Power transmission line WAPI network operation and maintenance management method and system

Abstract

The invention relates to a power transmission line WAPI network operation and maintenance management method and system, and the method comprises the steps: obtaining input information which is inputted by a user and corresponds to the operation and maintenance of a power transmission line WAPI network, and the input information at least comprises the operation and maintenance query information of one or more devices in the power transmission line WAPI network; inputting the input information into a target large model to obtain operation and maintenance result information; wherein the target large model is obtained by training a convolutional neural network model based on sample operation and maintenance data of the power transmission line WAPI network in advance; and executing a corresponding operation and maintenance response strategy based on the operation and maintenance result information.

Description

Technical Field

[0001] This disclosure relates to the field of power transmission line WAPI network technology, and in particular to a method and system for operation and maintenance management of power transmission line WAPI networks. Background Technology

[0002] Currently, traditional wireless communication is used in power transmission line monitoring, and data transmission relies heavily on the communication base stations of operators. However, power transmission lines are generally located in remote areas, and there may be no base stations or only a limited number of base stations near the power poles, making it difficult to provide full coverage for effective monitoring data transmission. Adding new base stations specifically for power transmission lines would increase unnecessary equipment costs, and traditional wireless communication, which transmits data through operator networks, cannot guarantee the security of power monitoring data.

[0003] One proposed technology involves installing multiple WAPI terminals on multiple towers along power transmission lines in areas where communication base stations have limited coverage, forming a WAPI (Wireless LAN Authentication and Privacy Infrastructure) network. This network enables a power transmission line WAPI network management system to acquire power monitoring data. However, after the power transmission line WAPI network management system is set up and operational, how to perform precise operation and maintenance, such as quickly and accurately locating faulty equipment or accurately determining the operating status of equipment, remains a gap in the industry. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a method and system for operation and maintenance management of a WAPI network for power transmission lines.

[0005] In a first aspect, embodiments of this disclosure provide a method for operation and maintenance management of a WAPI network for power transmission lines, the method comprising: Obtain user input information corresponding to the operation and maintenance of the power transmission line WAPI network, wherein the input information includes at least operation and maintenance query information of one or more devices in the power transmission line WAPI network; The input information is fed into the target large model to obtain the operation and maintenance result information; wherein, the target large model is pre-trained on a convolutional neural network model based on sample operation and maintenance data of the power transmission line WAPI network; The corresponding operation and maintenance response strategy is executed based on the operation and maintenance result information.

[0006] In one embodiment, the training process of the target large model includes: The sample operation and maintenance data is input into the convolutional neural network model for iterative training until the loss value of the loss function is less than or equal to a preset threshold, thereby ending the training to obtain the target large model. The convolutional neural network model includes a first network layer, a second network layer, and a third network layer. The first network layer is used to extract global feature information from the sample operation and maintenance data, the second network layer is used to extract local feature information from the sample operation and maintenance data, and the third network layer is used to generate target feature information based on the global and local feature information and input it into a fully connected layer to obtain output data. The loss function includes a first loss function and a second loss function. The first loss function characterizes the difference between the output data and the label data, and the second loss function characterizes the difference between the output of the third network layer and the output data.

[0007] In one embodiment, the number of convolutional layers in the first network layer is greater than the number of convolutional layers in the second network layer.

[0008] In one embodiment, the sample operation and maintenance data includes text data and annotation data of multiple different devices related to operation and maintenance.

[0009] In one embodiment, the maintenance result information includes at least one or more of the device's operating status information and fault identification result information.

[0010] In one embodiment, different maintenance response strategies correspond to different maintenance result information.

[0011] In one embodiment, the method further includes: generating and displaying operation and maintenance early warning information based on the operation and maintenance result information.

[0012] Secondly, embodiments of this disclosure provide a power transmission line WAPI network operation and maintenance management system, including: The acquisition module is used to acquire input information input by the user that corresponds to the operation and maintenance of the power transmission line WAPI network. The input information includes at least the operation and maintenance query information of one or more devices in the power transmission line WAPI network. The input module is used to input the input information into the target large model to obtain the operation and maintenance result information; wherein, the target large model is pre-trained on a convolutional neural network model based on sample operation and maintenance data of the power transmission line WAPI network; The processing module is used to execute the corresponding operation and maintenance response strategy based on the operation and maintenance result information.

[0013] Thirdly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power transmission line WAPI network operation and maintenance management method described in any of the above embodiments.

[0014] Fourthly, embodiments of this disclosure provide an electronic device, including: Processor; and Memory, used to store computer programs; The processor is configured to execute the power line WAPI network operation and maintenance management method of any of the above embodiments by executing the computer program.

[0015] The technical solution provided in this disclosure has the following advantages compared with the prior art: The transmission line WAPI network operation and maintenance management method and system provided in this disclosure acquire user-inputted information corresponding to the operation and maintenance of the transmission line WAPI network. The input information includes at least operation and maintenance query information for one or more devices in the transmission line WAPI network. The input information is then input into a target large-scale model to obtain operation and maintenance result information. The target large-scale model is pre-trained on a convolutional neural network model based on sample operation and maintenance data from the transmission line WAPI network. A corresponding operation and maintenance response strategy is executed based on the operation and maintenance result information. This embodiment utilizes a pre-trained operation and maintenance-related large-scale model to perform operation and maintenance queries and responses, enabling rapid and accurate location of faulty devices or accurate determination of device operating status in the transmission line WAPI network, thus achieving precise operation and maintenance of the transmission line WAPI network and filling a gap in the industry. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the WAPI network operation and maintenance management method for power transmission lines according to an embodiment of this disclosure; Figure 2 This is a schematic diagram of the architecture of a convolutional neural network model according to an embodiment of the present disclosure; Figure 3 This is a flowchart of a WAPI network operation and maintenance management method for power transmission lines according to another embodiment of this disclosure; Figure 4 This is a schematic diagram of the WAPI network operation and maintenance management system for power transmission lines, as described in this embodiment. Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0019] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0020] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0021] It should be understood that in the following text, "at least one item" refers to one or more items, and "more than one item" refers to two or more items. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0022] Figure 1 This is a flowchart illustrating a method for operation and maintenance management of a power transmission line WAPI network according to an embodiment of the present disclosure. This method can be executed by a computing device such as a computer or server, and may include the following steps: Step S101: Obtain the input information input by the user corresponding to the operation and maintenance of the power transmission line WAPI network. The input information includes at least the operation and maintenance query information of one or more devices in the power transmission line WAPI network.

[0023] For example, the multiple devices can be WAPI devices installed on the tower and monitoring devices such as cameras, temperature sensors, and humidity sensors. These are all devices in the power transmission line WAPI network. For information on power transmission line WAPI networks, please refer to patent document CN119997003A. The input information, i.e., the operation and maintenance query information, can be text information, such as "query the operating status and / or fault identification results of the target device in the power transmission line WAPI network of target region D". Specifically, the input information can be input by an input device on the computing device, such as a touch screen, keyboard, or other input device. The user can operate the computing device to perform the input operation. In other embodiments, if the computing device is a remote server, the input information can be input by the user through a terminal such as a smartphone or tablet, and then uploaded to the server by the terminal.

[0024] Step S102: Input the input information into the target large model to obtain the operation and maintenance result information; wherein, the target large model is pre-trained on a convolutional neural network model based on sample operation and maintenance data of the power transmission line WAPI network.

[0025] For example, in one embodiment, the sample operation and maintenance data may include text data and labeled data from multiple different devices related to operation and maintenance. These multiple different devices may be WAPI devices installed on the towers, as well as monitoring devices such as cameras, temperature sensors, and humidity sensors. During the operation of the power transmission line WAPI network, the network generates logs and monitoring data. This embodiment effectively collects, stores, analyzes, and utilizes this text data, performs data preprocessing, and uses feature engineering to obtain training text data, i.e., sample operation and maintenance data. Based on the sample operation and maintenance data, a convolutional neural network model is trained to obtain the target large model, i.e., the operation and maintenance large model of the power transmission line WAPI network.

[0026] In practical applications, input information such as the text "Query the operating status and / or fault identification results of the target equipment in the WAPI network of the power transmission line in the target region D" is input into the target large model. The model outputs operation and maintenance result information such as the operating status (normal, poor or abnormal) and / or fault identification results (such as fault level) of the target equipment.

[0027] Step S103: Execute the corresponding operation and maintenance response strategy based on the operation and maintenance result information.

[0028] For example, in one embodiment, the maintenance result information includes at least one or more of the device's operating status information and fault identification result information. In one embodiment, different maintenance response strategies correspond to different maintenance result information. For instance, when the target device's operating status is normal, the maintenance response strategy is to perform no manual operation. When the target device's operating status is abnormal, the maintenance response strategy may be to send a notification to maintenance personnel so that they can promptly inspect the target device on-site. As another example, the device's fault identification result, such as the fault level, can be divided into four levels, where level one is the lowest level fault with minimal impact on services, and level four is the highest level fault, potentially causing the entire power transmission line WAPI network to collapse. In this case, different response measures can be taken for different fault levels of the target device's fault identification result to ensure the network maintains stable operation.

[0029] The above-described scheme in this embodiment executes operation and maintenance queries and responses based on a pre-trained large-scale model related to operation and maintenance. It can quickly and accurately locate faulty equipment in the power transmission line WAPI network or accurately determine the operating status of equipment, thereby achieving precise operation and maintenance of the power transmission line WAPI network, which improves the accuracy of the operation and maintenance results of the power transmission line WAPI network and fills a gap in the industry.

[0030] To further improve the accuracy of the operation and maintenance results of the WAPI network of power transmission lines, in one embodiment, the training process of the target large model includes: inputting sample operation and maintenance data into the convolutional neural network model for iterative training until the loss value of the loss function is less than or equal to a preset threshold to end the training and obtain the target large model.

[0031] Among them, reference Figure 2 As shown, the convolutional neural network model may include a first network layer, a second network layer, and a third network layer, all of which can be convolutional layers. In this embodiment, the first network layer is used to extract global feature information of the sample operation and maintenance data, and the second network layer is used to extract local feature information of the sample operation and maintenance data. The third network layer is used to generate target feature information based on the global and local feature information and input it into a fully connected layer to obtain output data.

[0032] In this embodiment, the sample operation and maintenance data is text data. In existing technologies, the model needs to extract query keywords as feature information, such as feature vectors, during training. However, the existing technology fails to consider that these keywords themselves contain semantic information. In some cases, the extracted keywords, i.e., feature information, may not be accurate, leading to inaccurate results from the final trained model. Therefore, in this embodiment, at least one query keyword in the sample operation and maintenance data is extracted as local feature information, such as a feature vector. At the same time, the context information in the text sentence containing the query keyword is extracted as global feature information, such as the feature vector of the keyword corresponding to the context. Based on the local and global feature information, the final more accurate target feature information is determined and input into a fully connected layer (FC) to obtain the model's output data.

[0033] Accordingly, the model's loss functions are set as a first loss function and a second loss function. The first loss function characterizes the difference between the output data and the label data, while the second loss function characterizes the difference between the output of the third network layer and the output data. The first loss function is a constraint on the overall model, while the second loss function is a constraint on the third network layer within the model. In effect, it constrains the first and second network layers, and as part of the overall loss function of the convolutional neural network model, it influences the overall loss value of the model. By extracting different features from the first and second network layers respectively, and then comprehensively determining the final target features by the third network layer, and by combining the settings of the first and second loss functions to constrain training, more accurate feature information can be extracted from the sample maintenance data for constrained training. This allows the target model obtained after training to output more accurate maintenance results.

[0034] Therefore, based on the improved training and update process brought about by the aforementioned improved specific model architecture and specific loss function, using this pre-trained large-scale O&M-related model to execute O&M queries and responses can further enable O&M tasks such as quickly and accurately locating faulty equipment or accurately determining the operating status of equipment in the transmission line WAPI network, thereby achieving more precise O&M of the transmission line WAPI network and further improving the accuracy of O&M results.

[0035] In one embodiment, the loss value L of the loss function is equal to the sum of the loss values ​​L1 of the first loss function and L2 of the second loss function. Alternatively, L is equal to the weighted sum of the loss values ​​L1 and L2 of the first and second loss functions, along with their corresponding weights, where the weight of the second loss function is greater than that of the first loss function. This means that the target feature information determined by extracting global and local feature information has a greater influence weight. This allows the trained large-scale operation and maintenance model to execute operation and maintenance queries and responses, further enabling the rapid and accurate identification of faulty equipment or accurate determination of equipment operating status in the power transmission line WAPI network, thus achieving more precise operation and maintenance of the power transmission line WAPI network and further improving the accuracy of the operation and maintenance results.

[0036] In one embodiment, the number of convolutional layers in the first network layer is greater than the number of convolutional layers in the second network layer. Since the first network layer extracts global feature information from the sample operation and maintenance data, and its data volume is greater than that extracted by the second network layer, the number of convolutional layers in the first network layer is greater than the number of convolutional layers in the second network layer to improve the accuracy of feature extraction. This allows for the extraction of more accurate global feature information, thereby determining more accurate target feature information, and ultimately enabling the trained model to output more accurate operation and maintenance results.

[0037] Based on the aforementioned improvements to the specific model architecture, internal model units, and training / update processes brought about by the specific loss function, using this pre-trained large-scale O&M-related model to execute O&M queries and responses can further quickly and accurately locate faulty equipment or accurately determine the operating status of equipment in the power transmission line WAPI network, thereby achieving more precise O&M of the power transmission line WAPI network and further improving the accuracy of O&M results.

[0038] In one embodiment, reference Figure 3 As shown, the method may further include step S301: generating and displaying maintenance warning information based on the maintenance result information. For example, the maintenance warning information may be one or more of text, sound, and light. When, for example, the maintenance result information indicates a high fault level in the target equipment, a timely maintenance warning can be issued to clearly prompt relevant maintenance personnel to handle the situation promptly, ensuring the stable operation of the power transmission line WAPI network.

[0039] In complex environments or operating conditions, pre-trained AI models can be used to output maintenance warnings based on operational results (such as the simultaneous presence of abnormal operating status of target equipment and fault identification results, such as fault level four). This can further improve the accuracy of warnings under complex conditions and avoid false alarms. The AI ​​model can be pre-trained on an original AI model (including but not limited to convolutional neural networks) based on the operating status information and fault identification results, such as fault levels, of different equipment.

[0040] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps. Furthermore, it is readily understood that these steps may be executed synchronously or asynchronously, for example, in multiple modules / processes / threads.

[0041] like Figure 4 As shown in the figure, this disclosure provides a power transmission line WAPI network operation and maintenance management system, including: The acquisition module 401 is used to acquire input information input by the user corresponding to the operation and maintenance of the power transmission line WAPI network. The input information includes at least the operation and maintenance query information of one or more devices in the power transmission line WAPI network. The input module 402 is used to input the input information into the target large model to obtain operation and maintenance result information; wherein, the target large model is pre-trained on a convolutional neural network model based on sample operation and maintenance data of the power transmission line WAPI network; The processing module 403 is used to execute the corresponding operation and maintenance response strategy based on the operation and maintenance result information.

[0042] In one embodiment, the training process of the target large model includes: inputting sample operation and maintenance data into the convolutional neural network model for iterative training until the loss value of the loss function is less than or equal to a preset threshold to end the training and obtain the target large model; wherein, the convolutional neural network model includes a first network layer, a second network layer, and a third network layer, the first network layer is used to extract global feature information of the sample operation and maintenance data, the second network layer is used to extract local feature information of the sample operation and maintenance data, and the third network layer is used to generate target feature information based on the global feature information and local feature information and input it into a fully connected layer to obtain output data; the loss function includes a first loss function and a second loss function, the first loss function characterizing the difference between the output data and the label data, and the second loss function characterizing the difference between the output of the third network layer and the output data.

[0043] In one embodiment, the loss value L of the loss function is equal to the sum of the loss values ​​L1 of the first loss function and L2 of the second loss function; or, L is equal to the weighted sum of the loss values ​​L1 of the first loss function, L2 of the second loss function, and their corresponding weight values, wherein the weight value of the second loss function is greater than the weight value of the first loss function.

[0044] In one embodiment, the number of convolutional layers in the first network layer is greater than the number of convolutional layers in the second network layer.

[0045] In one embodiment, the sample operation and maintenance data includes text data and annotation data of multiple different devices related to operation and maintenance.

[0046] In one embodiment, the maintenance result information includes at least one or more of the device's operating status information and fault identification result information.

[0047] In one embodiment, different maintenance response strategies correspond to different maintenance result information.

[0048] In one embodiment, the system may further include an early warning module for generating and displaying operation and maintenance early warning information based on the operation and maintenance result information.

[0049] In one embodiment, a pre-trained AI model is used to output maintenance early warning information based on maintenance results.

[0050] Regarding the system in the above embodiments, the specific methods by which each module performs operations and the corresponding technical effects have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0051] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units. Components shown as modules or units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0052] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power transmission line WAPI network operation and maintenance management method of any of the above embodiments.

[0053] For example, the readable storage medium may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0054] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0055] This disclosure also provides an electronic device, including a processor and a memory, the memory being used to store a computer program. The processor is configured to execute the power transmission line WAPI network operation and maintenance management method described in any of the above embodiments by executing the computer program.

[0056] The following reference Figure 5 To describe an electronic device 600 according to this embodiment of the present invention. Figure 5 The electronic device 600 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0057] like Figure 5 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0058] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the above-described method embodiment section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform, as follows: Figure 1 The steps of the method shown are as follows.

[0059] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only memory unit (ROM) 6203.

[0060] The storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0061] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0062] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0063] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the method steps of the above embodiments according to the embodiments of this disclosure.

[0064] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0065] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for operation and maintenance management of a WAPI network for power transmission lines, characterized in that, The method includes: Obtain user input information corresponding to the operation and maintenance of the power transmission line WAPI network, wherein the input information includes at least operation and maintenance query information of one or more devices in the power transmission line WAPI network; The input information is fed into the target large model to obtain the operation and maintenance result information; wherein, the target large model is pre-trained on a convolutional neural network model based on sample operation and maintenance data of the power transmission line WAPI network; The corresponding operation and maintenance response strategy is executed based on the operation and maintenance result information.

2. The method according to claim 1, characterized in that, The training process of the target large model includes: The sample operation and maintenance data is input into the convolutional neural network model for iterative training until the loss value of the loss function is less than or equal to a preset threshold, thereby ending the training to obtain the target large model. The convolutional neural network model includes a first network layer, a second network layer, and a third network layer. The first network layer is used to extract global feature information from the sample operation and maintenance data, the second network layer is used to extract local feature information from the sample operation and maintenance data, and the third network layer is used to generate target feature information based on the global and local feature information and input it into a fully connected layer to obtain output data. The loss function includes a first loss function and a second loss function. The first loss function characterizes the difference between the output data and the label data, and the second loss function characterizes the difference between the output of the third network layer and the output data.

3. The method according to claim 2, characterized in that, The number of convolutional layers in the first network layer is greater than the number of convolutional layers in the second network layer.

4. The method according to any one of claims 1 to 3, characterized in that, The sample operation and maintenance data includes text data and labeled data from multiple different devices related to operation and maintenance.

5. The method according to any one of claims 1 to 3, characterized in that, The operation and maintenance result information includes at least one or more of the equipment's operating status information and fault identification result information.

6. The method according to claim 5, characterized in that, Different maintenance result information corresponds to different maintenance response strategies.

7. The method according to any one of claims 1 to 3, characterized in that, The method also includes: generating and displaying operation and maintenance early warning information based on the operation and maintenance result information.

8. A WAPI network operation and maintenance management system for power transmission lines, characterized in that, include: The acquisition module is used to acquire input information input by the user that corresponds to the operation and maintenance of the power transmission line WAPI network. The input information includes at least the operation and maintenance query information of one or more devices in the power transmission line WAPI network. The input module is used to input the input information into the target large model to obtain the operation and maintenance result information; wherein, the target large model is pre-trained on a convolutional neural network model based on sample operation and maintenance data of the power transmission line WAPI network; The processing module is used to execute the corresponding operation and maintenance response strategy based on the operation and maintenance result information.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power transmission line WAPI network operation and maintenance management method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: processor; as well as Memory, used to store computer programs; The processor is configured to execute the power transmission line WAPI network operation and maintenance management method according to any one of claims 1 to 7 by executing the computer program.

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