Infrared-ultraviolet dual-modal partial discharge detection method based on lightweight network

Abstract

The present application relates to an infrared-ultraviolet dual-modal partial discharge detection method based on a lightweight network. The method comprises: obtaining a discharge area in an infrared image corresponding to the area to be detected, and obtaining a discharge area in an ultraviolet image corresponding to the area to be detected; inputting the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information of the infrared image and partial discharge probability information of the ultraviolet image; determining a partial discharge detection result of the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image; the partial discharge detection result represents whether there is partial discharge in the area to be detected. The present method can utilize a lightweight model to analyze whether there is partial discharge in the area to be detected, simplify the discharge detection process, shorten the discharge detection time, and improve the detection efficiency of partial discharge detection.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment detection, and in particular to a lightweight network-based infrared-ultraviolet dual-mode partial discharge detection method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] In the insulation system of power equipment, uneven electric field distribution or defects in the insulation material can lead to small-scale, non-complete breakdown discharges in localized areas. Partial discharges typically occur in high-voltage equipment such as transformers, cables, switchgear, and generators. Partial discharges can damage the insulation of power equipment and affect the safe and stable operation of the power system.

[0003] Traditional technologies for partial discharge detection require complex analysis of power equipment images, which is cumbersome and time-consuming, and is not conducive to improving the detection efficiency of partial discharge detection. Summary of the Invention

[0004] Based on this, it is necessary to provide an infrared-ultraviolet dual-modal partial discharge detection method, device, computer equipment, computer-readable storage medium and computer program product based on a lightweight network, which can improve the detection efficiency of partial discharge detection, in order to address the above technical problems.

[0005] In a first aspect, the present application provides an infrared-ultraviolet dual-mode partial discharge detection method based on a lightweight network, comprising:

[0006] Acquire a discharge area in an infrared image corresponding to the area to be detected, and acquire a discharge area in an ultraviolet image corresponding to the area to be detected;

[0007] Inputting the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information of the infrared image and partial discharge probability information of the ultraviolet image;

[0008] A partial discharge detection result of the area to be detected is determined based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image; the partial discharge detection result indicates whether partial discharge exists in the area to be detected.

[0009] In one embodiment, obtaining the discharge area in the infrared image corresponding to the area to be detected includes:

[0010] Obtaining the temperature of each pixel in the infrared image;

[0011] For each pixel in the infrared image, if the temperature corresponding to the pixel is greater than a preset temperature threshold, the pixel is taken as a candidate pixel;

[0012] The discharge area in the infrared image corresponding to the area to be detected is determined according to the candidate pixel points.

[0013] In one embodiment, obtaining the discharge area in the ultraviolet image corresponding to the area to be detected includes:

[0014] Obtaining the number of photons corresponding to each pixel in the ultraviolet image;

[0015] For each pixel in the ultraviolet image, if the number of photons corresponding to the pixel is greater than a preset photon number threshold, the pixel is taken as a candidate pixel;

[0016] The discharge area in the ultraviolet image corresponding to the area to be detected is determined according to the candidate pixel points.

[0017] In one embodiment, determining the partial discharge detection result of the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image includes:

[0018] determining a first pixel threshold according to pixel values ​​of pixels corresponding to the discharge area in the infrared image;

[0019] screening out first target pixel points from pixel points corresponding to the discharge area in the infrared image according to the first pixel threshold;

[0020] determining a second pixel threshold according to pixel values ​​of pixels corresponding to the discharge area in the ultraviolet image;

[0021] screening out second target pixel points from pixel points corresponding to the discharge area in the ultraviolet image according to the second pixel threshold;

[0022] A partial discharge detection result of the area to be detected is determined according to the number of the first target pixels, the number of the second target pixels, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image.

[0023] In one embodiment, determining the partial discharge detection result of the area to be detected based on the number of the first target pixels, the number of the second target pixels, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image includes:

[0024] taking the product of the number of the first target pixels and the partial discharge probability information of the infrared image as a first product;

[0025] taking the product of the number of the second target pixels and the partial discharge probability information of the ultraviolet image as a second product;

[0026] determining a first sum based on the sum of the first product and the second product;

[0027] determining a second sum according to the sum of the partial discharge probability information of the infrared image and the partial discharge probability information of the ultraviolet image;

[0028] A partial discharge detection result of the area to be detected is determined according to a ratio between the first sum and the second sum.

[0029] In one embodiment, determining the first sum based on the sum of the first product and the second product includes:

[0030] Obtaining a weight of the infrared image and a weight of the ultraviolet image;

[0031] The first sum is determined according to the sum of the product of the weight of the infrared image and the first product and the product of the weight of the ultraviolet image and the second product.

[0032] In one embodiment, the method further comprises:

[0033] Performing grayscale processing on the initial infrared image corresponding to the area to be detected to obtain a processed infrared image, and performing grayscale processing on the initial ultraviolet image corresponding to the area to be detected to obtain a processed ultraviolet image;

[0034] performing denoising processing on the processed infrared image according to histogram mean information of the processed infrared image to obtain an infrared image corresponding to the area to be detected;

[0035] According to the histogram mean information of the processed ultraviolet image, the processed ultraviolet image is denoised to obtain the ultraviolet image corresponding to the area to be detected.

[0036] In one embodiment, the method further comprises:

[0037] Inputting the sample infrared image and the sample ultraviolet image into the lightweight image discharge detection model to be trained to obtain partial discharge probability information of the sample infrared image and partial discharge probability information of the sample ultraviolet image;

[0038] The lightweight image discharge detection model to be trained is trained according to the difference between the partial discharge information of the sample infrared image and the partial discharge probability information of the sample infrared image, and the difference between the partial discharge information of the sample ultraviolet image and the partial discharge probability information of the sample ultraviolet image.

[0039] In a second aspect, the present application also provides an infrared-ultraviolet dual-mode partial discharge detection device based on a lightweight network, comprising:

[0040] A determination module, configured to obtain a discharge area in an infrared image corresponding to the area to be detected, and obtain a discharge area in an ultraviolet image corresponding to the area to be detected;

[0041] a prediction module, configured to input the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information of the infrared image and partial discharge probability information of the ultraviolet image;

[0042] The detection module is configured to determine a partial discharge detection result of the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image; the partial discharge detection result indicates whether partial discharge exists in the area to be detected.

[0043] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0044] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0045] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0046] The above-mentioned infrared-ultraviolet dual-modal partial discharge detection method, device, computer equipment, computer-readable storage medium and computer program product based on lightweight network accurately analyze the discharge position in the infrared image and the ultraviolet image by obtaining the discharge area in the infrared image corresponding to the area to be detected, and obtaining the discharge area in the ultraviolet image corresponding to the area to be detected; input the infrared image and the ultraviolet image into the pre-trained lightweight image discharge detection model to obtain the partial discharge probability information of the infrared image and the partial discharge probability information of the ultraviolet image, thereby accurately analyzing the probability of partial discharge in the infrared image and the ultraviolet image using the lightweight image discharge detection model; according to the discharge area in the infrared image, the discharge area in the ultraviolet image, and the discharge area in the infrared image, the discharge location in the ultraviolet image is determined. The partial discharge probability information and the partial discharge probability information of the ultraviolet image are used to determine the partial discharge detection result of the area to be detected; the partial discharge detection result characterizes whether there is partial discharge in the area to be detected, so as to accurately analyze whether there is partial discharge in the area to be detected by combining the discharge area in the infrared image, the partial discharge probability of the infrared image, the discharge area in the ultraviolet image and the partial discharge probability of the ultraviolet image. The lightweight model can be used to analyze the probability of partial discharge in the image, and combined with the discharge area in the image, it is determined whether there is partial discharge in the area to be detected. There is no need to perform complex analysis on the power equipment image, but instead the discharge detection process is simplified by combining the lightweight model, thereby shortening the discharge detection analysis time, and thus improving the detection efficiency of partial discharge detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 FIG1 is a diagram of an application environment of an infrared-ultraviolet dual-mode partial discharge detection method based on a lightweight network in one embodiment;

[0049] Figure 2 1 is a flow chart of a lightweight network-based infrared-ultraviolet dual-mode partial discharge detection method in one embodiment;

[0050] Figure 3 1. A schematic diagram of a flow chart of infrared-ultraviolet dual-modal adaptive fusion partial discharge detection based on a lightweight network in one embodiment;

[0051] Figure 4 1 is a structural block diagram of an infrared-ultraviolet dual-mode partial discharge detection device based on a lightweight network in one embodiment;

[0052] Figure 5 The figure is a diagram of the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0054] The infrared-ultraviolet dual-mode partial discharge detection method based on a lightweight network provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be integrated with server 104, or placed on a cloud or other network server. Terminal 102 obtains the discharge area in the infrared image corresponding to the area to be inspected, and obtains the discharge area in the ultraviolet image corresponding to the area to be inspected. Terminal 102 inputs the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information for the infrared image and the ultraviolet image. Terminal 102 determines a partial discharge detection result for the area to be inspected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information in the infrared image, and the partial discharge probability information in the ultraviolet image. The partial discharge detection result indicates whether partial discharge exists in the area to be inspected. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. Server 104 can be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0055] In an exemplary embodiment, Figure 2 As shown, a lightweight network-based infrared-ultraviolet dual-mode partial discharge detection method is provided, which is described by taking the application of the method to a terminal as an example, and includes the following steps S202 to S206.

[0056] Step S202 : obtaining a discharge area in the infrared image corresponding to the area to be detected, and obtaining a discharge area in the ultraviolet image corresponding to the area to be detected.

[0057] The area to be detected may refer to a part or the entire area of ​​any power equipment in the power system.

[0058] The infrared image may refer to an image formed by an infrared remote sensor receiving infrared rays reflected from an object (such as power equipment) and obtained after preprocessing.

[0059] The ultraviolet image may refer to an image obtained after pre-processing an image acquired by ultraviolet imaging detection equipment for imaging power equipment.

[0060] The discharge area may refer to an area in the area to be detected where local discharge exists.

[0061] As an example, the terminal can analyze each pixel point in the infrared image corresponding to the detection area to determine the discharge area in the infrared image corresponding to the detection area; the terminal can also analyze each pixel point in the ultraviolet image corresponding to the detection area to determine the discharge area in the ultraviolet image corresponding to the detection area.

[0062] Step S204 : inputting the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information of the infrared image and partial discharge probability information of the ultraviolet image.

[0063] Among them, the lightweight image discharge detection model can refer to a model used to determine the probability of local discharge in an image. In practical applications, the lightweight image discharge detection model (such as the FalconNet model) can include a twin neural network and two independent fully connected layers.

[0064] The partial discharge probability information may refer to information output by the lightweight image discharge detection model and used to characterize the probability of partial discharge existing in the image.

[0065] As an example, the terminal can input the infrared image and ultraviolet image corresponding to the area to be detected into a pre-trained lightweight image discharge detection model. Since the lightweight image discharge detection model includes a twin neural network and two independent fully connected layers, the output result of the twin neural network after processing the infrared image corresponding to the area to be detected is input into the first fully connected layer in the lightweight image discharge detection model. The output result of the first fully connected layer can be used as the local discharge probability information of the infrared image. The output result of the twin neural network after processing the ultraviolet image corresponding to the area to be detected is input into the second fully connected layer in the lightweight image discharge detection model. The output result of the second fully connected layer can be used as the local discharge probability information of the ultraviolet image, wherein the first fully connected layer and the second fully connected layer are independent of each other.

[0066] Step S206 , determining a partial discharge detection result of the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image.

[0067] The partial discharge detection result may refer to information indicating whether there is partial discharge in the area to be detected.

[0068] As an example, the terminal can combine the characteristics of partial discharge in the infrared image and the characteristics of partial discharge in the ultraviolet image to analyze whether partial discharge exists in the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information in the infrared image, and the partial discharge probability information in the ultraviolet image, thereby determining a partial discharge detection result for the area to be detected. In practical applications, the terminal can first calculate discharge degree information used to characterize the probability of partial discharge in the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information in the infrared image, and the partial discharge probability information in the ultraviolet image. Then, the terminal can combine the discharge degree information with a preset threshold to determine a partial discharge detection result for the area to be detected. For example, if the discharge degree information is greater than the preset threshold, the terminal can determine that the partial discharge detection result for the area to be detected is that partial discharge exists in the area to be detected; if the discharge degree information is less than or equal to the preset threshold, the terminal can determine that the partial discharge detection result for the area to be detected is that partial discharge does not exist in the area to be detected.

[0069] In the above-mentioned infrared-ultraviolet dual-modal partial discharge detection method based on lightweight network, the discharge area in the infrared image corresponding to the area to be detected and the discharge area in the ultraviolet image corresponding to the area to be detected are obtained, so as to accurately analyze the discharge position in the infrared image and the ultraviolet image; the infrared image and the ultraviolet image are input into the pre-trained lightweight image discharge detection model to obtain the partial discharge probability information of the infrared image and the partial discharge probability information of the ultraviolet image, so as to accurately analyze the probability of partial discharge in the infrared image and the ultraviolet image using the lightweight image discharge detection model; according to the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image and the ultraviolet image, the discharge area in the ultraviolet image and the discharge area in the ultraviolet image are obtained. The partial discharge probability information of the infrared image is used to determine the partial discharge detection result of the area to be detected; the partial discharge detection result characterizes whether there is partial discharge in the area to be detected, so as to accurately analyze whether there is partial discharge in the area to be detected by combining the discharge area in the infrared image, the partial discharge probability of the infrared image, the discharge area in the ultraviolet image, and the partial discharge probability of the ultraviolet image. The lightweight model can be used to analyze the probability of partial discharge in the image, and combined with the discharge area in the image, it is determined whether there is partial discharge in the area to be detected. There is no need to perform complex analysis on the power equipment image, but instead the lightweight model is used to simplify the discharge detection process, thereby shortening the discharge detection analysis time, and thus improving the detection efficiency of partial discharge detection.

[0070] In an exemplary embodiment, obtaining a discharge area in an infrared image corresponding to the area to be detected includes: obtaining the temperature of each pixel in the infrared image; for each pixel in the infrared image, if the temperature corresponding to the pixel is greater than a preset temperature threshold, taking the pixel as a candidate pixel; and determining the discharge area in the infrared image corresponding to the area to be detected based on the candidate pixel.

[0071] The preset temperature threshold may include 1.5 times of the room temperature, and the preset temperature threshold may be flexibly adjusted and set based on actual conditions.

[0072] As an example, the terminal can determine the temperature of each pixel in the infrared image corresponding to the area to be detected based on the infrared image. For each pixel in the infrared image, the terminal can analyze the relationship between the temperature corresponding to each pixel and a preset temperature threshold. If the temperature corresponding to the pixel is greater than the preset temperature threshold, the terminal can use the pixel as a candidate pixel, traverse each pixel in the infrared image, and analyze whether the pixel can be used as a candidate pixel. The terminal can then determine the discharge area in the infrared image corresponding to the area to be detected based on each candidate pixel. In actual applications, the distribution area of ​​each candidate pixel in the infrared image can be used as the discharge area in the infrared image corresponding to the area to be detected.

[0073] In this embodiment, the temperature of each pixel in the infrared image is obtained; for each pixel in the infrared image, when the temperature corresponding to the pixel is greater than a preset temperature threshold, the pixel is used as a candidate pixel; based on the candidate pixel, the discharge area in the infrared image corresponding to the area to be detected is determined, and based on the temperature of the pixel in the infrared image, it is possible to analyze whether the pixel belongs to the discharge area, thereby accurately analyzing the discharge area in the infrared image corresponding to the area to be detected, and improving the accuracy of the discharge area in the infrared image corresponding to the area to be detected, so that local discharge detection can be performed in combination with the discharge area in the infrared image corresponding to the area to be detected, thereby improving the accuracy of local discharge detection.

[0074] In some embodiments, obtaining a discharge area in an ultraviolet image corresponding to the area to be detected includes: obtaining the number of photons corresponding to each pixel in the ultraviolet image; for each pixel in the ultraviolet image, if the number of photons corresponding to the pixel is greater than a preset photon number threshold, taking the pixel as a candidate pixel; and determining the discharge area in the ultraviolet image corresponding to the area to be detected based on the candidate pixel.

[0075] The number of photons corresponding to a pixel may refer to the number of ultraviolet photons received by the pixel.

[0076] Among them, the preset photon number threshold may include a calibrated photon number. The calibrated photon number may refer to performing a partial discharge experiment on the current equipment before inspection, by creating a known standard partial discharge, and treating the number of photons generated by the partial discharge on the equipment as the calibrated photon number.

[0077] As an example, the terminal can determine the number of photons corresponding to each pixel in the ultraviolet image based on the ultraviolet image corresponding to the area to be detected. For each pixel in the ultraviolet image, the terminal can analyze the relationship between the number of photons corresponding to each pixel and a preset photon number threshold. If the number of photons corresponding to a pixel is greater than the preset photon number threshold, the terminal can use the pixel as a candidate pixel, traverse each pixel in the ultraviolet image, and analyze whether the pixel can be used as a candidate pixel. The terminal can then determine the discharge area in the ultraviolet image corresponding to the area to be detected based on each candidate pixel. In actual applications, the distribution area of ​​each candidate pixel in the ultraviolet image can be used as the discharge area in the ultraviolet image corresponding to the area to be detected.

[0078] In this embodiment, the number of photons corresponding to each pixel in the ultraviolet image is obtained; for each pixel in the ultraviolet image, when the number of photons corresponding to the pixel is greater than a preset photon number threshold, the pixel is used as a candidate pixel; based on the candidate pixel, the discharge area in the ultraviolet image corresponding to the area to be detected is determined, and based on the number of photons of the pixel in the ultraviolet image, it is possible to analyze whether the pixel belongs to the discharge area, thereby accurately analyzing the discharge area in the ultraviolet image corresponding to the area to be detected, and improving the accuracy of the discharge area in the ultraviolet image corresponding to the area to be detected, so as to subsequently perform local discharge detection in combination with the discharge area in the ultraviolet image corresponding to the area to be detected, thereby improving the accuracy of local discharge detection.

[0079] In some embodiments, a local discharge detection result of the area to be detected is determined based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image, including: determining a first pixel threshold based on the pixel value of the pixel point corresponding to the discharge area in the infrared image; screening out a first target pixel point from the pixel points corresponding to the discharge area in the infrared image based on the first pixel threshold; determining a second pixel threshold based on the pixel value of the pixel point corresponding to the discharge area in the ultraviolet image; screening out a second target pixel point from the pixel point corresponding to the discharge area in the ultraviolet image based on the second pixel threshold; and determining the local discharge detection result of the area to be detected based on the number of the first target pixel points, the number of the second target pixel points, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image.

[0080] The first pixel threshold may include a preset ratio (eg, 20%) of the maximum pixel value among the pixel values ​​of the pixel points corresponding to the discharge area in the infrared image.

[0081] The first target pixel point may refer to a pixel point whose pixel value is greater than a first pixel threshold value among the pixel points corresponding to the discharge area in the infrared image.

[0082] The second pixel threshold may include a preset ratio (eg, 20%) of the maximum pixel value among the pixel values ​​of the pixel points corresponding to the discharge area in the ultraviolet image.

[0083] The second target pixel point may refer to a pixel point whose pixel value is greater than a second pixel threshold value among the pixel points corresponding to the discharge area in the ultraviolet image.

[0084] As an example, the terminal may first determine the maximum pixel value among the pixel values ​​of the pixels corresponding to the discharge area in the infrared image based on the pixel values ​​of the pixels corresponding to the discharge area in the infrared image. The terminal may then determine a first pixel threshold e1 based on the maximum pixel value. The terminal may then filter out the pixels corresponding to the discharge area in the infrared image, using the first pixel threshold e1, and select those pixels whose pixel values ​​are greater than the first pixel threshold e1 as first target pixels. The terminal may also first determine the maximum pixel value among the pixel values ​​of the pixels corresponding to the discharge area in the ultraviolet image based on the pixel values ​​of the pixels corresponding to the discharge area in the ultraviolet image. The terminal may then determine a second pixel threshold e2 based on the maximum pixel value. The terminal may then filter out the pixels corresponding to the discharge area in the ultraviolet image, using the second pixel threshold e2, and select those pixels whose pixel values ​​are greater than the second pixel threshold e2 as second target pixels. The terminal may then determine discharge level information representing the probability of partial discharge in the detection area based on the number of first target pixels, the number of second target pixels, partial discharge probability information of the infrared image, and partial discharge probability information of the ultraviolet image. The terminal may then determine a partial discharge detection result for the detection area by combining the discharge level information with a preset threshold.

[0085] In this embodiment, a first pixel threshold is determined based on the pixel values ​​of the pixels corresponding to the discharge area in the infrared image; a first target pixel is screened out from the pixels corresponding to the discharge area in the infrared image based on the first pixel threshold; a second pixel threshold is determined based on the pixel values ​​of the pixels corresponding to the discharge area in the ultraviolet image; a second target pixel is screened out from the pixels corresponding to the discharge area in the ultraviolet image based on the second pixel threshold; and based on the number of the first target pixels, the number of the second target pixels, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image, target pixels whose pixel values ​​meet preset requirements can be first screened out from the infrared image and the ultraviolet image respectively, and the partial discharge detection result of the area to be detected can be accurately generated by combining the number of target pixels and the partial discharge probability information of the infrared image and the partial discharge probability information of the ultraviolet image output by the lightweight model. This eliminates the need for complex analysis of the power equipment image and instead simplifies the discharge detection process by combining the lightweight model, shortens the discharge detection analysis time, and improves the detection efficiency of partial discharge detection.

[0086] In some embodiments, the local discharge detection result of the area to be inspected is determined based on the number of first target pixels, the number of second target pixels, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image, including: taking the product of the number of first target pixels and the local discharge probability information of the infrared image as the first product; taking the product of the number of second target pixels and the local discharge probability information of the ultraviolet image as the second product; determining a first sum based on the sum of the first product and the second product; determining a second sum based on the sum of the local discharge probability information of the infrared image and the local discharge probability information of the ultraviolet image; and determining the local discharge detection result of the area to be inspected based on the ratio between the first sum and the second sum.

[0087] As an example, the terminal can calculate the product between the number S1 of the first target pixels and the local discharge probability information P1 of the infrared image. In this case, the first product can be expressed as S1*P1; the terminal can calculate the product between the number S2 of the second target pixels and the local discharge probability information P2 of the ultraviolet image. In this case, the second product can be expressed as S2*P2; a first sum is determined based on the sum of the first product and the second product. In this case, the first sum can be expressed as S1*P1+S2*P2; the terminal can calculate the sum of the local discharge probability information P1 of the infrared image and the local discharge probability information P2 of the ultraviolet image. In this case, the second sum can be expressed as P1+P2; the terminal can calculate the discharge degree information used to characterize the probability of local discharge in the area to be detected based on the ratio (S1*P1+S2*P2) / (P1+P2) between the first sum and the second sum, and then determine the local discharge detection result of the area to be detected by combining the discharge degree information and a preset threshold. In practical applications, the number S1 of the first target pixels and the number S2 of the second target pixels can be used as information for adjusting the confidence level of the partial discharge probability information output by the lightweight image discharge detection model.

[0088] In this embodiment, by multiplying the number of first target pixels and the local discharge probability information of the infrared image as the first product; multiplying the number of second target pixels and the local discharge probability information of the ultraviolet image as the second product; determining a first sum based on the sum of the first product and the second product; determining a second sum based on the sum of the local discharge probability information of the infrared image and the local discharge probability information of the ultraviolet image; and determining a local discharge detection result of the area to be detected based on the ratio between the first sum and the second sum, it is possible to generate an accurate local discharge detection result by combining the number of first target pixels, the number of second target pixels, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image, thereby improving the accuracy of the local discharge detection result.

[0089] In some embodiments, determining the first sum based on the sum of the first product and the second product includes: obtaining the weight of the infrared image and the weight of the ultraviolet image; determining the first sum based on the sum of the product between the weight of the infrared image and the first product and the product between the weight of the ultraviolet image and the second product.

[0090] The weight of the infrared image may refer to information indicating the importance of the first product in the first sum. The weight of the ultraviolet image may refer to information indicating the importance of the second product in the first sum. The sum of the infrared image weight C1 and the ultraviolet image weight C2 may be 1. In practical applications, when partial discharge testing is performed on the area to be tested during the day or when the ambient temperature of the power equipment is above a preset ambient temperature threshold, C1 < C2; when partial discharge testing is performed on the area to be tested at night or when the ambient temperature of the power equipment is below the preset ambient temperature threshold, C1 > C2.

[0091] As an example, when calculating the first sum, the terminal can also obtain the weight of the infrared image and the weight of the ultraviolet image, and then the terminal can perform weighted summation on the first product and the second product according to the weight of the infrared image and the weight of the ultraviolet image to obtain the first sum. For example: the first product can be expressed as S1* P1, the first product can be expressed as S2* P2, the weight of the infrared image can be expressed as C1, and the weight of the ultraviolet image can be expressed as C2. At this time, the first sum can be expressed as: C1*S1* P1+ C2*S2* P2.

[0092] In this embodiment, the weight of the infrared image and the weight of the ultraviolet image are obtained; the first sum is determined according to the sum of the product of the weight of the infrared image and the first product and the product of the weight of the ultraviolet image and the second product. The first sum can be accurately calculated using the corresponding weights according to the partial discharge detection time / the ambient temperature of the equipment, thereby providing a data basis for the subsequent determination of the partial discharge detection result, thereby improving the accuracy of the partial discharge detection result.

[0093] In some embodiments, the above method also includes: performing grayscale processing on the initial infrared image corresponding to the detection area to obtain a processed infrared image, and performing grayscale processing on the initial ultraviolet image corresponding to the detection area to obtain a processed ultraviolet image; performing denoising processing on the processed infrared image according to the histogram mean information of the processed infrared image to obtain an infrared image corresponding to the detection area; performing denoising processing on the processed ultraviolet image according to the histogram mean information of the processed ultraviolet image to obtain an ultraviolet image corresponding to the detection area.

[0094] The initial infrared image may refer to an image formed when an infrared remote sensor receives infrared rays reflected from an object (such as an electric power device).

[0095] The processed infrared image may refer to an image obtained by performing grayscale processing on the initial infrared image.

[0096] The initial ultraviolet image may refer to an image obtained by imaging and collecting the power equipment using ultraviolet imaging detection equipment.

[0097] The processed ultraviolet image may refer to an image obtained by performing grayscale processing on the initial ultraviolet image.

[0098] The histogram mean information of the processed infrared image may refer to an average value of pixel values ​​of the processed infrared image calculated based on a statistical chart of the number of occurrences of each gray level in the processed infrared image.

[0099] The histogram mean information of the processed ultraviolet image may refer to an average value of pixel values ​​of the processed ultraviolet image calculated based on a statistical chart of the number of occurrences of each gray level in the processed ultraviolet image.

[0100] As an example, before performing partial discharge detection based on the initial infrared image and initial ultraviolet image of the area to be detected, the terminal may preprocess the initial infrared image and initial ultraviolet image. For example, the terminal may perform grayscale processing on the initial infrared image corresponding to the area to be detected to obtain a processed infrared image. The terminal may also perform grayscale processing on the initial ultraviolet image corresponding to the area to be detected to obtain a processed ultraviolet image. The terminal may then perform denoising on the processed infrared image based on the histogram mean information of the processed infrared image to obtain an infrared image corresponding to the area to be detected. The terminal may also perform denoising on the processed ultraviolet image based on the histogram mean information of the processed ultraviolet image to obtain an ultraviolet image corresponding to the area to be detected.

[0101] In this embodiment, the initial infrared image corresponding to the detection area is gray-scale processed to obtain a processed infrared image, and the initial ultraviolet image corresponding to the detection area is gray-scale processed to obtain a processed ultraviolet image; according to the histogram mean information of the processed infrared image, the processed infrared image is denoised to obtain an infrared image corresponding to the detection area; according to the histogram mean information of the processed ultraviolet image, the processed ultraviolet image is denoised to obtain an ultraviolet image corresponding to the detection area. The infrared image and the ultraviolet image can be pre-processed by gray-scale processing and denoising processing to avoid the influence of noise on subsequent partial discharge detection, thereby improving the accuracy of the partial discharge detection result.

[0102] In some embodiments, the above method also includes: inputting the sample infrared image and the sample ultraviolet image into the lightweight image discharge detection model to be trained to obtain local discharge probability information of the sample infrared image and local discharge probability information of the sample ultraviolet image; training the lightweight image discharge detection model to be trained based on the difference between the local discharge information of the sample infrared image and the local discharge probability information of the sample infrared image, and the difference between the local discharge information of the sample ultraviolet image and the local discharge probability information of the sample ultraviolet image.

[0103] Whether there is partial discharge in the sample infrared image (ie, partial discharge information of the sample infrared image) is known information, and whether there is partial discharge in the sample ultraviolet image (ie, partial discharge information of the sample ultraviolet image) is known information.

[0104] Among them, the sample infrared image and the sample ultraviolet image can be obtained through pre-calibration settings in the laboratory, the local discharge probability information of the sample infrared image can refer to information characterizing the probability of local discharge existing in the sample infrared image, and the local discharge probability information of the sample ultraviolet image can refer to information characterizing the probability of local discharge existing in the sample ultraviolet image.

[0105] As an example, to obtain a pre-trained lightweight image discharge detection model, the terminal can input a sample infrared image and a sample ultraviolet image into the lightweight image discharge detection model to be trained, obtaining local discharge probability information of the sample infrared image and the local discharge probability information of the sample ultraviolet image. The terminal can then train the lightweight image discharge detection model to be trained based on the difference between the local discharge information of the sample infrared image and the local discharge probability information of the sample infrared image, as well as the difference between the local discharge information of the sample ultraviolet image and the local discharge probability information of the sample ultraviolet image. In practical applications, when training the lightweight image discharge detection model to be trained, the structures in the twin neural network in the lightweight image discharge detection model that process infrared images and ultraviolet images respectively share the same set of weights and biases. The terminal can determine the loss function value of the lightweight image discharge detection model based on the difference between the local discharge information of the sample infrared image and the local discharge probability information of the sample infrared image. If the loss function value of the lightweight image discharge detection model does not meet the preset requirements, the model parameters of the lightweight image discharge detection model are adjusted, and the local discharge probability information of the sample infrared image and the local discharge probability information of the sample ultraviolet image are re-determined, and a new loss function value is calculated until the loss function value meets the preset requirements. At this time, the terminal can determine that the training of the lightweight image discharge detection model is completed.

[0106] In this embodiment, by inputting a sample infrared image and a sample ultraviolet image into a lightweight image discharge detection model to be trained, local discharge probability information of the sample infrared image and local discharge probability information of the sample ultraviolet image are obtained; based on the difference between the local discharge information of the sample infrared image and the local discharge probability information of the sample infrared image, as well as the difference between the local discharge information of the sample ultraviolet image and the local discharge probability information of the sample ultraviolet image, the lightweight image discharge detection model to be trained is trained. The training samples can be used to train the lightweight image discharge detection model, optimize the model performance of the lightweight image discharge detection model, and improve the accuracy of the local discharge probability information output by the lightweight image discharge detection model, so that the local discharge probability information can be subsequently combined to determine the local discharge detection result, thereby improving the accuracy of the local discharge detection result.

[0107] In some embodiments, as Figure 3 As shown, a flow chart of infrared-ultraviolet dual-modal adaptive fusion partial discharge detection based on a lightweight network is provided. The terminal can perform grayscale processing on the infrared image and ultraviolet image collected for the area to be detected to obtain a grayscale-processed infrared image and a grayscale-processed ultraviolet image, and then perform denoising processing on the grayscale-processed image based on the histogram mean. For example, the terminal can perform denoising processing on the grayscale-processed infrared image based on the histogram mean information of the grayscale-processed infrared image to obtain an infrared image based on the histogram mean processing; the terminal can perform denoising processing on the grayscale-processed ultraviolet image based on the histogram mean information of the grayscale-processed ultraviolet image to obtain an ultraviolet image based on the histogram mean processing. The terminal can then input the infrared image and the ultraviolet image processed based on the histogram mean into a pre-trained lightweight image discharge detection model (such as the FalconNet model). The structure in the lightweight image discharge detection model that processes infrared images can serve as the FalconNet for the infrared channel, and the structure in the lightweight image discharge detection model that processes ultraviolet images can serve as the FalconNet for the ultraviolet channel. The infrared and ultraviolet FalconNets share parameters. The infrared FalconNet analyzes the infrared image processed based on the histogram mean and outputs partial discharge probability information for the infrared image, representing the probability P1 of partial discharge in the infrared channel. The ultraviolet FalconNet analyzes the ultraviolet image processed based on the histogram mean and outputs partial discharge probability information for the ultraviolet image, representing the probability P2 of partial discharge in the ultraviolet channel.

[0108] The terminal can determine the discharge area from the infrared image processed by histogram averaging based on the temperature of each pixel in the infrared image processed by histogram averaging. The terminal can also determine the discharge area from the ultraviolet image processed by histogram averaging based on the number of photons at each pixel in the ultraviolet image processed by histogram averaging. The terminal can also calculate the number S1 of pixels having a pixel value greater than a threshold value e based on the pixel value of each pixel in the infrared image processed by histogram averaging, where the threshold value e when determining S1 can be the maximum pixel value among the pixel values ​​of the pixels in the discharge area in the infrared image processed by histogram averaging. The terminal can also calculate the number S2 of pixels having a pixel value greater than a threshold value e based on the pixel value of each pixel in the ultraviolet image processed by histogram averaging, where the threshold value e when determining S2 can be the maximum pixel value among the pixel values ​​of the pixels in the discharge area in the ultraviolet image processed by histogram averaging.

[0109] The terminal then calculates the discharge level of the area to be inspected based on the partial discharge probability information P1, partial discharge probability information P2, the number of pixels S1, the number of pixels S2, the infrared image weight C1, and the ultraviolet image weight C2. The discharge level can be expressed as: (C1*S1*P1+C2*S2*P2) / (S1+S2). Based on the discharge level of the area to be inspected, the terminal can analyze whether partial discharge exists in the area to be inspected, thereby determining the partial discharge detection result for the area to be inspected. In practical applications, if the discharge level of the area to be inspected is 80%, the terminal can combine it with a discharge level threshold (such as 50%) to determine that partial discharge exists in the area to be inspected.

[0110] In this embodiment, the captured infrared and ultraviolet images are grayscale processed; the grayscale-processed images are then subjected to histogram mean-based denoising to obtain infrared and ultraviolet discharge detection images containing the target location of partial discharges. The pixel areas of the target discharge locations in the denoised infrared and ultraviolet images are then calculated. Finally, a parameter-sharing bimodal detection model is designed based on the lightweight network FalconNet. The calculated pixel areas are used to adjust the model's output confidence, enhancing the algorithm's robustness and thereby determining the degree of discharge. Compared to traditional detection models, this method is more lightweight and utilizes the characteristic that histogram mean denoising results in different pixel areas of discharge locations in infrared and ultraviolet images under different discharge intensities. This enables the model to identify the degree of discharge, improving the efficiency and accuracy of partial discharge detection.

[0111] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0112] Based on the same inventive concept, embodiments of the present application also provide a lightweight network-based infrared-ultraviolet dual-modal partial discharge detection device for implementing the aforementioned lightweight network-based infrared-ultraviolet dual-modal partial discharge detection method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the lightweight network-based infrared-ultraviolet dual-modal partial discharge detection device provided below can be found in the above-mentioned limitations of the lightweight network-based infrared-ultraviolet dual-modal partial discharge detection method and are not further elaborated here.

[0113] In an exemplary embodiment, Figure 4 As shown, an infrared-ultraviolet dual-mode partial discharge detection device based on a lightweight network is provided, comprising: an acquisition module 402, a prediction module 404 and a detection module 406, wherein:

[0114] The acquisition module 402 is configured to acquire a discharge area in an infrared image corresponding to the area to be detected, and acquire a discharge area in an ultraviolet image corresponding to the area to be detected.

[0115] The prediction module 404 is configured to input the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information of the infrared image and partial discharge probability information of the ultraviolet image.

[0116] The detection module 406 is configured to determine a partial discharge detection result of the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image; the partial discharge detection result indicates whether partial discharge exists in the area to be detected.

[0117] In one of the exemplary embodiments, the acquisition module 402 is specifically used to obtain the temperature of each pixel point in the infrared image; for each pixel point in the infrared image, when the temperature corresponding to the pixel point is greater than a preset temperature threshold, the pixel point is used as a candidate pixel point; based on the candidate pixel point, the discharge area in the infrared image corresponding to the area to be detected is determined.

[0118] In one of the exemplary embodiments, the acquisition module 402 is specifically used to obtain the number of photons corresponding to each pixel in the ultraviolet image; for each pixel in the ultraviolet image, when the number of photons corresponding to the pixel is greater than a preset photon number threshold, the pixel is used as a candidate pixel; based on the candidate pixel, the discharge area in the ultraviolet image corresponding to the area to be detected is determined.

[0119] In one of the exemplary embodiments, the detection module 406 is further specifically used to determine a first pixel threshold based on the pixel values ​​of the pixel points corresponding to the discharge area in the infrared image; screen out first target pixel points from the pixel points corresponding to the discharge area in the infrared image based on the first pixel threshold; determine a second pixel threshold based on the pixel values ​​of the pixel points corresponding to the discharge area in the ultraviolet image; screen out second target pixel points from the pixel points corresponding to the discharge area in the ultraviolet image based on the second pixel threshold; and determine the local discharge detection result of the area to be detected based on the number of the first target pixel points, the number of the second target pixel points, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image.

[0120] In one exemplary embodiment, the detection module 406 is further configured to take the product of the number of the first target pixels and the local discharge probability information of the infrared image as a first product; take the product of the number of the second target pixels and the local discharge probability information of the ultraviolet image as a second product; determine a first sum based on the sum of the first product and the second product; determine a second sum based on the sum of the local discharge probability information of the infrared image and the local discharge probability information of the ultraviolet image; and determine a local discharge detection result of the area to be detected based on a ratio between the first sum and the second sum.

[0121] In one exemplary embodiment, the detection module 406 is further specifically used to obtain the weight of the infrared image and the weight of the ultraviolet image; and determine the first sum based on the sum of the product between the weight of the infrared image and the first product and the product between the weight of the ultraviolet image and the second product.

[0122] In one of the exemplary embodiments, the device also includes a preprocessing module, which is specifically used to perform grayscale processing on the initial infrared image corresponding to the area to be detected to obtain a processed infrared image, and perform grayscale processing on the initial ultraviolet image corresponding to the area to be detected to obtain a processed ultraviolet image; perform denoising processing on the processed infrared image according to the histogram mean information of the processed infrared image to obtain an infrared image corresponding to the area to be detected; perform denoising processing on the processed ultraviolet image according to the histogram mean information of the processed ultraviolet image to obtain an ultraviolet image corresponding to the area to be detected.

[0123] In one of the exemplary embodiments, the device also includes a model training module, which is specifically used to input a sample infrared image and a sample ultraviolet image into a lightweight image discharge detection model to be trained to obtain local discharge probability information of the sample infrared image and local discharge probability information of the sample ultraviolet image; and train the lightweight image discharge detection model to be trained based on the difference between the local discharge information of the sample infrared image and the local discharge probability information of the sample infrared image, and the difference between the local discharge information of the sample ultraviolet image and the local discharge probability information of the sample ultraviolet image.

[0124] Each module in the aforementioned lightweight network-based infrared-ultraviolet dual-modal partial discharge detection device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0125] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, mobile cellular networks, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements an infrared-ultraviolet dual-modal partial discharge detection method based on a lightweight network. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0126] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0129] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0131] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0132] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0133] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A lightweight network-based infrared-ultraviolet dual-mode partial discharge detection method, characterized in that: The method comprises: Acquire a discharge area in an infrared image corresponding to the area to be detected, and acquire a discharge area in an ultraviolet image corresponding to the area to be detected; Inputting the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information of the infrared image and partial discharge probability information of the ultraviolet image; Determining a local discharge detection result of the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image; the local discharge detection result represents whether local discharge exists in the area to be detected; comprising: determining a first pixel threshold according to the pixel values ​​of the pixels corresponding to the discharge area in the infrared image; screening out first target pixels from the pixels corresponding to the discharge area in the infrared image according to the first pixel threshold; determining a second pixel threshold according to the pixel values ​​of the pixels corresponding to the discharge area in the ultraviolet image; screening out second target pixels from the pixels corresponding to the discharge area in the ultraviolet image according to the second pixel threshold; determining a local discharge detection result of the area to be detected based on the number of the first target pixels, the number of the second target pixels, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image; Wherein, determining the local discharge detection result of the area to be inspected based on the number of the first target pixels, the number of the second target pixels, the local discharge probability information of the infrared image, and the local discharge probability information of the ultraviolet image includes: taking the product of the number of the first target pixels and the local discharge probability information of the infrared image as a first product; taking the product of the number of the second target pixels and the local discharge probability information of the ultraviolet image as a second product; determining a first sum based on the sum of the first product and the second product; determining a second sum based on the sum of the local discharge probability information of the infrared image and the local discharge probability information of the ultraviolet image; and determining the local discharge detection result of the area to be inspected based on a ratio between the first sum and the second sum.

2. The method according to claim 1, characterized in that The step of obtaining the discharge area in the infrared image corresponding to the area to be detected includes: Obtaining the temperature of each pixel in the infrared image; For each pixel in the infrared image, if the temperature corresponding to the pixel is greater than a preset temperature threshold, the pixel is taken as a candidate pixel; The discharge area in the infrared image corresponding to the area to be detected is determined according to the candidate pixel points.

3. The method according to claim 1, characterized in that The obtaining of the discharge area in the ultraviolet image corresponding to the area to be detected includes: Obtaining the number of photons corresponding to each pixel in the ultraviolet image; For each pixel in the ultraviolet image, if the number of photons corresponding to the pixel is greater than a preset photon number threshold, the pixel is taken as a candidate pixel; The discharge area in the ultraviolet image corresponding to the area to be detected is determined according to the candidate pixel points.

4. The method according to claim 1, wherein The determining a first sum according to the sum of the first product and the second product includes: Obtaining a weight of the infrared image and a weight of the ultraviolet image; The first sum is determined according to the sum of the product of the weight of the infrared image and the first product and the product of the weight of the ultraviolet image and the second product.

5. The method according to claim 1, characterized in that The method further comprises: Performing grayscale processing on the initial infrared image corresponding to the area to be detected to obtain a processed infrared image, and performing grayscale processing on the initial ultraviolet image corresponding to the area to be detected to obtain a processed ultraviolet image; performing denoising processing on the processed infrared image according to histogram mean information of the processed infrared image to obtain an infrared image corresponding to the area to be detected; According to the histogram mean information of the processed ultraviolet image, the processed ultraviolet image is denoised to obtain the ultraviolet image corresponding to the area to be detected.

6. An infrared-ultraviolet dual-mode partial discharge detection device based on a lightweight network, characterized in that: The device comprises: An acquisition module, configured to acquire a discharge area in an infrared image corresponding to the area to be detected, and to acquire a discharge area in an ultraviolet image corresponding to the area to be detected; a prediction module, configured to input the infrared image and the ultraviolet image into a pre-trained lightweight image discharge detection model to obtain partial discharge probability information of the infrared image and partial discharge probability information of the ultraviolet image; A detection module, configured to determine a partial discharge detection result of the area to be detected based on the discharge area in the infrared image, the discharge area in the ultraviolet image, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image; the partial discharge detection result characterizing whether partial discharge exists in the area to be detected; comprising: determining a first pixel threshold based on the pixel values ​​of the pixels corresponding to the discharge area in the infrared image; screening out first target pixels from the pixels corresponding to the discharge area in the infrared image based on the first pixel threshold; determining a second pixel threshold based on the pixel values ​​of the pixels corresponding to the discharge area in the ultraviolet image; screening out second target pixels from the pixels corresponding to the discharge area in the ultraviolet image based on the second pixel threshold; and determining the partial discharge detection result of the area to be detected based on the number of the first target pixels, the number of the second target pixels, the partial discharge probability information of the infrared image, and the partial discharge probability information of the ultraviolet image; The detection module is further configured to use the product of the number of the first target pixels and the partial discharge probability information of the infrared image as a first product; use the product of the number of the second target pixels and the partial discharge probability information of the ultraviolet image as a second product; determine a first sum based on the sum of the first product and the second product; determine a second sum based on the sum of the partial discharge probability information of the infrared image and the partial discharge probability information of the ultraviolet image; and determine a partial discharge detection result of the area to be detected based on a ratio between the first sum and the second sum.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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