Low-altitude route planning method based on mixed granularity grid

Abstract

The invention discloses a low-altitude route planning method based on mixed granularity grids, and belongs to the technical field of air traffic management for unmanned aerial vehicles. The problems that in the prior art, according to traditional low-altitude air route planning based on airspace grids of a unified scale, the fine-granularity air route planning efficiency is low, and a coarse-granularity route cannot be achieved are solved. An elliptical boundary is constructed according to starting and ending point coordinates of the unmanned aerial vehicle to obtain an elliptical region; the building is trimmed, and a building subset is constructed; performing spatial domain grid coding on the elliptical region to obtain an elliptical region grid; performing fine-grained grid division on the overlapped grids, and further judging whether the grids in the elliptical region intersect with the building subsets or not to obtain a separated grid set; and constructing an undirected graph, searching in the undirected graph through an A * path search algorithm to obtain a shortest path between a starting point and an ending point, and taking the shortest path as a low-altitude air route planning path. The airspace network modeling efficiency is effectively improved, and the method can be applied to low-altitude route planning.

Description

Technical Field

[0001] The present invention relates to a low-altitude route planning method, in particular to a low-altitude route planning method based on a mixed granularity grid, and belongs to the technical field of air traffic management for unmanned aerial vehicles. Background Art

[0002] "Heterogeneous, high-density, high-frequency, and high-complexity" are the basic attributes of future low-altitude flight activities. The scale of urban air traffic is expected to reach one million flights per year. At present, flight safety rules such as air collision avoidance have not yet been established, making it difficult to ensure all-weather, high-density, safe and efficient flight of aircraft.

[0003] In the prior art one, the patent document with the publication (announcement) number CN118654682A proposes a method, device, equipment and medium for drone path planning, the method including: obtaining a starting path node; generating a dynamic double-layer neighborhood of the starting path node based on a node neighborhood generation strategy and the starting path node; performing obstacle node detection on all adjacent nodes of each node in the dynamic double-layer neighborhood based on a detection strategy; when the detection result shows that the current adjacent node of each node is not an obstacle node, calculating the movement cost of the starting path node passing through each node or the current adjacent node of each node to reach the target path node based on a custom evaluation function, and selecting the best next path node from each node and the adjacent nodes of each node based on the minimum movement cost; taking the best next path node as the current starting path node, continuing to calculate and obtain the next path node until the target path node is reached; thereby finally obtaining the planned path of the drone. The above method can effectively improve the accuracy of path planning and reduce the cost of path planning; however, each time the shortest path is calculated, it is necessary to perform obstacle node detection on all adjacent nodes of each node contained in the dynamic double-layer neighborhood of the starting path node based on the detection strategy to obtain the obstacle node detection result. For large-scale obstacle collision detection in urban scenes, there is bound to be a problem of low efficiency; in the second prior art, the patent document with the publication (announcement) number CN118012103A discloses a method and system for low-altitude UAV flight path planning in cities, first obtaining the three-dimensional modeling data of the city, the three-dimensional modeling data including building models, road networks, terrain data and ground facility annotations; converting the three-dimensional modeling data into a graphic structure, the image structure including vertices, edges and faces, vertices representing spatial points in the three-dimensional modeling data, edges representing line segments connecting two vertices, and faces representing planes surrounded by a group of adjacent edges; using The algorithm generates flight paths based on graphic structures. This method makes full use of the data of the three-dimensional model of the digital city and quickly realizes the three-dimensional path planning of any two points, which greatly reduces the automation level of the path drawing of low-altitude UAV flights in the city. However, the method of constructing the three-dimensional model of the airspace lacks specific implementation steps, and the division of the airspace granularity is not clear. At the same time, the standard uses The algorithm is a standard heuristic path planning general algorithm and cannot support actual application in traffic scenarios.

[0004] In summary, a low-altitude route planning method based on a mixed granularity grid is needed to effectively reduce and prevent flight collision risks and improve the overall safety and efficiency of air traffic. Summary of the Invention

[0005] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0006] In view of this, in order to solve the problems in the prior art of low-altitude route planning based on a uniform-scale airspace grid, such as low efficiency of fine-grained route planning and inaccessibility of coarse-grained paths, the present invention provides a low-altitude route planning method based on a hybrid-granularity grid.

[0007] The technical solution is as follows: A low-altitude route planning method based on a mixed granularity grid includes the following steps:

[0008] S1. Construct an elliptical boundary based on the starting and ending coordinates of the low-altitude UAV to obtain an elliptical area, which is used as the feasible area for the low-altitude route, reducing the solution space for subsequent raster encoding and path search;

[0009] S2. Trim the buildings according to the minimum flight altitude of the drone, remove the buildings whose height is less than the minimum flight altitude of the drone, and construct a building subset;

[0010] S3. Perform spatial grid encoding on the elliptical area to generate a spatial grid set covering the elliptical area. Calculate the spatial intersection of the spatial grid set covering the elliptical area with the building subset, and select grids that overlap with the building subset to obtain the elliptical area grid.

[0011] S4. Fine-grained grid partitioning is performed on the overlapping grids. Whether the elliptical grids intersect with the building subset is further determined. Grids intersecting with buildings are screened to obtain a set of intersecting grids. This set of grids is further refined until the grid partitioning granularity reaches the minimum scale parameter. This results in an infeasible grid region. Grids separated from buildings are then merged to obtain a set of separated grids as a feasible grid region.

[0012] S5. Calculate the spatial relationship between the separated grids, obtain the adjacent grids of each grid, and construct an undirected graph, where the nodes of the graph are grid IDs, the edges of the graph are grid IDs and their adjacent grid IDs, and the cost of the edge is the spherical distance between the grid centroid and the adjacent grid centroid;

[0013] S6. Pass The path search algorithm searches in an undirected graph and calculates the shortest path between the starting and ending points, which is used as the low-altitude route planning path.

[0014] Furthermore, the step S1 specifically includes the following steps:

[0015] S11. Convert the latitude and longitude wgs_84 coordinates of the starting and ending points of the drone into the plane coordinates of the starting and ending points of the drone using the plane coordinate conversion formula;

[0016] S12. Use the Pythagorean theorem to calculate the straight-line distance between the starting and ending points;

[0017] S13. Define the major axis parameters and minor axis parameters of the ellipse, and calculate the plane coordinates of the ellipse boundary according to the ellipse formula;

[0018] S14. Convert the plane coordinates of the ellipse boundary into wgs_84 coordinates of the ellipse boundary using the wgs_84 coordinate conversion formula;

[0019] In S11, the plane coordinate conversion formula is expressed as: ;

[0020] in, is the radius of the Earth, is the longitude coordinate, is the latitude coordinate, is the natural logarithm, is the tangent function, is pi, The plane coordinates of the starting and ending points of the drone;

[0021] In S12, the straight-line distance d between the starting and ending plane coordinates of the drone is expressed as: ;

[0022] in, is the plane coordinate of the starting point of the UAV, is the plane coordinate of the UAV’s endpoint;

[0023] In S13, the ellipse formula is expressed as: ; ; ;

[0024] in, are the center coordinates of the ellipse, =( , ), is the length of the major axis, i.e. the major axis parameter of the ellipse, is the length of the minor axis, i.e. the minor axis parameter of the ellipse, is the plane coordinate of the ellipse boundary;

[0025] In the above S14, the coordinates of the ellipse boundary wgs_84 are expressed as: ;

[0026] in, is the base of the natural logarithm (e≈2.71828), and arctan is the inverse tangent function.

[0027] Furthermore, in S2, based on the comparison between the building height and the drone's minimum flight height parameter min_fly_height, the building data with a height greater than min_fly_height is retained to construct a building subset.

[0028] Furthermore, the step S3 specifically includes the following steps:

[0029] S31. Initialize the searched list searched_list and the to-be-checked list read_search_list, calculate the centroid of the elliptical area, obtain the coordinates of the centroid, perform GeoHash encoding on the coordinates of the centroid, add the grid ID encoded as 1 to the to-be-checked list read_search_list, extract the to-be-checked grid ID from the to-be-checked list read_search_list, determine the spatial relationship between the rectangular area of grid 1 and the elliptical area, obtain the GeoHash string list intersection_list=[] where the rectangular area of grid 1 intersects with the elliptical area, and the GeoHash string list within_list=[1] where the rectangular area of grid 1 contains the elliptical area, and add the checked grid 1 to the searched list searched_list=[1].

[0030] S32. Query the GeoHash rectangular region code [2,3,4,5,6,7,8,9] adjacent to the centroid grid 1 and determine whether it is in the searched_list. If so, skip it; otherwise, add it to the pending checklist read_search_list = [2,3,4,5,6,7,8,9]. Then, traverse the GeoHash grid IDs in the pending checklist read_search_list and determine the spatial relationship between the adjacent rectangular regions and the elliptical regions of grid 1. Obtain the GeoHash string list intersection_list = [4,7,8,9] where the adjacent rectangular regions of grid 1 intersect with the elliptical regions, and the GeoHash string list within_list = [2,3,5,4] where the adjacent rectangular regions of grid 1 contain the elliptical regions. Delete the grid ID from the pending checklist read_search_list after each determination.

[0031] S33. When the to-be-checked list read_search_list and the GeoHash string list within_list containing the rectangular area and the elliptical area are both empty, the judgment ends and the elliptical area grid is obtained. Otherwise, repeat step S32.

[0032] Furthermore, the step S4 specifically includes the following steps:

[0033] S41. Perform spatial position determination on the elliptical area grids from step S3 and the building subset data from step S2. Grids intersecting with buildings are screened out and marked as infeasible grids, obtaining a set of intersecting grids. Grids separated from buildings, i.e., non-intersecting areas, are marked as feasible grids and added to the separated grid set.

[0034] S42. Determine whether the grid division granularity is the minimum granularity parameter. If it is greater, further fine-grained grid division is performed on the infeasible grid, the granularity parameter is increased by one, and the process returns to step S41 to loop. Otherwise, the process exits to obtain the feasible grid area and the infeasible grid area.

[0035] Furthermore, the step S5 specifically includes the following steps:

[0036] S51. traverse and calculate whether the spatial relationship between each grid i and other grids j in the feasible grid list is connected. If connected, calculate the spherical distance d(ij) between the centroid coordinates of the connected grid and its adjacent grid;

[0037] S52. Generate edges ,side The cost of edge is d(ij), Node and nodes is the GeoHash grid code of grid i and grid j, node The coordinates are the centroid coordinates of grid i, node The coordinates are the centroid coordinates of grid j.

[0038] Furthermore, the step S6 specifically includes the following steps:

[0039] S61. The starting point coordinates and the ending point coordinates are GeoHash-encoded according to the grid encoding method of the elliptical area in step S3 to obtain the starting point node ID and the ending point node ID in the undirected graph;

[0040] S62. Utilization The path search algorithm performs a breadth-first search in an undirected graph to find the shortest path.

[0041] The beneficial effects of the present invention are as follows: the present invention uses GeoHash for grid coding and adopts a recursive algorithm to perform finer-grained grid coding on buildings near buildings and coarse-grained coding on airspace near non-buildings, thus achieving full coverage of the airspace with mixed-grained grids and using heuristics. The algorithm is used for low-altitude route planning. The present invention can quickly generate low-altitude feasible routes and avoid buildings, achieve strategic-level low-altitude flight conflict resolution, improve the safety level of low-altitude flight, shorten modeling time and improve modeling efficiency, and at the same time ensure fine-grained description of airspace obstacle objects and improve airspace utilization rate; the technical solution of the present invention quickly improves the efficiency of airspace network modeling by constructing a grid airspace coding method based on mixed granularity. The mixed granularity grid coding can divide the boundaries of buildings in a finer granularity and increase the area of feasible airspace regions, which is beneficial to the efficiency of airspace utilization in the context of large-scale low-altitude flight in the future. Taking 114.203, 22.686974 and 114.10972, 22.5841705 as the starting and ending points of the drone trip actually tested as an example, a single scale The airspace network modeling took 3133 seconds, with m as the scale unit, and the path planning took 11.94 seconds, with mixed scales. Coding took 22 seconds, path planning took 0.53 seconds, Coding takes 196 seconds and path planning takes 1.23 seconds, with efficiency significantly improved by 20 to 100 times. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0043] Figure 1 This is a flowchart of a low-altitude route planning method based on a mixed granularity grid;

[0044] Figure 2 This is a diagram showing the visualization effect of the ellipse boundary;

[0045] Figure 3 A visual diagram of a subset of buildings;

[0046] Figure 4 Schematic diagram of the process of encoding the elliptical area grid;

[0047] Figure 5 Schematic diagram of the process of latitude encoding;

[0048] Figure 6 A visual diagram of the elliptical area grid coding;

[0049] Figure 7 A visual diagram of the infeasible grid in the elliptical area;

[0050] Figure 8 A visualization diagram of a feasible grid with mixed granularity;

[0051] Figure 9 The diagrams are visualization diagrams of the shortest path; (a) is the overall visualization diagram of the path; (b) is the main local visualization diagram of the path; (c) is the secondary local visualization diagram of the path. DETAILED DESCRIPTION

[0052] To make the technical solutions and advantages of the embodiments of the present invention more clearly understood, exemplary embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments described are only a portion of the embodiments of the present invention, and are not an exhaustive list of all embodiments. It should be noted that the embodiments of the present invention and the features thereof may be combined with each other unless they conflict.

[0053] refer to Figures 1-9 The present embodiment is described in detail. A low-altitude route planning method based on a mixed granularity grid specifically includes the following steps:

[0054] S1. Construct an elliptical boundary based on the starting and ending coordinates of the low-altitude UAV to obtain an elliptical area, which is used as the feasible area for the low-altitude route, reducing the solution space for subsequent raster encoding and path search;

[0055] S2. Trim the buildings according to the minimum flight altitude of the drone, remove the buildings whose height is less than the minimum flight altitude of the drone, and construct a building subset;

[0056] S3. Perform spatial grid encoding on the elliptical area to generate a spatial grid set covering the elliptical area. Calculate the spatial intersection of the spatial grid set covering the elliptical area with the building subset, and select grids that overlap with the building subset to obtain the elliptical area grid.

[0057] S4. Fine-grained grid partitioning is performed on the overlapping grids. Whether the elliptical grids intersect with the building subset is further determined. Grids intersecting with buildings are screened to obtain a set of intersecting grids. This set of grids is further refined until the grid partitioning granularity reaches the minimum scale parameter. This results in an infeasible grid region. Grids separated from buildings are then merged to obtain a set of separated grids as a feasible grid region.

[0058] S5. Calculate the spatial relationship between the separated grids, obtain the adjacent grids of each grid, and construct an undirected graph, where the nodes of the graph are grid IDs, the edges of the graph are grid IDs and their adjacent grid IDs, and the cost of the edge is the spherical distance between the grid centroid and the adjacent grid centroid;

[0059] S6. Pass The path search algorithm searches in an undirected graph and calculates the shortest path between the starting and ending points, which is used as the low-altitude route planning path.

[0060] Furthermore, the step S1 specifically includes the following steps:

[0061] S11. Convert the latitude and longitude wgs_84 coordinates of the starting and ending points of the drone into the plane coordinates of the starting and ending points of the drone using the plane coordinate conversion formula;

[0062] S12. Use the Pythagorean theorem to calculate the straight-line distance between the starting and ending points;

[0063] S13. Define the major axis parameters and minor axis parameters of the ellipse, and calculate the plane coordinates of the ellipse boundary according to the ellipse formula;

[0064] S14. Convert the plane coordinates of the ellipse boundary into wgs_84 coordinates of the ellipse boundary using the wgs_84 coordinate conversion formula;

[0065] In S11, the plane coordinate conversion formula is expressed as: ;

[0066] in, is the radius of the Earth (the mean radius of the wgs_84 coordinate ellipsoid, =6371000m), is the longitude coordinate, is the latitude coordinate, is the natural logarithm, is the tangent function, is pi, The plane coordinates of the starting and ending points of the drone;

[0067] In S12, the straight-line distance d between the starting and ending plane coordinates of the drone is expressed as: ;

[0068] in, is the plane coordinate of the starting point of the UAV, is the plane coordinate of the UAV’s endpoint;

[0069] In S13, the ellipse formula is expressed as: ; ; ;

[0070] in, are the center coordinates of the ellipse, =( , ), is the length of the major axis, i.e. the major axis parameter of the ellipse, is the length of the minor axis, i.e. the minor axis parameter of the ellipse, is the plane coordinate of the ellipse boundary;

[0071] In the above S14, the coordinates of the ellipse boundary wgs_84 are expressed as: ;

[0072] in, is the base of the natural logarithm (e≈2.71828), and arctan is the inverse tangent function.

[0073] Furthermore, in S2, based on the comparison between the building height and the drone's minimum flight height parameter min_fly_height, the building data with a height greater than min_fly_height is retained and constructed as a building subset.

[0074] Furthermore, the step S3 specifically includes the following steps:

[0075] S31. Initialize the searched list searched_list and the to-be-checked list read_search_list, which are used to store the IDs of the grids whose spatial relationships have been determined and the IDs of the grids to be determined, respectively. Calculate the centroid of the elliptical area, obtain the coordinates of the centroid, perform GeoHash encoding on the coordinates of the centroid, add the grid ID encoded as 1 to the to-be-checked list read_search_list, retrieve the to-be-checked grid ID from the to-be-checked list read_search_list, determine the spatial relationship between the rectangular area of grid 1 and the elliptical area (intersection, separation, inclusion), obtain the GeoHash string list intersection_list=[] where the rectangular area of grid 1 intersects with the elliptical area, and the GeoHash string list within_list=[1] where the rectangular area of grid 1 contains the elliptical area, and add the checked grid 1 to the searched list searched_list=[1].

[0076] S32. Query the GeoHash rectangular region code [2,3,4,5,6,7,8,9] adjacent to the centroid grid 1 and determine whether it is in the searched_list. If so, skip it; otherwise, add it to the pending checklist read_search_list = [2,3,4,5,6,7,8,9]. Then, traverse the GeoHash grid IDs in the pending checklist read_search_list and determine the spatial relationship between the adjacent rectangular regions and the elliptical regions of grid 1. Obtain the GeoHash string list intersection_list = [4,7,8,9] where the adjacent rectangular regions of grid 1 intersect with the elliptical regions, and the GeoHash string list within_list = [2,3,5,4] where the adjacent rectangular regions of grid 1 contain the elliptical regions. Delete the grid ID from the pending checklist read_search_list after each determination.

[0077] S33. When the GeoHash string list within_list to be checked, read_search_list, and the rectangular and elliptical regions are both empty, the judgment is terminated to obtain the elliptical region grid. Otherwise, step S32 is repeated.

[0078] Specifically, the GeoHash encoding principle of latitude data is as follows:

[0079] Taking the coordinates "30.280245, 120.027162" as an example, to calculate its GeoHash string, first perform binary encoding on the latitude. Divide [-90, 90] into two equal parts. If "30.280245" falls in the right interval (0, 90], the first digit is 1. Divide (0, 90] into two equal parts. If "30.280245" falls in the left interval (0, 45], the second digit is 0. Repeat this step until the target interval becomes smaller and smaller, and the two endpoints of the interval get closer and closer to "30.280245". Through the above calculation, the latitude is encoded as 10111 00011. Similarly, encoding the longitude coordinate 116.390705 produces the longitude as 1101001011. Even digits represent longitude, and odd digits represent latitude. Referring to Table 1, the two codes are combined to generate a new string: 11100 1110100100 01111;

[0080] Table 1 Latitude and longitude group code table

[0081] coding 1 1 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 1 1 Serial number 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

[0082] Refer to Table 2. Use 0-9, bz (remove a, i, l, o) for a total of 32 letters to perform base32 encoding. Convert the new string: 11100 11101 00100 01111 into decimal code, which corresponds to 28, 29, 4, and 15. The corresponding decimal code in the encoding table is wx4g.

[0083] Table 2 GeoHash encoding table

[0084] Decimal encoding 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 base32 1 2 3 4 5 6 7 8 9 10 b c d e f g Decimal encoding 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 base32 h j k m n p q r s t u v w x y z

[0085] refer to Figure 6 The coverage area in the lower left corner shows the 1680th grid, i.e.

[1680] , and grid_id is the geohash code ws10kx3 of the grid.

[0086] Furthermore, the step S4 specifically includes the following steps:

[0087] S41. Perform spatial position determination on the elliptical area grids from step S3 and the building subset data from step S2. Grids intersecting with buildings are screened out and marked as infeasible grids, obtaining a set of intersecting grids. Grids separated from buildings, i.e., non-intersecting areas, are marked as feasible grids and added to the separated grid set.

[0088] S42. Determine whether the grid division granularity is the minimum granularity parameter. If it is, further fine-grained grid division is performed on the infeasible grid in the red area, the granularity parameter is increased by one, and the process returns to step S41 to loop. Otherwise, the process exits to obtain the feasible grid area and the infeasible grid area.

[0089] Furthermore, the step S5 specifically includes the following steps:

[0090] S51. traverse and calculate whether the spatial relationship between each grid i and other grids j in the feasible grid list is connected. If connected, calculate the spherical distance d(ij) between the centroid coordinates of the connected grid and its adjacent grid;

[0091] S52. Generate edges ,side The cost of edge is d(ij), Node and nodes is the GeoHash grid code of grid i and grid j, node The coordinates are the centroid coordinates of grid i, node The coordinates are the centroid coordinates of grid j.

[0092] Furthermore, the step S6 specifically includes the following steps:

[0093] S61. The starting point coordinates and the ending point coordinates are GeoHash-encoded according to the grid encoding method of the elliptical area in step S3 to obtain the starting point node ID and the ending point node ID in the undirected graph;

[0094] S62. Utilization The path search algorithm performs a breadth-first search in an undirected graph to find the shortest path.

[0095] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A low-altitude route planning method based on a mixed granularity grid, characterized in that: The following steps are involved: S1. Construct an elliptical boundary based on the starting and ending coordinates of the low-altitude UAV to obtain an elliptical area, which is used as the feasible area for the low-altitude route, reducing the solution space for subsequent raster encoding and path search; S2. Trim the buildings according to the minimum flight altitude of the drone, remove the buildings whose height is less than the minimum flight altitude of the drone, and construct a building subset; S3. Perform spatial grid encoding on the elliptical area to generate a spatial grid set covering the elliptical area. Calculate the spatial intersection of the spatial grid set covering the elliptical area with the building subset, and select grids that overlap with the building subset to obtain the elliptical area grid. S4. Fine-grained grid partitioning is performed on the overlapping grids. Whether the elliptical grids intersect with the building subset is further determined. Grids intersecting with buildings are screened to obtain a set of intersecting grids. This set of grids is further refined until the grid partitioning granularity reaches the minimum scale parameter. This results in an infeasible grid region. Grids separated from buildings are then merged to obtain a set of separated grids as a feasible grid region. S5. Calculate the spatial relationship between the separated grids, obtain the adjacent grids of each grid, and construct an undirected graph, where the nodes of the graph are grid IDs, the edges of the graph are grid IDs and their adjacent grid IDs, and the cost of the edge is the spherical distance between the grid centroid and the adjacent grid centroid; S6. Pass The path search algorithm searches in an undirected graph and calculates the shortest path between the starting and ending points, which is used as the low-altitude route planning path.

2. The low-altitude route planning method based on a mixed granularity grid according to claim 1, characterized in that: Said S1 specifically includes the following steps: S11. Convert the latitude and longitude wgs_84 coordinates of the starting and ending points of the drone into the plane coordinates of the starting and ending points of the drone using the plane coordinate conversion formula; S12. Use the Pythagorean theorem to calculate the straight-line distance between the starting and ending points; S13. Define the major axis parameters and minor axis parameters of the ellipse, and calculate the plane coordinates of the ellipse boundary according to the ellipse formula; S14. Convert the plane coordinates of the ellipse boundary into wgs_84 coordinates of the ellipse boundary using the wgs_84 coordinate conversion formula; In S11, the plane coordinate conversion formula is expressed as: ； in, is the radius of the Earth, is the longitude coordinate, is the latitude coordinate, is the natural logarithm, is the tangent function, is pi, The plane coordinates of the starting and ending points of the drone; In S12, the straight-line distance d between the starting and ending plane coordinates of the drone is expressed as: ； in, is the plane coordinate of the starting point of the UAV, is the plane coordinate of the UAV’s endpoint; In S13, the ellipse formula is expressed as: ； ； ； in, are the center coordinates of the ellipse, =( , ), is the length of the major axis, i.e. the major axis parameter of the ellipse, is the length of the minor axis, i.e. the minor axis parameter of the ellipse, is the plane coordinate of the ellipse boundary; In the above S14, the coordinates of the ellipse boundary wgs_84 are expressed as: ； in, is the base of the natural logarithm (e≈2.71828), and arctan is the inverse tangent function.

3. The low-altitude route planning method based on a mixed granularity grid according to claim 2, characterized in that: In S2, based on the comparison between the building height and the drone's minimum flight height parameter min_fly_height, the building data with a height greater than min_fly_height is retained to construct a building subset.

4. The low-altitude route planning method based on a mixed granularity grid according to claim 3 is characterized in that: The S3 specifically includes the following steps: S31. Initialize the searched list searched_list and the to-be-checked list read_search_list, calculate the centroid of the elliptical area, obtain the coordinates of the centroid, perform GeoHash encoding on the coordinates of the centroid, add the grid ID encoded as 1 to the to-be-checked list read_search_list, extract the to-be-checked grid ID from the to-be-checked list read_search_list, determine the spatial relationship between the rectangular area of grid 1 and the elliptical area, obtain the GeoHash string list intersection_list=[] where the rectangular area of grid 1 intersects with the elliptical area, and the GeoHash string list within_list=[1] where the rectangular area of grid 1 contains the elliptical area, and add the checked grid 1 to the searched list searched_list=[1]. S32. Query the GeoHash rectangular region code [2,3,4,5,6,7,8,9] adjacent to the centroid grid 1 and determine whether it is in the searched_list. If so, skip it; otherwise, add it to the pending checklist read_search_list = [2,3,4,5,6,7,8,9]. Then, traverse the GeoHash grid IDs in the pending checklist read_search_list and determine the spatial relationship between the adjacent rectangular regions and the elliptical regions of grid 1. Obtain the GeoHash string list intersection_list = [4,7,8,9] where the adjacent rectangular regions of grid 1 intersect with the elliptical regions, and the GeoHash string list within_list = [2,3,5,4] where the adjacent rectangular regions of grid 1 contain the elliptical regions. Delete the grid ID from the pending checklist read_search_list after each determination. S33. When the to-be-checked list read_search_list and the GeoHash string list within_list containing the rectangular area and the elliptical area are both empty, the judgment ends and the elliptical area grid is obtained. Otherwise, repeat step S32.

5. The low-altitude route planning method based on a mixed granularity grid according to claim 4 is characterized in that: The S4 specifically includes the following steps: S41. Perform spatial position determination on the elliptical area grids from step S3 and the building subset data from step S2. Grids intersecting with buildings are screened out and marked as infeasible grids, obtaining a set of intersecting grids. Grids separated from buildings, i.e., non-intersecting areas, are marked as feasible grids and added to the separated grid set. S42. Determine whether the grid division granularity is the minimum granularity parameter. If it is greater, further fine-grained grid division is performed on the infeasible grid, the granularity parameter is increased by one, and the process returns to step S41 to loop. Otherwise, the process exits to obtain the feasible grid area and the infeasible grid area.

6. The low-altitude route planning method based on a mixed granularity grid according to claim 5, characterized in that: The S5 specifically includes the following steps: S51. traverse and calculate whether the spatial relationship between each grid i and other grids j in the feasible grid list is connected. If connected, calculate the spherical distance d(ij) between the centroid coordinates of the connected grid and its adjacent grid; S52. Generate edges ,side The cost of edge is d(ij), Node and nodes is the GeoHash grid code of grid i and grid j, node The coordinates are the centroid coordinates of grid i, node The coordinates are the centroid coordinates of grid j.

7. The low-altitude route planning method based on a mixed granularity grid according to claim 6, characterized in that: The S6 specifically includes the following steps: S61. The starting point coordinates and the ending point coordinates are GeoHash-encoded according to the grid encoding method of the elliptical area in step S3 to obtain the starting point node ID and the ending point node ID in the undirected graph; S62. Utilization The path search algorithm performs a breadth-first search in an undirected graph to find the shortest path.

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