CN114299359A - Method, equipment and storage medium for detecting transmission line fault - Google Patents
Method, equipment and storage medium for detecting transmission line fault Download PDFInfo
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Abstract
The method, the equipment and the storage medium for detecting the transmission line faults solve the technical problems that potential safety hazards existing in lines cannot be comprehensively and accurately judged through manual detection, and the overhauling cost is too high. The method comprises the following steps: acquiring a plurality of sample images based on inspection equipment, and constructing a first multi-scale image data set based on the plurality of sample images; determining a fault sample image in the first multi-scale image dataset and performing fault marking to generate a second multi-scale image dataset; determining a first fault classification network, and training the first fault classification network based on a second multi-scale image data set to obtain a first fault classification model; when the transmission line fault is detected, the image to be analyzed acquired by the inspection equipment is input into the first fault classification model, and an alarm is given according to the fault classification result. According to the method, the potential safety hazard of the line can be comprehensively and accurately judged, so that the overhaul cost is greatly reduced.
Description
Technical Field
The present disclosure relates to the field of transmission line fault detection technologies, and in particular, to a method, a device, and a storage medium for detecting a transmission line fault.
Background
The transmission line usually spans several hundred kilometers, is exposed to the outside for a long time, and is subjected to various natural environments or artificial damages and the like, so that the safety of the whole power grid system is damaged. Therefore, regular inspection of the transmission line is an effective measure for preventing transmission line faults.
In order to solve the problem that the safety of the whole power grid system is damaged due to various natural environments or human damage of the power transmission line, the conventional inspection mode still remains to manually inspect the power transmission line. Because the condition of misjudgment or missed judgment inevitably occurs in manual detection, the potential safety hazard of the circuit cannot be comprehensively and accurately judged, and the overhaul cost is greatly increased.
Disclosure of Invention
The embodiment of the application provides a method, equipment and a storage medium for detecting the faults of a power transmission line, and solves the technical problem that potential safety hazards existing in the line cannot be comprehensively and accurately judged through manual detection, so that the overhaul cost is greatly increased.
In a first aspect, an embodiment of the present application provides a method for detecting a fault of a power transmission line, where the method includes: acquiring a plurality of sample images based on inspection equipment, and constructing a first multi-scale image data set based on the plurality of sample images; determining a fault sample image in the first multi-scale image data set, and carrying out fault category marking on the fault sample image to generate a second multi-scale image data set; determining a first fault classification network, and training the first fault classification network based on a second multi-scale image data set to obtain a converged first fault classification model; and under the condition that the fault of the power transmission line needs to be detected, inputting the image to be analyzed acquired by the inspection equipment into the first fault classification model, determining a fault classification result, and giving an alarm according to the fault classification result.
According to the method for detecting the transmission line fault, firstly, a plurality of sample images acquired by inspection equipment are constructed into a first multi-scale data set; then, fault category marking is carried out on the fault sample images in the first multi-scale image data set, and the marked sample images form a second multi-scale image data set; and training the first fault classification network based on the second multi-scale data set to obtain a first fault classification model, inputting an image to be analyzed into the first fault classification model when the transmission line fault is detected, and determining a fault classification result so as to give an alarm. According to the method, the condition that manual detection can be misjudged or missed is effectively avoided, potential safety hazards existing in the line can be comprehensively and accurately judged, and therefore the overhaul cost is greatly reduced.
In an implementation manner of the present application, determining the first fault classification network specifically includes: adding a preset spatial pyramid pooling layer before a full connection layer of a preset deep learning network to generate a multi-scale image deep learning network; and replacing the standard convolution calculation mode in the multi-scale image deep learning network with a depth separable convolution calculation mode to obtain a first fault classification network.
According to the embodiment of the application, the space pyramid pooling layer is added before the full-connection layer of the preset depth network, so that the obtained first fault classification network can process sample images of any scale input into the first fault classification network; the standard convolution calculation mode in the deep network is replaced by the deep separable convolution calculation mode, so that the parameter scale in the obtained first fault classification network can be greatly reduced. The first fault classification network obtained by the method can process sample images of any scale for identification processing, and the required storage space and the machine computing power are greatly reduced.
In one implementation of the present application, after obtaining the converged first fault classification model, the method further comprises: determining a plurality of corresponding first training parameters in the first fault classification model, and simplifying and adjusting the plurality of first training parameters to obtain a plurality of second training parameters; transmitting a plurality of second training parameters into a second fault classification network to determine a second fault classification model; and setting the second fault classification model in the inspection equipment so that the inspection equipment can analyze the acquired image to be analyzed in real time.
In one implementation of the present application, before the number of second training parameters is introduced into the second fault classification network, the method includes: determining an adapted second network computing logic based on the type of the inspection equipment; based on the second network computing logic, the first fault classification network is reconstructed to obtain a second fault classification network.
In an implementation manner of the present application, constructing a first multi-scale image dataset based on a plurality of sample images specifically includes: performing segmentation processing on the plurality of sample images to determine a plurality of sample image groups; the sample image group comprises a preset number of sample images with different scales; a first multi-scale image dataset is generated based on the number of sample image groups.
In an implementation manner of the present application, the fault marking of the fault sample image to generate the second multi-scale image dataset specifically includes: determining the position of the fault in the sample image, and marking the fault sample image based on the position; determining the fault type of the power transmission line in the fault sample image, and marking the fault sample image based on the fault type; a second multi-scale image dataset is constructed and the marked failure sample images are added to the second multi-scale image dataset.
In an implementation manner of the present application, determining a fault type of a power transmission line in a fault sample image specifically includes: determining a first fault type and a corresponding first fault type grade of the power transmission line in the fault sample image; and determining the fault type of the transmission line in the fault sample image based on the first fault category and the first fault category grade.
In an implementation manner of the present application, the warning according to the fault classification result specifically includes: determining a second fault category and a corresponding second fault category grade in the fault classification result; and determining a preset alarm mode to alarm based on the second fault category and the second fault category grade.
In a second aspect, an embodiment of the present application further provides an apparatus for detecting a power transmission line fault, where the apparatus includes: a processor; and a memory having executable code stored thereon, which when executed, causes the processor to perform a method according to any one of claims 1-8.
In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for detecting a power transmission line fault, where computer-executable instructions are stored, and the computer-executable instructions are configured to: acquiring a plurality of sample images based on inspection equipment, and constructing a first multi-scale image data set based on the plurality of sample images; determining a fault sample image in the first multi-scale image dataset, and performing fault marking on the fault sample image to generate a second multi-scale image dataset; determining a first fault classification network, and training the first fault classification network based on a second multi-scale image data set to obtain a converged first fault classification model; and under the condition that the fault of the power transmission line needs to be detected, inputting the image to be analyzed acquired by the inspection equipment into the first fault classification model, determining a fault classification result, and giving an alarm according to the fault classification result.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a method for detecting a fault of a power transmission line according to an embodiment of the present disclosure;
fig. 2 is a schematic view of an internal structure of a device for detecting a power transmission line fault according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a method, equipment and a storage medium for detecting the faults of a power transmission line, and solves the technical problem that potential safety hazards existing in the line cannot be comprehensively and accurately judged through manual detection, so that the overhaul cost is greatly increased.
The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for detecting a power transmission line fault according to an embodiment of the present disclosure. As shown in fig. 1, a method for detecting a power transmission line fault provided in an embodiment of the present application mainly includes the following steps:
It should be noted that the inspection equipment of the present application includes but is not limited to inspection helicopters or inspection unmanned aerial vehicles, and can be selected according to specific application scenarios. The sample image is based on the image acquisition equipment arranged on the inspection equipment, and the acquired image of the area to be inspected is obtained. It can be understood that the image of the region to be inspected includes the power transmission line to be inspected.
Further, the acquired plurality of sample images are constructed into a first multi-scale image dataset. The images contained in the first multi-scale image data set are a plurality of sample images, and each sample image can be in different scales; for example, the first multi-scale image dataset includes both sample images at a scale of 300 × 300 and sample images at a scale of 400 × 400.
In an embodiment of the present application, the present application may further perform segmentation processing on the obtained sample image to determine a sample image group corresponding to the sample image; the sample image group comprises a preset number of sample images with different scales.
It should be noted that, the following segmentation methods may be adopted for segmenting the sample image: firstly, segmenting the sample image according to different scales aiming at a fault central point on the power transmission line; and secondly, aiming at the region corresponding to the fault in the sample image, segmenting the sample image according to different scales. The first division method is used when a certain failure feature on the sample image is divided, and the second division method is used when a plurality of failure features on the sample image are divided.
Firstly, a certain part of the power transmission line in the obtained sample image is damaged, and other parts are intact, so that information only needs to be extracted from the damaged part, the sample image is continuously segmented according to the scale sequence from small to large by taking the part with the fault as the center until the information which influences the damage of the part is segmented, and the segmented sample images with different scales form a plurality of sample image groups.
And secondly, the electric transmission line in the acquired sample image has a plurality of damages with different sizes and scales, and the damaged parts are scattered at different positions of the sample image, so that the sample image needs to be segmented with different sizes according to the size of the damaged scale, and the segmented sample images with different scales form a plurality of sample image groups.
It can be understood that the two manners may also be used in combination, that is, when the power transmission line in the obtained sample image has a plurality of different-scale damages, the second division manner is first adopted for the sample image, and then the first division manner is adopted for each divided part.
By means of segmentation processing of the sample images, sample image groups with different visual depths of a certain sample image can be obtained, and therefore follow-up training of the first fault classification network can be more accurate.
In an embodiment of the application, the sample image group may also be a complete sample image containing different information and having different scales, that is, a plurality of sample images obtained by the inspection device are not subjected to any segmentation processing, a plurality of sample image groups are directly obtained, a part of the sample images in the plurality of sample images obtained by the inspection device may also be subjected to segmentation processing, other sample images retain the original scale size, and thus the segmented sample images with different scales and the complete sample image form a first multi-scale image data set.
In one embodiment of the present application, labeling the failure sample images in the first multi-scale dataset includes location labeling the failure in the failure sample images and failure type labeling the failure sample images.
In an embodiment of the present application, a fault type of a power transmission line in a fault sample image is determined, specifically: the method comprises the steps of determining a first fault category and a first fault category grade of the power transmission line in a fault sample image, wherein the first fault category comprises but is not limited to problems of damage, pollution, color change and the like, and the first fault category grade can be set according to the damage degree of the first fault to the power transmission line, and can be divided into but not limited to a first grade, a second grade, a third grade and the like. Determining the fault type of the power transmission line in the fault sample image according to the first fault type and the first fault type grade, for example: a damaged first grade, a damaged second grade, a damaged third grade, a foul first grade, a foul second grade, a foul third grade, a color-changing first grade, a color-changing second grade, a color-changing third grade, etc.
In an embodiment of the present application, after marking the fault sample image in the first multi-scale data set, a second multi-scale image data set is constructed (it can be understood that the newly constructed set is an empty set), and then the fault sample image marked with the specific fault position and the fault type of the power transmission line is added to the second multi-scale image data set.
in one embodiment of the application, in order to enable the deep network to process sample images of any scale for identification processing, the required storage space and the mechanical calculation power are small, and the application constructs a first fault classification network. The first fault classification network construction comprises: adding a spatial pyramid pooling layer before a full connection layer of a preset deep learning network and replacing a standard convolution calculation mode in the preset deep learning network with a deep separable convolution calculation mode.
In an embodiment of the application, because a full connection layer of a traditional deep learning network needs to have fixed input, and a spatial pyramid pooling layer can generate output with fixed scale from input sample images with any scale, a spatial pyramid pooling layer is added before the full connection layer of the deep learning network, so that the deep learning network can realize input of multi-scale sample images.
In an embodiment of the present application, since a model obtained by server-side training generally includes a large number of parameters, and a complex deep learning environment needs to be deployed, is limited by hardware facilities such as a memory, and cannot be directly deployed to a device side. In order to consider that follow-up inspection equipment such as an unmanned aerial vehicle system can be used for carrying a fault classification model, and the capability of real-time analysis is achieved. In the network training process, the standard convolution is replaced by the deep separable convolution, and the parameter scale is reduced. The convolution is divided into depth convolution and point-by-point convolution by the depth separable convolution, so that the calculation cost can be effectively reduced, and the memory occupation can be reduced.
In one embodiment of the present application, after determining the first fault classification network, the first fault classification network is trained using the second multi-scale image dataset, resulting in a converged first fault classification model.
And 104, under the condition that the fault of the power transmission line needs to be detected, inputting the image to be analyzed acquired by the inspection equipment into the first fault classification model, determining a fault classification result, and performing pre-warning according to the fault classification result.
In an embodiment of the application, after the first fault classification model is obtained, under the condition that the power transmission line needs to be detected, the inspection equipment obtains an image to be analyzed, and then transmits the image back to the first fault classification model arranged on the server, so that a fault classification result is determined. It can be understood that the determined fault classification result includes a second fault category corresponding to the image to be analyzed and a corresponding second fault category level.
In an embodiment of the present application, after the fault classification result is determined, an alarm is performed according to a preset alarm manner. The alarm mode may be selected based on a specific application environment, which is not limited herein.
It can be understood that the inspection equipment acquires the image to be analyzed and then transmits the image back to the first fault classification model arranged on the server for detection, and the effect of real-time detection cannot be achieved. Therefore, the embodiment of the application also sets a fault classification model in the inspection equipment. So that the inspection equipment can analyze the acquired image to be analyzed in real time.
It should be noted that the first fault classification model is a fault classification model adapted to the server, and if the inspection equipment is equipped with the fault classification model, the first fault classification model needs to be adjusted.
Specifically, a plurality of corresponding first training parameters in the first fault classification model need to be adjusted; it is understood that the first training parameters are parameters for training convergence. Because the unmanned aerial vehicle system needs a relatively simple fault classification model to realize the real-time analysis of the power transmission line, a large number of parameters in the first fault classification model obtained by training the first fault classification network need to be simplified and adjusted to obtain a plurality of second training parameters. For example: one of the first training parameters is 0.15963456, and the corresponding second training parameter after simplification is 0.16.
In addition, a second fault classification network is required to be constructed for adapting the inspection equipment. The second fault classification network is constructed by firstly determining an adaptive second network computing logic based on the type of the routing inspection equipment, namely rewriting a network computing logic, and reconstructing the first fault classification network based on the rewritten network computing logic to obtain the second fault classification network.
After the second fault classification network is constructed, the obtained second training parameters are transmitted into the second fault classification network to determine a second fault classification model.
In an embodiment of the application, after the second fault classification model is obtained, the fault classification model can be applied to the inspection equipment, so that the inspection equipment can analyze the acquired image to be analyzed in real time.
It should be noted that after the second fault classification model is applied to the inspection equipment, it is not necessary to upload each image to be analyzed, which is acquired by the inspection equipment, to the server for detection. Further, since the second fault classification model has a limited accuracy, it is preferable to use the first fault classification model in combination with the second fault classification model. A certain image to be analyzed acquired by the inspection equipment is uploaded to the server to be detected regularly, so that the detection precision can be ensured, and the detection real-time performance can be ensured.
Based on the same inventive concept, the embodiment of the application also provides equipment for detecting the transmission line fault, and the internal structure of the equipment is shown in fig. 2.
Fig. 2 is a schematic view of an internal structure of a device for detecting a power transmission line fault according to an embodiment of the present application. As shown in fig. 2, the apparatus includes: a processor 201; a memory 202 having executable instructions stored thereon that, when executed, cause the processor 201 to perform a method of detecting a power transmission line fault as described above.
In an embodiment of the present application, the processor 201 is configured to obtain a plurality of sample images based on the inspection equipment, and construct a first multi-scale image dataset based on the plurality of sample images; determining a fault sample image in the first multi-scale image dataset, and performing fault marking on the fault sample image to generate a second multi-scale image dataset; determining a first fault classification network, and training the first fault classification network based on a second multi-scale image data set to obtain a converged first fault classification model; and under the condition that the fault of the power transmission line needs to be detected, inputting the image to be analyzed acquired by the inspection equipment into the first fault classification model, determining a fault classification result, and giving an alarm according to the fault classification result.
Some embodiments of the present application provide a non-volatile computer storage medium corresponding to fig. 1 for detecting a power transmission line fault, the medium storing computer-executable instructions configured to:
acquiring a plurality of sample images based on inspection equipment, and constructing a first multi-scale image data set based on the plurality of sample images;
determining a fault sample image in the first multi-scale image dataset, and performing fault marking on the fault sample image to generate a second multi-scale image dataset;
determining a first fault classification network, and training the first fault classification network based on a second multi-scale image data set to obtain a converged first fault classification model;
and under the condition that the fault of the power transmission line needs to be detected, inputting the image to be analyzed acquired by the inspection equipment into the first fault classification model, determining a fault classification result, and giving an alarm according to the fault classification result.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. Especially, for the internet of things device and medium embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the partial description of the method embodiments.
The system and the medium provided by the embodiment of the application correspond to the method one to one, so the system and the medium also have the beneficial technical effects similar to the corresponding method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method of detecting a fault in a power transmission line, the method comprising:
acquiring a plurality of sample images based on inspection equipment, and constructing a first multi-scale image data set based on the plurality of sample images;
determining a fault sample image in the first multi-scale image dataset and fault marking the fault sample image to generate a second multi-scale image dataset;
determining a first fault classification network and training the first fault classification network based on the second multi-scale image dataset to obtain a converged first fault classification model;
and under the condition that the fault of the power transmission line needs to be detected, inputting the image to be analyzed acquired by the inspection equipment into the first fault classification model, determining a fault classification result, and giving an alarm according to the fault classification result.
2. The method according to claim 1, wherein determining the first fault classification network specifically includes:
adding a preset spatial pyramid pooling layer before a full connection layer of a preset deep learning network to generate a multi-scale image deep learning network;
and replacing the standard convolution calculation mode in the multi-scale image deep learning network with a depth separable convolution calculation mode to obtain a first fault classification network.
3. The method of claim 1, wherein after obtaining the converged first fault classification model, the method further comprises:
determining a plurality of corresponding first training parameters in the first fault classification model, and simplifying and adjusting the plurality of first training parameters to obtain a plurality of second training parameters;
transmitting the second training parameters into a second fault classification network to determine a second fault classification model;
and setting the second fault classification model in the inspection equipment so that the inspection equipment can analyze the acquired image to be analyzed in real time.
4. The method of claim 3, wherein before the number of second training parameters is introduced into the second fault classification network, the method comprises: determining an adapted second network computing logic based on the type of the inspection device;
reconstructing the first fault classification network based on the second network computing logic to obtain a second fault classification network.
5. The method according to claim 1, wherein constructing a first multi-scale image dataset based on the plurality of sample images specifically comprises:
performing segmentation processing on the plurality of sample images to determine a plurality of sample image groups; the sample image group comprises a preset number of sample images with different scales;
generating the first multi-scale image dataset based on the number of sample image groups.
6. The method according to claim 1, wherein the fault marking of the fault sample image to generate a second multi-scale image dataset comprises:
determining the position of a fault in the image, and marking the fault sample image based on the position; and the number of the first and second groups,
determining the fault type of the power transmission line in the fault sample image, and marking the fault sample image based on the fault type;
constructing a second multi-scale image dataset and adding the marked fault sample images to the second multi-scale image dataset.
7. The method according to claim 6, wherein determining the type of the fault of the power transmission line in the fault sample image specifically includes:
determining a first fault type and a corresponding first fault type grade of the power transmission line in the fault sample image;
and determining the fault type of the transmission line in the fault sample image based on the first fault category and the first fault category grade.
8. The method for detecting the transmission line fault according to claim 1, wherein the alarming according to the fault classification result specifically includes:
determining a second fault category and a corresponding second fault category grade in the fault classification result;
and determining a preset alarm mode to alarm based on the second fault category and the second fault category grade.
9. An apparatus for detecting a fault in a power transmission line, the apparatus comprising:
a processor;
and a memory having executable code stored thereon, which when executed, causes the processor to perform a method as claimed in any one of claims 1-8.
10. A non-transitory computer storage medium storing computer-executable instructions for detecting transmission line faults, the computer-executable instructions configured to:
acquiring a plurality of sample images based on inspection equipment, and constructing a first multi-scale image data set based on the plurality of sample images;
determining a fault sample image in the first multi-scale image dataset and fault marking the fault sample image to generate a second multi-scale image dataset;
determining a first fault classification network and training the first fault classification network based on the second multi-scale image dataset to obtain a converged first fault classification model;
and under the condition that the fault of the power transmission line needs to be detected, inputting the image to be analyzed acquired by the inspection equipment into the first fault classification model, determining a fault classification result, and giving an alarm according to the fault classification result.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114926723A (en) * | 2022-06-10 | 2022-08-19 | 国网江苏省电力有限公司电力科学研究院 | Method, terminal and storage medium for identifying and triggering alarm of interference objects around power transmission line |
| CN115620496A (en) * | 2022-09-30 | 2023-01-17 | 北京国电通网络技术有限公司 | Fault alarm method, device, equipment and medium applied to power transmission line |
| CN116029537A (en) * | 2023-03-29 | 2023-04-28 | 中国南方电网有限责任公司超高压输电公司广州局 | Power transmission line inspection method, device, computer equipment and storage medium |
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| CN115620496B (en) * | 2022-09-30 | 2024-04-12 | 北京国电通网络技术有限公司 | Fault alarm method, device, equipment and medium applied to power transmission line |
| CN116029537A (en) * | 2023-03-29 | 2023-04-28 | 中国南方电网有限责任公司超高压输电公司广州局 | Power transmission line inspection method, device, computer equipment and storage medium |
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