A method for automatic route planning based on time parameters

Abstract

The present invention relates to an automatic route planning method based on time parameters, belonging to the field of aviation control technology. The method converts the planned routes and no-fly zones managed in an airspace manager into grid codes with time attributes, and calculates the occupation periods of all planned routes for each grid code. When planning routes, the grid codes storing the precise occupation periods are converted into grids with the same time parameters, and the grid consumption values ​​are changed accordingly according to the time parameters, thereby enabling more optimized utilization of airspace resources and obtaining more optimized route planning results.

Description

Technical Field

[0001] The present invention relates to the field of aviation control technology, and in particular to a method for automatic route planning based on time parameters. Background Art

[0002] Route planning technology is an important sub-technical field of airspace management. By identifying areas occupied by existing routes and no-fly zones in the managed airspace, it automatically identifies available airspace, compares cost losses, and seeks the optimal path for the planned route.

[0003] Currently, there are many mature algorithms and technical applications in the field of route planning technology. The most commonly used method is to first establish the topological relationship between the waypoints of the planned route through raster conversion technology. Then, based on the Dijkstra algorithm (i.e., DJ algorithm) or A* algorithm, starting from a waypoint, the grid nodes with the lowest cost are sequentially searched and connected in sequence to obtain the route planning result. However, the shortcomings of existing technologies are that route planning does not take time factors into account, and the planning results are not optimal. At the same time, raster conversion operations based on coordinates and elevation values ​​are highly complex, slow, and require a large amount of calculations. When large amounts of data are required for route calculations, it is difficult to achieve a fast and timely response and cannot meet application needs.

[0004] Therefore, it is necessary to study a route planning method that takes time factors into consideration, so as to achieve more optimized use of airspace resources and route planning results, and to meet the rapid response needs in large-scale route calculation scenarios with low computational complexity and very low computational cost. Summary of the Invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a method for automatic route planning based on time parameters to solve the problems of existing technologies such as insufficient optimization of route planning and airspace resource utilization, high computational complexity, and slow response speed.

[0006] In one aspect, an embodiment of the present invention provides a method for automatically planning routes based on time parameters. An airspace manager stores data on planned routes, no-fly zones, and corresponding grid codes in an airspace. Each grid code is provided with information about occupied periods. The airspace manager automatically plans routes using the following method:

[0007] Enter the waypoints and sailing time of the new route to be planned;

[0008] According to the occupation period information of each grid code, find out the grid code occupied during the navigation time and save it;

[0009] Locate each grid covered by the occupied grid code in the grid map, and save the occupied period information and the empty consumption value of the corresponding grid code in the covered grid, wherein the empty consumption value indicates that the consumption value of the grid is empty during the occupied period;

[0010] Establishing a topological relationship between the waypoints based on the eight-directional connectivity of the grid;

[0011] Planning an effective route between the waypoints based on the topological relationship between the waypoints and the principle of minimum cost;

[0012] The valid routes between the waypoints are converted into grid codes, and the occupied time period information of each grid code of the valid routes is estimated and stored.

[0013] The beneficial effects of the above technical solution are as follows: the application of grid code conversion grid greatly reduces the computational complexity of route planning; the grid converted from the grid code with time parameter also has time parameter, which can obtain more optimized route planning results.

[0014] Based on the further improvement of the above method, the following method is used to estimate the single grid code occupancy time of the route:

[0015] For a route, the time to reach each waypoint on the route is estimated based on the aircraft speed and the distance between each waypoint;

[0016] For a grid code of the route, find the projection points of the four corner points of the grid corresponding to the grid code on the route;

[0017] Based on the arrival time difference of the two waypoints closest to the grid, the arrival time of each projection point is estimated by the time linear difference averaging method;

[0018] The earliest arrival time and the latest arrival time of the projection point are used as the time period during which the route occupies the grid code.

[0019] The beneficial effect of the above further improvement scheme is that estimating the time that a route occupies a grid code can help plan a more optimized route.

[0020] Based on a further improvement of the above method, finding the projection points of the four corner points of the grid corresponding to the grid code on the route includes:

[0021] The connection point on the route of the line with the shortest distance between each corner point of the four corner points of the grid corresponding to the grid code and the route is used as the projection point of each corner point on the route.

[0022] The beneficial effect of the above further improved solution is that the accurate projection point of the grid code on the route can be found, so that the time the route occupies the grid code is estimated more accurately.

[0023] Based on the further improvement of the above method, the airspace manager calculates the coordinates of the four corner points of the occupied grid code one by one, calculates the covered grid based on the coordinate range covered by the four corner point coordinates, and saves the occupied period information of the occupied grid code to the covered grid, and sets the covered grid consumption value during the occupied period to a null value and saves it.

[0024] The beneficial effects of the above-mentioned further improvement scheme are: the amount of grid code conversion grid operation is very small, and the grid that stores the time parameters can be used to plan a more optimized route.

[0025] Based on a further improvement of the above method, the effective route grid between waypoints is planned based on the topological relationship between waypoints and the minimum cost principle, which means selecting two adjacent waypoints among the waypoints, one as the starting point and the other as the end point, and using the starting point as the initial node, and finding the effective route grid from the starting point to the end point based on the following steps:

[0026] S1: Push the node into the heap manager;

[0027] S2: The heap manager finds a non-null neighborhood grid in the eight-directional connectivity neighborhood grid of the node based on the node arrival time, pushes the non-null neighborhood grid into the heap manager, and finds a neighborhood grid with the minimum cost from the non-null neighborhood grid as the updated node; updates the cost value of the eight-directional connectivity neighborhood grid based on the cost value of the current node and the existing cost value of the eight-directional connectivity neighborhood grid; repeats step S2 until the eight-directional connectivity neighborhood grid of the node includes the end point;

[0028] S3. Connect the grids corresponding to all nodes in sequence as the valid route grids of the starting point and the end point.

[0029] The beneficial effect of the above-mentioned further improvement scheme is that the algorithm based on the grid with time parameters is more optimized than the existing technology, and a more optimized route can be planned.

[0030] Based on a further improvement of the above method, the cost value of the eight-directional connected neighborhood grid is updated based on the cost value of the current node and the existing cost value of the eight-directional connected neighborhood grid by the following method, the method comprising:

[0031] The cost value of the current node is summed with the existing cost values ​​of the eight-directional connectivity neighborhood grid of the current node to obtain the cost value assigned by the neighborhood grid;

[0032] For the eight-directional connectivity neighborhood grid of the current node already in the heap manager, determining whether the cost value assigned by the neighborhood grid is greater than the existing cost value of the neighborhood grid; if so, updating the existing cost value of the neighborhood grid to the cost value assigned by the neighborhood grid; otherwise, not updating the existing cost value of the neighborhood grid;

[0033] For the eight-directional connectivity neighborhood grid of the current node that is pushed into the heap manager for the first time, the existing cost value of the neighborhood grid is updated to the cost value assigned by the neighborhood grid.

[0034] The beneficial effect of the above-mentioned further improved solution is that the neighborhood grid with the minimum cost value can be quickly found through the grid cost value, thereby realizing fast route planning.

[0035] Based on a further improvement of the above method, the valid routes between the waypoints are converted into grid codes by the following method:

[0036] Starting from the starting point of the actual flyable route, dividing the route into segments with a preset threshold as a step size;

[0037] A distance constraint grid set is calculated for each route segment one by one, and a grid deduplication calculation and aggregation calculation are performed to generate the actual flyable route grid code.

[0038] The beneficial effect of the above-mentioned further improved solution is: based on the grid code quickly calculating the grid position of the conflicting route, the shortest distance between the routes is further calculated, thereby further improving the calculation accuracy of the technical solution.

[0039] Based on a further improvement of the above method, the calculation of each route segment and the establishment of a distance constraint grid set are completed through the following steps, which include:

[0040] If the two endpoints of the route segment are in the same grid, the grid is selected as the selected grid. If the selected grid does not exist in the distance constraint grid set, the selected grid is added to the distance constraint grid set.

[0041] If the two endpoints of the route segment are in different grids, the two different grids are added to the distance-constrained grid set, and a first selected area is obtained based on the longitude and latitude of the two different grids. Redundant grids are removed from the first selected area, and grids that meet preset conditions are selected from the remaining grids in the first selected area and added to the distance-constrained grid set.

[0042] The selected grid or the first selected area is expanded based on the preset threshold to obtain a second selected area, redundant grids are removed from the second selected area, and grids that meet preset conditions are selected from the remaining grids in the second selected area to be added to the distance constrained grid set.

[0043] The beneficial effects of the above-mentioned further improvement scheme are: by setting the safety threshold for route data, the route is segmented, so that the grid set within a threshold range for each route segment can be efficiently calculated without having to calculate all intersecting grids, which greatly reduces the amount of calculation and algorithm complexity; and the calculation result of the previous route segment can effectively reduce the amount of calculation for the next segment, further improving the efficiency of grid code calculation.

[0044] Based on a further improvement of the above method, the preset threshold is an aircraft safety threshold, and a grid code width equal to the aircraft safety threshold or any one of the two closest adjacent level grid code widths is selected as the lowest level grid in the distance constraint grid set.

[0045] The beneficial effect of the above-mentioned further improvement scheme is: setting the safety threshold to be consistent with the aircraft safety threshold, and determining the lowest level of the grid map based on the aircraft safety threshold, is an appropriate and reasonable choice for the grid map to process the route. It neither greatly increases the computational burden nor greatly affects the distance constraint. It can also make the route segments approximate straight lines when dividing the route, thereby reducing the computational complexity.

[0046] Based on the further improvement of the above method, the polymerization comprises the following steps:

[0047] SA1: sorting the grid codes of the distance constraint grid set by size;

[0048] SA2: grouping every four adjacent grid codes in the sorted grid code sequence into the same group; SA3: determining whether the grid codes in each group are at the same level and have the same upper level grid code;

[0049] If so, the grid codes of the same group are deleted and the upper level grid code corresponding to the grid code of the group is added to obtain an updated distance constraint grid set, and the process returns to step SA1. When there is no group of the four adjacent grid codes that can be merged into the upper level grid code, the process ends.

[0050] The beneficial effect of the above further improvement scheme is that the aggregated grid set is convenient for subsequent applications and saves subsequent storage and processing resources.

[0051] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols denote the same components.

[0053] Figure 1 This is a schematic diagram of automatic route replanning according to an embodiment of the present invention.

[0054] Figure 2 A schematic diagram of a grid code set covered by waypoint distance constraints according to an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the projection points of the grid code on the flight path according to an embodiment of the present invention;

[0056] Figure 4 A schematic diagram of a grid code conversion grid according to an embodiment of the present invention;

[0057] Figure 5 This is the time grid diagram of waypoint A in an embodiment of the present invention;

[0058] Figure 6 This is a grid diagram of the grid 33 connected to waypoint A in an embodiment of the present invention;

[0059] Figure 7 This is a schematic diagram of route segmentation according to an embodiment of the present invention;

[0060] Figure 8 This is a schematic diagram of a first selected area according to an embodiment of the present invention;

[0061] Figure 9 This is a schematic diagram of a second selected area according to an embodiment of the present invention;

[0062] Figure 10 Schematic diagram of grid code aggregation operation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] To facilitate understanding of the technical solution of the present invention, the following lists specific explanations of the professional terms that appear or are involved:

[0064] GeoSOT grid: GeoSOT (Geographic Coordinate Subdividing Grid with OneDimension Integral Coding on 2n-Tree) is based on 2 n A one-dimensional integer array of global latitude and longitude grids. The GeoSOT grid achieves a whole-degree and whole-minute integer quadtree grid by spatially expanding the latitude and longitude range of the Earth's surface three times (expanding the Earth's geographic space to 512°, 1° to 64', and 1' to 64") Based on the GeoSOT gridding principle, GeoSOT grid codes use 64-bit codes to identify each level of grid. The longest code bit is a 32-bit quaternary value. Bits 1-9 represent the degree grid code, bits 10-15 represent the hierarchical grid code, bits 16-21 represent the second grid code, and bits 22-32 represent the sub-second grid code. The length of the code is the grid level. The GeoSOT grid is divided into 32 levels, ranging from global to centimeter scale, evenly dividing the Earth's surface into multiple levels of grids. These grids form a global quadtree system. The area ratio between upper and lower levels of the GeoSOT grid is roughly 4:1, and varies evenly.

[0065] Grid code: A multi-scale, discrete, global geographic grid coding model developed based on a global grid, suitable for navigation and positioning services. This grid coding model proposes a unified identification and expression method for global spatial regional location information, capable of identifying both locations and regions. It features non-overlapping boundaries, orthogonal grids, consistent longitude and latitude, and good compatibility with traditional data specifications, as well as the ability to represent points and surfaces in an integrated manner. Through the use of integer encoding, this coding greatly simplifies the complexity of identifying, expressing, and calculating location information. It effectively addresses the organization of massive, multi-source, and heterogeneous spatial information in terms of information calculation speed, information indexing efficiency, and information exchange and integration. The grid coding of the present invention adopts the GeoSOT partitioning scheme. GeoSOT expands the earth's surface space three times, that is, the latitude (-90° to 90°) and longitude (-180° to 180°) are expanded to (-256° to 256°), 1° is expanded to 64', and 1' is expanded to 64", achieving equal degree, equal division, and equal second partitioning. GeoSOT belongs to the equal longitude and latitude quadtree partitioning grid system, with a total of 32 partitioning levels. From the top level 1, which can express a grid of 256°, to the bottom level 32, which can express a grid of 1 / 2048" (about 1.5 cm).

[0066] Grid Aggregation: Because the grid code used in this invention uses a quadtree grid system with equal longitude and latitude, theoretically all grids are constructed by descending from the top grid (256°*256°). Grid aggregation is the reverse of this process, aggregating four adjacent grids that meet the coding rules into a single grid at the next higher level.

[0067] Mesh operations primarily involve performing spatial operations on linear or planar objects within a spatial domain using mesh sets. These operations, such as intersection, union, and erase, have different practical implications. Intersection operations represent the shared mesh area between two objects; union operations represent the mesh area covered by both objects; and erase operations represent the mesh area left after one object is subtracted from another.

[0068] The so-called eight-way grid connectivity can also be called eight-neighborhood connectivity or eight-connected region connectivity. The so-called eight-connected regions or eight-neighborhoods refer to the eight directions above, below, left, right, upper left, upper right, lower left, and lower right of the corresponding position. These are the immediately adjacent positions and the diagonally adjacent positions, a total of eight directions, hence the name eight-connected regions or eight-neighborhoods.

[0069] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0070] A specific embodiment of the present invention discloses a method for automatically planning a route based on time parameters, such as Figure 1 shown.

[0071] The airspace manager stores the planned routes, no-fly zone data, and corresponding grid codes in the airspace. Each grid code is provided with occupied time period information. The airspace manager automatically plans routes using the following method:

[0072] Enter the waypoints and sailing time of the new route to be planned;

[0073] According to the occupation period information of each grid code, find out the grid code occupied during the navigation time and save it;

[0074] Locate each grid covered by the occupied grid code in the grid map, and save the occupied period information and the empty consumption value of the corresponding grid code in the covered grid, wherein the empty consumption value indicates that the consumption value of the grid is empty during the occupied period;

[0075] Establishing a topological relationship between the waypoints based on the eight-directional connectivity of the grid;

[0076] Planning an effective route between the waypoints based on the topological relationship between the waypoints and the principle of minimum cost;

[0077] The valid routes between the waypoints are converted into grid codes, and the occupied time period information of each grid code of the valid routes is estimated and stored.

[0078] Specifically, in the technical solution disclosed in this embodiment, the airspace manager includes data on planned routes and no-fly zones. This data is converted into grid codes and stored in the airspace manager. The grid codes are converted to the corresponding grid code based on the specified modified GeoSOT level and the longitude and latitude coordinates of the point, allowing for rapid positioning of the corresponding grid. Complete grid information includes: grid code, grid coordinates, grid level, and other information.

[0079] For example, Figure 2 Taking the coordinates of point A (39°54′20″N, 116°25′29″E) as an example, the 16-level grid code (grid size is about 1km) is calculated using the following algorithm:

[0080] ① First, encode the longitude and latitude coordinates of point A according to the grid size comparison table below. The encoding process is to divide the coordinate value by the grid size of each level continuously. The quotient is the grid code value of that level until the specified encoding level is reached.

[0081] The longitude value 116°25′29″ is encoded at level 16 as follows:

[0082]

[0083] The 16th level code for latitude 39°54′20″ is as follows:

[0084]

[0085] The final 16th-level binary grid code of point A obtained by cross-coding latitude and longitude is: 00000111010011101010110110100100;

[0087] If converted into quaternary, it is: 0013103222312210.

[0088]

[0089] Table 1: Grid size comparison table

[0090] ② The grid rectangle coordinates are calculated as follows:

[0091] Rect.Left=116°25′29″-29″=116°25′;

[0092] Rect.Bottom=39°54′20″-20″=39°54′;

[0093] Rect.Right=Rect.Left+32″=116°25′32″;

[0094] Rect.Top=Rect.Bottom+32″=39°54′32″;

[0095] ③ Grid code set calculation considering distance constraints:

[0096] like Figure 2 As shown in the figure, the calculation requirement is not only to obtain the grid information of point A, but also to obtain the information of all grids within a certain range from point A. The distance constraint range is expressed by a dotted box in the figure. The actual calculation result is the set of all grids with diagonal lines. There are a total of 9 grids here, each of which includes the grid code of the corresponding level and the corresponding grid coordinates and other information.

[0097] Specifically, in this embodiment, the planned routes and no-fly zone grid codes saved in the airspace manager all include time parameter information. The time parameter information can be the total flight time of the corresponding planned route. For example, if the total flight time of the planned route is from 9:00 to 10:00, the total flight time can be saved in the converted grid code of the planned route, and the grid code can be set to an exclusive state within the total flight time.

[0098] Of course, the above method is not optimal. Therefore, the technical solution disclosed in this embodiment also includes estimating the more accurate time information of the planned route occupying the grid code according to the flight time, and saving it to the corresponding grid code. When the accurate time information arrives, the corresponding grid code is set to an exclusive state in turn, thereby achieving optimal utilization of airspace resources.

[0099] Furthermore, the occupancy time of a single grid code of a route is estimated by the following method:

[0100] For a route, the time to reach each waypoint on the route is estimated based on the aircraft speed and the distance between each waypoint;

[0101] For a grid code of the route, find the projection points of the four corner points of the grid corresponding to the grid code on the route;

[0102] Based on the arrival time difference of the two waypoints closest to the grid, the arrival time of each projection point is estimated by the time linear difference averaging method;

[0103] The earliest arrival time and the latest arrival time of the projection point are used as the time period during which the route occupies the grid code.

[0104] Specifically, the time occupied by a single grid code of the route is estimated by dividing the arrival time difference of known waypoints by the average speed. Generally, when planning a route, the planned route period and the waypoints to be passed are known, and the time to arrive at the planned waypoints can be estimated. The method for estimating the time of the planned waypoints can be the distance between the waypoints / the average speed of the aircraft, wherein the average speed of the aircraft can be estimated by the maximum speed of the aircraft, or the sum of the average speeds of all previous flights of the aircraft / the number of flights. After estimating the arrival time of the waypoints, first find the projection points of the four corner points of the grid on the route, calculate the corresponding arrival times of the projection points respectively by time linear interpolation, then find the earliest and latest arrival times of the projection points, subtract the earliest time from the latest time, and the time occupied by a single grid code of the route is obtained. For each grid of the route, the occupied time of each grid needs to be calculated and saved.

[0105] For example, Figure 3 As shown, first calculate the arrival time of each waypoint based on the known speed value of each waypoint, which is recorded as: T A , T B , T C , T D ;

[0106] The four corner points of the grid G ​​are projected onto the route, and the arrival time of the corresponding projection points is calculated. The specific calculation can be obtained by linear interpolation of the time of the previous and next waypoints of the segment where the projection point is located. Take T1 as an example: let the projection point corresponding to T1 be P1, then T1 = T B +L B P1*(T C -T B ) / L BC ; where L B P1 and L BC are the distances between the two related points.

[0107] Through the above step 2, obtain T1, T2, T3, T4; the time period of grid G ​​is the interval [T FROM ,T TO ]:T FROM , T TO Obtained by the following formula:

[0108] T FROM =MIN(T1, T2, T3, T4);

[0109] T TO =MAX(T1, T2, T3, T4).

[0110] Furthermore, finding the projection points of the four corner points of the grid corresponding to the grid code on the route includes:

[0111] The connection point on the route of the line with the shortest distance between each corner point of the four corner points of the grid corresponding to the grid code and the route is used as the projection point of each corner point on the route.

[0112] Specifically, the grid code is represented by rectangles of different sizes. Based on the irregular characteristics of the route and the uncertainty of the route in the grid code, it is more accurate to obtain the closest point of the grid code on the route and use the closest point as the projection point of the grid code on the route. Figure 3 As shown, the relationship between the minimum distance between the four corner points of the grid code and the route includes the following situations:

[0113] Take the corner point as the starting point and draw a perpendicular line to the route, with the other end point of the perpendicular line located on the route. Figure 3 The projection point corresponding to time T1 and the projection point corresponding to time T2 are shown;

[0114] Take the corner point as the starting point and connect it with the waypoints on the route to form a straight line segment. Take the waypoints on the route connected by the straight line segment with the shortest distance, such as Figure 3 The projection point corresponding to time T3 is shown in the figure;

[0115] The corner point coincides with a waypoint on the route, such as Figure 3 The projection point corresponding to time T4 is shown in the figure.

[0116] Determine whether the three relationships between the four corner points of the grid code and the route exist, and calculate the minimum value of the relationships. The point on the route corresponding to the minimum value is used as the projection point of the corresponding corner point on the route.

[0117] Furthermore, the airspace manager calculates the coordinates of the four corner points of the occupied grid code one by one, calculates the covered grid based on the coordinate range covered by the four corner point coordinates, saves the occupied period information of the occupied grid code to the covered grid, and sets the covered grid consumption value during the occupied period to a null value and saves it.

[0118] Specifically, for each occupied grid code, the area covered by the occupied grid code is determined based on the coordinates of the four corner points of the occupied grid code, and the corresponding covered grid can be found. Since each occupied grid code stores occupied time period information, the corresponding grid will also store the occupied time period information, and the cost value of the occupied time period will be set to a null value and stored.

[0119] For example, after a planned route or no-fly zone is converted to a grid code from the airspace manager, the grid code includes the time parameters occupied by the planned route or no-fly zone, such as Figure 4 As shown, the index positions of the four corner points of the grid code rectangle in the grid data are calculated respectively, and are recorded as: LT(X L ,Y T )、RT(X R ,Y T )、RB(X R ,Y B )、LB(X L ,Y B );

[0120] Put the row and column numbers in (X L ,X R )、(Y B ,Y T ) is set to NOVALUE, which marks it as an invalid area for route planning.

[0121] Furthermore, the planning of the effective route grid between the waypoints based on the topological relationship between the waypoints and the minimum cost principle refers to selecting two adjacent waypoints among the waypoints, one as the starting point and the other as the end point, taking the starting point as the initial node, and finding the effective route grid from the starting point to the end point based on the following steps:

[0122] S1: Push the node into the heap manager;

[0123] S2: The heap manager finds a non-null neighborhood grid in the eight-directional connectivity neighborhood grid of the node based on the node arrival time, pushes the non-null neighborhood grid into the heap manager, and finds a neighborhood grid with the minimum cost from the non-null neighborhood grid as the updated node; updates the cost value of the eight-directional connectivity neighborhood grid based on the cost value of the current node and the existing cost value of the eight-directional connectivity neighborhood grid; repeats step S2 until the eight-directional connectivity neighborhood grid of the node includes the end point;

[0124] S3. Connect the grids corresponding to all nodes in sequence as the valid route grids of the starting point and the end point.

[0125] Furthermore, the updating of the cost value of the eight-directional connected neighborhood grid based on the cost value of the current node and the existing cost value of the eight-directional connected neighborhood grid is completed by the following method, the method comprising:

[0126] The cost value of the current node is summed with the existing cost values ​​of the eight-directional connectivity neighborhood grid of the current node to obtain the cost value assigned by the neighborhood grid;

[0127] For the eight-directional connectivity neighborhood grid of the current node already in the heap manager, determining whether the cost value assigned by the neighborhood grid is greater than the existing cost value of the neighborhood grid; if so, updating the existing cost value of the neighborhood grid to the cost value assigned by the neighborhood grid; otherwise, not updating the existing cost value of the neighborhood grid;

[0128] For the eight-directional connectivity neighborhood grid of the current node that is pushed into the heap manager for the first time, the existing cost value of the neighborhood grid is updated to the cost value assigned by the neighborhood grid.

[0129] Specifically, this embodiment adds consideration of time parameters based on the DJ algorithm or the A* algorithm. All grids not only save the consumption value, but also save the time information based on the occupancy of the grid code. Based on the time information, the grid consumption value may be transformed into a null value, so that the consumption value saved by the grid has a dynamic transformation feature based on the change of time.

[0130] For example, Figure 5 For the convenience of expression, Figure 5 The grid cost values ​​for non-null grids are all set to 1.0. When planning a route from waypoint A to waypoint B, at the time of waypoint A, based on the eight-way connectivity of the grids, the neighboring nodes grids 23, 13, 14, 15, and 25 are found. Since the cost values ​​of grids 23, 13, 14, 15, and 25 are all 1.0, grid 23 is arbitrarily selected as the node with the minimum cost value, and waypoint A, grids 23, 13, 14, 15, and 25 are pushed into the heap manager, and the grids 23, 13, 14, 15, and 25 all include the time of arrival at the corresponding grids;

[0131] Then find the neighboring node grid based on the eight-directional connectivity of grid 23, such as Figure 6 As shown, it can be seen that due to the change of time Figure 6 Raster images and Figure 5 The grid diagram is different. The neighborhood node grids of grid 23 are: grids 14, 32, and 33. Figure 5 At the middle moment, the neighboring node grids of grid 23 are: grids 12, 13, 14, 22, and 32. Since the cost values ​​of grids 12, 32, and 33 are all 1.0, grid 33 is arbitrarily selected as the node grid with the minimum cost value, and grids 32 and 33 are pushed into the heap manager. Since grids 32 and 33 are both newly added neighboring grid nodes, the cost value of grid 23, 1.0, is summed with the existing cost values ​​of grids 32 and 33, 1.0, to obtain an assigned cost value of 2.0. The assigned cost values ​​are then used to replace the existing cost values ​​of grids 32 and 33, respectively. At this time, the heap manager includes the node grids: waypoint A, grids 13, 14, 15, 23, 25, 32, and 33.

[0132] ...Continue to iterate the algorithm downward, and finally find the minimum cost path from waypoint A to waypoint B: waypoint A-grid 23-grid 33-waypoint B.

[0133] This embodiment calculates the time from the node to the neighboring node based on each loop iteration, saves the time of the neighboring node in the domain node grid, and when searching for the neighboring node based on the eight-directional connectivity of the grid based on the time of the domain node, the cost value change of the grid can be obtained in real time, bypassing the grid whose cost value is set to a null value. Based on the accurate eight-directional connectivity relationship, it is helpful to obtain more optimized route planning results and optimize the use of airspace resources.

[0134] Furthermore, the valid routes between the waypoints are converted into grid codes by the following method:

[0135] Starting from the starting point of the actual flyable route, dividing the route into segments with a preset threshold as a step size;

[0136] A distance constraint grid set is calculated for each route segment one by one, and a grid deduplication calculation and aggregation calculation are performed to generate the actual flyable route grid code.

[0137] Furthermore, the preset threshold is an aircraft safety threshold, and a grid code width equal to the aircraft safety threshold or any one of the two closest adjacent grid code widths is selected as the lowest level grid in the distance constraint grid set.

[0138] Specifically, the lowest-level grid code width depends on the aircraft's own safety threshold, which is a factory parameter based on the aircraft's maneuverability properties. Preferably, a grid code width that is equal to or closest to the aircraft's own safety threshold is selected as the lowest-level grid code width. The advantage of this approach is that it not only ensures the aircraft's safety threshold range, but also prevents the selection of a grid code width that is too large to cause coarse-grained errors, or too small to cause low grid code accuracy, resulting in an increase in invalid calculations.

[0139] For example, assuming that the lowest level grid is level 16, according to Table 1, the size of the level 16 grid is 1 km, and the size is 32″, then the maximum value of the preset threshold is the width of the level 16 grid.

[0140] Specifically, starting from the starting point A, a temporary intermediate node is inserted between AB with a threshold as the step length to segment the route. The threshold is preferably set to the width of the lowest level grid. If the route AB is less than one step length, no point insertion is required. When the distance between the last inserted node and B is less than one step length, point insertion is also stopped, such as Figure 7Seven intermediate nodes are inserted to form route segments AA1, A1A2, ..., A7B.

[0141] Then, as Figure 8 As shown, each segment is processed, starting with segment AA1, and the grid codes of point A and point A1 are calculated as G A ,G A1 , if G A =G A1 , indicating that A and A1 are in the same grid. At this time, the grid where points A and A1 are located is saved in the distance constraint grid set G.

[0142] It should be noted that the distance constraint grid set is initially set to an empty set. After traversing all route segments AA1, A1A2, ..., A7B, the grid code corresponding to each route segment is stored in the set.

[0143] Furthermore, the calculation of each route segment one by one and the establishment of a distance constraint grid set are completed by the following steps, which include:

[0144] If the two endpoints of the route segment are in the same grid, the grid is selected as the selected grid. If the selected grid does not exist in the distance constraint grid set, the selected grid is added to the distance constraint grid set.

[0145] If the two endpoints of the route segment are in different grids, the two different grids are added to the distance-constrained grid set, and a first selected area is obtained based on the longitude and latitude of the two different grids. Redundant grids are removed from the first selected area, and grids that meet preset conditions are selected from the remaining grids in the first selected area and added to the distance-constrained grid set.

[0146] The selected grid or the first selected area is expanded based on the preset threshold to obtain a second selected area, redundant grids are removed from the second selected area, and grids that meet preset conditions are selected from the remaining grids in the second selected area to be added to the distance constrained grid set.

[0147] like Figure 8 As shown, if the grid code G where point A is located is A The grid code G where point A1 is located A1 Different, they are merged into a large grid G Inner Specifically, according to G A , G A1 The range of the grids where point A and point A1 are located is sorted respectively by their longitude and latitude, and the range of the grids where point A and point A1 are located with the largest difference in longitude and latitude is taken as the first selected area, that is, G Inner .

[0148] For example, assume that the grid longitude and latitude of point A is:

[0149] ARect.Left = 116°25′;

[0150] ARect.Bottom=39°54′32″;

[0151] ARect.Right=116°25′32″;

[0152] ARect.Top=39°54′;

[0153] The grid longitude and latitude of point A1 are:

[0154] A1Rect.Left=116°25′32″;

[0155] A1Rect.Bottom=39°55′4″;

[0156] A1Rect.Right=116°26′4″;

[0157] A1Rect.Top=39°54′32″;

[0158] Sort the longitudes of points A and A1, and we get: ARect.Left, ARect.Right, A1Rect.Left, A1Rect.Right. It can be seen that the difference between the longitudes of ARect.Left and A1Rect.Right is the largest, so keep these two longitudes as G Inner Similarly, obtain the two latitudes with the largest difference between point A and point A1, ARect.Top and A1Rect.Bottom, as G Inner The latitude of G Inner The grid code contained in the rectangular area.

[0159] Get G Inner Remove G from A , G A1 The remaining two grid codes are marked as G LT , G RB .

[0160] Judge AA1 and G respectively LT ,G RB The distance relationship, if G LT ,G RB If the distance between the farthest point of the mid-distance route segment AA1 and the perpendicular line of the route segment AA1 is less than the preset threshold, then G LT and G RB Save to the distance constraint grid set G.

[0161] Then, continue to expand G according to the preset threshold Inner Cheng G Outer , specifically, from G Inner The top, bottom, left, and right sides extend outward by a threshold width, such as Figure 9 As shown, in the embodiment G Inner The original rectangular area containing 2*2, that is, 4 grids, is expanded to G Outer After that, it becomes a 4*4 rectangular area, making G Outer Just cover all grids within the AA1 segment threshold range.

[0162] Specifically, from G Outer Remove G from Inner The remaining grids are strictly compared with AA1, and the grids with distances less than the threshold are saved in the distance constraint grid set G.

[0163] After processing segment AA1, the next step is to process segment A1A2. This segment is processed similarly to segment AA1, but due to the proximity of AA1 and A1A2, there will be duplicate grid areas at the junction. Many grids have already been marked and retained during segment AA1. Therefore, removing redundant grids allows us to skip the repeated verification of overlapping grids between AA1 and A1A2, eliminating the need for secondary processing. Instead, we only need to process newly added grids whose distance from the previously processed grid exceeds a preset threshold, reducing the number of comparisons. This process continues until the final route segment is processed, ultimately forming a complete grid code set.

[0164] Furthermore, the deduplication refers to performing the following steps on the grid codes of the distance-constrained grid set, the steps comprising:

[0165] sorting the grid codes included in the distance constraint grid set by size;

[0166] Comparing the sorted grid codes in pairs;

[0167] If the two adjacent grid codes are identical, any one of the two adjacent identical grid codes is deleted from the distance-constrained grid set.

[0168] Specifically, when the conversion of the route grid code obtained by calculation is completed, some repeated grid codes will be generated due to reasons such as line segmentation processing.

[0169] Since each grid has a unique grid code, the grid set is quickly sorted according to the size of the grid code, and the adjacent grid codes are compared. If the adjacent grid code values ​​are the same, one is removed and the grid set is scanned once to complete the deduplication operation.

[0170] Furthermore, the polymerization comprises the following steps:

[0171] SA1: sorting the grid codes of the distance constraint grid set by size;

[0172] SA2: grouping every four adjacent grid codes in the sorted grid code sequence into the same group.

[0173] SA3: Determine whether the grid codes in each group are at the same level and the grid codes of the previous level are the same.

[0174] If so, the grid codes of the same group are deleted and the upper level grid code corresponding to the grid code of the group is added to obtain an updated distance constraint grid set, and the process returns to step SA1. When there is no group of the four adjacent grid codes that can be merged into the upper level grid code, the process ends.

[0175] Specifically, according to the principle of minimum grid codes, it is necessary to aggregate the grid codes of some areas upwards to reduce the number of grid codes to a minimum.

[0176] Since the grid coding principle follows the equal longitude and latitude quadtree grid system, through sorting, if four adjacent grid codes simultaneously meet the following requirements: Same level, The upper level grid codes are all the same; then the four grid codes can be aggregated into one upper level grid code.

[0177] For example, Figure 10 As shown:

[0178] The large grid where point C is located is actually composed of 4 small grids G C1 , G C2 , G C3 , G C4 , the level of the small grid is 16, and the grid codes are:

[0179] G C1 =00000111010011101010110110100100;

[0180] G C2 =00000111010011101010110110100101;

[0181] G C3 =00000111010011101010110110100110;

[0182] G C4 =00000111010011101010110110100111;

[0183] Because G C1 ,G C2 ,G C3 ,G C4 The upper level grid code is 000001110100111010101101101001, recorded as G C ,

[0184] Meet the merging requirements, the merged grid G C Become the 15th level grid.

[0185] Theoretically, after a grid code is aggregated once, it can continue to aggregate upwards until all adjacent grid codes no longer meet the conditions, and then the aggregation process is stopped.

[0186] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0187] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for automatic route planning based on time parameters, characterized in that: The airspace manager stores the planned routes, no-fly zone data, and corresponding grid codes in the airspace. Each grid code is provided with occupied time period information. The airspace manager automatically plans routes using the following method: Enter the waypoints and sailing time of the new route to be planned; According to the occupation period information of each grid code, find out the grid code occupied during the navigation time and save it, where: The following method is used to estimate the occupancy period of a single grid code on a route: For a route, the time to reach each waypoint on the route is estimated based on the aircraft speed and the distance between each waypoint; For a grid code of the route, find the projection points of the four corner points of the grid corresponding to the grid code on the route; Based on the arrival time difference of the two waypoints closest to the grid, the arrival time of each projection point is estimated by the time linear difference averaging method; The earliest arrival time and the latest arrival time of the projection point are used as the time period during which the route occupies the grid code; Locate each grid covered by the occupied grid code in the grid map, and save the occupied period information and the empty consumption value of the corresponding grid code in the covered grid, wherein the empty consumption value indicates that the consumption value of the grid is empty during the occupied period; Establishing a topological relationship between the waypoints based on the eight-directional connectivity of the grid; Planning an effective route between the waypoints based on the topological relationship between the waypoints and the principle of minimum cost; The valid routes between the waypoints are converted into grid codes, and the occupied time period information of each grid code of the valid routes is estimated and stored, wherein: The valid route between the waypoints is converted into a grid code by the following method: Starting from the starting point of the actual flyable route, dividing the route into segments with a preset threshold as a step size; Calculate each route segment one by one and establish a distance constraint grid set, and generate the actual flyable route grid code through grid deduplication calculation and aggregation calculation, where: The calculation of each route segment one by one and the establishment of a distance constraint grid set are completed by the following steps, which include: If the two endpoints of the route segment are in the same grid, the grid is selected as the selected grid. If the selected grid does not exist in the distance constraint grid set, the selected grid is added to the distance constraint grid set. If the two endpoints of the route segment are in different grids, the two different grids are added to the distance-constrained grid set, and a first selected area is obtained based on the longitude and latitude of the two different grids. Redundant grids are removed from the first selected area, and grids that meet preset conditions are selected from the remaining grids in the first selected area and added to the distance-constrained grid set. The selected grid or the first selected area is expanded based on the preset threshold to obtain a second selected area, redundant grids are removed from the second selected area, and grids that meet preset conditions are selected from the remaining grids in the second selected area to be added to the distance constrained grid set.

2. The method for automatic route planning based on time parameters according to claim 1, characterized in that: The step of finding the projection points of the four corner points of the grid corresponding to the grid code on the route includes: The connection point on the route of the line with the shortest distance between each corner point of the four corner points of the grid corresponding to the grid code and the route is used as the projection point of each corner point on the route.

3. The method for automatic route planning based on time parameters according to claim 2, characterized in that: The airspace manager calculates the coordinates of the four corner points of the occupied grid code one by one, calculates the covered grid based on the coordinate range covered by the four corner point coordinates, saves the occupied period information of the occupied grid code to the covered grid, and sets the covered grid consumption value during the occupied period to a null value and saves it.

4. The method for automatic route planning based on time parameters according to claim 1, characterized in that: Planning the effective route grid between waypoints based on the topological relationship between waypoints and the minimum cost principle refers to selecting two adjacent waypoints among the waypoints, one as the starting point and the other as the end point, using the starting point as the initial node, and finding the effective route grid from the starting point to the end point based on the following steps: S1: Push the node into the heap manager; S2: The heap manager finds a non-null neighborhood grid in the eight-directional connectivity neighborhood grid of the node based on the node arrival time, pushes the non-null neighborhood grid into the heap manager, and finds a neighborhood grid with the minimum cost from the non-null neighborhood grid as the updated node; updates the cost value of the eight-directional connectivity neighborhood grid based on the cost value of the current node and the existing cost value of the eight-directional connectivity neighborhood grid; Repeat step S2 until the eight-directional connectivity neighborhood grid of the node includes the endpoint; S3. Connect the grids corresponding to all nodes in sequence as the valid route grids of the starting point and the end point.

5. The method for automatic route planning based on time parameters according to claim 4, characterized in that: The updating of the cost value of the eight-directional connected neighborhood grid based on the cost value of the current node and the existing cost value of the eight-directional connected neighborhood grid is completed by the following method, the method comprising: The cost value of the current node is summed with the existing cost values ​​of the eight-directional connectivity neighborhood grid of the current node to obtain the cost value assigned by the neighborhood grid; For the eight-directional connectivity neighborhood grid of the current node already in the heap manager, determining whether the cost value assigned by the neighborhood grid is greater than the existing cost value of the neighborhood grid; if so, updating the existing cost value of the neighborhood grid to the cost value assigned by the neighborhood grid; otherwise, not updating the existing cost value of the neighborhood grid; For the eight-directional connectivity neighborhood grid of the current node that is pushed into the heap manager for the first time, the existing cost value of the neighborhood grid is updated to the cost value assigned by the neighborhood grid.

6. The method for automatic route planning based on time parameters according to claim 1, characterized in that: The preset threshold is an aircraft safety threshold, and a grid code width equal to the aircraft safety threshold or any one of the two closest adjacent grid code widths is selected as the lowest level grid in the distance constraint grid set.

7. The method for automatic route planning based on time parameters according to claim 1, characterized in that: The aggregation calculation includes the following steps: SA1: sorting the grid codes of the distance constraint grid set by size; SA2: grouping every four adjacent grid codes in the sorted grid code sequence into the same group; SA3: determining whether the grid codes in each group are at the same level and have the same upper level grid code; If yes, then delete the grid codes of the same group and add the upper level grid code corresponding to the grid code of each group to obtain an updated distance constraint grid set, and return to step SA1; When there is no grouping of the four adjacent grid codes that can be merged into the upper level grid code, the process ends.

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