Operation and maintenance method, device and equipment of power transmission line, storage medium and program product

Abstract

The invention relates to an operation and maintenance method, device and equipment of a power transmission line, a storage medium and a program product. The method comprises the following steps: acquiring an inspection plan and equipment archive information of a power transmission line, and generating an intelligent work order according to the inspection plan and the equipment archive information; sending the intelligent work order to a user terminal, and obtaining an inspection result returned by the user terminal and obtained by inspection of a user; the inspection result comprises a surface image and / or operation data of the target line component; synchronizing the inspection result to the digital twin of the power transmission line for prediction and analysis to obtain the potential fault risk of the target line component; and determining an operation and maintenance result of the power transmission line according to the potential fault risk of the target line component. By collecting the surface image and the operation data of the target line component, a rich data basis is provided for determining the fault risk of the power transmission line; and the inspection result is subjected to system simulation processing and multi-dimensional analysis through the digital twinborn body, so that the operation and maintenance accuracy of the power transmission line is improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, equipment, storage medium, and program product for the operation and maintenance of transmission lines. Background Technology

[0002] With the rapid advancement of power grid construction, the coverage of transmission lines continues to expand, and the proportion of lines traversing complex terrains such as mountains, rivers, and coastlines is increasing year by year. As the backbone network of the power system, the safe and stable operation of transmission lines is of paramount importance.

[0003] Traditional power transmission line operation and maintenance methods mainly rely on manual periodic inspections, and the data obtained from these inspections are analyzed to determine the operating status of the power transmission lines. Based on the operating status, "post-incident maintenance" or "periodic maintenance" is carried out to achieve stable operation of the power transmission lines.

[0004] However, the above-mentioned operation and maintenance methods have the technical problem of low accuracy. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, equipment, storage medium, and program product for the operation and maintenance of transmission lines that can improve the accuracy of line operation and maintenance, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for the operation and maintenance of transmission lines, including:

[0007] Obtain the inspection plan and equipment file information of the transmission line, and generate a smart work order based on the inspection plan and equipment file information;

[0008] The intelligent work order is sent to the user terminal, and the inspection results obtained by the user during the inspection are returned by the user terminal; the inspection results include surface images and / or operating data of the target line components;

[0009] The inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components;

[0010] The operation and maintenance results of the transmission line are determined based on the potential failure risks of the target line components.

[0011] In one embodiment, the inspection results include surface images of the target line components, and synchronizing the inspection results to a digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components includes:

[0012] The surface image is analyzed using the digital twin of the transmission line to obtain risk parameters, which include appearance deviation, structural anomaly, defect index, and contextual consistency score.

[0013] The potential failure risk of the target circuit component is determined based on the appearance deviation, the structural anomaly, the defect index, and the contextual consistency score.

[0014] In one embodiment, the digital twin of the transmission line includes a first matching module, a second matching module, a third matching module, and a fourth matching module. The step of analyzing the surface image using the digital twin of the transmission line to obtain risk parameters includes:

[0015] The surface image is input into the first matching module for texture comparison with the standard surface image of the target circuit component to obtain the appearance deviation.

[0016] The surface image is input to the second matching module for comparison of key points with the standard surface image of the target circuit component to obtain the structural anomaly degree.

[0017] The surface image is input to the third matching module for thermal image comparison with the standard surface image of the target circuit component to obtain the defect index;

[0018] The surface image is input into the fourth matching module for structural comparison with the standard surface image of the target circuit component to obtain a context consistency score.

[0019] In one embodiment, determining the potential failure risk of the target circuit component based on the appearance deviation, the structural anomaly, the defect index, and the contextual consistency score includes:

[0020] The failure risk score of the target circuit component is obtained by weighted summation of the appearance deviation, the structural anomaly, the defect index, and the context consistency score.

[0021] The fault risk score is matched with the risk range of the preset fault level to determine the target risk range in which the fault risk score is located, and the fault level of the target line component is determined according to the preset fault level of the target risk range.

[0022] In one embodiment, the digital twin includes a feature extraction module, a prediction module, and a safety threshold comparison module. The inspection results include the operational data. The step of synchronizing the inspection results to the transmission line digital twin for predictive analysis to obtain the potential fault risks of the target line components includes:

[0023] The operational data is input into the feature extraction module for feature extraction to obtain target feature data; the target feature data includes temperature and vibration amplitude.

[0024] The target feature data is input into the prediction module for prediction to obtain the change trend curve of the target feature of the target line component;

[0025] The trend curve is input into the comparison module and compared with the safety threshold curve of the target line component to obtain the potential failure risk of the target line component.

[0026] In one embodiment, the method further includes:

[0027] For the twin line component in the digital twin of the transmission line that corresponds to the target line component, parameter perturbations corresponding to the potential fault risks are injected into the twin line component;

[0028] Initiate multiphysics coupling simulation to calculate the mechanical stress redistribution index and electrical characteristic change index of each twin line component in the digital twin of the transmission line after fault injection;

[0029] The mechanical overload rate and electrical static margin of the twin circuit components are determined based on the mechanical stress redistribution index and the electrical characteristic change index.

[0030] The risk level of the transmission line is determined based on the mechanical overload rate and / or the electrical static margin.

[0031] Secondly, this application also provides an operation and maintenance device for transmission lines, comprising:

[0032] The generation module is used to obtain the inspection plan and equipment file information of the transmission line, and generate intelligent work orders based on the inspection plan and equipment file information;

[0033] The acquisition module is used to send the intelligent work order to the user terminal and acquire the inspection results returned by the user terminal. The inspection results include surface images and / or operating data of the target line components.

[0034] The prediction module is used to synchronize the inspection results to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components.

[0035] The determination module is used to determine the operation and maintenance results of the transmission line based on the potential failure risks of the target line components.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] Obtain the inspection plan and equipment file information of the transmission line, and generate a smart work order based on the inspection plan and equipment file information;

[0038] The intelligent work order is sent to the user terminal, and the inspection results obtained by the user during the inspection are returned by the user terminal; the inspection results include surface images and / or operating data of the target line components;

[0039] The inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components;

[0040] The operation and maintenance results of the transmission line are determined based on the potential failure risks of the target line components.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] Obtain the inspection plan and equipment file information of the transmission line, and generate a smart work order based on the inspection plan and equipment file information;

[0043] The intelligent work order is sent to the user terminal, and the inspection results obtained by the user during the inspection are returned by the user terminal; the inspection results include surface images and / or operating data of the target line components;

[0044] The inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components;

[0045] The operation and maintenance results of the transmission line are determined based on the potential failure risks of the target line components.

[0046] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0047] Obtain the inspection plan and equipment file information of the transmission line, and generate a smart work order based on the inspection plan and equipment file information;

[0048] The intelligent work order is sent to the user terminal, and the inspection results obtained by the user during the inspection are returned by the user terminal; the inspection results include surface images and / or operating data of the target line components;

[0049] The inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components;

[0050] The operation and maintenance results of the transmission line are determined based on the potential failure risks of the target line components.

[0051] The aforementioned operation and maintenance methods, devices, equipment, storage media, and program products for transmission lines acquire inspection plans and equipment file information for the transmission lines, generate intelligent work orders based on these information, send the intelligent work orders to user terminals, and obtain inspection results returned by the user terminals. These inspection results include surface images and / or operational data of the target line components. The inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components. The operation and maintenance results of the transmission line are determined based on the potential fault risks of the target line components. By generating intelligent work orders, the rapid execution of inspection tasks is achieved, improving the operation and maintenance efficiency of transmission lines. Simultaneously, surface images and operational data of target line components were collected, providing a rich data foundation for determining transmission line fault risks. Furthermore, the inspection results were processed through system simulation and multi-dimensional analysis using a digital twin, improving the accuracy of fault risk prediction. Finally, based on the bidirectional interactive operation and maintenance scenario between the physical site and the digital virtual entity, data support was provided for condition-based maintenance and intelligent decision-making of transmission lines. Compared with the problem of low operation and maintenance accuracy caused by traditional manual inspections, this method improves the accuracy of line operation and maintenance by performing multi-level analysis of multi-dimensional inspection results based on a digital twin. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a structural block diagram of the computer equipment used in the operation and maintenance method of a transmission line in one embodiment;

[0054] Figure 2 This is a flowchart illustrating a method for the operation and maintenance of a transmission line in one embodiment;

[0055] Figure 3 This is a flowchart illustrating the process of determining potential failure risks of circuit components in one embodiment;

[0056] Figure 4 This is a flowchart illustrating the process of determining risk parameters in one embodiment;

[0057] Figure 5 This is a flowchart illustrating the process for determining the potential failure risk of circuit components in another embodiment;

[0058] Figure 6 This is a flowchart illustrating the process for determining potential failure risks of circuit components in yet another embodiment;

[0059] Figure 7 This is a flowchart illustrating the process of determining the risk level of a transmission line in one embodiment;

[0060] Figure 8 This is a flowchart illustrating the operation and maintenance method for a transmission line in another embodiment;

[0061] Figure 9 This is a structural block diagram of a power transmission line operation and maintenance device in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0063] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0064] With the rapid advancement of power grid construction, the coverage of transmission lines continues to expand, and the proportion of lines traversing complex terrains such as mountains, rivers, and coastlines is increasing year by year. As the backbone of the power system, the safe and stable operation of transmission lines is crucial. Traditional transmission line operation and maintenance mainly relies on regular manual inspections, analyzing the data obtained during these inspections to determine the operating status of the transmission lines, and then conducting "post-incident maintenance" or "periodic maintenance" based on this status to achieve stable operation. However, the above-mentioned operation and maintenance methods suffer from low accuracy.

[0065] In view of the above-mentioned technical problems, this application provides a method for the operation and maintenance of transmission lines that can improve the accuracy of operation and maintenance. The following embodiments will specifically illustrate the method for the operation and maintenance of transmission lines.

[0066] The operation and maintenance method for transmission lines provided in this application embodiment can be applied to, for example, Figure 1 The computer device shown can be a server, and its internal structure diagram can be as follows. Figure 1As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores digital twins of the transmission line, surface images of components, and operational data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for the operation and maintenance of a power transmission line.

[0067] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0068] In one exemplary embodiment, such as Figure 2 As shown, a method for the operation and maintenance of power transmission lines is provided. This embodiment illustrates the method by applying it to computer equipment. In this embodiment, the method includes:

[0069] S201: Obtain the inspection plan and equipment file information of the transmission line, and generate intelligent work orders based on the inspection plan and equipment file information.

[0070] The inspection plan may include, but is not limited to, information such as inspection location, inspection time, inspection target, and inspection terminals. The number of inspection targets and terminals can be one or more. Equipment file information can include information on all equipment and components covered by the transmission line. The smart work order includes the optimal inspection route, mandatory inspection points, and the data types to be collected. Mandatory inspection points are the key components that must be inspected in the current inspection operation and can be dynamically adjusted based on equipment type, usage time, historical defect records, and a family defect database. Data types can be image data of components (e.g., insulators, fittings, conductors, tower foundations) and operational data of components (e.g., temperature, sag, vibration). The smart work order also includes multimedia guidance with selectable inspection points and standard operating procedures. Selectable inspection points can be adaptively set according to the actual inspection scenario and are not limited here.

[0071] In the embodiments of this application, after the computer device obtains the pre-stored inspection plan and equipment file information of the transmission line from the database, it parses the inspection plan to obtain the mandatory inspection points, the data types to be collected, and route-related data. The route-related data may include the inspection starting point, the location of the mandatory inspection points, the performance parameters of the inspection terminal, real-time traffic and geographic information, and the priority of equipment health status obtained from the digital twin. The route-related data is input into a multi-objective optimization model for processing to obtain the optimal inspection route. Optionally, the optimization objectives include: shortest total travel time, lowest total energy consumption, avoidance of special terrain, and priority coverage weight for areas with high historical failure rates and current weather warning areas.

[0072] S202, send the intelligent work order to the user terminal, and obtain the inspection results returned by the user terminal.

[0073] The inspection results include surface images and / or operational data of the target line components. Surface images can be high-resolution images of insulators, fittings, conductors, tower foundations, or other line components; there are no restrictions here. Operational data can include temperature, current, voltage, partial discharge, sag, aerobatic vibration data, power, etc., or other operational data, collected according to the actual scenario requirements; there are no restrictions here.

[0074] In the embodiments of this application, after receiving a smart work order, the computer device can send the smart work order to the corresponding user terminal based on the correspondence between the smart work order and the inspection terminal. The user terminal performs inspection according to the optimal inspection route in the smart work order, and at the mandatory inspection point, it takes a surface image of the target line component through an image acquisition device and collects the operating data of the target line component through sensors. The user terminal transmits the inspection results to the computer device for subsequent fault detection.

[0075] S203 synchronizes the inspection results to the digital twin of the transmission line for predictive analysis, thereby obtaining the potential fault risks of the target line components.

[0076] The transmission line data twin is a virtual mapping of the physical transmission line, including a 3D model, physical attributes, historical status data, a standard status database, and multiple image processing and data processing modules. Potential fault risks can include information such as risk location, risk type, risk level, risk probability, and impact range. Risk levels are categorized as low, medium, high, and extremely high. Each risk level corresponds to a fault mode. For example, when the line component is a tower, the fault mode corresponding to extremely high risk is complete fracture; the fault mode corresponding to high risk is a 60% loss of grip strength.

[0077] In the embodiments of this application, the inspection results are first synchronized to the digital twin of the transmission line to update the historical status information. Predictive analysis is then performed on the updated surface images and operational data of the target line components. Optionally, when the inspection result is a surface image, the inspection result is synchronized to the image processing module of the digital twin of the transmission line for predictive analysis to obtain image feature analysis results. Based on the image feature analysis results, the potential fault risk of the target line components is determined. When the inspection result is operational data, the inspection result is synchronized to the data processing module of the digital twin of the transmission line for predictive analysis to obtain data analysis results. Based on the data analysis results, the potential fault risk of the target line components is determined. When the inspection result is both a surface image and operational data, the inspection result is synchronized to the image processing module and data processing module of the digital twin of the transmission line for predictive analysis to obtain image feature analysis results and data analysis results, respectively. Based on the image feature analysis results and data analysis results, the potential fault risk of the target line components is determined.

[0078] S204, determine the operation and maintenance results of the transmission line based on the potential failure risks of the target line components.

[0079] The maintenance results can include whether maintenance is required or not.

[0080] In the embodiments of this application, for the potential fault risks of the target line components, the risk level is extracted from the potential fault risks and analyzed. When the risk level exceeds a predetermined threshold, it indicates that the current transmission line needs maintenance, and a corresponding maintenance or special inspection work order is generated and dispatched. After the maintenance is completed, the current status information of the target line components in the digital twin of the transmission line is updated. When the risk level does not exceed the predetermined threshold, it indicates that the current transmission line does not need maintenance, and the current status information of the target line components in the digital twin of the transmission line remains unchanged.

[0081] The aforementioned operation and maintenance method for transmission lines involves acquiring the transmission line inspection plan and equipment file information, generating intelligent work orders based on these information, sending the intelligent work orders to the user terminal, and obtaining the inspection results returned by the user. The inspection results include surface images and / or operational data of the target line components. The inspection results are then synchronized to the transmission line's digital twin for predictive analysis to determine the potential fault risks of the target line components. Finally, the operation and maintenance results for the transmission line are determined based on these potential fault risks. By generating intelligent work orders, the rapid execution of inspection tasks is achieved, improving the efficiency of transmission line operation and maintenance. Simultaneously, surface images and operational data of target line components were collected, providing a rich data foundation for determining transmission line fault risks. Furthermore, the inspection results were processed through system simulation and multi-dimensional analysis using a digital twin, improving the accuracy of fault risk prediction. Finally, based on the bidirectional interactive operation and maintenance scenario between the physical site and the digital virtual entity, data support was provided for condition-based maintenance and intelligent decision-making of transmission lines. Compared with the problem of low operation and maintenance accuracy caused by traditional manual inspections, this method improves the accuracy of line operation and maintenance by performing multi-level analysis of multi-dimensional inspection results based on a digital twin.

[0082] In one exemplary embodiment, such as Figure 3 As shown, the inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components, including:

[0083] S301 uses a digital twin of the transmission line to analyze the surface image and obtain risk parameters.

[0084] The risk parameters include appearance deviation, structural anomaly, defect index, and contextual consistency score. The digital twin of a transmission line may include a first matching module, a second matching module, a third matching module, and a fourth matching module, and may also include a feature extraction module and an image conversion module. The first matching module is used to detect whether the texture features of the surface image are standard; the second matching module is used to detect whether the key point positions of the surface image are standard; the third matching module is used to detect whether the thermal imaging features of the surface image are standard; and the fourth matching module is used to detect whether the component connection relationships of the surface image are standard. The digital twin of the transmission line also pre-stores standard surface images of the line components.

[0085] In the embodiments of this application, when analyzing the surface image using a digital twin of a transmission line, optionally, the surface image is input to a first matching module for texture comparison with a standard surface image of the target line component to obtain the appearance deviation; the surface image is input to a second matching module for key point comparison with a standard surface image of the target line component to obtain the structural anomaly; the surface image is input to a third matching module for thermal image comparison with a standard surface image of the target line component to obtain the defect index; and the surface image is input to a fourth matching module for structural comparison with a standard surface image of the target line component to obtain the context consistency score. It should be noted that the analysis of the surface image by the first, second, third, and fourth matching modules is not restricted by order; they can be executed sequentially or in parallel, and this is not limited here.

[0086] S302 determines the potential failure risk of target circuit components based on appearance deviation, structural anomaly, defect index, and contextual consistency score.

[0087] In the embodiments of this application, for the determined appearance deviation, structural anomaly, defect index, and contextual consistency score, optionally, the weights corresponding to the appearance deviation, structural anomaly, defect index, and contextual consistency score are obtained. The appearance deviation, structural anomaly, defect index, and contextual consistency score are then weighted and summed with their corresponding weights to obtain a risk score. This risk score is then matched with a preset risk level scoring range to determine the potential failure risk of the target line component based on the matching result. The weights are adaptively set according to the degree of influence of each indicator on the failure result, and are not limited here. Optionally, the maximum value among the appearance deviation, structural anomaly, defect index, and contextual consistency score is determined, and the maximum value is matched with a preset risk level scoring range to determine the potential failure risk of the target line component based on the matching result.

[0088] The above method evaluates surface images using multi-dimensional indicators, thereby improving the stability of determining the fault risk of circuit components.

[0089] In one exemplary embodiment, such as Figure 4 As shown, risk parameters are obtained by analyzing surface images using a digital twin of the transmission line, including:

[0090] S401, the surface image is input to the first matching module for texture comparison with the standard surface image of the target circuit component to obtain the appearance deviation.

[0091] The appearance deviation, ranging from 0 to 1, represents the degree of defects such as cracks, dirt, and corrosion in circuit components. The greater the appearance deviation, the more significant the defects in the circuit components.

[0092] In the embodiments of this application, when the surface image and the standard surface image of the target circuit component are input into the first matching module, the trained convolutional neural network is used to extract features from the surface image and the standard surface image to obtain texture feature vectors and standard texture feature vectors. The cosine similarity or Euclidean distance between the texture feature vectors and the standard texture feature vectors is calculated to obtain the appearance deviation.

[0093] S402, the surface image is input to the second matching module for comparison with the key points of the standard surface image of the target circuit component to obtain the structural anomaly degree.

[0094] The structural anomaly degree, ranging from 0 to 1, characterizes the degree of deformation features such as bending, torsion, displacement, or loss of circuit components. The higher the structural anomaly degree, the more significant the deformation features of the circuit components.

[0095] In the embodiments of this application, when the surface image and the standard surface image of the target circuit component are input to the second matching module, a key point feature extraction algorithm is used to extract key point features from the surface image and the standard surface image of the target circuit component, obtaining key points in the surface image and the standard surface image. The homography matrix or affine transformation matrix between the key points in the surface image and the standard surface image is calculated using the RANSAC algorithm, and the displacement residuals of the matched point pairs are analyzed. The displacement residuals are normalized to obtain the structural anomaly degree. The key point feature extraction algorithm can be Scale Invariant Feature Transform (SIFT), ORB, or a deep learning-based key point detector (such as SuperPoint), or other key point feature extraction algorithms, which are not limited here.

[0096] S403, input the surface image to the third matching module for comparison with the thermal image of the standard surface image of the target circuit component to obtain the defect index.

[0097] The defect index, ranging from 0 to 1, characterizes the degree of specific defects in circuit components, such as corrosion, temperature variations, and contamination. The higher the defect index, the more significant the specific defect in the circuit component.

[0098] In the embodiments of this application, when the surface image and the standard surface image of the target circuit component are input to the third matching module, the channel images (e.g., a*b chromaticity channels) of the surface image and the standard surface image of the target circuit component in a preset color space, the infrared thermal image, and the grayscale image corresponding to the surface image are obtained. The cosine similarity or Euclidean distance of the channel images of the surface image and the standard surface image of the target circuit component in the preset color space is calculated to obtain the corrosion coverage. The temperature distribution of the infrared thermal images of the surface image and the standard surface image of the target circuit component is compared to obtain the maximum temperature rise value. The entropy or uniformity of the grayscale image is calculated, and the calculation result is matched with the standard range of the preset dirt level to obtain the dirt level score. The corrosion coverage, the maximum temperature rise value, and the dirt level score are weighted and summed, and the summation result is normalized to obtain the defect index. The weights of the corrosion coverage, the maximum temperature rise value, and the dirt level score are set according to the actual scenario requirements and are not limited here.

[0099] S404, the surface image is input to the fourth matching module for structural comparison with the standard surface image of the target circuit component to obtain the context consistency score.

[0100] The context consistency score, ranging from 0 to 1, represents the degree of impact of component installation location on the overall line. A higher context consistency score indicates a more significant impact from incorrect component installation.

[0101] In the embodiments of this application, when the surface image and the standard surface image of the target line component are input to the fourth matching module, the line component and the surrounding components are defined as a graph structure, the nodes in each graph structure are component sub-regions, the edges are the connection relationships of the components, the graph structure features in the target line component and the standard surface image are extracted, the similarity between the two graph structure features is calculated, and the context consistency score is obtained.

[0102] The above method evaluates surface images using multi-dimensional indicators, thereby improving the stability of determining the fault risk of circuit components.

[0103] In one exemplary embodiment, such as Figure 5 As shown, the potential failure risk of target circuit components is determined based on appearance deviation, structural anomaly, defect index, and contextual consistency score, including:

[0104] S501, the weighted sum of appearance deviation, structural anomaly, defect index and context consistency score is used to obtain the fault risk score of the target line component;

[0105] In the embodiments of this application, after obtaining the appearance deviation, structural anomaly, defect index, and context consistency score, the computer device acquires the weights corresponding to these scores. These weights are pre-trained or pre-set based on the component type and the importance of the indicators. For example, for insulators, the appearance deviation has the highest weight; for hardware, the defect index has the highest weight. The appearance deviation, structural anomaly, defect index, and context consistency score are then weighted and summed with their corresponding weights to obtain the fault risk score of the target line component.

[0106] S502, match the fault risk score with the risk range of the preset fault level, determine the target risk range where the fault risk score is located, and determine the fault level of the target line component according to the preset fault level of the target risk range.

[0107] The preset fault level risk range can be: low risk [0, 30), medium risk [30, 60), high risk [60, 80), and extremely high risk [80, 100]. Other risk ranges are also possible and are not restricted here.

[0108] In the embodiments of this application, the risk ranges of preset fault levels are traversed, and the fault risk score of the target line component is matched with each risk range. When the fault risk score falls within a certain risk range, the preset fault risk level corresponding to that risk range is determined as the fault level of the target line component. For example, when the fault risk score of the target line component is 65 points, it falls within the risk range of [60, 80), and the fault level of the target line component is determined to be medium risk.

[0109] In one exemplary embodiment, such as Figure 6 As shown, the digital twin includes a feature extraction module, a prediction module, and a safety threshold comparison module. Inspection results include operational data. These inspection results are synchronized to the transmission line digital twin for predictive analysis to obtain the potential fault risks of the target line components, including:

[0110] S601, input the running data into the feature extraction module for feature extraction to obtain the target feature data.

[0111] The target feature data includes temperature and vibration amplitude. The feature extraction module can be a convolutional neural network or other filtering algorithms, such as attribute filtering algorithms, rule filtering algorithms, and variance filtering algorithms; there are no restrictions here.

[0112] In the embodiments of this application, after the running data is input to the feature extraction module, optionally, the temperature and vibration amplitude in the running data are extracted using a convolutional neural network to obtain target feature data. Alternatively, a filtering algorithm is used to filter the running data containing temperature and vibration attributes to obtain the target feature data.

[0113] S602, the target feature data is input into the prediction module for prediction, and the change trend curve of the target feature of the target line component is obtained.

[0114] The prediction module can be a time series prediction algorithm, such as one or a combination of linear trend methods, moving average methods, exponential smoothing methods, recurrent neural network models, or long short-term memory network models, or other time series prediction algorithms; there are no restrictions here. The trend curves can include temperature change trend curves and vibration amplitude change trend curves.

[0115] In the embodiments of this application, when the target feature data is input to the prediction module for prediction, the first option is to select any one of the preset time-series prediction algorithms to predict the temperature data and vibration amplitude, and obtain the temperature change trend curve and the vibration amplitude change trend curve.

[0116] Alternatively, a second approach is to select any two preset time-series prediction algorithms to predict both temperature data and vibration amplitude. Specifically, the first selected time-series prediction algorithm is used to predict the temperature data, yielding a temperature change trend curve; the second selected time-series prediction algorithm is used to predict the vibration amplitude, yielding a vibration amplitude change trend curve. For example, the moving average method can be used to predict the temperature data, resulting in a temperature change trend curve; a recurrent neural network model can be used to predict the vibration amplitude, yielding a vibration amplitude change trend curve.

[0117] S603, input the trend curve to the comparison module to compare it with the safety threshold curve of the target line component, and obtain the potential failure risk of the target line component.

[0118] In the embodiments of this application, after obtaining the trend curve, the trend curve is input to the comparison module and compared with the safety threshold curve, that is, the temperature trend curve is compared with the temperature safety threshold curve, and the vibration amplitude trend curve is compared with the vibration amplitude safety threshold curve. When the value of the trend curve exceeds the safety threshold at the corresponding time, the proportion of the number of target features exceeding the safety threshold among all the numbers of the same target feature is determined. The proportion is matched with the over-limit range of the preset fault level. When the proportion is within a certain over-limit range, the preset fault risk level corresponding to the over-limit range is determined as the fault level of the target line component. For example, when the proportion of the target line component is 35%, it is in the risk range of [30%, 60%), and the fault level of the target line component is determined to be medium risk.

[0119] The above method improves the accuracy of fault detection for line components by analyzing operational data.

[0120] Optionally, when the inspection results include surface images and operational data, the inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components. This includes: comparing the surface images and standard surface images of the target line components using a similarity comparison algorithm to obtain image differences; normalizing the image differences to obtain a first fault score; extracting features from the operational data to obtain target feature data; using a time-series prediction algorithm to predictively analyze the target feature data to obtain a trend curve; comparing the trend curve with a preset safety threshold curve in the digital twin of the transmission line; when the value of the trend curve exceeds the safety threshold at the corresponding time, determining the proportion of the number of target features exceeding the safety threshold among all the numbers of the same target feature, and using this proportion as a second fault score; weighted summing of the first and second fault scores to obtain a comprehensive score for the line component; matching the comprehensive score with a preset fault level score range; when the comprehensive score falls within a certain score range, determining the preset fault risk level corresponding to that score range as the fault level of the target line component.

[0121] The above method uses digital twins to perform multi-level and multi-dimensional analysis of inspection results, integrating vision, time sequence, correlation and system simulation, which improves the accuracy of predicting fault risks of line components and is conducive to the precise operation and maintenance of transmission lines.

[0122] In one exemplary embodiment, such as Figure 7 As shown, the method also includes:

[0123] S701, for the twin line component corresponding to the target line component in the digital twin of the transmission line, injects parameter disturbances corresponding to potential fault risks into the twin line component.

[0124] In the embodiments of this application, the twin line component corresponding to the target line component marked as having a high risk level of potential fault risk is determined in the digital twin of the transmission line, and the parameters or state of the twin line component are modified according to the type of the target line component and the predicted fault mode.

[0125] S702, initiate multiphysics coupling simulation to calculate the mechanical stress redistribution index and electrical characteristic change index of each twin line component in the digital twin of the transmission line after fault injection.

[0126] Among them, the mechanical stress redistribution index can be used to represent the load borne after fault injection; the electrical characteristic change index can be used to represent the simulated clearance distance.

[0127] In the embodiments of this application, mechanical stress redistribution simulation, electrical characteristic change simulation, and thermo-coupling simulation are initiated on the digital twin of the transmission line to calculate the dynamic response process of the digital twin after fault injection, and to identify primary affected components, potential cascading fault points, and line operating states. Optionally, components that directly bear abnormal stress or electrical impact after fault injection are identified as primary affected components; and components whose state values ​​exceed the failure threshold among the primary affected components are identified as potential cascading fault points; the line operating state after fault injection can be line arc protection operation, single-phase grounding, or phase-to-phase short circuit. The mechanical stress redistribution index and electrical characteristic change index of the primary affected components are calculated.

[0128] S703 determines the mechanical overload rate and electrical static adequacy of twin circuit components based on the mechanical stress redistribution index and the electrical characteristic change index.

[0129] In the embodiments of this application, the mechanical stress redistribution index is divided by the ultimate load to obtain the mechanical overload rate of the twin circuit component; the electrical characteristic change index is divided by the minimum distance required by the safety regulations to obtain the electrical static adequacy of the twin circuit component.

[0130] S704, determine the risk level of a transmission line based on the mechanical overload rate and / or electrical static adequacy.

[0131] In the embodiments of this application, the mechanical overload rate and electrical static margin are normalized. Optionally, a first approach is to match the normalized mechanical overload rate with the risk level of the transmission line. If the normalized mechanical overload rate falls within the mechanical overload rate range corresponding to the risk level of a certain transmission line, then the corresponding risk level is taken as the risk level of the transmission line. Optionally, a second approach is to match the normalized electrical static margin with the risk level of the transmission line. If the normalized electrical static margin falls within the electrical static margin range corresponding to the risk level of a certain transmission line, then the corresponding risk level is taken as the risk level of the transmission line. Alternatively, a third approach is to match the normalized mechanical overload rate with the risk level of the transmission line, and to match the normalized electrical static margin with the risk level of the transmission line. If the normalized mechanical overload rate falls within the mechanical overload rate range corresponding to the risk level of a certain transmission line, and the normalized electrical static margin falls within the electrical static margin range corresponding to the risk level of a certain transmission line, then the highest risk level among the corresponding risk levels is taken as the risk level of the transmission line.

[0132] In addition to the methods of all the above embodiments, a method for the operation and maintenance of transmission lines is also provided, such as... Figure 8 As shown, the method includes:

[0133] S801 acquires the inspection plan and equipment file information of the transmission line, and generates intelligent work orders based on the inspection plan and equipment file information;

[0134] S802 sends the intelligent work order to the user terminal and obtains the inspection results returned by the user terminal; the inspection results include surface images and / or operating data of the target line components.

[0135] S803, input the surface image to the first matching module for texture comparison with the standard surface image of the target circuit component to obtain the appearance deviation;

[0136] S804, input the surface image to the second matching module to compare the key points with the standard surface image of the target circuit component to obtain the structural anomaly degree;

[0137] S805, input the surface image to the third matching module to compare it with the thermal image of the standard surface image of the target circuit component to obtain the defect index;

[0138] S806, input the surface image to the fourth matching module for structural comparison with the standard surface image of the target circuit component, and obtain the context consistency score.

[0139] S807 calculates the failure risk score of the target circuit component by weighting and summing the appearance deviation, structural anomaly, defect index, and contextual consistency score.

[0140] S808 matches the fault risk score with the risk range of the preset fault level to determine the target risk range where the fault risk score is located, and determines the fault level of the target line component based on the preset fault level of the target risk range.

[0141] S809: Input the running data into the feature extraction module for feature extraction to obtain target feature data; the target feature data includes temperature and vibration amplitude.

[0142] S810, input the target feature data into the prediction module for prediction, and obtain the change trend curve of the target feature of the target line component;

[0143] S811, input the trend curve to the comparison module to compare it with the safety threshold curve of the target line component, and obtain the potential failure risk of the target line component.

[0144] S812 determines the operation and maintenance results of the transmission line based on the potential failure risks of the target line components.

[0145] S813, for the twin line component corresponding to the target line component in the digital twin of the transmission line, inject parameter perturbation corresponding to potential fault risks into the twin line component;

[0146] S814, initiate multiphysics coupling simulation to calculate the mechanical stress redistribution index and electrical characteristic change index of each twin line component in the digital twin of the transmission line after fault injection;

[0147] S815, determine the mechanical overload rate and electrical static margin of twin circuit components based on the mechanical stress redistribution index and the electrical characteristic change index;

[0148] S816, determine the risk level of a transmission line based on mechanical overload rate and / or electrical static margin.

[0149] Each of the above steps has been described in the foregoing embodiments. For details, please refer to the foregoing content. They will not be repeated here.

[0150] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0151] Based on the same inventive concept, this application also provides a transmission line operation and maintenance device for implementing the above-mentioned transmission line operation and maintenance method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more transmission line operation and maintenance device embodiments provided below can be found in the limitations of the transmission line operation and maintenance method above, and will not be repeated here.

[0152] In one exemplary embodiment, such as Figure 9 As shown, a transmission line operation and maintenance device is provided, comprising: a generation module 91, an acquisition module 92, a prediction module 93, and a determination module 94, wherein:

[0153] The generation module 91 is used to obtain the inspection plan and equipment file information of the transmission line, and generate intelligent work orders based on the inspection plan and equipment file information;

[0154] The acquisition module 92 is used to send the intelligent work order to the user terminal and acquire the inspection results returned by the user terminal. The inspection results include surface images and / or operating data of the target line components.

[0155] Prediction module 93 is used to synchronize the inspection results to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components.

[0156] Module 94 is used to determine the operation and maintenance results of the transmission line based on the potential failure risks of the target line components.

[0157] In an exemplary embodiment, the prediction module 93 described above includes:

[0158] The analysis unit is used to analyze surface images using a digital twin of the transmission line to obtain risk parameters, including appearance deviation, structural anomaly, defect index, and contextual consistency score.

[0159] The determination unit is used to determine the potential failure risk of target circuit components based on appearance deviation, structural anomaly, defect index, and contextual consistency score.

[0160] In an exemplary embodiment, the analysis unit described above includes:

[0161] The first comparison subunit is used to input the surface image into the first matching module for texture comparison with the standard surface image of the target circuit component to obtain the appearance deviation.

[0162] The second comparison subunit is used to input the surface image into the second matching module to compare the key points with the standard surface image of the target circuit component to obtain the structural anomaly degree.

[0163] The third comparison subunit is used to input the surface image into the third matching module to compare it with the thermal image of the standard surface image of the target circuit component to obtain the defect index.

[0164] The fourth comparison subunit is used to input the surface image into the fourth matching module for structural comparison with the standard surface image of the target circuit component to obtain the context consistency score.

[0165] In an exemplary embodiment, the determining unit described above is configured to:

[0166] The calculation sub-unit is used to perform a weighted summation of appearance deviation, structural anomaly, defect index and context consistency score to obtain the fault risk score of the target line component;

[0167] The matching subunit is used to match the fault risk score with the risk range of the preset fault level, determine the target risk range where the fault risk score is located, and determine the fault level of the target line component according to the preset fault level of the target risk range.

[0168] In an exemplary embodiment, the prediction module 93 described above includes:

[0169] The feature extraction unit is used to input the running data into the feature extraction module for feature extraction to obtain target feature data; the target feature data includes temperature and vibration amplitude.

[0170] The prediction unit is used to input target feature data into the prediction module for prediction, and obtain the change trend curve of the target feature of the target line component.

[0171] The comparison unit is used to input the trend curve into the comparison module for comparison with the safety threshold curve of the target line component, so as to obtain the potential failure risk of the target line component.

[0172] In one exemplary embodiment, the above-described apparatus further includes:

[0173] The injection module is used to inject parameter disturbances corresponding to potential fault risks into the twin line components in the digital twin of the transmission line corresponding to the target line component.

[0174] The simulation module is used to initiate multiphysics coupling simulation and calculate the mechanical stress redistribution index and electrical characteristic change index of each twin line component in the digital twin of the transmission line after fault injection.

[0175] The first determining module is used to determine the mechanical overload rate and electrical static adequacy of twin circuit components based on the mechanical stress redistribution index and the electrical characteristic change index.

[0176] The second determination module is used to determine the risk level of the transmission line based on the mechanical overload rate and / or electrical static adequacy.

[0177] The various modules in the aforementioned transmission line operation and maintenance equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0178] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the following steps:

[0179] Obtain inspection plans and equipment file information for power transmission lines, and generate intelligent work orders based on the inspection plans and equipment file information;

[0180] The intelligent work order is sent to the user terminal, and the inspection results obtained by the user during the inspection are returned by the user terminal; the inspection results include surface images and / or operating data of the target line components;

[0181] The inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components;

[0182] The operation and maintenance results of the transmission line are determined based on the potential failure risks of the target line components.

[0183] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0184] Risk parameters are obtained by analyzing surface images using a digital twin of the transmission line; these parameters include appearance deviation, structural anomaly, defect index, and contextual consistency score.

[0185] The potential failure risk of the target circuit components is determined based on appearance deviation, structural anomaly, defect index, and contextual consistency score.

[0186] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0187] The surface image is input into the first matching module for texture comparison with the standard surface image of the target circuit component to obtain the appearance deviation.

[0188] The surface image is input into the second matching module for comparison with the key points of the standard surface image of the target circuit component to obtain the structural anomaly degree.

[0189] The surface image is input into the third matching module for comparison with the thermal image of the standard surface image of the target circuit component to obtain the defect index;

[0190] The surface image is input into the fourth matching module for structural comparison with the standard surface image of the target circuit component to obtain the context consistency score.

[0191] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0192] The failure risk score of the target circuit component is obtained by weighted summation of appearance deviation, structural anomaly, defect index and context consistency score;

[0193] The fault risk score is matched with the risk range of the preset fault level to determine the target risk range in which the fault risk score is located, and the fault level of the target line component is determined according to the preset fault level of the target risk range.

[0194] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0195] The running data is input into the feature extraction module for feature extraction to obtain the target feature data; the target feature data includes temperature and vibration amplitude.

[0196] The target feature data is input into the prediction module for prediction, and the change trend curve of the target feature of the target line component is obtained.

[0197] The trend curve is input into the comparison module and compared with the safety threshold curve of the target line component to obtain the potential failure risk of the target line component.

[0198] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0199] For the twin line components in the digital twin of the transmission line corresponding to the target line component, parameter perturbations corresponding to potential fault risks are injected into the twin line components;

[0200] Initiate multiphysics coupling simulation to calculate the mechanical stress redistribution index and electrical characteristic change index of each twin line component in the digital twin of the transmission line after fault injection;

[0201] The mechanical overload rate and electrical static margin of twin circuit components are determined based on the mechanical stress redistribution index and the electrical characteristic change index.

[0202] The risk level of a transmission line is determined based on the mechanical overload rate and / or electrical static margin.

[0203] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0204] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0205] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0206] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0207] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for the operation and maintenance of a power transmission line, characterized in that, The method includes: Obtain the inspection plan and equipment file information of the transmission line, and generate a smart work order based on the inspection plan and equipment file information; The intelligent work order is sent to the user terminal, and the inspection results obtained by the user during the inspection are returned by the user terminal; the inspection results include surface images and / or operating data of the target line components; The inspection results are synchronized to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components; The operation and maintenance results of the transmission line are determined based on the potential failure risks of the target line components.

2. The method according to claim 1, characterized in that, The inspection results include surface images of the target line components. Synchronizing the inspection results to a digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components includes: The surface image is analyzed using the digital twin of the transmission line to obtain risk parameters, which include appearance deviation, structural anomaly, defect index, and contextual consistency score. The potential failure risk of the target circuit component is determined based on the appearance deviation, the structural anomaly, the defect index, and the contextual consistency score.

3. The method according to claim 2, characterized in that, The digital twin of the transmission line includes a first matching module, a second matching module, a third matching module, and a fourth matching module. The analysis of the surface image using the digital twin of the transmission line to obtain risk parameters includes: The surface image is input into the first matching module for texture comparison with the standard surface image of the target circuit component to obtain the appearance deviation. The surface image is input to the second matching module for comparison of key points with the standard surface image of the target circuit component to obtain the structural anomaly degree. The surface image is input to the third matching module for thermal image comparison with the standard surface image of the target circuit component to obtain the defect index; The surface image is input into the fourth matching module for structural comparison with the standard surface image of the target circuit component to obtain a context consistency score.

4. The method according to claim 2, characterized in that, The determination of the potential failure risk of the target circuit component based on the appearance deviation, the structural anomaly, the defect index, and the contextual consistency score includes: The failure risk score of the target circuit component is obtained by weighted summation of the appearance deviation, the structural anomaly, the defect index, and the context consistency score. The fault risk score is matched with the risk range of the preset fault level to determine the target risk range in which the fault risk score is located, and the fault level of the target line component is determined according to the preset fault level of the target risk range.

5. The method according to claim 1, characterized in that, The digital twin includes a feature extraction module, a prediction module, and a safety threshold comparison module. The inspection results include the operational data. Synchronizing the inspection results to the transmission line digital twin for predictive analysis to obtain the potential fault risks of the target line components includes: The operational data is input into the feature extraction module for feature extraction to obtain target feature data; the target feature data includes temperature and vibration amplitude. The target feature data is input into the prediction module for prediction to obtain the change trend curve of the target feature of the target line component; The trend curve is input into the comparison module and compared with the safety threshold curve of the target line component to obtain the potential failure risk of the target line component.

6. The method according to claim 1, characterized in that, The method further includes: For the twin line component in the digital twin of the transmission line that corresponds to the target line component, parameter perturbations corresponding to the potential fault risks are injected into the twin line component; Initiate multiphysics coupling simulation to calculate the mechanical stress redistribution index and electrical characteristic change index of each twin line component in the digital twin of the transmission line after fault injection; The mechanical overload rate and electrical static margin of the twin circuit components are determined based on the mechanical stress redistribution index and the electrical characteristic change index. The risk level of the transmission line is determined based on the mechanical overload rate and / or the electrical static margin.

7. A power transmission line operation and maintenance device, characterized in that, The device includes: The generation module is used to obtain the inspection plan and equipment file information of the transmission line, and generate intelligent work orders based on the inspection plan and equipment file information; The acquisition module is used to send the intelligent work order to the user terminal and acquire the inspection results returned by the user terminal; the inspection results include surface images and / or operating data of the target line components. The prediction module is used to synchronize the inspection results to the digital twin of the transmission line for predictive analysis to obtain the potential fault risks of the target line components. The determination module is used to determine the operation and maintenance results of the transmission line based on the potential failure risks of the target line components.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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