has strong adaptability and wide application value.

[0139] Specifically, using the preliminary ultraviolet image enhancement coefficient to adjust the contrast of the preliminary ultraviolet image and the preliminary visible light image includes:

[0140] Extracting grayscale values ​​of pixels contained in the preliminary visible light image as second grayscale value data;

[0141] Extracting the central grayscale value and the observed difference value of the preliminary ultraviolet image;

[0142] Obtaining a preliminary visible light image enhancement coefficient by using the central grayscale value and the observed difference of the preliminary ultraviolet image in combination with each grayscale value data in the second grayscale value data;

[0143] The preliminary visible light image enhancement coefficient is obtained by the following formula:

[0144]

[0145] Among them, S k represents the preliminary visible light image enhancement coefficient; m represents the number of grayscale data contained in the second grayscale value data; H 02i represents the gray value corresponding to the i-th second gray value data; H 02i+1 represents the gray value corresponding to the i+1th second gray value data; H 01z Represents the central gray value of the preliminary UV image; H 02z represents the central grayscale value of the preliminary visible light image; f represents the adjustment factor, and the adjustment factor is obtained by the following formula:

[0146]

[0147] Where, f represents the adjustment factor; H 01b represents the standard deviation of the grayscale value corresponding to the first grayscale value data; H 02b represents the gray value standard deviation corresponding to the second gray value data;

[0148] The contrast between the preliminary ultraviolet light image and the preliminary visible light image is adjusted using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient.

[0149] The technical effect of the above technical solution is: by calculating the enhancement coefficients of the preliminary ultraviolet image and the preliminary visible light image and adjusting the contrast of the two images, the dynamic range of the image can be significantly expanded. This means that the details of the bright and dark parts of the image can be better preserved and displayed, thereby improving the overall visual effect of the image. The technical solution uses information such as the central grayscale value, the observed difference, and the standard deviation of the grayscale value to calculate the enhancement coefficient, which helps to achieve image contrast balance. This not only avoids the problem of overexposure or underexposure of the image, but also makes different areas in the image more visually balanced and coordinated. In the process of contrast adjustment, the technical solution focuses on preserving and enhancing the details of the image. By finely adjusting the grayscale value of each pixel, the texture, edge and other detail information in the image can be made clearer and more prominent. While enhancing the image details, the technical solution can also suppress the noise in the image to a certain extent. This is because when calculating the enhancement coefficient, statistical information such as the standard deviation of the grayscale value is considered, which helps to distinguish between noise and real signals, thereby effectively suppressing noise. The technical solution has strong adaptability. It can calculate the enhancement coefficient based on the actual gray value distribution of the preliminary ultraviolet image and the preliminary visible light image, so it is suitable for image contrast adjustment under different scenes and lighting conditions. By adjusting the parameters in the formula (such as the adjustment factor f), the intensity of the contrast adjustment can be flexibly controlled. This provides users with more options to optimize the image effect according to actual needs. When calculating the enhancement coefficient, this technical solution mainly relies on statistical information such as the standard deviation of the gray value and the center gray value. The calculation of this information is relatively simple and efficient, so a faster processing speed can be achieved while ensuring image quality. Due to its high computational efficiency, this technical solution can be applied to real-time image processing systems. For example, in the fields of video surveillance, autonomous driving, etc., real-time contrast adjustment of images can be achieved, thereby improving the performance and reliability of the system.

[0150] In summary, the technical effects of the above technical solution in terms of performance indicators are mainly reflected in the significant improvement of contrast adjustment effect, image detail retention and enhancement, adaptability and flexibility, and computational efficiency and real-time performance. These effects make the technical solution have broad application prospects and potential value in the field of image processing.

[0151] Specifically, adjusting the contrast of the preliminary ultraviolet light image and the preliminary visible light image by using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient includes:

[0152] Extracting preliminary visible light image enhancement coefficients and preliminary ultraviolet light image enhancement coefficients;

[0153] Obtaining a comprehensive contrast adjustment coefficient using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient;

[0154] The comprehensive contrast adjustment coefficient is obtained by the following formula:

[0155]

[0156] Among them, S x Comprehensive contrast adjustment factor; S k represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 Respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient;

[0157] Performing contrast adjustment on the preliminary visible light image using the comprehensive contrast adjustment coefficient to obtain a preliminary visible light image after contrast adjustment;

[0158] The contrast value corresponding to the preliminary visible light image after contrast adjustment is obtained by the following formula:

[0159]

[0160] Among them, D k represents the contrast value corresponding to the preliminary visible light image after contrast adjustment; D k0 Indicates the contrast value of the preliminary visible light image before adjustment; S x Comprehensive contrast adjustment factor; S k represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 Respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient;

[0161] Using the comprehensive contrast adjustment coefficient to perform contrast adjustment on the preliminary ultraviolet image to obtain a preliminary ultraviolet image after contrast adjustment;

[0162] The contrast value corresponding to the preliminary ultraviolet image after contrast adjustment is obtained by the following formula:

[0163]

[0164] Among them, D z represents the contrast value corresponding to the preliminary ultraviolet image after contrast adjustment; D z0 Indicates the contrast value before adjustment corresponding to the preliminary ultraviolet image; S x Comprehensive contrast adjustment factor; Sk represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 They respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient.

[0165] The technical effect of the above technical solution is: by calculating the comprehensive contrast adjustment coefficient, the technical solution can comprehensively consider the contrast characteristics of the preliminary visible light image and the preliminary ultraviolet light image, and achieve more accurate and comprehensive contrast adjustment. The comprehensive contrast adjustment coefficient combines the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient, and introduces a preset weight coefficient, making the adjustment process more flexible and controllable. The technical solution provides a quantitative calculation method for the contrast value, which can accurately reflect the changes before and after the contrast adjustment, and helps users to understand the adjustment effect more intuitively. Through the quantitative control of the contrast value, the user can more accurately adjust the contrast of the image to meet different application requirements. In the process of contrast adjustment, the technical solution focuses on retaining and enhancing the details of the image. By finely adjusting the gray value of each pixel, the texture, edge and other detail information in the image can be made clearer and more prominent. Detail retention and enhancement help improve the readability and recognition of the image, and provide better data support for subsequent image analysis, target recognition and other tasks.

[0166] The image after contrast adjustment will have a significant improvement in visual effects, including brighter colors, clearer contours and richer details. The improved visual effect helps to enhance the user's viewing experience and makes the image more in line with the user's visual needs and aesthetic standards. Contrast adjustment not only improves the visual effect of the image, but also increases the amount of information in the image. By enhancing the contrast of the image, more image information can be retained and displayed. The increased amount of image information helps to improve the analysis and processing capabilities of the image and provide a better data basis for subsequent image processing tasks. The technical solution has strong adaptability. It can calculate the comprehensive contrast adjustment coefficient based on the actual contrast characteristics of the preliminary visible light image and the preliminary ultraviolet light image, so it is suitable for image contrast adjustment under different scenes and lighting conditions. By adjusting the preset weight coefficients k01 and k02, the user can flexibly control the contribution of the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient to the comprehensive contrast adjustment coefficient. The adjustability of the parameters provides users with more options to optimize the contrast adjustment effect of the image according to actual needs.

[0167] In summary, the technical effects of the above technical solution in terms of performance indicators are mainly reflected in the significant improvement of contrast adjustment effect, image detail retention and enhancement, overall improvement of image quality, and adaptability and flexibility. These effects make the technical solution have broad application prospects and potential value in the field of image processing.

[0168] Perform image fusion on the target processed image in S3, including:

[0169] The preliminary ultraviolet image and the preliminary visible light image in the target processing image are aligned by using an image registration technology, and in the image alignment process, a feature point matching model is used to perform alignment processing, and the feature point matching model is retrieved from a database;

[0170] After the preliminary ultraviolet image and the preliminary visible light image are aligned, the preliminary ultraviolet image and the preliminary visible light image are merged using a pixel-level fusion method;

[0171] After the images are merged, image fusion processing is performed. The image fusion processing is to calculate the weighted average value of each corresponding pixel of the preliminary ultraviolet light image and the preliminary visible light image. After the calculation is completed, the pixel value of the preliminary ultraviolet light image and the preliminary visible light image after fusion is obtained;

[0172] Performing contrast enhancement and brightness adjustment on the preliminary ultraviolet light image and the preliminary visible light image after image fusion processing;

[0173] After contrast enhancement and brightness adjustment, the target fused image is obtained.

[0174] Specifically, ultraviolet imaging technology can detect partial discharge phenomena on the surface of power equipment, while visible light images provide intuitive morphological information of the equipment. By combining the two through image fusion, the discharge point can be located more accurately and the detection accuracy can be improved. The application of the feature point matching model further improves the accuracy of image alignment, thereby ensuring that the fused image can truly reflect the status of the equipment. The pixel-level fusion method enables the information of the preliminary ultraviolet image and the preliminary visible light image to be fully integrated. The weighted average calculation of each corresponding pixel not only retains the discharge information in the ultraviolet image, but also incorporates the morphological information in the visible light image, enhancing the richness and completeness of the image information. Contrast enhancement and brightness adjustment further improve the visual effect of the image, making the discharge phenomenon more obvious and easy to observe and analyze. Image fusion technology can realize rapid detection of the status of power equipment. Through one shot and processing, a fused image containing discharge information and morphological information can be obtained, without multiple shots and separate analysis, which improves the detection efficiency. In addition, image fusion technology can also reduce the impact of human factors on detection results and improve the objectivity and accuracy of detection. By training machine learning models, it can automatically identify and analyze discharge phenomena and morphological information in fused images, further improving the degree of automation and accuracy of detection.

[0175] In order to solve the problem in the prior art that the processed image data is not effectively confirmed for data features, and the feature data is not analyzed and abnormality judged more accurately, which leads to the inability to judge the detection results with the highest efficiency, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0176] In S4, the target fusion image is used to identify the discharge target, and the discharge target in the identified target fusion image is marked. After the marking is completed, integration is performed, including:

[0177] The target fusion image is separated from the foreground and background by edge detection, wherein the foreground in the target fusion image is the discharge target recognition area, and the background in the target fusion image is the remaining area except the foreground;

[0178] Extract features of the foreground in the target fusion image, including shape features, texture features, spectrum features, color features, brightness features, time data, intensity data, position data and spatial relationship features;

[0179] The extracted features are used to identify discharge targets using convolutional neural networks;

[0180] Marking and integrating the identified discharge targets;

[0181] Finally, the charged identification data is obtained.

[0182] Specifically, a comprehensive feature extraction is performed on the foreground (discharge target recognition area) in the target fusion image, including shape features, texture features, spectral features, color features, brightness features, time data, intensity data, position data and spatial relationship features. These features together constitute a complete description of the discharge target, which helps to improve the accuracy of recognition. The convolutional neural network (CNN) is used to perform discharge target recognition on the extracted features, which can learn more abstract and advanced feature representations, thereby achieving accurate recognition of the discharge target. The edge detection method is used to separate the foreground and background in the target fusion image, making the discharge target recognition area clearer. This separation method not only helps to reduce the interference of background information, but also improves the flexibility of image processing. In the feature extraction stage, the scheme integrates multiple feature information and makes full use of the diversity of image data. In the target fusion stage, by integrating information from different sources, the flexibility and robustness of image processing are further enhanced. By marking and integrating the identified discharge targets, the scheme can generate charged recognition data. These data provide strong support for live detection of power equipment, help to timely discover and deal with potential discharge problems, and achieve a comprehensive description and accurate identification of discharge targets by integrating multiple feature information. The convolutional neural network has strong generalization capabilities and can adapt to the discharge target identification needs in different scenarios.

[0183] Perform data analysis on the live identification data in S5, screen abnormal data based on the analysis results, determine the abnormal degree of the screened abnormal data, and issue an early warning based on the determination results, including:

[0184] The charged identification data is preprocessed, and the data preprocessing includes removing noise, processing missing data and normalizing data;

[0185] Use feature selection algorithm to extract key indicator data of discharge characteristics from charged identification data;

[0186] Using historical data to train a deep learning model, wherein the historical data and the deep learning model are retrieved from a database;

[0187] After the deep learning model training is completed, the key indicator data of the discharge characteristics in the charged identification data are analyzed.

[0188] The analysis results are confirmed as abnormal data, wherein the analysis results are compared with the standard data for data thresholds, and according to the threshold comparison results, the analysis results that are not within the standard data threshold range are marked as abnormal data;

[0189] Determine the degree of abnormality based on the abnormal threshold range of abnormal data;

[0190] The degree of abnormality is divided into mild abnormality, moderate abnormality and severe abnormality;

[0191] Carry out early warning processing of different intensities according to different abnormality levels;

[0192] Abnormal data and non-abnormal data, as well as the abnormal degree and warning information of abnormal data are uniformly marked as standard live detection data.

[0193] Specifically, by removing noise from the data, the accuracy and reliability of the data can be improved, providing a high-quality data foundation for subsequent analysis. Missing data may affect the accuracy of the analysis results. By processing missing data through appropriate methods (such as interpolation, filling, etc.), the integrity of the data can be ensured. Normalizing the data to the same order of magnitude helps to eliminate the dimensional differences between different features and improve the accuracy and generalization ability of the model. Using feature selection algorithms to extract key indicator data of discharge characteristics can reduce the dimension of the data and improve analysis efficiency while retaining the data that has the greatest influence on discharge characteristics. Using historical data to train deep learning models can learn the complex patterns and laws of discharge characteristics and improve the analysis accuracy of live identification data. Deep learning models can process large-scale data and have strong adaptability. They can cope with different power equipment and different types of discharges. By comparing thresholds with standard data, abnormal data can be accurately confirmed to avoid false positives and negatives. Judging the degree of abnormality based on the abnormal threshold range of abnormal data is helpful to take different treatment measures for discharge problems of different severity and improve maintenance efficiency. Different intensity early warning treatments are carried out according to different abnormal degrees to ensure that maintenance personnel are aware of the severity of the discharge problem in a timely manner and take corresponding maintenance measures to avoid damage to power equipment and safety accidents. By timely discovering and handling discharge problems, the normal operation of power equipment can be ensured and the stability and safety of the power grid can be improved.

[0194] In order to solve the problem in the prior art that the final test results are not generated and presented in the inspection report, which makes it impossible for the staff to make faster abnormal decisions based on the test results, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0195] The UV imaging live detection system for power equipment includes:

[0196] Live detection report generation unit, used for:

[0197] Generate a report using a visualization tool for standard live-test data, wherein the report layout is designed before generating the report;

[0198] The report layout includes text data, image data, table data and chart data. The chart data includes pie charts, curve charts, heat maps and bar charts.

[0199] After the report layout design is completed, use the visualization tool to generate a report of the abnormal data and non-abnormal data in the standard live detection data;

[0200] The abnormal degree and warning information in the abnormal data are marked separately;

[0201] Finally, the generated report is transmitted to the display terminal for display. When the report is displayed, an abnormal warning is synchronously issued according to the warning information.

[0202] Specifically, by integrating text data, image data, table data and chart data (including pie charts, curve charts, heat maps and bar charts) into the report, the test results are made more intuitive and easy to understand. Different data types can display the results of live detection from different angles, which helps the testers to understand the status of the equipment more comprehensively. During the report generation process, abnormal data and its abnormality degree and warning information are individually marked, which helps the testers to quickly identify and locate the problem, so as to take timely and effective measures. Visualization tools can automatically convert live detection data into reports, reducing the time for manual analysis and data collation, and improving detection efficiency. Rich data display forms (such as pie charts, curve charts, etc.) can help testers analyze data more deeply, discover potential trends and problems, and provide decision support for equipment maintenance and repair. When the report is displayed on the display terminal, abnormal warnings are synchronously issued according to the warning information, which helps to achieve real-time monitoring and warnings, and prevent unexpected power outages and damage caused by equipment failures. By designing a unified report layout and data display method, the standardization and normalization of live detection reports can be ensured, which facilitates communication and collaboration between different personnel.

[0203] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0204] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for detecting power equipment charged by ultraviolet imaging, characterized in that: The steps include: S1: Equipment detection and optical signal collection: Before collecting optical signals, the collection equipment is first tested. After all the equipment is tested and qualified, optical signal collection is performed. After the collection is completed, the signal data to be processed is obtained. S2: Image generation and processing: Generate an image of the signal data to be processed, perform image preprocessing on the generated image, and obtain a target processed image after image preprocessing; S3: Image fusion: fusing the target processed image, which includes an ultraviolet light image and a visible light image, to obtain a target fused image; S4: Fusion image recognition: the target fusion image is used for discharge target recognition, and the discharge target in the recognized target fusion image is marked. After marking, the discharge target is integrated to obtain the charged recognition data; S5: Identification data analysis and early warning: Analyze the live identification data, screen abnormal data based on the analysis results, judge the degree of abnormality of the screened abnormal data, and issue an early warning based on the judgment results.

2. The method for detecting power equipment charged by ultraviolet imaging according to claim 1 is characterized in that: In S1, the acquisition equipment is tested, and optical signal acquisition is performed after all the equipment passes the test, including: The acquisition equipment includes an ultraviolet imager, which collects ultraviolet light signals and visible light signals; Before collecting ultraviolet and visible light signals, the ultraviolet imager is first tested; Among them, the equipment inspection of UV imagers includes appearance inspection, function inspection, performance inspection and safety inspection; After all the equipment are tested and qualified, signal collection will be carried out; if the equipment is tested and qualified, repair or replacement will be carried out; The ultraviolet imager converts the collected ultraviolet light signals and visible light signals into digital signals, and performs digital signal processing after the conversion is completed; Digital signal processing includes signal amplification and filtering; After digital signal processing, the signal data to be processed is obtained.

3. The method for detecting power equipment charged by ultraviolet imaging according to claim 2 is characterized in that: In step S2, the signal data to be processed is image-generated, and the generated image is image-preprocessed, including: Reconstructing the signal data to be processed into digital signals by using digital signal processing technology, and forming preliminary image data of the signal data to be processed after the digital signal reconstruction; The preliminary image data of the signal data to be processed includes a preliminary ultraviolet light image and a preliminary visible light image; Performing image preprocessing on the preliminary ultraviolet light image and the preliminary visible light image; Image preprocessing includes denoising, enhancing, rectifying, cropping and scaling the image; Unifying the formats of the preliminary ultraviolet light image and the preliminary visible light image after image preprocessing; After the format is unified, the target processed image is obtained.

4. The method for detecting power equipment charged by ultraviolet imaging according to claim 3 is characterized in that: Performing image enhancement on the preliminary ultraviolet light image and the preliminary visible light image, comprising: Extracting grayscale values ​​of pixels contained in the preliminary ultraviolet image as first grayscale value data; Extracting the central grayscale value of the preliminary visible light image; Using the central grayscale value of the preliminary visible light image and the central grayscale value of the preliminary ultraviolet light image to obtain a difference between the central grayscale values ​​as an observed difference; Obtaining a preliminary ultraviolet image enhancement coefficient using the first gray value data and the central gray value of the preliminary visible light image in combination with an observed difference; Wherein, the preliminary ultraviolet image enhancement coefficient is obtained by the following formula: Among them, S z represents the preliminary ultraviolet image enhancement coefficient; n represents the number of gray values ​​contained in the first gray value data; H 01i represents the gray value corresponding to the i-th first gray value data; H 01i+1 represents the grayscale value corresponding to the i+1th first grayscale value data; H 01z Represents the central gray value of the preliminary UV image; H 02z Represents the central gray value of the preliminary visible light image; H 01b represents the standard deviation of the grayscale value corresponding to the first grayscale value data; H 02b represents the gray value standard deviation corresponding to the second gray value data; The contrast of the preliminary ultraviolet light image and the preliminary visible light image is adjusted by using the preliminary ultraviolet light image enhancement coefficient.

5. The method for detecting power equipment charged by ultraviolet imaging according to claim 4 is characterized in that: Using the preliminary ultraviolet image enhancement coefficient to adjust the contrast of the preliminary ultraviolet image and the preliminary visible light image includes: Extracting grayscale values ​​of pixels contained in the preliminary visible light image as second grayscale value data; Extracting the central grayscale value and the observed difference value of the preliminary ultraviolet image; Obtaining a preliminary visible light image enhancement coefficient by using the central grayscale value and the observed difference of the preliminary ultraviolet image in combination with each grayscale value data in the second grayscale value data; The preliminary visible light image enhancement coefficient is obtained by the following formula: Among them, S k represents the preliminary visible light image enhancement coefficient; m represents the number of grayscale data contained in the second grayscale value data; H 02i represents the gray value corresponding to the i-th second gray value data; H 02i+1 represents the gray value corresponding to the i+1th second gray value data; H 01z Represents the central gray value of the preliminary UV image; H 02z represents the central grayscale value of the preliminary visible light image; f represents the adjustment factor, and the adjustment factor is obtained by the following formula: Where, f represents the adjustment factor; H 01b represents the standard deviation of the grayscale value corresponding to the first grayscale value data; H 02b represents the gray value standard deviation corresponding to the second gray value data; The contrast between the preliminary ultraviolet light image and the preliminary visible light image is adjusted using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient.

6. The method for detecting power equipment charged by ultraviolet imaging according to claim 5 is characterized in that: The contrast adjustment of the preliminary ultraviolet light image and the preliminary visible light image using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient comprises: Extracting preliminary visible light image enhancement coefficients and preliminary ultraviolet light image enhancement coefficients; Obtaining a comprehensive contrast adjustment coefficient using the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient; The comprehensive contrast adjustment coefficient is obtained by the following formula: Among them, S x Comprehensive contrast adjustment factor; S k represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 Respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient; Performing contrast adjustment on the preliminary visible light image using the comprehensive contrast adjustment coefficient to obtain a preliminary visible light image after contrast adjustment; The contrast value corresponding to the preliminary visible light image after contrast adjustment is obtained by the following formula: Among them, D k represents the contrast value corresponding to the preliminary visible light image after contrast adjustment; D k0 Indicates the contrast value of the preliminary visible light image before adjustment; S x Comprehensive contrast adjustment factor; S k represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 Respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient; Using the comprehensive contrast adjustment coefficient to perform contrast adjustment on the preliminary ultraviolet image to obtain a preliminary ultraviolet image after contrast adjustment; The contrast value corresponding to the preliminary ultraviolet image after contrast adjustment is obtained by the following formula: Among them, D z represents the contrast value corresponding to the preliminary ultraviolet image after contrast adjustment; D z0 Indicates the contrast value before adjustment corresponding to the preliminary ultraviolet image; S x Comprehensive contrast adjustment factor; S k represents the preliminary visible light image enhancement coefficient; S z represents the preliminary UV image enhancement coefficient; k 01 and k 02 They respectively represent the preset weight coefficients corresponding to the preliminary visible light image enhancement coefficient and the preliminary ultraviolet light image enhancement coefficient.

7. The method for detecting power equipment charged by ultraviolet imaging according to claim 3 is characterized in that: Perform image fusion on the target processed image in S3, including: The preliminary ultraviolet image and the preliminary visible light image in the target processing image are aligned by using an image registration technology, and in the image alignment process, a feature point matching model is used to perform alignment processing, and the feature point matching model is retrieved from a database; After the preliminary ultraviolet image and the preliminary visible light image are aligned, the preliminary ultraviolet image and the preliminary visible light image are merged using a pixel-level fusion method; After the images are merged, image fusion processing is performed. The image fusion processing is to calculate the weighted average value of each corresponding pixel of the preliminary ultraviolet light image and the preliminary visible light image. After the calculation is completed, the pixel value of the preliminary ultraviolet light image and the preliminary visible light image after fusion is obtained; Performing contrast enhancement and brightness adjustment on the preliminary ultraviolet light image and the preliminary visible light image after image fusion processing; After contrast enhancement and brightness adjustment, the target fused image is obtained.

8. The method for detecting power equipment charged by ultraviolet imaging according to claim 7, characterized in that: In S4, the target fusion image is used to identify the discharge target, and the discharge target in the identified target fusion image is marked. After the marking is completed, integration is performed, including: The target fusion image is separated from the foreground and background by edge detection, wherein the foreground in the target fusion image is the discharge target recognition area, and the background in the target fusion image is the remaining area except the foreground; Extract features of the foreground in the target fusion image, including shape features, texture features, spectrum features, color features, brightness features, time data, intensity data, position data and spatial relationship features; The extracted features are used to identify discharge targets using convolutional neural networks; Marking and integrating the identified discharge targets; Finally, the charged identification data is obtained.

9. The method for detecting power equipment charged by ultraviolet imaging according to claim 8, characterized in that: Perform data analysis on the live identification data in S5, screen abnormal data based on the analysis results, determine the abnormal degree of the screened abnormal data, and issue an early warning based on the determination results, including: The charged identification data is preprocessed, and the data preprocessing includes removing noise, processing missing data and normalizing data; Use feature selection algorithm to extract key indicator data of discharge characteristics from charged identification data; Using historical data to train a deep learning model, wherein the historical data and the deep learning model are retrieved from a database; After the deep learning model training is completed, the key indicator data of the discharge characteristics in the charged identification data are analyzed; The analysis results are confirmed as abnormal data, wherein the analysis results are compared with the standard data for data thresholds, and according to the threshold comparison results, the analysis results that are not within the standard data threshold range are marked as abnormal data; Determine the degree of abnormality based on the abnormal threshold range of abnormal data; The degree of abnormality is divided into mild abnormality, moderate abnormality and severe abnormality; Carry out early warning processing of different intensities according to different abnormality levels; Abnormal data and non-abnormal data, as well as the abnormal degree and warning information of abnormal data are uniformly marked as standard live detection data.

10. An ultraviolet imaging charged detection system for electric power equipment, applied in the ultraviolet imaging charged detection method for electric power equipment as claimed in claim 9, characterized in that: include: Live detection report generation unit, used for: Generate a report using a visualization tool for standard live-test data, wherein the report layout is designed before generating the report; The report layout includes text data, image data, table data and chart data. The chart data includes pie charts, curve charts, heat maps and bar charts. After the report layout design is completed, use the visualization tool to generate a report of the abnormal data and non-abnormal data in the standard live detection data; The abnormal degree and warning information in the abnormal data are marked separately; Finally, the generated report is transmitted to the display terminal for display. When the report is displayed, an abnormal warning is synchronously issued according to the warning information.

Images