A low-altitude air route planning method based on hybrid granularity grid

Abstract

The application discloses a kind of low-altitude air route planning methods based on hybrid granularity grid, belong to the air traffic management technical field for unmanned aerial vehicle.Solve the low-altitude air route planning of the airspace grid of traditional uniform scale based in prior art exists the problem that coarse granularity path is not reached, low efficiency of fine granularity air route planning;The application constructs elliptical boundary according to the coordinates of unmanned aerial vehicle starting and ending point, obtains elliptical region;To building edge, construct building subset;Elliptical region is carried out airspace grid coding, and elliptical region grid is obtained;Fine granularity grid division is carried out to overlapping grid, further judge whether elliptical region grid and building subset intersect, obtain the set of separate grid;Undirected graph is constructed, search in undirected graph by A* path search algorithm, obtain the shortest path of starting and ending point, and it is used as low-altitude air route planning path.The application effectively improves airspace network modeling efficiency, and can be applied to low-altitude air 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 a flight path based on a graph structure, which fully utilizes the data of the three-dimensional model of the digital city, quickly realizes three-dimensional path planning of any two points, greatly saves the automation level of path drawing of the city low-altitude unmanned aerial vehicle flight, but the method for constructing the three-dimensional model of the airspace lacks specific implementation steps, and the division of the airspace granularity is not clear, and the standard used The algorithm is a standard heuristic path planning general algorithm, which cannot support the actual application of the traffic scene.

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

[0005] In the following, a brief summary of the present application is given in order to provide a basic understanding of some aspects of the present application. It should be understood that this summary is not a comprehensive overview of the present application. It is not intended to identify key or important parts of the present application nor is it intended to limit the scope of the present application. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.

[0006] In view of this, in order to solve the problems of low efficiency of fine granularity air route planning and unreachability of coarse granularity path in the conventional low-altitude air route planning based on a uniform scale airspace grid, the present application provides a low-altitude air route planning method based on a hybrid granularity grid.

[0007] The technical solution is as follows: a low-altitude air route planning method based on a hybrid granularity grid, comprising the following steps:

[0008] S1. An ellipse boundary is constructed according to the coordinates of the starting and ending points of the low-altitude flight unmanned aerial vehicle, and an elliptical region is obtained, which is used as a low-altitude air route feasible region to reduce the solution space of subsequent grid coding and path search;

[0009] S2. According to the minimum flight height of the unmanned aerial vehicle, the buildings are trimmed, and the buildings with a height less than the minimum flight height of the unmanned aerial vehicle are deleted to construct a building subset;

[0010] S3. The airspace grid coding is performed on the elliptical region to generate an airspace grid set covering the elliptical region, and the spatial intersection of the airspace grid set covering the elliptical region and the building subset is calculated to screen out the grids overlapping with the building subset, and an elliptical region grid is obtained;

[0011] S4. Fine-grained grid division is performed on the overlapping grid, further judgment is made on whether the elliptical region grid intersects with the building subset, the grid intersecting with the building is screened, the intersecting grid set is obtained and is refined again until the grid division granularity reaches the minimum scale parameter, the infeasible grid region is obtained, and the grid away from the building is merged to obtain the away grid set as the feasible grid region;

[0012] S5. The spatial relationship of the away grid is calculated to obtain the adjacent grid of each grid, an undirected graph is constructed, the nodes of the graph are set as the grid ID, the edges of the graph are set as the grid ID and the adjacent grid ID, and the cost of the edge is set as the spherical distance between the grid centroid and the adjacent grid centroid;

[0013] S6. Through The path search algorithm searches in the undirected graph, and the shortest path between the start and end points is calculated and obtained as the low-altitude air route planning path.

[0014] Further, in S1, the following steps are specifically included:

[0015] S11. The longitude and latitude wgs_84 coordinates of the start and end points of the unmanned aerial vehicle are converted into the plane coordinates of the start and end points of the unmanned aerial vehicle through a plane coordinate conversion formula;

[0016] S12. The straight-line distance of the plane coordinates of the start and end points is calculated by using the Pythagorean theorem;

[0017] S13. The long axis parameter and the short axis parameter of the ellipse are defined, and the ellipse boundary plane coordinates are calculated according to the ellipse formula;

[0018] S14. The ellipse boundary plane coordinates are converted into the ellipse boundary wgs_84 coordinates through a wgs_84 coordinate conversion formula;

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

[0020] ;

[0021] Wherein, is the radius of the earth, is the longitude coordinate, is the latitude coordinate, is the natural logarithm, is the tangent function, is the constant pi, is the plane coordinates of the start and end points of the unmanned aerial vehicle;

[0022] In S12, the straight-line distance d of the plane coordinates of the start and end points of the unmanned aerial vehicle is represented as:

[0023] ;

[0024] wherein, is the starting point of the UAV in the plane coordinate, is the end point of the UAV in the plane coordinate;

[0025] In the S13, the elliptic formula is expressed as:

[0026] ;

[0027] ;

[0028] ;

[0029] wherein, is the center coordinate of the ellipse, = ( , ), is the length of the major axis of the ellipse, i.e., the long axis parameter of the ellipse, is the length of the minor axis of the ellipse, i.e., the short axis parameter of the ellipse, is the boundary plane coordinate of the ellipse;

[0030] In the S14, the elliptic boundary wgs_84 coordinate is expressed as:

[0031] ;

[0032] wherein, is the base of the natural logarithm (e≈2.71828), and arctan is the inverse tangent function.

[0033] Further, in the S2, according to the comparison between the height of the building height and the minimum flight height of the UAV min_fly_height parameter, the building data with height greater than min_fly_height is retained, and a building building subset is constructed.

[0034] Further, in the S3, the following steps are specifically included:

[0035] S31. Initialize the searched list searched_list and the read_search_list, calculate the centroid point of the elliptical region, obtain the centroid point coordinates, encode the centroid point coordinates by GeoHash, add the grid ID with the code of 1 to the read_search_list, take out the grid ID to be judged from the read_search_list, judge the spatial relationship between the grid 1 rectangular region and the elliptical region, obtain the GeoHash string list intersection_list=[] of the grid 1 rectangular region and the elliptical region existing intersection relationship and the GeoHash string list within_list=[1] of the grid 1 rectangular region and the elliptical region existing containing relationship, and add the checked grid 1 to the searched_list=[1];

[0036] S32. Query the adjacent GeoHash rectangular region codes [2, 3, 4, 5, 6, 7, 8, 9] of the centroid point grid 1, judge whether it is in the searched_list, if yes, skip, otherwise add to the read_search_list=[2, 3, 4, 5, 6, 7, 8, 9], traverse the GeoHash grid ID in the read_search_list, judge the spatial relationship between the adjacent rectangular region of grid 1 and the elliptical region, obtain the GeoHash string list intersection_list=[4, 7, 8, 9] of the adjacent rectangular region of grid 1 and the elliptical region existing intersection relationship and the GeoHash string list within_list=[2, 3, 5, 4] of the adjacent rectangular region of grid 1 and the elliptical region existing containing relationship, delete the grid ID after each judgment from the read_search_list;

[0037] S33. When the read_search_list and the GeoHash string list within_list are empty, end the judgment, obtain the elliptical region grid, otherwise repeat step S32.

[0038] Further, in the S4, the following steps are specifically included:

[0039] S41. Perform spatial position judgment on the elliptical region grid of step S3 and the building subset data of step S2, filter out the grid intersecting with the building, mark it as an unfeasible grid, obtain the intersection grid set, mark the grid away from the building as a feasible grid, and add it to the away grid set;

[0040] 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.

[0041] Furthermore, the step S5 specifically includes the following steps:

[0042] 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;

[0043] 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.

[0044] Furthermore, the step S6 specifically includes the following steps:

[0045] 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;

[0046] S62. Utilization The path search algorithm performs a breadth-first search in an undirected graph to find the shortest path.

[0047] 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 performs low-altitude route planning, the low-altitude feasible route can be quickly generated, buildings can be avoided, strategic low-altitude flight conflict resolution can be realized, the low-altitude flight safety level is improved, the modeling time is shortened, the modeling efficiency is improved, meanwhile, fine-grained airspace obstacle object description can be ensured, and the airspace utilization rate is improved; the technical scheme of the application constructs a hybrid granularity-based grid airspace coding method, quickly improves the efficiency of airspace network modeling, the hybrid granularity grid coding can divide the building boundary in a finer granularity, improves the area of the feasible airspace region, and is beneficial to the airspace utilization efficiency in the future large-scale low-altitude flight background, for example, taking 114.203, 22.686974 and 114.10972, 22.5841705 as the actual test unmanned aerial vehicle route start and end points, the single scale Airspace network modeling takes 3133 seconds, m is a scale unit, path planning takes 11.94 seconds, hybrid scale Encoding takes 22 seconds, path planning takes 0.53 seconds, Encoding takes 196 seconds, path planning takes 1.23 seconds, and the efficiency is obviously improved by more than 20 to 100 times. BRIEF DESCRIPTION OF DRAWINGS

[0048] The drawings described herein are used to provide further understanding of the application, and form a part of the application, the schematic embodiments of the application and the description thereof are used to explain the application, and do not constitute improper limitation on the application. In the drawings:

[0049] Figure 1 It is a flowchart of a low-altitude route planning method based on a hybrid granularity grid;

[0050] Figure 2 It is a visualization effect schematic diagram of an ellipse boundary;

[0051] Figure 3 It is a visualization schematic diagram of a building building subset;

[0052] Figure 4 It is a flowchart of ellipse region grid coding;

[0053] Figure 5 It is a flowchart of latitude coding;

[0054] Figure 6 It is a visualization schematic diagram of ellipse region grid coding;

[0055] Figure 7 It is a visualization schematic diagram of ellipse region unfeasible grid;

[0056] Figure 8 It is a visualization schematic diagram of hybrid granularity feasible grid;

[0057] Figure 9Fig. 1 is a schematic diagram of the visualization of the shortest path; (a) is a schematic diagram of the overall visualization of the path; (b) is a schematic diagram of the main local visualization of the path; and (c) is a schematic diagram of the secondary local visualization of the path. DETAILED DESCRIPTION

[0058] In order to make the technical solutions and advantages of the embodiments of the present application clearer, the exemplary embodiments of the present application are further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0059] REFERENCE Figures 1-9 In detail, the embodiment is a low-altitude air route planning method based on a hybrid granularity grid, and specifically includes the following steps:

[0060] S1. An ellipse boundary is constructed according to the coordinates of the starting and ending points of the low-altitude unmanned aerial vehicle, and an elliptical region is obtained, which is used as a low-altitude air route feasible region to reduce the solution space of subsequent grid coding and path searching;

[0061] S2. According to the minimum flight height of the unmanned aerial vehicle, the buildings are trimmed, and the buildings with a height less than the minimum flight height of the unmanned aerial vehicle are deleted to construct a building subset;

[0062] S3. The elliptical region is subjected to airspace grid coding to generate an airspace grid set covering the elliptical region, and the spatial intersection of the airspace grid set covering the elliptical region and the building subset is calculated to screen out the overlapping grids with the building subset, and an elliptical region grid is obtained;

[0063] S4. The overlapping grids are subjected to fine-grained grid division, and it is further judged whether the elliptical region grid intersects with the building subset, and the grids intersecting with the building are screened out to obtain an intersection grid set which is further refined until the grid division granularity reaches a minimum scale parameter to obtain an infeasible grid region, and the grids away from the building are combined to obtain an away grid set as a feasible grid region;

[0064] S5. The spatial relationship of the away grids is calculated to obtain the adjacent grids of each grid, a graph is constructed, the nodes of the graph are set as the grid IDs, the edges of the graph are set as the grid IDs and their adjacent grid IDs, and the cost of the edge is set as the spherical distance between the grid centroid and the adjacent grid centroid;

[0065] S6. The path searching algorithm is searched in the graph to calculate the shortest path between the starting point and the ending point, and the shortest path is used as the low-altitude air route planning path.

[0066] ​Further, the S1 specifically comprises the following steps:

[0067] S11. converting the longitude and latitude wgs_84 coordinates of the starting and ending points of the unmanned aerial vehicle into the plane coordinates of the starting and ending points of the unmanned aerial vehicle through a plane coordinate conversion formula;

[0068] S12. calculating the straight line distance of the plane coordinates of the starting and ending points by using the Pythagorean theorem;

[0069] S13. defining the parameters of the long axis and the short axis of the ellipse, and calculating the plane coordinates of the boundary of the ellipse according to the ellipse formula;

[0070] S14. converting the plane coordinates of the boundary of the ellipse into the wgs_84 coordinates of the boundary of the ellipse through a wgs_84 coordinate conversion formula;

[0071] In the S11, the plane coordinate conversion formula is represented as:

[0072] ;

[0073] wherein, is the earth radius (the average 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 the circular constant, is the plane coordinate of the starting and ending points of the unmanned aerial vehicle;

[0074] In the S12, the straight line distance d of the plane coordinates of the starting and ending points of the unmanned aerial vehicle is represented as:

[0075] ;

[0076] wherein, is the plane coordinate of the starting point of the unmanned aerial vehicle, is the plane coordinate of the ending point of the unmanned aerial vehicle;

[0077] In the S13, the ellipse formula is represented as:

[0078] ;

[0079] ;

[0080] ;

[0081] wherein, is the center coordinate of the ellipse, = ( , ), is a length of a long semi-axis, i.e., an ellipse long axis parameter, is a length of a short semi-axis, i.e., an ellipse short axis parameter, is a plane coordinate of an ellipse boundary;

[0082] In the S14, the ellipse boundary wgs_84 coordinate is expressed as:

[0083] ;

[0084] wherein, is a base number of a natural logarithm (e≈2.71828), and arctan is an inverse tangent function.

[0085] Further, in the S2, according to a comparison between a height of a building height and a minimum flight height of a UAV min_fly_height, building data with height greater than min_fly_height is reserved to construct a building building subset.

[0086] Further, in the S3, the following steps are specifically included:

[0087] S31. Initialize a searched list searched_list and a read_search_list, which are respectively used to store grid IDs of which spatial relationships have been judged and grid IDs to be judged, calculate a centroid point of the ellipse region, obtain a centroid point coordinate, perform GeoHash encoding on the centroid point coordinate, add a grid ID with an encoding of 1 to the read_search_list, take out a grid ID to be judged from the read_search_list, judge a spatial relationship (intersection, disjunction, inclusion) between a grid 1 rectangular region and the ellipse region, obtain a GeoHash string list intersection_list=[] of which the grid 1 rectangular region and the ellipse region have an intersection relationship and a GeoHash string list within_list=[1] of which the grid 1 rectangular region and the ellipse region have an inclusion relationship, and add the checked grid 1 to the searched_list=[1];

[0088] S32. Query the adjacent GeoHash rectangular region code [2, 3, 4, 5, 6, 7, 8, 9] of the center point grid 1, judge whether it is in the searched list searched_list, if yes, skip, otherwise add to the read_search_list=[2, 3, 4, 5, 6, 7, 8, 9] to be checked list, traverse the GeoHash grid ID in the read_search_list to be checked list, judge the spatial relationship between the adjacent rectangular region of the grid 1 and the elliptical region, get the GeoHash string list intersection_list=[4, 7, 8, 9] of the adjacent rectangular region of the grid 1 and the elliptical region existing intersection relationship and the GeoHash string list within_list=[2, 3, 5, 4] of the adjacent rectangular region of the grid 1 and the elliptical region existing containing relationship, delete the grid ID after each judgment from the read_search_list to be checked list;

[0089] S33. When the read_search_list to be checked list and the rectangular region and the elliptical region existing containing GeoHash string list within_list are empty, end the judgment, get the elliptical region grid, otherwise repeat step S32;

[0090] Specifically, the latitude data GeoHash encoding principle is as follows:

[0091] Taking the coordinates "30.280245, 120.027162" as an example, the GeoHash string is calculated, first, the latitude is binary coded, and [-90, 90] is divided into 2 parts, "30.280245" falls in the right interval (0, 90], so the first bit is 1, and (0, 90] is divided into 2 parts, "30.280245" falls in the left interval (0, 45], so the second bit is 0, repeat the above steps, the target interval will be smaller and smaller, and the two endpoints of the interval will be closer and closer to "30.280245"; through the above calculation, the latitude produces the code 10111 00011; similarly, the longitude coordinate 116.390705 is coded, and the longitude produces the code 1101001011, the even bits are longitude, and the odd bits are latitude, refer to Table 1, combine the two string codes to generate a new string: 11100 1110100100 01111;

[0092] Table 1 Latitude and longitude code table

[0093] Encoding 1 1 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 1 1 Sequence number 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

[0094] Referring to Table 2, using 0-9, b-z (remove a, i, l, o) 32 letters for base32 encoding, the new string: 11100 11101 00100 01111 is converted into decimal encoding, corresponding to 28, 29, 4, 15, and the corresponding encoding of the query encoding table is wx4g.

[0095] Table 2 GeoHash encoding table

[0096] 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

[0097] Reference Figure 6 The lower left corner of the coverage area shows the number of the 1680th grid, that is,

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

[0098] Further, in the S4, the following steps are specifically included:

[0099] S41. Spatial position judgment is performed on the elliptical area grid of step S3 and the building building subset data of step S2, the grid intersecting with the building building is filtered out and marked as an unfeasible grid, an intersection grid set is obtained, and the grid away from the building building, i.e. the non-intersecting area, is marked as a feasible grid and added to the away grid set;

[0100] S42. It is judged whether the division granularity of the grid is the minimum granularity parameter, if greater than, the unfeasible grid of the red area is further divided into fine granularity grids, the granularity parameter is added by one, and step S41 is returned for circulation, otherwise, the process is exited, and the feasible grid area and the unfeasible grid area are obtained.

[0101] Further, in the S5, the following steps are specifically included:

[0102] S51. The spatial relationship of each grid i and other grid j in the feasible grid list is traversed to calculate whether they are adjacent, if adjacent, the spherical distance d(ij) of the centroid coordinates of the adjacent grid and its adjacent grid is calculated;

[0103] S52. The edge The cost of the edge is d(ij), the nodes and the node of the edge are the GeoHash grid encodings of the grid i and the grid j, the node coordinate is the centroid coordinate of the grid i, and the node coordinate is the centroid coordinate of the grid j.

[0104] Further, in the S6, the following steps are specifically included:

[0105] S61. The edge coordinates of the start point and the end point are GeoHash raster encoded according to the raster encoding method of the elliptical region of step S3 to obtain the start node ID and the end node ID in the undirected graph;

[0106] S62. The path search algorithm is used to perform a breadth-first search in the undirected graph to obtain the shortest path.

[0107] Although the present application has been described in terms of limited number of embodiments, those skilled in the art, with the benefit of the descriptions above, will appreciate that other embodiments can be envisaged within the scope of the application as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and can not have been selected to delineate or circumscribe the subject application. Accordingly, a number of modifications and variations are possible in light of the above teachings without departing from the scope and spirit of the subject application. The disclosure of the present application is illustrative and not restrictive, the scope of the application being defined by the appended claims.

Claims

1. A method for low altitude route planning based on hybrid granularity grid, characterized in that, The method comprises the following steps: S1. Constructing an elliptical boundary according to the starting and ending point coordinates of the low-altitude unmanned aerial vehicle, obtaining an elliptical region, and taking the elliptical region as a low-altitude air route feasible region to reduce the solution space of subsequent grid coding and path searching; S2. According to the minimum flight height of the unmanned aerial vehicle, the building is trimmed, the building with a height less than the minimum flight height of the unmanned aerial vehicle is deleted, and a building subset is constructed; S3. The elliptical region is subjected to airspace grid coding to generate an airspace grid set covering the elliptical region, and the spatial intersection of the airspace grid set covering the elliptical region and the building subset is calculated to screen out the overlapping grids with the building subset, thereby obtaining an elliptical region grid; S4. The overlapping grids are subjected to fine-grained grid division, and it is further judged whether the elliptical region grid intersects with the building subset, the grids intersecting with the building are screened out, a set of intersecting grids is obtained, and is further refined until the grid division granularity reaches a minimum scale parameter, thereby obtaining an infeasible grid region, and the grids not intersecting with the building are combined to obtain a set of feasible grid regions; S5. The spatial relationship of the non-intersecting grids is calculated to obtain the adjacent grids of each grid, a graph is constructed, 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 centroid points of the grids; In S5, the following steps are included: S51. The spatial relationship of each grid i and other grids j in the feasible grid list is calculated, and if the grids are adjacent, the spherical distance d(ij) between the centroid coordinates of the adjacent grids is calculated; 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; S6. by The path search algorithm searches in an undirected graph, calculates the shortest path from the starting point to the ending point, and takes the shortest path as the low-altitude air route planning path. In S6, the following steps are included: S61. The edge coordinates of the start point and the end point are GeoHash raster encoded according to the raster encoding method of the elliptical region of step S3 to obtain the start node ID and the end node ID in the undirected graph; S62. Utilizing The path search algorithm performs a breadth-first search in an undirected graph to obtain the shortest path.

2. The method of claim 1, wherein, In S1, the following steps are included: S11. The latitude and longitude wgs_84 coordinates of the starting and ending points of the unmanned aerial vehicle are converted into the plane coordinates of the starting and ending points of the unmanned aerial vehicle by a plane coordinate conversion formula; S12. The straight-line distance of the plane coordinates of the starting and ending points is calculated by using the Pythagorean theorem; S13. The long-axis parameter and the short-axis parameter of the ellipse are defined, and the plane coordinates of the elliptical boundary are calculated according to the elliptical formula; S14. The plane coordinates of the elliptical boundary are converted into the wgs_84 coordinates of the elliptical boundary by a wgs_84 coordinate conversion formula; In S11, the plane coordinate conversion formula is represented as: ； wherein, R is the radius of the earth, L is the longitude coordinate, B is the latitude coordinate, ln is the natural logarithm, tan is the tangent function, pi is the circle constant, is the plane coordinate of the starting point and the ending point of the UAV. In S12, the straight-line distance d of the plane coordinates of the starting and ending points of the unmanned aerial vehicle is represented as: ； wherein, is the starting point plane coordinate of the UAV, is the end point plane coordinate of the UAV; In S13, the elliptical formula is represented as: ； ； ； wherein is the center coordinate of the ellipse, = ( , ), is the length of the semi-major axis, i.e. the ellipse long axis parameter, is the length of the semi-minor axis, i.e. the ellipse short axis parameter, is the ellipse boundary plane coordinate; In S14, the wgs_84 coordinates of the elliptical boundary are represented as: ； wherein is the base of the natural logarithm, e ~ 2.71828, and arctan is the inverse tangent function.

3. The method of claim 2, wherein, In S2, according to the comparison between the height height of the building and the minimum flight height min_fly_height parameter of the unmanned aerial vehicle, the building data with height greater than min_fly_height is retained to construct a building subset.

4. The method of claim 3, wherein, In S3, the following steps are included: S31. Initialize the searched list searched_list and the read_search_list, calculate the centroid point of the elliptical region, obtain the centroid point coordinates, encode the centroid point coordinates by GeoHash, add the grid ID with the code 1 to the read_search_list, take out the grid ID to be judged from the read_search_list, judge the spatial relationship between the grid 1 rectangular region and the elliptical region, obtain the GeoHash string list intersection_list=[] of the grid 1 rectangular region and the elliptical region existing intersection relationship and the GeoHash string list within_list=[1] of the grid 1 rectangular region and the elliptical region existing containing relationship, and add the checked grid 1 to the searched_list=[1]; S32. Query the adjacent GeoHash rectangular region codes [2, 3, 4, 5, 6, 7, 8, 9] of the centroid point grid 1, judge whether it is in the searched_list, if yes, skip, otherwise add to the read_search_list=[2, 3, 4, 5, 6, 7, 8, 9], traverse the GeoHash grid ID in the read_search_list, judge the spatial relationship between the adjacent rectangular region of grid 1 and the elliptical region, obtain the GeoHash string list intersection_list=[4, 7, 8, 9] of the adjacent rectangular region of grid 1 and the elliptical region existing intersection relationship and the GeoHash string list within_list=[2, 3, 5, 4] of the adjacent rectangular region of grid 1 and the elliptical region existing containing relationship, delete the grid ID after each judgment from the read_search_list; S33. When the read_search_list and the GeoHash string list within_list are empty, end the judgment, obtain the elliptical region grid, otherwise repeat step S32.

5. The method of claim 4, wherein, In the S4, the following steps are specifically included: S41. Perform spatial position judgment on the elliptical region grid of step S3 and the building subset data of step S2, filter out the grid intersecting with the building, mark as unfeasible grid, obtain the intersection grid set, mark the grid away from the building as feasible grid, and add to the away grid set; S42. Judge whether the division granularity of the grid is the minimum granularity parameter, if the division granularity of the grid is greater than the minimum granularity parameter, further fine-grained grid division is performed on the unfeasible grid, add one to the granularity parameter, return to step S41 for circulation, otherwise exit, obtain the feasible grid region and the unfeasible grid region.

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