Unmanned aircraft low-altitude route planning method and equipment

Abstract

The invention discloses an unmanned aircraft low-altitude route planning method and device, and the method comprises the steps: obtaining the coverage data of a target region; the target area is divided into a plurality of continuous square grids with the same size; the target area comprises a route starting point and a route ending point; calculating the grid confidence of the square grid according to the coverage data; constructing a mesh topology structure of the target area according to the coverage data; determining the path cost of a plurality of topological paths in the mesh topological structure according to the grid confidence; and obtaining a target route from the route starting point to the route ending point according to the path cost. According to the method, the path cost is taken as a unified selection standard, so that different planning designers can obtain the same route planning result under the same condition, and the stability of the planning result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude route planning, and in particular to a method and device for low-altitude route planning of an unmanned aerial vehicle. Background Art

[0002] Low-altitude route planning refers to planning a better flight path that meets multiple constraints between the starting point and the end point of the route within the low-altitude airspace, taking into account factors such as flight safety and public impact, minimizing flight distance and reducing flight costs.

[0003] At present, low-altitude route planning and design personnel mainly use the following methods when conducting route planning and design: after determining the market demand and the starting and ending locations of the route, before route planning, route planning and design personnel need to collect a series of relevant data such as population distribution, building models, road distribution, rivers, lakes, mountains and forests, and infrastructure distribution in the area where the route is to fly, to provide data support for route planning; then superimpose the base map data, combine quantitative and qualitative analysis methods, and planners need to consider many constraints, starting from the starting point, to determine the direction of the route; for multiple optional flight paths, designers need to analyze and verify each plan and select the optimal flight path. After completing the route planning, planners need to repeatedly check and adjust the planned route to gradually form the final route plan.

[0004] Therefore, when planning existing low-altitude routes, the route direction is subjectively judged by planners and designers. There is a lack of unified selection criteria for multiple direction options, which may lead to different results selected by different planners and designers, resulting in low stability of planning results. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method and device for low-altitude route planning of unmanned aerial vehicles, which can provide a unified selection standard to ensure that different planning and design personnel can obtain the same route planning results under the same conditions, thereby improving the stability of the planning results.

[0006] In order to solve the above problems, the present invention is implemented according to the following scheme: A method for low-altitude route planning of an unmanned aerial vehicle is provided, comprising: Obtain coverage data of the target area; the target area is divided into a plurality of continuous square grids of the same size; the target area includes the route starting point and the route ending point; Calculate the grid confidence of the square grid based on the coverage data; Based on the coverage data, a mesh topology structure of the target area is constructed; Determine the path costs of multiple topological paths in the mesh topology structure according to the grid confidence; According to the path cost, the target route from the starting point to the end point of the route is obtained.

[0007] Compared with the prior art, the beneficial effects of the unmanned aerial vehicle low-altitude route planning method of the present invention are as follows: by determining the path costs of multiple topological paths in a mesh topological structure and determining the target route based on the path costs, the path cost is used as a unified selection criterion to ensure that different planning and design personnel can obtain the same route planning results under the same conditions, thereby improving the stability of the planning results.

[0008] Optionally, the grid confidence includes route safety, operational reliability, and public acceptability.

[0009] Optionally, the calculating the grid confidence of the square grid according to the coverage data includes: Determine the influencing factors and their corresponding weights; Based on the coverage data, the evaluation scores of the square grid on the influencing factors are calculated; The grid confidence is determined based on the evaluation scores and weights corresponding to the influencing factors.

[0010] Optionally, constructing a mesh topology structure of the target area according to the coverage data includes: According to the coverage data, determine the grid center point corresponding to each square grid in the target area; According to the center point of the grid, a mesh topological structure of the target area is constructed.

[0011] Optionally, constructing a mesh topology structure of the target area according to the grid center point includes: Select a grid center point in the target area as the reference center point; Determine the adjacent center points of the reference center point; Connect the reference center point with its adjacent center points to form a topological structure of the reference center point; Repeat the above steps for all grid center points in the target area to construct the topological structure of each grid center point in the target area; The topological structure of each grid center point in the target area constitutes a mesh topological structure of the target area.

[0012] Optionally, the mesh topology structure includes multiple topological nodes and multiple topological paths; Each grid center point corresponds to a node, and the connection line between two nodes corresponds to a topological path; The path types of topological paths include: vertical paths, diagonal paths, and penalty paths.

[0013] Optionally, determining the path costs of multiple topological paths in the mesh topological structure according to the grid confidence includes: According to the path type of the topological path, determine the calculation formula of the path cost; According to the calculation formula and grid confidence, the path cost of each topological path is calculated.

[0014] Optionally, the coverage data includes the coordinates of the route starting point and the route ending point; The method of obtaining a target route from the route starting point to the route ending point according to the path cost includes: According to the coordinates of the route starting point and the route ending point, determining the route starting grid and the route ending grid in a plurality of square grids; The node corresponding to the grid center point of the route start grid in the mesh topology structure is determined as the route start node; The node corresponding to the grid center point of the route termination grid in the mesh topology structure is determined as the route termination node; Determine the target route based on the path cost, route starting node and route ending node.

[0015] Optionally, determining the target route according to the path cost, the route start node and the route end node includes: Adopt the shortest path algorithm to determine the target path under the mesh topology based on the path cost; The target path under the mesh topology structure is converted into a target route, which is the actual driving route of the unmanned aerial vehicle.

[0016] A computer device is also provided, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the route planning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the route planning method of the present invention; Figure 2 It is a schematic diagram of the topological structure of the present invention; Figure 3 It is a target path schematic diagram of the present invention; Figure 4 Schematic diagram of the target route of the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0019] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0020] See also Figure 1 As shown, the present invention provides a method for planning a low-altitude route for an unmanned aerial vehicle, comprising: S1: Obtain coverage data of the target area; the target area includes the starting point and the ending point of the route. When planning the route, the influence of the surrounding environment needs to be considered. Therefore, it is necessary to appropriately expand the scope of route planning on the basis of including the starting point and the ending point of the route. It can be expanded to a certain extent according to the standards of towns, streets, districts, and urban areas, that is, the target area includes a series of geographical areas that completely cover the starting point and the ending point of the route, and expand to a certain extent; the coverage data includes but is not limited to a series of relevant data such as population distribution, building models, road distribution, rivers, lakes, mountains and forests, and infrastructure distribution in the target area, providing data support for route planning; for example, population distribution data is used to determine whether the area is densely populated. When planning the route, it is necessary to avoid passing through densely populated areas as much as possible; infrastructure data includes relevant information such as the location of infrastructure such as power lines and communication facilities. When planning the route, it is necessary to avoid passing through infrastructure as much as possible to avoid affecting it.

[0021] After obtaining a series of relevant data such as population distribution, building models, road distribution, rivers, lakes, forests, infrastructure distribution, etc. in the target area, these data need to be processed through the GIS system (geographic information system) to convert data in different formats into a unified format, such as Shapefile, GeoJSON, GML, KML / KMZ, DWG, DXF and other formats. Processing the data in a unified format through the GIS system can improve the efficiency and accuracy of data processing.

[0022] The target area is divided into a plurality of continuous square grids of the same size. In the present invention, the size of the square grid is 100 meters * 100 meters. The tools used to divide the target area into a plurality of square grids include but are not limited to GIS data processing software such as ArcGIS and QGIS, and geographic information processing databases such as PostGIS, ArcSDE, MySQL Spatial, and Oracle Spatial.

[0023] By dividing the target area into multiple square grids, the complex geographic space can be abstracted into a regular grid structure, simplifying spatial analysis and calculation; at the same time, rasterization allows geographic data to be quantified and standardized, facilitating spatial analysis, assessment of influencing factors and route planning; through rasterization, data such as population distribution, building models, road distribution, rivers, lakes, mountains and forests, and infrastructure distribution in the target area can be processed and analyzed more efficiently, providing data support for subsequent route planning.

[0024] S2: Calculate the grid confidence of the square grid based on the coverage data; the grid confidence includes route safety, operation reliability, and public acceptability. That is, when calculating the grid confidence, it is necessary to calculate the scores of the three dimensions of route safety, operation reliability, and public acceptability.

[0025] Table 1 shows the influencing factors and their corresponding weights of each dimension, as shown below

[0026] As shown in Table 1, the three dimensions of route safety, operational reliability, and public acceptability include influencing factors and their corresponding weights respectively; the influencing factors and their corresponding weights are formulated in accordance with the "Specifications for the Planning of Logistics Routes for Light and Small Unmanned Aerial Vehicles in Urban Scenarios" MH / T 4054-2022 issued by the Civil Aviation Administration of China, which has a strong guiding role in the calculation of grid confidence in low-altitude route planning scenarios, but the index system should be regarded as exemplary and non-restrictive. Therefore, in actual applications, the influencing factors can be selected according to the actual application scenarios, and the weights corresponding to each influencing factor can be dynamically adjusted. The sum of the weights corresponding to each influencing factor should be ensured to be 1.

[0027] Based on the coverage data, the grid confidence of the square grid is calculated, including: First, determine the influencing factors to be actually selected and their corresponding weights; then, based on the coverage data, calculate the evaluation score of the square grid on the influencing factors, and the evaluation score is used to measure the importance or influence of the influencing factor on the square grid; before calculating the evaluation score, it is necessary to divide the coverage data into sub-data corresponding to each square grid based on the basis for dividing the target area into multiple square grids, and the sub-data corresponding to all square grids constitute the coverage data of the target area.

[0028] According to the sub-data corresponding to the square grid, the evaluation scores of each influencing factor of the square grid are calculated. The range setting and scoring standard of the evaluation score are not restricted here. The calculation formula is as follows:

[0029] in, Influencing factors The evaluation score of is the number of the influencing factor, Influencing factors The calculation results are: For the scoring criteria, is the standard number of divided scores, For satisfy Time influence factors Score of; Assume satisfy , then The value is .

[0030] After calculating the evaluation scores of the influencing factors for each square grid respectively, the scores of each square grid in each dimension are calculated according to the evaluation scores and weights corresponding to the influencing factors.

[0031] The route safety score is calculated according to the following formula:

[0032]

[0033] in Indicates the safety of the route. Score the route safety. , , , They represent the safety of ground personnel, safety of ground facilities, difficulty of search, and secondary damage indicators respectively. , , , They represent the weights of ground personnel safety, ground facility safety, search difficulty, and secondary damage indicators, respectively. , , , They represent the average scores of the square grids that the route passes through in terms of ground personnel safety, ground facility safety, search difficulty, and secondary damage indicators. The weight that represents the safety of the route.

[0034] Evaluate the operational reliability score, which is calculated according to the following formula:

[0035]

[0036] in Indicates the operating reliability. is the operational reliability score, , , , , , They represent meteorological environment, electromagnetic environment, obstacle environment, navigation capability, communication performance, and surveillance performance indicators respectively. , , , , , They represent the weights of meteorological environment, electromagnetic environment, obstacle environment, navigation capability, communication performance, and surveillance performance indicators respectively. , , , , , They represent the average scores of the square grids that the route passes through in terms of meteorological environment, electromagnetic environment, obstacle environment, navigation capability, communication performance, and surveillance performance indicators. Represents the weight of operational reliability.

[0037] The public acceptability score of the route is calculated according to the following formula:

[0038]

[0039] in Indicates public acceptability. Score for public acceptability, , Respectively represent the privacy factor and noise factor indicators, , Respectively represent the weights of privacy factors and noise factors, , They represent the average scores of the square grids that the route passes through in terms of privacy factors and noise factors. A weight representing public acceptability.

[0040] Finally, according to the scores and weights of each dimension, the grid confidence of each square grid is calculated. The calculation formula is as follows:

[0041] in, For the The confidence level of a square grid, is the number of the square grid, is the score of the i-th dimension, is the weight of the i-th dimension, is the number of the dimension, The total number of dimensions for the raster confidence.

[0042] When implementing step S2, it can be implemented by writing SQL statements, using functions including but not limited to intersection, coverage, inclusion, buffer analysis, etc. of the PostGIS database. After running the SQL statements, the calculation of the raster confidence in step S2 can be automatically completed.

[0043] By calculating the grid confidence of the square grid, the scores of the three dimensions of route safety, operational reliability and public acceptability can be comprehensively considered, thus providing a multi-dimensional evaluation basis for route planning. This multi-dimensional evaluation method can ensure that the route achieves the best balance in terms of safety, reliability and public acceptance, and improve the safety and reliability of unmanned aerial vehicles flying at low altitudes.

[0044] S3: Based on the coverage data, a mesh topology structure of the target area is constructed, including: First, according to the coverage data, that is, according to the sub-data of each square grid, the grid center point of each square grid is determined. The grid center point is the geometric center point of each square grid. The geometric center point is the average position of all points in the square grid, which can balance the influence of each part in the square grid and ensure that the overall characteristics of the square grid can be accurately reflected in the subsequent route planning. When determining the grid center point, the geometric center point of each square grid can be quickly calculated in batches with the help of GIS data processing software and geographic information processing database. The calculation function of the geometric center point of the square grid is not constrained here.

[0045] After determining the grid center point of each square grid, select any grid center point in the target area as the reference center point, determine the adjacent center points of the reference center point, connect the reference center point with its adjacent center points, and construct the topological structure of the reference center point; repeat the above steps for all grid center points in the target area to construct the topological structure of each grid center point in the target area; the topological structure of each grid center point in the target area constitutes the mesh topological structure of the target area, that is, by constructing the connection relationship between the grid center points in the target area, a mesh topological structure covering all grid center points in the target area can be constructed, and the mesh topological structure includes the connection relationship between all grid center points in the target area.

[0046] The topological structure of each grid center point is constructed according to the following formula:

[0047] in, The reference center point The topological structure of Center point The set of adjacent center points of The reference center point The adjacency matrix of an undirected graph is is the number of the square grid corresponding to the reference center point, The reference center point The adjacent center points of The reference center point and adjacent center points The connecting edge of The reference center point The number of adjacent center points of Figure 2 The reference center point Schematic diagram of the topological structure, Figure 2 J in 1 -j 8 is the adjacent center point of the reference center point j.

[0048] When implementing step S3, it can be implemented by writing SQL statements, using functions including but not limited to intersection, contact, containment and segment generation of the PostGIS database. After running the SQL statements, the construction of the mesh topology structure of the target area in step S3 can be automatically completed.

[0049] By constructing a mesh topology structure of the target area, the connection relationship between the center points of the square grids can be clarified, that is, the connection relationship between the square grids in the target area can be clarified, which is convenient for path search and optimization during route planning; the mesh topology structure represents the spatial relationship between the center points of the grids in the form of nodes and connecting edges, that is, the mesh topology structure can be used to represent the spatial relationship between the square grids, so that when planning a route, it can be carried out in the node network of the mesh topology structure, with each square grid represented by a node, which reduces the computational complexity when planning a route and improves the efficiency of route planning.

[0050] S4: According to the grid confidence, determine the path costs of multiple topological paths in the mesh topological structure; the mesh topological structure includes multiple topological nodes and multiple topological paths, each grid center point corresponds to a topological node, and the connection line between two topological nodes corresponds to a topological path, that is, each topological path can be expressed as ; The path types of topological paths include vertical paths, diagonal paths and penalty paths.

[0051] by Figure 2 The topological structure diagram shown in FIG. 1 illustrates the path type of the topological path. Figure 2 J and are some nodes in the mesh topology, and the connecting edges between the nodes are the topological paths. Figure 2 In , , , , , , , is a partial topological path in the mesh topology, where Figure 2 middle , , , The path types corresponding to these four topological paths are vertical paths. , , , The path type corresponding to the four topological paths is a diagonal path. When the grid confidence of the square grid corresponding to any of the two nodes in the topological path is equal to the maximum grid confidence, the path type of the topological path is preferentially defined as a penalty path. That is, when When the topological path The path type is preferentially defined as a penalty path, where is the grid confidence of the square grid corresponding to node j, For Node The grid confidence of the corresponding square grid, is the maximum value of grid confidence.

[0052] Based on the grid confidence, the path costs of multiple topological paths in the mesh topology are determined, including: According to the path type of the topological path, the calculation formula of the path cost is determined. The calculation formulas of the path cost of different path types are as follows:

[0053] in, For Node and nodes The connecting edge of is the topological path. is the number of the square grid corresponding to the node, For Node The adjacent nodes of For Node The number of adjacent nodes of is the topological path The path cost, For Node The grid confidence of the corresponding square grid, For Node The grid confidence of the corresponding square grid, is the maximum value of grid confidence, is the penalty cost parameter. The penalty cost parameter can take a larger value according to the situation. Since the shortest path algorithm is used in route planning, the larger the penalty cost parameter is, the larger the path cost is, the stronger the constraint of the grid confidence is, and the square grid belongs to the strictly controlled area and must be avoided; it means that when automatically planning the route, the larger the path cost of the topological path is, the greater the risk of the topological path is, that is, the topological path with a small path cost is preferred in planning. Therefore, the penalty cost parameter is not specifically constrained and can be set in combination with the upper constraints of route planning and actual needs.

[0054] When implementing step S4, it can be implemented by writing a SQL statement and using functions including but not limited to line segment length calculation of the PostGIS database. After running the SQL statement, the path cost calculation for each topological path in step S4 can be automatically completed.

[0055] By determining the path cost of the topological path based on the grid confidence, the grid confidence can be effectively incorporated into the path planning considerations to ensure that the route avoids high-risk areas during planning. In particular, by introducing the penalty path, the possibility of the route passing through the high-risk area can be strictly controlled, further improving the safety of the route. The calculation method of the path cost makes route planning more intelligent and refined, and can dynamically adjust the route according to the actual situation.

[0056] S5: According to the path cost, the target route from the route starting point to the route ending point is obtained, including: The coordinates of the route starting point and the route ending point are obtained in the coverage data, and the route starting grid and the route ending grid are determined from multiple square grids in the target area according to the route starting point coordinates and the route ending point coordinates. Specifically, the square grid in the target area that covers the route starting point coordinates is used as the route starting grid, and the square grid in the target area that covers the route ending point coordinates is used as the route ending grid.

[0057] Then, according to the route start grid and the route end grid, the route start node and the route end node are determined in the mesh topology structure, and each grid center point corresponds to a node in the mesh topology structure. Therefore, the node corresponding to the grid center point of the route start grid in the mesh topology structure is used as the route start node, and the node corresponding to the grid center point of the route end grid in the mesh topology structure is used as the route end node.

[0058] Finally, the target route is determined based on the path cost, route start node, and route end node, including: The shortest path algorithm is adopted to determine the target path under the mesh topology structure based on the path cost of each square grid in the target area; the shortest path algorithm adopted here is the Dijkstra algorithm commonly used in the prior art. In the prior art, the shortest path algorithm is based on the actual distance between nodes to determine the shortest path, but the present invention also determines the target path based on the path cost, and because the path cost is calculated based on the grid confidence, the target path finally obtained is the optimal path that comprehensively considers the three factors of path cost, grid confidence and path distance.

[0059] Because the mesh topology structure represents the spatial relationship between grid center points in the form of nodes and connecting edges, the target path is the planar projection path of the route, that is, the target path under the mesh topology structure needs to be converted into a target route, that is, according to the relationship between the route starting point coordinates, the route starting grid, and the route starting node, as well as the relationship between the route ending point coordinates, the route ending grid, and the route ending node, the target route can be obtained by replacing each node in the target path with the coordinate information in the coverage data. The target route is the actual driving route of the unmanned aerial vehicle.

[0060] When the step S5 is specifically implemented, the automatic calculation of the target route can be realized by writing a sql statement with the help of functions including but not limited to the shortest path analysis pgr_dijkstra of the PostGIS database.

[0061] By adopting the shortest path algorithm and combining it with the path cost, we can get the optimal route that takes into account the path cost, grid confidence and path distance. This method can not only ensure the shortest path of the route, but also avoid high-risk areas to the greatest extent, thus improving the safety and reliability of the route.

[0062] The present invention determines the path costs of multiple topological paths in a mesh topological structure, determines the target route based on the path costs, and uses the path costs as a unified selection criterion, thereby ensuring that different planning and design personnel can obtain the same route planning results under the same conditions, thereby improving the stability of the planning results.

[0063] After obtaining the target route, use visualization technology to program and realize the visualization of low-altitude routes. For details, please refer to Figure 3 , which is a schematic diagram of the target path of the present invention, wherein the visualization technology may adopt WebGL technology including but not limited to Baidu MapVGL, AutoNavi LOCA, mapbox and other GIS visualization software; by visualizing the target route, the technicians can clearly see the flight route of the unmanned aerial vehicle.

[0064] See also Figure 4 As shown, it is a schematic diagram of the target route of the present invention, and it can be seen from the figure that the scores of the target route in the three dimensions of route safety, operation reliability, and public acceptability are similar, that is, the performance of the target route in each dimension is relatively balanced, that is, it is a route with good comprehensive performance and can meet the requirements of multiple dimensions at the same time.

[0065] The present invention also provides a computer device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the above-mentioned route planning method.

[0066] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0067] The memory can be used to store the computer program or module, and the processor implements various functions of the route planning method by running or executing the computer program or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0068] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for low-altitude route planning for an unmanned aerial vehicle, characterized in that: include: Get coverage data for the target area; The target area is divided into a plurality of continuous square grids of the same size; the target area includes the route starting point and the route ending point; Calculate the grid confidence of the square grid based on the coverage data; Based on the coverage data, a mesh topology structure of the target area is constructed; Determine the path costs of multiple topological paths in the mesh topology structure according to the grid confidence; According to the path cost, the target route from the starting point to the end point of the route is obtained.

2. The method for low-altitude route planning of an unmanned aerial vehicle according to claim 1, characterized in that: The grid confidence includes route safety, operational reliability, and public acceptability.

3. The method for low-altitude route planning of an unmanned aerial vehicle according to claim 2, characterized in that: The step of calculating the grid confidence of the square grid according to the coverage data comprises: Determine the influencing factors and their corresponding weights; Based on the coverage data, the evaluation scores of the square grid on the influencing factors are calculated; The grid confidence is determined based on the evaluation scores and weights corresponding to the influencing factors.

4. The method for low-altitude route planning of an unmanned aerial vehicle according to claim 1, characterized in that: The step of constructing a mesh topology structure of the target area according to the coverage data includes: According to the coverage data, determine the grid center point corresponding to each square grid in the target area; According to the center point of the grid, a mesh topological structure of the target area is constructed.

5. A method for low-altitude route planning for an unmanned aerial vehicle according to claim 4, characterized in that: The method of constructing a mesh topological structure of the target area according to the grid center point includes the following steps: Step 1: Select a grid center point in the target area as the reference center point; Step 2: Determine the adjacent center points of the reference center point; Step 3: Connect the reference center point with its adjacent center points to form a topological structure of the reference center point; Step 4: Repeat steps 1 to 3 for all grid center points in the target area to construct the topological structure of each grid center point in the target area; Step 5: The topological structure of each grid center point in the target area constitutes a mesh topological structure of the target area.

6. A method for low-altitude route planning for an unmanned aerial vehicle according to claim 4, characterized in that: The mesh topology structure includes a plurality of topological nodes and a plurality of topological paths; Each grid center point corresponds to a node, and the connection line between two nodes corresponds to a topological path; The path types of topological paths include: vertical paths, diagonal paths, and penalty paths.

7. A method for low-altitude route planning for an unmanned aerial vehicle according to claim 6, characterized in that: Determining the path costs of multiple topological paths in the mesh topological structure according to the grid confidence includes: According to the path type of the topological path, determine the calculation formula of the path cost; According to the calculation formula and grid confidence, the path cost of each topological path is calculated.

8. A method for low-altitude route planning for an unmanned aerial vehicle according to claim 7, characterized in that: The coverage data includes the coordinates of the route starting point and the route ending point; The method of obtaining a target route from the route starting point to the route ending point according to the path cost includes: According to the coordinates of the route starting point and the route ending point, determining the route starting grid and the route ending grid in a plurality of square grids; The node corresponding to the grid center point of the route start grid in the mesh topology structure is determined as the route start node; The node corresponding to the grid center point of the route termination grid in the mesh topology structure is determined as the route termination node; Determine the target route based on the path cost, route starting node and route ending node.

9. A method for low-altitude route planning for an unmanned aerial vehicle according to claim 8, characterized in that: Determining the target route according to the path cost, the route start node and the route end node includes: Adopt the shortest path algorithm to determine the target path under the mesh topology based on the path cost; The target path under the mesh topology structure is converted into a target route, which is the actual driving route of the unmanned aerial vehicle.

10. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the route planning method as described in any one of claims 1 to 9.

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