Path planning method, device and equipment for mountain photovoltaic power station and medium

Abstract

The invention relates to the field of mountain photovoltaic power station inspection, in particular to a mountain photovoltaic power station path planning method, device, equipment and medium, and the method comprises the steps: obtaining the road network data of a main road of a mountain photovoltaic power station, the static wall data of each partition of the mountain photovoltaic power station, and the access coordinates of the partitions; acquiring positions of multiple target nodes, wherein the positions comprise a plurality of to-be-inspected positions and an inspection starting point position; performing multi-target sorting according to the to-be-inspected position and the inspection starting point position to obtain a multi-target planning sequence; aiming at any two first target nodes in the planning sequence, performing inter-target path planning according to the positions of the two first target nodes, the inlet and outlet coordinates of the partition and the first data; and according to an inter-target path planning result between every two target nodes in the planning sequence, an inspection path plan is obtained. According to the invention, the path can be accurately planned for the mountain photovoltaic power station.

Description

Technical Field

[0001] The present application relates to the field of inspection of mountain photovoltaic power stations, and in particular to a path planning method, device, equipment and medium for mountain photovoltaic power stations. Background Art

[0002] Equipment inspections currently rely primarily on satellite positioning for basic location determination, supplemented by electronic maps for route planning. Furthermore, drone inspection technology is often used to assist with positioning, using aerial images to mark equipment locations and provide feedback to maintenance personnel.

[0003] However, in large-scale mountain photovoltaic power plants, weak signals make satellite positioning difficult, and electronic maps are inaccurate, making route planning less reliable during inspections. Therefore, accurately planning routes for mountain photovoltaic power plants is a pressing technical challenge for those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to provide a path planning method, device, equipment and medium for a mountain photovoltaic power station, which can accurately plan the path for a mountain photovoltaic power station.

[0005] In a first aspect, a path planning method for a mountain photovoltaic power station is provided, comprising: obtaining road network data of a main road of the mountain photovoltaic power station, static wall data of each partition of the mountain photovoltaic power station, and partition import and export coordinates, wherein the static wall data comprises partition range vector data and photovoltaic panel vector data within the partition; obtaining positions of multiple target nodes, wherein the positions comprise multiple positions to be inspected and inspection starting positions; sorting multiple targets according to the positions to be inspected and the inspection starting positions to obtain a planning sequence of the multiple targets; for any two first target nodes in the planning sequence, performing inter-target path planning according to the positions of the two first target nodes, partition import and export coordinates, and first data; if the two first target nodes are both in the same partition, the first data comprises static wall data; if the two first target nodes are not in the same partition, the first data comprises static wall data and road network data; obtaining an inspection path planning according to the inter-target path planning result between each two target nodes in the planning sequence.

[0006] In a preferred example, the present application can be further configured as follows: multiple targets are sorted according to the positions to be inspected and the inspection starting positions to obtain a planning order for the multiple targets, including: selecting a preset number of ants, taking the inspection starting position as the starting position, and determining a position to be inspected for each ant; determining the position to be inspected for each ant until an ant path constructed by each ant is obtained; determining the distance of each ant path, where the distance of the ant path is the sum of the distances between the nodes of the ant path; based on the distance of each ant path, selecting the ant path with the shortest distance as the optimal path for the current round, and performing iterative processing to obtain the final optimal path to determine the planning order for the multiple targets.

[0007] In a preferred example, the present application can be further configured to: determine the distance of each ant path, including: for two adjacent second target nodes of the ant path, determine whether the two adjacent second target nodes of the ant path are in the same partition; if they are in the same partition, use Euclidean distance to determine the node-to-node distance between the two adjacent second target nodes; if they are not in the same partition, use Euclidean distance to determine the first distance between each second target node and the nearest neighbor's import and export position; determine the second distance between the two nearest neighbor's import and export positions based on the road network data; determine the node-to-node distance between the two adjacent second target nodes based on the first distance and the second distance; determine the distance of the ant path based on the node-to-node distance of the two adjacent second target nodes.

[0008] In a preferred example, the present application can be further configured to: obtain static wall data of each partition of the mountain photovoltaic power station, including: obtaining drone aerial images of each partition of the mountain photovoltaic power station; compressing the drone aerial images to obtain processed images; performing edge extraction based on the processed images to obtain static wall data of each partition of the mountain photovoltaic power station.

[0009] In a preferred example, the present application can be further configured as follows: according to the positions of the two first target nodes, the partition import and export coordinates and the first data, the path planning between targets is performed pixel by pixel, including: if the two first target nodes are in the same partition, the path planning between targets is performed according to the positions of the two first target nodes and the static wall data; if the two first target nodes are not in the same partition, the path planning between targets is performed according to the positions of the two first target nodes, the static wall data and the road network data.

[0010] In a preferred example, the present application can be further configured as follows: based on the positions of the two first target nodes, the static wall data and the road network data, inter-target path planning is performed, including: determining the import and export positions of the nearest neighbors corresponding to the two first target nodes respectively; if the starting point of the two first target nodes is within the partition, based on the position of the starting point, the corresponding import and export position of the nearest neighbor and the static wall data, determining the first sub-path planning between the two first target nodes and the corresponding import and export positions of the nearest neighbors pixel by pixel; based on the import and export positions of the nearest neighbors corresponding to the two first target nodes respectively and the road network data, determining the second sub-path planning between the import and export ; The inter-target path planning includes: the first sub-path planning and the second sub-path planning; if the starting point of the two first target nodes is not in the partition, the intermediate segment path between the line segment of the starting point in the road network data, the import and export position of the nearest neighbor of the starting point and the import and export position of the nearest neighbor of the end point is determined according to the road network data; the third sub-path planning between the import and export of the nearest neighbor corresponding to the starting point to the end point is determined according to the line segment and the intermediate segment path; the fourth sub-path planning between the import and export position of the nearest neighbor of the end point and the position of the end point is determined pixel by pixel. The inter-target path planning includes: the third sub-path planning and the fourth sub-path planning.

[0011] In a preferred example, the present application can be further configured as follows: determining the third sub-path planning between the nearest neighbor entrances and exits corresponding to the starting point to the end point based on the line segment and the intermediate segment path, including: if the intermediate segment path includes the line segment, then removing the path between the starting point and the nearest neighbor entrance and exit positions of the starting point in the intermediate segment path to obtain the third sub-path planning; if the intermediate segment path does not include the line segment, then determining the path between the starting point in the line segment and the nearest neighbor entrance and exit positions of the starting point and the intermediate segment path as the third sub-path planning.

[0012] In a second aspect, a path planning device for a mountain photovoltaic power station is provided, comprising: a first acquisition module for acquiring road network data of a main road of the mountain photovoltaic power station, static wall data of each partition of the mountain photovoltaic power station, and partition import and export coordinates; a second acquisition module for acquiring positions of multiple target nodes, the positions including multiple positions to be inspected and inspection starting positions; a multi-target sorting module for sorting multiple targets according to the positions to be inspected and the inspection starting positions to obtain a planning sequence of the multiple targets; an inter-target path planning module for performing inter-target path planning for any two first target nodes in the planning sequence according to the positions of the two first target nodes, partition import and export coordinates, and first data, where if the two first target nodes are both in the same partition, the first data includes static wall data; if the two first target nodes are not in the same partition, the first data includes static wall data and road network data; a determination module for obtaining an inspection path plan based on the inter-target path planning result between each two target nodes in the planning sequence.

[0013] In a third aspect, an electronic device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the path planning method for a mountain photovoltaic power station as described in any one of the first aspects when running the computer program.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein at least one program code is stored in the computer-readable storage medium, and the program code is loaded and executed by a processor to implement the path planning method for a mountain photovoltaic power station as described in any one of the first aspects.

[0015] In a fifth aspect, a computer program product is provided, comprising a computer program or instructions, which, when executed by a processor, implements the path planning method for a mountain photovoltaic power station as described in any one of the first aspects.

[0016] In summary, the path planning method for a mountain photovoltaic power station provided by the present application includes the following beneficial technical effects: obtaining the road network data, static wall data, and partition import and export coordinates, positions to be inspected, and inspection starting positions of the mountain photovoltaic power station. First, multi-target sorting is performed between nodes to obtain a reasonable planning sequence; for any two first target nodes in the planning sequence, pixel-level search is used according to their positions, partition import and export coordinates, and first data to perform inter-target path planning, thereby ensuring the accuracy and feasibility of path planning, especially when inspecting across partitions, the road network data can be fully utilized; by integrating the inter-target path planning results between each two target nodes, a complete inspection path planning is obtained; the present application realizes efficient and accurate planning of inspection paths, reduces inspection time and costs, and improves the operation and maintenance efficiency of mountain photovoltaic power stations. In addition, the present application also provides a path planning device, equipment, and medium for a mountain photovoltaic power station, all of which have the above-mentioned beneficial technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 This is a flow chart of a path planning method for a mountain photovoltaic power station provided in an embodiment of the present application.

[0019] Figure 2 This is a flowchart of a multi-objective sorting process based on an ant colony algorithm provided in an embodiment of the present application.

[0020] Figure 3 This is a flowchart of determining the third sub-path planning provided by an embodiment of the present application.

[0021] Figure 4 This is a flowchart of a path planning process between targets provided in an embodiment of the present application.

[0022] Figure 5a This is a schematic diagram of single-point navigation within a partition provided in an embodiment of the present application.

[0023] Figure 5b This is a schematic diagram of multi-point navigation within a partition provided in an embodiment of the present application.

[0024] Figure 6a This is a schematic diagram of single-point navigation between partitions provided in an embodiment of the present application.

[0025] Figure 6bThis is a schematic diagram of inter-partition multipoint navigation provided by an embodiment of the present application.

[0026] Figure 7a This is a schematic diagram of a trunk road to single point navigation provided in an embodiment of the present application.

[0027] Figure 7b This is a schematic diagram of trunk road to multi-point navigation provided in an embodiment of the present application.

[0028] Figure 8 This is a structural diagram of a path planning device for a mountain photovoltaic power station provided in an embodiment of the present application.

[0029] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by patent law.

[0031] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the permission of the object, the permission of the relevant department, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the permission of the object.

[0032] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0034] Currently, basic positioning relies primarily on satellite positioning, supplemented by electronic maps for route planning. Drone inspection technology is also often used to assist with positioning, using aerial images to mark equipment locations and provide feedback to power plant operators.

[0035] However, these technologies cannot provide a reliable path planning method in large-scale mountain photovoltaic power station scenarios.

[0036] In order to solve the above problems, the present application provides a path planning and navigation technology for operation and maintenance personnel of mountain photovoltaic power stations based on drone aerial photography. The present invention obtains high-precision base map images of power stations through drone aerial photography, and extracts road networks through algorithms. It is necessary to solve the problems of vegetation coverage, shadow areas, terrain undulations, uneven lighting, etc. that affect the recognition of road network features. At the path planning level, the road network of mountain power stations is often accompanied by complex terrain such as steep slopes, cliffs, and gullies, and a comprehensive analysis based on high-precision terrain data and road network information is required. If the traditional path planning algorithm does not deeply integrate the terrain details of the aerial map, it is easy to plan unfeasible or dangerous paths and cannot meet actual needs. At the same time, in terms of navigation, it is necessary to combine the road network and the actual location of the equipment, and to achieve the optimal route design for multiple faulty equipment. This can greatly improve the efficiency of operation and maintenance personnel in finding faulty equipment and reduce their safety risks.

[0037] The present application embodiment provides a path planning method for a mountain photovoltaic power station, such as Figure 1 As shown, the method provided in the embodiment of the present application can be executed by an electronic device, and the electronic device is a server. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the electronic device can be directly or indirectly connected via wired or wireless communication. The embodiment of the present application does not limit this. The method includes: S101, obtaining road network data of the main road of the mountain photovoltaic power station, static wall data of each partition of the mountain photovoltaic power station, and the partition import and export coordinates. The static wall data includes: partition range vector data, photovoltaic panel vector data within the partition.

[0038] A mountain photovoltaic power station refers to a photovoltaic power generation system built on mountainous terrain, utilizing solar energy resources and underutilized land to convert solar energy into electricity. In this embodiment of the present application, the mountain photovoltaic power station is divided into multiple zones, each containing multiple photovoltaic panels. Each zone can be bounded by a fence, and the roads between zones form a road network. The road network data for the main roads represents the layout of the roads within the mountain photovoltaic power station area, including their location, direction, and connectivity, and indicates the distribution of roads within the power station. The static wall data for each zone of the mountain photovoltaic power station refers to a fixed, unchanging data set associated with the zone. It can be represented as a set of obstacle data, indicating locations that are inaccessible to operation and maintenance personnel. This static wall data can include: zone range vector data and photovoltaic panel vector data within the zone. The zone range vector data represents the boundary range of each zone, accurately describing the zone's outline in vector form, making it easier to determine the zone's size and shape. The photovoltaic panel vector data within the zone represents the distribution of photovoltaic panels within the zone. Each zone corresponds to one or more zone entrances and exits. The zone entrance and exit coordinates represent the specific locations of each zone entrance and exit in the coordinate system, allowing accurate identification of the entrance and exit locations.

[0039] In this embodiment, since complete road network data within a mountain photovoltaic power station is difficult to obtain directly, even manual annotation cannot be very accurate. Therefore, in this embodiment, the mountain photovoltaic power station is divided into multiple sub-areas, each with fixed entrances and exits, and only the entrances and exits are accessible. The main roads outside the sub-areas can be manually annotated to obtain road network data, and then, within the sub-areas, a pixel-by-pixel navigation method is used for route planning.

[0040] In the embodiments of this application, the included path planning algorithm requires trunk road network data, zone range vector data, zone entry and exit coordinate data, and zone photovoltaic panel vector data. The trunk road network data is primarily used for trunk road path planning; the photovoltaic panel vector data and zone range vector data are primarily used for static wall generation for zone path planning.

[0041] The purpose of static walls is to provide prior data for the path planning algorithm. Specifically, the coordinates of the walls are considered impassable, which is used to determine the navigation route. This step is primarily used for pixel-by-pixel navigation within a partition. Therefore, the wall data consists of multiple coordinate points. As you can see, the edges of the photovoltaic panels and the partitions are designed as walls. To better adapt to the pixel-by-pixel navigation algorithm, the static walls are converted from the geographic coordinate system to the pixel coordinate system.

[0042] To further reduce the size of the offline file, the number of static walls needs to be further controlled. In practice, in addition to edge extraction (using the number of pixels as static walls), image compression is also performed to reduce the pixel resolution. Specifically, drone-photographed images of the photovoltaic power plant are obtained and compressed to reduce the pixel resolution. The images of the photovoltaic power plant are annotated with road network data, partition range vector data, and partition entry and exit coordinates. Furthermore, photovoltaic panels in the images are identified to obtain photovoltaic panel vector data. Based on the photovoltaic panel vector data and partition range vector data, static wall data is determined.

[0043] S102: Acquire the locations of multiple target nodes, where the locations include multiple locations to be inspected and an inspection starting point.

[0044] Among them, the multi-target node is multiple target points and inspection starting points, and the inspection starting point is its current location; the multiple locations to be inspected are locations that need to be inspected, which can be the locations of maintenance targets, such as photovoltaic panels, inverters, brackets and other equipment.

[0045] After completing S101 and S102, in this embodiment, the path planning algorithm is the core of the path planning and navigation technology. Due to its universality, it can be reused for navigation across different photovoltaic power plants. To address the needs of multi-target navigation applications, a two-stage algorithm solution was designed. The first stage prioritizes multiple navigation targets (maintenance targets, multiple photovoltaic panels) and determines a target navigation sequence for the shortest route (step S103). The second stage, based on the navigation sequence, plans a path between the two targets (step S104).

[0046] S103 , sorting multiple targets according to the positions to be inspected and the inspection starting positions to obtain a planning order for the multiple targets.

[0047] In some embodiments, multi-target node sorting can be achieved through various methods: Alternatively, an ant colony algorithm can be used to sort multiple target nodes using the Euclidean distance between any two points as a cost function. Alternatively, a genetic algorithm or particle swarm optimization can be used to sort multiple target nodes. It is understood that other methods can also be used to achieve multi-target node sorting, which are not limited here.

[0048] This sorting process ensures that in the subsequent inter-node path planning, the inter-node path planning can be carried out according to the sorted node order, that is, the priority order.

[0049] S104 : For any two first target nodes in the planning sequence, perform inter-target path planning according to the positions of the two first target nodes, the partition import and export coordinates, and the first data.

[0050] If the two first target nodes are both in the same partition, the first data includes: static wall data; if the two first target nodes are not in the same partition, the first data includes: static wall data and road network data.

[0051] This step can plan the paths between nodes and obtain the target path planning results between every two target nodes in the planning sequence.

[0052] S105 , obtaining an inspection path plan according to the inter-target path planning result between every two target nodes in the planning sequence.

[0053] According to the planning order, the target path planning results between every two target nodes are connected to obtain the inspection path planning.

[0054] It can be seen that in the embodiment of the present application, the road network data, static wall data, and the coordinates of the partition entrance and exit, the position to be inspected, and the inspection starting point of the mountain photovoltaic power station are obtained. First, multi-target sorting is performed between nodes to obtain a reasonable planning order; for any two first target nodes in the planning order, pixel-level search is used according to their positions, partition entrance and exit coordinates, and the first data to perform inter-target path planning, ensuring the accuracy and feasibility of path planning, especially when inspecting across partitions, the road network data can be fully utilized; by integrating the inter-target path planning results between each two target nodes, a complete inspection path planning is obtained; the present application realizes efficient and accurate planning of inspection paths, reduces inspection time and costs, and improves the operation and maintenance efficiency of mountain photovoltaic power stations.

[0055] A possible implementation method of the embodiment of the present application is that S103 sorts multiple targets according to the position to be inspected and the inspection starting position to obtain a planning order of the multiple targets, including: selecting a preset number of ants, taking the inspection starting position as the starting position, and determining a position to be inspected for each ant; determining the position to be inspected for each ant until an ant path constructed by each ant is obtained; determining the distance of each ant path, the distance of the ant path is the sum of the distances between the nodes of the ant path; according to the distance of each ant path, selecting the ant path with the shortest distance as the best path for the current round, and performing iterative processing to obtain the final best path to determine the planning order of the multiple targets.

[0056] The ant colony algorithm (ACO) explains its specific process: Ants' paths represent feasible solutions to the problem to be optimized. All paths taken by the entire ant colony constitute the solution space. As ants release pheromones during their travels, these pheromones evaporate over time. Therefore, ants with shorter paths release more pheromones, and therefore tend to choose paths rich in pheromones. Over time, the pheromone concentration on shorter paths gradually increases, and an increasing number of ants choose these paths. Ultimately, through positive feedback, the ants converge on the optimal path, which corresponds to the optimal solution to the problem to be optimized.

[0057] See also Figure 2 , Figure 2 This is a flowchart of a multi-objective sorting process based on an ant colony algorithm provided by an embodiment of the present application, including: data preprocessing; initialization parameters, wherein the initialization parameters include a preset number m of ants, a pheromone volatility coefficient, and the number of iterations; placing m ants, and placing ants at preset m points of the positions to be inspected; determining a current candidate road set for each ant, the current candidate road set being all positions to be inspected except the inspection starting point and the current ant's position; selecting the next moving position for each ant based on probability in the candidate road set; judging whether the path is completed, that is, judging whether the ant has completed all the nodes. An ant starts from the starting point and visits a series of nodes in sequence. The target location is listed. If the path is not complete, the process returns to the step of determining the current candidate path set for each ant. If so, the best path among the m ants is selected. In each round (iteration), the shortest path among all the current ant paths is selected as the optimal path for the current round. Distances between nodes are generally calculated using Euclidean distance to determine whether the maximum number of iterations has been reached. If so, the optimal path is determined. If not, the pheromone is updated, which includes pheromone volatilization and pheromone enhancement. Pheromone volatilization is the process of automatically and gradually reducing the concentration of pheromones on each path, while pheromone enhancement is the process of enhancing the pheromone of the determined optimal path. When the number of iterations has been reached, the shortest path is selected as the final optimal path based on the paths obtained in the final round to determine the planning order for multiple objectives.

[0058] It can be seen that in the embodiment of the present application, planning the target sequence through the ant algorithm can further improve the planning accuracy.

[0059] Furthermore, considering that in scenarios with complex road conditions and changeable terrain, factors such as the actual road direction are not taken into account, sorting based solely on Euclidean distance may result in insufficient sorting accuracy, affecting the accuracy and practicality of path planning. In the embodiment of the present application, the cost function of the Euclidean distance can be modified, and a distance that is closer to the actual situation can be used as a new cost function. It is mainly to calculate the Euclidean distance from the starting point and the end point to the nearest entrance and exit in the partition, determine the actual distance between the two entrances and exits, and use the sum of these two distances as the cost function of the ant colony algorithm, so as to greatly improve the accuracy of multi-target sorting.

[0060] Based on this, a possible implementation method of an embodiment of the present application is to determine the distance of each ant path, including: for two adjacent second target nodes of the ant path, determining whether the two adjacent second target nodes of the ant path are in the same partition; if they are in the same partition, using Euclidean distance to determine the node-to-node distance between the two adjacent second target nodes; if they are not in the same partition, using Euclidean distance to determine the first distance between each second target node and the nearest neighbor's import and export position; determining the second distance between the two nearest neighbor's import and export positions based on road network data; determining the node-to-node distance between the two adjacent second target nodes based on the first distance and the second distance; and determining the distance of the ant path based on the node-to-node distance between the two adjacent second target nodes.

[0061] For two adjacent target nodes u and v in an ant path, the distance is calculated as follows: Case 1: Nodes u and v are in the same partition. The Euclidean distance is used directly to calculate the distance between them. Case 2: Nodes u and v are not in the same partition. The nearest neighbor's entry and exit locations are found. For node u, all entry and exit locations in its partition are found, and the Euclidean distance from node u to each entry and exit location is calculated. The nearest entry and exit location eu is selected. Similarly, for node v, all entry and exit locations in its partition are found, and the Euclidean distance from node v to each entry and exit location is calculated. The nearest entry and exit location ev is selected. Calculate the Euclidean distance d1 from node u to eu. Calculate the Euclidean distance d2 from node v to ev. Based on the road network data, find the actual road distance droad between eu and ev. Therefore, the total distance between node u and node v is: dtotal = d1 + droad + d2.

[0062] Furthermore, after obtaining the distances between all adjacent nodes, they are summarized and added to obtain the distance of the ant path.

[0063] It can be seen that in the embodiment of the present application, by combining partitions, import and export locations and road network data, the actual driving distance between cross-partition nodes can be calculated more accurately.

[0064] In a possible implementation of the embodiment of the present application, the static wall data further includes: terrain obstacle vector data.

[0065] A possible implementation method of an embodiment of the present application, obtaining static wall data of each partition of a mountain photovoltaic power station in S101, includes: obtaining drone aerial images of each partition of the mountain photovoltaic power station; compressing the drone aerial images to obtain processed images; and performing edge extraction based on the processed images to obtain static wall data of each partition of the mountain photovoltaic power station.

[0066] Acquire drone aerial images of various partitions of a mountain photovoltaic power station, and then compress the images, specifically using lossless compression, to significantly reduce the file size while ensuring visual quality. Due to the reduced data volume, the amount of data obtained when further edge extraction is performed to determine static wall data is relatively small.

[0067] When performing edge extraction, algorithms such as Canny edge detection, Sobel edge detection, and LoG can be used to extract edges, and then edge connection can be performed to remove noise edges.

[0068] Furthermore, when using a drone for aerial photography, you can also set the drone's shooting height and angle to ensure sufficient image overlap and perform image stitching, further improving image accuracy. Furthermore, if the light is low when taking photos, you can appropriately increase the ISO parameter, reduce the shutter speed, and increase the amount of light entering.

[0069] It can be seen that in the embodiment of the present application, by compressing the image, the pixel resolution can be reduced, thereby controlling the number of static walls.

[0070] Furthermore, for path planning between two nodes, we can use Algorithm / Dijkstra algorithm, D* algorithm for pixel-level path search. Algorithm as an example The algorithm flow includes: 1. Add the starting point to the open list. 2. Traverse the open list and find the node with the smallest F value, and use it as the current node to be processed. Move the node to the close list. F=G+H, G is the cost of moving from the starting point to the specified point, and H is the estimated cost of moving from the specified point to the end point. 3. For the adjacent nodes of the current node. If it is unreachable or in the close list, ignore it. Otherwise, do the following: If the node is not in the open list, add it to the open list, and set the current square as its parent, and record the F, G, and H values ​​of the node. If it is already in the open list, check whether this path (that is, reaching it via the current square) is better, using the G value as a reference. A smaller G value indicates that this is a better path. If so, set its parent node as the current node and recalculate the G and F values. 4. Repeat the above steps until the end point is added to the open list, at which point the path has been found, or the search for the end point fails, and the open list is empty, and there is no path. 5. Save the path. Starting from the end point, each square moves along the parent node until it reaches the starting point to obtain the shortest path.

[0071] In one possible implementation of the embodiment of the present application, S104 performs inter-target path planning according to the positions of the two first target nodes, the zone import and export coordinates, and the first data, including:

[0072] If the two first target nodes are in the same partition, the path planning between the targets is performed pixel by pixel based on the positions of the two first target nodes and the static wall data;

[0073] If the two first target nodes are not in the same partition, the path planning between the targets is performed based on the positions of the two first target nodes, static wall data and road network data.

[0074] Because the route is planned from the inspection starting point and includes multiple locations to be inspected, the locations of two adjacent first target nodes can be the inspection starting point and the first location to be inspected, or two locations in between (the two locations to be inspected can be in the same zone or different zones). The inspection starting point can be within the zone or in the road network outside the zone, while the locations to be inspected are generally within the zone.

[0075] Based on this, if the two first target nodes are in the same partition, the path planning between the targets can be performed by pixel-by-pixel search based on the A* algorithm / Dijkstra algorithm / D* algorithm. Specifically, taking the A* algorithm as an example, the positions of the two first target nodes are obtained and set as the starting position and the end position, and the static wall data, that is, the pixel-level information of the obstacle, is obtained. The starting point is added to the open list (Open List) and its F=G+H value is recorded (G is the actual cost, and H is the heuristic estimated cost). The specific calculation method is no longer limited in the embodiment of this application, and the user can set it according to actual needs. Take the pixel with the smallest F from the open list as the current node. Check the adjacent pixels (usually 8 neighborhoods): If it is a walkable area (non-wall and unvisited), calculate its G and H values. If the pixel is in the closed list (Closed List) and the new G value is better, it is updated. Until the end point is found, the path planning between the targets is realized.

[0076] If the two first target nodes are not in the same partition, there are two situations: one is: the starting position is in the road network and the end position is in the partition; the other is that the starting position and the end position are not in the same partition. In this case, the path planning between the targets can be performed based on the positions of the two first target nodes, static wall data and road network data.

[0077] In one achievable manner, if the two first target nodes are not in the same partition, the manner of planning the path between the targets includes:

[0078] SA1. Determine the import and export positions of the nearest neighbors corresponding to the two first target nodes.

[0079] In one feasible manner, when determining the entrance and exit positions of the nearest neighbor corresponding to the first target node, the distance between the position of the first target node and each entrance and exit position of the partition where it is located can be determined by using the Euclidean distance, and then the entrance and exit position corresponding to the shortest distance is selected as the entrance and exit position of the nearest neighbor corresponding to the first target node; or, based on the position of the first target node and each entrance and exit position of the partition where it is located, as well as the static wall data of the partition, the distance between the first target node and each entrance and exit position of the partition where it is located can be determined by using the Euclidean distance. Algorithm / Dijkstra algorithm / The algorithm searches pixel by pixel and plans the path between targets to ensure that obstacles are avoided and the path is as short as possible. Then, the distance of the path corresponding to each entrance and exit is determined, and then the entrance and exit position corresponding to the shortest distance is selected as the entrance and exit position of the nearest neighbor corresponding to the first target node.

[0080] SA2. If the starting point of the two first target nodes is within the partition, the first sub-path planning between the two first target nodes and the corresponding nearest neighbor's import and export positions is determined pixel by pixel based on the position of the starting point, the corresponding nearest neighbor's import and export positions, and the static wall data; the second sub-path planning between the imports and exports is determined based on the import and export positions and road network data of the nearest neighbors corresponding to the two first target nodes respectively; the inter-target path planning includes: the first sub-path planning and the second sub-path planning.

[0081] In the embodiment of the present application, it indicates that the two first target nodes are located in two partitions respectively. Then the path between the two first target nodes includes: a first sub-path planning between the starting point and the first entrance and exit, a second sub-path planning between the first entrance and exit and the second entrance and exit corresponding to the end point, and a first sub-path planning between the second entrance and exit corresponding to the end point and the end point. The first sub-path planning can be adopted Algorithm / Dijkstra algorithm / The algorithm,searches pixel by pixel to plan the path between targets, while,the second sub-path planning combines the actual path of the road network for,planning.

[0082] SA3. If the starting point of the two first target nodes is not within the partition, determine the middle segment path between the line segment of the starting point in the road network data, the entry and exit positions of the nearest neighbor of the starting point, and the entry and exit positions of the nearest neighbor of the end point based on the road network data; determine the third sub-path planning between the entry and exit positions of the nearest neighbors corresponding to the starting point and the end point based on the line segment and the middle segment path; determine the fourth sub-path planning between the entry and exit positions of the nearest neighbor of the end point and the position of the end point pixel by pixel. The inter-target path planning includes: the third sub-path planning and the fourth sub-path planning.

[0083] In an embodiment of the present application, if the starting point of the two first target nodes is not within the partition, it means that the starting point is likely to be the inspection starting point. At this time, based on the road network data, the middle segment path between the line segment of the starting point in the road network data, the import and export positions of the nearest neighbor of the starting point, and the import and export positions of the nearest neighbor of the end point can be determined.

[0084] Then, the third and fourth sub-path plans are determined. For the third sub-path plan, if the intermediate path segment includes the line segment, the path between the starting point and the nearest neighbor's entrance and exit locations in the intermediate path segment is removed to obtain the third sub-path plan. If the intermediate path segment does not include the line segment, the path between the starting point and the nearest neighbor's entrance and exit locations in the line segment and the path are determined as the third sub-path plan.

[0085] For example, see Figure 3, where a, b, and e are the target locations, q and p are the entrances and exits, c is the intersection of L1 and L2 in the road network data, and d is the intersection of L1 and L3 in the road network data. If a is the starting point and e is the ending point, then the line segment from the starting point in the road network data is a→c, and the two nearest entrances and exits are q and p respectively. The intermediate path between the nearest neighbor entrance and exit of the starting point and the nearest neighbor entrance and exit of the ending point is q→c→d→p. In this case, a→c is not within q→c→d→p, so the line segment is split to obtain a→q, and the final third subpath is planned as a→q→c→d→p.

[0086] If b is the starting point and e is the end point, the line segment of the starting point in the road network data is b→c, the two nearest neighbor entrances and exits are q and p respectively, and the intermediate segment path between the entrance and exit positions of the nearest neighbor of the starting point and the entrance and exit positions of the nearest neighbor of the end point is q→c→d→p (subdivided into q→b→c→d→p). At this time, the intermediate segment path contains the line segment b→c, so remove q→b in the intermediate segment path and get b→c→d→p as the third sub-path planning.

[0087] For the fourth sub-path planning, based on the entrance and exit positions of the nearest neighbors of the endpoint and the location of the endpoint, the A* algorithm / Dijkstra algorithm / D* algorithm is used to search pixel by pixel to plan the path between targets.

[0088] Based on the above, see Figure 4 , Figure 4 This is a flow chart of path planning between targets provided in an embodiment of the present application, wherein s and e represent the starting point start and the end point end; snd and end represent the starting point's nearest entrance and exit tartNearDoor and the key point's nearest entrance and exit endNearDoor; line represents the line segment where the starting point s is located; way represents the intermediate segment path between snd and end.

[0089] If the starting point s and the end point e are in the same partition, the A* algorithm is directly used to find the optimal path pixel by pixel.

[0090] If the starting point s and the end point e are not in the same partition, it is necessary to determine whether the starting point s is in the partition.

[0091] If the starting point is within the partition, path planning is divided into three phases: planning the path between the starting point and its nearest neighbor, planning the path between the starting point's nearest neighbor and the destination's nearest neighbor, and planning the path between the destination's nearest neighbor and the destination. The first and second phases use the pixel-by-pixel A* algorithm, while the intermediate node-to-node path (between the two entrances and exits) uses a node-by-node path planning algorithm.

[0092] If the starting point is not within the partition, the line segment where the starting point is located and the path between the two nearest nodes of the starting and ending points need to be found. If the way contains the line segment, the way needs to be split, removing the multiple line segments between the starting point and the starting point's nearest node, and then adding them to the planned path. The path from the end point to the nearest node of the end point is also added. If the way does not contain the line segment, the line needs to be split, selecting multiple line segments between the starting point and the starting point's nearest node and adding them to the path. At the same time, the path between the two nearest nodes of the starting and ending points and the path from the end point to the nearest node of the end point are added. If the starting point is not within the partition and the nearest node of the starting point and the nearest node of the end point are the same point, only the line needs to be split and added to the path, and the path between the end point and the nearest node of the end point is added.

[0093] Furthermore, in the path planning algorithm, since the static "wall" offline file used is a record of pixel coordinates, the planned route is also a pixel coordinate, which needs to be converted into the WGS84 (EPSG: 4326) coordinate system for use. Since the inter-target path planning adopts a pixel-by-pixel search algorithm, the planned path will have problems such as severe distortion, and the path and trajectory can be simplified. A possible implementation method of the embodiment of the present application, after obtaining the inspection path planning based on the inter-target path planning results between each two target nodes in the planning sequence, also includes: performing multi-segment smoothing on the inspection path planning. To address this problem, the Bessel spline algorithm / Douglas Peucker algorithm / B-spline algorithm are used to smooth the polylines.

[0094] Furthermore, it is understandable that in existing technologies, satellite positioning is significantly affected by mountainous terrain obstruction and multipath effects, and signals are easily interrupted or exhibit large deviations, making it difficult to meet the needs of precise equipment-level positioning in complex mountainous environments. Electronic maps often lack detailed annotations of subtle terrain, hidden paths, and equipment-intensive areas within mountain power stations. Navigation route planning is divorced from the actual terrain, greatly reducing its practicality. In addition, there is a lack of intelligent recognition and avoidance capabilities for obstacles such as mountain vegetation cover, steep cliffs, and fences. Operation and maintenance personnel are prone to getting stuck in dead ends or dangerous situations when following navigation, and are unable to effectively combine the real-time operating status of power station equipment (such as the priority of faulty equipment) for dynamic navigation optimization. As a result, the efficiency and reliability of positioning navigation are difficult to meet actual operation and maintenance needs.

[0095] However, in this application, it is not necessary to obtain all road networks, only the road networks on the main roads need to be obtained, and path planning is performed within the power station partition with photovoltaic panels as obstacles, saving the initial data acquisition cost; the multi-target node sorting algorithm adopts a method of combining the Euclidean distance within the partition with the real distance of the main road, taking into account a certain accuracy while speeding up the path planning speed; the path planning scheme between two nodes is designed in different ways of intra-partition navigation and inter-partition navigation. The intra-partition navigation uses photovoltaic panels as obstacles and adopts pixel-by-pixel navigation for path planning; the inter-partition path planning adopts node-by-node navigation between entrances and exits to speed up navigation efficiency.

[0096] For this application, the offline navigation system of the mountain photovoltaic power station is tested from three perspectives: navigation within a partition, navigation between different partitions, and navigation from the main road to the target partition.

[0097] For navigation within a partition. See Figure 5a and Figure 5b , Figure 5a This is a schematic diagram of single-point navigation within a partition provided by an embodiment of the present application, wherein the white triangle represents the starting point and the white circle represents the target point, and the curve between the two is the path planning. Figure 5b This is a schematic diagram of multi-point navigation within a zone, as provided by an embodiment of this application. The curve represents the planned path. Rigorous testing was conducted on photovoltaic panel navigation within the same zone, including path planning by clicking on a single photovoltaic panel target and multiple photovoltaic panel targets located in the same zone as the starting point. The experimental results demonstrate that this system can accurately plan routes.

[0098] For navigation between partitions. See Figure 6a and Figure 6b . Figure 6a This is a schematic diagram of single-point navigation between partitions provided in an embodiment of the present application, wherein the white triangle represents the starting point and the white circle represents the target point, and the curve between the two is the path planning. Figure 6b This is a schematic diagram of multi-point navigation between partitions provided in an embodiment of the present application, and the curve represents path planning.

[0099] In addition to testing PV panel navigation within the same zone, we also conducted in-depth research on PV panel navigation across different zones. Experimental results show that the system can quickly and accurately plan paths for both single and multiple targets. This further demonstrates the system's superior performance and efficiency when handling targets in different zones.

[0100] For navigation from main road to target area. Figure 7a and Figure 7b , Figure 7a This is a schematic diagram of a trunk road to single point navigation provided by an embodiment of the present application. Figure 7b This is a schematic diagram of a trunk road to multi-point navigation provided by the embodiment of this application. In addition to considering the navigation situation where the starting point is within the partition, we also fully consider various scenarios in actual applications, especially the situation where the starting point is on the trunk road. For this scenario, we conducted detailed tests and obtained the following results: Figure 7a and Figure 7b These results further verify the stability and reliability of the system in different scenarios, providing users with a more comprehensive and efficient photovoltaic panel navigation service.

[0101] The following is an introduction to a path planning device for a mountain photovoltaic power station provided by an embodiment of the present application. The path planning device for the mountain photovoltaic power station described below and the path planning method for the mountain photovoltaic power station described above can be referred to each other. The path planning device for the mountain photovoltaic power station of this embodiment is set in an electronic device, and the reference Figure 8 , Figure 8 This is a structural block diagram of a path planning device for a mountain photovoltaic power station in one embodiment of the present application, including: a first acquisition module 210, used to obtain road network data of the main road of the mountain photovoltaic power station, static wall data of each partition of the mountain photovoltaic power station, and partition import and export coordinates; a second acquisition module 220, used to obtain the positions of multiple target nodes, the positions including multiple positions to be inspected and inspection starting positions; a multi-target sorting module 230, used to sort multiple targets according to the positions to be inspected and the inspection starting positions to obtain a planning sequence for the multiple targets; an inter-target path planning module 240, used to plan an inter-target path for any two first target nodes in the planning sequence according to the positions of the two first target nodes, the partition import and export coordinates, and the first data. If the two first target nodes are both in the same partition, the first data includes: static wall data; if the two first target nodes are not in the same partition, the first data includes: static wall data and road network data; a determination module 250, used to obtain an inspection path plan based on the inter-target path planning results between each two target nodes in the planning sequence.

[0102] In one feasible manner, the multi-target sorting module 230 is used to: select a preset number of ants, use the inspection starting point as the starting position, and determine a position to be inspected for each ant; determine the position to be inspected for each ant until an ant path constructed by each ant is obtained; determine the distance of each ant path, where the distance of the ant path is the sum of the distances between the nodes of the ant path; based on the distance of each ant path, select the ant path with the shortest distance as the optimal path for the current round, and perform iterative processing to obtain the final optimal path to determine the planning order of multiple targets.

[0103] In one achievable manner, the multi-target sorting module 230 is used to: determine, for two adjacent second target nodes of an ant path, whether the two adjacent second target nodes of the ant path are in the same partition; if they are in the same partition, use Euclidean distance to determine the node-to-node distance between the two adjacent second target nodes; if they are not in the same partition, use Euclidean distance to determine the first distance between each second target node and the import and export position of the nearest neighbor; determine the second distance between the import and export positions of the two nearest neighbors based on the road network data; determine the node-to-node distance between the two adjacent second target nodes based on the first distance and the second distance; and determine the distance of the ant path based on the node-to-node distance of the two adjacent second target nodes.

[0104] In one feasible manner, the first acquisition module 210 is used to: obtain drone aerial images of each partition of the mountain photovoltaic power station; compress the drone aerial images to obtain processed images; and perform edge extraction based on the processed images to obtain static wall data of each partition of the mountain photovoltaic power station.

[0105] In one achievable method, the inter-target path planning module 240 is used to: if the two first target nodes are in the same partition, perform inter-target path planning pixel by pixel based on the positions of the two first target nodes and static wall data; if the two first target nodes are not in the same partition, perform inter-target path planning based on the positions of the two first target nodes, static wall data and road network data.

[0106] In one achievable manner, the inter-target path planning module 240 is used to: determine the entry and exit positions of the nearest neighbors corresponding to the two first target nodes respectively; if the starting point of the two first target nodes is within the partition, determine the first sub-path planning between the two first target nodes and the corresponding nearest neighbor entry and exit positions pixel by pixel based on the position of the starting point, the entry and exit positions of the corresponding nearest neighbors and the static wall data; determine the second sub-path planning between the entry and exit positions based on the entry and exit positions of the nearest neighbors corresponding to the two first target nodes and the road network data; the inter-target path planning includes: the first sub-path planning and the second sub-path planning; if the starting point of the two first target nodes is not within the partition, determine the intermediate segment path between the line segment of the starting point in the road network data, the entry and exit positions of the nearest neighbors of the starting point and the entry and exit positions of the nearest neighbors of the end point according to the road network data; determine the third sub-path planning between the entry and exit positions of the nearest neighbors corresponding to the starting point to the end point according to the line segment and the intermediate segment path; determine the fourth sub-path planning between the entry and exit positions of the nearest neighbors of the end point and the position of the end point pixel by pixel, and the inter-target path planning includes: the third sub-path planning and the fourth sub-path planning.

[0107] In one feasible manner, the inter-target path planning module 240 is used to: if the intermediate segment path includes the line segment, then remove the path between the starting point and the nearest neighbor's import and export positions in the intermediate segment path to obtain the third sub-path planning; if the intermediate segment path does not include the line segment, then determine the path between the starting point and the nearest neighbor's import and export positions in the line segment and the intermediate segment path as the third sub-path planning.

[0108] An electronic device is provided in an embodiment of the present application, such as Figure 9 As shown, Figure 9 The electronic device 300 shown includes: at least one processor 301 ( Figure 9 301 and memory 303. The processor 301 and memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in practical applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0109] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0110] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0111] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0112] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0113] Figure 9 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0114] An embodiment of the present application provides a computer-readable storage medium, which stores at least one program code. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content of the aforementioned method embodiment.

[0115] An embodiment of the present application provides a computer program product, including a computer program or instructions, which implements the corresponding contents of the aforementioned method embodiment when the computer program or instructions are executed by a processor.

[0116] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0117] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A path planning method for a mountain photovoltaic power station, characterized in that: include: Obtaining road network data of the main road of the mountain photovoltaic power station, static wall data of each partition of the mountain photovoltaic power station, and the coordinates of the partition entrance and exit. The static wall data includes: partition range vector data and photovoltaic panel vector data within the partition; Obtaining the locations of multiple target nodes, including multiple locations to be inspected and an inspection starting point; Sorting multiple targets according to the positions to be inspected and the inspection starting positions to obtain a planning order for the multiple targets; For any two first target nodes in the planning sequence, inter-target path planning is performed according to the positions of the two first target nodes, the zone entrance and exit coordinates, and the first data. If the two first target nodes are both in the same zone, the first data includes: static wall data; if the two first target nodes are not in the same zone, the first data includes: static wall data and road network data; An inspection path plan is obtained according to the inter-target path planning result between every two target nodes in the planning sequence.

2. The path planning method for a mountain photovoltaic power station according to claim 1, characterized in that: Sorting multiple targets according to the positions to be inspected and the inspection starting positions to obtain a planning sequence for the multiple targets includes: Select a preset number of ants, use the inspection starting point as the starting position, and determine a position to be inspected for each ant; Determine the location to be inspected for each ant until the ant path constructed by each ant is obtained; Determine the distance of each ant path. The distance of an ant path is the sum of the distances between the nodes of the ant path. According to the distance of each ant path, the ant path with the shortest distance is selected as the best path for the current round, and it is iterated to obtain the final best path to determine the planning order of multiple objectives.

3. The path planning method for a mountain photovoltaic power station according to claim 2, characterized in that: Determine the distance of each ant's path, including: For two adjacent second target nodes of the ant path, determining whether the two adjacent second target nodes of the ant path are in the same partition; If they are in the same partition, the inter-node distance between two adjacent second target nodes is determined using Euclidean distance; If they are not in the same partition, the first distance between each second target node and the nearest neighbor's entrance and exit locations is determined using Euclidean distance; the second distance between the two nearest neighbor's entrance and exit locations is determined based on the road network data; and the inter-node distance between two adjacent second target nodes is determined based on the first distance and the second distance. The distance of the ant path is determined according to the distance between two adjacent second target nodes.

4. The path planning method for a mountain photovoltaic power station according to claim 1, characterized in that: Obtain static wall data for each partition of a mountain photovoltaic power station, including: Obtain drone aerial images of each subarea of ​​a mountain photovoltaic power station; Compressing the drone aerial image to obtain a processed image; Edge extraction is performed based on the processed image to obtain static wall data of each partition of the mountain photovoltaic power station.

5. The path planning method for a mountain photovoltaic power station according to any one of claims 1 to 4, characterized in that: According to the positions of the two first target nodes, the zone import and export coordinates and the first data, a path planning between the targets is performed, including: If the two first target nodes are in the same partition, performing inter-target path planning pixel by pixel based on the positions of the two first target nodes and the static wall data; If the two first target nodes are not in the same partition, inter-target path planning is performed based on the positions of the two first target nodes, the static wall data, and the road network data.

6. The path planning method for a mountain photovoltaic power station according to claim 5, characterized in that: Performing inter-target path planning according to the positions of the two first target nodes, the static wall data, and the road network data, including: Determine the import and export positions of the nearest neighbors corresponding to the two first target nodes respectively; If the starting point of the two first target nodes is within the partition, a first sub-path plan between the two first target nodes and the corresponding nearest neighbor's entrance and exit positions is determined pixel by pixel based on the position of the starting point, the corresponding nearest neighbor's entrance and exit positions, and the static wall data; a second sub-path plan between the entrances and exits is determined based on the nearest neighbor's entrance and exit positions corresponding to the two first target nodes and the road network data; the inter-target path plan includes: the first sub-path plan and the second sub-path plan; If the starting point of the two first target nodes is not within the partition, determine the intermediate segment path between the line segment of the starting point in the road network data, the import and export position of the nearest neighbor of the starting point, and the import and export position of the nearest neighbor of the end point according to the road network data; determine the third sub-path planning between the import and export position of the nearest neighbor corresponding to the starting point to the end point according to the line segment and the intermediate segment path; determine the fourth sub-path planning between the import and export position of the nearest neighbor of the end point and the position of the end point pixel by pixel, and the inter-target path planning includes: the third sub-path planning and the fourth sub-path planning.

7. The path planning method for a mountain photovoltaic power station according to claim 6, characterized in that: Determining a third sub-path plan between the nearest entrance and exit corresponding to the starting point and the end point according to the line segment and the intermediate path includes: If the middle segment path includes the line segment, then removing the path between the starting point and the nearest entrance and exit positions of the starting point in the middle segment path to obtain the third sub-path plan; If the middle segment path does not include the line segment, the path between the starting point in the line segment and the nearest entrance and exit position of the starting point and the middle segment path are determined as the third sub-path planning.

8. A path planning device for a mountain photovoltaic power station, characterized in that: include: The first acquisition module is used to obtain the road network data of the main road of the mountain photovoltaic power station, the static wall data of each partition of the mountain photovoltaic power station, and the coordinates of the partition entrance and exit; A second acquisition module is used to acquire the positions of multiple target nodes, wherein the positions include multiple positions to be inspected and an inspection starting position; A multi-target sorting module is used to sort multiple targets according to the positions to be inspected and the inspection starting positions to obtain a planning order for the multiple targets; An inter-target path planning module is configured to plan an inter-target path for any two first target nodes in a planning sequence according to the positions of the two first target nodes, the zone entry and exit coordinates, and the first data. If the two first target nodes are both in the same zone, the first data includes static wall data; if the two first target nodes are not in the same zone, the first data includes static wall data and road network data. The determination module is used to obtain the inspection path planning according to the target path planning result between every two target nodes in the planning sequence.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the path planning method for a mountain photovoltaic power station according to any one of claims 1 to 7 when running the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one program code, and the program code is loaded and executed by the processor to implement the path planning method for a mountain photovoltaic power station according to any one of claims 1 to 7.

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