Cable line inspection path planning method based on Beidou positioning

Abstract

The invention provides a cable line inspection path planning method based on Beidou positioning, and relates to the technical field of power cable inspection, and the method comprises the steps: inputting ground surface and infrastructure deformation data, cable state data and real-time meteorological grid data into a space-time geographic weighted regression model, and generating a dynamic risk field with a geographic grid as a unit; distributing an environment construction task to a mobile beacon cluster through a distributed contract network protocol based on multi-objective optimization; the inspection unit performs local obstacle avoidance and trajectory smoothing by adopting a fusion predictive control algorithm based on the guidance of the virtual inspection channel, and collects real-time sensing data; and comparing the real-time sensing data with an expected state in the digital twinborn model, and when the deviation exceeds a preset tolerance, triggering redeployment of the mobile beacon cluster and dynamic reconstruction of the virtual inspection channel. According to the invention, the risk can be accurately identified and quantified, so that a predictive inspection strategy is generated.

Description

Technical Field

[0001] This invention relates to the field of cable inspection technology, and in particular to a cable line inspection path planning method based on BeiDou positioning. Background Technology

[0002] With the continuous expansion of power grids, the inspection and maintenance of cable lines face increasingly severe challenges. Traditional inspection methods mainly rely on manual labor or pre-programmed automated equipment, with fixed inspection routes that are difficult to cope with complex geographical environments and sudden risks. Although the BeiDou Navigation Satellite System provides basic support for outdoor positioning, there are still risks of inaccurate positioning and communication interruptions in mountainous areas, urban canyons, or under severe weather conditions.

[0003] Currently, some advanced inspection solutions are beginning to utilize sensor data and static beacons for path planning. However, these solutions have significant shortcomings: First, they are typically based on static or historical data for risk assessment, failing to integrate multi-source information such as real-time weather and geological deformation, leading to delayed risk warnings; second, the fixed locations of beacon nodes after deployment prevent adaptive adjustments based on dynamically changing risk fields, potentially causing inspection paths to fail in emergencies; third, existing path planning methods often focus on a single objective (such as the shortest path), failing to comprehensively consider multi-objective collaborative optimization such as deployment energy consumption and network connectivity, making it difficult to achieve the optimal balance between emergency rescue and routine inspections.

[0004] Therefore, a cable line inspection path planning method based on BeiDou positioning is proposed. Summary of the Invention

[0005] This manual provides a cable line inspection route planning method based on BeiDou positioning, which can accurately identify and quantify risks, thereby generating a predictive inspection strategy.

[0006] This manual provides a cable line inspection route planning method based on BeiDou positioning, including: It acquires surface and infrastructure deformation data provided by Beidou monitoring terminals, cable status data provided by sensors deployed on the cable body, and real-time meteorological grid data provided by meteorological data sources; The surface and infrastructure deformation data, the cable status data, and the real-time meteorological grid data are input into the spatiotemporal geographic weighted regression model to generate a dynamic risk field in geographic grid units. Identify high-risk core areas in the dynamic risk field and generate corresponding environment construction tasks for each core area; The environment building task is assigned to the mobile beacon cluster using a distributed contract network protocol based on multi-objective optimization; The mobile beacon cluster plans a cooperative movement path based on an improved particle swarm optimization algorithm, aiming to minimize global deployment energy consumption and maximize network connectivity, and moves to the location specified by the task. Using the location of the deployed mobile beacon cluster as control points, a virtual inspection channel is generated in the digital twin model using B-spline curve fitting technology. Guided by the virtual inspection channel, the inspection unit uses a fusion predictive control algorithm to perform local obstacle avoidance and trajectory smoothing, and collects real-time perception data. The real-time sensing data is compared with the expected state in the digital twin model. When the deviation exceeds the preset tolerance, the redeployment of the mobile beacon cluster and the dynamic reconstruction of the virtual inspection channel are triggered.

[0007] Optionally, identifying high-risk core areas in the dynamic risk field and generating corresponding environment construction tasks for each core area includes: When a high-risk core area is identified and there are risks of both communication interruption and positioning inaccuracy, and the two are highly overlapping in space, the composite task is generated. The composite task instruction is assigned to a subset of beacons, which construct a multifunctional node at the target location that combines self-organizing network communication relay function and dynamic differential positioning service function. Some beacons are designated as positioning reference stations, while the remaining beacons share their differential data and synchronously construct communication links.

[0008] Optionally, the mobile beacon cluster plans a cooperative movement path based on an improved particle swarm optimization algorithm, aiming to minimize global deployment energy consumption and maximize network connectivity, including: When the environment construction task is an emergency rescue task, the weight of maximizing network connectivity in the optimization objective is automatically raised to the highest priority. When the environment building task is a routine inspection task, the weight of minimizing global deployment energy consumption in the optimization objective is set to the highest priority.

[0009] Optionally, the step of using the location of the deployed mobile beacon cluster as control points to generate a virtual inspection channel in the digital twin model using B-spline curve fitting technology includes: The main inspection channel is generated based on the geometric center of all mobile beacons in the deployed mobile beacon cluster; Branch inspection channels are generated based on the distribution characteristics of each beacon subgroup; The main inspection channel and the branch inspection channels together form a complete inspection path network with a tree topology.

[0010] Optionally, comparing the real-time perceived data with the expected state in the digital twin model includes: Compare the instantaneous value deviation between the real-time sensing data and the expected state, and / or compare the degree of consistency between the changing trend of the real-time sensing data and the changing trend of the expected state. When either the instantaneous value deviation or the degree of agreement between the change trend and the instantaneous value deviation exceeds a preset tolerance, the reconstruction mechanism is triggered.

[0011] Optionally, after allocating the environment building task to the mobile beacon cluster via a distributed contract network protocol based on multi-objective optimization, the process includes: In the mobile beacon cluster, each mobile beacon determines its role in task execution through distributed negotiation. The roles include a leading node, a secondary node, and a backup node. The mobile beacon assumes different functional responsibilities in subsequent path planning and task execution based on its different role.

[0012] Optional, also includes: Record the historical usage data for each of the aforementioned virtual inspection channels; Based on machine learning algorithms, the correlation between historical usage data and channel parameters is analyzed, and the curvature radius, channel width, and safety margin parameters of subsequently generated virtual inspection channels are automatically adjusted.

[0013] This specification provides a cable fault detection device based on intelligent sensors, comprising: The acquisition module is used to acquire surface and infrastructure deformation data provided by the Beidou monitoring terminal, cable status data provided by sensors deployed on the cable body, and real-time meteorological grid data provided by meteorological data sources; The processing module is used to input the surface and infrastructure deformation data, the cable status data, and the real-time meteorological grid data into the spatiotemporal geographic weighted regression model to generate a dynamic risk field in geographic grid units. The construction module is used to identify high-risk core areas in the dynamic risk field and generate corresponding environment construction tasks for each core area; The allocation module is used to allocate the environment building tasks to the mobile beacon cluster via a distributed contract network protocol based on multi-objective optimization. The planning module is used by the mobile beacon cluster to plan a cooperative movement path based on an improved particle swarm optimization algorithm, with the goal of minimizing global deployment energy consumption and maximizing network connectivity, and to move to the position specified by the task. The fitting module is used to generate virtual inspection channels in the digital twin model by using the positions of the deployed mobile beacon clusters as control points and B-spline curve fitting technology. The smoothing module is used by the inspection unit to perform local obstacle avoidance and trajectory smoothing based on the virtual inspection channel, and to collect real-time sensing data by employing a fusion predictive control algorithm. The adjustment module is used to compare the real-time sensing data with the expected state in the digital twin model. When the deviation exceeds the preset tolerance, it triggers the redeployment of the mobile beacon cluster and the dynamic reconstruction of the virtual inspection channel.

[0014] Optionally, the building module includes: When a high-risk core area is identified and there are risks of both communication interruption and positioning inaccuracy, and the two are highly overlapping in space, the composite task is generated. The composite task instruction is assigned to a subset of beacons, which construct a multifunctional node at the target location that combines self-organizing network communication relay function and dynamic differential positioning service function. Some beacons are designated as positioning reference stations, while the remaining beacons share their differential data and synchronously construct communication links.

[0015] Optionally, the planning module includes: When the environment construction task is an emergency rescue task, the weight of maximizing network connectivity in the optimization objective is automatically raised to the highest priority. When the environment building task is a routine inspection task, the weight of minimizing global deployment energy consumption in the optimization objective is set to the highest priority.

[0016] Optionally, the fitting module includes: The main inspection channel is generated based on the geometric center of all mobile beacons in the deployed mobile beacon cluster; Branch inspection channels are generated based on the distribution characteristics of each beacon subgroup; The main inspection channel and the branch inspection channels together form a complete inspection path network with a tree topology.

[0017] Optionally, the adjustment module includes: Compare the instantaneous value deviation between the real-time sensing data and the expected state, and / or compare the degree of consistency between the changing trend of the real-time sensing data and the changing trend of the expected state. When either the instantaneous value deviation or the degree of agreement between the change trend and the instantaneous value deviation exceeds a preset tolerance, the reconstruction mechanism is triggered.

[0018] Optionally, after the allocation module, the following is included: In the mobile beacon cluster, each mobile beacon determines its role in task execution through distributed negotiation. The roles include a leading node, a secondary node, and a backup node. The mobile beacon assumes different functional responsibilities in subsequent path planning and task execution based on its different role.

[0019] Optional, also includes: Record the historical usage data for each of the aforementioned virtual inspection channels; Based on machine learning algorithms, the correlation between historical usage data and channel parameters is analyzed, and the curvature radius, channel width, and safety margin parameters of subsequently generated virtual inspection channels are automatically adjusted.

[0020] This specification also provides an electronic device, wherein the electronic device includes: A processor; and a memory storing computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0021] This specification also provides a computer-readable storage medium that stores one or more programs that, when executed by a processor, implement any of the methods described above.

[0022] In this invention, by constructing a dynamic risk field integrating surface deformation, cable status, and real-time meteorological data, the system can accurately identify and quantify risks, thereby generating predictive inspection strategies. When real-time data deviates from the expected state of the digital twin model, the system automatically triggers a reconstruction mechanism, ensuring that the inspection path always matches the real environmental state, greatly improving the accuracy and safety of inspections. Using a distributed contract network protocol based on multi-objective optimization for task allocation, combined with an improved particle swarm optimization algorithm for path planning, the system can intelligently adjust the weight and priority of optimization objectives according to the urgency of the task. This not only ensures the fastest possible restoration of communication and positioning in emergency situations but also minimizes global energy consumption in routine tasks, achieving precise and efficient resource allocation. Through the collaborative deployment and role negotiation of mobile beacon clusters, the system can quickly construct a temporary network with both communication relay and differential positioning functions in high-risk areas. A tree-like topology virtual inspection channel generated using B-spline curve fitting provides a smooth, coherent, and comprehensive guidance path for the inspection units. This structure ensures unobstructed main channels while enabling detailed inspection of risk areas through branch channels, forming an inspection network that combines global efficiency with local precision. By recording and analyzing the historical usage of virtual inspection channels and automatically adjusting channel parameters using machine learning algorithms, the system can learn from past experience and continuously optimize the quality of subsequently generated inspection channels. This ensures that parameters such as curvature, width, and safety margin better meet actual inspection needs, thereby achieving continuous evolution of system performance. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A schematic diagram illustrating the principle of a cable fault detection method based on intelligent sensors, provided in the embodiments of this specification; Figure 2 A schematic diagram of a cable fault detection device based on a smart sensor provided in the embodiments of this specification; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification; Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification. Detailed Implementation

[0025] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0026] The following is in conjunction with the appendix Figure 1-4 Exemplary embodiments of the invention will be described more fully here. However, exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the figures denote the same or similar elements, components, or parts, and therefore repeated descriptions of them are omitted.

[0027] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.

[0028] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.

[0029] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.

[0032] Figure 1 This specification provides a schematic diagram of a cable fault detection method based on intelligent sensors, which may include: S110: Acquire surface and infrastructure deformation data provided by Beidou monitoring terminals, cable status data provided by sensors deployed on the cable body, and real-time meteorological grid data provided by meteorological data sources; In the specific implementation of this specification, the data includes millimeter-precision surface deformation and infrastructure settlement data provided by BeiDou monitoring terminals deployed on poles or the ground; cable temperature, strain, current carrying capacity, and other status data provided by distributed optical fiber sensors (DTS / DFOS) pre-laid on the cable body; and real-time meteorological grid data accessed from the National Meteorological Center, including precipitation, wind speed, lightning activity, etc.

[0033] S120: Input the surface and infrastructure deformation data, the cable status data, and the real-time meteorological grid data into the spatiotemporal geographic weighted regression model to generate a dynamic risk field in geographic grid units; In the specific implementation of this specification, surface and infrastructure deformation data, cable status data, and real-time meteorological grid data are unified into a spatiotemporal reference and then input into a pre-trained spatiotemporal geographic weighted regression (GTWR) model. This model can consider the heterogeneity of geographical locations and temporal nonstationarity, outputting a dynamic risk field covering the entire inspection area in units of geographic grids (e.g., 30m × 30m). The risk value of each grid is a comprehensive index between 0 and 1, quantifying the probability of geological disasters, cable faults, or inspection obstruction occurring at that location.

[0034] S130: Identify high-risk core areas in the dynamic risk field and generate corresponding environment construction tasks for each core area; In the specific implementation of this specification, a clustering algorithm (such as DBSCAN) is used to automatically identify high-risk core areas in the risk field, and a specific environment construction task is generated for each core area. The task content includes the target area coordinates, risk level and required functions.

[0035] Optionally, S130 includes: When a high-risk core area is identified and there are risks of both communication interruption and positioning inaccuracy, and the two are highly overlapping in space, the composite task is generated. The composite task instruction is assigned to a subset of beacons, which construct a multifunctional node at the target location that combines self-organizing network communication relay function and dynamic differential positioning service function. Some beacons are designated as positioning reference stations, while the remaining beacons share their differential data and synchronously construct communication links.

[0036] In the specific implementation of this specification, during the process of identifying high-risk core areas and generating environment construction tasks, the system performs a fine-grained judgment of risk types. For example, analysis reveals that a certain core area suffers from severe weather conditions that cause wireless signal attenuation and multipath effects that affect satellite signal reception due to heavy rainfall and geological instability. This indicates a combined risk of communication interruption and positioning inaccuracy in the area. In this case, the system no longer generates a single communication relay or positioning assistance task, but rather a composite task. The assigned beacon subset of this task requires the construction of a multi-functional node network within the target area. Specifically, beacons with stronger computing power and higher-precision positioning modules (such as UAVs equipped with high-precision boards) are prioritized as positioning reference stations, performing high-precision fixed-point observations in place and calculating differential correction information. The remaining beacons in the cluster act as rover stations, sharing the differential data to improve their own positioning accuracy while using self-organizing network communication modules (such as Mesh networks) to build a stable communication link with the reference stations and other beacons, thus simultaneously solving the two major challenges of positioning and communication in a single operation.

[0037] S140: The environment building task is assigned to the mobile beacon cluster via a distributed contract network protocol based on multi-objective optimization; In a specific implementation of this specification, this task is advertised to a cluster of mobile beacons (e.g., a group of unmanned vehicles or drones) within the network using a distributed contract network protocol based on multi-objective optimization.

[0038] Optionally, after S140, the following steps are included: In the mobile beacon cluster, each mobile beacon determines its role in task execution through distributed negotiation. The roles include a leading node, a secondary node, and a backup node. The mobile beacon assumes different functional responsibilities in subsequent path planning and task execution based on its different role.

[0039] In the specific implementation described in this specification, after a task is successfully assigned via the Distributed Contract Network protocol, the mobile beacon cluster does not immediately begin operation but enters a brief distributed negotiation phase. Each mobile beacon in the cluster, based on its own capabilities (such as computing power, sensor accuracy, and battery level) and its committed contribution during the bidding process, autonomously determines its role in executing the task by exchanging negotiation messages. The most capable beacon, designated as the core of the task, becomes the leading node, responsible for the final decision on path planning and cluster coordination; other beacons providing primary support become auxiliary nodes, obeying the instructions of the leading node; beacons with low remaining power or located at the edge may self-organize as backup nodes, remaining silent normally and taking over work when the leading node fails. After roles are determined, each mobile beacon will assume different functional responsibilities in subsequent path planning and task execution. For example, the leading node is responsible for calculating the global path and collecting data, while auxiliary nodes are responsible for relay communication and edge awareness, thereby achieving load balancing and functional optimization within the cluster.

[0040] S150: The mobile beacon cluster plans a cooperative movement path based on an improved particle swarm optimization algorithm, aiming to minimize global deployment energy consumption and maximize network connectivity, and moves to the location specified by the task. In the specific implementation described in this specification, each mobile beacon in the cluster bids based on its remaining power, the distance between its current location and the target area, and its own functional integrity. The protocol ultimately determines the winning subset of beacons with the goal of maximizing the probability of task completion and minimizing the total energy consumption of the cluster. The winning mobile beacon cluster then runs an improved particle swarm optimization (PSO) algorithm. The fitness function of this algorithm considers both the total energy consumption of the mobile path and the connectivity of the communication network formed after deployment, planning an optimal cooperative mobile path to the target area.

[0041] Optionally, S150 includes: When the environment construction task is an emergency rescue task, the weight of maximizing network connectivity in the optimization objective is automatically raised to the highest priority. When the environment building task is a routine inspection task, the weight of minimizing global deployment energy consumption in the optimization objective is set to the highest priority.

[0042] In the specific implementation of this specification, when the mobile beacon cluster uses the improved Particle Swarm Optimization (PSO) algorithm to plan its path, the system dynamically adjusts the weights of the optimization objectives based on the urgency of the task. Specifically, the algorithm's fitness function is defined as: F = w1 * (total energy consumption) + w2 * (1 / network connectivity), achieving multi-objective optimization through weights w1 and w2. When the task type is identified as an emergency rescue task (such as troubleshooting after an earthquake or flood), the system automatically raises the weight w2, which maximizes network connectivity, to the highest priority (e.g., setting w1 = 0.2, w2 = 0.8). This means the algorithm will prioritize seeking the path that will enable the beacon cluster to form the most stable and powerful communication network after deployment, even if the path may be longer and more energy-intensive. Conversely, when the task is a routine inspection task, the system sets the weight w1, which minimizes global deployment energy consumption, to the highest priority (e.g., setting w1 = 0.8, w2 = 0.2). In this case, the algorithm will prioritize selecting the path with the shortest total travel distance and the lowest power consumption to achieve energy saving and extend equipment life.

[0043] S160: Using the location of the deployed mobile beacon cluster as control points, a virtual inspection channel is generated in the digital twin model using B-spline curve fitting technology. In the specific implementation of this specification, after the beacon cluster moves to the designated location and is deployed, its precise coordinates in the physical world (obtained through BeiDou differential positioning) are used as control points and transmitted to the digital twin model. The model uses B-spline curve fitting technology to pass through these control points and generate a smooth, continuous virtual inspection channel.

[0044] Optionally, S160 includes: The main inspection channel is generated based on the geometric center of all mobile beacons in the deployed mobile beacon cluster; Branch inspection channels are generated based on the distribution characteristics of each beacon subgroup; The main inspection channel and the branch inspection channels together form a complete inspection path network with a tree topology.

[0045] In the specific implementation of this specification, when generating a virtual inspection channel using B-spline curve fitting in the digital twin model, the specific steps are as follows: First, calculate the geometric center coordinates of all deployed mobile beacons. Using all these center points as a set of core control points, generate a smooth main inspection channel that runs through the main risk area through B-spline curve fitting. This channel constitutes the "main artery" of the inspection path. Second, for the beacon subgroups deployed separately to perform specific sub-tasks (such as multi-angle monitoring of a collapse point), generate branch inspection channels connected to the main channel, using the beacon positions of each subgroup as control points. Finally, the main inspection channel and multiple branch inspection channels are interconnected to form a comprehensive and clearly structured tree-like topology inspection path network, thereby achieving multi-level, blind-spot-free inspection coverage of complex risk areas.

[0046] S170: Based on the guidance of the virtual inspection channel, the inspection unit uses a fusion predictive control algorithm to perform local obstacle avoidance and trajectory smoothing, and collects real-time perception data. In the specific implementation of this specification, the inspection unit (such as an inspection robot or drone) that performs the actual inspection task uses this virtual channel as a reference path and adopts a fusion predictive control algorithm (such as combining model predictive control MPC with obstacle potential field method) to perform real-time local obstacle avoidance and trajectory smoothing. At the same time, it uses its own onboard sensors to collect real-time perception data (such as visible light images, infrared temperature, and local meteorological information).

[0047] S180: Compare the real-time sensing data with the expected state in the digital twin model. When the deviation exceeds the preset tolerance, trigger the redeployment of the mobile beacon cluster and the dynamic reconstruction of the virtual inspection channel.

[0048] In the specific implementation of this specification, the continuously collected real-time sensing data is compared with the expected state deduced from the digital twin model based on physical laws and historical data. Once a data deviation (such as the actual temperature far exceeding the model's expected value) is detected to exceed the preset tolerance, the system immediately determines that the current environment has changed significantly, automatically triggers the reconstruction mechanism, instructs the mobile beacon cluster to re-plan and deploy, and updates the virtual inspection channel accordingly, forming a complete "perception-decision-action" closed loop.

[0049] Optionally, S180 includes: Compare the instantaneous value deviation between the real-time sensing data and the expected state, and / or compare the degree of consistency between the changing trend of the real-time sensing data and the changing trend of the expected state. When either the instantaneous value deviation or the degree of agreement between the change trend and the instantaneous value deviation exceeds a preset tolerance, the reconstruction mechanism is triggered.

[0050] In the specific implementation of this specification, when comparing real-time sensing data with the expected state in the digital twin model, the system implements a dual verification mechanism. The first layer is instantaneous value deviation comparison, which directly compares the absolute or relative difference between the sensor reading (e.g., cable joint temperature of 75℃) and the model's expected value (e.g., 65℃). The second layer is trend consistency comparison, which analyzes whether the slope of change of real-time sensing data over a period of time (e.g., temperature rises by 2℃ per minute) significantly deviates from the trend predicted by the model (e.g., the model predicts that the temperature should rise by 0.5℃ per minute due to increased load). The system sets preset tolerances for these two types of comparisons (e.g., instantaneous temperature difference exceeding 15℃, or trend slope deviation exceeding 50%). In actual operation, as long as either the instantaneous value deviation or the trend consistency exceeds its corresponding tolerance, the system will immediately determine that the state is abnormal, trigger the reconstruction mechanism, and initiate path replanning and deployment.

[0051] Optional, also includes: Record the historical usage data for each of the aforementioned virtual inspection channels; Based on machine learning algorithms, the correlation between historical usage data and channel parameters is analyzed, and the curvature radius, channel width, and safety margin parameters of subsequently generated virtual inspection channels are automatically adjusted.

[0052] In the specific implementation of this specification, a file is created for each generated and used virtual inspection channel, recording its historical usage data, including but not limited to: the actual smoothness of the inspection unit passing through the channel (calculated using IMU data), the frequency of obstacles appearing around the channel, the task completion time, and whether reconstruction was triggered. Periodically, the system analyzes the correlation between this massive amount of historical usage data and channel parameters based on machine learning algorithms (e.g., using random forest or gradient boosting decision tree models). Through analysis, the model may discover patterns such as "when the radius of curvature is less than X meters, the probability of inspection unit jitter increases by 40%" or "in Y-type terrain, the channel width needs to be greater than Z meters to ensure safety." Based on these learned patterns, the system automatically adjusts the parameters of subsequently generated virtual inspection channels, such as appropriately increasing the radius of curvature to improve passage smoothness, widening the channel width to increase safety space, or dynamically setting a more adaptive safety margin, thereby enabling the path planning system to continuously evolve, becoming increasingly intelligent and efficient.

[0053] In this invention, by constructing a dynamic risk field integrating surface deformation, cable status, and real-time meteorological data, the system can accurately identify and quantify risks, thereby generating predictive inspection strategies. When real-time data deviates from the expected state of the digital twin model, the system automatically triggers a reconstruction mechanism, ensuring that the inspection path always matches the real environmental state, greatly improving the accuracy and safety of inspections. Using a distributed contract network protocol based on multi-objective optimization for task allocation, combined with an improved particle swarm optimization algorithm for path planning, the system can intelligently adjust the weight and priority of optimization objectives according to the urgency of the task. This not only ensures the fastest possible restoration of communication and positioning in emergency situations but also minimizes global energy consumption in routine tasks, achieving precise and efficient resource allocation. Through the collaborative deployment and role negotiation of mobile beacon clusters, the system can quickly construct a temporary network with both communication relay and differential positioning functions in high-risk areas. A tree-like topology virtual inspection channel generated using B-spline curve fitting provides a smooth, coherent, and comprehensive guidance path for the inspection units. This structure ensures unobstructed main channels while enabling detailed inspection of risk areas through branch channels, forming an inspection network that combines global efficiency with local precision. By recording and analyzing the historical usage of virtual inspection channels and automatically adjusting channel parameters using machine learning algorithms, the system can learn from past experience and continuously optimize the quality of subsequently generated inspection channels. This ensures that parameters such as curvature, width, and safety margin better meet actual inspection needs, thereby achieving continuous evolution of system performance.

[0054] Figure 2 This specification provides a schematic diagram of a cable fault detection device based on a smart sensor, which may include: The acquisition module 10 is used to acquire surface and infrastructure deformation data provided by the Beidou monitoring terminal, cable status data provided by sensors deployed on the cable body, and real-time meteorological grid data provided by meteorological data sources; The processing module 20 is used to input the surface and infrastructure deformation data, the cable status data, and the real-time meteorological grid data into the spatiotemporal geographic weighted regression model to generate a dynamic risk field in geographic grid units. The construction module 30 is used to identify high-risk core areas in the dynamic risk field and generate corresponding environment construction tasks for each core area; The allocation module 40 is used to allocate the environment building task to the mobile beacon cluster via a distributed contract network protocol based on multi-objective optimization. The planning module 50 is used to plan a cooperative movement path for the mobile beacon cluster based on an improved particle swarm optimization algorithm, with the goal of minimizing global deployment energy consumption and maximizing network connectivity, and to move to the position specified by the task. The fitting module 60 is used to generate a virtual inspection channel in the digital twin model by using the location of the deployed mobile beacon cluster as control points and B-spline curve fitting technology. The smoothing module 70 is used by the inspection unit to perform local obstacle avoidance and trajectory smoothing based on the virtual inspection channel, using a fusion predictive control algorithm, and collecting real-time sensing data. The adjustment module 80 is used to compare the real-time sensing data with the expected state in the digital twin model. When the deviation exceeds the preset tolerance, it triggers the redeployment of the mobile beacon cluster and the dynamic reconstruction of the virtual inspection channel.

[0055] Optionally, the building module 30 includes: When a high-risk core area is identified and there are risks of both communication interruption and positioning inaccuracy, and the two are highly overlapping in space, the composite task is generated. The composite task instruction is assigned to a subset of beacons, which construct a multifunctional node at the target location that combines self-organizing network communication relay function and dynamic differential positioning service function. Some beacons are designated as positioning reference stations, while the remaining beacons share their differential data and synchronously construct communication links.

[0056] Optionally, the planning module 50 includes: When the environment construction task is an emergency rescue task, the weight of maximizing network connectivity in the optimization objective is automatically raised to the highest priority. When the environment building task is a routine inspection task, the weight of minimizing global deployment energy consumption in the optimization objective is set to the highest priority.

[0057] Optionally, the fitting module 60 includes: The main inspection channel is generated based on the geometric center of all mobile beacons in the deployed mobile beacon cluster; Branch inspection channels are generated based on the distribution characteristics of each beacon subgroup; The main inspection channel and the branch inspection channels together form a complete inspection path network with a tree topology.

[0058] Optionally, the adjustment module 80 includes: Compare the instantaneous value deviation between the real-time sensing data and the expected state, and / or compare the degree of consistency between the changing trend of the real-time sensing data and the changing trend of the expected state. When either the instantaneous value deviation or the degree of agreement between the change trend and the instantaneous value deviation exceeds a preset tolerance, the reconstruction mechanism is triggered.

[0059] Optionally, after the allocation module 40, the following is included: In the mobile beacon cluster, each mobile beacon determines its role in task execution through distributed negotiation. The roles include a leading node, a secondary node, and a backup node. The mobile beacon assumes different functional responsibilities in subsequent path planning and task execution based on its different role.

[0060] Optional, also includes: Record the historical usage data for each of the aforementioned virtual inspection channels; Based on machine learning algorithms, the correlation between historical usage data and channel parameters is analyzed, and the curvature radius, channel width, and safety margin parameters of subsequently generated virtual inspection channels are automatically adjusted.

[0061] The functions of the apparatus in this embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0062] Based on the same inventive concept, embodiments of this specification also provide an electronic device.

[0063] The following describes embodiments of the electronic device of the present invention, which can be considered as specific implementations of the methods and apparatus embodiments of the present invention described above. Details described in the embodiments of the electronic device of the present invention should be considered as supplements to the methods or apparatus embodiments described above; details not disclosed in the embodiments of the electronic device of the present invention can be implemented with reference to the methods or apparatus embodiments described above.

[0064] Figure 3 This is a schematic diagram of an electronic device provided as an embodiment of this specification. Refer to the following... Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0065] like Figure 3 As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including storage unit 320 and processing unit 310), a display unit 340, etc.

[0066] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the processing method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 310 can perform, for example... Figure 1The steps are shown.

[0067] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache storage unit 3202, and may further include a read-only memory unit (ROM) 3203.

[0068] The storage unit 320 may also include a program / utility 3204 having a set (at least one) program module 3205, such program module 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0069] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0070] Electronic device 300 can also communicate with one or more external devices 400 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable viewers to interact with electronic device 300, and / or with any device that enables electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Network adapter 360 can communicate with other modules of electronic device 300 via bus 330. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0071] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the method described above according to this invention. When the computer program is executed by a data processing device, it enables the computer-readable medium to implement the method described above, i.e.: as... Figure 1 The method shown.

[0072] Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification.

[0073] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer-readable media. A computer-readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0074] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0075] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the audience's computing device, partially on the audience's device, as a standalone software package, partially on the audience's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the audience's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0076] In summary, the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that in practice, general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0077] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0079] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for cable line inspection path planning based on Beidou positioning, characterized in that, The method comprises the following steps: obtaining ground and infrastructure deformation data provided by a Beidou monitoring terminal, cable state data provided by a sensor deployed on a cable body, and real-time weather grid data provided by a weather data source; inputting the ground and infrastructure deformation data, the cable state data, and the real-time weather grid data into a spatiotemporal geographically weighted regression model to generate a dynamic risk field in geographic grid units; identifying high-risk core areas in the dynamic risk field and generating corresponding environment construction tasks for each core area; allocating the environment construction tasks to a mobile beacon cluster based on a multi-objective optimization distributed contract net protocol; the mobile beacon cluster plans a collaborative mobile path based on an improved particle swarm optimization algorithm, aiming to minimize global deployment energy consumption and maximize network connectivity, and moves to the position specified by the task; using the positions of the deployed mobile beacon cluster as control points, generating a virtual inspection channel in the digital twin model using B-spline curve fitting technology; the inspection unit uses a fusion predictive control algorithm to perform local obstacle avoidance and trajectory smoothing based on the guidance of the virtual inspection channel, and collects real-time perception data; comparing the real-time perception data with the expected state in the digital twin model, and triggering redeployment of the mobile beacon cluster and dynamic reconstruction of the virtual inspection channel when the deviation exceeds the preset tolerance. 2.The method of claim 1, wherein, The method of identifying high-risk core areas in the dynamic risk field and generating corresponding environment construction tasks for each core area comprises: when the identified high-risk core areas have both communication interruption and positioning misalignment risks, and the two risks are highly overlapped in space, a composite task is generated; the beacon subset to which the composite task instructions are allocated constructs a multifunctional node with both ad hoc network communication relay function and dynamic differential positioning service function at the target position, wherein part of the beacons are designated as positioning reference stations, and the remaining beacons share their differential data and synchronously construct communication links. 3.The method of claim 1, wherein, The method of the mobile beacon cluster planning a collaborative mobile path based on an improved particle swarm optimization algorithm, aiming to minimize global deployment energy consumption and maximize network connectivity, comprises: when the environment construction task is an emergency rescue task, the weight of maximizing network connectivity in the optimization target is automatically promoted to the highest priority; when the environment construction task is a regular inspection task, the weight of minimizing global deployment energy consumption in the optimization target is set to the highest priority.

4. The cable line inspection path planning method based on Beidou positioning of claim 1, wherein, The method of using the positions of the deployed mobile beacon cluster as control points, generating a virtual inspection channel in the digital twin model using B-spline curve fitting technology, comprises: generating a main inspection channel based on the geometric centers of all mobile beacons in the deployed mobile beacon cluster; generating branch inspection channels based on the distribution characteristics of each beacon subgroup; the main inspection channel and the branch inspection channels together form a complete inspection path network in a tree topology.

5. The cable line inspection path planning method based on Beidou positioning of claim 1, wherein, The method of comparing the real-time perception data with the expected state in the digital twin model comprises: comparing the instantaneous value deviation of the real-time sensing data from the expected state, and / or, comparing the change trend of the real-time sensing data with the change trend of the expected state; when either of the instantaneous value deviation and the change trend deviation exceeds a preset tolerance, triggering a reconstruction mechanism. 6.The method of claim 1, wherein, after the environment construction task is allocated to the mobile beacon cluster through the distributed contract network protocol based on multi-objective optimization, comprising: each mobile beacon in the mobile beacon cluster determines its role in task execution through distributed negotiation, including a leading node, an auxiliary node, and a backup node; the mobile beacon assumes different functional responsibilities in subsequent path planning and task execution based on different roles.

7. The cable line inspection path planning method based on Beidou positioning according to claim 1, characterized in that, Also comprising: recording historical use effect data of each virtual inspection channel; based on machine learning algorithm, analyzing the correlation between the historical use effect data and the channel parameters, and automatically adjusting the curvature radius, channel width and safety margin parameters of the subsequently generated virtual inspection channel.

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