Geofence mesh classification and mesh matching system and method for unmanned aerial vehicle navigation

Abstract

The invention discloses a geo-fence mesh classification and mesh matching system and method for unmanned aerial vehicle navigation in the technical field of unmanned aerial vehicle monitoring. The method comprises the following steps: receiving geo-fence data containing fence polygons; generating a fence mesh based on the geo-fence data; performing intersection matching processing on the boundary lines of the fence polygon and the fence grids, and identifying intersected fence grids; all the fence grids are classified and identified as internal grids, boundary grids and external grids based on the intersected fence grids; and after receiving real-time unmanned aerial vehicle position data, matching the unmanned aerial vehicle position data with internal, boundary and external grids. According to the method, the fence grids are classified, the efficiency of subsequent judgment of the real-time position relation between the unmanned aerial vehicle and the fence is greatly improved, and the requirement for real-time response of the large-data-volume unmanned aerial vehicle is met.

Description

Technical Field

[0001] The present invention relates to a geographic fence grid classification and grid matching system and method for unmanned aerial vehicle (UAV) navigation, and belongs to the technical field of UAV monitoring. Background Art

[0002] The drone safety monitoring platform needs to maintain real-time monitoring of all drones above the designated area, and it needs to simultaneously issue alarm information and report any abnormal drones.

[0003] In existing technologies, as the scope of surveillance expands, the number of vertices on the fence boundary increases. As the number of drones increases, traditional methods for determining the positional relationship between drones and the fenced area become less efficient and unable to meet the requirements for real-time response to large amounts of drone data. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a geographic fence grid classification and grid matching system and method for drone navigation. The fence grids are classified and processed, which greatly improves the efficiency of subsequent real-time position relationship judgment between drones and fences, and meets the requirements of real-time response of drones with large amounts of data.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for geo-fence grid classification and grid matching for drone navigation, comprising:

[0007] Receive geofence data containing a fence polygon;

[0008] Generate a fence grid based on geofence data;

[0009] Perform intersection matching on the fence polygon boundary line and the fence grid, and identify the intersecting fence grids;

[0010] Based on the intersecting fence grids, all fence grids are classified into internal, boundary and external grids;

[0011] After receiving real-time drone position data, it is matched with the interior, boundary, and exterior grids.

[0012] Furthermore, receiving geofence data containing the fence polygon boundary line includes: automatically reading the geofence file at the specified location, and receiving geofence data sent by other business platforms through the message middleware; the geofence file includes the latitude and longitude coordinates of the vertices of the fence polygon boundary line, the upper and lower height limits of the fence, and the fence buffer threshold information.

[0013] Furthermore, a fence grid is generated based on the geo-fence data, including:

[0014] Calculate the rectangular range that encloses the entire fence based on the longitude and latitude coordinates of the fence polygon;

[0015] Apply double fence threshold D to the rectangular range TH Extension of distance;

[0016] Divide the expanded rectangular range into a grid, and set the length and width of the grid to D TH , generate the grid set S GRID .

[0017] Furthermore, the fence polygon boundary line and the fence grid are intersected and matched to identify the intersecting fence grids, including:

[0018] Take out the boundary segments of the fence polygon boundary line one by one, and compare the boundary segments and the grid set S GRID The grid in the grid is intersected;

[0019] In response to the grid set S GRID If any grid in the grid intersects with a boundary line segment, the index value of the intersecting boundary line segment is saved in the grid, and the grid is assigned to a temporary grid set S T GRID In, S T GRID ∈S GRID .

[0020] Furthermore, for the boundary segments and grid set S GRID The grid in the grid is intersected, including:

[0021] Get the bounding rectangle of the boundary segment;

[0022] Get all grids that intersect with the range rectangle, recorded as grid set B GRID ;

[0023] Take out grid set B one by one GRID For each grid G ​​in , determine whether G intersects with the boundary segment;

[0024] In response to the endpoint of the boundary line segment falling in the grid G ​​or the boundary line segment intersecting with the four boundary line segments of the grid G, it is determined that the grid G ​​and the boundary line segment intersect; otherwise, it is determined that the grid G ​​and the boundary line segment do not intersect.

[0025] Furthermore, based on the intersecting fence grids, all fence grids are classified and identified as internal, boundary, and external grids, including:

[0026] The grid set S GRID Split into three disjoint subsets S 1 GRID 、S2 GRID and S 3 GRID , where S 1 GRID is the set of grids near the fence polygon boundary line, S 2 GRID is the set of grids completely outside the fence, S 3 GRID is the set of grids that are completely inside the fence;

[0027] Sequentially take out the grid set S T GRID Each grid in is denoted as G C ;

[0028] Calculate the acquisition and G C The other eight directly adjacent grids are denoted as G i , i∈[1,8];

[0029] like Then G i Join S 1 GRID Finally, put G C Also joined S 1 GRID , until S is processed T GRID All grids in ;

[0030] S 1 GRID After marking all the grids in the grid as boundary grid attributes, clear S T GRID ;

[0031] The grid set S GRID Upper left corner grid G LT Join S T GRID middle;

[0032] Take out S in sequence T GRID Each grid G ​​in T , get and G T The other K directly adjacent grids are denoted as G i , i∈[1,K],K≤8; for G i Judge one by one: If and Then G i Join S 2 GRID In the meantime, G i Append to S TGRID In the process, after processing the adjacent grid, G T Also joined S 2 GRID and from S T GRID Delete G T , loop processing S T GRID All grids in the T GRID All meshes in the are deleted;

[0033] S 2 GRID Mark all grids in as external grid attributes, and set S GRID All unmarked grids in the grid are marked as internal grid attributes and grouped into S 3 GRID middle.

[0034] Furthermore, after receiving the real-time drone position data, it is matched with the internal, boundary and external grids, including:

[0035] Get the drone's location;

[0036] Calculate the position of the drone in the grid set S according to the drone's position coordinates GRID The row and column indices in ;

[0037] Based on the UAV in the grid collection S GRID The row and column indexes in the grid set S are used to calculate the position of the drone. GRID The corresponding grid in is denoted as G P ;

[0038] In response to G P ∈S 2 GRID When , it is determined that the current position of the drone is outside the fence boundary;

[0039] In response to G P ∈S 3 GRID When , it is determined that the current position of the drone is inside the fence;

[0040] In response to G P ∈S 1 GRID , it is determined that the current position of the drone is near the fence polygon boundary line.

[0041] In a second aspect, the present invention provides a geo-fence grid classification and grid matching system for drone navigation, comprising:

[0042] Data receiving module: receives geo-fence data including fence polygons;

[0043] Grid generation module: Generates fence grid based on geo-fence data;

[0044] Grid identification module: performs intersection matching processing on the fence polygon boundary line and the fence grid, and identifies the intersecting fence grids;

[0045] Mesh classification module: Based on the intersecting fence meshes, all fence meshes are classified into internal, boundary and external meshes;

[0046] Position determination module: After receiving real-time drone position data, it matches it with the internal, boundary and external grids.

[0047] In a third aspect, the present invention provides a geo-fence grid classification and grid matching device for drone navigation, including a processor and a storage medium;

[0048] The storage medium is used to store instructions;

[0049] The processor is configured to operate according to the instructions to execute the steps of any of the above methods.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] First, this invention takes into account the fact that geofences can be imported once and used multiple times in a drone safety monitoring platform. It proposes a method for converting the polygons of the fence area into two-dimensional meshes, providing technical support for subsequent high-performance computing. It also classifies the fence meshes into three categories: internal meshes, boundary meshes, and external meshes, greatly improving the efficiency of subsequent real-time positional relationship determination between drones and fences.

[0053] Second, the present invention supports a dual-mode fence update mechanism that can load static geofence data at system startup and receive dynamic fence updates in real time through the message middleware. This overlay update strategy ensures the timeliness of fence information while reducing resource consumption caused by frequent system updates.

[0054] 3. The present invention innovatively adopts a dual grid division mechanism of basic grid construction and intelligent boundary matching, generates an extended envelope rectangle based on the fence polygon, and constructs a square space grid system with the fence threshold as the unit; accurately identifies boundary interaction grids through the line segment-grid fast intersection judgment algorithm, significantly improving the efficiency of spatial relationship judgment. In the specific grid classification algorithm process, the calculation of the boundary grid set is the basis; another core innovation lies in the rapid generation algorithm of the external grid set. The algorithm uses the connectivity relationship of the grid for rapid diffusion, and can find all external grids at one time. This process is very fast. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 A geo-fence grid diagram of the geo-fence grid classification and grid matching method for drone navigation provided in Example 1 of the present invention;

[0057] Figure 2 A fence grid classification identification diagram for the geo-fence grid classification and grid matching method for drone navigation provided in Example 1 of the present invention;

[0058] Figure 3 An eight-adjacent grid diagram for the geo-fence grid classification and grid matching method for drone navigation provided in Example 1 of the present invention;

[0059] Figure 4 This is a drone grid matching diagram for the geo-fence grid classification and grid matching method for drone navigation provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0060] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0061] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0062] Example 1:

[0063] This embodiment provides a method for geo-fence grid classification and grid matching for drone navigation, specifically including the following:

[0064] 1. Geofence area polygon reading

[0065] There are generally two ways to set up geofences on the drone security monitoring platform: one is to automatically read the geofence file of the specified location when the software starts, including the vertex latitude and longitude coordinates of the fence polygon boundary line, the upper and lower limit heights of the fence, and the fence buffer threshold (denoted as: D TH , the same below). Another method is to receive geofence data from other business platforms through message middleware. If the geofence file has already been read when the security monitoring platform starts up, and new geofence data is subsequently received through the message middleware, the new data will overwrite the previous settings. Generally, frequent changes to geofence data are not recommended; set it once after the monitoring platform starts up.

[0066] 2. Geofencing Grid

[0067] The geofence gridding process is divided into: (1) generating a fence grid from the fence; (2) intersecting and matching the fence polygon boundary line with the fence grid. The polygon corresponding to the geofence is denoted as Z. GEO , the fence threshold is D TH , the fence polygon boundary line is L B The specific steps for fence gridding are as follows:

[0068] ① First, calculate the rectangular range of the entire fence based on the latitude and longitude coordinates of the fence polygon, and record it as R. TH The expansion of the distance, the expanded rectangular range is still recorded as R;

[0069] ② Divide R into a grid, and set the length and width of the grid to D TH , that is, each grid has a side length of D TH The width and height of R are D W 、D H , then the corresponding number of grid rows and columns is N ROW 、N COL They are:

[0070] N ROW =INT(D H / D TH )+1;

[0071] N COL =INT(D W / D TH )+1;

[0072] The grid division starts from the upper left corner of R, and the generated grid set is recorded as: S GRID ;

[0073] ③ Take out L segment by segment B The boundary segment S AB and S GRID The grid in the grid is intersected and judged by the line segment S AB For example:

[0074] S1. Get line segment S AB The range rectangle Bound;

[0075] S2. Get all grids that intersect with Bound and record them as grid set B GRID ;

[0076] S3, take out B one by one GRID Each grid in is denoted as G, and for G and line segment S AB Make an intersection judgment;

[0077] S4. If point A or B falls in grid G, then grid G ​​and line segment S AB intersect;

[0078] S5. If the line segment S AB and the four boundary segments of G, then the grid G ​​and the line segment S AB They also intersect;

[0079] S6. If S4 and S5 are not satisfied, then the grid G ​​and line segment S AB do not intersect;

[0080] If a grid and S AB If there is an intersection, save S in the grid AB The index value (ie S AB In L B The line segment index in , and the grid is assigned to a temporary grid set, denoted as: S T GRID , that is, S T GRID ∈S GRID ;

[0081] ④The algorithm ends.

[0082] Fence area polygons (ABCDEFA) are meshed, with a mesh width of one D TH , the bounding rectangle of polygon (ABCDEFA) is: R(P LT ,P RT ,P RB ,P LB ), double D for R TH The distance is expanded, that is, Figure 1 The collection of grids shown. Figure 1 The grid set with the dotted line is S TGRID .

[0083] 3. Fence Grid Marking

[0084] After fence gridding, two fence grid sets are formed: S T GRID and S GRID , and S T GRID ∈S GRID , S T GRID All grids that intersect with the fence polygon boundary line are stored in S GRID is the complete set of all grids. In the fence grid marking module, we will GRID Perform grid classification, and the S GRID will be split into three mutually disjoint subsets, namely: S 1 GRID 、S 2 GRID 、S 3 GRID , where S 1 GRID is the set of grids near the fence polygon boundary line, S 2 GRID It is a set of grids that are completely outside the fence (the closest distance to the boundary line is greater than D TH ), S 3 GRID It is a set of grids that are completely inside the fence (the closest distance to the boundary line is greater than D TH ), namely S GRID =S 1 GRID +S 2 GRID +S 3 GRID After such classification, it will greatly facilitate the subsequent judgment of the relationship between the real-time position of the drone and the fence.

[0085] The grid marking algorithm process is as follows:

[0086] ① Take out S in sequence T GRID Each grid in is denoted as: G C , calculate and obtain G C The other eight directly adjacent grids are denoted as: G i , i∈[1,8]. C The boundary line segment index value stored in is assigned to each G i , if G iIf the same index value has been saved in G, there is no need to add it again to avoid storing duplicate boundary line segment index values ​​in the grid. C The boundary index value saved in is assigned to each G around it i ,Purpose Each boundary grid needs to record the index value of the boundary line segments near it in order to ,perform high-performance distance calculation and point and fence position relationship determination ,later;G i The reason for deduplication of boundary segment indexes is to avoid the same boundary segment index being recorded multiple times in the same grid;

[0087] like Then G i Join S 1 GRID Finally, put G C Also joined S 1 GRID Until S is processed T GRID All grids in ;

[0088] ② For S 1 GRID Mark all grids in as boundary grid attributes and clear S T GRID , waiting for subsequent backup;

[0089] ③Since we are generating S GRID 2 times D TH Distance expansion can ensure S GRID The upper left corner of grid G LT Definitely not in S 1 GRID Middle, that is, G LT Not the boundary grid, put G LT Join S T GRID middle;

[0090] ④ Take out S in sequence T GRID Each grid in is denoted as G T , get and G T The other directly adjacent grids (number <= 8, denoted as: K) are denoted as: G i , i∈[1,K]. i Judge one by one: If G i Not a boundary grid (i.e. ),and Then G i Join S 2 GRID In the meantime, G iAppend to S T GRID In the process, after processing the adjacent grid, finally G T Also joined S 2 GRID and from S T GRID Delete G T ;

[0091] ⑤Cyclic call ④Process S T GRID All grids in , up to S T GRID All the grids in are deleted and the loop ends;

[0092] ⑥To S 2 GRID Mark all meshes in as external mesh attributes;

[0093] ⑦ Put S GRID All unmarked grids in the grid are marked as internal grid attributes and grouped into S 3 GRID In, S 3 GRID That is, the set of grids that are completely inside the fence;

[0094] ⑧The algorithm ends.

[0095] like Figure 2 As shown, the grid with the dotted line is the fence boundary grid set S 1 GRID , S 1 GRID The blank grid outside the fence is the external grid set S 2 GRID , S 1 GRID The internal blank grid is the fence internal grid set S 3 GRID . Figure 3 Shown G C The 8 adjacent grids G i , i∈[1,8].

[0096] 4. UAV real-time location data reception

[0097] Drone safety monitoring platforms typically use message-based middleware to continuously obtain real-time drone location data. This includes key information such as the drone's ID, latitude and longitude, heading, pitch, and speed. The latest location data automatically overwrites the previously acquired location data. The drone safety monitoring platform regularly obtains the latest drone location information within the monitoring area and simultaneously transmits this location data to various monitoring algorithms for evaluation and calculation. Different monitoring algorithms are responsible for generating corresponding drone alerts. Finally, the safety monitoring platform aggregates these different alerts and sends them externally via the message-based middleware.

[0098] 5. UAV Mesh Matching

[0099] After completing the above preparations, we can then perform grid matching on the real-time received drone position data. The algorithm process is relatively simple. UAV , the algorithm is as follows:

[0100] ①According to P UAV Coordinate calculation of drone in S GRID The row and column indices in are calculated as follows:

[0101] Row=INT((P UAV .yR.top) / (-D TH ))

[0102] Col=INT((P UAV .xR.left) / D TH )

[0103] Among them, P UAV .y is P UAV The latitude value, P UAV .x is P UAV Longitude value, R.top is S GRID The upper boundary value of the envelope rectangle R, R.left is the left boundary value of R.

[0104] ② According to the row and column index values ​​Row and Col obtained in ① above, calculate and obtain P UAV In S GRID The corresponding grid in is denoted as G P , if G P ∈S 2 GRID or G P ∈S 3 GRID , then you can immediately end the P UAV If G P ∈S 2 GRID, it means that the current position of the drone has gone beyond the fence boundary, and an alarm message can be given; if G P ∈S 3 GRID , it means that the current position of the drone is inside the fence, and the distance between the drone and the fence boundary is greater than D TH , it is a safe flight and no warning is required. End the detection of the drone;

[0105] ③If G P ∈S 1 GRID , then P UAV Near the fence polygon boundary line, further judgment is needed UAV Inside or outside the fence;

[0106] ④) The algorithm ends.

[0107] like Figure 4 As shown, P 1 UAV ,P 2 UAV ,P 3 UAV ,P 4 UAV ,P 5 UAV The real-time positions of different drones are matched with the corresponding grid attributes: P 2 UAV , P 4 UAV is the boundary grid, P 1 UAV , P 5 UAV is the internal grid, P 3 UAV is the external grid. From this, we can directly end the 1 UAV , P 3 UAV , P 5 UAV The remaining P 2 UAV , P 4 UAV Further identification is required. In fact, for geofenced areas within prefecture-level cities, after gridding, the majority of the grids have internal attributes, leaving relatively few boundary and external grids. Assuming the distribution of real-time drones within the fenced area is uniform, we can identify most drones by matching the internal, external, and boundary grids. This process is virtually time-saving, significantly improving identification efficiency.

[0108] Example 2:

[0109] The geo-fence grid classification and grid matching system for drone navigation can implement the geo-fence grid classification and grid matching method for drone navigation described in Example 1, including:

[0110] Data receiving module: receives geo-fence data including fence polygons;

[0111] Grid generation module: Generates fence grid based on geo-fence data;

[0112] Grid identification module: performs intersection matching processing on the fence polygon boundary line and the fence grid, and identifies the intersecting fence grids;

[0113] Mesh classification module: Based on the intersecting fence meshes, all fence meshes are classified into internal, boundary and external meshes;

[0114] Position determination module: After receiving real-time drone position data, it matches it with the internal, boundary and external grids.

[0115] Example 3:

[0116] The embodiment of the present invention further provides a geo-fence grid classification and grid matching device for drone navigation, which can implement the geo-fence grid classification and grid matching method for drone navigation described in the first embodiment, including a processor and a storage medium;

[0117] The storage medium is used to store instructions;

[0118] The processor is configured to operate according to the instructions to execute the steps of the following method:

[0119] Receive geofence data containing a fence polygon;

[0120] Generate a fence grid based on geofence data;

[0121] Perform intersection matching on the fence polygon boundary line and the fence grid, and identify the intersecting fence grids;

[0122] Based on the intersecting fence grids, all fence grids are classified into internal, boundary and external grids;

[0123] After receiving real-time drone position data, it is matched with the interior, boundary, and exterior grids.

[0124] Example 4:

[0125] An embodiment of the present invention further provides a computer-readable storage medium that can implement the geo-fence grid classification and grid matching method for drone navigation described in Example 1. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method steps:

[0126] Receive geofence data containing a fence polygon;

[0127] Generate a fence grid based on geofence data;

[0128] Perform intersection matching on the fence polygon boundary line and the fence grid, and identify the intersecting fence grids;

[0129] Based on the intersecting fence grids, all fence grids are classified into internal, boundary and external grids;

[0130] After receiving real-time drone position data, it is matched with the interior, boundary, and exterior grids.

[0131] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

[0132] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A geo-fence grid classification and grid matching method for drone navigation, characterized by: include: Receive geofence data containing a fence polygon; Generate a fence grid based on geofence data; Perform intersection matching on the fence polygon boundary line and the fence grid, and identify the intersecting fence grids; Based on the intersecting fence grids, all fence grids are classified into internal, boundary and external grids; After receiving real-time drone position data, it is matched with the interior, boundary, and exterior grids.

2. The geo-fence grid classification and grid matching method for drone navigation according to claim 1 is characterized in that: Receiving geo-fence data containing fence polygon boundary lines, including: automatically reading geo-fence files at specified locations, and receiving geo-fence data sent by other business platforms through message middleware; the geo-fence files include the latitude and longitude coordinates of the vertices of the fence polygon boundary lines, the upper and lower height limits of the fence, and the fence buffer threshold information.

3. The geo-fence grid classification and grid matching method for drone navigation according to claim 1 is characterized in that: Generate a fence grid based on geofence data, including: Calculate the rectangular range that encloses the entire fence based on the longitude and latitude coordinates of the fence polygon; Apply double fence threshold D to the rectangular range TH Extension of distance; Divide the expanded rectangular range into a grid, and set the length and width of the grid to D TH , generate the grid set S GRID .

4. The geo-fence grid classification and grid matching method for drone navigation according to claim 3 is characterized in that: Perform intersection matching on the fence polygon boundary line and the fence grid, and identify the intersecting fence grids, including: Take out the boundary segments of the fence polygon boundary line one by one, and compare the boundary segments and the grid set S GRID The grid in the grid is intersected; In response to the grid set S GRID If any grid in the grid intersects with a boundary line segment, the index value of the intersecting boundary line segment is saved in the grid, and the grid is assigned to a temporary grid set S T GRID In, S T GRID ∈S GRID .

5. The geo-fence grid classification and grid matching method for drone navigation according to claim 4 is characterized in that: For boundary segments and grid set S GRID The grid in the grid is intersected, including: Get the bounding rectangle of the boundary segment; Get all grids that intersect with the range rectangle, recorded as grid set B GRID ; Take out grid set B one by one GRID For each grid G ​​in , determine whether G intersects with the boundary segment; In response to the endpoint of the boundary line segment falling in the grid G ​​or the boundary line segment intersecting with the four boundary line segments of the grid G, it is determined that the grid G ​​and the boundary line segment intersect; otherwise, it is determined that the grid G ​​and the boundary line segment do not intersect.

6. The geo-fence grid classification and grid matching method for drone navigation according to claim 4 is characterized in that: Based on the intersecting fence meshes, all fence meshes are classified into internal, boundary, and external meshes, including: The grid set S GRID Split into three disjoint subsets S 1 GRID 、S 2 GRID and S 3 GRID , where S 1 GRID is the set of grids near the fence polygon boundary line, S 2 GRID is the set of grids completely outside the fence, S 3 GRID is the set of grids that are completely inside the fence; Sequentially take out the grid set S T GRID Each grid in is denoted as G C ; Calculate the acquisition and G C The other eight directly adjacent grids are denoted as G i , i∈[1,8]; like Then G i Join S 1 GRID Finally, put G C Also joined S 1 GRID , until S is processed T GRID All grids in ; S 1 GRID After marking all the grids in the grid as boundary grid attributes, clear S T GRID ; The grid set S GRID Upper left corner grid G LT Join S T GRID middle; Take out S in sequence T GRID Each grid G ​​in T , get and G T The other K directly adjacent grids are denoted as G i , i∈[1,K],K≤8; for G i Judge one by one: If and Then G i Join S 2 GRID In the meantime, G i Append to S T GRID In the process, after processing the adjacent grid, G T Also joined S 2 GRID and from S T GRID Delete G T , loop processing S T GRID All grids in the T GRID All meshes in the are deleted; S 2 GRID Mark all grids in as external grid attributes, and set S GRID All unmarked grids in the grid are marked as internal grid attributes and grouped into S 3 GRID middle.

7. The method for geo-fence grid classification and grid matching for drone navigation according to claim 6, characterized in that: After receiving real-time drone position data, it is matched against the interior, boundary, and exterior grids, including: Get the drone's location; Calculate the position of the drone in the grid set S according to the drone's position coordinates GRID The row and column indices in ; Based on the UAV in the grid collection S GRID The row and column indexes in the grid set S are used to calculate the position of the drone. GRID The corresponding grid in is denoted as G P ; In response to G P ∈S 2 GRID When , it is determined that the current position of the drone is outside the fence boundary; In response to G P ∈S 3 GRID When , it is determined that the current position of the drone is inside the fence; In response to G P ∈S 1 GRID , it is determined that the current position of the drone is near the fence polygon boundary line.

8. A geo-fence grid classification and grid matching system for drone navigation, characterized by: include: Data receiving module: receives geo-fence data including fence polygons; Grid generation module: Generates fence grid based on geo-fence data; Grid identification module: performs intersection matching processing on the fence polygon boundary line and the fence grid, and identifies the intersecting fence grids; Mesh classification module: Based on the intersecting fence meshes, all fence meshes are classified into internal, boundary and external meshes; Position determination module: After receiving real-time drone position data, it matches it with the internal, boundary and external grids.

9. A geo-fence grid classification and grid matching device for drone navigation, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images