A gridding method and system based on UAV flight trajectory data

Abstract

This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a gridding method and system based on UAV flight trajectory data. The method includes the following steps: constructing a spatial network segmentation and coding system based on UAV operational characteristics to obtain the target geographical region corresponding to the target airspace; dividing the target geographical region at the target spatial grid level to generate an initial spatial grid list corresponding to the target geographical region; and generating a target spatial grid list corresponding to the target airspace based on the initial spatial grid list and the historical flight trajectory data of the target UAV. It can be seen that by performing differentiated gridding processing on the airspace according to the density of UAV flight activities, the same size airspace grid is avoided in areas with dense UAV flight activities and areas with sparse UAV flight activities, which would cause great inconvenience to the management of UAV flight activities under dense flight activities.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a gridding method and system based on UAV flight trajectory data. Background Technology

[0002] In the field of unmanned aerial vehicles (UAVs), multiple operators often operate within the same airspace during UAV flight. Typically, the airspace grid shape for UAVs is regular. For example, in a given airspace, both densely populated and sparsely populated areas use the same size airspace grid, which greatly complicates the management of UAV flight activities during periods of high density. Therefore, differentiating the airspace grid based on the density of UAV flight activities has become a pressing technical problem to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention provides a gridding method based on UAV flight trajectory data, the method comprising the following steps:

[0004] S100, obtain the target geographic region corresponding to the target airspace;

[0005] S200: Divide the target geographic region into target spatial grids at the target spatial grid level and generate an initial spatial grid list corresponding to the target geographic region;

[0006] S300: Based on the initial spatial grid list and the historical flight trajectory data of the target UAV, generate a target spatial grid list for the target airspace pair.

[0007] Specifically, in step S100, the target airspace is the target vector plane.

[0008] Specifically, in step S100, the target geographical area is rectangular.

[0009] Specifically, step S100 also includes the following steps;

[0010] S101, Map the target airspace onto the target map and obtain the maximum longitude (LON) of the initial geographic region corresponding to the target. max LON (Minimum Longitude) min Maximum latitude LAT max and minimum latitude LAT min ;

[0011] S102, according to LON max LON min LAT max and LAT minGenerate the target geographic region corresponding to the target airspace. The size of the target geographic region includes its length and width. The length of the target geographic region is Max(△LON, △LAT), and the width is Min(△LON, △LAT). The target latitude difference is △LAT, and the target longitude difference △LON satisfies the following condition: △LON = LON. max -LON min The target latitude difference ΔLAT satisfies the following condition: ΔLAT = LAT max -LAT min .

[0012] Specifically, step S200 also includes the following steps:

[0013] S201, according to LON max LON min LAT max and LAT min Determine the target spatial grid level;

[0014] S202, Generate an initial spatial grid list B = (B1, B2, ..., B...) corresponding to the target geographic region at the target spatial grid level. j B m ), B j Let j be the j-th initial spatial grid, j = 1, 2, ..., m, where m is the number of initial spatial grids corresponding to the target geographic region.

[0015] This invention also protects a gridding system based on UAV flight trajectory data, the system comprising:

[0016] The first execution module is used to obtain the target geographical region corresponding to the target airspace;

[0017] The second execution module is used to divide the target geographic region into target spatial grids and generate an initial spatial grid list corresponding to the target geographic region.

[0018] The third execution module is used to generate a target spatial grid list for the target airspace pair based on the initial spatial grid list and the historical flight trajectory data of the target UAV.

[0019] Specifically, the target spatial domain is the target vector plane.

[0020] Specifically, the target geographical area is rectangular.

[0021] Specifically, the first execution module includes:

[0022] The first acquisition module is used to map the target airspace onto the target map and obtain the maximum longitude (LON) of the initial geographic region corresponding to the target. max LON (Minimum Longitude) min Maximum latitude LAT max and minimum latitude LAT min ;

[0023] The first generation module is used to generate LON. max LON min LAT max and LAT min Generate the target geographic region corresponding to the target airspace. The size of the target geographic region includes its length and width. The length of the target geographic region is Max(△LON, △LAT), and the width is Min(△LON, △LAT). The target latitude difference is △LAT, and the target longitude difference △LON satisfies the following condition: △LON = LON. max -LON min The target latitude difference ΔLAT satisfies the following condition: ΔLAT = LAT max -LAT min .

[0024] Specifically, the second execution module includes:

[0025] The second acquisition module is used to obtain information based on LON. max LON min LAT max and LAT min Determine the target spatial grid level;

[0026] The second generation module is used to generate an initial spatial grid list B = (B1, B2, ..., B...) corresponding to the target geographic region at the target spatial grid level. j B m ), B j Let j be the j-th initial spatial grid, j = 1, 2, ..., m, where m is the number of initial spatial grids corresponding to the target geographic region.

[0027] The present invention has at least the following beneficial effects: a gridding method based on UAV flight trajectory data, the method comprising the following steps: obtaining the target geographical region corresponding to the target airspace; dividing the target geographical region into target spatial grids at the target spatial grid level to generate an initial spatial grid list corresponding to the target geographical region; generating a target spatial grid list for the target airspace pair based on the initial spatial grid list and the historical flight trajectory data of the target UAV; it can be seen that by performing differentiated gridding processing on the airspace according to the density of UAV flight activities, the same size airspace grid is avoided for both densely populated and sparsely populated UAV flight activity areas, which leads to great inconvenience in the management of UAV flight activities under dense flight activities. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating a method for gridding flight trajectory data of unmanned aerial vehicles (UAVs) provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar tasks and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0032] Example 1

[0033] like Figure 1 As shown, Embodiment 1 of the present invention provides a gridding method for UAV flight trajectory data, the method comprising the following steps:

[0034] S100, obtain the target geographic region corresponding to the target airspace;

[0035] S200: Divide the target geographic region into target spatial grids at the target spatial grid level and generate an initial spatial grid list corresponding to the target geographic region;

[0036] S300: Based on the initial spatial grid list and the historical flight trajectory data of the target UAV, generate a target spatial grid list for the target airspace pair.

[0037] Specifically, in step S100, the target airspace is the target vector plane.

[0038] Furthermore, in step S100, the target geographical area is rectangular.

[0039] In a specific embodiment, in the target spatial grid list in steps S100 to S300, the spatial grid is under the spatial grid coding and partitioning system based on UAV operation characteristics (excluding polar regions). The specific spatial grid coding and partitioning system based on UAV operation characteristics is as follows;

[0040]

[0041]

[0042]

[0043]

[0044] Specifically, step S100 also includes the following steps;

[0045] S101, Map the target airspace onto the target map and obtain the maximum longitude (LON) of the initial geographic region corresponding to the target. max Minimum Longitude LON min Maximum latitude LAT max and minimum latitude LAT min ;

[0046] S102, according to LON max LON min LAT max and LAT minGenerate the target geographic region corresponding to the target airspace. The size of the target geographic region includes its length and width. The length of the target geographic region is Max(△LON, △LAT), and the width is Min(△LON, △LAT). The target latitude difference is △LAT, and the target longitude difference △LON satisfies the following condition: △LON = LON. max -LON min The target latitude difference ΔLAT satisfies the following condition: ΔLAT = LAT max -LAT min .

[0047] Specifically, step S200 also includes the following steps:

[0048] S201, according to LON max LON min LAT max and LAT min Determine the target spatial grid level;

[0049] S202, Generate an initial spatial grid list B = (B1, B2, ..., B...) corresponding to the target geographic region at the target spatial grid level. j B m ), B j Let j be the j-th initial spatial grid, j = 1, 2, ..., m, where m is the number of initial spatial grids corresponding to the target geographic region.

[0050] Furthermore, step S201 also includes the following steps:

[0051] Get the longitude mean (LON) 0 and latitude mean LAT 0 Among them, LON 0 Meets the following conditions: LON 0 = (LON max +LON min ) / 2, LAT 0 Meets the following conditions: Latitude Mean (LAT) 0 =(LAT max +LAT min ) / 2;

[0052] According to LON 0 and LAT 0 Construct a rectangular central geographic region, wherein the dimensions of the central geographic region include its length and its width, and the length of the central geographic region is Max(LON). 0 LAT 0The width of the intermediate geographic region is Min(LON). 0 LAT 0 );

[0053] Obtain the preset spatial grid size information D = (D1, D2, ..., D...). r D s ), D r =(D r1 D r2 ), D r1 D is the preset spatial grid length at level r. r2 It is the preset spatial grid width at level r; D is the spatial grid level sorted from largest to smallest;

[0054] The size of the intermediate geographic region is matched with the preset spatial grid size to obtain the matching degree F = (F1, F2, ..., F...). r F s ), F r It is D r The corresponding matching degree, F r The following conditions must be met:

[0055]

[0056] Select the spatial grid level corresponding to the maximum value in F as the target spatial grid level.

[0057] Specifically, the S300 procedure also includes the following steps:

[0058] S301, Obtain the historical flight trajectory dataset H = (H1, H2, ..., H...) of the target UAV within a preset time period. g H z ), H g H represents the g-th historical flight trajectory data of the target UAV, where g ranges from 1 to z, z is the number of historical flight trajectory data of the target UAV and z≥10, and H represents the continuous historical flight trajectory data.

[0059] S302, process H to obtain the historical flight trajectory point list K = (K1, K2, ..., K...). x , ..., K y ), K x y is the xth historical flight trajectory of the target UAV, and the value of y ranges from 1 to y, where y is the number of historical flight trajectory points of the target UAV and y≥10.

[0060] S303, combine K and B to obtain the target trajectory point density G = (G1, G2, ..., G...). j , ..., Gm ), G j It is B j The corresponding trajectory point density; where G j The following conditions must be met:

[0061] G j =N j / S j N j The query result is located in B from K. j Number of historical flight track points in the interior, S j It is B j The corresponding area.

[0062] S304, based on G, determine the final spatial grid level set, wherein, in step S304, based on G... j Query G from the preset spatial grid level mapping table. j The corresponding preset spatial grid level is used as the final spatial grid level. The preset spatial grid level mapping table includes several preset spatial grid levels and the trajectory point density interval corresponding to each preset spatial grid level. When G j When the trajectory point density range is within a certain preset spatial grid level, the preset spatial grid level is determined to be G. j The corresponding final spatial grid level;

[0063] S305, Based on the final spatial grid level set and B, determine the final grid list C = (C1, C2, ..., C...). j C m ), C j =(C j1 C j2 C jp C jq ), where C jp For B j The p-th final spatial grid level grid, p=1,2,...,q, where q is the number of grids at the final spatial grid level.

[0064] In summary, this embodiment provides a gridding method for UAV flight trajectory data. The method includes the following steps: obtaining the target geographical region corresponding to the target airspace; dividing the target geographical region into target spatial grids at the target spatial grid level to generate an initial spatial grid list corresponding to the target geographical region; and generating a target spatial grid list for the target airspace pair based on the initial spatial grid list and the historical flight trajectory data of the target UAV. It can be seen that by performing differentiated gridding processing on the airspace according to the density of UAV flight activities, the use of the same size airspace grid in areas with dense UAV flight activities and areas with sparse UAV flight activities can be avoided, which would cause great inconvenience to the management of UAV flight activities under dense flight activities.

[0065] Example 2

[0066] Embodiment 2 of the present invention provides a gridding system based on UAV flight trajectory data, the system comprising:

[0067] The first execution module is used to obtain the target geographical region corresponding to the target airspace;

[0068] The second execution module is used to divide the target geographic region into target spatial grids and generate an initial spatial grid list corresponding to the target geographic region.

[0069] The third execution module is used to generate a target spatial grid list for the target airspace pair based on the initial spatial grid list and the historical flight trajectory data of the target UAV.

[0070] Specifically, the target spatial domain is the target vector plane.

[0071] Furthermore, the target geographical area is rectangular.

[0072] Specifically, the first execution module includes:

[0073] The first acquisition module is used to map the target airspace onto the target map and obtain the maximum longitude (LON) of the initial geographic region corresponding to the target. max LON (Minimum Longitude) min Maximum latitude LAT max and minimum latitude LAT min ;

[0074] The first generation module is used to generate LON. max LON min LAT max and LAT minGenerate the target geographic region corresponding to the target airspace. The size of the target geographic region includes its length and width. The length of the target geographic region is Max(△LON, △LAT), and the width is Min(△LON, △LAT). The target latitude difference is △LAT, and the target longitude difference △LON satisfies the following condition: △LON = LON. max -LON min The target latitude difference ΔLAT satisfies the following condition: ΔLAT = LAT max -LAT min .

[0075] Specifically, the second execution module:

[0076] The second acquisition module is used to obtain information based on LON. max LON min LAT max and LAT min Determine the target spatial grid level;

[0077] The second generation module is used to generate an initial spatial grid list B = (B1, B2, ..., B...) corresponding to the target geographic region at the target spatial grid level. j B m ), B j Let j be the j-th initial spatial grid, j = 1, 2, ..., m, where m is the number of initial spatial grids corresponding to the target geographic region.

[0078] Furthermore, the second acquisition module includes:

[0079] The third acquisition module is used to obtain the longitude mean (LON). 0 and latitude mean LAT 0 Among them, LON 0 Meets the following conditions: LON 0 = (LON max +LON min ) / 2, LAT 0 Meets the following conditions: Latitude Mean (LAT) 0 =(LAT max +LAT min ) / 2;

[0080] The third generation module is used to generate based on LON. 0 and LAT 0 Construct a rectangular central geographic region, wherein the dimensions of the central geographic region include its length and its width, and the length of the central geographic region is Max(LON). 0 LAT 0The width of the intermediate geographic region is Min(LON). 0 LAT 0 );

[0081] The fourth acquisition module is used to acquire the preset spatial grid size information D = (D1, D2, ..., D...). r D s ), D r =(D r1 D r2 ), D r1 D is the preset spatial grid length at level r. r2 It is the preset spatial grid width at level r; D is the spatial grid level sorted from largest to smallest;

[0082] The fifth acquisition module is used to match the size of the intermediate geographic region with the preset spatial grid size to obtain the matching degree F = (F1, F2, ..., F...) of the intermediate geographic region. r F s ), F r It is D r The corresponding matching degree, F r Meets the following conditions:

[0083]

[0084] Select the spatial grid level corresponding to the maximum value in F as the target spatial grid level.

[0085] Specifically, the third execution module includes:

[0086] The sixth acquisition module is used to acquire the historical flight trajectory dataset H = (H1, H2, ..., H2) of the target UAV within a preset time period.

[0087] H g H z ), H g H represents the g-th historical flight trajectory data of the target UAV, where g ranges from 1 to z, z is the number of historical flight trajectory data of the target UAV and z≥10, and H represents the continuous historical flight trajectory data.

[0088] The seventh acquisition module is used to process H to obtain a list of historical flight trajectory points of the target UAV, K = (K1, K2, ..., K...). x , ..., K y ), K x y is the xth historical flight trajectory of the target UAV, and the value of y ranges from 1 to y, where y is the number of historical flight trajectory points of the target UAV and y≥10.

[0089] The eighth acquisition module is used to obtain the target trajectory point density G = (G1, G2, ..., G) corresponding to B by comparing K and B. j , ..., G m ), G j It is B j The corresponding trajectory point density; where G j Meets the following conditions:

[0090] G j =N j / S j N j The query result is located in B from K. j Number of historical flight track points in the interior, S j It is B j The corresponding area.

[0091] The fourth generation module is used to determine the final spatial grid level set based on G, wherein, in step S304, based on G... j Query G from the preset spatial grid level mapping table. j The corresponding preset spatial grid level is used as the final spatial grid level. The preset spatial grid level mapping table includes several preset spatial grid levels and the trajectory point density interval corresponding to each preset spatial grid level. When G j When the trajectory point density range is within a certain preset spatial grid level, the preset spatial grid level is determined to be G. j The corresponding final spatial grid level;

[0092] The fifth generation module is used to determine the final grid list C = (C1, C2, ..., C...) based on the final spatial grid level set and B. j C m ), C j =(C j1 C j2 C jp C jq ), where C jp For B j The p-th final spatial grid level grid, p=1,2,...,q, where q is the number of grids at the final spatial grid level.

[0093] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A gridding method for UAV flight trajectory data, characterized in that, The method includes the following steps: S100, obtain the target geographic region corresponding to the target airspace; S200: Divide the target geographic region into target spatial grids at the target spatial grid level and generate an initial spatial grid list corresponding to the target geographic region; S300: Based on the initial spatial grid list and the historical flight trajectory data of the target UAV, a target spatial grid list for the target airspace pair is generated; step S300 also includes the following steps: S301, Obtain the historical flight trajectory dataset H = (H1, H2, ..., Hg, ...) of the target UAV within a preset time period. Hg is the g-th historical flight trajectory data of the target UAV, where g ranges from 1 to z, z is the number of historical flight trajectory data of the target UAV and z≥10, and Hg represents the continuous historical flight trajectory data. S302, process H to obtain the historical flight trajectory point list K = (K1, K2, ..., Kx, ..., Ky) of the target UAV, where Kx is the xth historical flight trajectory of the target UAV, the value of y ranges from 1 to y, and y is the number of historical flight trajectory points of the target UAV and y≥10. S303, by combining K and B, obtain the target trajectory point density G = (G1, G2, ..., Gj, ..., Gm) corresponding to B, where Gj is the trajectory point density corresponding to Bj; where Gj satisfies the following condition: Gj = Nj / Sj, where Nj is the number of historical flight trajectory points within Bj retrieved from K, and Sj is the area corresponding to Bj; S304, Based on G, determine the final spatial grid level set. In step S304, based on Gj, query the preset spatial grid level mapping table. The preset spatial grid level corresponding to Gj is used as the final spatial grid level. The preset spatial grid level mapping table includes several preset spatial grid levels and the trajectory point density interval corresponding to each preset spatial grid level. When Gj is in the trajectory point density interval corresponding to a certain preset spatial grid level, the preset spatial grid level is determined as the final spatial grid level corresponding to Gj. S305. Based on the final spatial grid level set and B, determine the final grid list C = (C1, C2, ..., Cj, ..., Cm) and Cj = (Cj1, Cj2, ..., Cjp, ..., Cjq), where Cjp is the p-th grid of the final spatial grid level in Bj, p = 1, 2, ..., q, and q is the number of grids in the final spatial grid level.

2. The gridding method for UAV flight trajectory data according to claim 1, characterized in that, In step S100, the target spatial domain is the target vector plane.

3. The gridding method for UAV flight trajectory data according to claim 2, characterized in that, In step S100, the target geographic area is rectangular.

4. The gridding method for UAV flight trajectory data according to claim 1, characterized in that, Step S100 also includes the following steps; S101, map the target airspace onto the target map, and obtain the maximum longitude LONmax, minimum longitude LONmin, maximum latitude LATmax, and minimum latitude LATmin of the initial geographic region corresponding to the target airspace; S102, based on LONmax, LONmin, LATmax, and LATmin, generate the target geographic region corresponding to the target airspace. The size of the target geographic region includes the length and width of the target geographic region. The length of the target geographic region is Max(△LON, △LAT), and the width of the target geographic region is Min(△LON, △LAT). The target latitude difference is △LAT. The target longitude difference △LON satisfies the following conditions: △LON = LONmax - LONmin, and the target latitude difference △LAT satisfies the following conditions: △LAT = LATmax - LATmin.

5. The gridding method for UAV flight trajectory data according to claim 1, characterized in that, The S200 procedure also includes the following steps: S201, based on LONmax, LONmin, LATmax, and LATmin, determines the target spatial grid level; step S201 also includes the following steps: Obtain the longitude mean LON0 and latitude mean LAT0; where LON0 meets the following condition: LON0 = (LONmax + LONmin) / 2, and LAT0 meets the following condition: latitude mean LAT0 = (LATmax + LATmin) / 2; Based on LON0 and LAT0, construct the central geographic region of a rectangle, wherein the size of the central geographic region includes the length and the width of the central geographic region, the length of the central geographic region is Max(LON0, LAT0), and the width of the central geographic region is Min(LON0, LAT0). Obtain the preset spatial grid size information D = (D1, D2, ..., Dr, ..., Ds), Dr = (Dr1, Dr2), where Dr1 is the preset spatial grid length at level r, and Dr2 is the preset spatial grid width at level r; D is sorted in descending order of spatial grid level. The size of the intermediate geographic region is matched with the preset spatial grid size to obtain the matching degree F = (F1, F2, ..., Fr, ..., Fs) corresponding to the intermediate geographic region, where Fr is the matching degree corresponding to Dr, and Fr meets the following condition: ； Select the spatial grid level corresponding to the maximum value in F as the target spatial grid level; S202, at the target spatial grid level, generate an initial spatial grid list B = (B1, B2, ..., Bj, ..., Bm) corresponding to the target geographic region, where Bj is the j-th initial spatial grid, j = 1, 2, ..., m, and m is the number of initial spatial grids corresponding to the target geographic region.

6. A gridded system based on UAV flight trajectory data, characterized in that, The system includes: The first execution module is used to obtain the target geographical region corresponding to the target airspace; The second execution module is used to divide the target geographic region into target spatial grids and generate an initial spatial grid list corresponding to the target geographic region. The third execution module is used to generate a target spatial grid list for the target airspace pair based on the initial spatial grid list and the historical flight trajectory data of the target UAV; the third execution module includes: The sixth acquisition module is used to acquire the historical flight trajectory dataset H = (H1, H2, ..., H2) of the target UAV within a preset time period. Hg, ..., Hz), Hg is the g-th historical flight trajectory data of the target UAV, the value of g ranges from 1 to z, z is the number of historical flight trajectory data of the target UAV and z≥10, where H represents the continuous historical flight trajectory data; The seventh acquisition module is used to process H to obtain a list of historical flight trajectory points of the target UAV K = (K1, K2, ..., Kx, ..., Ky), where Kx is the xth historical flight trajectory of the target UAV, the value of y ranges from 1 to y, and y is the number of historical flight trajectory points of the target UAV and y≥10; The eighth acquisition module is used to obtain the target trajectory point density G = (G1, G2, ..., Gj, ..., Gm) corresponding to B by comparing K and B, where Gj is the trajectory point density corresponding to Bj; and Gj satisfies the following condition: Gj = Nj / Sj, where Nj is the number of historical flight trajectory points within Bj retrieved from K, and Sj is the area corresponding to Bj; The fourth generation module is used to determine the final spatial grid level set based on G. In step S304, the preset spatial grid level corresponding to Gj is retrieved from the preset spatial grid level mapping table based on Gj and is used as the final spatial grid level. The preset spatial grid level mapping table includes several preset spatial grid levels and the trajectory point density interval corresponding to each preset spatial grid level. When Gj is in the trajectory point density interval corresponding to a certain preset spatial grid level, the preset spatial grid level is determined as the final spatial grid level corresponding to Gj. The fifth generation module is used to determine the final grid list C = (C1, C2, ..., Cj, ..., Cm) and Cj = (Cj1, Cj2, ..., Cjp, ..., Cjq) based on the final spatial grid level set and B, where Cjp is the p-th grid of the final spatial grid level in Bj, p = 1, 2, ..., q, and q is the number of grids in the final spatial grid level.

7. The gridded system based on UAV flight trajectory data according to claim 6, characterized in that, The target airspace is the target vector plane.

8. The gridded system based on UAV flight trajectory data according to claim 7, characterized in that, The target geographical area is rectangular.

9. The gridded system based on UAV flight trajectory data according to claim 6, characterized in that, The first execution module includes; The first acquisition module is used to map the target airspace onto the target map and obtain the maximum longitude LONmax, minimum longitude LONmin, maximum latitude LATmax, and minimum latitude LATmin of the initial geographic region corresponding to the target airspace. The first generation module is used to generate a target geographic region corresponding to the target airspace based on LONmax, LONmin, LATmax, and LATmin. The size of the target geographic region includes the length and width of the target geographic region. The length of the target geographic region is Max(△LON, △LAT), and the width of the target geographic region is Min(△LON, △LAT). The target latitude difference is △LAT. The target longitude difference △LON satisfies the following conditions: △LON = LONmax - LONmin, and the target latitude difference △LAT satisfies the following conditions: △LAT = LATmax - LATmin.

10. The gridded system based on UAV flight trajectory data according to claim 6, characterized in that, The second execution module includes: The second acquisition module is used to determine the target spatial grid level based on LONmax, LONmin, LATmax, and LATmin; the second acquisition module includes: The third acquisition module is used to acquire the longitude mean LON0 and the latitude mean LAT0; where LON0 meets the following condition: LON0 = (LONmax + LONmin) / 2, and LAT0 meets the following condition: latitude mean LAT0 = (LATmax + LATmin) / 2; The third generation module is used to construct a rectangular intermediate geographic region based on LON0 and LAT0, wherein the size of the intermediate geographic region includes the length and width of the intermediate geographic region, the length of the intermediate geographic region is Max(LON0, LAT0), and the width of the intermediate geographic region is Min(LON0, LAT0). The fourth acquisition module is used to acquire the preset spatial grid size information D=(D1, D2, ..., Dr, ..., Ds), Dr=(Dr1, Dr2), where Dr1 is the preset spatial grid length at level r, Dr2 is the preset spatial grid width at level r, and D is sorted in descending order of spatial grid level. The fifth acquisition module is used to match the size of the intermediate geographic region with the preset spatial grid size to obtain the matching degree F = (F1, F2, ..., Fr, ..., Fs) corresponding to the intermediate geographic region, where Fr is the matching degree corresponding to Dr, and Fr meets the following conditions: ； Select the spatial grid level corresponding to the maximum value in F as the target spatial grid level; The second generation module is used to generate an initial spatial grid list B = (B1, B2, ..., Bj, ..., Bm) corresponding to the target geographic region at the target spatial grid level, where Bj is the j-th initial spatial grid, j = 1, 2, ..., m, and m is the number of initial spatial grids corresponding to the target geographic region.

Images