Fault detection method, system, device and medium for electric power equipment

Abstract

The present invention discloses a fault detection method, system, device, and medium for power equipment. The method comprises obtaining infrared images, location information, and ambient temperature of each power equipment, sorting the infrared images of the equipment at the same ambient temperature based on position order to obtain a sequence of infrared images to be detected, selecting the image with the highest cluster repetition from the sequence as the target infrared image according to a preset rule, and processing and obtaining a first fault area image in the target image using a preset model. Using the first fault area image, the other images in the sequence are sequentially deduplicated. After each deduplication, the model is re-applied to the other deduplicated images to identify a new fault area image and use it to update the first fault area image. The last updated first fault area image is used as the fault area identification result for the power equipment corresponding to the sequence. The present invention reduces the amount of data processed by the model and improves the speed and stability of the model processing.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment detection, and in particular relates to a fault detection method, system, equipment and medium for power equipment. Background Art

[0002] With the large-scale access of ultra-high voltage power equipment and AC / DC hybrid power equipment, the coverage of the power grid has expanded significantly. Faced with the diverse and large number of power equipment, relying solely on traditional manual detection methods to detect power equipment faults is increasingly unable to meet the current maintenance needs.

[0003] Currently, the use of technologies such as image recognition in the field of artificial intelligence can process and analyze the collected infrared images, which can assist or even replace manual fault diagnosis of power equipment, greatly improving inspection efficiency and reducing maintenance costs. However, if all the collected image data is input into the model for identification one by one, the excessive amount of data may affect the speed of fault detection and even easily cause the system to crash. Summary of the Invention

[0004] In response to the deficiencies of the prior art, the present application provides a method, system, device and medium for fault detection of power equipment, which reduces the amount of data processed by the model and improves the speed and stability of model processing.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for detecting a fault in an electric power device, comprising:

[0006] Obtaining infrared images, location information, and ambient temperature of each power device to be detected, and sorting the infrared images of each power device to be detected at the same ambient temperature based on the order of their locations to obtain at least one sequence of infrared images to be detected;

[0007] According to a preset extraction rule, the sequence of infrared images to be detected is divided into a target infrared image and a first subsequence; wherein the target infrared image is the infrared image to be detected with the largest degree of repetition of clustered regions in the sequence of infrared images to be detected;

[0008] Detecting the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image;

[0009] performing deduplication processing on each infrared image to be detected in the first subsequence in sequence based on the first fault area image, and detecting the deduplicated infrared image to be detected according to the target detection model during each deduplication processing to obtain a second fault area image corresponding to the deduplicated infrared image to be detected, and updating the first fault area image according to the current second fault area image during each deduplication processing;

[0010] The most recently updated first fault area image is used as the fault area identification result of each electric device corresponding to the infrared image sequence to be detected.

[0011] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by grouping infrared images of electrical equipment under the same ambient temperature and sorting them according to the location information of the equipment, the interference caused by ambient temperature differences can be reduced; by selecting images with the highest cluster area repetition, it is convenient to determine the fault-frequent areas; the fault area in the target image is identified by the trained model to obtain the first fault area image; the first fault area image is used to deduplicate other images in the subsequence to avoid repeated detection of the same fault area, thereby reducing the data processing amount of the model and improving the speed and stability of the model detection; the model is then used to detect the deduplicated images to ensure comprehensive detection and enhance the fault detection capability of the entire system.

[0012] In some embodiments of the first aspect of the present application, dividing the sequence of infrared images to be detected into target infrared images and a first subsequence according to a preset extraction rule includes:

[0013] Obtaining pixel values ​​of all feature points of a first infrared image to be detected in the sequence of infrared images to be detected, and clustering all feature points based on the magnitude of the pixel values ​​to obtain at least one clustering region;

[0014] The cluster area repetition of each infrared image to be detected in the sequence of infrared images to be detected is calculated, and the infrared image to be detected with the largest cluster area repetition in the sequence of infrared images to be detected is selected as the target infrared image, and the target infrared image is removed from the sequence of infrared images to be detected to obtain a first subsequence corresponding to the target infrared image.

[0015] The above embodiment has the following beneficial effects: by extracting the target infrared image, it is convenient to determine the fault-frequently occurring area, and then the fault-frequently occurring area image is used to deduplicate other images in the subsequence, and then the deduplicated image is used to identify each fault area, thereby reducing the amount of data processed by the model during subsequent fault detection and improving detection efficiency.

[0016] In some embodiments of the first aspect of the present application, detecting the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image includes:

[0017] Input the target infrared image into a pre-built image detection model and output the corresponding one or more first regions; the first regions are composed of feature points whose pixel values ​​are greater than a first preset threshold;

[0018] The center points of the one or more first areas are obtained respectively, and circles are drawn with each center point as the center and the closest distance from each center point to the edge of each first area as the radius to obtain a first fault area image corresponding to the target infrared image; wherein the first fault area image contains at least one fault area.

[0019] The above embodiment has the following beneficial effects: the fault area in the target image is identified by the trained model, and a first fault area image is obtained, which provides a basis for further analysis and troubleshooting.

[0020] In some embodiments of the first aspect of the present application, the deduplication processing of each infrared image to be detected in the first subsequence according to the first fault area image includes:

[0021] Acquire a fault area in the first fault area image, and slide on an edge of the fault area based on a preset sliding window to acquire a transition area connected to the fault area;

[0022] matching the feature points in the transition area with the feature points of the next infrared image to be detected in the first subsequence, and aligning the first fault area image with the next infrared image to be detected according to the matching result;

[0023] The aligned target area is removed from the next infrared image to be detected to obtain an infrared sub-image to be detected corresponding to the next infrared image to be detected, wherein the target area includes the fault area and the transition area.

[0024] The above embodiment has the following beneficial effects: before using the model to detect other images in the subsequence, the first fault area image is used to deduplicate other images in the subsequence, thereby avoiding repeated detection of the same fault area, reducing the amount of data for model detection, and improving the speed and stability of model processing.

[0025] In some embodiments of the first aspect of the present application, during each deduplication process, detecting the deduplicated infrared image to be detected according to the target detection model to obtain a second fault area image corresponding to the deduplicated infrared image to be detected, and updating the first fault area image according to the current second fault area image during each deduplication process, including:

[0026] Inputting the infrared sub-image to be detected into the image detection model and outputting one or more corresponding second regions; the second regions are composed of feature points whose pixel values ​​are greater than a second preset threshold;

[0027] Obtaining the center points of the one or more second areas respectively, and drawing circles with each center point as the center and the closest distance from each center point to the edge of each second area as the radius, to obtain a second fault area image corresponding to the target infrared image; wherein the second fault area image contains at least one fault area;

[0028] The second fault area image is merged into the first fault area image to update the first fault area image.

[0029] The above embodiment has the following beneficial effects: by reapplying the target detection model to the image to be detected with the known fault area removed, new or hidden fault areas can be discovered, ensuring comprehensive detection and enhancing the fault detection capability of the entire system. By using the new fault area image to update the first fault area image, a more comprehensive fault distribution situation can be obtained, and it also provides a better basis for cyclically repeating the detection of the next image to be detected.

[0030] In a second aspect, an embodiment of the present application further provides a fault detection system for an electric power device, comprising: an acquisition module, an extraction module, a first identification module, and a circulation module;

[0031] The acquisition module is used to acquire the infrared image to be detected, the location information and the ambient temperature of each power device at the same ambient temperature, and sort the infrared images to be detected of each power device at the same ambient temperature based on the order of the locations to obtain at least one sequence of infrared images to be detected;

[0032] The extraction module is used to divide the infrared image sequence to be detected into a target infrared image and a first subsequence according to a preset extraction rule; wherein the target infrared image is the infrared image to be detected with the largest cluster area repetition in the infrared image sequence to be detected;

[0033] The first recognition module is used to detect the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image;

[0034] The loop module is used to perform deduplication processing on each infrared image to be detected in the first subsequence in sequence according to the first fault area image, and during each deduplication processing, detect the deduplicated infrared image to be detected according to the target detection model to obtain the second fault area image corresponding to the deduplicated infrared image to be detected, and during each deduplication processing, update the first fault area image according to the current second fault area image.

[0035] In some embodiments of the second aspect of the present application, the extraction module includes: a clustering unit and a calculation unit;

[0036] The clustering unit is used to obtain pixel values ​​of all feature points of the first infrared image to be detected in the sequence of infrared images to be detected, and cluster all feature points based on the size of the pixel values ​​to obtain at least one clustering area;

[0037] The calculation unit is used to calculate the cluster area repetition of each infrared image to be detected in the infrared image sequence to be detected, select the infrared image to be detected with the largest cluster area repetition in the infrared image sequence to be detected as the target infrared image, and remove the target infrared image from the infrared image sequence to be detected to obtain a first subsequence corresponding to the target infrared image.

[0038] In some embodiments of the second aspect of the present application, the first recognition module includes: a first model recognition unit and a first image generation unit;

[0039] The first model recognition unit is used to input the target infrared image into a pre-built image detection model and output one or more corresponding first regions; the first region is composed of feature points whose pixel values ​​are greater than a first preset threshold;

[0040] The first image generation unit is used to respectively obtain the center points of the one or more first areas, and draw circles with each center point as the center and the closest distance from each center point to the edge of each first area as the radius, to obtain a first fault area image corresponding to the target infrared image; wherein, the first fault area image contains at least one fault area.

[0041] In some embodiments of the second aspect of the present application, the recycling module includes: a deduplication unit;

[0042] The deduplication unit is used to obtain the fault area in the first fault area image, and slide on the edge of the fault area based on a preset sliding window to obtain a transition area connected to the fault area, match the feature points in the transition area with the feature points of the next infrared image to be detected in the first subsequence, and align the first fault area image and the next infrared image to be detected according to the matching result, remove the aligned target area in the next infrared image to be detected, and obtain the infrared sub-image to be detected corresponding to the next infrared image to be detected, wherein the target area includes the fault area and the transition area.

[0043] In some embodiments of the second aspect of the present application, the loop module further comprises: a repeat identification unit;

[0044] The repeated recognition unit is used to input the infrared sub-image to be detected into the image detection model and output the corresponding one or more second areas; the second area is composed of each feature point whose pixel value is greater than a second preset threshold, and the center points of the one or more second areas are obtained respectively, and circles are drawn with each center point as the center and the closest distance from each center point to the edge of each second area as the radius to obtain a second fault area image corresponding to the target infrared image, wherein the second fault area image contains at least one fault area, and the second fault area image is merged into the first fault area image to update the first fault area image.

[0045] Compared with the prior art, the above embodiment has the following beneficial effects: by grouping infrared images of power equipment under the same ambient temperature and sorting them according to the location information of the equipment, the interference caused by ambient temperature differences can be reduced; by selecting images with the highest cluster area repetition, it is convenient to determine the fault-frequent areas; the fault area in the target image is identified by the trained model to obtain the first fault area image; the first fault area image is used to deduplicate other images in the subsequence to avoid repeated detection of the same fault area, thereby reducing the data processing amount of the model and improving the speed and stability of the model detection; the model is then used to detect the deduplicated images to ensure comprehensive detection and enhance the fault detection capability of the entire system.

[0046] In a third aspect, an embodiment of the present application also provides a fault detection device for an electric power device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the steps of a method for fault detection of an electric power device.

[0047] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for detecting a fault of an electric power device are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 : A flow chart of a method for detecting a fault in an electric power device provided in some embodiments of the present invention.

[0049] Figure 2 : A structural diagram of a fault detection system for electric power equipment provided in some embodiments of the present invention.

[0050] Figure 3 : A structural diagram of a fault detection device for an electric power device provided in some embodiments of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1:

[0053] Please refer to Figure 1 , a fault detection method for electric power equipment provided by an embodiment of the present invention, comprising steps S1 to S5:

[0054] Step S1: Obtain the infrared image, location information and ambient temperature of each power device to be detected, and sort the infrared images of each power device to be detected at the same ambient temperature based on the order of location to obtain at least one infrared image sequence to be detected.

[0055] In this embodiment, images of each power device are captured by an infrared image capture device, and the location information corresponding to each infrared image and the ambient temperature of the power device during the capture are recorded. By grouping the infrared images of the power devices at the same ambient temperature together and sorting them according to the location information of the devices, the interference caused by the difference in ambient temperature can be reduced, thereby ensuring the reliability of subsequent fault detection.

[0056] Step S2: dividing the infrared image sequence to be detected into a target infrared image and a first subsequence according to a preset extraction rule; wherein the target infrared image is the infrared image to be detected with the largest cluster area repetition in the infrared image sequence to be detected.

[0057] In some embodiments of the present application, step S2 can be implemented by the following preferred implementations:

[0058] S21: Obtaining pixel values ​​of all feature points of a first infrared image to be detected in the sequence of infrared images to be detected, and clustering all feature points based on the magnitude of the pixel values ​​to obtain at least one clustering region;

[0059] S22: calculating the cluster region repetition of each infrared image to be detected in the sequence of infrared images to be detected, selecting the infrared image to be detected with the largest cluster region repetition in the sequence of infrared images to be detected as the target infrared image, and removing the target infrared image from the sequence of infrared images to be detected to obtain a first subsequence corresponding to the target infrared image;

[0060] Furthermore, as an optimization, the formula for calculating the repetitiveness of cluster regions can be as follows:

[0061] where f i is the repetition degree of the clustering area of ​​the i-th infrared image to be detected, a1 is the number of infrared images to be detected that contain the first clustering area after excluding the i-th infrared image to be detected in the infrared image sequence to be detected, a2 is the number of infrared images to be detected that contain the second clustering area after excluding the i-th infrared image to be detected in the infrared image sequence to be detected, an is the number of infrared images to be detected that contain the n-th clustering area after excluding the i-th infrared image to be detected in the infrared image sequence to be detected, t is the number of other infrared images to be detected in the infrared image sequence to be detected excluding the i-th infrared image to be detected, T i is the total number of clustered regions in the i-th infrared image to be detected;

[0062] For example, in a sequence of infrared images to be detected, infrared image A contains clusters a, b, c, and d; infrared image B contains clusters a, e, f, and h; and infrared image C contains clusters a and e. Therefore, the cluster repetition of infrared image A is (1+0+0+0) / 4=0.25, the cluster repetition of infrared image B is (1+0.5+0+0) / 4=0.375, and the cluster repetition of infrared image C is (1+0.5) / 2=0.75. Therefore, infrared image C is selected as the target infrared image.

[0063] In this embodiment, by extracting the target infrared image, it is convenient to determine the fault-frequently occurring area, and then use the fault-frequently occurring area image to deduplicate other images in the subsequence, and then use the deduplicated image to identify each fault area, thereby reducing the amount of data processed by the model during subsequent fault detection and improving detection efficiency.

[0064] Step S3: Detecting the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image.

[0065] In some embodiments of the present application, step S3 may be implemented by the following preferred implementations:

[0066] S31: Input the target infrared image into a pre-built image detection model and output one or more corresponding first regions; the first regions are composed of feature points whose pixel values ​​are greater than a first preset threshold;

[0067] S32: Obtain the center points of the one or more first areas respectively, and draw circles with each center point as the center and the closest distance from each center point to the edge of each first area as the radius, to obtain a first fault area image corresponding to the target infrared image; wherein the first fault area image contains at least one fault area.

[0068] In this embodiment, the fault area in the target image is identified by using the trained model to obtain a first fault area image, which provides a basis for further analysis and troubleshooting.

[0069] Step S4: performing deduplication processing on each infrared image to be detected in the first subsequence in sequence according to the first fault area image.

[0070] In some embodiments of the present application, step S4 can be implemented by the following preferred implementations:

[0071] S41: Acquire a fault area in the first fault area image, and slide on the edge of the fault area based on a preset sliding window to acquire a transition area connected to the fault area;

[0072] S42: matching the feature points in the transition area with the feature points of the next infrared image to be detected in the first subsequence, and aligning the first fault area image with the next infrared image to be detected according to the matching result;

[0073] S43: removing the aligned target area from the next infrared image to be detected to obtain an infrared sub-image to be detected corresponding to the next infrared image to be detected, wherein the target area includes the fault area and the transition area.

[0074] In this embodiment, before using the model to detect other images in the subsequence, the first fault area image is used to match the other images in the subsequence, and then the same parts as those in the first fault area image are removed from the other images, thereby avoiding repeated detection of the same fault area, reducing the amount of data detected by the model, and improving the speed and stability of model processing.

[0075] Step S5: During each deduplication process, the deduplicated infrared image to be detected is detected according to the target detection model to obtain the second fault area image corresponding to the deduplicated infrared image to be detected, and during each deduplication process, the first fault area image is updated according to the current second fault area image.

[0076] In some embodiments of the present application, step S5 may be implemented by the following preferred implementations:

[0077] S51: Inputting the infrared sub-image to be detected into the image detection model and outputting one or more corresponding second regions; the second regions are composed of feature points whose pixel values ​​are greater than a second preset threshold;

[0078] S52: Obtaining the center points of the one or more second areas, respectively, and drawing circles with each center point as the center and the closest distance from each center point to the edge of each second area as the radius, to obtain a second fault area image corresponding to the target infrared image; wherein the second fault area image contains at least one fault area;

[0079] S53: Merge the second fault area image into the first fault area image to update the first fault area image.

[0080] In this embodiment, the target detection model is applied again to the image to be detected with the known fault area removed, so that new or hidden fault areas can be discovered, ensuring comprehensive detection and enhancing the fault detection capability of the entire system. Using the new fault area image to update the first fault area image can obtain a more comprehensive fault distribution, and also provide a better basis for cyclically repeating the detection of the next image to be detected.

[0081] In summary, compared with the prior art, the embodiments of the present application have the following beneficial effects: by grouping infrared images of power equipment under the same ambient temperature and sorting them according to the location information of the equipment, the interference caused by ambient temperature differences can be reduced; by selecting images with the highest cluster area repetition, it is convenient to determine fault-frequent areas; the fault area in the target image is identified by the trained model to obtain the first fault area image; the first fault area image is used to deduplicate other images in the subsequence to avoid repeated detection of the same fault area, thereby reducing the data processing amount of the model and improving the speed and stability of model detection; the model is then used to detect the deduplicated image to ensure comprehensive detection and enhance the fault detection capability of the entire system.

[0082] Example 2:

[0083] Please refer to Figure 2 Based on the same inventive concept, an embodiment of the present invention discloses a fault detection system for electric power equipment, including: an acquisition module M1, an extraction module M2, a first identification module M3 and a circulation module M4.

[0084] Among them, the acquisition module M1 is used to obtain the infrared image to be detected, location information and ambient temperature of each power device at the same ambient temperature, and sort the infrared images to be detected of each power device at the same ambient temperature based on the order of location to obtain at least one infrared image sequence to be detected.

[0085] The acquisition module M1 of this embodiment groups infrared images of power equipment at the same ambient temperature into one group and sorts them according to the location information of the equipment, thereby reducing interference caused by ambient temperature differences and ensuring the reliability of subsequent fault detection.

[0086] The extraction module M2 is used to divide the infrared image sequence to be detected into a target infrared image and a first subsequence according to a preset extraction rule; wherein the target infrared image is the infrared image to be detected with the largest cluster area repetition in the infrared image sequence to be detected;

[0087] The extraction module M2 includes: a clustering unit and a calculation unit; the clustering unit is used to obtain pixel values ​​of all feature points of the first infrared image to be detected in the infrared image sequence to be detected, and cluster all feature points based on the size of the pixel values ​​to obtain at least one cluster area; the calculation unit is used to calculate the repetition of the cluster areas of each infrared image to be detected in the infrared image sequence to be detected, and select the infrared image to be detected with the largest repetition of the cluster areas in the infrared image sequence to be detected as the target infrared image, and remove the target infrared image from the infrared image sequence to be detected to obtain a first subsequence corresponding to the target infrared image;

[0088] Furthermore, as an optimization, the formula for calculating the repetitiveness of cluster regions can be as follows:

[0089] where f i is the repetition degree of the clustering area of ​​the i-th infrared image to be detected, a1 is the number of infrared images to be detected that contain the first clustering area after excluding the i-th infrared image to be detected in the infrared image sequence to be detected, a2 is the number of infrared images to be detected that contain the second clustering area after excluding the i-th infrared image to be detected in the infrared image sequence to be detected, an is the number of infrared images to be detected that contain the n-th clustering area after excluding the i-th infrared image to be detected in the infrared image sequence to be detected, t is the number of other infrared images to be detected in the infrared image sequence to be detected excluding the i-th infrared image to be detected, T i is the total number of clustered regions in the i-th infrared image to be detected.

[0090] The extraction module M2 of this embodiment extracts the target infrared image to facilitate the determination of the fault-frequently occurring areas, and subsequently uses the fault-frequently occurring area images to deduplicate other images in the subsequence, and then uses the deduplicated images to identify each fault area, thereby reducing the amount of data processed by the model during subsequent fault detection and improving detection efficiency.

[0091] The first recognition module M3 is used to detect the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image;

[0092] The first recognition module M3 includes: a first model recognition unit and a first image generation unit; the first model recognition unit is used to input the target infrared image into a pre-built image detection model and output the corresponding one or more first areas; the first area is composed of various feature points whose pixel values ​​are greater than a first preset threshold; the first image generation unit is used to obtain the center points of the one or more first areas respectively, and draw circles with each center point as the center and the closest distance from each center point to the edge of each first area as the radius to obtain a first fault area image corresponding to the target infrared image; wherein, the first fault area image contains at least one fault area.

[0093] The first recognition module M3 of this embodiment recognizes the fault area in the target image through the trained model to obtain a first fault area image, which provides a basis for further analysis and troubleshooting.

[0094] The loop module M4 is configured to sequentially perform deduplication processing on each infrared image to be detected in the first subsequence based on the first fault area image, and during each deduplication processing, detect the deduplicated infrared image to be detected based on the target detection model to obtain a second fault area image corresponding to the deduplicated infrared image to be detected, and during each deduplication processing, update the first fault area image based on the current second fault area image;

[0095] The loop module M4 includes: a deduplication unit; the deduplication unit is used to obtain the fault area in the first fault area image, and slide on the edge of the fault area based on a preset sliding window to obtain a transition area connected to the fault area, match the feature points in the transition area with the feature points of the next infrared image to be detected in the first subsequence, and align the first fault area image and the next infrared image to be detected according to the matching result, remove the aligned target area in the next infrared image to be detected, and obtain the infrared sub-image to be detected corresponding to the next infrared image to be detected, wherein the target area includes the fault area and the transition area.

[0096] The loop module M4 of this embodiment uses the first fault area image to deduplicate other images in the subsequence before using the model to detect other images in the subsequence, thereby avoiding repeated detection of the same fault area, reducing the amount of data for model detection, and improving the speed and stability of model processing.

[0097] The loop module M4 also includes: a repeated recognition unit; the repeated recognition unit is used to input the infrared sub-image to be detected into the image detection model and output the corresponding one or more second areas; the second area is composed of each feature point whose pixel value is greater than a second preset threshold, and the center points of the one or more second areas are obtained respectively, and circles are drawn with each center point as the center and the closest distance from each center point to the edge of each second area as the radius to obtain a second fault area image corresponding to the target infrared image; wherein, the second fault area image contains at least one fault area; the second fault area image is merged into the first fault area image to update the first fault area image.

[0098] The loop module M4 of this embodiment reapplies the target detection model to the image to be detected with the known fault area removed, which can discover new or hidden fault areas, ensure comprehensive detection, and enhance the fault detection capability of the entire system. Using the new fault area image to update the first fault area image can obtain a more comprehensive fault distribution situation, and also provide a better basis for cyclically repeating the detection of the next image to be detected.

[0099] In summary, compared with the prior art, the embodiments of the present application have the following beneficial effects: by grouping infrared images of power equipment under the same ambient temperature and sorting them according to the location information of the equipment, the interference caused by ambient temperature differences can be reduced; by selecting images with the highest cluster area repetition, it is convenient to determine fault-frequent areas; the fault area in the target image is identified by the trained model to obtain the first fault area image; the first fault area image is used to deduplicate other images in the subsequence to avoid repeated detection of the same fault area, thereby reducing the data processing amount of the model and improving the speed and stability of model detection; the model is then used to detect the deduplicated image to ensure comprehensive detection and enhance the fault detection capability of the entire system.

[0100] The division of the modules described above is merely a logical function division. In actual implementation, there may be other division methods, for example, multiple modules may be combined or integrated into another system.

[0101] Example 3:

[0102] Figure 3 The structural diagram of a fault detection device for an electric power device of the present application is presented. Figure 3 As shown, the fault detection device may include: a processor N1, a memory N2, a data interface N3 and a communication bus N4.

[0103] Among them: the processor N1, the memory N2, and the data interface N3 communicate with each other through the communication bus N4; the data interface N3 is used for data communication with other devices such as input devices or output devices; the processor N1 is used to execute the program N5, which can specifically execute the relevant steps in the above-mentioned embodiment of the fault detection method of the power equipment.

[0104] Specifically, the program N5 may include program code, which includes computer-executable instructions.

[0105] Processor N1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. A fault detection device for power equipment includes one or more processors, which may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0106] The memory N2 is used to store the program N5. The memory N2 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0107] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. In addition, the embodiments of the present application are not directed to any particular programming language.

[0108] Example 4:

[0109] An embodiment of the present invention also provides a computer-readable storage medium, which stores at least one executable instruction. When the executable instruction is run on a fault detection device / system of an electric power device, the fault detection device / system of the electric power device enables the fault detection method of the electric power device in any of the above-mentioned method embodiments.

[0110] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. Similarly, in order to streamline the application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the application, the various features of the embodiments of the application are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are hereby clearly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the application.

[0111] Those skilled in the art will appreciate that the modules in the devices of the embodiments can be adaptively changed and installed in one or more devices different from the embodiments. The modules, units, or components in the embodiments can be combined into one module, unit, or component, and furthermore, they can be divided into multiple submodules, subunits, or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

Claims

1. A method for detecting a fault in an electric power device, characterized in that: include: Obtaining infrared images, location information, and ambient temperature of each power device to be detected, and sorting the infrared images of each power device to be detected at the same ambient temperature based on the order of their locations to obtain at least one sequence of infrared images to be detected; According to a preset extraction rule, the sequence of infrared images to be detected is divided into a target infrared image and a first subsequence; wherein the target infrared image is the infrared image to be detected with the largest degree of repetition of clustered regions in the sequence of infrared images to be detected; Detecting the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image; performing deduplication processing on each infrared image to be detected in the first subsequence in sequence based on the first fault area image, and detecting the deduplicated infrared image to be detected according to the target detection model during each deduplication processing to obtain a second fault area image corresponding to the deduplicated infrared image to be detected, and updating the first fault area image according to the current second fault area image during each deduplication processing; The most recently updated first fault area image is used as the fault area identification result of each electric device corresponding to the infrared image sequence to be detected.

2. A method for detecting a fault in an electric power device according to claim 1, characterized in that: The step of dividing the infrared image sequence to be detected into target infrared images and a first subsequence according to a preset extraction rule includes: Obtaining pixel values ​​of all feature points of a first infrared image to be detected in the sequence of infrared images to be detected, and clustering all feature points based on the magnitude of the pixel values ​​to obtain at least one clustering region; The cluster area repetition of each infrared image to be detected in the sequence of infrared images to be detected is calculated, and the infrared image to be detected with the largest cluster area repetition in the sequence of infrared images to be detected is selected as the target infrared image, and the target infrared image is removed from the sequence of infrared images to be detected to obtain a first subsequence corresponding to the target infrared image.

3. A method for detecting a fault in an electric power device according to claim 2, characterized in that: The detecting the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image includes: Input the target infrared image into a pre-built image detection model and output the corresponding one or more first regions; the first regions are composed of feature points whose pixel values ​​are greater than a first preset threshold; The center points of the one or more first areas are obtained respectively, and circles are drawn with each center point as the center and the closest distance from each center point to the edge of each first area as the radius to obtain a first fault area image corresponding to the target infrared image; wherein the first fault area image contains at least one fault area.

4. A method for detecting a fault in an electric power device according to claim 3, characterized in that: The step of sequentially performing deduplication processing on each infrared image to be detected in the first subsequence according to the first fault area image includes: Acquire a fault area in the first fault area image, and slide on an edge of the fault area based on a preset sliding window to acquire a transition area connected to the fault area; matching the feature points in the transition area with the feature points of the next infrared image to be detected in the first subsequence, and aligning the first fault area image with the next infrared image to be detected according to the matching result; The aligned target area is removed from the next infrared image to be detected to obtain an infrared sub-image to be detected corresponding to the next infrared image to be detected, wherein the target area includes the fault area and the transition area.

5. A method for detecting a fault in an electric power device according to claim 4, characterized in that: During each deduplication process, the deduplicated infrared image to be detected is detected according to the target detection model to obtain a second fault area image corresponding to the deduplicated infrared image to be detected, and during each deduplication process, the first fault area image is updated according to the current second fault area image, including: Inputting the infrared sub-image to be detected into the image detection model and outputting one or more corresponding second regions; the second regions are composed of feature points whose pixel values ​​are greater than a second preset threshold; Obtaining the center points of the one or more second areas respectively, and drawing circles with each center point as the center and the closest distance from each center point to the edge of each second area as the radius, to obtain a second fault area image corresponding to the target infrared image; wherein the second fault area image contains at least one fault area; The second fault area image is merged into the first fault area image to update the first fault area image.

6. A fault detection system for power equipment, characterized in that: include: an acquisition module, an extraction module, a first recognition module, and a circulation module; The acquisition module is used to acquire the infrared image to be detected, the location information and the ambient temperature of each power device at the same ambient temperature, and sort the infrared images to be detected of each power device at the same ambient temperature based on the order of the locations to obtain at least one sequence of infrared images to be detected; The extraction module is used to divide the infrared image sequence to be detected into a target infrared image and a first subsequence according to a preset extraction rule; wherein the target infrared image is the infrared image to be detected with the largest cluster area repetition in the infrared image sequence to be detected; The first recognition module is used to detect the target infrared image according to a preset target detection model to obtain a first fault area image corresponding to the target infrared image; The loop module is used to perform deduplication processing on each infrared image to be detected in the first subsequence in sequence according to the first fault area image, and during each deduplication processing, detect the deduplicated infrared image to be detected according to the target detection model to obtain the second fault area image corresponding to the deduplicated infrared image to be detected, and during each deduplication processing, update the first fault area image according to the current second fault area image.

7. A fault detection system for electric power equipment according to claim 6, characterized in that: The extraction module includes: a clustering unit and a calculation unit; The clustering unit is used to obtain pixel values ​​of all feature points of the first infrared image to be detected in the sequence of infrared images to be detected, and cluster all feature points based on the size of the pixel values ​​to obtain at least one clustering area; The calculation unit is used to calculate the cluster area repetition of each infrared image to be detected in the infrared image sequence to be detected, select the infrared image to be detected with the largest cluster area repetition in the infrared image sequence to be detected as the target infrared image, and remove the target infrared image from the infrared image sequence to be detected to obtain a first subsequence corresponding to the target infrared image.

8. A fault detection system for electric power equipment according to claim 7, characterized in that: The first recognition module includes: a first model recognition unit and a first image generation unit; The first model recognition unit is used to input the target infrared image into a pre-built image detection model and output one or more corresponding first regions; the first region is composed of feature points whose pixel values ​​are greater than a first preset threshold; The first image generation unit is used to respectively obtain the center points of the one or more first areas, and draw circles with each center point as the center and the closest distance from each center point to the edge of each first area as the radius, to obtain a first fault area image corresponding to the target infrared image; wherein, the first fault area image contains at least one fault area.

9. A fault detection system for electric power equipment according to claim 8, characterized in that: The circulation module includes: a deduplication unit; The deduplication unit is used to obtain the fault area in the first fault area image, and slide on the edge of the fault area based on a preset sliding window to obtain a transition area connected to the fault area, match the feature points in the transition area with the feature points of the next infrared image to be detected in the first subsequence, and align the first fault area image and the next infrared image to be detected according to the matching result, remove the aligned target area in the next infrared image to be detected, and obtain the infrared sub-image to be detected corresponding to the next infrared image to be detected, wherein the target area includes the fault area and the transition area.

10. A fault detection system for electric power equipment according to claim 9, characterized in that: The loop module further includes: a repetition recognition unit; The repeated recognition unit is used to input the infrared sub-image to be detected into the image detection model and output the corresponding one or more second areas; the second area is composed of each feature point whose pixel value is greater than a second preset threshold, and the center points of the one or more second areas are obtained respectively, and circles are drawn with each center point as the center and the closest distance from each center point to the edge of each second area as the radius to obtain a second fault area image corresponding to the target infrared image, wherein the second fault area image contains at least one fault area, and the second fault area image is merged into the first fault area image to update the first fault area image.

11. A fault detection device for an electric power device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is loaded into a processor, the steps of the fault detection method for electric power equipment according to any one of claims 1 to 5 are implemented.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the fault detection method for electric power equipment according to any one of claims 1 to 5 are implemented.

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