Electric power wire fixing operation method and system

Abstract

The invention discloses an electric power wire fixing operation method and system, and relates to the related field of electric power equipment maintenance, and the method comprises the steps: creating a wire bottle removing task, activating a video collection device to execute the data collection of a bottle removing position, and adding a supplementary data set to the wire bottle removing task; calling the fault wire data to identify the stability of the fault wire; calling the associated lead data, and performing complexity identification of overline repair by using the associated lead data; after balance analysis is carried out on the first task execution complexity and the second task execution complexity, an operation scheme mapped with the wire bottle-off task is configured; and after the crawling robot is conveyed to the target wire through the unmanned aerial vehicle, the crawling robot controls the electric power wire fixing device to repair the wire at the bottle removing point position based on the operation scheme so as to complete wire fixing operation. The technical problem of low efficiency and safety of existing electric power wire fixing operation is solved, and the technical effect of improving the efficiency and safety of wire fixing operation is achieved.

Description

Technical Field

[0001] This application relates to the field of power equipment maintenance, and particularly to a method and system for fixing power conductors during operation. Background Art

[0002] The failure of conductors to come off the insulator seriously affects the stability and power supply reliability of the power system. Timely repair of these failures is crucial for ensuring normal power consumption in society. Currently, the main methods to solve this problem rely on manual identification and repair of conductor insulator detachment, or use drones equipped with simple tools for preliminary processing, followed by manual intervention to complete fine repair operations. The current methods are limited by the efficiency and safety of manual operations and the limitations of drone operation capabilities, resulting in problems such as slow response speed, high operation difficulty, and low repair efficiency. These problems are particularly prominent in complex terrains or adverse weather conditions.

[0003] In the current related technologies, there are technical problems of low efficiency and safety in power conductor fixing operations. Summary of the Invention

[0004] This application provides a method and system for fixing power conductors during operation. By creating a conductor insulator detachment task, activating a video acquisition device to collect data on the detachment position, constructing a supplementary data set and integrating it into the task, calling the faulty conductor data in the supplementary data set according to the detachment point, evaluating the stability of the faulty conductor, determining the complexity of the first task execution, similarly calling the associated conductor data, analyzing the complexity of cross-line repair, establishing the complexity of the second task execution, performing a balance analysis on the complexities of the two task executions, configuring an operation plan matching the conductor insulator detachment task accordingly, using a drone to transport a crawling robot to the target conductor position, and the crawling robot controlling a power conductor fixing device according to the operation plan to repair the detachment point and complete the conductor fixing operation, etc., the technical effect of improving the efficiency and safety of conductor fixing operations is achieved.

[0005] The present application provides a method for fixing power conductors, including: creating a conductor bottle removal task, activating a video acquisition device to perform data acquisition at the bottle removal position, establishing a supplementary data set, and adding the supplementary data set to the conductor bottle removal task; calling the faulty conductor data in the supplementary data set according to the bottle removal point to identify the stability of the faulty conductor, and establishing the first task execution complexity; calling the associated conductor data in the supplementary data set according to the bottle removal point, using the associated conductor data to identify the complexity of cross-line repair, and establishing the second task execution complexity; after performing a balance analysis on the first task execution complexity and the second task execution complexity, configuring an operation plan mapped to the conductor bottle removal task; after using a drone to transport a crawling robot to the target conductor, the crawling robot controls a power conductor fixing device to repair the conductor at the bottle removal point position based on the operation plan, so as to complete the conductor fixing operation.

[0006] In a possible implementation manner, the step of calling the faulty conductor data in the supplementary data set according to the bottle removal point to identify the stability of the faulty conductor and establishing the first task execution complexity performs the following processing: identifying the faulty conductor fixing points in the faulty conductor data, and establishing a first influence feature according to the faulty conductor fixing points; obtaining the material property data of the faulty conductor, and establishing a second influence feature according to the material property data; performing environmental data monitoring, establishing an environmental data set, where the environmental data set includes wind force data and wind direction data, and establishing a third influence feature according to the environmental data set; obtaining the self-weights of the crawling robot and the power conductor fixing device, and establishing a fourth influence feature according to the self-weights; identifying the stability of the faulty conductor according to the first influence feature, the second influence feature, the third influence feature, and the fourth influence feature, and establishing the first task execution complexity.

[0007] In a possible implementation manner, the step of identifying the stability of the faulty conductor according to the first influence feature, the second influence feature, the third influence feature, and the fourth influence feature performs the following processing: establishing a three-dimensional simulation scenario, and inputting the first influence feature, the second influence feature, the third influence feature, and the fourth influence feature into the three-dimensional simulation scenario; obtaining the calibrated operation distance of the power conductor fixing device, configuring the fourth influence feature according to the calibrated operation distance, and then using the three-dimensional simulation scenario to perform conductor galloping fitting to establish a conductor galloping fitting result; obtaining the stable operation plan of the crawling robot in a stable state, and performing scenario superposition fitting according to the conductor galloping fitting result and the stable operation plan to complete the stability identification.

[0008] In a possible implementation, for the scenario superposition fitting based on the wire galloping fitting result and the stable operation plan, the following processing is further performed: obtaining the operation limit range of the power wire fixing device; performing the cooperation fitting of the crawling robot and the power wire fixing device within the operation limit range, reconstructing the fourth influence feature according to the cooperation fitting result, and establishing equipment stability compensation; establishing operation complexity compensation based on the cooperation fitting result and the stable operation plan; and completing the scenario superposition fitting by using the equipment stability compensation and the operation complexity compensation.

[0009] In a possible implementation, for the establishment of the first task execution complexity, the following processing is further performed: using a drone to perform scanning and monitoring of the faulty wire to establish a scanning data set; after performing feature recognition on the scanning data set, establishing local deformation features; using the local deformation features as additional influence features, and performing calculation and compensation of the first task execution complexity according to the deformation magnitude and deformation position.

[0010] In a possible implementation, for the complexity recognition of cross-line repair using the associated wire data and the establishment of the second task execution complexity, the following processing is performed: reading the cross-line operation direction and cross-line operation distance according to the associated wire data; establishing a fifth influence feature by using the cross-line operation direction and cross-line operation distance; obtaining the fixed point data in the associated wire data, and establishing a sixth influence feature according to the fixed point data; and establishing the second task execution complexity according to the fifth influence feature, the sixth influence feature, the second influence feature, the third influence feature, and the fourth influence feature, where the material properties of the associated wire and the faulty wire are the same.

[0011] In a possible implementation, after performing a balance analysis on the first task execution complexity and the second task execution complexity and configuring an operation plan mapped to the wire unclamping task, the following processing is performed: determining whether both the first task execution complexity and the second task execution complexity meet a preset complexity threshold; if both the first task execution complexity and the second task execution complexity meet the preset complexity threshold, generating a joint fixing plan, where the joint fixing plan is a plan for using the faulty wire as the operation wire of the crawling robot and jointly fixing the faulty wire by using the associated wire; and using the joint fixing plan as the operation plan mapped to the wire unclamping task.

[0012] In a possible implementation, for the crawling robot to control the power wire fixing device to repair the wire at the unclamping point position based on the operation plan, the following processing is performed: establishing a position anomaly warning sequence according to the operation plan; performing operation warning recognition of the crawling robot by using the position anomaly warning sequence to establish a warning signal; and reporting abnormal operation according to the warning signal.

[0013] In a possible implementation, after reporting the abnormal operation according to the warning signal, the following processing is performed: performing self-response correction fitting according to the warning signal to generate a self-response correction fitting result; synchronously sending the self-response correction fitting result and the warning signal to an administrator; and when receiving the execution feedback from the administrator, performing operation compensation management by using the self-response correction fitting result.

[0014] The present application also provides a power conductor fixing operation system, including: a supplementary data set establishment module, configured to create a conductor bottle-off task, activate a video acquisition device to perform data acquisition of the bottle-off position, establish a supplementary data set, and add the supplementary data set to the conductor bottle-off task; a first task execution complexity establishment module, configured to identify the stability of a faulty conductor by calling the faulty conductor data in the supplementary data set according to the bottle-off point, and establish a first task execution complexity; a second task execution complexity establishment module, configured to identify the complexity of cross-line repair by calling the associated conductor data in the supplementary data set according to the bottle-off point, and establish a second task execution complexity; an operation plan configuration module, configured to perform a balance analysis on the first task execution complexity and the second task execution complexity, and configure an operation plan mapped to the conductor bottle-off task; and a conductor fixing module, configured to use a drone to transport a crawling robot to a target conductor, and then the crawling robot controls a power conductor fixing device to repair the conductor at the bottle-off point position based on the operation plan, so as to complete the conductor fixing operation.

[0015] It is intended to propose a power conductor fixing operation method and system through the present application. First, a conductor bottle-off task is created, a video acquisition device is activated to perform data acquisition of the bottle-off position, a supplementary data set is established, and the supplementary data set is added to the conductor bottle-off task. Then, the stability of the faulty conductor is identified by calling the faulty conductor data in the supplementary data set according to the bottle-off point, and a first task execution complexity is established. Next, the complexity of cross-line repair is identified by calling the associated conductor data in the supplementary data set according to the bottle-off point, and a second task execution complexity is established. Then, after performing a balance analysis on the first task execution complexity and the second task execution complexity, an operation plan mapped to the conductor bottle-off task is configured. Finally, after using a drone to transport a crawling robot to a target conductor, the crawling robot controls a power conductor fixing device to repair the conductor at the bottle-off point position based on the operation plan, so as to complete the conductor fixing operation. The technical effect of improving the efficiency and safety of the conductor fixing operation is achieved. Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 It is a schematic flowchart of a method for fixing a power conductor provided by an embodiment of the present application.

[0018] Figure 2 It is a schematic structural diagram of a system for fixing a power conductor provided by an embodiment of the present application.

[0019] Explanation of reference numerals: Supplementary dataset establishment module 10, First task execution complexity establishment module 20, Second task execution complexity establishment module 30, Operation plan configuration module 40, Conductor fixing module 50. Detailed implementation manners

[0020] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0022] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. The terms "first" and "second" are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0023] An embodiment of the present application provides a method for fixing a power conductor, as Figure 1 shown, the method comprising: Step S100, creating a conductor unbolting task, activating a video acquisition device to perform data acquisition of the unbolting position, establishing a supplementary data set, and adding the supplementary data set to the conductor unbolting task.

[0024] Specifically, a conductor unbolting task is created through a central control system (for example, a management system based on a cloud platform). This system receives a fault signal from the power grid monitoring system and automatically generates a task instruction. A high-definition camera installed on a drone or at a fixed position is used as the video acquisition device, and the camera is activated through a remote control signal to start video data acquisition of the unbolting position. The video data collected by the camera is transmitted to the central control system in real time through a wireless communication module (such as a 4G / 5G network or Wi-Fi). The system performs preliminary processing on the video data and extracts key frame images. The collected key frame images, geographical location information (obtained through a GPS module), and environmental parameters (such as wind speed, temperature, etc.) are integrated into a supplementary data set and stored in a task database. Among them, the supplementary data set is a data set containing information such as images, positions, and environmental parameters of the faulty conductor and associated conductors, and is used to support complexity assessment and job plan generation. For example, when the power grid monitoring system detects a conductor unbolting fault at a certain location, the central control system automatically creates a task and sends an instruction to a nearby drone. The drone carries a high-definition camera and flies to the fault point, and the camera starts to collect video data and transmits the data to the system in real time through a 5G network. The system extracts key frame images and generates a supplementary data set in combination with GPS positioning data and environmental sensor data.

[0025] Step S200: Call the faulty wire data in the supplementary data set according to the bottle removal point to identify the stability of the faulty wire, and establish the complexity of the first task execution.

[0026] Specifically, call the faulty wire data related to the bottle removal point from the task database, including key frame images, wire positions, environmental parameters, etc. Use computer vision algorithms (such as convolutional neural networks, CNNs, in deep learning) to analyze the images of the faulty wire, and identify stability indicators such as the swing amplitude and tightness of the wire. According to the stability identification results, combined with environmental parameters (such as wind speed, temperature, etc.), calculate the complexity of the first task execution through a preset complexity evaluation model, which can be a rule-based system or a machine learning model. Among them, the complexity of the first task execution is a quantitative indicator reflecting the difficulty of repairing the stability of the faulty wire, calculated based on the swing amplitude of the wire, environmental parameters, etc. For example, the system calls the image data of the faulty wire and analyzes the swing amplitude of the wire through the CNN algorithm. If the swing amplitude of the wire is large and the current wind speed is high, the system will determine that the complexity of the first task execution is high.

[0027] In a possible implementation, the step of calling the faulty wire data in the supplementary data set according to the bottle removal point to identify the stability of the faulty wire and establish the complexity of the first task execution, step S200 further includes step S210: Identify the fixed points of the faulty wire in the faulty wire data, and establish the first influence feature according to the fixed points of the faulty wire. Specifically, use computer vision algorithms (such as object detection algorithms in deep learning, such as YOLO or Faster R-CNN) to analyze the image data of the faulty wire, and identify the positions of the fixed points of the faulty wire. These fixed points are the connection points of the wire to the insulator or other support structures. Extract key features from the identified fixed points, such as the coordinate positions, shapes, sizes, etc. of the fixed points, and store these features as the first influence feature in the system.

[0028] Step S220: Obtain the material property data of the faulty wire, and establish the second influence feature according to the material property data. Specifically, query the material property data of the faulty wire from the power grid equipment management system, including the material of the wire (such as aluminum, copper, etc.), diameter, tensile strength, etc. Standardize the queried material property data and extract the key parameters as the second influence feature. For example, the system queries from the power grid equipment management system that the material of the faulty wire is aluminum, the diameter is 20mm, and the tensile strength is 300MPa. After standardizing these parameters, the system uses them as the second influence feature.

[0029] Step S230: Perform environmental data monitoring, establish an environmental data set. The environmental data set includes wind force data and wind direction data. Establish a third influencing feature based on the environmental data set. Specifically, deploy an anemometer and a wind vane at the operation site to monitor wind force and wind direction data in real time. Transmit the monitored wind force and wind direction data to the central control system through a wireless communication module. The system processes and analyzes the data, and extracts key features as the third influencing feature. For example, the system monitors that the current wind force is 5 m / s and the wind direction is northeast through the anemometer and wind vane installed at the operation site. After processing these data by the system, they are used as the third influencing feature.

[0030] Step S240: Obtain the self-weight of the crawling robot and the power wire fixing device, and establish a fourth influencing feature based on the self-weight. Specifically, install weight sensors on the crawling robot and the power wire fixing device to measure the self-weight of the device in real time. Transmit the measured self-weight data to the central control system through a wireless communication module. The system processes the data and extracts key features as the fourth influencing feature. For example, the system measures that the self-weight of the device is 10 kg through the weight sensor installed on the crawling robot. After processing this data by the system, it is used as the fourth influencing feature.

[0031] Step S250: Perform stability identification of the faulty wire based on the first influencing feature, the second influencing feature, the third influencing feature, and the fourth influencing feature, and establish the complexity of the first task execution. Specifically, input the first, second, third, and fourth influencing features into a pre-trained multi-feature fusion model. This model can be a machine learning-based classifier (such as a support vector machine SVM) or a deep learning model (such as a neural network). The model outputs the stability identification result of the faulty wire based on the input features, and establishes the complexity of the first task execution based on this result. For example, the system inputs the first influencing feature (fixing point position and shape), the second influencing feature (wire material, diameter, tensile strength), the third influencing feature (wind force, wind direction), and the fourth influencing feature (device self-weight) into the pre-trained SVM model. The model outputs the stability identification result of the faulty wire as "medium stability", and establishes the complexity of the first task execution as "medium" accordingly. This implementation method can more accurately identify the stability state of the faulty wire by comprehensively considering influencing features in multiple aspects such as the fixing point position, wire material properties, environmental factors, and device self-weight. This multi-dimensional evaluation method can help the system generate a more reasonable and safer operation plan, ensure the smooth progress of the repair task, and at the same time reduce operation risks and improve operation efficiency.

[0032] In a possible implementation manner, for the stability identification of the faulty wire according to the first influencing feature, the second influencing feature, the third influencing feature, and the fourth influencing feature, step S250 further includes step S251 of establishing a three-dimensional simulation scenario and inputting the first influencing feature, the second influencing feature, the third influencing feature, and the fourth influencing feature into the three-dimensional simulation scenario. Specifically, use professional three-dimensional modeling software (such as SolidWorks, AutoCAD, or 3D Studio Max) to establish three-dimensional models of power wires, insulators, crawling robots, and power wire fixing devices. Build a three-dimensional simulation scenario including wires, insulators, robots, and devices in simulation software (such as ANSYS, MATLAB / Simulink, or VRML). This scenario can simulate the real physical environment, including gravity, wind force, friction, etc. Input the first influencing feature (fixed point position and shape), the second influencing feature (wire material properties), the third influencing feature (environmental data such as wind force and wind direction), and the fourth influencing feature (device self-weight) as parameters into the simulation scenario. For example, use SolidWorks to establish three-dimensional models of wires and insulators, and then build a simulation environment in MATLAB / Simulink. Input parameters such as the position coordinates of the fixed point, the material and diameter of the wire, the current wind force and wind direction, and the device self-weight into the simulation environment to simulate the state of the wire under the current environment.

[0033] Step S252, obtain the calibration operation distance of the power wire fixing device. After configuring the fourth influencing feature according to the calibration operation distance, use the three-dimensional simulation scenario to perform wire galloping fitting and establish a wire galloping fitting result. Specifically, obtain the calibration operation distance of the power wire fixing device under different environmental conditions through experiments or historical data, that is, the maximum distance at which the device can effectively operate. Adjust the parameters of the fourth influencing feature (device self-weight) in the simulation scenario according to the calibration operation distance to ensure the accuracy of the simulation results. Use the physical model and dynamic equations in the simulation scenario, combined with the input feature parameters, and perform wire galloping fitting through numerical simulation methods (such as finite element analysis or dynamic simulation). The fitting results include the swing amplitude, frequency, and direction of the wire, etc. For example, it is known through experiments that the effective operation distance of the power wire fixing device is 2 meters. In the simulation scenario, adjust the influence range of the device self-weight according to this distance. Then, use the dynamic simulation toolbox in MATLAB, combined with the material properties of the wire and the environmental wind force data, to perform wire galloping fitting. The fitting results show that the swing amplitude of the wire under the current wind force is 30 cm and the frequency is 10 times per minute.

[0034] Step S253: Obtain the stable operation plan of the crawling robot in the stable state, and perform scene superposition fitting according to the wire galloping fitting result and the stable operation plan to complete stability identification. Specifically, obtain the operation plan of the crawling robot in the stable state from its control system, including the movement path, operation steps, and time series of the robot. Superpose and fit the wire galloping fitting result with the stable operation plan of the crawling robot. Through the collision detection and path planning functions in the simulation software, analyze the operation feasibility and stability of the robot under the condition of wire galloping. According to the superposition fitting result, evaluate the stability of the faulty wire during the robot operation and generate the first task execution complexity. For example, obtain the operation plan of the crawling robot in the stable state from its control system, including the movement path and operation steps of the robot from the starting point to the ending point. Superpose and fit the swing amplitude and frequency shown in the wire galloping fitting result with the movement path of the robot. The simulation results show that the robot can still safely complete the operation under the condition of wire swing, but the operation time will increase by 10%. According to these results, the system evaluates the stability of the faulty wire as "medium" and establishes the first task execution complexity as "medium". This implementation method can simulate the wire state in the real environment by establishing a three-dimensional simulation scene and inputting various influencing features. Combining the calibrated operation distance and the wire galloping fitting result, further analyze the feasibility and stability of the crawling robot in actual operation. This comprehensive evaluation method can provide an accurate complexity evaluation for the subsequent repair task, ensure the rationality and safety of the operation plan, and improve the success rate and efficiency of the repair operation.

[0035] In a possible implementation manner, for the step of performing scene superposition fitting according to the wire galloping fitting result and the stable operation plan, step S253 further includes step S2531: Obtain the operation limit interval of the power wire fixing device. Specifically, measure the operation limit interval of the power wire fixing device under different environmental conditions through experiments, including the longest and shortest operation distances. These data can be obtained through laboratory tests or historical operation records. Extract key parameters from the measurement data, such as the longest operation distance and the shortest operation distance, and store them as the operation limit interval in the system. For example, through experimental measurement, it is known that the longest operation distance of the power wire fixing device is 3 meters and the shortest operation distance is 1 meter. The system stores these parameters as the operation limit interval.

[0036] Step S2532: Perform the fitting of the crawling robot and the power wire fixing device within the operation limit range, reconstruct the fourth influencing feature according to the fitting result, and establish equipment stability compensation. Specifically, in the three-dimensional simulation scenario, simulate the cooperative operation of the crawling robot and the power wire fixing device within the operation limit range. Through dynamic simulation and path planning algorithms, analyze the cooperative effects at different distances. According to the fitting result, adjust the fourth influencing feature (device self-weight), and calculate the equipment stability compensation. The equipment stability compensation is used to correct the stability difference of the device at different operation distances. For example, in the three-dimensional simulation scenario, simulate the cooperative operation of the crawling robot and the power wire fixing device at different operation distances. The simulation results show that at the longest operation distance (3 meters), the device has high stability, but the operation complexity increases; at the shortest operation distance (1 meter), the operation complexity is low, but the device stability is poor. The system reconstructs the fourth influencing feature based on these results and calculates the equipment stability compensation value.

[0037] Step S2533: Establish operation complexity compensation based on the fitting result and the stable operation plan. Specifically, combine the fitting result and the stable operation plan of the crawling robot, and calculate the operation complexity compensation through a preset complexity evaluation model. This model can be a rule-based system or a machine learning model. According to the model output, calculate the operation complexity compensation value, which is used to correct the complexity evaluation in the operation plan. For example, the system combines the fitting result and the stable operation plan of the crawling robot, and calculates the operation complexity compensation value through the complexity evaluation model. If the operation complexity increases by 20% at the longest operation distance, the system takes this value as the operation complexity compensation.

[0038] Step S2534: Complete the scene superposition fitting by using the equipment stability compensation and the operation complexity compensation. Specifically, apply the equipment stability compensation and the operation complexity compensation to the three-dimensional simulation scenario for the final scene superposition fitting. Through the collision detection and path planning functions in the simulation software, analyze the operation feasibility and stability of the robot under the condition of wire galloping. According to the superposition fitting result, evaluate the stability of the faulty wire during the robot operation and generate the final stability recognition result. For example, the system applies the equipment stability compensation and the operation complexity compensation to the three-dimensional simulation scenario for the final scene superposition fitting. The simulation results show that after considering the compensation, the robot can still complete the operation safely under the condition of wire galloping, but the operation time will increase by 15%. The system generates the final stability recognition result based on these results. This implementation method more accurately evaluates the stability of the faulty wire during the actual repair process by comprehensively considering the operation limit range of the power wire fixing device, equipment stability, and operation complexity.

[0039] In a possible implementation, the establishment of the first task execution complexity, step S200 further includes step S260, using a drone to scan and monitor the faulty wire to establish a scan data set. Specifically, the drone is equipped with a lidar or a high-resolution camera for scanning and monitoring the faulty wire. During flight, the drone collects three-dimensional point cloud data or high-resolution images of the wire through the lidar or camera, and transmits these data to the central control system in real time through a wireless communication module (such as a 4G / 5G network). The collected data is stored as a scan data set for subsequent feature recognition and analysis. For example, the drone carrying the lidar flies to the location of the faulty wire and scans the wire through the lidar to generate high-precision three-dimensional point cloud data. These data are transmitted to the central control system in real time through the 5G network and stored as a scan data set.

[0040] Step S270, after performing feature recognition on the scan data set, establish local deformation features. Specifically, use point cloud processing software (such as CloudCompare, PCL) to preprocess the scan data set, including operations such as denoising, filtering, and segmentation. Then, identify the local deformation features of the wire, such as bending, twisting, and fracture, through computer vision algorithms (such as segmentation networks in deep learning, such as U-Net). Store the identified local deformation features in the system, including information such as the location, size, and type of the deformation.

[0041] Step S280, use the local deformation features as additional influencing features and perform calculation compensation for the first task execution complexity according to the deformation size and deformation location. Specifically, use the local deformation features (such as deformation size and location) as additional influencing features and input them into a preset complexity evaluation model. This model can be a rule-based system or a machine learning model for evaluating the impact of local deformation on task execution complexity. According to the model output, calculate the compensation value for the first task execution complexity. The compensation value is used to adjust the initial complexity evaluation result to ensure that it more accurately reflects the complexity of the actual repair task. For example, the system inputs the local deformation features (such as the location and size of the bending deformation) into the complexity evaluation model. The model calculates the compensation value for the first task execution complexity to be 15% according to the severity of the deformation. The system applies this compensation value to the initial complexity evaluation result to generate the final first task execution complexity. This implementation method uses drone scanning monitoring and feature recognition technologies to conduct a detailed evaluation of the local deformation of the faulty wire, incorporates it as an additional influencing feature into the complexity evaluation, and ensures the accuracy of the complexity evaluation through compensation calculation, thus providing strong support for the success of the entire repair task.

[0042] Step S300: Invoke the associated wire data in the supplementary dataset according to the bottle removal point, identify the complexity of cross-wire repair using the associated wire data, and establish the complexity of the second task execution.

[0043] Specifically, extract the associated wire data adjacent to the bottle removal point from the supplementary dataset, including the tension, position, connection method, etc. of the wire. Use mechanical models and computer simulation techniques to analyze the force changes and possible interference situations of the associated wire during the repair process. By simulating the repair process, evaluate the complexity of cross-wire repair. Combine the simulation results and actual environmental parameters, and calculate the complexity of the second task execution through a preset complexity evaluation model. The complexity of the second task execution is a quantitative indicator reflecting the difficulty of cross-wire repair, calculated based on the force changes, position relationships, etc. of the associated wire. For example, the system invokes the tension data and position information of adjacent wires, and simulates the force changes of the wire during the repair process through a mechanical model. If it is found that the repair process may cause large displacements or tension changes in adjacent wires, the system will determine that the complexity of the second task execution is high.

[0044] In a possible implementation manner, for the step of identifying the complexity of cross-wire repair using the associated wire data and establishing the complexity of the second task execution, step S300 further includes step S310: Read the cross-wire operation direction and cross-wire operation distance according to the associated wire data. Specifically, read the data of the associated wire from the supplementary dataset, including the position, direction, and length information of the wire. Extract the cross-wire operation direction and cross-wire operation distance through Geographic Information System (GIS) software or a dedicated data parsing tool. Store the extracted cross-wire operation direction and distance information as structured data for subsequent processing. For example, the system reads the geographic coordinates and direction information of the associated wire from the supplementary dataset, and calculates through GIS software that the cross-wire operation direction is the northeast direction and the cross-wire operation distance is 50 meters. These information are stored as cross-wire operation direction and distance data.

[0045] Step S320: Establish the fifth influence feature using the cross-wire operation direction and cross-wire operation distance. Specifically, calculate the fifth influence feature according to the cross-wire operation direction and distance. This is completed through a mathematical model or empirical formula. For example, consider the influence of direction and distance on the repair operation. Store the calculated fifth influence feature in the system for subsequent complexity evaluation. For example, the system calculates the fifth influence feature value as 0.8 according to the cross-wire operation direction (northeast direction) and distance (50 meters) through a mathematical model. This value reflects the comprehensive influence of the cross-wire operation direction and distance on the repair operation.

[0046] Step S330: Obtain the fixed-point data in the associated wire data, and establish the sixth influence feature according to the fixed-point data. Specifically, use computer vision algorithms (such as object detection algorithms) or point cloud processing techniques to identify the positions and types of fixed points from the associated wire data. Calculate the sixth influence feature according to the positions and types of fixed points. For example, the closer the position of the fixed point is to the fault point, the higher the influence feature value. Store the calculated sixth influence feature in the system. For example, the system identifies the position of the fixed point on the associated wire through point cloud processing techniques and calculates that the value of the sixth influence feature is 0.6. This value reflects the potential impact of the fixed point on the repair operation.

[0047] Step S340: Establish the second task execution complexity according to the fifth influence feature, the sixth influence feature, the second influence feature, the third influence feature, and the fourth influence feature, where the material properties of the associated wire and the fault wire are the same. Specifically, input the fifth influence feature, the sixth influence feature, the second influence feature (material property), the third influence feature (environmental data), and the fourth influence feature (device self-weight) into a comprehensive evaluation model. This model can be a rule-based system or a machine learning model for evaluating the complexity of the cross-line repair task. Calculate the second task execution complexity according to the model output. The complexity value reflects the overall difficulty of the cross-line repair task. For example, the system inputs the fifth influence feature (0.8), the sixth influence feature (0.6), the second influence feature (wire material property), the third influence feature (wind force and wind direction data), and the fourth influence feature (device self-weight) into the comprehensive evaluation model. The model outputs that the second task execution complexity is "high".

[0048] Step S400: After performing a balance analysis on the first task execution complexity and the second task execution complexity, configure an operation plan mapped to the wire unhooking task.

[0049] Specifically, input the first task execution complexity and the second task execution complexity into the balance analysis module. This module uses optimization algorithms (such as genetic algorithms or linear programming) to find the optimal operation plan. Generate a specific operation plan according to the balance analysis result, including repair steps, required tools, operation sequence, etc. The operation plan is stored in the task database in the form of instructions. For example, the system analyzes that the first task execution complexity is high and the second task execution complexity is medium. Through the optimization algorithm, the system decides to perform local repairs with higher stability first and then gradually expand to cross-line repairs, generating a detailed operation plan.

[0050] In a possible implementation, after performing a balance analysis on the execution complexity of the first task and the execution complexity of the second task, a job plan mapped to the wire unhooking task is configured. Step S400 further includes step S410 of determining whether both the execution complexity of the first task and the execution complexity of the second task meet a preset complexity threshold. Specifically, two complexity thresholds are preset in the system, corresponding to the execution complexity of the first task and the execution complexity of the second task respectively. These thresholds are set based on historical data, expert experience, or experimental results. The calculated execution complexity of the first task and the execution complexity of the second task are compared with the preset thresholds. A logic judgment module (such as a conditional statement) is used to determine whether both complexities meet the preset thresholds. For example, the system presets the execution complexity threshold of the first task as "medium" and the execution complexity threshold of the second task as "low". The execution complexity of the first task calculated by the system is "medium", and the execution complexity of the second task is "low". Through the logic judgment module, the system confirms that both complexities meet the preset thresholds.

[0051] Step S420, if both the execution complexity of the first task and the execution complexity of the second task meet the preset complexity threshold, a joint fixing plan is generated. The joint fixing plan is a plan that uses the faulty wire as the working wire of the crawling robot and uses the associated wire to jointly fix the faulty wire. Specifically, a plan generation module is used to generate a joint fixing plan according to preset rules or algorithms. This module is a rule-based system or an optimization algorithm. The joint fixing plan refers to a plan that uses the faulty wire as the working wire of the crawling robot and uses the associated wire to jointly fix the faulty wire. Specifically, it includes determining the path of the crawling robot, the operation steps, and the time sequence. The generated joint fixing plan is stored in the system for subsequent invocation. For example, the system generates a joint fixing plan according to preset rules. The plan includes the path of the crawling robot from the starting point to the faulty wire, the operation steps (such as the selection of the fixing point, the binding operation, etc.), and the time sequence. The system stores this plan in the task database.

[0052] Step S430, use the joint fixing plan as the job plan mapped to the wire unhooking task. Specifically, a task mapping module is used to map the generated joint fixing plan to the wire unhooking task. This module can be a simple database operation or a complex task scheduling system. The joint fixing plan is bound to the wire unhooking task to ensure that the correct plan is invoked when the task is executed. If the task conditions change (such as the update of environmental data), the task mapping module can update or adjust the plan. For example, the system maps the generated joint fixing plan to the wire unhooking task. In the task database, the ID of the joint fixing plan is bound to the ID of the wire unhooking task. When the task is executed, the system invokes the joint fixing plan according to the binding relationship.

[0053] In step S500, after using the drone to transport the crawling robot to the target wire, the crawling robot controls the wire fixing device for the power wire to repair the wire at the position of the bottle detachment point based on the operation plan, so as to complete the wire fixing operation.

[0054] Specifically, the wire fixing device for the power wire includes an insulating operating rod, a fixing head, a one-way bearing, a binding coil, and a guiding groove. Among them, the insulating operating rod is used for remote operation to reduce the risk of electric shock. The fixing head includes a first hoop and two second hoops, and is used to fix the wire and the insulating porcelain bottle. The one-way bearing is used to control the winding and contraction of the binding coil to ensure firm fixation. The binding coil is used to fix the wire on the insulating porcelain bottle and is made of high-strength materials (such as steel stranded wire or iron wire). The guiding groove is used to guide the wire into place to ensure that the wire is correctly placed in the groove of the insulating porcelain bottle.

[0055] The drone accurately locates the target wire position through GPS and visual recognition technologies. After the drone reaches the target position, it releases the crawling robot. The crawling robot is equipped with a wire fixing device for the power wire and receives the operation plan instructions sent by the central control system through the wireless communication module, and controls the wire fixing device for the power wire to repair the wire according to the instructions. Specifically, the crawling robot operates the insulating operating rod through a robotic arm or a similar device to control the movement and operation of the wire fixing device for the power wire. The fallen wire is guided into the groove of the insulating porcelain bottle by using the guiding groove on the fixing device. The winding of the binding coil is controlled by the one-way bearing to firmly fix the wire on the insulating porcelain bottle. The crawling robot conducts a final inspection to ensure that the wire is firmly fixed. After the repair is completed, the drone retrieves the crawling robot to complete the entire repair task. In the embodiment of the present application, a wire bottle detachment task is created, and the video acquisition device is activated to collect data at the bottle detachment position, a supplementary data set is constructed and integrated into the task, the fault wire data in the supplementary data set is called according to the bottle detachment point to evaluate the stability of the fault wire, the complexity of the first task execution is determined, the associated wire data is also called, the complexity of the cross-wire repair is analyzed, the complexity of the second task execution is established, a balance analysis is performed on the complexities of the two task executions, and accordingly an operation plan matching the wire bottle detachment task is configured. The drone is used to transport the crawling robot to the target wire position, and the crawling robot controls the wire fixing device for the power wire according to the operation plan to repair the bottle detachment point and complete the wire fixing operation and other technical means, achieving the technical effect of improving the efficiency and safety of the wire fixing operation.

[0056] In a possible implementation, the crawling robot controls the wire fixing device to repair the wire at the bottle dropping point position based on the operation plan. Step S500 further includes step S510 of establishing a position anomaly warning sequence according to the operation plan. Specifically, obtain the detailed operation plan from the central control system, including information such as the path of the crawling robot, operation steps, time series, etc. Generate a position anomaly warning sequence according to the key positions and operation steps in the operation plan. The warning sequence is a list containing multiple warning points, and each warning point corresponds to a specific operation position and expected state. Set threshold parameters for each warning point, such as position deviation threshold, time threshold, etc., for judging whether to trigger a warning. For example, the operation plan stipulates that the crawling robot needs to operate at three key positions on the wire, namely position A, position B, and position C. The system generates a warning sequence based on these positions, sets the position deviation threshold to ±5 cm and the time threshold to ±30 s for each position. The warning sequence is stored in the system for subsequent real-time monitoring.

[0057] Step S520, use the position anomaly warning sequence to perform operation warning identification of the crawling robot and establish a warning signal. Specifically, the position and state of the crawling robot are monitored in real time through sensors on the crawling robot (such as GPS, IMU, lidar, etc.). Compare the real-time monitored position data with the expected position in the warning sequence to judge whether it exceeds the preset threshold. If it is found that the position deviation or time deviation exceeds the threshold, the system generates a warning signal and records the specific time and position information. For example, when the crawling robot is performing a task, the real-time position monitoring system finds that its actual position at position A deviates from the expected position by 6 cm, exceeding the preset ±5 cm threshold. The system immediately generates a warning signal, records the specific time and position information, and sends the warning signal to the central control system.

[0058] Step S530, report abnormal operations according to the warning signal. Specifically, record the generated warning signal in the abnormal operation log, including information such as warning time, position, deviation value, etc. Send an alarm notification to the operator through the central control system, which can be a text message, email or in-system message. According to the warning signal, the system can automatically adjust the operation plan, such as pausing the operation, adjusting the path or re-planning the task. For example, after receiving the warning signal, the central control system records it in the abnormal operation log and sends a text message notification to the operator. At the same time, the system automatically adjusts the operation plan, pauses the operation of the crawling robot at position A, and re-plans the path to ensure the accuracy of subsequent operations. This implementation ensures the operation accuracy and safety of the crawling robot during the repair task by establishing a position anomaly warning sequence, performing operation warning identification and reporting abnormal operations.

[0059] In a possible implementation, after reporting the abnormal operation according to the warning signal, step S500 further includes step S540 of performing self-response correction fitting according to the warning signal to generate a self-response correction fitting result. Specifically, after receiving the warning signal, the system analyzes the specific information in the warning signal, including the warning time, location, deviation value, etc. According to the warning signal, the system automatically invokes a preset correction fitting algorithm to adjust the current operation plan. The correction fitting algorithm can be a rule-based system or a machine learning model for generating correction measures. The correction fitting algorithm generates specific correction measures according to the warning signal, including adjusting the path, operation steps or time series of the crawling robot. These correction measures are recorded as the self-response correction fitting result. For example, when the system receives a warning signal indicating that the actual position of the crawling robot at position A deviates from the expected position by 6 centimeters, after analyzing the warning signal, the system invokes the correction fitting algorithm to generate correction measures, such as adjusting the path of the crawling robot to realign it with the expected position. These correction measures are recorded as the self-response correction fitting result.

[0060] Step S550: Synchronously send the self-response correction fitting result and the warning signal to the administrator. Specifically, the system synchronously sends the self-response correction fitting result and the warning signal to the administrator's terminal device through a wireless communication module (such as 4G / 5G network or Wi-Fi). The system notifies the administrator via text message, email or in-system message to ensure that the administrator can receive the relevant information in a timely manner.

[0061] Step S560: When receiving the execution feedback from the administrator, perform operation compensation management using the self-response correction fitting result. Specifically, the system receives the execution feedback from the administrator through the wireless communication module. The feedback can be an instruction to confirm the execution of the correction measures or further adjustment suggestions. According to the administrator's feedback, the system further adjusts the current operation plan or confirms the execution of the correction measures. These adjustments or confirmations are recorded in the operation compensation management module. The system updates the operation plan in the task database according to the records in the operation compensation management module to ensure the accuracy of subsequent operations. For example, after receiving the self-response correction fitting result and the warning signal, the administrator confirms the execution of the correction measures through the mobile application. After receiving the administrator's feedback, the system updates the operation plan in the task database to ensure that the crawling robot performs tasks according to the new path and operation steps.

[0062] In the above text, with reference to Figure 1 a method for fixing a power conductor according to an embodiment of the present invention is described in detail. Next, with reference to Figure 2 a power conductor fixing operation system according to an embodiment of the present invention will be described.

[0063] A power wire fixing operation system according to an embodiment of the present invention is used to solve the technical problems of low efficiency and safety existing in the existing power wire fixing operation, and achieve the technical effect of improving the efficiency and safety of the wire fixing operation. A power wire fixing operation system includes: a supplementary data set establishment module 10, a first task execution complexity establishment module 20, a second task execution complexity establishment module 30, an operation plan configuration module 40, and a wire fixing module 50.

[0064] The supplementary data set establishment module 10 is used to create a wire bottle-off task, activate the video acquisition device to perform data acquisition of the bottle-off position, establish a supplementary data set, and add the supplementary data set to the wire bottle-off task; the first task execution complexity establishment module 20 is used to call the faulty wire data in the supplementary data set according to the bottle-off point to identify the stability of the faulty wire, and establish the first task execution complexity; the second task execution complexity establishment module 30 is used to call the associated wire data in the supplementary data set according to the bottle-off point, and use the associated wire data to identify the complexity of cross-wire repair, and establish the second task execution complexity; the operation plan configuration module 40 is used to perform a balance analysis on the first task execution complexity and the second task execution complexity, and configure an operation plan mapped to the wire bottle-off task; the wire fixing module 50 is used to use a drone to transport the crawling robot to the target wire, and the crawling robot controls the power wire fixing device to repair the wire at the bottle-off point position based on the operation plan to complete the wire fixing operation.

[0065] Next, the specific configuration of the first task execution complexity establishment module 20 will be described in detail. As described above, the first task execution complexity establishment module 20 calls the faulty wire data in the supplementary data set according to the bottle-off point to identify the stability of the faulty wire and establish the first task execution complexity. The first task execution complexity establishment module 20 may further include: a faulty wire fixing point identification unit for identifying the faulty wire fixing point in the faulty wire data and establishing a first influence feature according to the faulty wire fixing point; a material property data acquisition unit for acquiring the material property data of the faulty wire and establishing a second influence feature according to the material property data; an environmental data monitoring unit for performing environmental data monitoring and establishing an environmental data set, where the environmental data set includes wind force data and wind direction data, and establishing a third influence feature according to the environmental data set; a device self-weight acquisition unit for acquiring the device self-weight of the crawling robot and the power wire fixing device and establishing a fourth influence feature according to the device self-weight; a faulty wire stability identification unit for identifying the stability of the faulty wire according to the first influence feature, the second influence feature, the third influence feature, and the fourth influence feature, and establishing the first task execution complexity.

[0066] Among them, for the stability identification of the faulty wire based on the first influencing feature, the second influencing feature, the third influencing feature, and the fourth influencing feature, the faulty wire stability identification unit may further include: a three-dimensional simulation scene establishment sub-unit for establishing a three-dimensional simulation scene and inputting the first influencing feature, the second influencing feature, the third influencing feature, and the fourth influencing feature into the three-dimensional simulation scene; a wire galloping fitting sub-unit for obtaining the calibration operation distance of the power wire fixing device, configuring the fourth influencing feature according to the calibration operation distance, and then using the three-dimensional simulation scene to perform wire galloping fitting to establish a wire galloping fitting result; a scene superposition fitting sub-unit for obtaining the stable operation plan of the crawling robot in the stable state and performing scene superposition fitting according to the wire galloping fitting result and the stable operation plan to complete the stability identification.

[0067] Among them, for the scene superposition fitting according to the wire galloping fitting result and the stable operation plan, the scene superposition fitting sub-unit may further include: an operation limit interval acquisition component for acquiring the operation limit interval of the power wire fixing device; an equipment stability compensation establishment component for performing the cooperative fitting of the crawling robot and the power wire fixing device within the operation limit interval, reconstructing the fourth influencing feature according to the cooperative fitting result, and establishing equipment stability compensation; an operation complexity compensation establishment component for establishing operation complexity compensation based on the cooperative fitting result and the stable operation plan; a scene superposition fitting component for completing the scene superposition fitting by using the equipment stability compensation and the operation complexity compensation.

[0068] Among them, for the establishment of the first task execution complexity, the first task execution complexity establishment module 20 may further include: a scanning data set establishment unit for using a drone to perform scanning and monitoring of the faulty wire and establishing a scanning data set; a local deformation feature establishment unit for performing feature recognition on the scanning data set and establishing local deformation features; a complexity calculation and compensation unit for using the local deformation features as additional influencing features and performing the calculation and compensation of the first task execution complexity according to the deformation magnitude and deformation position.

[0069] Next, the specific configuration of the second task execution complexity establishment module 30 will be described in detail. As described above, by using the associated wire data to identify the complexity of cross-wire repair and establish the second task execution complexity, the second task execution complexity establishment module 30 may further include: a cross-wire operation information reading unit for reading the cross-wire operation direction and cross-wire operation distance according to the associated wire data; a fifth influence feature establishment unit for establishing a fifth influence feature by using the cross-wire operation direction and cross-wire operation distance; a sixth influence feature establishment unit for obtaining the fixed point data in the associated wire data and establishing a sixth influence feature according to the fixed point data; a second task execution complexity establishment unit for establishing the second task execution complexity according to the fifth influence feature, the sixth influence feature, the second influence feature, the third influence feature, and the fourth influence feature, wherein the material properties of the associated wire and the faulty wire are the same.

[0070] Next, the specific configuration of the operation plan configuration module 40 will be described in detail. As described above, after performing a balance analysis on the first task execution complexity and the second task execution complexity, an operation plan mapped to the wire unhooking task is configured. The operation plan configuration module 40 may further include: a judgment unit for judging whether both the first task execution complexity and the second task execution complexity meet a preset complexity threshold; a combined fixation plan generation unit for generating a combined fixation plan if both the first task execution complexity and the second task execution complexity meet the preset complexity threshold, where the combined fixation plan is a plan that uses the faulty wire as the operation wire of the crawling robot and uses the associated wire for joint fixation of the faulty wire; an operation plan acquisition unit for using the combined fixation plan as the operation plan mapped to the wire unhooking task.

[0071] Next, the specific configuration of the wire fixation module 50 will be described in detail. As described above, the crawling robot controls the power wire fixation device to repair the wire at the unhooking point position based on the operation plan. The wire fixation module 50 may further include: a position anomaly warning sequence establishment unit for establishing a position anomaly warning sequence according to the operation plan; an operation warning recognition unit for using the position anomaly warning sequence to perform operation warning recognition of the crawling robot and establish a warning signal; an abnormal operation reporting unit for reporting an abnormal operation according to the warning signal.

[0072] Among them, after reporting abnormal operations according to the warning signal, the wire fixing module 50 may further include: a self-response correction fitting unit for performing self-response correction fitting according to the warning signal to generate a self-response correction fitting result; a synchronous sending unit for synchronously sending the self-response correction fitting result and the warning signal to an administrator; and an operation compensation management unit for performing operation compensation management by using the self-response correction fitting result when receiving the execution feedback from the administrator.

[0073] The power wire fixing operation system provided by the embodiment of the present invention can execute the power wire fixing operation method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0074] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included various units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for facilitating mutual distinction and do not limit the protection scope of the present invention.

[0075] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for fixing an electric power conductor during operation, characterized in that, The method includes: Creating a conductor unclamping task, activating the video acquisition device to perform data acquisition of the unclamping position, establishing a supplementary data set, and adding the supplementary data set to the conductor unclamping task; Calling the faulty conductor data in the supplementary data set according to the unclamping point to identify the stability of the faulty conductor, and establishing the first task execution complexity; Calling the associated conductor data in the supplementary data set according to the unclamping point, using the associated conductor data to identify the complexity of cross-line repair, and establishing the second task execution complexity; After performing a balance analysis on the first task execution complexity and the second task execution complexity, configuring an operation plan mapped to the conductor unclamping task; After using the drone to transport the crawling robot to the target conductor, the crawling robot controls the power conductor fixing device to repair the conductor at the unclamping point position based on the operation plan to complete the conductor fixing operation.

2. The method for fixing an electric power conductor during operation according to claim 1, characterized in that, The step of calling the faulty conductor data in the supplementary data set according to the unclamping point to identify the stability of the faulty conductor and establishing the first task execution complexity includes: Identifying the faulty conductor fixing points in the faulty conductor data, and establishing the first influence feature according to the faulty conductor fixing points; Obtaining the material property data of the faulty conductor, and establishing the second influence feature according to the material property data; Performing environmental data monitoring, establishing an environmental data set, where the environmental data set includes wind force data and wind direction data, and establishing the third influence feature according to the environmental data set; Obtaining the device self-weights of the crawling robot and the power conductor fixing device, and establishing the fourth influence feature according to the device self-weights; Identifying the stability of the faulty conductor according to the first influence feature, the second influence feature, the third influence feature, and the fourth influence feature, and establishing the first task execution complexity.

3. The method for fixing an electric power conductor during operation according to claim 2, wherein, The step of identifying the stability of the faulty conductor according to the first influence feature, the second influence feature, the third influence feature, and the fourth influence feature includes: Establishing a three-dimensional simulation scenario, and inputting the first influence feature, the second influence feature, the third influence feature, and the fourth influence feature into the three-dimensional simulation scenario; Obtaining the calibrated operation distance of the power conductor fixing device, configuring the fourth influence feature according to the calibrated operation distance, and then using the three-dimensional simulation scenario to perform conductor galloping fitting to establish a conductor galloping fitting result; Obtaining the stable operation plan of the crawling robot in the stable state, and performing scene superposition fitting according to the conductor galloping fitting result and the stable operation plan to complete the stability identification.

4. The method for fixing an electric power conductor during operation according to claim 3, wherein The step of performing scene superposition fitting according to the conductor galloping fitting result and the stable operation plan further includes: Obtaining the operation limit interval of the power conductor fixing device; Performing the cooperation fitting of the crawling robot and the power conductor fixing device within the operation limit interval, reconstructing the fourth influence feature according to the cooperation fitting result, and establishing the equipment stability compensation; Establishing the operation complexity compensation based on the cooperation fitting result and the stable operation plan; Using the equipment stability compensation and the operation complexity compensation to complete the scene superposition fitting.

5. The method for fixing an electric power conductor during operation according to claim 2, characterized in that, The step of establishing the first task execution complexity further includes: Use a drone to scan and monitor the faulty wire, and establish a scanning data set; After performing feature recognition on the scanning data set, establish local deformation features; Take the local deformation features as additional influencing features, and perform calculation compensation for the execution complexity of the first task according to the deformation magnitude and deformation position.

6. The method for fixing an electric power conductor during operation according to claim 2, wherein The complexity identification of cross-line repair using the associated wire data to establish the execution complexity of the second task includes: Read the cross-line operation direction and cross-line operation distance according to the associated wire data; Establish a fifth influencing feature using the cross-line operation direction and cross-line operation distance; Obtain the fixed point data in the associated wire data, and establish a sixth influencing feature according to the fixed point data; Establish the execution complexity of the second task according to the fifth influencing feature, the sixth influencing feature, the second influencing feature, the third influencing feature, and the fourth influencing feature, where the material properties of the associated wire and the faulty wire are the same.

7. The method for fixing an electric power conductor as claimed in claim 1, wherein After performing a balance analysis on the execution complexity of the first task and the execution complexity of the second task, configure an operation plan mapped to the wire unbolting task, including: Judge whether the execution complexity of the first task and the execution complexity of the second task both meet the preset complexity threshold; If the execution complexity of the first task and the execution complexity of the second task both meet the preset complexity threshold, generate a joint fixing plan, where the joint fixing plan is a plan to use the faulty wire as the operation wire of the crawling robot and use the associated wire to jointly fix the faulty wire; Take the joint fixing plan as the operation plan mapped to the wire unbolting task.

8. The method for fixing an electric power conductor during operation according to claim 1, wherein The crawling robot controls the wire at the unbolting point position of the power wire fixing device based on the operation plan, including: Establish a position anomaly warning sequence according to the operation plan; Perform operation warning recognition of the crawling robot using the position anomaly warning sequence to establish a warning signal; Report abnormal operations according to the warning signal.

9. The method for fixing an electric power conductor during operation according to claim 8, wherein After reporting abnormal operations according to the warning signal, it includes: Perform self-response correction fitting according to the warning signal to generate a self-response correction fitting result; Synchronously send the self-response correction fitting result and the warning signal to the administrator; When receiving the execution feedback from the administrator, perform operation compensation management using the self-response correction fitting result.

10. A power conductor fixing operation system, characterized in that, The system is used to implement a power wire fixing operation method according to any one of claims 1-9, and the system includes: A supplementary data set establishment module for creating a wire unbolting task, activating a video acquisition device to perform data acquisition at the unbolting position, establishing a supplementary data set, and adding the supplementary data set to the wire unbolting task; A first task execution complexity establishment module for identifying the stability of the faulty wire by calling the faulty wire data in the supplementary data set according to the unbolting point, and establishing the execution complexity of the first task; A second task execution complexity establishment module for calling the associated wire data in the supplementary data set according to the unbolting point, and identifying the complexity of cross-line repair using the associated wire data to establish the execution complexity of the second task; The operation plan configuration module is used to configure an operation plan mapped to the conductor unhooking task after performing a balance analysis on the first task execution complexity and the second task execution complexity; The conductor fixing module is used to transport the crawling robot to the target conductor by using a drone, and then the crawling robot controls the electric conductor fixing device to repair the conductor at the unhooking point position based on the operation plan, so as to complete the conductor fixing operation.

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