Unmanned aerial vehicle power transmission, transformation and distribution flight path planning method and system

Abstract

According to the unmanned aerial vehicle power transmission, transformation and distribution flight path planning method and system provided by the invention, through breakthrough of a three-dimensional modeling technology and deep fusion of an unmanned aerial vehicle technology and a power grid inspection service, transmission, transformation and distribution professional equipment inspection resources are further optimized, and the inspection efficiency and the equipment operation and maintenance quality are improved. Through the unmanned aerial vehicle gridding inspection platform deployed in the inspection dense area, optimal combination of inspection routes of power transformation equipment, power transmission equipment and power distribution equipment and automatic compiling and execution of an inspection plan are completed, and the automation level of inspection is improved. Through the scheduling of the management platform, the inspection work of power transmission, power transformation and power distribution is completed by the same aircraft. And the nest communicates with the unmanned aerial vehicle in real time, so that remote scheduling and real-time data return of the unmanned aerial vehicle are realized. The shooting angle, the pitch angle, the shooting distance and the hovering time parameters are improved, and a personalized shooting route plan is designed, so that an inspection shooting picture is clearer and more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of power transmission line inspection and fault identification, and in particular to a method and system for unmanned aerial vehicle (UAV) power transmission, transformation and distribution trajectory planning. Background Art

[0002] The current model for power transmission and distribution drone inspections relies on on-site personnel issuing tasks. While drones operate autonomously, they still require human supervision. Substation drone inspections rely on simple nests, requiring manual battery replacement, thus failing to meet the requirements for unmanned operation. Furthermore, power transmission and distribution drones utilize point-to-point communication, making remote control and real-time data transmission impossible. They can only operate at close range on-site. The transmission, processing, and analysis of on-site data still require human intervention, and the full unmanned operation of the service process needs to be strengthened.

[0003] Power transmission, substation, and distribution departments have established drone inspection programs based on their respective specialized needs. However, these specialized applications face technical barriers, preventing cross-use and reuse, which aligns with the company's development strategy of centralized operations and inspections as part of its digital transformation. Furthermore, the execution time and frequency of drone missions are dictated by the respective specialized operations and maintenance strategies, resulting in idle resources during non-mission periods. The existing platforms are in the early stages of application or construction, and their support capabilities lag. The sheer volume of drone inspection data, generated by daily tasks, makes data management extremely challenging, and the full potential of intelligent technology is not being fully realized.

[0004] The ground control system is the core hub for UAV mission execution, responsible for flight monitoring, route planning, data management, and emergency response. Route planning and electronic maps are essential components of the system, and their performance directly impacts the integrity and availability of the entire system. Currently, several commonly used small UAV ground stations in the industry simply utilize a limited number of APIs provided by the map provider for flight monitoring and route planning, failing to fully utilize the map's value. This hinders the smooth execution of many missions.

[0005] Therefore, designing a UAV transmission, transformation and distribution trajectory planning method to automate the inspection of transmission, transformation and distribution, executing inspection tasks according to the platform's data processing capabilities and the status of the UAV, and obtaining clearer and more accurate pictures are important means and urgent tasks 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 UAV transmission, transformation and distribution trajectory planning method that can perform integrated automated inspection of transmission, transformation and distribution, perform inspection tasks based on the platform's data processing capabilities and the status of the UAV, and obtain clearer and more accurate shooting images. It is easy to promote and apply, and the operation is relatively simple, making it suitable for promotion and application in more scenarios.

[0007] The present application provides a method and system for planning the trajectory of UAV transmission, transformation and distribution. According to 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 management platform for transmission, transformation and distribution UAVs. By laying out machine nests in the grid, a grid-based UAV inspection network covering the entire area is formed. According to the different characteristics of 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 for guiding the flight position and action is generated and stored in the database for the platform to call. The transmission and distribution overhead line channels and refined inspection routes and refined transformation inspection routes are imported into the UAV grid management platform. The inspection platform can automatically associate the inspection plan of the production system or automatically respond to temporary tasks issued by production personnel. The routes are optimized and combined, and the inspection speed of the drone is optimized from the perspective of drone endurance and the distance of the inspection line to generate the optimal inspection route; the optimal inspection route and the status data of the drone are input into the preset task scheduling model, and the scheduling algorithm can be used to realize cluster tasks through task decomposition, execution and collaboration between machine nests; the drone controls the optimal inspection route and task scheduling to conduct inspections. During the inspection process, the host in the hangar will transmit the real-time video data of the drone camera to the drone grid management platform through the wireless private network through the streaming server, realizing real-time video stream monitoring of the drone; after the drone inspection is completed, the drone can push the images taken during the inspection to the platform through the data network card, and the platform will perform unified summary and recovery of the data, and the platform can use the platform to view the images taken during the inspection and detailed information on time.

[0008] In a first aspect, an embodiment of the present application provides a method for planning a trajectory of a UAV power transmission, transformation and distribution system, the method comprising: S1: Divide the target area into multiple grids based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors. Build a grid-based management platform for transmission, transformation, and distribution drones. Deploy drone nests within the grids to form a grid-based drone inspection network covering the entire area. S2: Based on the different characteristics of power transmission, substation, and distribution equipment, the flight waypoint sequence and photo-taking actions of the drone inspection are planned and designed, and a dedicated route file guiding the flight position and action is generated and stored in the database for the platform to call; S3: Transmission and distribution overhead line channels, refined inspection routes, and refined substation inspection routes are imported into the drone grid management platform. The inspection platform can automatically link the inspection plan of the production system or automatically optimize the route combination based on temporary tasks issued by production personnel. The inspection speed of the drone is optimized based on the drone's endurance and the distance of the inspection route, generating the optimal inspection route. S4: Inputting the optimal patrol route and the status data of the drone into a preset task scheduling model, and utilizing the scheduling algorithm to realize the cluster task through task decomposition, execution and coordination between drone nests; S5: The drone controls the optimal patrol route and task scheduling to conduct patrols. During the patrol process, 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 the wireless private network, realizing real-time video stream monitoring of the drone. After the drone patrol is completed, the drone can push the images taken during the patrol to the platform through the data network card, and the platform will perform unified summary and recovery of the data. The platform can also view the images taken during the patrol and detailed information on time.

[0009] Optionally, in an implementation of the first aspect of the present invention, S1: dividing the target area into multiple grids based on power grid equipment distribution, inspection frequency, and terrain and environmental factors, building a grid management platform for transmission, transformation, and distribution drones, and forming a grid-based drone inspection network covering the entire area by deploying drone nests within the grids, including: Using the coordinate data of transmission and distribution equipment, combined 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 transmission, transformation and distribution equipment, dividing the distribution of power grid equipment into high-density areas; Combine DEM data and satellite images to extract and generate topographic features, and divide the target area into different types: plains, mountains, and waters; Based on the distribution of power grid equipment, inspection frequency and drone endurance, the area where the transmission and distribution equipment is located is divided into several inspection grids. Machine nests are arranged in each grid to realize autonomous take-off and landing and cluster control of drones, forming a drone inspection network with full coverage.

[0010] Optionally, in an implementation of the first aspect of the present invention, S2: planning and designing the flight waypoint sequence and photo-taking actions of the drone inspection based on the different characteristics of the power transmission, transformation, and distribution equipment, generating a dedicated route file guiding the flight location and actions, and storing it in a database for the platform to call, includes: Use LiDAR to collect high-precision 3D point cloud data of transmission lines or substations, extract spatial information of key target points, and generate preliminary routes based on this information; According to the inspection objectives, parameters are designed for each waypoint, including shooting angle, distance, and height, and waypoints are planned in sequence according to the order of parameters; Use Dijkstra algorithm to optimize path planning to ensure the shortest and safest route; Add auxiliary points and set a reasonable safety distance between adjacent waypoints to prevent the drone from accidentally hitting the equipment during flight and ensure the safety of the route; According to 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 that all inspection targets are covered; Rehearse the route in a simulated environment to check for safety hazards or missed areas, and make adjustments based on actual conditions; Manual review and fine-tuning: Based on actual on-site conditions, fine-tune waypoint locations and parameters to ensure the route meets actual needs; The generated route file should be stored in KML or JSON format for easy subsequent use.

[0011] Optionally, in an implementation of the first aspect of the present invention, setting the shooting angle, pitch angle, shooting distance, and hovering time parameters of each waypoint according to inspection requirements includes: Adjust the shooting distance according to the flight direction, and the adjustment range is:

[0012] in , The adjustment range of the flight coordinates on the x and y coordinates respectively, is the real-time coordinate of the drone at the previous moment, Predict the next moment coordinates for the drone, Parameters for adjusting the flight direction; Adjust the shooting angle and pitch angle according to the flight direction, and the adjustment range is:

[0013] in They are the real-time shooting angle and pitch angle adjustment range of the drone on the x and y coordinates, Parameters for adjusting the tangent direction of flight.

[0014] Optionally, in an implementation of the first aspect of the present invention, the step of arriving at waypoints in a predetermined order and performing the photo-taking task includes: Adjust the shooting angle, pitch angle, shooting distance, and hovering time parameters according to the preset adjustment range, and then start shooting; When the shooting requirements for key areas cannot be met, according to the control instructions, the scene image of the object to be photographed is obtained, a three-dimensional point cloud model of the object to be photographed is established, and a corresponding route shooting template is set. The data of the three-dimensional point cloud model of the object to be photographed is substituted into the shooting template to generate a shooting route plan, wherein the shooting route plan includes circular shooting at different angles and up and down shooting.

[0015] Optionally, in an implementation of the first aspect of the present invention, acquiring a scene image of an object to be photographed, establishing a three-dimensional point cloud model of the object to be photographed, setting a corresponding route shooting template, substituting data of the three-dimensional point cloud model of the object to be photographed into the shooting template, and generating a shooting route plan includes: Determining the shape of the object to be photographed based on the three-dimensional point cloud model of the object to be photographed; Establishing a corresponding minimum circumscribed cylinder according to the shape of the object to be photographed; Obtaining parameters of the minimum circumscribed cylinder, substituting the parameters into the shooting template, and generating a route file; Setting a shooting route plan around the outer surface of the minimum circumscribed cylinder according to the route file, and performing circular shooting or up and down shooting of the object to be photographed at different angles; The different angles are the tilt angles for the surround shooting, and the route for the circular shooting is: , in, is the route coordinate for the surrounding shooting, are the coordinates of the key points of the surround shooting, is the number of routes, is the number of waypoints, The length of the surround shot. is the height of the object to be photographed, in degrees, The respective orbiting directions are equivalent to the various inclination angles of the coordinate axes. The tilt angle of the height vector is equivalent to the orbital direction.

[0016] Optionally, in an implementation of the first aspect of the present invention, the step S4: inputting the optimal patrol route and the drone status data into a preset task scheduling model, and utilizing a scheduling algorithm to implement cluster tasks through task decomposition, execution, and collaboration between drone nests, includes: S4.1, preprocessing the input data, wherein the optimal patrol route data includes a waypoint sequence, a time window, a priority, and a region weight, and the UAV status data includes a battery capacity, a cruising speed, a sensor status, a UAV status, a no-fly zone, weather conditions, and communication coverage environment constraints; S4.2 builds a task scheduling model for a hierarchical and clustered network topology, combining centralized planning with 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 task sequences 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 strategies. Task decomposition involves dividing the patrol area into grids or polygons, and then splitting it according to task type. Tasks are first assigned to key areas, and then internal routes are planned. In a dynamic environment, heuristic algorithms are used to adaptively plan reconnaissance paths. The resource allocation optimization objective function is to minimize the total task time, maximize the coverage or balance the load, and combine the battery life and sensor matching constraints; The dynamic collaborative strategy includes charging relay, data relay, and conflict resolution strategies in drone-nest collaboration. The wireless charging collaborative scheduling unit plans the charging sequence of drones to achieve power balance. The drone cluster reduces energy consumption by jointly optimizing flight trajectory and speed under the edge computing framework. The communication and information sharing mechanism in drone cluster collaborative computing is also included. S4.4, Status Monitoring and Adaptive Adjustment: Through real-time feedback loops and fault-tolerant mechanisms, the automated scheduling system needs to continuously monitor the status of drones and reschedule as needed. Task scheduling strategies based on drone performance differences can improve task completion rates. S4.5, real-time feedback loop: Each drone reports its location and battery level via heartbeat packets; the central scheduler re-evaluates the task queue every 5 seconds, triggering rescheduling when battery degradation exceeds expectations. Fault-tolerance mechanism: When a drone fails, a backup drone is deployed from the nearest drone nest, and coverage gaps are replanned. S4.6, output and execution, through the output and execution monitoring of the scheduling instructions of the drone grid management platform, provides an interactive interface to display the drone status and task progress.

[0017] In a second aspect, an embodiment of the present application provides a UAV power transmission, transformation and distribution trajectory planning system, which is applied to the UAV power transmission, transformation and distribution trajectory planning method according to any one of claims 1 to 7, and is characterized by comprising: Platform construction module: Divide the target area into multiple grids based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors. Build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a grid-based drone inspection network covering the entire area is formed. Route file generation module: Based on the different characteristics of power transmission, substation, and distribution equipment, the flight waypoint sequence and photo-taking actions of the drone inspection are planned and designed, and a dedicated route file that guides the flight position and action is generated and stored in the database for the platform to call; Route Optimization Module: Transmission and distribution overhead line channels, refined inspection routes, and refined substation inspection routes are imported into the drone grid management platform. The inspection platform can automatically link the inspection plan of the production system or automatically optimize the route combination based on temporary tasks issued by production personnel. The drone inspection speed is optimized based on the drone's endurance and the distance of the inspection route to generate the optimal inspection route. Task scheduling module: inputs the optimal patrol route and the status data of the UAV into a preset task scheduling model, and uses the scheduling algorithm to realize cluster tasks through task decomposition, execution and coordination between drone nests; Patrol monitoring module: The UAV controls the optimal patrol route and task scheduling to conduct patrols. During the patrol process, the host in the hangar and the streaming server will transmit the real-time video data of the UAV camera to the UAV grid management platform through the wireless private network, realizing real-time video stream monitoring of the UAV; after the UAV patrol is completed, the UAV can push the images taken during the patrol to the platform through the data network card, and the platform will perform unified summary and recovery of the data. The platform can also view the images taken during the patrol and detailed information on time.

[0018] In a third aspect, an embodiment of the present application provides an electronic device, characterized by including: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the UAV power transmission, transformation and distribution trajectory planning method as described in the first aspect when executing the instructions.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a program, and the program instructs a device to execute the UAV power transmission, transformation and distribution trajectory planning method as described in the first aspect.

[0020] The present application provides a method and system for planning the trajectory of UAV transmission, transformation and distribution. According to 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 management platform for transmission, transformation and distribution UAVs. By laying out machine nests in the grid, a grid-based UAV inspection network covering the entire area is formed. According to the different characteristics of 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 for guiding the flight position and action is generated and stored in the database for the platform to call. The transmission and distribution overhead line channels and refined inspection routes and refined transformation inspection routes are imported into the UAV grid management platform. The inspection platform can automatically associate the inspection plan of the production system or automatically respond to temporary tasks issued by production personnel. The routes are optimized and combined, and the inspection speed of the drone is optimized from the perspective of drone endurance and the distance of the inspection line to generate the optimal inspection route; the optimal inspection route and the status data of the drone are input into the preset task scheduling model, and the scheduling algorithm can be used to realize cluster tasks through task decomposition, execution and collaboration between machine nests; the drone controls the optimal inspection route and task scheduling to conduct inspections. During the inspection process, the host in the hangar will transmit the real-time video data of the drone camera to the drone grid management platform through the wireless private network through the streaming server, realizing real-time video stream monitoring of the drone; after the drone inspection is completed, the drone can push the images taken during the inspection to the platform through the data network card, and the platform will perform unified summary and recovery of the data, and the platform can use the platform to view the images taken during the inspection and detailed information on time.

[0021] Beneficial effects: (1) Through breakthroughs in 3D modeling technology and the deep integration of drone technology with power grid inspection services, we will further optimize the inspection resources of transmission, transformation and distribution equipment, and improve inspection efficiency and equipment operation and maintenance quality.

[0022] (2) By deploying a grid inspection platform of drones in inspection-intensive areas, the optimized combination of inspection routes for substation equipment, transmission equipment, and distribution equipment and the automatic compilation and execution of inspection plans are completed, thereby improving the automation level of inspections.

[0023] (3) Through the management platform scheduling, the same aircraft can complete the inspection work of power transmission, power transformation and power distribution.

[0024] (4) The machine nest communicates with the drone in real time, realizing remote dispatch of the drone and real-time data transmission.

[0025] (5) By improving the shooting angle, pitch angle, shooting distance and hovering time parameters, and designing a personalized shooting route plan, the inspection pictures are made clearer and more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1A flowchart of a method for planning a flight path of a UAV for power transmission, transformation and distribution is provided in an embodiment of the present application.

[0027] Figure 2 A flowchart of a task scheduling method is provided in an embodiment of the present application.

[0028] Figure 3 A schematic diagram of a system module for planning a flight path of a UAV for power transmission, transformation and distribution is provided in an embodiment of the present application.

[0029] Figure 4 A schematic diagram of an electronic device is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application.

[0031] It should be noted that "at least one" in the embodiments of the present application means one or more, and more means two or more. Unless otherwise defined, all the technical and scientific terms used in the present application have the same meanings as those commonly understood by the person skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments of the present application, and are not intended to limit the present application.

[0032] It should be noted that the terms "first", "second", and the like in the embodiments of the present application are only used for the purpose of distinguishing description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying sequence. The features limited by "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the terms "exemplary" or "for example" are used to mean as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the terms "exemplary" or "for example" are used in the specific manner to present the relevant concept.

[0033] Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor are within the scope of protection of the present application.

[0034] Embodiment one

[0035] This embodiment provides a method and system for planning the transmission, transformation and distribution trajectory of drones. Through breakthroughs in three-dimensional modeling technology and the deep integration of drone technology with power grid inspection services, it further optimizes the inspection resources of transmission, transformation and distribution professional equipment, improves inspection efficiency and equipment operation and maintenance quality. By deploying a drone grid inspection platform in inspection-intensive areas, the optimized combination of inspection routes for substation equipment, transmission equipment and distribution equipment and the automatic compilation and execution of inspection plans are completed, thereby improving the automation level of inspections. Through scheduling via the management platform, the same aircraft can complete the inspection work of transmission, transformation and distribution. The machine nest communicates with the drone in real time to realize remote scheduling of the drone and real-time data transmission. By improving the shooting angle, pitch angle, shooting distance and hovering time parameters, and designing a personalized shooting route plan, the inspection pictures are made clearer and more accurate.

[0036] Figure 1 A flow chart of a method for planning the trajectory of power transmission, transformation and distribution for a UAV provided in one embodiment of the present application.

[0037] like Figure 1 As shown, a UAV power transmission, transformation and distribution trajectory planning method includes: S1: Divide the target area into multiple grids based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors. Build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a grid-based drone inspection network covering the entire area is formed.

[0038] It is understood that in this embodiment, S1: based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors, the target area is divided into multiple grids, and a grid management platform for transmission, transformation, and distribution drones is constructed. By deploying drone nests within the grids, a grid-based drone inspection network covering the entire area is formed, including: Using the coordinate data of transmission and distribution equipment, combined 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 transmission, transformation and distribution equipment, dividing the distribution of power grid equipment into high-density areas; Combine DEM data and satellite images to extract and generate topographic features, and divide the target area into different types: plains, mountains, and waters; Based on the distribution of power grid equipment, inspection frequency and drone endurance, the area where the transmission and distribution equipment is located is divided into several inspection grids. Machine nests are arranged in each grid to realize autonomous take-off and landing and cluster control of drones, forming a drone inspection network with full coverage.

[0039] Specifically, to meet the daily inspection needs of power transmission, transformation, and distribution, densely populated areas are divided into grids. By deploying drone nests within these grids, a grid-based, fully covered drone inspection network is formed. Each grid assesses the O&M workload based on the type and quantity of equipment under its jurisdiction and the corresponding inspection cycle, and determines the number of drone nests deployed within the grid based on the workload. For key and important inspection targets or areas where hidden dangers are frequently discovered, the inspection frequency required is higher, so the number or type of drone nests deployed within the corresponding grid needs to be increased.

[0040] Specifically, DBSCAN is a density-based clustering algorithm. Its core idea is to divide high-density areas in the data space into clusters by defining density-connected point sets, while also being able to identify noise points and abnormal data. The DBSCAN algorithm sets two key parameters: the neighborhood radius (Eps) and the minimum number of points (MinPts). For any point p in the data set, if the number of points contained in its neighborhood is greater than or equal to MinPts, the point is called a core point; if the number of points contained in the neighborhood is less than MinPts, the point may be marked as a boundary point or a noise point. By continuously expanding the set of density-reachable points, DBSCAN can identify areas with sufficiently high density and divide them into clusters. Using the DBSCAN spatial clustering algorithm to perform spatial clustering analysis on transmission and distribution equipment can divide the distribution of power grid equipment into high-density areas.

[0041] In spatial cluster analysis of power grid equipment, DBSCAN can effectively handle noisy data and clustering problems involving non-convex shapes. For example, in analyzing the location coordinates of power equipment, the DBSCAN algorithm can identify differences between power lines and other ground noise points, thereby segmenting high-density areas such as power lines. Furthermore, the DBSCAN algorithm does not require a predefined number of clusters, making it more flexible and efficient when processing complex power grid data. Using the DBSCAN spatial clustering algorithm for spatial cluster analysis of transmission and distribution equipment is an efficient and reliable method. It can automatically identify high-density areas and segment the distribution patterns of power grid equipment, while also exhibiting strong noise immunity and adaptability to complex shapes. By rationally setting parameters and combining improved algorithms, the clustering effect can be further enhanced, providing strong support for power grid management and maintenance.

[0042] S2: Based on the different characteristics of power transmission, substation and distribution equipment, the flight waypoint sequence and photo-taking actions of the drone inspection are planned and designed, and a special route file guiding the flight position and action is generated and stored in the database for the platform to call.

[0043] It is understood that in this embodiment, S2: planning and designing the flight waypoint sequence and photo-taking actions of the drone inspection based on the different characteristics of the power transmission, transformation, and distribution equipment, generating a dedicated route file guiding the flight position and action, and storing it in the database for the platform to call, includes: Use LiDAR to collect high-precision 3D point cloud data of transmission lines or substations, extract spatial information of key target points, and generate preliminary routes based on this information; According to the inspection objectives, parameters are designed for each waypoint, including shooting angle, distance, and height, and waypoints are planned in sequence according to the order of parameters; Use Dijkstra algorithm to optimize path planning to ensure the shortest and safest route; Add auxiliary points and set a reasonable safety distance between adjacent waypoints to prevent the drone from accidentally hitting the equipment during flight and ensure the safety of the route; According to 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 that all inspection targets are covered; Rehearse the route in a simulated environment to check for safety hazards or missed areas, and make adjustments based on actual conditions; Manual review and fine-tuning: Based on actual on-site conditions, fine-tune waypoint locations and parameters to ensure the route meets actual needs; The generated route file should be stored in KML or JSON format for easy subsequent use.

[0044] The Dijkstra algorithm is a classic single-source shortest path algorithm applicable to the shortest path problem on non-negatively weighted graphs. Its core concept is to compute the shortest paths from a starting point to all other nodes by gradually expanding the shortest path tree. In path planning, the Dijkstra algorithm can effectively solve the shortest path problem from a starting point to a destination with high computational efficiency and accuracy. By converting the risk map into a weighted directed graph and using path risk minimization as the objective function, the traditional Dijkstra algorithm is improved to generate the optimal path with the lowest 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: taking into account factors such as distance, safety factor, and airspace restrictions. Safety parameters are incorporated into the weight calculation: weather condition weight adjustment, avoidance of no-fly / restricted zones, terrain obstacle safety distance, and airspace classification restrictions. Priority queues (minimum heaps) are used to improve efficiency. In multi-UAV trajectory planning, a time window conflict judgment model can be introduced to screen non-conflicting routes from all feasible shortest paths. It can also be combined with heuristic functions: in power inspection route planning, by adding heuristic functions and combining them with the Dijkstra algorithm, the path can be dynamically adjusted to avoid potential threats.

[0045] Specifically, the shooting angle, pitch angle, shooting distance and hovering time parameters of each waypoint are set according to the inspection requirements, including: Adjust the shooting distance according to the flight direction, and the adjustment range is:

[0046] in , The adjustment range of the flight coordinates on the x and y coordinates respectively, is the real-time coordinate of the drone at the previous moment, Predict the next moment coordinates for the drone, Parameters for adjusting the flight direction; Adjust the shooting angle and pitch angle according to the flight direction, and the adjustment range is:

[0047] in They are the real-time shooting angle and pitch angle adjustment range of the drone on the x and y coordinates, Parameters for adjusting the tangent direction of flight.

[0048] Specifically, flight direction adjustment depends primarily on the drone's real-time (x, y) coordinates and its predicted next-moment coordinates. Changes in these coordinates directly influence the adjustment of the shooting distance. For example, by calculating the difference between the drone's current coordinates and the next predicted coordinates, the drone's movement in the x and y directions can be determined, thereby adjusting the shooting distance. This adjustment can be implemented within the drone's flight control system, such as a PID controller for pitch and attitude angle adjustment, ensuring stable tracking and distance adjustment.

[0049] Shooting angle and pitch angle adjustment are achieved through the drone platform's angle control. Pitch angle adjustment is typically achieved by feeding back signals from the platform's pitch angle sensor to the controller, where it is corrected using a PID control algorithm. Furthermore, depending on mission requirements, users can manually set the pitch angle parameters at waypoints, such as setting a -90° pitch angle at a waypoint to achieve a specific shooting effect.

[0050] Changes in flight direction affect the adjustment of shooting angles. For example, as the drone flies along a route, the platform needs to adjust its pitch angle based on the flight direction to keep the target in the center of the frame. This adjustment can be accomplished using preset waypoint parameters. For example, setting a different pitch angle value at each waypoint allows the drone to gradually transition to the pitch angle of the next waypoint during flight.

[0051] Parameter settings are fundamental to achieving these adjustments. For example, users can set waypoint parameters during a drone mission, including the pitch angle and turning radius between waypoints. Furthermore, by acquiring real-time parameters like the drone's altitude and speed, users can dynamically adjust the shooting angle and pitch angle to suit different flight environments.

[0052] Specifically, the process of arriving at waypoints in a predetermined order and performing the photo-taking task includes: Adjust the shooting angle, pitch angle, shooting distance, and hovering time parameters according to the preset adjustment range, and then start shooting; When the shooting requirements for key areas cannot be met, according to the control instructions, the scene image of the object to be photographed is obtained, a three-dimensional point cloud model of the object to be photographed is established, and a corresponding route shooting template is set. The data of the three-dimensional point cloud model of the object to be photographed is substituted into the shooting template to generate a shooting route plan, wherein the shooting route plan includes circular shooting at different angles and up and down shooting.

[0053] Specifically, the step of acquiring a scene image of an object to be photographed, establishing a three-dimensional point cloud model of the object to be photographed, setting a corresponding route shooting template, substituting data of the three-dimensional point cloud model of the object to be photographed into the shooting template, and generating a shooting route plan includes: Determining the shape of the object to be photographed based on the three-dimensional point cloud model of the object to be photographed; Establishing a corresponding minimum circumscribed cylinder according to the shape of the object to be photographed; Obtaining parameters of the minimum circumscribed cylinder, substituting the parameters into the shooting template, and generating a route file; Setting a shooting route plan around the outer surface of the minimum circumscribed cylinder according to the route file, and performing circular shooting or up and down shooting of the object to be photographed at different angles; The different angles are the tilt angles for the surround shooting, and the route for the circular shooting is: , in, is the route coordinate for the surrounding shooting, are the coordinates of the key points of the surround shooting, is the number of routes, is the number of waypoints, The length of the surround shot. is the height of the object to be photographed, in degrees, The respective orbiting directions are equivalent to the various inclination angles of the coordinate axes. The tilt angle of the height vector is equivalent to the orbital direction.

[0054] Specifically, the shape of the object to be photographed is first analyzed using a 3D point cloud model. This step forms the basis for subsequent route planning, aiming to determine the object's geometric features and provide a basis for route design. Based on the object's shape, a minimum circumscribed cylinder is calculated and constructed. This cylinder completely encompasses the target object, and its parameters (such as radius and height) are used for subsequent route planning. The parameters of the minimum circumscribed cylinder are substituted into the capture template to generate a route file. These parameters include the cylinder's radius, height, and other geometric information related to the target object. Based on the generated route file, a capture route is set around the outer surface of the minimum circumscribed cylinder. Capture can be performed in a circular or up-and-down manner. Circular capture is suitable for single buildings or landmarks, while up-and-down capture is suitable for complex terrain or multi-layered objects. The key to circular capture is adjusting the camera's tilt angle to ensure high-quality images from multiple perspectives. For example, the tilt angle (θ) of the capture direction relative to the coordinate axes and the tilt angle (φ) of the height vector must be precisely set to avoid blind spots and maximize data overlap. Based on the loop aerial photography model, route parameters include the relative altitude of the aerial track, heading overlap, and lateral overlap. These parameters directly impact the quality and accuracy of the 3D model. For example, heading overlap and lateral overlap must meet certain requirements to ensure accurate matching between images. The resulting route file will guide the drone along the pre-set trajectory for data collection. The drone must maintain a stable tilt angle and altitude during flight to ensure image coverage and quality.

[0055] After data collection is complete, the images are processed using specialized software (such as Pix4D and Agisoft Metashape), including steps such as feature point matching, point cloud generation, and dense point cloud generation. Finally, a 3D model is generated, followed by texture mapping and optimization. This entire process emphasizes comprehensive management, from 3D point cloud modeling to route planning, image acquisition, and 3D modeling. By properly configuring route parameters and shooting angles, the accuracy and efficiency of 3D modeling can be effectively improved.

[0056] S3: Import transmission and distribution overhead line channels, refined inspection routes, and refined substation inspection routes into the drone grid management platform. The inspection platform can automatically link the inspection plan of the production system or automatically optimize the routes based on temporary tasks issued by production personnel. It can optimize the drone inspection speed from the perspective of drone endurance and inspection route distance to generate the optimal inspection route.

[0057] It will be appreciated that in this embodiment, drone inspection speed optimization is typically based on a comprehensive consideration of flight distance, flight time, and the drone's 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, strict route planning standards can be established to ensure the safety and efficiency of drone inspections.

[0058] 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. This can be achieved by generating ranking assignments and comprehensive analysis models to optimize the drone's flight path. Furthermore, the autonomous drone inspection system enables panoramic monitoring and intelligent route planning for UHV transmission lines.

[0059] Through a grid-based drone management platform, combined with intelligent algorithms and multi-disciplinary collaborative operations, it is possible to automatically optimize the combination of refined inspection routes for transmission lines, distribution network overhead lines, and substations. The optimal inspection route is generated based on the drone's endurance and the distance to be inspected. This not only improves inspection efficiency but also reduces labor costs, providing a strong guarantee for the safe and stable operation of power grid equipment.

[0060] S4: Input the optimal patrol route and the status data of the UAV into a preset task scheduling model, and use the scheduling algorithm to realize the cluster task through task decomposition, execution and coordination between the drone nests.

[0061] It is understood that in this embodiment, the step S4: inputting the optimal patrol route and the status data of the drone into a preset task scheduling model, and utilizing a scheduling algorithm to implement cluster tasks through task decomposition, execution, and collaboration between drone nests, includes: S4.1, preprocessing the input data, wherein the optimal patrol route data includes a waypoint sequence, a time window, a priority, and a region weight, and the UAV status data includes a battery capacity, a cruising speed, a sensor status, a UAV status, a no-fly zone, weather conditions, and communication coverage environment constraints; S4.2 builds a task scheduling model for a hierarchical and clustered network topology, combining centralized planning with 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 task sequences 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 strategies. Task decomposition involves dividing the patrol area into grids or polygons, and then splitting it according to task type. Tasks are first assigned to key areas, and then internal routes are planned. In a dynamic environment, heuristic algorithms are used to adaptively plan reconnaissance paths. The resource allocation optimization objective function is to minimize the total task time, maximize the coverage or balance the load, and combine the battery life and sensor matching constraints; The dynamic collaborative strategy includes charging relay, data relay, and conflict resolution strategies in drone-nest collaboration. The wireless charging collaborative scheduling unit plans the charging sequence of drones to achieve power balance. The drone cluster reduces energy consumption by jointly optimizing flight trajectory and speed under the edge computing framework. The communication and information sharing mechanism in drone cluster collaborative computing is also included. S4.4, Status Monitoring and Adaptive Adjustment: Through real-time feedback loops and fault-tolerant mechanisms, the automated scheduling system needs to continuously monitor the status of drones and reschedule as needed. Task scheduling strategies based on drone performance differences can improve task completion rates. S4.5, real-time feedback loop: Each drone reports its location and battery level via heartbeat packets; the central scheduler re-evaluates the task queue every 5 seconds, triggering rescheduling when battery degradation exceeds expectations. Fault-tolerance mechanism: When a drone fails, a backup drone is deployed from the nearest drone nest, and coverage gaps are replanned. S4.6, output and execution, through the output and execution monitoring of the scheduling instructions of the drone grid management platform, provides an interactive interface to display the drone status and task progress.

[0062] Specifically, S4.1 input data includes optimal patrol route data, including waypoint sequences, time windows, priorities, regional weights, and drone status data, including battery capacity, cruising speed, and sensor status. A centralized task decomposition approach divides tasks based on range and drone capabilities to meet mission completion time and coverage requirements. S4.2 constructs a task scheduling model based on a hierarchical clustered network topology, combining centralized planning with distributed execution. This multi-layered task scheduling model based on a hierarchical clustered network topology is a key step in module-level resource virtualization and task decomposition. Centralized task allocation optimizes task sequences through a centralized system, while distributed task allocation offers the advantages of rapid response and flexible adjustment. S4.3 Scheduling algorithms include task decomposition, resource allocation optimization, and dynamic coordination strategies. Multi-objective optimization and task scheduling algorithms, including genetic algorithms and particle swarm optimization, can be used for task decomposition and resource allocation optimization. Furthermore, the application of heuristic algorithms and hybrid algorithm strategies in drone swarm task scheduling is mentioned, which can be used in dynamic coordination strategies. S4.4 implements state monitoring and adaptive adjustment of the automated scheduling system through real-time feedback loops and fault-tolerant mechanisms. The drone swarm management system utilizes distributed scheduling and dynamic adjustment mechanisms to address communication failures and drone outages. Its risk-aware task scheduling approach optimizes task allocation by combining task completion time with additional overhead time, meeting real-time adjustment requirements. S4.5 Each drone reports its location and battery level via heartbeat packets. The central scheduler re-evaluates the task queue every 5 seconds, enabling real-time monitoring of drone status and rescheduling as needed. S4.6 The drone grid management platform monitors the output and execution of dispatch instructions and provides an interactive interface displaying drone status and task progress. The communication and information sharing mechanisms within drone swarm collaborative computing support real-time data transmission and task execution monitoring.

[0063] S5: The drone controls the optimal patrol route and task scheduling to conduct patrols. During the patrol process, 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 the wireless private network, realizing real-time video stream monitoring of the drone. After the drone patrol is completed, the drone can push the images taken during the patrol to the platform through the data network card, and the platform will perform unified summary and recovery of the data. The platform can also view the images taken during the patrol and detailed information on time.

[0064] It is understood that in this embodiment, during the drone's patrol process, the real-time video data collected by the camera is transmitted to the drone grid management platform via a wireless private network, enabling real-time video streaming monitoring of the drone. This real-time transmission method benefits from the large bandwidth characteristics of the 5G network, which can effectively reduce latency and improve clarity. In addition, the application of edge computing mode further reduces the time it takes to transmit data to the cloud, improving the efficiency of real-time monitoring.

[0065] Specifically, the images and video data taken during the inspection can be transmitted in real time to the management platform through a wireless private network or a 5G network. For example, the drone is equipped with a high-definition camera, and the image data is transmitted back to the management platform in real time through low-latency communication technology. Some systems also support noise reduction processing and image optimization of collected data to improve data quality. After the drone completes the inspection task, the images and video data taken can be pushed to the platform through a data card for unified induction and recovery. The platform can classify and manage the images, time, and other detailed information taken during the inspection, and support subsequent data analysis and report generation. In addition, the platform can use AI algorithms to detect targets and identify abnormalities in video streams, and timely discover potential problems and issue warnings.

[0066] Embodiment Two

[0067] As shown in Figure 3 The present application provides a UAV transmission and distribution flight path planning system, which is applied to the UAV transmission and distribution flight path planning method as described in Embodiment One, and includes a platform building module 11, a flight path file generation module 12, a flight path optimization module 13, a task scheduling module 14, and an inspection monitoring module 15.

[0068] It can be understood that in the present embodiment, the platform building 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, to build a grid-based management platform for transmission, transformation, and distribution UAVs, and to form a UAV inspection network covering the entire area by arranging nests in the grids.

[0069] It can be understood that in the present embodiment, the flight path file generation module 12 is used to plan and design the flight point order and photographing actions of UAV inspection according to the different characteristics of transmission, transformation, and distribution equipment, to generate a special flight path file guiding the position and actions of flight, and to store it in the database for the platform to call.

[0070] It can be understood that in the present embodiment, the flight path optimization module 13 is used to import the overhead line paths of transmission and distribution, fine inspection flight paths of transmission, and fine inspection flight paths of transformation into the grid-based management platform for UAVs, so that the inspection platform can automatically associate the inspection plan of the production system or automatically optimize and combine the flight paths according to the temporary tasks issued by production personnel, optimize the UAV inspection speed from the perspective of UAV endurance and inspection route distance, and generate the optimal inspection flight path.

[0071] It can be understood that in the present embodiment, the task scheduling module 14 is used to input the optimal inspection flight path and the state data of the UAV into a preset task scheduling model, and to realize cluster tasks through task decomposition, execution, and coordination between nests by using scheduling algorithms.

[0072] It can be understood that in this embodiment, the patrol monitoring module 15 is used for the drone to control the optimal patrol route and task scheduling for patrol. During the patrol process, the host in the hangar and the streaming server will respectively transmit the real-time video data of the drone camera to the drone grid management platform through the wireless private network, thereby realizing real-time video stream monitoring of the drone; after the drone patrol is completed, the drone can push the images taken during the patrol to the platform through the data network card, and the platform will perform unified summary and recovery of the data, and the platform can use the platform to provide detailed information on the images taken during the patrol and time.

[0073] The present application provides a method and system for planning the transmission, transformation and distribution trajectory of unmanned aerial vehicles. Through breakthroughs in three-dimensional modeling technology and the deep integration of unmanned aerial vehicle technology with power grid inspection services, it further optimizes the inspection resources of transmission, transformation and distribution professional equipment, and improves inspection efficiency and equipment operation and maintenance quality. By deploying a grid inspection platform of unmanned aerial vehicles in inspection-intensive areas, the optimized combination of inspection routes for substation equipment, transmission equipment and distribution equipment and the automatic compilation and execution of inspection plans are completed, thereby improving the automation level of inspections. Through scheduling via a management platform, the same aircraft can complete the inspection work of transmission, transformation and distribution. The machine nest communicates with the unmanned aerial vehicle in real time to realize remote scheduling of the unmanned aerial vehicle and real-time data transmission. By improving the shooting angle, pitch angle, shooting distance and hovering time parameters, and designing a personalized shooting route plan, the images taken during the inspection are made clearer and more accurate.

[0074] Figure 4 This is an electronic device provided by an embodiment of the present application. Figure 4 As shown, the electronic device includes at least the following parts: a processor 101 and a memory 100 , a communication interface 103 , and a bus 102 .

[0075] In the embodiment of the present application, the memory 100 is used to store instructions executable by the processor 101. The processor 101 is configured to execute the instructions to implement the following Figure 3 The equipment module shown is for UAV power transmission, transformation and distribution trajectory planning.

[0076] In an embodiment of the present application, a computer-readable storage medium includes instructions, and the instructions instruct a device to execute the method of the first aspect. For example, the instructions instruct the device to execute Figure 1 The method is shown in the process steps.

[0077] The program running in the electronic device involved in one embodiment of the present application may be a program that controls a central processing unit (CPU) and the like to implement the functions of the above-mentioned embodiment involved in one embodiment of the present invention (a program that causes a computer to function). The information processed by these devices is temporarily stored in random access memory (RAM) during processing, and then stored in various ROMs such as read-only memory (Flash ROM) and hard disk drives (HDDs), where it is read, modified, and written as needed by the CPU.

[0078] It should be noted that a portion of the electronic device of the above embodiment may also be implemented by a computer. In this case, a program for implementing the control function may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer and executed.

[0079] It should be noted that the "computer" mentioned here refers to a computer built into an electronic device, employing hardware including an operating system (OS) and peripheral devices. Furthermore, "computer-readable recording medium" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into computers.

[0080] Furthermore, "computer-readable recording media" may include: media that dynamically store programs for a short period of time, such as communication lines when transmitting programs via networks such as the Internet or communication lines such as telephone lines; and media that store programs for a fixed period of time, such as volatile memory within computers acting as servers or clients in this context. Furthermore, the aforementioned program may be a program for implementing a portion of the aforementioned functions, or a program that can achieve the aforementioned functions by combining with a program already stored in a computer.

[0081] Furthermore, the electronic device in the above-described embodiments can also be implemented as a collection of multiple devices (a device group). Each device comprising the device group may include some or all of the functions or functional blocks of the electronic device in the above-described embodiments. A device group only needs to include all of the functions or functional blocks of the electronic device.

[0082] Those skilled in the art should recognize that the above embodiments are merely intended to illustrate the present application and are not intended to limit the present application. As long as they are within the spirit of the present application, appropriate changes and modifications to the above embodiments are within the scope of protection claimed in the present application.

Claims

1. A UAV power transmission, transformation and distribution trajectory planning method, characterized in that: The method comprises: S1: Divide the target area into multiple grids based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors. Build a grid-based management platform for transmission, transformation, and distribution drones. Deploy drone nests within the grids to form a grid-based drone inspection network covering the entire area. S2: Based on the different characteristics of power transmission, substation, and distribution equipment, the flight waypoint sequence and photo-taking actions of the drone inspection are planned and designed, and a dedicated route file guiding the flight position and action is generated and stored in the database for the platform to call; S3: Transmission and distribution overhead line channels, refined inspection routes, and refined substation inspection routes are imported into the drone grid management platform. The inspection platform can automatically link the inspection plan of the production system or automatically optimize the route combination based on temporary tasks issued by production personnel. The drone inspection speed is optimized based on the drone's endurance and the distance of the inspection route to generate the optimal inspection route. S4: Inputting the optimal patrol route and the status data of the UAV into a preset task scheduling model, and utilizing the scheduling algorithm to realize the cluster task through task decomposition, execution and coordination between the drone nests; S 5: The drone controls the optimal patrol route and task scheduling to conduct patrols. During the patrol process, 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 the wireless private network, realizing real-time video stream monitoring of the drone. After the drone patrol is completed, the drone can push the images taken during the patrol to the platform through the data network card, and the platform will perform unified summary and recovery of the data. The platform can also view the images taken during the patrol and detailed information on time.

2. The UAV power transmission, transformation and distribution trajectory planning method according to claim 1 is characterized in that: S1: Divide the target area into multiple grids based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors, and build a grid management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a grid-based drone inspection network covering the entire area is formed, including: Using the coordinate data of transmission and distribution equipment, combined 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 transmission, transformation and distribution equipment, dividing the distribution of power grid equipment into high-density areas; Combine DEM data and satellite images to extract and generate topographic features, and divide the target area into different types: plains, mountains, and waters; Based on the distribution of power grid equipment, inspection frequency and drone endurance, the area where the transmission and distribution equipment is located is divided into several inspection grids. Machine nests are arranged in each grid to realize autonomous take-off and landing and cluster control of drones, forming a drone inspection network with full coverage.

3. The UAV power transmission, transformation and distribution trajectory planning method according to claim 2 is characterized in that: S2: Based on the different characteristics of power transmission, substation, and distribution equipment, the flight waypoint sequence and photo-taking actions of the drone inspection are planned and designed, and a dedicated route file guiding the flight position and action is generated and stored in the database for the platform to call, including: Use LiDAR to collect high-precision 3D point cloud data of transmission lines or substations, extract spatial information of key target points, and generate preliminary routes based on this information; According to the inspection objectives, parameters are designed for each waypoint, including shooting angle, distance, and height, and waypoints are planned in sequence according to the order of parameters; Use Dijkstra algorithm to optimize path planning to ensure the shortest and safest route; Add auxiliary points and set a reasonable safety distance between adjacent waypoints to prevent the drone from accidentally hitting the equipment during flight and ensure the safety of the route; According to 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 that all inspection targets are covered; Rehearse the route in a simulated environment to check for safety hazards or missed areas, and make adjustments based on actual conditions; Manual review and fine-tuning: Based on actual on-site conditions, fine-tune waypoint locations and parameters to ensure the route meets actual needs; The generated route file should be stored in KML or JSON format for easy subsequent use.

4. The UAV power transmission, transformation and distribution trajectory planning method according to claim 3 is characterized in that: According to the inspection requirements, the shooting angle, pitch angle, shooting distance and hovering time parameters of each waypoint are set, including: Adjust the shooting distance according to the flight direction, and the adjustment range is: where r 2 =(x-x0) 2 (y-y0) 2 , Δx, Δy are the adjustment ranges of the flight coordinates on the x and y coordinates respectively, (x0, y0) is the real-time coordinates of the drone at the previous moment, and (x, y) is the coordinates predicted by the drone at the next moment. k1, k2, and k3 are parameters for adjusting the flight direction; Adjust the shooting angle and pitch angle according to the flight direction, and the adjustment range is: where x p ,y p They are the adjustment range of the drone's real-time shooting angle and pitch angle in the x and y coordinates respectively, and p1 and p2 are the parameters adjusted in the tangential direction of the flight.

5. The UAV power transmission, transformation and distribution trajectory planning method according to claim 4 is characterized in that: The process of arriving at the waypoints in a predetermined order and performing the photo-taking task includes: Adjust the shooting angle, pitch angle, shooting distance, and hovering time parameters according to the preset adjustment range, and then start shooting; When the shooting requirements for key areas cannot be met, according to the control instructions, the scene image of the object to be photographed is obtained, a three-dimensional point cloud model of the object to be photographed is established, and a corresponding route shooting template is set. The data of the three-dimensional point cloud model of the object to be photographed is substituted into the shooting template to generate a shooting route plan, wherein the shooting route plan includes circular shooting at different angles and up and down shooting.

6. The method for UAV power transmission, transformation and distribution trajectory planning according to claim 5, characterized in that: The method includes 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 route shooting template, substituting data of the three-dimensional point cloud model of the object to be photographed into the shooting template, and generating a shooting route plan, including: Determining the shape of the object to be photographed based on the three-dimensional point cloud model of the object to be photographed; Establishing a corresponding minimum circumscribed cylinder according to the shape of the object to be photographed; Obtaining parameters of the minimum circumscribed cylinder, substituting the parameters into the shooting template, and generating a route file; Setting a shooting route plan around the outer surface of the minimum circumscribed cylinder according to the route file, and performing circular shooting or up and down shooting of the object to be photographed at different angles; The different angles are the tilt angles for the surround shooting, and the route for the circular shooting is: Among them, (x i,j ,y i,j ) is the coordinate of the route for the surrounding shooting, (x a ,y a ) are the coordinates of the key points of the surround shooting, i is the number of routes, j is the number of waypoints, f is the length of the surround shooting, h is the height of the object to be photographed, α and β are the tilt angles of the coordinate axis in the surround direction, and θ is the tilt angle of the height vector in the surround direction.

7. The UAV power transmission, transformation and distribution trajectory planning method according to claim 1 is characterized in that: S4: Inputting the optimal patrol route and the status data of the drone into a preset task scheduling model, and utilizing a scheduling algorithm to realize cluster tasks through task decomposition, execution, and collaboration between drone nests, including: S 4.1, preprocessing the input data, wherein the optimal patrol route data includes a waypoint sequence, a time window, a priority, and a region weight, and the UAV status data includes a battery capacity, a cruising speed, a sensor status, a UAV status, a no-fly zone, weather conditions, and a communication coverage range environmental constraint; S.4.2: Construct a task scheduling model for a hierarchical and clustered network topology, combining centralized planning with distributed execution. The central scheduler is responsible for global optimization of task allocation, while the local decision module is responsible for real-time adjustment of obstacle avoidance and power management. Centralized task allocation optimizes task sequences through the central system, while distributed task allocation focuses on rapid response and flexible adjustment. S 4.3, the scheduling algorithm includes task decomposition, resource allocation optimization, and dynamic coordination strategies. Task decomposition involves dividing the patrol area into grids or polygons and splitting it according to task type. Tasks are first assigned to key areas, followed by internal route planning. In a dynamic environment, heuristic algorithms are used to adaptively plan reconnaissance paths. The resource allocation optimization objective function is to minimize the total task time, maximize the coverage or balance the load, and combine the battery life and sensor matching constraints; The dynamic collaborative strategy includes charging relay, data relay, and conflict resolution strategies in drone-nest collaboration. The wireless charging collaborative scheduling unit plans the charging sequence of drones to achieve power balance. The drone cluster reduces energy consumption by jointly optimizing flight trajectory and speed under the edge computing framework. The communication and information sharing mechanism in drone cluster collaborative computing is also included. S 4.4, Status Monitoring and Adaptive Adjustment: Through real-time feedback loops and fault-tolerant mechanisms, the automated scheduling system needs to continuously monitor the status of drones and reschedule as needed. Task scheduling strategies based on drone performance differences can improve task completion rates; S 4.5, real-time feedback loop: Each drone reports its location and battery level via heartbeat packets; the central scheduler re-evaluates the task queue every 5 seconds, triggering rescheduling when battery degradation exceeds expectations; fault-tolerance mechanism: When a drone fails, a backup drone is deployed from the nearest drone nest, and coverage gaps are replanned; S 4.6, Output and Execution, provides an interactive interface to display the status of drones and task progress through the output and execution monitoring of scheduling instructions on the drone grid management platform.

8. A UAV power transmission, transformation and distribution trajectory planning system, applied to the UAV power transmission, transformation and distribution trajectory planning method according to any one of claims 1 to 7, characterized in that: include: Platform construction module: Divide the target area into multiple grids based on the distribution of power grid equipment, inspection frequency, and terrain and environmental factors. Build a grid-based management platform for transmission, transformation, and distribution drones. By deploying drone nests within the grids, a grid-based drone inspection network covering the entire area is formed. Route file generation module: Based on the different characteristics of power transmission, substation, and distribution equipment, the flight waypoint sequence and photo-taking actions of the drone inspection are planned and designed, and a dedicated route file that guides the flight position and action is generated and stored in the database for the platform to call; Route Optimization Module: Transmission and distribution overhead line channels, refined inspection routes, and refined substation inspection routes are imported into the drone grid management platform. The inspection platform can automatically link the inspection plan of the production system or automatically optimize the route combination based on temporary tasks issued by production personnel. The drone inspection speed is optimized based on the drone's endurance and the distance of the inspection route to generate the optimal inspection route. Task scheduling module: inputs the optimal patrol route and the status data of the UAV into a preset task scheduling model, and uses the scheduling algorithm to realize cluster tasks through task decomposition, execution and coordination between drone nests; Patrol monitoring module: The drone controls the optimal patrol route and dispatches tasks for patrol. During the patrol process, the host in the hangar and the streaming server transmit the real-time video data of the drone camera to the drone grid management platform via a wireless private network, realizing real-time video streaming monitoring of the drone; After the drone inspection is completed, the drone can push the images taken during the inspection to the platform through the data network card. The platform will then summarize and restore the data. The platform can also provide detailed information on the images taken during the inspection and the time.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the UAV power transmission, transformation and distribution trajectory planning method as described in any one of claims 1 to 7 when executing the instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, and the program instructs a device to execute the UAV power transmission, transformation and distribution trajectory planning method according to any one of claims 1 to 7.

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