110 KV power transmission line unmanned aerial vehicle cluster inspection control method and system

Abstract

The invention discloses a 110KV power transmission line unmanned aerial vehicle cluster inspection control method and system, and relates to the technical field of power systems and automatic control, and the method comprises the steps: generating an inspection region three-dimensional map through first data, and carrying out the distributed task distribution through a first algorithm; performing path search based on three-dimensional expansion of a second algorithm; and a third control algorithm is adopted to realize unmanned aerial vehicle cluster coordinated flight, and a dynamic window method is combined to realize real-time obstacle avoidance. According to the method, the flight path of the unmanned aerial vehicle can be more accurately planned through three-dimensional expansion of an existing path planning algorithm in combination with topographic data, and unnecessary flight time and energy consumption are reduced; through cluster collaborative operation, multiple sections of power transmission lines can be inspected at the same time, the inspection time is greatly shortened, the operation efficiency is improved, all corners of the data lines are fully covered, and the blind area or missing detection problem is avoided.

Description

Technical Field

[0001] The present invention relates to the field of power system and automation control technology, and specifically to a 110KV transmission line UAV cluster inspection control method and system. Background Art

[0002] With the rapid development of drone technology, its application in the field of power line inspection is becoming more and more extensive. Traditional inspection methods are mainly based on single-machine methods. The load capacity of a single machine is limited, which limits the equipment carried, and is limited in terms of cruising distance and monitoring content. If a complex inspection task is to be completed, it is necessary to dispatch multiple times or frequently change different equipment, which will cause problems such as low load capacity and inspection efficiency.

[0003] In recent years, drone swarm inspection technology has gradually become a research hotspot. The collaborative operation of multiple drones can effectively improve inspection efficiency and coverage. However, the existing drone swarm inspection control methods have problems such as long inspection time, low coverage, difficulty in processing inspection data and scene analysis, immature drone collaborative control technology, and easy interference with communications. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing UAV cluster inspection control method has the problems of unreasonable task allocation, low path planning efficiency, poor stability of cluster collaborative flight, and how to achieve real-time obstacle avoidance.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a 110KV transmission line drone cluster inspection control method, comprising generating a three-dimensional map of the inspection area by using first data, and using a first algorithm to perform distributed task allocation; performing path search based on a three-dimensional expansion of a second algorithm; and using a third control algorithm to achieve coordinated flight of drone clusters, combined with a dynamic window method for real-time obstacle avoidance.

[0007] As a preferred solution of the 110KV transmission line drone cluster inspection control method described in the present invention, the use of the first data includes classifying the pre-processed first data into ground features and non-ground features.

[0008] As a preferred solution of the 110KV transmission line drone cluster inspection control method described in the present invention, wherein: the generation of a three-dimensional map of the inspection area includes generating a three-dimensional map by interpolating and gridding the preprocessed first data; the three-dimensional map includes a digital elevation model and a digital terrain model.

[0009] As a preferred solution of the 110KV transmission line drone cluster inspection control method described in the present invention, the use of the first algorithm for distributed task allocation includes using the first algorithm, combined with the first data, to distribute tasks based on terrain data.

[0010] As a preferred solution of the 110KV transmission line drone cluster inspection control method described in the present invention, the path search based on the three-dimensional expansion of the second algorithm includes three-dimensionally expanding the second algorithm, and performing path search by finding the path with the minimum cost by expanding neighbor nodes.

[0011] As a preferred solution of the 110KV transmission line UAV cluster inspection control method described in the present invention, the use of a third control algorithm to achieve coordinated flight of the UAV cluster includes using a third control algorithm to stabilize the UAV cluster in a coordinated flight formation in three-dimensional space based on attraction, repulsion and alignment force.

[0012] As a preferred solution of the 110KV transmission line UAV cluster inspection control method described in the present invention, the real-time obstacle avoidance combined with the dynamic window method includes the UAV evaluating multiple feasible paths through a dynamic window according to the current state, and selecting the optimal path for execution.

[0013] Another object of the present invention is to provide a 110KV transmission line drone cluster inspection control system, which can help drones form a coordinated flight formation in three-dimensional space through a cluster control algorithm and based on three behavioral forces, avoiding collisions or flying out of the formation, thereby solving the problem of immaturity of drone collaborative control technology in the prior art.

[0014] As a preferred solution of the 110KV transmission line UAV cluster inspection control system described in the present invention, it includes: a task planning and allocation module, a three-dimensional path planning module, and a cluster collaborative flight module; the task planning and allocation module is used to generate a three-dimensional map of the inspection area by using the first data, and use the first algorithm to perform distributed task allocation; the three-dimensional path planning module is used to perform path search based on the three-dimensional extension of the second algorithm; the cluster collaborative flight module is used to adopt a third control algorithm to realize coordinated flight of UAV clusters, combined with the dynamic window method for real-time obstacle avoidance.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a 110KV transmission line drone cluster inspection control method.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a 110KV transmission line drone cluster inspection control method.

[0017] Beneficial effects of the present invention: The 110KV transmission line UAV cluster inspection control method provided by the present invention can more intelligently allocate tasks according to terrain complexity and obstacle distribution by using pre-processed radar point cloud data for classification, combined with terrain data, and using advanced distributed task allocation algorithms, ensuring that the tasks performed by each UAV are adapted to its capabilities and environment, thereby improving the overall inspection efficiency; by three-dimensional expansion of the existing path planning algorithm, combined with terrain data, the flight path of the UAV can be planned more accurately, reducing unnecessary flight time and energy consumption; by adopting a cluster control algorithm, based on three types of behavioral forces, the coordinated flight of the UAV in three-dimensional space is realized, the formation is kept stable, and the flexibility and adaptability of cluster flight are improved; through cluster collaborative operation, multiple sections of transmission lines can be inspected at the same time, greatly shortening the inspection time, improving the operating efficiency, and comprehensively covering every corner of the data line, avoiding blind spots or missed inspections. The present invention has achieved better results in inspection efficiency, inspection coverage, and cooperative control of UAV clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 An overall flow chart of a 110KV transmission line drone cluster inspection control method provided for the first embodiment of the present invention.

[0020] Figure 2 An overall schematic diagram of a 110KV transmission line drone cluster inspection control method provided for the first embodiment of the present invention.

[0021] Figure 3 This is a diagram of an inspection simulation implementation example of a 110KV transmission line UAV cluster inspection control method provided in the second embodiment of the present invention.

[0022] Figure 4 An overall flow chart of a 110KV transmission line drone cluster inspection control system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] Example 1, reference Figure 1-Figure 2 , is an embodiment of the present invention, and provides a 110KV transmission line UAV cluster inspection control method, comprising:

[0025] S1: Generate a three-dimensional map of the inspection area by using the first data, and use the first algorithm to perform distributed task allocation.

[0026] Furthermore, utilizing the first data includes classifying the preprocessed first data into ground features and non-ground features.

[0027] It should be noted that the first data may be point cloud data generated by a drone equipped with a lidar system flying over the inspection area, or optical data provided by a drone carrying optical impact equipment through flight photography to provide detailed information on surface features and objects, or other data that can generate a three-dimensional map of the inspection area.

[0028] In an embodiment of the present application, the first data used is point cloud data generated by a lidar system carried by a drone when flying in an inspection area. Radar point cloud data is a high-density three-dimensional spatial data set. The drone generates point cloud data by flying in the inspection area with the lidar system carried by the drone. These point cloud data contain thousands of three-dimensional data points, and each point represents the spatial coordinates (x, y, z) of a specific location. The lidar can penetrate vegetation and obtain the accurate material of the ground and obstacles.

[0029] In an optional embodiment, the first data uses optical data of surface features and detailed information of ground objects provided by optical imaging equipment carried by a drone through flight photography. During the cruise, the high-resolution optical camera carried by the drone will automatically shoot a series of images. After the flight, the collected image data will be downloaded to the ground station, and the data will be processed to generate complete and clear optical images. The processed images will be analyzed to identify the features within the inspection area.

[0030] It should also be noted that the preprocessing includes removing noise points from the first data and classifying the data using a filtering algorithm.

[0031] It should also be noted that generating the three-dimensional map of the inspection area includes generating the three-dimensional map by interpolating and gridding the preprocessed first data;

[0032] Three-dimensional maps include digital elevation models and digital terrain models;

[0033] According to the features in the first data, a three-dimensional elevation network is generated by interpolation to obtain a digital elevation model containing all the features; ground features are extracted, buildings and vegetation are removed, and a digital terrain model of the ground is generated by gridding.

[0034] It should also be noted that digital elevation models can be used to analyze the height changes of the entire area, and digital terrain models can be used to analyze the terrain undulations and foundation design of the line.

[0035] It should also be noted that a grid map is generated based on the processed first data, and the grid map divides the area into small grids, each grid corresponding to a determined terrain height or obstacle information.

[0036] It should also be noted that using the first algorithm to distribute distributed tasks includes distributing tasks based on terrain data by using the first algorithm in combination with the first data;

[0037] Combining the first data includes decomposing the inspection task into a plurality of subtasks according to the terrain characteristics of the first data, and setting a priority for each subtask, such as allocating priority to drones with sufficient power in areas with more complex terrain;

[0038] Considering the direct impact of high-voltage transmission line characteristics on flight and control to ensure the safety and reliability of the inspection process, a safety area is defined according to the electric field distribution of the 110kV transmission line. Based on this safety area, the drone will maintain a certain safety distance when approaching the power line to avoid interference from the electric field. When assigning tasks, the system marks the task area close to the transmission line and assigns it to a drone with anti-electromagnetic interference capabilities, or selects a specific flight altitude to reduce electromagnetic interference.

[0039] It should also be noted that the first algorithm may be an auction algorithm, or a particle swarm optimization algorithm, or other algorithms suitable for distributed task allocation.

[0040] In the embodiment of the present application, the first algorithm uses an auction algorithm. By considering the power and distance of the drone and the terrain complexity of the inspection area, the cost function of the auction algorithm is designed, and the task planning is performed based on the actual terrain data; assuming that the task T j The terrain height difference in the area is h j , the obstacle density is o j , then the cost function is expanded as:

[0041]

[0042] Among them, d ijIndicates the current distance between the UAV and the mission location, E max Indicates the maximum battery capacity of the drone, E i Indicates the current battery status of the drone, t ij represents the estimated time to complete task j, α, β, and γ represent the importance coefficients of distance, remaining power, and time, respectively, and h j Indicates the elevation change of the area, o j Indicates the complexity of obstacles in the area, f em (d em ) indicates that the electromagnetic field interference increases with the distance d em The attenuation function ensures that the cost increases when flying in high electric field areas. ε, δ, and σ represent weight coefficients, which are used to balance the impact of different factors on the cost.

[0043] The estimated time includes flight time and mission execution time;

[0044] When bidding, the drone will adjust the cost function according to the terrain information of the mission area. If there are dense obstacles or steep terrain in a certain area, the h in the cost function will be j , o j Improve, so that the tasks in this area are assigned to drones with sufficient power and strong obstacle avoidance capabilities.

[0045] In an optional embodiment, the first algorithm uses a particle swarm optimization algorithm, which regards the drone as a particle and initializes a group of particles. Each particle represents a potential task allocation plan. Each particle has its own position and speed in the search space. The speed and position of the drone are initialized according to the drone performance and formation. The cost function is used to evaluate the performance of each particle, that is, the cost and efficiency of each task allocation plan. Based on the performance of each particle, the optimal performance of the entire group is calculated, the task plan corresponding to each particle is output, and then allocated.

[0046] S2: Perform path search based on the three-dimensional extension of the second algorithm.

[0047] Furthermore, performing path search based on the three-dimensional extension of the second algorithm includes performing three-dimensional extension of the second algorithm, and performing path search by finding a path with minimum cost by expanding neighbor nodes;

[0048] In path planning, a three-dimensional grid map is used to describe the inspection area. The map divides the three-dimensional space into voxels, and each voxel represents the spatial information of a specific area. The spatial information includes terrain height, obstacles, and path passability.

[0049] It should be noted that the electromagnetic interference area and electric field strength around the high-voltage transmission line are incorporated into the three-dimensional path planning. According to the electric field distribution of the 110kV transmission line, an "electromagnetic influence zone" is set. The UAV needs to avoid obstacles while avoiding this area or choose a flight altitude to keep the UAV away from the influence of strong electric fields.

[0050] It should also be noted that the second algorithm may be an A* search algorithm, or a Dijkstra algorithm, or other algorithms suitable for path planning.

[0051] In this embodiment, the second algorithm uses the A* search algorithm for three-dimensional expansion. In the three-dimensional space, the heuristic function h(n) estimates the three-dimensional Euclidean distance from the current n to the target node, and the current starting point p of the drone is start and the target point p goal Mapped to a three-dimensional grid map, the electromagnetic interference area and electric field strength around the high-voltage transmission line are incorporated into the three-dimensional path planning model. According to the electric field distribution of the 110kV transmission line, an "electromagnetic influence zone" is set. The drone needs to avoid obstacles while trying to avoid this area or choose a flight altitude to keep it away from the influence of strong electric fields.

[0052] Considering the horizontal distance, adding the height change and the obstacle cost, the cost function in three-dimensional path planning is expressed as:

[0053] f(n)=g(n)+h(n)

[0054] Among them, g(n) represents the actual cost from the starting point to node n, and h(n) represents the heuristic estimated cost from node n to the target point, using the three-dimensional Euclidean distance;

[0055] The actual cost g(n) includes the flight distance cost d(n) and the altitude change cost h change (n) and the obstacle avoidance cost o(n), expressed as:

[0056] g(n)=d(n)+λ1·h change (n)+λ2·o(n)+λ3·f em (d em )

[0057] Among them, λ1, λ2, and λ3 represent weight coefficients used to adjust the importance of different costs in path planning;

[0058] The heuristic cost h(n) is estimated using the three-dimensional Euclidean distance, expressed as:

[0059]

[0060] Among them, x goal,y goal 、z goal Respectively represent the three-dimensional position of the target point;

[0061] Starting from the starting point, the path with the lowest cost is found by expanding the neighbor nodes. In three-dimensional space, each node has 26 possible neighbor nodes. The A* search algorithm checks the passability of each neighbor node and selects the best path based on the cost function. The path searched by the algorithm is usually composed of multiple straight line segments, and further path smoothing is required to ensure the stability of the drone flight.

[0062] In an optional embodiment, the second algorithm uses the Dijkstra algorithm. The specific steps of the Dijkstra algorithm to implement optimal path planning are to create a graph model, wherein the nodes of the graph model represent key points in the inspection area, such as the turning points of the transmission line or the checkpoint lights, and the edges represent the connections between the nodes. Each edge has a weight, which represents the cost or price of flying the path. The cost or price of the drones is different according to the performance of the drones. The starting point and target point of the drone cluster are determined. The starting point can be the take-off position of the drone, and the target point is the key point of the inspection task or the end point of the entire inspection task. For each node in the graph, Create a distance table at the point to record the shortest path length from the starting point to each node. Initially, the distance from the starting point to itself is 0, and the distance to all other nodes is infinite. All nodes are marked as unvisited. Starting from the starting point, visit each node directly connected to it and update their distance tables. Select the unvisited node with the smallest distance as the next visit point. For each visited node, check whether the path through the node to the adjacent node is shorter than the currently known path. If so, update the distance table and path record. Update iteratively and finally confirm the optimal flight path of the drone cluster in the inspection task based on the distance table and path record.

[0063] S3: The third control algorithm is used to achieve coordinated flight of UAV clusters, combined with the dynamic window method for real-time obstacle avoidance.

[0064] Furthermore, adopting a third control algorithm to achieve coordinated flight of a swarm of drones includes adopting a third control algorithm to stabilize the flight of the swarm of drones in a coordinated flight formation in three-dimensional space based on attraction, repulsion, and alignment force.

[0065] It should be noted that the third control algorithm may be a cluster control algorithm or other algorithms suitable for coordinated flight of drone clusters.

[0066] In this embodiment, the third control algorithm uses a cluster control algorithm, which is based on three behavioral forces: attraction, repulsion, and alignment;

[0067] The position, speed and neighbor relationship in three-dimensional space are modeled using the cluster control algorithm. The state of each drone in three-dimensional space includes the position p i (t), speed v i (t) and the distance d to the neighboring drone ij ;

[0068] Position p i (t), expressed as:

[0069] p i (t) = [x i ,y i ,z i ]

[0070] Speed i (t), expressed as:

[0071]

[0072] Attraction is the force that brings drones closer together, the compactness of the location cluster. In three-dimensional space, the calculation formula of attraction is expressed as:

[0073]

[0074] Among them, η ij (t) represents the attraction coefficient;

[0075] Repulsive force is the force that moves two drones away from each other to avoid collision when they are too close. In three-dimensional space, the calculation formula of repulsive force is expressed as:

[0076]

[0077] Among them, ρ ij (t) represents the rejection coefficient;

[0078] The repulsion coefficient is inversely proportional to the distance between drones. When the distance is too small, the repulsion force increases sharply;

[0079] The alignment force is a force that ensures that the flight direction of the drone is consistent with that of its neighbors, so that all drones in the cluster fly in the same direction. The calculation formula of the alignment force is expressed as:

[0080]

[0081] The total motion force of each drone is the weighted sum of attraction, repulsion, and alignment forces, expressed as:

[0082]

[0083] The combined force acts on the drone’s flight control system, adjusting the drone’s speed and direction in real time, ensuring the drone maintains a safe distance and follows the cluster.

[0084] It should also be noted that the real-time obstacle avoidance combined with the dynamic window method includes the drone evaluating multiple feasible paths through a dynamic window according to the current state and selecting the optimal path for execution;

[0085] Obtain the current obstacle position, size and shape information from the three-dimensional grid map, generate the obstacle set around the drone, and generate feasible speed and steering angle combinations, i.e., feasible speed space, based on the drone's current flight status;

[0086] Through the dynamic window method, the feasible range of the current speed v and angular velocity ω of the UAV is searched;

[0087] According to the physical characteristics of the drone, including the maximum acceleration, steering angle limit, etc., a feasible speed space is generated, which is expressed as:

[0088] V={(v,ω)v min ≤v≤v max ,ω min ≤ω≤ω max}

[0089] Each speed combination is scored and the comprehensive score of the path in terms of avoiding obstacles, approaching the target and flight efficiency is calculated;

[0090] At each moment, the drone selects an optimal v and ω from the velocity space, safely avoids obstacles and moves toward the target point;

[0091] The optimization objective U(v,ω) consists of three main parts, expressed as:

[0092] U(v,ω)=α'·heading(v,ω)+β'·velocity(v,ω)-γ'·obstacle(v,ω)

[0093] Among them, heading represents the direction cost between the UAV and the target point, which encourages the UAV to fly in the target direction; velocity represents the speed cost of the UAV, which ensures flight efficiency and avoids too low speed; obstacle represents the obstacle cost, which ensures that the UAV avoids obstacles and maintains a safe distance; α', β', and γ' represent different weight parameters, which are used to adjust the relative importance of different targets in path planning.

[0094] The path with the highest score is selected, and the speed and direction of the drone are updated according to the path. The drone moves along the selected optimal path and re-evaluates obstacles and paths at every moment to ensure real-time obstacle avoidance.

[0095] It should also be noted that when optimizing the function, the system also considers constraints, which include obstacle distance constraints as well as speed and steering constraints;

[0096] Among them, the obstacle distance constraint is to ensure that the distance between the drone and the obstacle is always greater than the safety distance, otherwise the calculated path is invalid;

[0097] Speed ​​and steering constraints limit the maximum speed and steering angle of the drone, ensuring that flight performance complies with physical limitations during flight.

[0098] Example 2, reference Figure 3 , which is an embodiment of the present invention, provides a 110KV transmission line UAV cluster inspection control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0099] like Figure 3 A specific simulation example is shown in the figure. In the figure, the green dot represents the target point, the red circle area represents the obstacle area, the sky blue circle area represents the electromagnetic interference area, and the dark blue circle represents the drone, a total of six. This embodiment simulates the process of multiple drones working together to complete the inspection task in a complex power transmission line environment. A geographical environment containing a variety of terrains, such as mountains, plains, rivers, etc. and obstacles, such as transmission towers, trees, buildings, etc., is constructed in the simulation environment to simulate the actual power transmission line inspection scene. Six drones were used in the experiment, and each drone can collect and obtain the terrain and obstacle information of the inspection area. The drone cluster inspection control method proposed in the present invention is adopted.

[0100] Specific as Figure 3 As shown, first is the initial stage of the inspection process, the drones are at the origin of the base, then the inspection path is planned to avoid obstacles, and the inspection is carried out after the planning is completed. During the inspection process, the drones first cooperate to complete the inspection task 1. If the drones are insufficient in power during the inspection process and are unable to complete the inspection task, they will return to the base first, and the remaining drones will perform their other inspection tasks respectively. Finally, the inspection is completed and all drones return to the base.

[0101] This simulation embodiment greatly shortens the inspection time and improves the inspection coverage rate of the transmission line. Through intelligent scheduling and task allocation, a 100% task completion rate is achieved, and the collaboration between drones is efficient and avoids mutual interference. The inspection process not only successfully avoids obstacles, but also successfully avoids the impact of electromagnetic interference on drone flight by optimizing paths and selecting specific flight altitudes, and can ensure the safe flight of drones in complex environments. Therefore, the 110KV transmission line drone cluster inspection control method proposed in the present invention has significant advantages in improving inspection efficiency, coverage, data collection accuracy, drone collaboration efficiency, path planning optimization degree, and obstacle avoidance success rate, and can effectively avoid the impact of electromagnetic interference on drone flight.

[0102] Example 3, reference Figure 4 , as an embodiment of the present invention, provides a 110KV transmission line UAV cluster inspection control system, including a task planning and allocation module, a three-dimensional path planning module, and a cluster collaborative flight module;

[0103] The task planning and allocation module is used to generate a three-dimensional map of the inspection area by using the first data, and use the first algorithm to perform distributed task allocation; the three-dimensional path planning module is used to perform path search based on the three-dimensional extension of the second algorithm; the cluster collaborative flight module is used to adopt the third control algorithm to realize the coordinated flight of the drone cluster, combined with the dynamic window method for real-time obstacle avoidance.

[0104] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0106] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0107] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A 110KV transmission line drone cluster inspection control method, characterized in that: include: Generate a three-dimensional map of the inspection area by using the first data, and perform distributed task allocation using the first algorithm; Perform path search based on the three-dimensional extension of the second algorithm; The third control algorithm is used to realize coordinated flight of UAV clusters, combined with the dynamic window method for real-time obstacle avoidance.

2. The 110KV transmission line UAV cluster inspection control method according to claim 1 is characterized in that: The utilizing the first data includes classifying the preprocessed first data into ground features and non-ground features.

3. The 110KV transmission line UAV cluster inspection control method according to claim 2 is characterized in that: Generating the three-dimensional map of the inspection area includes generating the three-dimensional map by interpolating and gridding the pre-processed first data; The three-dimensional map includes a digital elevation model and a digital terrain model.

4. The 110KV transmission line UAV cluster inspection control method according to claim 3 is characterized in that: The using the first algorithm to perform distributed task allocation includes using the first algorithm in combination with the first data to perform distributed task allocation based on the terrain data.

5. The 110KV transmission line UAV cluster inspection control method according to claim 4 is characterized in that: The path search based on the three-dimensional extension of the second algorithm includes three-dimensionally extending the second algorithm and performing the path search by finding the path with the minimum cost by expanding neighbor nodes.

6. The 110KV transmission line UAV cluster inspection control method according to claim 5 is characterized in that: The use of the third control algorithm to achieve coordinated flight of the drone cluster includes using the third control algorithm to stabilize the drone cluster in a coordinated flight formation in three-dimensional space based on attraction, repulsion and alignment force.

7. The 110KV transmission line UAV cluster inspection control method according to claim 6 is characterized in that: The real-time obstacle avoidance combined with the dynamic window method includes the UAV evaluating multiple feasible paths through a dynamic window according to the current state, and selecting the optimal path for execution.

8. A system using the 110KV transmission line UAV cluster inspection control method as described in any one of claims 1 to 7, characterized in that: Including task planning and allocation module, 3D path planning module, cluster collaborative flight module; The task planning and allocation module is used to generate a three-dimensional map of the inspection area by using the first data and perform distributed task allocation using the first algorithm; The three-dimensional path planning module is used to perform path search based on the three-dimensional extension of the second algorithm; The cluster cooperative flight module is used to adopt the third control algorithm to realize the coordinated flight of the UAV cluster and combine the dynamic window method to avoid obstacles in real time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the 110KV transmission line drone cluster inspection control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the 110KV transmission line drone cluster inspection control method described in any one of claims 1 to 7 are implemented.

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