An unmanned aerial vehicle transmission and distribution flight path planning method and system

Abstract

The application provides a kind of unmanned aerial vehicle transmission and distribution power track planning method and system, through the breakthrough of three-dimensional modeling technology and the deep integration of unmanned aerial vehicle technology and power grid inspection business, further optimizes transmission, transformation and distribution professional equipment inspection resources, improves the inspection efficiency and equipment operation quality. Through the grid inspection platform of unmanned aerial vehicle deployed in the intensive inspection area, the optimization combination of power transformation equipment, power transmission equipment and power distribution equipment inspection route and the automatic preparation and execution of inspection plan are completed, and the automation level of inspection is improved. Through the management platform scheduling, the same aircraft completes the inspection work of power transmission, power transformation and power distribution. The nest and unmanned aerial vehicle realize real-time communication, realize remote scheduling and real-time data return of unmanned aerial vehicle. By improving the shooting angle, pitch angle, shooting distance and hovering time parameters, and designing personalized shooting route plan, the picture of inspection shooting is more clear and accurate.

Description

Technical Field

[0001] This application relates to the field of power transmission line inspection and fault identification technology, and in particular to a method and system for UAV power transmission and distribution trajectory planning. Background Technology

[0002] The current model for power transmission and distribution drone inspections involves on-site task assignment by personnel. Although the drones fly automated routes, they still require human supervision. Substation drone inspections, based on simple drone housings, require manual battery swapping, thus failing to meet the requirements for unmanned operation. Secondly, power transmission and distribution drones use point-to-point communication, making remote control and real-time data transmission impossible; they can only operate at close range on-site. Data transmission, processing, and analysis still require human intervention, and the goal of achieving fully automated operation across the entire process needs further improvement.

[0003] Power transmission, substation, and distribution systems are implementing drone inspection projects based on their respective professional needs. However, technical barriers exist between these specialties, preventing cross-functionality and aligning with the company's digital transformation strategy of centralized operation and maintenance and centralized inspection. Furthermore, the execution time and frequency of drone missions are dictated by each specialty's operational strategy, leading to resource idleness during off-peak hours. Current platforms are in their early application or development stages, exhibiting lagging support capabilities. Drone inspections generate massive amounts of data, with daily tasks producing staggering volumes, making data management extremely difficult and hindering the full realization of intelligent capabilities.

[0004] The ground control system is the core hub for UAV mission execution, responsible for flight monitoring, flight path planning, data management, and emergency control. Flight path planning and electronic maps are essential modules in the system, and their performance directly affects the integrity and availability of the entire system. Currently, several commonly used small UAV ground stations in the industry simply use a limited number of APIs provided by map providers for flight monitoring and flight path planning electronic maps, which cannot fully realize the value of the maps. This makes it difficult to execute many missions smoothly.

[0005] Therefore, designing a drone-based power transmission and distribution trajectory planning method to automate the inspection of power transmission, substation, and distribution systems, and to execute inspection tasks based on the platform's data processing capabilities and the drone's status, while obtaining clearer and more accurate images, is an important means and urgent task to ensure the safe and stable operation of the power grid. Summary of the Invention

[0006] In view of this, it is necessary to provide a drone flight path planning method for power transmission, transformation and distribution, which can perform integrated automated detection of power transmission, transformation and distribution inspection work. It can perform inspection tasks based on the platform's data processing capabilities and the drone's status, and obtain clearer and more accurate images. It is easy to promote and apply, relatively simple to operate, and suitable for promotion and application in more scenarios.

[0007] This application provides a method and system for UAV flight path planning in power transmission, transformation, and distribution. Based on the distribution of power grid equipment, inspection frequency, and terrain factors, the target area is divided into multiple grids, constructing a grid-based management platform for transmission, transformation, and distribution UAVs. By deploying UAV nests within the grids, a grid-based UAV inspection network covering the entire area is formed. Considering the different characteristics of transmission, transformation, and distribution equipment, the flight waypoint sequence and photographic actions of UAV inspections are planned and designed, generating dedicated flight path files guiding flight positions and actions, which are stored in a database for later use by the platform. The transmission and distribution overhead line channels and refined inspection routes, as well as refined substation inspection routes, are imported into the UAV grid management platform. The inspection platform can automatically link to the inspection plans of the production system or automatically perform inspections based on temporary tasks assigned by production personnel. The flight paths are optimized by combining drones with other drones to improve inspection speed based on drone endurance and route distance, generating an optimal patrol route. This optimal route and drone status data are input into a pre-defined task scheduling model. The scheduling algorithm enables clustered tasks through task decomposition, execution, and inter-drone collaboration. The drones control the optimal patrol route and task scheduling for patrols. During the patrol, the main unit in the hangar and the streaming server transmit real-time video data from the drone cameras to the drone grid management platform via a dedicated wireless network, enabling real-time video stream monitoring. After the patrol is completed, the drones push the captured images to the platform via a data network card. The platform then performs unified data aggregation and recovery, providing detailed information on the captured images and time.

[0008] In a first aspect, embodiments of this application provide a method for planning the flight path of an unmanned aerial vehicle (UAV) power transmission and distribution system, the method comprising:

[0009] S1: Based on the distribution of power grid equipment, inspection frequency, and terrain environment factors, the target area is divided into multiple grids to build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a drone inspection network based on the grid and covering the entire area is formed.

[0010] S2: Based on the different characteristics of power transmission, transformation, and distribution equipment, the flight waypoint sequence and photo-taking actions of UAV inspection are planned and designed, and a special route file guiding the flight position and actions is generated and stored in the database for the platform to call.

[0011] S3: Import the overhead transmission and distribution lines and refined inspection routes, as well as the refined inspection routes of substations, into the UAV grid management platform. The inspection platform can automatically associate with the inspection plan of the production system or automatically optimize the route combination according to the temporary tasks issued by the production personnel. It can optimize the inspection speed of the UAV from the perspective of UAV endurance and inspection route distance, and generate the optimal inspection route.

[0012] S4: Input the optimal patrol route and UAV status data into the preset task scheduling model. Using the scheduling algorithm, cluster tasks can be achieved through task decomposition, execution and inter-nest collaboration.

[0013] S5: The drone is controlled to perform patrols along the optimal patrol route and with task scheduling. During the patrol, the main unit in the hangar and the streaming server will transmit real-time video data from the drone's camera to the drone's gridded management platform via a private wireless network, enabling real-time video stream monitoring of the drone. After the drone patrol is completed, the drone can push the images captured during the patrol to the platform via the data network card. The platform will then perform unified data summarization and recovery, and can provide detailed information on the images captured during the patrol, including time information.

[0014] Optionally, in one implementation of the first aspect of the present invention, step S1: dividing the target area into multiple grids based on the distribution of power grid equipment, inspection frequency, and terrain environmental factors, constructing a grid-based management platform for transmission, transformation, and distribution UAVs, and forming a grid-based UAV inspection network covering the entire area by deploying UAV nests within the grids, including:

[0015] Using the coordinate data of transmission and distribution equipment and combining it with Geographic Information System (GIS) technology, a three-dimensional model of the transmission line is generated, and high-precision three-dimensional information of the transmission line corridor is collected through laser point cloud data.

[0016] The DBSCAN spatial clustering algorithm is used to perform spatial clustering analysis on power transmission, transformation and distribution equipment, and the distribution of power grid equipment is divided into high-density areas;

[0017] By combining DEM data and satellite imagery, topographic features are extracted and generated, and the target area is divided into different types such as plains, mountains and water areas.

[0018] Based on the distribution of power grid equipment, inspection frequency, and drone endurance, the area where the transmission, transformation, and distribution equipment is located is divided into several inspection grids. Each grid is equipped with drone nests to enable autonomous take-off and landing and swarm control of drones, forming a drone inspection network covering the entire area.

[0019] Optionally, in one implementation of the first aspect of the present invention, step S2: planning and designing the flight waypoint sequence and photographing actions of the UAV inspection based on the different characteristics of power transmission, transformation, and distribution equipment, generating a dedicated flight path file guiding the flight position and actions, and storing it in a database for later use by the platform, including:

[0020] High-precision 3D point cloud data of power transmission lines or substations are collected by lidar, spatial information of key target locations is extracted, and preliminary flight routes are generated based on this information.

[0021] Based on the inspection objectives, parameters are designed for each waypoint, including shooting angle, distance, and altitude. Waypoints are planned sequentially according to the order of these parameters.

[0022] Dijkstra's algorithm is used to optimize path planning, ensuring the shortest and safest route.

[0023] Add auxiliary points and set reasonable safety distances between adjacent waypoints to avoid drones accidentally hitting equipment during flight and to ensure the safety of the flight path;

[0024] According to the inspection requirements, set the shooting angle, pitch angle, shooting distance and hovering time parameters for each waypoint;

[0025] Arrive at waypoints in the predetermined order and perform photo-taking tasks to ensure coverage of all inspection targets;

[0026] The route is rehearsed in a simulated environment to check for any safety hazards or omissions, and adjustments are made based on the actual situation; manual review and fine-tuning: the waypoint positions and parameters are fine-tuned based on the actual on-site situation to ensure that the route meets the actual needs;

[0027] The generated route files should be stored in KML or JSON format for easy retrieval later.

[0028] Optionally, in one implementation of the first aspect of the present invention, setting the shooting angle, pitch angle, shooting distance, and hovering time parameters for each waypoint according to the inspection requirements includes:

[0029] The shooting distance is adjusted according to the flight direction, with the adjustment range being:

[0030]

[0031] in , Adjustments to the flight coordinates in the x and y coordinates respectively. The real-time coordinates of the drone at the previous moment. Predict the next coordinates for the drone. Parameters used to adjust the flight direction;

[0032] Adjust the shooting angle and pitch angle according to the flight direction, with the adjustment range being:

[0033]

[0034] in These represent the adjustment ranges of the drone's real-time shooting angle and pitch angle on the x and y coordinates, respectively. Parameters used to adjust the tangential direction of flight.

[0035] Optionally, in one implementation of the first aspect of the present invention, the step of arriving at waypoints sequentially and performing the photographing task according to a predetermined order includes:

[0036] Adjust the shooting angle, pitch angle, shooting distance, and hovering time parameters according to the preset adjustment range, and then take a picture;

[0037] When shooting of key areas cannot meet the shooting requirements, the scene image of the object to be shot is acquired according to the control command, a three-dimensional point cloud model of the object to be shot is established, and a corresponding flight path shooting template is set. The data of the three-dimensional point cloud model of the object to be shot is substituted into the shooting template to generate a shooting flight path plan. The shooting flight path plan includes circular shooting at different angles and up-and-down shooting.

[0038] Optionally, in one implementation of the first aspect of the present invention, the step of acquiring a scene image of the object to be photographed, establishing a three-dimensional point cloud model of the object to be photographed, setting a corresponding flight path shooting template, and substituting the data of the three-dimensional point cloud model of the object to be photographed into the shooting template to generate a shooting flight path plan includes:

[0039] Determine the shape of the object to be photographed based on its 3D point cloud model;

[0040] Establish the corresponding minimum circumscribed cylinder based on the shape of the object to be photographed;

[0041] Obtain the parameters of the minimum circumscribed cylinder, substitute the parameters into the shooting template, and generate a flight path file;

[0042] According to the flight path file, a shooting flight path plan is set around the outer surface of the smallest circumscribed cylinder, and the object to be photographed is shot in a circular or up-and-down motion at different angles.

[0043] Wherein, the different angles refer to the tilt angles of the circumferential shooting, and the flight path for the circular circumferential shooting is:

[0044] ,

[0045] in, For the coordinates of the flight path used in the circumferential shooting, The coordinates of the key points for the surround shooting. For the number of routes, Number of waypoints The length of the surround shot, The height of the object to be photographed. The directions of rotation correspond to the various tilt angles of the coordinate axes. This is the tilt angle, which is equivalent to the height vector in the circumferential direction.

[0046] Optionally, in one implementation of the first aspect of the present invention, step S4: inputting the optimal patrol route and UAV status data into a preset task scheduling model, and using a scheduling algorithm to achieve cluster tasks through task decomposition, execution, and inter-nest collaboration, including:

[0047] S4.1, preprocess the input data, wherein the optimal patrol route data includes waypoint sequence, time window, priority, and regional weight, and the UAV status data includes battery capacity, cruise speed, sensor status, UAV status, no-fly zone, weather conditions, and communication coverage environmental constraints.

[0048] S4.2, construct a task scheduling model for a hierarchical and clustered network topology, combining centralized planning and distributed execution. The central scheduler is responsible for global optimization of task allocation, while the local decision-making module is responsible for real-time adjustment of obstacle avoidance and power management. Centralized task allocation optimizes the task sequence through the central system, while distributed task allocation focuses on rapid response and flexible adjustment.

[0049] S4.3, the scheduling algorithm includes task decomposition, resource allocation optimization and dynamic coordination strategy; wherein, the task decomposition is to divide the inspection area into grids or polygons and split it according to the task type, first allocate key area tasks, then plan internal routes, and in the dynamic environment, adaptively plan the reconnaissance path through heuristic algorithm.

[0050] The resource allocation optimization objective function is to minimize the total task time, maximize coverage or balance the load, and combines battery life and sensor matching constraints.

[0051] The dynamic collaborative strategy includes charging relay, data relay, and conflict resolution strategies in drone swarm collaboration. The wireless charging collaborative scheduling unit plans the charging sequence of drones to achieve power balance. Under the edge computing framework, the drone swarm reduces energy consumption by jointly optimizing flight trajectory and speed. The communication and information sharing mechanism in drone swarm collaborative computing.

[0052] S4.4 Status Monitoring and Adaptive Adjustment: Through real-time feedback loop and fault tolerance mechanism, the automated scheduling system needs to continuously monitor the status of UAVs and reschedule them as needed. The task scheduling strategy based on the performance differences of UAVs can improve the task completion rate.

[0053] S4.5, Real-time Feedback Loop: Each drone reports its location and battery level via heartbeat packets; the central scheduler reassesses the task queue every 5 seconds, triggering rescheduling when battery degradation exceeds expectations; Fault Tolerance Mechanism: When a drone malfunctions, a backup drone is released from the nearest drone nest, and the coverage gap area is replanned.

[0054] S4.6 Output and Execution: Through the output and execution monitoring of the scheduling commands of the UAV grid management platform, an interactive interface is provided to display the UAV status and mission progress.

[0055] Secondly, embodiments of this application provide a UAV power transmission and distribution trajectory planning system, applied to the UAV power transmission and distribution trajectory planning method as described in any one of claims 1 to 7, characterized in that it includes:

[0056] Platform construction module: Based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors, the target area is divided into multiple grids to build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a drone inspection network based on the grid and covering the entire area is formed.

[0057] Flight route file generation module: Based on the different characteristics of power transmission, substation and distribution equipment, the module plans and designs the flight waypoint sequence and photo-taking actions of UAV inspection, generates special flight route files that guide the flight positions and actions, and stores them in the database for the platform to call.

[0058] The route optimization module imports the overhead transmission and distribution lines and the refined inspection routes of substations into the UAV grid management platform. The inspection platform can automatically associate with the inspection plan of the production system or automatically optimize the route combination according to the temporary tasks issued by the production personnel. It optimizes the inspection speed of the UAV from the perspective of UAV endurance and inspection route distance, and generates the optimal inspection route.

[0059] Task scheduling module: Input the optimal patrol route and UAV status data into the preset task scheduling model, and use the scheduling algorithm to realize cluster tasks through task decomposition, execution and inter-nest collaboration;

[0060] Patrol and monitoring module: The drone controls the optimal patrol route and schedules tasks for patrol. During the patrol, the host in the hangar and the streaming server transmit real-time video data from the drone's camera to the drone grid management platform via a private wireless network, enabling real-time video stream monitoring of the drone. After the drone patrol is completed, the drone can push the images captured during the patrol to the platform via the data network card. The platform then performs unified data summarization and recovery, and can provide detailed information on the images captured during the patrol, including time information.

[0061] Thirdly, embodiments of this application provide an electronic device, characterized in that it includes:

[0062] processor;

[0063] Memory used to store processor-executable instructions;

[0064] The processor is configured to implement the UAV power transmission and distribution trajectory planning method as described in the first aspect when executing the instructions.

[0065] Fourthly, embodiments of this application provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores a program that instructs a device to execute the UAV power transmission and distribution trajectory planning method as described in the first aspect.

[0066] This application provides a method and system for UAV flight path planning in power transmission, transformation, and distribution. Based on the distribution of power grid equipment, inspection frequency, and terrain factors, the target area is divided into multiple grids, constructing a grid-based management platform for transmission, transformation, and distribution UAVs. By deploying UAV nests within the grids, a grid-based UAV inspection network covering the entire area is formed. Considering the different characteristics of transmission, transformation, and distribution equipment, the flight waypoint sequence and photographic actions of UAV inspections are planned and designed, generating dedicated flight path files guiding flight positions and actions, which are stored in a database for later use by the platform. The transmission and distribution overhead line channels and refined inspection routes, as well as refined substation inspection routes, are imported into the UAV grid management platform. The inspection platform can automatically link to the inspection plans of the production system or automatically perform inspections based on temporary tasks assigned by production personnel. The flight paths are optimized by combining drones with other drones to improve inspection speed based on drone endurance and route distance, generating an optimal patrol route. This optimal route and drone status data are input into a pre-defined task scheduling model. The scheduling algorithm enables clustered tasks through task decomposition, execution, and inter-drone collaboration. The drones control the optimal patrol route and task scheduling for patrols. During the patrol, the main unit in the hangar and the streaming server transmit real-time video data from the drone cameras to the drone grid management platform via a dedicated wireless network, enabling real-time video stream monitoring. After the patrol is completed, the drones push the captured images to the platform via a data network card. The platform then performs unified data aggregation and recovery, providing detailed information on the captured images and time.

[0067] Beneficial effects:

[0068] (1) By breaking through the three-dimensional modeling technology and deeply integrating UAV technology with power grid inspection business, we can further optimize the inspection resources of transmission, transformation and distribution equipment, and improve inspection efficiency and equipment operation and maintenance quality.

[0069] (2) By deploying a grid-based inspection platform for unmanned aerial vehicles in densely populated inspection areas, the inspection routes for substation equipment, transmission equipment, and distribution equipment are optimized and the inspection plans are automatically compiled and executed, thereby improving the level of automation of inspections.

[0070] (3) Through the management platform scheduling, the same aircraft can complete the inspection of power transmission, transformation and distribution.

[0071] (4) The nest communicates with the UAV in real time to realize remote scheduling of UAV and real-time data transmission.

[0072] (5) By improving the shooting angle, pitch angle, shooting distance and hovering time parameters, and designing personalized shooting route plans, the images captured by the inspection are clearer and more accurate. Attached Figure Description

[0073] Figure 1 This is a schematic diagram of a UAV power transmission and distribution trajectory planning method provided in an embodiment of this application.

[0074] Figure 2 This is a task scheduling flowchart provided for an embodiment of this application.

[0075] Figure 3 A schematic diagram of a UAV power transmission and distribution trajectory planning system module provided in an embodiment of this application.

[0076] Figure 4 A schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0077] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0078] It should be noted that, in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0079] It should be noted that in the embodiments of this application, the terms "first," "second," etc., are used only for descriptive purposes and should not be construed as indicating or implying relative importance, nor as indicating or implying order. Features specified as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0080] Based on the embodiments described in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0081] Example 1

[0082] This embodiment provides a method and system for UAV (Unmanned Aerial Vehicle) power transmission and distribution trajectory planning. Through breakthroughs in 3D modeling technology and deep integration of UAV technology with power grid inspection operations, it further optimizes inspection resources for transmission, transformation, and distribution equipment, improving inspection efficiency and equipment maintenance quality. By deploying a UAV gridded inspection platform in densely populated inspection areas, it optimizes the combination of inspection routes for substation, transmission, and distribution equipment, and automatically generates and executes inspection plans, improving the automation level of inspections. Through management platform scheduling, a single aircraft can complete the inspection of transmission, transformation, and distribution equipment. Real-time communication between the UAV's control center and the UAV enables remote scheduling of the UAV and real-time data transmission. By improving parameters such as shooting angle, pitch angle, shooting distance, and hovering time, and designing personalized shooting route plans, the inspection footage becomes clearer and more accurate.

[0083] Figure 1 This is a schematic diagram of a UAV power transmission and distribution trajectory planning method provided in an embodiment of this application.

[0084] like Figure 1 As shown, a method for planning the flight path of a UAV in power transmission and distribution includes:

[0085] S1: Based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors, the target area is divided into multiple grids to build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a drone inspection network based on the grid and covering the entire area is formed.

[0086] It is understood that in this embodiment, S1: Based on the distribution of power grid equipment, inspection frequency, and terrain environmental factors, the target area is divided into multiple grids to construct a grid-based management platform for transmission, transformation, and distribution UAVs. By deploying UAV nests within the grids, a UAV inspection network based on the grid and covering the entire area is formed, including:

[0087] Using the coordinate data of transmission and distribution equipment and combining it with Geographic Information System (GIS) technology, a three-dimensional model of the transmission line is generated, and high-precision three-dimensional information of the transmission line corridor is collected through laser point cloud data.

[0088] The DBSCAN spatial clustering algorithm is used to perform spatial clustering analysis on power transmission, transformation and distribution equipment, and the distribution of power grid equipment is divided into high-density areas;

[0089] By combining DEM data and satellite imagery, topographic features are extracted and generated, and the target area is divided into different types such as plains, mountains and water areas.

[0090] Based on the distribution of power grid equipment, inspection frequency, and drone endurance, the area where the transmission, transformation, and distribution equipment is located is divided into several inspection grids. Each grid is equipped with drone nests to enable autonomous take-off and landing and swarm control of drones, forming a drone inspection network covering the entire area.

[0091] Specifically, to meet the daily inspection needs of power transmission, substation, and distribution systems, densely populated areas are divided into grids. By deploying drone nests within these grids, a grid-based drone inspection network covering the entire area is formed. The maintenance workload within each grid is assessed based on the type and quantity of equipment under its jurisdiction and the corresponding inspection cycle. The number of drone nests deployed within the grid is determined according to the workload. For critical and important inspection targets or areas where potential hazards are frequently discovered, the required inspection frequency is higher; therefore, the number or type of drone nests deployed within the corresponding grids needs to be increased.

[0092] Specifically, DBSCAN is a density-based clustering algorithm. Its core idea is to divide high-density regions in the data space into clusters by defining a density-connected set of points, while also identifying noise points and outliers. The DBSCAN algorithm sets two key parameters: the neighborhood radius (Eps) and the minimum number of points (MinPts). For any point p in the dataset, if the number of points in its neighborhood is greater than or equal to MinPts, then the point is called a core point; if the number of points in its neighborhood is less than MinPts, then the point may be marked as a boundary point or a noise point. By continuously expanding the density-reachable point set, DBSCAN can identify regions with sufficiently high density and divide them into clusters. Using the DBSCAN spatial clustering algorithm to perform spatial clustering analysis on power transmission and distribution equipment can divide the distribution of power grid equipment into high-density regions.

[0093] In spatial clustering analysis of power grid equipment, DBSCAN effectively handles noisy data and clustering problems with non-convex shapes. For example, in the analysis of power equipment location coordinates, the DBSCAN algorithm can identify the differences between power lines and other ground features, thus dividing high-density areas such as power lines. Furthermore, the DBSCAN algorithm does not require pre-setting the number of clusters, making it more flexible and efficient in processing complex power grid data. Using the DBSCAN spatial clustering algorithm for spatial clustering analysis of transmission, transformation, and distribution equipment is an efficient and reliable method. It can automatically identify high-density areas and divide the distribution patterns of power grid equipment, while also possessing advantages such as strong noise resistance and adaptability to complex shapes. By reasonably setting parameters and combining with improved algorithms, the clustering effect can be further enhanced, providing strong support for power grid management and maintenance.

[0094] S2: Based on the different characteristics of power transmission, substation, and distribution equipment, the flight waypoint sequence and photo-taking actions of UAV inspections are planned and designed, generating dedicated flight path files that guide the flight positions and actions, and storing them in the database for the platform to call.

[0095] It is understood that in this embodiment, S2: For the different characteristics of power transmission, transformation, and distribution equipment, the flight waypoint sequence and photographing actions of the UAV inspection are planned and designed to generate a dedicated flight path file guiding the flight position and actions, which is stored in a database for later use by the platform, including:

[0096] High-precision 3D point cloud data of power transmission lines or substations are collected by lidar, spatial information of key target locations is extracted, and preliminary flight routes are generated based on this information.

[0097] Based on the inspection objectives, parameters are designed for each waypoint, including shooting angle, distance, and altitude. Waypoints are planned sequentially according to the order of these parameters.

[0098] Dijkstra's algorithm is used to optimize path planning, ensuring the shortest and safest route.

[0099] Add auxiliary points and set reasonable safety distances between adjacent waypoints to avoid drones accidentally hitting equipment during flight and to ensure the safety of the flight path;

[0100] According to the inspection requirements, set the shooting angle, pitch angle, shooting distance and hovering time parameters for each waypoint;

[0101] Arrive at waypoints in the predetermined order and perform photo-taking tasks to ensure coverage of all inspection targets;

[0102] The route is rehearsed in a simulated environment to check for any safety hazards or omissions, and adjustments are made based on the actual situation; manual review and fine-tuning: the waypoint positions and parameters are fine-tuned based on the actual on-site situation to ensure that the route meets the actual needs;

[0103] The generated route files should be stored in KML or JSON format for easy retrieval later.

[0104] Dijkstra's algorithm is a classic single-source shortest path algorithm suitable for shortest path problems in unweighted graphs. Its core idea is to progressively expand the shortest path tree to calculate the shortest path from the starting point to all other nodes. In path planning, Dijkstra's algorithm can effectively solve the shortest path problem from the starting point to the destination, while maintaining high computational efficiency and accuracy. By transforming the risk map into a weighted directed graph and using the minimum path risk as the objective function, the traditional Dijkstra's algorithm is improved to generate the optimal path with the minimum risk. Specifically, the route area is modeled as a graph structure: Nodes: key waypoints, airports, navigation points, etc. Edges: feasible routes connecting nodes. Edge weights: considering factors such as distance, safety factor, and airspace restrictions. Safety parameters are incorporated into the weight calculation: weather condition weight adjustment, no-fly / restricted area avoidance, terrain obstacle safety distance, and airspace classification restrictions. A priority queue (min-heap) is used to improve efficiency. Furthermore, in multi-UAV trajectory planning, a time window conflict judgment model can be introduced to filter out non-conflicting routes from all feasible shortest paths. Heuristic functions can also be combined: in power line inspection route planning, by adding heuristic functions and combining them with Dijkstra's algorithm, the path can be dynamically adjusted to avoid potential threats.

[0105] Specifically, according to the inspection requirements, the shooting angle, pitch angle, shooting distance, and hovering time parameters for each waypoint are set, including:

[0106] The shooting distance is adjusted according to the flight direction, with the adjustment range being:

[0107]

[0108] in , Adjustments to the flight coordinates in the x and y coordinates respectively. The real-time coordinates of the drone at the previous moment. Predict the next coordinates for the drone. Parameters used to adjust the flight direction;

[0109] Adjust the shooting angle and pitch angle according to the flight direction, with the adjustment range being:

[0110]

[0111] in These represent the adjustment ranges of the drone's real-time shooting angle and pitch angle on the x and y coordinates, respectively. Parameters used to adjust the tangential direction of flight.

[0112] Specifically, the adjustment of flight direction mainly relies on the drone's real-time coordinates (x, y) and the predicted coordinates for the next moment. Changes in these coordinates directly affect the adjustment range of the shooting distance. For example, by calculating the difference between the drone's current coordinates and the next predicted coordinates, the range of movement of the drone in the x and y directions can be determined, thereby adjusting the shooting distance. This adjustment method can be achieved through the drone's flight control system, such as a PID controller used for adjusting pitch and attitude angles, ensuring that the drone can stably track the target and adjust the shooting distance.

[0113] The shooting angle and pitch angle are adjusted through the angle control of the drone platform. Pitch angle adjustments are typically made by receiving feedback from the platform's pitch angle sensor to the controller, which then corrects the adjustment using a PID control algorithm. Furthermore, depending on mission requirements, users can manually set the pitch angle parameters for waypoints, such as setting a -90° pitch angle at a waypoint to achieve specific shooting effects.

[0114] Changes in flight direction affect the adjustment of the shooting angle. For example, when a drone flies along a flight path, the platform needs to adjust the pitch angle according to the flight direction to keep the target in the center of the frame. This adjustment can be accomplished through preset waypoint parameters, such as setting different pitch angle values ​​at each waypoint, so that the drone gradually transitions to the pitch angle of the next waypoint during flight.

[0115] Parameter settings are fundamental to achieving the above adjustments. For example, users can set waypoint parameters during drone flight missions, including the pitch angle of the waypoints and the turning radius between waypoints. Furthermore, by acquiring real-time parameters such as the drone's altitude and speed, the shooting angle and pitch angle can be dynamically adjusted to adapt to different flight environments.

[0116] Specifically, the step of arriving at waypoints in a predetermined order and performing the photographing task includes:

[0117] Adjust the shooting angle, pitch angle, shooting distance, and hovering time parameters according to the preset adjustment range, and then take a picture;

[0118] When shooting of key areas cannot meet the shooting requirements, the scene image of the object to be shot is acquired according to the control command, a three-dimensional point cloud model of the object to be shot is established, and a corresponding flight path shooting template is set. The data of the three-dimensional point cloud model of the object to be shot is substituted into the shooting template to generate a shooting flight path plan. The shooting flight path plan includes circular shooting at different angles and up-and-down shooting.

[0119] Specifically, the steps of acquiring scene images of the object to be photographed, establishing a 3D point cloud model of the object to be photographed, setting a corresponding flight path template, and substituting the data of the 3D point cloud model of the object to be photographed into the shooting template to generate a shooting flight path plan include:

[0120] Determine the shape of the object to be photographed based on its 3D point cloud model;

[0121] Establish the corresponding minimum circumscribed cylinder based on the shape of the object to be photographed;

[0122] Obtain the parameters of the minimum circumscribed cylinder, substitute the parameters into the shooting template, and generate a flight path file;

[0123] According to the flight path file, a shooting flight path plan is set around the outer surface of the smallest circumscribed cylinder, and the object to be photographed is shot in a circular or up-and-down motion at different angles.

[0124] Wherein, the different angles refer to the tilt angles of the circumferential shooting, and the flight path for the circular circumferential shooting is:

[0125] ,

[0126] in, For the coordinates of the flight path used in the circumferential shooting, The coordinates of the key points for the surround shooting. For the number of routes, Number of waypoints The length of the surround shot, The height of the object to be photographed. The directions of rotation correspond to the various tilt angles of the coordinate axes. This is the tilt angle, which is equivalent to the height vector in the circumferential direction.

[0127] Specifically, firstly, the shape of the object to be photographed is analyzed using a 3D point cloud model. This step is fundamental to subsequent flight path planning, aiming to determine the object's geometric features and thus provide a basis for flight path design. Based on the shape of the object, a minimum circumscribed cylinder is calculated and constructed. This cylinder completely contains the target object, and its parameters (such as radius and height) will be used for subsequent flight path planning. The parameters of the minimum circumscribed cylinder are substituted into the shooting template to generate a flight path file. These parameters include the cylinder's radius, height, and other geometric information related to the target object. Based on the generated flight path file, the shooting flight path is set around the outer surface of the minimum circumscribed cylinder. The shooting method can be circular or vertical. Circular shooting is suitable for single buildings or landmarks, while vertical shooting is suitable for complex terrain or multi-layered target objects. The key to circular shooting is adjusting the camera's tilt angle to ensure high-quality images are obtained from multiple perspectives. For example, the tilt angle (θ) of the circling direction relative to the coordinate axes and the tilt angle (φ) of the height vector need to be precisely set to avoid blind spots and improve data overlap. Based on the loop aerial photography model, flight path parameters include the relative altitude of the aerial photography track, forward overlap rate, and lateral overlap rate. These parameters directly affect the quality and accuracy of the 3D model. For example, the forward and lateral overlap rates must meet certain requirements to ensure the matching accuracy between images. The final generated flight path file will guide the UAV to fly along the preset trajectory for data collection. During flight, the UAV must maintain a stable tilt angle and altitude to ensure image coverage and quality.

[0128] After data acquisition, the images are processed using professional software (such as Pix4D and Agisoft Metashape), including feature point matching, point cloud generation, and dense point cloud generation. Finally, a 3D model is generated, and texture mapping and optimization are performed. The entire process emphasizes end-to-end management from 3D point cloud modeling to flight path planning, image acquisition, and 3D modeling. By appropriately setting flight path parameters and shooting angles, the accuracy and efficiency of 3D modeling can be effectively improved.

[0129] S3: Import the overhead transmission and distribution lines and refined inspection routes, as well as the refined inspection routes of substations, into the UAV grid management platform. The inspection platform can automatically associate with the inspection plan of the production system or automatically optimize the route combination according to the temporary tasks issued by the production personnel. It can optimize the inspection speed of UAVs from the perspective of UAV endurance and inspection route distance, and generate the optimal inspection route.

[0130] Understandably, in this embodiment, the optimization of drone inspection speed is typically based on a comprehensive consideration of flight distance, flight time, and drone endurance. Artificial intelligence can be used to optimize drone trajectory planning, ensuring a smooth flight path and avoiding areas with strong electric fields, thereby improving inspection efficiency. Furthermore, establishing strict route planning standards can ensure the safety and efficiency of drone inspections.

[0131] Specifically, when performing inspection missions, drones can dynamically optimize their flight paths based on the operating status of power grid equipment, ambient wind speed, and the drone's endurance. Flight paths can be optimized by generating ranking assignments and comprehensive analysis models. Furthermore, autonomous drone inspection systems enable panoramic monitoring and intelligent path planning of ultra-high-voltage transmission lines.

[0132] By utilizing a drone-based grid management platform, combined with intelligent algorithms and multi-disciplinary collaborative operations, the system can automatically optimize and combine precise inspection routes for transmission lines, distribution network overhead lines, and substations. It generates optimal inspection routes based on drone endurance and the distance of the inspection lines. This not only improves inspection efficiency but also reduces labor costs, providing strong support for the safe and stable operation of power grid equipment.

[0133] S4: Input the optimal patrol route and UAV status data into the preset task scheduling model. The scheduling algorithm can realize cluster tasks through task decomposition, execution and inter-nest collaboration.

[0134] It is understood that in this embodiment, step S4: inputting the optimal patrol route and UAV status data into a preset task scheduling model, and using a scheduling algorithm to achieve cluster tasks through task decomposition, execution, and inter-nest collaboration, includes:

[0135] S4.1, preprocess the input data, wherein the optimal patrol route data includes waypoint sequence, time window, priority, and regional weight, and the UAV status data includes battery capacity, cruise speed, sensor status, UAV status, no-fly zone, weather conditions, and communication coverage environmental constraints.

[0136] S4.2, construct a task scheduling model for a hierarchical and clustered network topology, combining centralized planning and distributed execution. The central scheduler is responsible for global optimization of task allocation, while the local decision-making module is responsible for real-time adjustment of obstacle avoidance and power management. Centralized task allocation optimizes the task sequence through the central system, while distributed task allocation focuses on rapid response and flexible adjustment.

[0137] S4.3, the scheduling algorithm includes task decomposition, resource allocation optimization and dynamic coordination strategy; wherein, the task decomposition is to divide the inspection area into grids or polygons and split it according to the task type, first allocate key area tasks, then plan internal routes, and in the dynamic environment, adaptively plan the reconnaissance path through heuristic algorithm.

[0138] The resource allocation optimization objective function is to minimize the total task time, maximize coverage or balance the load, and combines battery life and sensor matching constraints.

[0139] The dynamic collaborative strategy includes charging relay, data relay, and conflict resolution strategies in drone swarm collaboration. The wireless charging collaborative scheduling unit plans the charging sequence of drones to achieve power balance. Under the edge computing framework, the drone swarm reduces energy consumption by jointly optimizing flight trajectory and speed. The communication and information sharing mechanism in drone swarm collaborative computing.

[0140] S4.4 Status Monitoring and Adaptive Adjustment: Through real-time feedback loop and fault tolerance mechanism, the automated scheduling system needs to continuously monitor the status of UAVs and reschedule them as needed. The task scheduling strategy based on the performance differences of UAVs can improve the task completion rate.

[0141] S4.5, Real-time Feedback Loop: Each drone reports its location and battery level via heartbeat packets; the central scheduler reassesses the task queue every 5 seconds, triggering rescheduling when battery degradation exceeds expectations; Fault Tolerance Mechanism: When a drone malfunctions, a backup drone is released from the nearest drone nest, and the coverage gap area is replanned.

[0142] S4.6 Output and Execution: Through the output and execution monitoring of the scheduling commands of the UAV grid management platform, an interactive interface is provided to display the UAV status and mission progress.

[0143] Specifically, S4.1 input data includes optimal patrol route data, including waypoint sequences, time windows, priorities, regional weights, and UAV status data, including battery capacity, cruise speed, and sensor status. A centralized architecture-based task decomposition method divides tasks according to range and UAV capabilities to meet task completion time and coverage requirements. S4.2 Constructs a hierarchical clustered network topology task scheduling model, combining centralized planning and distributed execution. The multi-layered task scheduling model based on hierarchical clustered network topology is a crucial step in module-level resource virtualization and task decomposition. Centralized task allocation optimizes task sequences through a central system, while distributed task allocation offers advantages such as rapid response and flexible adjustment. S4.3 Scheduling algorithms include task decomposition, resource allocation optimization, and dynamic collaborative strategies, as well as multi-objective optimization and task scheduling algorithms, including genetic algorithms and particle swarm optimization algorithms. These algorithms can be used for task decomposition and resource allocation optimization. Furthermore, the application of heuristic algorithms and hybrid algorithm strategies in UAV swarm task scheduling is mentioned, which can be used for dynamic collaborative strategies. S4.4 Real-time feedback loops and fault-tolerant mechanisms enable automated scheduling system status monitoring and adaptive adjustment. The drone swarm management system employs a distributed scheduling and dynamic adjustment mechanism to handle communication failures and drone malfunctions. Based on a risk-aware task scheduling method, it optimizes task allocation by combining task completion time and additional overhead time, addressing real-time adjustment needs. In S4.5, each drone reports its location and battery level via heartbeat packets. The central scheduler reassesses the task queue every 5 seconds, enabling real-time monitoring of drone status and rescheduling as needed. S4.6 monitors the output and execution of scheduling commands through a drone grid management platform, providing an interactive interface to display drone status and task progress. The communication and information sharing mechanisms in the drone swarm collaborative computing support real-time data transmission and task execution monitoring.

[0144] S5: The drone controls the optimal patrol route and schedules tasks for patrol. During the patrol, the host in the hangar and the streaming server will transmit the real-time video data from the drone's camera to the drone's grid management platform via a private wireless network to achieve real-time video stream monitoring of the drone. After the drone patrol is completed, the drone can push the images captured during the patrol to the platform via the data network card. The platform will then perform unified data summarization and recovery, and can provide detailed information on the images captured during the patrol, including time information.

[0145] Understandably, in this embodiment, during the patrol process, the drone transmits real-time video data captured by the camera to the drone's gridded management platform via a private wireless network, enabling real-time video stream monitoring of the drone. This real-time transmission method benefits from the high bandwidth of the 5G network, effectively reducing latency and improving clarity. Furthermore, the application of edge computing further reduces the time for data transmission to the cloud, improving the efficiency of real-time monitoring.

[0146] Specifically, images and video data captured during the inspection can be transmitted to the management platform in real time via a private wireless network or 5G network. For example, drones equipped with high-definition cameras can transmit image data back to the control platform in real time through low-latency communication technology. Some systems also support noise reduction and image optimization of the collected data to improve data quality. After the drone completes its inspection mission, the captured images and video data can be pushed to the platform via a data network card for unified summarization and recovery. The platform can classify and manage detailed information such as images and time captured during the inspection, and supports subsequent data analysis and report generation. In addition, the platform can use AI algorithms to perform target detection and anomaly identification on the video stream, promptly identifying potential problems and issuing early warnings.

[0147] Example 2

[0148] like Figure 3 As shown, this application provides a UAV power transmission and distribution trajectory planning system, which is applied to the UAV power transmission and distribution trajectory planning method described in Embodiment 1, including: a platform construction module 11, a route file generation module 12, a route optimization module 13, a task scheduling module 14, and a patrol and monitoring module 15.

[0149] It is understood that in this embodiment, the platform construction module 11 is used to divide the target area into multiple grids according to the distribution of power grid equipment, inspection frequency, and terrain environmental factors, and to build a grid-based management platform for transmission, transformation, and distribution UAVs. By deploying UAV nests in the grid, a UAV inspection network based on the grid and covering the entire area is formed.

[0150] It is understood that in this embodiment, the flight path file generation module 12 is used to plan and design the flight waypoint sequence and photo-taking actions of the UAV inspection based on the different characteristics of power transmission, substation and distribution equipment, generate a special flight path file that guides the flight position and actions, and store it in the database for the platform to call.

[0151] It is understood that in this embodiment, the route optimization module 13 is used to import the overhead transmission and distribution lines and refined inspection routes and substation refined inspection routes into the UAV grid management platform. The inspection platform can automatically associate with the inspection plan of the production system or automatically optimize and combine the routes according to the temporary tasks issued by the production personnel. It optimizes the inspection speed of the UAV from the perspective of UAV endurance and inspection route distance, and generates the optimal inspection route.

[0152] It is understood that in this embodiment, the task scheduling module 14 is used to input the optimal patrol route and UAV status data into a preset task scheduling model, and the scheduling algorithm can realize cluster tasks through task decomposition, execution and inter-nest collaboration.

[0153] Understandably, in this embodiment, the patrol monitoring module 15 is used for UAV control of the optimal patrol route and task scheduling for patrol. During the patrol, the host in the hangar and the streaming server will respectively transmit the real-time video data of the UAV camera to the UAV grid management platform through a wireless private network to realize real-time video stream monitoring of the UAV. After the UAV patrol is completed, the UAV can push the images captured during the patrol to the platform through the data network card, and the platform will perform unified data summarization and recovery. The platform can also provide detailed information on the images captured during the patrol, including time information.

[0154] This application provides a method and system for UAV (Unmanned Aerial Vehicle) power transmission and distribution trajectory planning. Through breakthroughs in 3D modeling technology and deep integration of UAV technology with power grid inspection operations, it further optimizes inspection resources for transmission, transformation, and distribution equipment, improving inspection efficiency and equipment maintenance quality. By deploying a UAV gridded inspection platform in densely populated inspection areas, it optimizes the combination of inspection routes for substation, transmission, and distribution equipment, and automatically generates and executes inspection plans, improving the automation level of inspections. Through management platform scheduling, a single aircraft can complete the inspection of transmission, transformation, and distribution equipment. Real-time communication between the UAV's control center and the UAV enables remote scheduling of the UAV and real-time data transmission. By improving parameters such as shooting angle, pitch angle, shooting distance, and hovering time, and designing personalized shooting route plans, the inspection footage becomes clearer and more accurate.

[0155] Figure 4 This is an electronic device provided in one embodiment of this application. For example... Figure 4 As shown, the electronic device includes at least the following components: processor 101 and memory 100, communication interface 103, and bus 102.

[0156] In this embodiment, the memory 100 is used to store executable instructions of the processor 101, which, when configured to execute instructions, implements... Figure 3 The device module shown is for planning the flight path of a UAV power transmission and distribution system.

[0157] In embodiments of this application, a computer-readable storage medium includes instructions that instruct a device to perform the method as described in the first aspect. For example, the instructions instruct the device to perform... Figure 1 The method is shown in the process steps.

[0158] In one embodiment of this application, the program operating in the electronic device may be a program that controls a central processing unit (CPU) or similar device to achieve the functions of the above-described embodiments of the present invention (a program that enables the computer to function). The information processed by these devices is then temporarily stored in random access memory (RAM) during processing, and subsequently stored in various ROMs such as read-only memory (FlashROM) or hard disk drives (HDDs), and read, corrected, and written by the CPU as needed.

[0159] It should be noted that a portion of the electronic device described in the above embodiments can also be implemented using a computer. In this case, the program for implementing the control function can be recorded on a computer-readable recording medium, and the program recorded on the recording medium can be read into the computer and executed.

[0160] It should be noted that the term "computer" as used here refers to a computer built into an electronic device, employing hardware including an operating system and peripheral devices. Furthermore, "computer-readable recording media" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard drives built into a computer.

[0161] Furthermore, a "computer-readable recording medium" can include: a medium that dynamically stores a program for a short period of time, such as a communication line used when transmitting a program via a network such as the Internet or a communication line such as a telephone line; or a medium that stores a program for a fixed period of time, such as volatile memory inside a computer that serves as a server or client in this case. In addition, the aforementioned program can be a program used to implement the above-mentioned functions, or it can be a program that can implement the above-mentioned functions by combining with programs already recorded in the computer.

[0162] Furthermore, the electronic device in the above embodiments can also be implemented as an assembly (device group) composed of multiple devices. Each device constituting the device group can possess some or all of the functions or functional blocks of the electronic device in the above embodiments. As a device group, it is sufficient to have all the functions or functional blocks of the electronic device.

[0163] Those skilled in the art should recognize that the above embodiments are only used to illustrate this application and are not intended to limit this application. Any appropriate changes and variations made to the above embodiments within the essential spirit and scope of this application fall within the scope of protection claimed in this application.

Claims

1. A method for planning the flight path of an unmanned aerial vehicle (UAV) in power transmission and distribution, characterized in that, The method includes: S1: Based on the distribution of power grid equipment, inspection frequency, and terrain environment factors, the target area is divided into multiple grids to build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a drone inspection network based on the grid and covering the entire area is formed. S2: Based on the different characteristics of power transmission, substation, and distribution equipment, the flight waypoint sequence and photographic actions for UAV inspections are planned and designed, generating dedicated flight path files that guide the flight positions and actions, and storing them in a database for use by the platform, including: High-precision 3D point cloud data of power transmission lines or substations are collected by lidar, spatial information of key target locations is extracted, and preliminary flight routes are generated based on this information. Based on the inspection objectives, parameters are designed for each waypoint, including shooting angle, distance, and altitude. Waypoints are planned sequentially according to the order of these parameters. Dijkstra's algorithm is used to optimize route planning, ensuring the shortest and safest flight path. Add auxiliary points and set reasonable safety distances between adjacent waypoints to avoid drones accidentally hitting equipment during flight and to ensure the safety of the flight path; According to the inspection requirements, set the shooting angle, pitch angle, shooting distance and hovering time parameters for each waypoint; Arrive at waypoints in the predetermined order and perform photo-taking tasks to ensure coverage of all inspection targets; The route is rehearsed in a simulated environment to check for any safety hazards or omissions, and adjustments are made based on the actual situation; manual review and fine-tuning: the waypoint positions and parameters are fine-tuned based on the actual on-site situation to ensure that the route meets the actual needs; The generated route files should be stored in KML or JSON format for easy retrieval later; The process involves setting parameters for the shooting angle, pitch angle, shooting distance, and hovering time for each waypoint according to inspection requirements, including: The shooting distance is adjusted according to the flight direction, with the adjustment range being: ； in , Adjustments to the flight coordinates in the x and y coordinates respectively. The real-time coordinates of the drone at the previous moment. Predict the next coordinates for the drone. Parameters used to adjust the flight direction; Adjust the shooting angle and pitch angle according to the flight direction, with the adjustment range being: ； in These represent the adjustment ranges of the drone's real-time shooting angle and pitch angle on the x and y coordinates, respectively. Parameters used to adjust the tangential direction of flight; The process of arriving at waypoints in a predetermined order and performing photographic tasks includes: Adjust the shooting angle, pitch angle, shooting distance, and hovering time parameters according to the preset adjustment range, and then take a picture; When shooting of key areas cannot meet the shooting requirements, according to control commands, scene images of the object to be shot are acquired, a 3D point cloud model of the object to be shot is established, and a corresponding flight path shooting template is set. The data of the 3D point cloud model of the object to be shot is substituted into the shooting template to generate a shooting flight path plan. The shooting flight path plan includes circular shooting at different angles and up-and-down shooting, including: Determine the shape of the object to be photographed based on its 3D point cloud model; Establish the corresponding minimum circumscribed cylinder based on the shape of the object to be photographed; Obtain the parameters of the minimum circumscribed cylinder, substitute the parameters into the shooting template, and generate a flight path file; According to the flight path file, a shooting flight path plan is set around the outer surface of the smallest circumscribed cylinder, and the object to be photographed is shot in a circular or up-and-down motion at different angles. Wherein, the different angles refer to the tilt angles of the circumferential shooting, and the flight path for the circular circumferential shooting is: ， in, For the coordinates of the flight path used in the circumferential shooting, The coordinates of the key points for the surround shooting. For the number of routes, Number of waypoints The length of the surround shot, The height of the object to be photographed. The directions of rotation correspond to the various tilt angles of the coordinate axes. The tilt angle is equivalent to the tilt angle of the height vector in the orbital direction; S3: Import the overhead transmission and distribution lines and refined inspection routes, as well as the refined inspection routes of substations, into the UAV grid management platform. The inspection platform can automatically associate with the inspection plan of the production system or automatically optimize the route combination according to the temporary tasks issued by the production personnel. It can optimize the inspection speed of the UAV from the perspective of UAV endurance and inspection route distance, and generate the optimal inspection route. S4: Input the optimal patrol route and UAV status data into the preset task scheduling model. Using the scheduling algorithm, cluster tasks can be achieved through task decomposition, execution and inter-nest collaboration. S5: The drone controls the optimal patrol route and schedules tasks for patrol. During the patrol, the host in the hangar and the streaming server will transmit the real-time video data from the drone's camera to the drone's grid management platform via a private wireless network to achieve real-time video stream monitoring of the drone. After the drone patrol is completed, the drone can push the images captured during the patrol to the platform via the data network card. The platform will then perform unified data summarization and recovery, and can provide detailed information on the images captured during the patrol, including time information.

2. The method for planning the flight path of a UAV power transmission and distribution system according to claim 1, characterized in that, S1: Based on the distribution of power grid equipment, inspection frequency, and terrain factors, the target area is divided into multiple grids, and a grid-based management platform for transmission, transformation, and distribution UAVs is constructed. By deploying UAV nests within the grids, a grid-based UAV inspection network covering the entire area is formed, including: Using the coordinate data of transmission and distribution equipment and combining it with Geographic Information System (GIS) technology, a three-dimensional model of the transmission line is generated, and high-precision three-dimensional information of the transmission line corridor is collected through laser point cloud data. The DBSCAN spatial clustering algorithm is used to perform spatial clustering analysis on power transmission, transformation and distribution equipment, and the distribution of power grid equipment is divided into high-density areas; By combining DEM data and satellite imagery, topographic features are extracted and generated, and the target area is divided into different types such as plains, mountains and water areas. Based on the distribution of power grid equipment, inspection frequency, and drone endurance, the area where the transmission, transformation, and distribution equipment is located is divided into several inspection grids. Each grid is equipped with drone nests to enable autonomous take-off and landing and swarm control of drones, forming a drone inspection network covering the entire area.

3. The method for planning the flight path of a UAV power transmission and distribution system according to claim 1, characterized in that, Step S4: Input the optimal patrol route and UAV status data into a preset task scheduling model. Using a scheduling algorithm, cluster tasks can be achieved through task decomposition, execution, and inter-nest collaboration, including: S4.1, preprocess the input data, wherein the optimal patrol route data includes waypoint sequence, time window, priority, and area weight, and the UAV status data includes battery capacity, cruise speed, sensor status, UAV status, no-fly zone, weather conditions, and communication coverage environmental constraints. S4.2, construct a task scheduling model for a hierarchical and clustered network topology, combining centralized planning and distributed execution. The central scheduler is responsible for global optimization of task allocation, while the local decision-making module is responsible for real-time adjustment of obstacle avoidance and power management. Centralized task allocation optimizes the task sequence through the central system, while distributed task allocation focuses on rapid response and flexible adjustment. S4.3, the scheduling algorithm includes task decomposition, resource allocation optimization and dynamic coordination strategy; wherein, the task decomposition is to divide the inspection area into grids or polygons and split it according to the task type, first allocate key area tasks, then plan internal routes, and in the dynamic environment, adaptively plan the reconnaissance path through heuristic algorithm. The resource allocation optimization objective function is to minimize the total task time, maximize coverage or balance the load, and combines battery life and sensor matching constraints. The dynamic collaborative strategy includes charging relay, data relay, and conflict resolution strategies in drone swarm collaboration. The wireless charging collaborative scheduling unit plans the charging sequence of drones to achieve power balance. Under the edge computing framework, the drone swarm reduces energy consumption by jointly optimizing flight trajectory and speed. The communication and information sharing mechanism in drone swarm collaborative computing. S4.4 Status Monitoring and Adaptive Adjustment: Through real-time feedback loop and fault tolerance mechanism, the automated scheduling system needs to continuously monitor the status of UAVs and reschedule them as needed. The task scheduling strategy based on the performance differences of UAVs can improve the task completion rate. S4.5, Real-time Feedback Loop: Each drone reports its location and battery level via heartbeat packets; the central scheduler reassesses the task queue every 5 seconds, triggering rescheduling when battery degradation exceeds expectations; Fault Tolerance Mechanism: When a drone malfunctions, a backup drone is released from the nearest drone nest, and the coverage gap area is replanned. S4.6 Output and Execution: Through the output and execution monitoring of the scheduling instructions of the UAV grid management platform, an interactive interface is provided to display the UAV status and mission progress.

4. A UAV power transmission and distribution trajectory planning system, applied to the UAV power transmission and distribution trajectory planning method as described in any one of claims 1 to 3, characterized in that, include: Platform construction module: Based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors, the target area is divided into multiple grids to build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a drone inspection network based on the grid and covering the entire area is formed. Flight route file generation module: Based on the different characteristics of power transmission, substation and distribution equipment, the module plans and designs the flight waypoint sequence and photo-taking actions of UAV inspection, generates special flight route files that guide the flight positions and actions, and stores them in the database for the platform to call. The route optimization module imports the overhead transmission and distribution lines and the refined inspection routes of substations into the UAV grid management platform. The inspection platform can automatically associate with the inspection plan of the production system or automatically optimize the route combination according to the temporary tasks issued by the production personnel. It optimizes the inspection speed of the UAV from the perspective of UAV endurance and inspection route distance, and generates the optimal inspection route. Task scheduling module: Input the optimal patrol route and UAV status data into the preset task scheduling model, and use the scheduling algorithm to realize cluster tasks through task decomposition, execution and inter-nest collaboration; Patrol and monitoring module: The drone controls the optimal patrol route and task scheduling to carry out patrols. During the patrol, the host in the hangar and the streaming server will transmit the real-time video data of the drone camera to the drone grid management platform through a private wireless network to realize real-time video stream monitoring of the drone. After the drone inspection is completed, the drone can push the images captured during the inspection to the platform via the data network card. The platform will then perform unified data summarization and recovery, and can provide the inspection images and detailed time information through the platform.

5. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the UAV power transmission and distribution trajectory planning method as described in any one of claims 1 to 3 when executing the instructions.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that instructs the device to perform the UAV power transmission and distribution trajectory planning method as described in any one of claims 1 to 3.

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