Power grid defect diagnosis method and device, computer equipment and storage medium

Abstract

The embodiment of the invention provides a power grid defect diagnosis method and device, computer equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring a defect collection rule, a plurality of inspection images and position information corresponding to the inspection images; the plurality of inspection images are images shot for a plurality of designated areas of at least one tower in the power grid; inputting the inspection image into a defect identification model to extract equipment component characteristics, and performing defect prediction based on the extracted equipment component characteristics to obtain first defect information corresponding to the inspection image; according to the position information corresponding to the inspection image, first defect information corresponding to the inspection image is updated, and second defect information corresponding to the inspection image is obtained; determining a defect point set of any tower in the power grid according to a defect collection rule and the second defect information respectively corresponding to the plurality of inspection images; and outputting the defect point set of any tower according to the tower dimension, so that the diagnosis accuracy of the power grid defects can be improved.

Description

Technical Field

[0001] The embodiments of the present specification relate to the field of image processing technology, and specifically to a method, apparatus, computer equipment and storage medium for diagnosing power grid defects. Background Art

[0002] Overhead lines in power grids are transmission lines that are assumed to be above the ground, with transmission conductors fixed on multiple poles and towers erected on the ground to transmit electricity. With the rapid development of the power industry, the scale and complexity of power grids are increasing, and overhead lines in power grids are also susceptible to defects caused by meteorological and environmental factors (such as strong winds, lightning strikes, dirt, ice and snow, etc.), thus causing failures.

[0003] In the related technology, when diagnosing defects and operating and maintaining overhead lines in the power grid, it is usually dependent on manual inspections and experience-based judgments. The defect diagnosis accuracy is low and the operation and maintenance difficulty is high, which can hardly meet the current power grid operation needs.

[0004] Therefore, there is an urgent need to provide a power grid defect diagnosis method to improve the accuracy of power grid defect diagnosis. Summary of the invention

[0005] In view of this, multiple embodiments of this specification are dedicated to providing a power grid defect diagnosis method, apparatus, computer equipment and storage medium to improve the diagnosis accuracy of power grid defects.

[0006] The embodiment of the present specification provides a method for diagnosing defects in a power grid, wherein the power grid includes a plurality of pole towers in an overhead line; the method includes: obtaining a defect collection rule, a plurality of inspection images, and position information corresponding to the inspection images; wherein the plurality of inspection images are images taken of a plurality of designated areas of at least one of the pole towers, and the defect collection rule is used to collect and count defect points in a plurality of designated areas of the pole tower; the inspection image is input into a pre-trained defect recognition model to extract device component features, so as to perform defect prediction based on the extracted device component features, and obtain first defect information corresponding to the inspection image; wherein the first defect information includes the inspection image The type and quantity of defect points in the designated area corresponding to the inspection image; according to the position information corresponding to the inspection image, the first defect information corresponding to the inspection image is updated to obtain the second defect information corresponding to the inspection image; wherein the second defect information includes the type, quantity and position of the defect points in the designated area corresponding to the inspection image; according to the defect collection rule and the second defect information corresponding to each of the multiple inspection images, the defect point set of any of the towers in the power grid is determined; wherein the defect point set includes the second defect information corresponding to each of the multiple designated areas in the tower; according to the tower dimension, the defect point set of any of the towers is output.

[0007] In some embodiments, the designated area is any one of a tower body area, a large side channel, and a small side channel; the tower body area is any one of the entire tower, a tower top, a tower head, a tower body, a left cross arm, and a right cross arm; the inspection image corresponding to the large side channel is a large side channel inspection image, the inspection image corresponding to the small side channel is a small side channel inspection image, and the inspection image corresponding to the tower body area is a tower body inspection image; determining the defect point set of any tower in the power grid according to the defect collection rule and the second defect information corresponding to each of the multiple inspection images, includes: for any tower, merging and deduplicating the second defect information corresponding to each of the multiple tower body inspection images in the tower to obtain the tower body defect point set of the tower; merging the second defect information corresponding to each of the large side channel inspection image and the small side channel inspection image of the tower with the tower body defect point set of the tower to obtain the defect point set of the tower.

[0008] In some embodiments, before inputting the inspection image into the defect recognition model, the method further includes: preprocessing the inspection image to obtain the preprocessed inspection image; wherein the preprocessing includes at least one of image denoising, image enhancement and image segmentation.

[0009] In some implementations, the method further includes: for any defect point set of the pole tower, outputting the defect point set of the pole tower according to the type dimension of the defect points in the defect point set.

[0010] In some embodiments, the types of defect points include at least one of the following: dirty insulators, discharge marks on insulators, tree and bamboo barriers, bird nests on pole towers, untied conductors, and damaged conductors.

[0011] In some embodiments, the defect recognition model is obtained by the following training method: constructing a training image sample set; wherein the training image sample set includes multiple training image samples, and the labels of the training image samples are the types of defect points; performing model training on the initial model according to the training image sample set and the labels to obtain the defect recognition model; wherein the initial model is built based on any one of the autoencoder, Transformer, and convolutional neural network models.

[0012] An embodiment of the present specification provides a power grid defect diagnosis device, wherein the power grid includes multiple pole towers in an overhead line; the device includes: a data acquisition module, used to obtain defect collection rules, multiple inspection images and position information corresponding to the inspection images; wherein the multiple inspection images are images taken of multiple designated areas of at least one of the pole towers, and the defect collection rules are used to collect and count defect points in multiple designated areas of the pole tower; a defect prediction module, used to input the inspection image into a pre-trained defect recognition model to extract equipment component features, so as to perform defect prediction based on the extracted equipment component features, and obtain first defect information corresponding to the inspection image; wherein the first defect information includes the defect information corresponding to the inspection image The type and number of defect points in the designated area; a defect location module, used to update the first defect information corresponding to the inspection image according to the position information corresponding to the inspection image, and obtain the second defect information corresponding to the inspection image; wherein the second defect information includes the type, number and position of the defect points in the designated area corresponding to the inspection image; a defect collection module, used to determine the defect point set of any of the towers in the power grid according to the defect collection rule and the second defect information corresponding to each of the multiple inspection images; wherein the defect point set includes the second defect information corresponding to each of the multiple designated areas in the tower; a defect output module, used to output the defect point set of any of the towers according to the tower dimension.

[0013] In some embodiments, the designated area is any one of the tower body area, the large side channel and the small side channel; the tower body area is any one of the entire tower, the tower top, the tower head, the tower body, the left cross arm and the right cross arm; the inspection image corresponding to the large side channel is the large side channel inspection image, the inspection image corresponding to the small side channel is the small side channel inspection image, and the inspection image corresponding to the tower body area is the tower body inspection image; the defect collection module is also used to: for any of the towers, merge and remove duplicates of the second defect information corresponding to each of the multiple tower body inspection images in the tower to obtain the tower body defect point set of the tower; merge the second defect information corresponding to the large side channel inspection image and the small side channel inspection image of the tower with the tower body defect point set of the tower to obtain the defect point set of the tower.

[0014] An embodiment of the present specification provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the power grid defect diagnosis method described in any of the above embodiments is implemented.

[0015] An embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the power grid defect diagnosis method described in any of the above embodiments is implemented.

[0016] In multiple implementations provided in this specification, by photographing at least one pole tower in an overhead line of a power grid, multiple inspection images and their corresponding position information are obtained, and defect aggregation rules for aggregating and counting defect points in multiple specified areas of the pole tower are obtained. Then, the inspection image is input into a pre-trained defect recognition model to extract equipment component features, so as to perform defect prediction based on the extracted equipment component features, and obtain first defect information corresponding to the inspection image, the first defect information including the type and number of defect points in the specified area corresponding to the inspection image. Then, according to the position information corresponding to the inspection image, the inspection image is updated. The method comprises the following steps: first defect information corresponding to the inspection image is obtained, and second defect information corresponding to the inspection image is obtained, the second defect information including the type, quantity and position of the defect points in the designated area corresponding to the inspection image; then, according to the defect aggregation rule and the second defect information corresponding to multiple inspection images of any tower in the power grid, the defect point set of the tower is determined, the defect point set of the tower includes the second defect information corresponding to multiple designated areas in the tower; then, after determining the defect point set corresponding to at least one tower in the power grid, the defect point set of any tower is output according to the tower dimension, so that the diagnosis accuracy of power grid defects can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a power grid defect diagnosis system provided for an embodiment of this specification; Figure 2 A schematic diagram of a flow chart of a power grid defect diagnosis method provided in an embodiment of this specification; Figure 3 A schematic diagram of a power grid defect diagnosis device provided in an embodiment of this specification; Figure 4 A schematic diagram of a computer device provided for embodiments of the present specification. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solution of this specification, the technical solution in the implementation of this specification will be clearly and completely described below in conjunction with the drawings in the implementation of this specification. Obviously, the described implementation is only a part of the implementation of this specification, not all of the implementations. Based on the implementation in this specification, all other implementations obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0019] This specification provides an example of a scenario for a power grid fault diagnosis system. Figure 1 , Figure 1 This is a schematic diagram of a power grid defect diagnosis system provided in an embodiment of the present specification. The power grid defect diagnosis system 100 can be used to diagnose defects in one or more towers 110 of overhead lines in a power grid. The power grid defect diagnosis system 100 may include a data acquisition device 120, a defect diagnosis device 130 and a defect output device 140.

[0020] The data acquisition device 120 can be used to acquire images of the overhead lines of the power grid or one or more pole towers 110 in the overhead lines during the inspection process, and obtain inspection images of one or more pole towers 110 in the overhead lines. Exemplarily, the data acquisition device 120 can be an image acquisition device or a visual device that can acquire inspection images or inspection videos in real time during the inspection process, for example, it can be a drone with a camera. After the data acquisition device 120 acquires the inspection images or inspection videos, the acquired inspection images or inspection videos can be transmitted to the defect diagnosis device 130.

[0021] The defect diagnosis device 130 can be a client or a server. The client can be an electronic device with network access capability. For example, the client can be a desktop computer, a tablet computer, a laptop computer, a smart phone, a digital assistant, etc. Alternatively, the client can also be software that can be run in the electronic device. The server can be an electronic device with certain computing and processing capabilities. It can have a network communication module, a processor, a memory, etc. Exemplarily, the server can also be a distributed server, which can be a system with multiple processors, memories, network communication modules, etc. operating in collaboration. Alternatively, the server can also be a server cluster formed by several servers. Alternatively, with the development of science and technology, the server can also be a new technical means that can realize the corresponding functions of the implementation method of the specification. For example, it can be a new form of "server" based on quantum computing.

[0022] Exemplarily, the defect diagnosis device 130 may perform defect diagnosis based on the inspection image after receiving the inspection image. Alternatively, after receiving the inspection video, the inspection image may be acquired based on the inspection video, and defect diagnosis may be performed based on the inspection image. Exemplarily, after performing defect diagnosis to determine the defect point or defect information, the defect diagnosis device 130 may output the defect through the defect output device 140.

[0023] The defect output device 140 may include an image output device. For example, the defect output device may include a display or other display terminal. As another example, the defect output device 140 may also include a voice output device such as a microphone to assist in voice output or alarm.

[0024] This specification provides a method for diagnosing power grid defects. Figure 2 , Figure 2 It is a flowchart of a power grid defect diagnosis method provided by this embodiment. This embodiment provides method operation steps as shown in the flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one execution method among many step execution sequences, and does not represent the only execution sequence. When the actual system or server product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment (for example, in a parallel processor or multi-threaded processing environment). The power grid defect diagnosis method can be applied to the defect diagnosis equipment in the power grid defect diagnosis system, such as Figure 2 As shown, the power grid defect diagnosis method may include the following steps.

[0025] Step S210: Obtain defect collection rules, multiple inspection images and location information corresponding to the inspection images; wherein the multiple inspection images are images taken of multiple designated areas of at least one tower, and the defect collection rules are used to collect and count defect points in multiple designated areas of the tower.

[0026] In some cases, the video acquisition device can inspect one or more towers of overhead lines in the power grid, and during the inspection process, capture multiple designated areas of one or more towers in the overhead lines to obtain multiple inspection images. In other words, the multiple inspection images may include inspection images corresponding to multiple designated areas of a tower, or may include inspection images corresponding to multiple designated areas of multiple towers.

[0027] In some other cases, the video acquisition device may record the corresponding position information when shooting any pole tower, that is, the inspection image acquired by the video acquisition device carries the corresponding position information.

[0028] The multiple designated areas of the tower may refer to multiple shooting angles of the data acquisition device during the inspection of the tower.

[0029] Specifically, the defect diagnosis device may receive a plurality of inspection images and the position information carried by each inspection image from the video acquisition device.

[0030] Exemplarily, the defect diagnosis device may obtain defect aggregation rules or may pre-store defect aggregation rules to facilitate subsequent aggregation and statistics of defect points in multiple designated areas of the tower.

[0031] Step S220: Input the inspection image into a pre-trained defect recognition model to extract equipment component features, perform defect prediction based on the extracted equipment component features, and obtain first defect information corresponding to the inspection image; wherein the first defect information includes the type and number of defect points in the specified area corresponding to the inspection image.

[0032] Specifically, the defect diagnosis equipment can be deployed with a pre-trained defect recognition model. After the inspection image is input into the defect recognition model, the defect recognition model can extract the equipment component features in the inspection image, and perform defect prediction based on the extracted equipment component features to obtain the first defect information of the inspection image.

[0033] The equipment component feature may be a feature of an area of ​​interest in the inspection image. For example, the area of ​​interest in the inspection image may refer to an equipment component area, such as an insulator area, a pole tower area, a conductor area, a cross arm area, etc. For example, the area of ​​interest in the inspection image may also be a tree or bamboo barrier area.

[0034] Exemplarily, since the inspection image of any tower corresponds to a designated area of ​​the tower, that is, the first defect information of any inspection image corresponds to the designated area corresponding to the inspection image. The first defect information includes the type and number of defect points in the corresponding designated area.

[0035] As an example, the types of defect points may include at least one of the following: insulator contamination, insulator discharge marks, tree and bamboo barriers, tower bird nests, wire unbound, wire damage. It is understandable that the types of defect points may also include other types, such as insulator damage, wire ice, etc.

[0036] Step S230: According to the position information corresponding to the inspection image, the first defect information corresponding to the inspection image is updated to obtain the second defect information corresponding to the inspection image; wherein the second defect information includes the type, quantity and position of defect points in the specified area corresponding to the inspection image.

[0037] Specifically, after determining the first defect information of the inspection image, the first defect information of the inspection image can be updated based on the position information corresponding to the inspection image to obtain the second defect information of the inspection image. Exemplarily, after determining the type and number of defect points in the specified area corresponding to the inspection image, the position of the defect points in the specified area corresponding to the inspection image can be located according to the position information corresponding to the inspection image, that is, the position of the defect points in the specified area corresponding to the inspection image is determined. In this way, the type, number and position of the defect points in the specified area corresponding to any inspection image can be determined.

[0038] Step S240: Determine a defect point set of any tower in the power grid according to the defect aggregation rule and the second defect information corresponding to each of the plurality of inspection images; wherein the defect point set includes the second defect information corresponding to each of the plurality of designated areas in the tower.

[0039] The defect aggregation rule may refer to counting and determining the second defect information corresponding to each tower in the overhead line of the power grid according to the tower dimension, that is, counting and determining the second defect information corresponding to multiple designated areas of each tower in the overhead line according to the tower dimension. The defect aggregation rule can describe the statistical logic of the set of defect points corresponding to any tower to reduce duplication and missed detection of defect points.

[0040] Step S250: output the defect point set of any tower according to the tower dimension.

[0041] Specifically, the defect point set of at least one tower in the power grid may be sent to the defect output device, so that the defect output device outputs the defect point set of any tower according to the tower dimension.

[0042] In the above implementation manner, corresponding inspection images and their position information are obtained for multiple designated areas corresponding to each of the multiple towers of the overhead lines in the power grid. Then, the inspection images are input into a pre-trained defect recognition model to extract equipment component features, so as to perform defect prediction based on the extracted equipment component features and obtain first defect information of the inspection images. Then, according to the position information corresponding to the inspection images, the first defect information corresponding to the inspection images is updated to obtain second defect information corresponding to the inspection images. Then, according to the defect collection rules and the second defect information corresponding to each of the multiple inspection images, the defect point set of any tower in the power grid is determined, and the defect point set of any tower is output according to the tower dimension. In this way, the efficiency of power grid defect inspection can be improved while the diagnostic accuracy can be improved.

[0043] In some embodiments, the designated area may be any one of the tower body area, the large side channel, and the small side channel.

[0044] Among them, the large side channel refers to the space or path of the pole tower facing the receiving end, and the small side channel refers to the space or path of the pole tower facing the transmitting end.

[0045] The tower body area can be any one of the whole tower, tower top, tower head, tower body, left cross arm and right cross arm.

[0046] Correspondingly, the inspection image corresponding to the large side channel is the large side channel inspection image, the inspection image corresponding to the small side channel is the small side channel inspection image, and the inspection image corresponding to the tower body area is the tower body inspection image.

[0047] In this embodiment, determining the defect point set of any tower in the power grid according to the defect aggregation rule and the second defect information corresponding to each of the plurality of inspection images may include the following steps S310-S320.

[0048] Step S310: for any tower, the second defect information corresponding to each of the plurality of tower body inspection images in the tower is merged and deduplicated to obtain a tower body defect point set of the tower.

[0049] Specifically, during the inspection of the tower by the data acquisition equipment, the perspectives and information captured by multiple shooting angles are different, and the perspectives and information captured by different shooting angles may be different. For example, the perspectives captured for the entire tower, tower top, tower head, tower body, left cross arm, right cross arm and other tower body areas may be repeated or omitted. Therefore, the defect information corresponding to the inspection images of multiple tower bodies can be merged and deduplicated according to the defect collection rules, thereby improving the accuracy of defect diagnosis and reducing the false detection rate of defect diagnosis.

[0050] Step S320: merging the second defect information corresponding to the large side channel inspection image and the small side channel inspection image of the tower with the tower body defect point set of the tower to obtain the defect point set of the tower.

[0051] Specifically, the perspectives captured for the large side channel, small side channel and tower body area are usually not repeated or the repetition rate is not high. Therefore, the defect information corresponding to the tower body inspection image, the large side channel inspection image and the small side channel inspection image can be merged. In this way, a set of defect points of the tower can be obtained.

[0052] In some implementations, the defect aggregation rule may be used for statistical logic between defect points of the same type in the second defect information corresponding to each of multiple inspection images of any tower, thereby determining a set of defect points of the same type for the tower.

[0053] The following explanation is given by taking tree and bamboo barrier as an example of the defect point type. For any pole tower, multiple pole tower body inspection images, or in other words, the tree and bamboo barrier defect points in the second defect information corresponding to the inspection images of the pole tower body areas such as the entire tower, tower top, tower head, tower body, left crossarm, and right crossarm are merged and deduplicated to obtain the tree and bamboo barrier defect points in the pole tower body area of ​​the tower. Then, the tree and bamboo barrier defect points of the large side channel are determined according to the second defect information corresponding to the inspection image of the large side channel, and the tree and bamboo barrier defect points of the small side channel are determined according to the second defect information corresponding to the inspection image of the small side channel. Then, the tree and bamboo barrier defect points in the pole tower body area, the tree and bamboo barrier defect points of the large side channel, and the tree and bamboo barrier defect points of the small side channel are summed to obtain the tree and bamboo barrier defect points of the pole tower.

[0054] As an example, continue to use the tree and bamboo barrier defect point as an example to explain, the defect aggregation rule can be expressed as ,in, It represents the defect points of tree and bamboo barriers in multiple tower body areas after merging and deduplication, B represents the defect points of tree and bamboo barriers in the large side channel, and C represents the defect points of tree and bamboo barriers in the small side channel.

[0055] In some implementations, the power grid defect diagnosis method may further include: for a defect point set of any tower, outputting the defect point set of the tower according to the type dimension of the defect points in the defect point set.

[0056] In some embodiments, before inputting the inspection image into the defect recognition model, the power grid diagnosis method may further include: preprocessing the inspection image to obtain a preprocessed inspection image; wherein the preprocessing includes at least one of image denoising, image enhancement, and image segmentation.

[0057] In some implementations, the defect recognition model can be obtained through the training method of the following steps S410 - S420 .

[0058] Step S410: construct a training image sample set; wherein the training image sample set includes a plurality of training image samples, and the labels of the training image samples are the types of defect points.

[0059] The training image samples may be collected by data collection equipment at historical moments.

[0060] Step S420: training the initial model according to the training image sample set and labels to obtain a defect recognition model; wherein the initial model is built based on any one of the autoencoder, Transformer, and convolutional neural network models.

[0061] Exemplarily, the Convolutional Neural Networks (CNN) model may adopt any one of LeNet, VGG, ResNet, and DenseNet.

[0062] The embodiments of this specification provide a power grid defect diagnosis device. The power grid defect diagnosis device can be applied to a defect diagnosis device in a power grid defect diagnosis system. Figure 3 The power grid defect diagnosis device may include a data acquisition module 510 , a defect prediction module 520 , a defect location module 530 , a defect collection module 540 and a defect output module 550 .

[0063] The data acquisition module 510 is used to acquire defect collection rules, multiple inspection images, and location information corresponding to the inspection images; wherein the multiple inspection images are images taken for multiple designated areas of at least one tower, and the defect collection rules are used to collect and count defect points in multiple designated areas of the tower; The defect prediction module 520 is used to input the inspection image into a pre-trained defect recognition model to extract equipment component features, so as to perform defect prediction based on the extracted equipment component features, and obtain first defect information corresponding to the inspection image; wherein the first defect information includes the type and number of defect points in a specified area corresponding to the inspection image; The defect location module 530 is used to update the first defect information corresponding to the inspection image according to the position information corresponding to the inspection image, and obtain the second defect information corresponding to the inspection image; wherein the second defect information includes the type, quantity and position of the defect points in the specified area corresponding to the inspection image; The defect collection module 540 is used to determine the defect point set of any tower in the power grid according to the defect collection rule and the second defect information corresponding to each of the plurality of inspection images; wherein the defect point set includes the second defect information corresponding to each of the plurality of designated areas in the tower; The defect output module 550 is used to output the defect point set of any tower according to the tower dimension.

[0064] In some embodiments, the designated area is any one of the tower body area, the large side channel and the small side channel; the tower body area is any one of the entire tower, the tower top, the tower head, the tower body, the left cross arm and the right cross arm; the inspection image corresponding to the large side channel is the large side channel inspection image, the inspection image corresponding to the small side channel is the small side channel inspection image, and the inspection image corresponding to the tower body area is the tower body inspection image.

[0065] In this embodiment, the defect collection module 540 can also be used to: for any tower, merge and remove duplicate second defect information corresponding to each of multiple tower body inspection images in the tower to obtain a tower body defect point set of the tower; merge the second defect information corresponding to each of the large side channel inspection image and the small side channel inspection image of the tower with the tower body defect point set of the tower to obtain a tower defect point set.

[0066] The specific functions and effects achieved by the power grid defect diagnosis device can be explained in other embodiments of this specification, and will not be repeated here. Each module in the power grid defect diagnosis device can be implemented in whole or in part by software, hardware, and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0067] The embodiments of the present specification also provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computer, the computer executes the power grid defect diagnosis method in any of the above embodiments.

[0068] The embodiments of this specification also provide a computer program product including instructions, and when the instructions are executed by a computer, the computer executes the power grid defect diagnosis method in any of the above embodiments.

[0069] The embodiments of the present specification also provide a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the power grid defect diagnosis method in the above-mentioned embodiment is implemented.

[0070] In some embodiments, see Figure 4 The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, and a communication interface connected through a system bus. Among them, 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 an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a power grid defect diagnosis method is implemented.

[0071] It should be understood that the specific examples herein are only intended to help those skilled in the art to better understand the embodiments of the present specification, rather than to limit the scope of the present invention.

[0072] It can be understood that in the various implementations of this specification, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of this specification.

[0073] It can be understood that the various embodiments described in this specification can be implemented individually or in combination, and the embodiments of this specification are not limited to this.

[0074] Unless otherwise specified, all technical and scientific terms used in the embodiments of this specification have the same meaning as those generally understood by those skilled in the art of the technical field of this specification. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the scope of this specification. The term "and / or" used in this specification includes any and all combinations of one or more related listed items. The singular forms of "a", "above", and "the" used in the embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0075] It can be understood that the processor of the embodiment of this specification can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method implementation can be completed by the hardware integrated logic circuit or software instructions in the processor. The above processor can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiment of this specification can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of this specification can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor are combined and executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0076] It is understood that the memory in the embodiments of this specification may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (programmable ROM, PROM), an erasable programmable read-only memory (erasablePROM, EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory may be a random access memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0077] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0078] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0079] The above is only a specific implementation of this specification, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this specification, which should be included in the protection scope of this specification. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for diagnosing power grid defects, characterized in that: The power grid includes a plurality of towers in an overhead line; the method includes: Obtaining defect collection rules, multiple inspection images, and location information corresponding to the inspection images; wherein the multiple inspection images are images taken of multiple designated areas of at least one of the towers, and the defect collection rules are used to collect and count defect points in the multiple designated areas of the towers; Input the inspection image into a pre-trained defect recognition model to extract equipment component features, so as to perform defect prediction based on the extracted equipment component features, and obtain first defect information corresponding to the inspection image; wherein the first defect information includes the type and number of defect points in the specified area corresponding to the inspection image; According to the position information corresponding to the inspection image, the first defect information corresponding to the inspection image is updated to obtain the second defect information corresponding to the inspection image; wherein the second defect information includes the type, quantity and position of the defect points in the specified area corresponding to the inspection image; Determine a defect point set of any of the towers in the power grid according to the defect collection rule and the second defect information corresponding to each of the plurality of inspection images; wherein the defect point set includes the second defect information corresponding to each of the plurality of designated areas in the tower; According to the tower dimensions, a defect point set of any of the towers is output.

2. The power grid defect diagnosis method according to claim 1, characterized in that: The designated area is any one of the tower body area, the large side channel and the small side channel; the tower body area is any one of the entire tower, the tower top, the tower head, the tower body, the left cross arm and the right cross arm; the inspection image corresponding to the large side channel is the large side channel inspection image, the inspection image corresponding to the small side channel is the small side channel inspection image, and the inspection image corresponding to the tower body area is the tower body inspection image; The step of determining a defect point set of any of the towers in the power grid according to the defect aggregation rule and the second defect information corresponding to each of the plurality of inspection images comprises: For any of the pole towers, merging and removing duplicates of the second defect information corresponding to a plurality of the pole tower body inspection images in the pole tower to obtain a pole tower body defect point set of the pole tower; The second defect information corresponding to the large side channel inspection image and the small side channel inspection image of the tower and the tower body defect point set of the tower are merged to obtain the defect point set of the tower.

3. The power grid defect diagnosis method according to claim 1, characterized in that: Before inputting the inspection image into the defect recognition model, the method further includes: Preprocessing is performed on the inspection image to obtain the preprocessed inspection image; wherein the preprocessing includes at least one of image denoising, image enhancement and image segmentation.

4. The power grid defect diagnosis method according to claim 1, characterized in that: The method further comprises: For any defect point set of the pole tower, the defect point set of the pole tower is output according to the type dimension of the defect points in the defect point set.

5. The power grid defect diagnosis method according to claim 1, characterized in that: The types of defect points include at least one of the following: dirty insulators, discharge marks on insulators, tree and bamboo barriers, bird nests on poles and towers, untied conductors, and damaged conductors.

6. The power grid defect diagnosis method according to any one of claims 1 to 5, characterized in that: The defect recognition model is obtained by the following training method: Constructing a training image sample set; wherein the training image sample set includes a plurality of training image samples, and the labels of the training image samples are the types of defect points; The initial model is trained according to the training image sample set and the labels to obtain the defect recognition model; wherein the initial model is built based on any one of an autoencoder, a Transformer, and a convolutional neural network model.

7. A power grid defect diagnosis device, characterized in that: The power grid includes a plurality of pole towers in an overhead line; the device includes: A data acquisition module, used to acquire defect collection rules, multiple inspection images and position information corresponding to the inspection images; wherein the multiple inspection images are images taken for multiple designated areas of at least one of the towers, and the defect collection rules are used to collect and count defect points in the multiple designated areas of the towers; A defect prediction module, used for inputting the inspection image into a pre-trained defect recognition model to extract equipment component features, so as to perform defect prediction based on the extracted equipment component features, and obtain first defect information corresponding to the inspection image; wherein the first defect information includes the type and number of defect points in the specified area corresponding to the inspection image; A defect location module, used to update the first defect information corresponding to the inspection image according to the position information corresponding to the inspection image, and obtain the second defect information corresponding to the inspection image; wherein the second defect information includes the type, quantity and position of the defect points in the specified area corresponding to the inspection image; A defect collection module, used to determine a defect point set of any of the towers in the power grid according to the defect collection rule and the second defect information corresponding to each of the plurality of inspection images; wherein the defect point set includes the second defect information corresponding to each of the plurality of designated areas in the tower; The defect output module is used to output a set of defect points of any of the towers according to the tower dimensions.

8. The power grid defect diagnosis device according to claim 7, characterized in that: The designated area is any one of the tower body area, the large side channel and the small side channel; the tower body area is any one of the entire tower, the tower top, the tower head, the tower body, the left cross arm and the right cross arm; the inspection image corresponding to the large side channel is the large side channel inspection image, the inspection image corresponding to the small side channel is the small side channel inspection image, and the inspection image corresponding to the tower body area is the tower body inspection image; The defect collection module is also used to: for any of the towers, merge and remove duplicate second defect information corresponding to each of the multiple tower body inspection images in the tower to obtain a tower body defect point set of the tower; merge the second defect information corresponding to each of the large side channel inspection image and the small side channel inspection image of the tower with the tower body defect point set of the tower to obtain a defect point set of the tower.

9. 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 power grid defect diagnosis method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the power grid defect diagnosis method according to any one of claims 1 to 6 is implemented.

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