Time-of-flight-based spatial gridding method, electronic device, and storage medium

Abstract

The present invention provides a method for airspace gridding based on flight time, an electronic device, and a storage medium, which relate to the technical field of airspace gridding. The method comprises: obtaining a flight path RE of a target UAV; determining a level L of an airspace grid corresponding to RE; obtaining target airspace grids of each level L corresponding to RE to obtain an airspace grid list A; determining a code of each target airspace grid at level L to obtain a target airspace grid code list G. L ; According to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid to obtain the time list to be coded t; encode each time to be coded in t to obtain the L-level time coding list T L ; T L i Add to G L i In order to obtain the target airspace grid code including the flight time; the present invention can determine whether the airspace grid is occupied according to the corresponding time code information, thereby avoiding the occurrence of collision.

Description

Technical Field

[0001] The present invention relates to the technical field of spatial gridding, and in particular to a spatial gridding method based on time of flight, an electronic device and a storage medium. Background Art

[0002] Drones (UAVs) are widely used in various application scenarios, including agricultural applications, environmental and wildlife monitoring, surveillance, public safety, three-dimensional (3D) modeling, photogrammetry, and logistics. Drones offer many advantages, including high maneuverability, low energy consumption, and real-time monitoring capabilities. However, as the number of unmanned aerial vehicles (UAVs) increases, managing them becomes more challenging, especially in low-altitude airspace due to complex issues of security, privacy, and flexibility. Airspace management is typically divided into several three-dimensional grids based on their corresponding latitude, longitude, and altitude. However, other objects may fly into the airspace at different times. If the airspace is divided based solely on latitude, longitude, and altitude, there is still a risk of collision between drones. Summary of the Invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to a first aspect of the present application, a method for spatial gridding based on time of flight is provided, the method comprising the following steps:

[0005] S100, obtaining a flight path RE of a target UAV; wherein RE includes a take-off point, a landing point, and several waypoints of the target UAV.

[0006] S200: Determine the level L of the airspace grid corresponding to the RE according to the take-off point and landing point corresponding to the RE.

[0007] S300, obtain the target airspace grid of each L level corresponding to RE to obtain the airspace grid list A=(A1, A2, ..., A i ,…,A n ), i=1, 2,..., n; among them, A i is the i-th L-level target airspace grid corresponding to RE, n is the number of L-level target airspace grids corresponding to RE; the target airspace grid is the airspace grid that RE passes through.

[0008] S400, according to A, determine the L-level code of each target airspace grid to obtain the target airspace grid code list G L =(G L 1, G L 2,…,G L i ,…,G L n); where G L i A i The corresponding target spatial grid code; G L i =(G L i，P , G L i，H );G L i，P is the latitude and longitude code of the target airspace grid of the i-th L level corresponding to RE, G L i，H It is the height code of the target spatial grid of the i-th level L corresponding to RE.

[0009] S500, according to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid to obtain the time list to be encoded t=(t1, t2, ..., t i ,…,t n ); where t i Fly the target drone to A i The time of time.

[0010] S600, encode each time to be encoded in t to obtain an L-level time encoding list T L =(T L 1. T L 2,…,T L i ,…,T L n ); where T L i t i The corresponding time code.

[0011] S700, T L i Add to G L i In order to get A i The corresponding target airspace grid coding GA including flight time L i =(G L i，P , G L i，H , T L i ).

[0012] According to another aspect of the present application, a non-transitory computer-readable storage medium is also provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned flight time-based airspace gridding method.

[0013] According to another aspect of the present application, an electronic device is provided, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0014] The present invention has at least the following beneficial effects:

[0015] The airspace gridding method based on flight time of the present invention obtains the flight path RE of the target UAV; determines the level L of the airspace grid corresponding to RE according to the take-off point and landing point corresponding to RE; obtains the target airspace grid of each level L corresponding to RE to obtain an airspace grid list A; determines the L-level code of each target airspace grid according to A to obtain the target airspace grid code list G L ; According to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid to obtain the time list to be coded t; encode each time to be coded in t to obtain the L-level time coding list T L ; T L i Add to G L i In order to get A i The corresponding target airspace grid code includes the flight time; through the above method, the time information corresponding to the flight path of the target UAV is encoded and added to the corresponding target airspace grid code, so that the airspace grid code contains the time information of the corresponding flight path, so that the target UAV can determine whether the airspace grid is occupied according to the corresponding time code information before flying into the target airspace grid, thereby avoiding collision. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of a time-of-flight spatial gridding method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] It should be noted that, based on this disclosure, those skilled in the art will appreciate that an aspect described herein can be implemented independently of any other aspect, and that two or more of these aspects can be combined in various ways. For example, any number of the aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement such an apparatus and / or practice such a method.

[0020] The following will refer to Figure 1 The flowchart of the spatial domain gridding method based on flight time is shown, which introduces a spatial domain gridding method based on flight time.

[0021] The flight time-based spatial gridding method may include the following steps:

[0022] S100, obtaining a flight path RE of a target UAV; wherein RE includes a take-off point, a landing point, and several waypoints of the target UAV.

[0023] In this embodiment, the target UAV can be any UAV; before executing the corresponding flight mission, the target UAV will plan a corresponding flight path; it can be understood that the flight path includes the take-off point, landing point and several waypoints of the target UAV during flight.

[0024] S200: Determine the level L of the airspace grid corresponding to the RE according to the take-off point and landing point corresponding to the RE.

[0025] In this embodiment, the airspace grid can be used to divide the airspace outside the earth into airspace grids using a three-dimensional geographic spatial reference system (GeoSOT-3D). Specifically, the earth can be subdivided through iteration. First, the longitude and latitude of the earth (180°×360°) are expanded to a 512°×512° grid. Second, each 1° is expanded to 64'. Finally, each 1' is expanded to 64", obtaining a two-dimensional quadtree subdivision at the degree, minute, and second levels. In addition, the two-dimensional global subdivision framework is extended to a third spatial dimension: elevation. The division of the elevation dimension is directly performed using a binary search method. The entire range of the elevation dimension is divided from the center of the earth to 50,000 kilometers above sea level.

[0026] The largest grid segment at Level 0, the highest level of GeoSOT-3D, can represent the entire Earth's space, while the smallest grid segment at Level 32, the lowest level, can represent centimeters. This allows the GeoSOT-3D grid system to not only possess the characteristics of a global multidimensional octree hierarchy but also be consistent across different applications. GeoSOT-3D encoding has an octree-like structure, capable of displaying 3D components. It should be noted that those skilled in the art can use GeoSOT-3D to grid the airspace corresponding to the RE according to actual needs, and this will not be elaborated upon here.

[0027] Furthermore, step S200 may include the following steps:

[0028] S210, obtaining the straight-line distance S between the take-off point and the landing point corresponding to RE RE .

[0029] In this embodiment, the take-off point and landing point corresponding to the RE have corresponding longitude and latitude information, so that the S can be determined according to the corresponding longitude and latitude information. RE .

[0030] S220, according to S RE And the preset straight-line distance route type mapping table QR, determine S RE The corresponding route type; wherein, QR includes several rows, each row corresponds to a route type, and each route type corresponds to a straight-line distance range and an airspace grid level.

[0031] In this embodiment, it can be understood that S RE The size of will determine the corresponding route type; for example, S RE If it is at the township level and does not cross the county, then the corresponding route type is township level; the relative airspace grid level is also larger; QR can be shown in Table 1:

[0032] Table 1

[0033]

[0034] FW1, FW2, FW3 and FW4 in the above QR are preset straight-line distance ranges, which can be determined by S RE Which straight-line distance range does it belong to? This determines the corresponding route type. For example, S RE In FW4, determine S RE The corresponding route type is township level.

[0035] S230, according to S RE The corresponding route type determines the level L of the airspace grid corresponding to RE.

[0036] In this embodiment, after the route type is determined, the level of the corresponding airspace grid can be further determined; for example, if the route type is township level, then L=21.

[0037] S300, obtain the target airspace grid of each L level corresponding to RE to obtain the airspace grid list A=(A1, A2, ..., A i ,…,A n ), i=1, 2,..., n; among them, A i is the i-th L-level target airspace grid corresponding to RE, n is the number of L-level target airspace grids corresponding to RE; the target airspace grid is the airspace grid that RE passes through.

[0038] In this embodiment, GeoSOT-3D can be used to encode the airspace outside the earth at levels 0-32. After determining L, it is only necessary to obtain the target airspace grid at level L, and there is no need to obtain airspace grids at other levels. This reduces the amount of calculation and improves calculation efficiency. Whether the airspace grid to be judged is the target airspace grid can be determined by judging whether the airspace grid contains the take-off point, landing point or passing point on RE.

[0039] S400, according to A, determine the L-level code of each target airspace grid to obtain the target airspace grid code list G L =(G L 1, G L 2,…,G L i ,…,G L n ); where G L i A i The corresponding target spatial grid code; G L i =(G L i，P , G L i，H );G L i，P is the latitude and longitude code of the target airspace grid of the i-th L level corresponding to RE, G L i，H It is the height code of the target spatial grid of the i-th level L corresponding to RE.

[0040] In this embodiment, each L-level target airspace grid corresponds to a target airspace grid code, and the target airspace grid code corresponding to each target airspace grid can be obtained, thereby obtaining G L ; Understandably, G L i It only contains latitude, longitude and altitude information, but does not contain time information.

[0041] S500, according to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid to obtain the time list to be encoded t=(t1, t2, ..., t i ,…,t n ); where t i Fly the target drone to A i The time of time.

[0042] In this embodiment, when planning the flight path of the target UAV, the take-off point, landing point and waypoint contain corresponding time information, that is, the time to fly to the corresponding waypoint; thus, the time for the target UAV to fly to each target airspace grid can be determined, thereby obtaining the time list t to be encoded; for example, the target UAV flies to A i The time is 10:10:20, then t i =10:10:20.

[0043] S600, encode each time to be encoded in t to obtain an L-level time encoding list T L =(T L 1. T L 2,…,T L i ,…,T L n ); where T L i t i The corresponding time code.

[0044] Furthermore, step S600 may include the following steps:

[0045] S610, obtain t i Corresponding hour i 、minutesmin i and seconds i .

[0046] S620, for hour i 、min i and second i Convert binary integer to get hour i 、min i and second i The corresponding binary integer hour' i 、min' i and second' i ; Among them, hour' i =hour i ×32 / 24;min'i =min i ×64 / 60;second' i =second i ×64 / 60.

[0047] S630, will hour' i 、min' i and second' i Convert to binary code to get T L i .

[0048] In this embodiment, a time dimension is added, using a time segment-by-segment dichotomy and multi-scale coding, and relying on code clustering to quickly retrieve data containing time information. This information primarily takes the form of a common master time binary expansion within a limited time domain, expanding a year to 16 months, a month to 32 days, an hour to 32 hours, an hour to 64 minutes, and a second to 1024 microseconds.

[0049] Due to the unique characteristics of drone-transmitted data, a three-fold expansion method was used to fit the binary integer profile. Specifically, the daily time span of 0–24 hours was expanded to 0–32 hours, 0–60 minutes was extended to 0–64 minutes, and 0–60 seconds was extended to 0–64 seconds. The three-fold expansion method is shown in Table 2.

[0050] Table 2

[0051]

[0052] The advantage of integer bifurcation is that data within the same time period can be given the same prefix, and data requests within a time period can be based on the database prefix index, rather than sequentially searching the entire timestamp information across domains. This process ultimately simplifies time period queries in database retrieval to simple binary code matching queries, significantly shortening the retrieval cycle.

[0053] Furthermore, before step S610, the method may further include the following steps:

[0054] S601, if the hour corresponding to t1 is the same as t n If the corresponding hours are different, proceed to S610; otherwise, proceed to S602.

[0055] S602, obtain t i Corresponding minutes min i and seconds i .

[0056] S603, for min iand second i Perform binary integer conversion to get the min i and second i The corresponding binary integer min' i and second' i ; Among them, min' i =min i ×64 / 60;second' i =second i ×64 / 60.

[0057] S604, min' i and second' i Convert to binary code to get T L i .

[0058] In this embodiment, the above method can be used to determine whether binary conversion of hours is required. That is, if the hours do not change from the start to the end of the flight, binary conversion of hours is not required, thereby reducing the amount of calculation and improving conversion efficiency.

[0059] S700, T L i Add to G L i In order to get A i The corresponding target airspace grid coding GA including flight time L i =(G L i，P , G L i，H , T L i ).

[0060] In this embodiment, the time information corresponding to RE is encoded and added to G L i So we get A i The corresponding target airspace grid coding GA including flight time L i .

[0061] Furthermore, after step S700, the method may further include the following steps:

[0062] S800, when the target drone flies into A i When the preset time is reached, get A i The corresponding occupation time code is used to obtain the occupation time code list ZT i =(ZT i，1 , ZTi，2 ,…,ZT i，j ,…,ZT i，f(i) ), j = 1, 2, ..., f(i); where ZT i，j A i The corresponding j-th occupied time code, f(i) is A i The number of corresponding occupied time codes; the time corresponding to the occupied time code is A i Occupied time.

[0063] In this embodiment, if other drones plan their flight paths, they will send the occupied airspace grids and corresponding time codes; the preset duration can be 2 seconds.

[0064] S810, traverse ZT i , if ZT i There exists any L i The occupation time code that meets the preset occupation judgment condition is determined by A i is the airspace grid to be avoided; otherwise, determine A i It is the non-obstacle avoidance airspace grid.

[0065] In this embodiment, it is understood that it takes a certain amount of time for a drone to fly from the target airspace grid to the target airspace grid, and each drone does not fly into the corresponding target airspace grid at the same time; therefore, step S810 may include the following steps:

[0066] S811, get the target drone to fly out of A i The time code corresponding to the time TH i .

[0067] S812, if ZT i There exists any one in T L i and TH i The occupation time code between i is the airspace grid to be avoided; otherwise, determine A i It is the non-obstacle avoidance airspace grid.

[0068] In this embodiment, determine A i After the airspace to be avoided is gridded, the RE needs to be replanned to avoid collision with the target UAV.

[0069] The flight time-based airspace gridding method of this embodiment obtains the flight path RE of the target UAV; determines the level L of the airspace grid corresponding to RE based on the take-off point and landing point corresponding to RE; obtains the target airspace grid of each level L corresponding to RE to obtain an airspace grid list A; determines the L-level code of each target airspace grid based on A to obtain the target airspace grid code list G L ; According to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid to obtain the time list to be coded t; encode each time to be coded in t to obtain the L-level time coding list T L ; T L i Add to G L i In order to get A i The corresponding target airspace grid code includes the flight time; through the above method, the time information corresponding to the flight path of the target UAV is encoded and added to the corresponding target airspace grid code, so that the airspace grid code contains the time information of the corresponding flight path, so that the target UAV can determine whether the airspace grid is occupied according to the corresponding time code information before flying into the target airspace grid, thereby avoiding collision.

[0070] In an exemplary embodiment, determining A i After the airspace to be avoided is meshed, the RE needs to be replanned. The flight path can be replanned by the following steps:

[0071] Q100, obtain each airspace grid adjacent to the airspace grid DQ where the target UAV is currently flying, to obtain an adjacent airspace grid list C = (C1, C2, ..., C a ,…,C b ), a=1, 2, ..., b; where C a is the ath spatial grid adjacent to DQ, and b is the number of spatial grids adjacent to DQ.

[0072] In this embodiment, the airspace grid exists in three dimensions. There are multiple grids adjacent to the airspace grid DQ where the target UAV is currently flying. Each airspace grid adjacent to DQ can be obtained to obtain C.

[0073] Q200, obtain the estimated cost of each adjacent spatial grid in DQ and C to obtain the estimated cost list F = (F1, F2, ..., F a ,…,F b ); where F a DQ and C a The estimated cost of F a =ga +h a +W a ;g a is the path cost from the airspace grid where the take-off point is located to DQ, h a DQ to C a The heuristic estimate of the estimated cost, W a C a The corresponding increased weight value; g a and h a According to DQ and C a The latitude and longitude difference, altitude difference and time difference between them are obtained; W a According to C a Risk level, risk level weight and occupation C a The type of drone you get.

[0074] In this embodiment, based on the A* algorithm, C a The risk level, risk level weight and target drone type are comprehensively used to obtain DQ and C a The estimated cost F a .

[0075] The degree of area occupancy is the first factor for drone avoidance; when the selected airspace grid is occupied or marked as a no-fly zone, the weight value is maximized to achieve circumvention of the area.

[0076] Due to the particularity of drone airspace, the traffic volume of drones in the grid is combined with the risk level of the current grid. The risk level of the current area is proportional to the drone traffic volume, and different traffic thresholds are set to divide the risk level.

[0077] Taking into account the passability of different types of drones in the airspace grid, if the risk level is too high, that is, when the drone traffic in the current grid is large, the pass rate of micro drones is higher than that of larger drones. Conventional drone types usually include large, medium, small and micro drones, which can be further subdivided by their weight.

[0078] Furthermore, g a This can be determined by the following steps:

[0079] Q210, obtain DQ and C a The height difference Δh a =|h DQ -hC a |, DQ and C a The distance difference Δd between a =((Δlat a ) 2 +(Δlon a )2 ) 1 / 2 ; Among them, h DQ is the height of DQ, hC a C a Height; Δlat a DQ and C a The latitude difference between DQ and C, Δlon is the latitude difference between DQ and C a The longitude difference between them.

[0080] Q211, get the target drone into DQ and C a The time interval ΔT a =|T DQ -T a |; Among them, T DQ is the time when the target UAV enters the DQ, T a Enter C for the target drone a Time; T DQ and T a It is obtained through the time code in the grid code of the corresponding spatial grid.

[0081] Q212, based on Δh, Δd and ΔTa, determine g a =g'+α×Δh a +β×Δd a +δΔT a ; Wherein, α is the first weight factor of the preset height difference, β is the second weight factor of the preset distance difference, and δ is the third weight factor of the preset time interval; g' is the cumulative cost of the target UAV from the corresponding airspace grid of the take-off point to the previous airspace grid of the DQ.

[0082] In this embodiment, through the above steps, the longitude and latitude changes, altitude changes and time changes between the airspace grids can be comprehensively considered, so that the calculated F a More reasonable and accurate; α, β and δ can be obtained through a large number of experiments and fine-tuned according to the results of each experiment.

[0083] Furthermore, h a This can be determined by the following steps:

[0084] Q220, based on Δh, Δd and ΔTa, determine h a =ε×Δh a +γ×Δd a +θΔT a ; Wherein, ε is the fourth weight factor of the preset height difference, γ is the fifth weight factor of the preset distance difference, and θ is the sixth weight factor of the preset time interval.

[0085] In this embodiment, ε, γ, and θ can also be obtained through a large number of experiments and fine-tuning according to the results of each experiment.

[0086] Furthermore, W a This can be determined by the following steps:

[0087] Q230, if C a is occupied, then determine W a =MQ; where MQ is the preset value.

[0088] In this embodiment, if C a is occupied, then C a The weight value of C is set to a very high value. In this formula, the value of MQ can be set to 3000; this value can ensure that C a The weight value is relatively high.

[0089] Q231, if C a If not occupied, get C a The corresponding risk level r, the corresponding weight ω r The risk value de corresponding to the type of target drone.

[0090] Q232, according to r, ω r and de, determine W a =(ω r ×r)×((NUM+de) / NUM); where NUM is the number of preset drone types.

[0091] In this embodiment, if C a Not occupied, consider C a Risk level r, risk level weight ω r The risk value de corresponding to the type of target drone; ω r It is a preset value and can be obtained through a large number of experiments.

[0092] Furthermore, de can be determined by the following steps:

[0093] Q21, obtain the weight ZT of the target UAV.

[0094] Q22, based on ZT, determine the level JB of the drone type corresponding to the target drone.

[0095] Q23, identify JB as de.

[0096] Specifically, the drone types are divided into NUM types according to their weight, and the drones are ranked from light to heavy. For example, the level of micro drones is 1, the level of small drones is 2, and the level of medium drones is 3, increasing in sequence. If the target drone is a small drone, JB is 2; so that W a The value of becomes larger as the level of the target drone increases.

[0097] Furthermore, NUM can be 3 or 4. When NUM is 4, a large drone can be added, and the corresponding level is 4.

[0098] Furthermore, r can be determined by the following steps:

[0099] Q24, obtain the current UAV traffic λ.

[0100] In this embodiment, the current drone traffic may be the traffic of drones in a certain area around the target drone.

[0101] Q25, determine r based on λ, where r satisfies the following relationship:

[0102]

[0103] Among them, λ1 is the preset first drone traffic threshold, λ2 is the preset second drone traffic threshold, and λ3 is the preset third drone traffic threshold.

[0104] Q300, based on F, determine the target estimated cost F min =MIN(F); where MIN() is a preset minimum value function.

[0105] In summary, considering the three factors, the total cost is updated in real time according to the provided calculation formula. The target UAV's path is updated in real time to ensure that the UAV achieves the optimal shortest path under the constraints during flight.

[0106] Q400, F min The corresponding airspace grid is determined as the next airspace grid for the target UAV to be used for path planning.

[0107] In this embodiment, GeoSOT-4D grid is used to divide the airspace information and set the encoding to realize the gridding of the airspace. The obtained encoding value is used to divide the airspace grid cube and build an airspace information database. The airspace encoding data is used to calculate the airspace grid range of the UAV during flight, and the four-dimensional data is analyzed in real time to generate the flight path of the UAV.

[0108] Furthermore, this embodiment transforms airspace into multi-dimensional data, with each independent location having a unique code. Based on the results of the airspace's spatiotemporal partitioning, the track search phase can utilize an improved airspace grid trajectory planning algorithm, using the drone's reachable neighborhood grids as units. The optimal neighborhood grid is searched for during flight, forming the most feasible flight path.

[0109] In this example, experiments were conducted to verify the improvements to drone management achieved by using GeoSOT-4D subdivision grids. The implementation of a grid changes the data storage architecture. Previously, spatial information about routes and other objects was recorded using ObjectIDs. In our design, a database primarily based on GeoSOT-4D encoding was constructed, storing both air routes and object data in a single database. An airspace environment at 44° latitude and 29.8° longitude was simulated, and several real-world track data were added to the airspace. After completing the airspace modeling, the number of stored tracking records and data size were compared using latitude, longitude, time, and different grid levels. The amount of data and information recorded by the subdivision grid increases with the level, as higher grid levels provide more detailed descriptions of routes and spatial objects, requiring more storage space. If the data is not converted to a grid representation, both the data size and the number of tracking records are larger than after conversion. Experimental results show that subdivision significantly reduces data storage. Data size using traditional latitude and longitude representation is approximately 1.5 times that of using a 21-level grid, which is considered a high grid level.

[0110] Due to the massive amount of real-time environmental data transmitted by drones, traditional latitude and longitude methods result in significant planning time and storage requirements. However, GeoSOT-4D meets the requirements for fast drone path design and can quickly complete drone path construction at a high grid level.

[0111] Within the airspace grid database, GeoSOT-4D can establish grid code databases of varying levels and sizes based on actual needs, storing airspace, drone, time, and other information at the grid level to accommodate the current drone flight. Within the same airspace, the storage size is half that of the coordinate method. In terms of the time consumed for neighborhood locations, GeoSOT-4D takes approximately 30% of the database index coordinate method, while the time consumed for path planning based on total coordinates is one-seventh of the computation time of conventional algorithm path planning. It is easy to understand that GeoSOT-4D's spatiotemporal gridding, as a storage method for airspace representation during drone flight, offers significant advantages over traditional latitude and longitude methods.

[0112] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0113] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0114] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0115] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0116] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0117] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0118] An embodiment of the present invention further provides an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.

[0119] The electronic device is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0120] The electronic device is implemented as a general-purpose computing device. Components of the electronic device may include, but are not limited to, the at least one processor, the at least one memory, and a bus connecting different system components (including the memory and the processor).

[0121] The memory stores program codes, which can be executed by the processor, so that the processor performs the steps of various embodiments described in this specification.

[0122] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0123] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0124] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0125] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0126] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0127] An embodiment of the present invention further provides a computer program product comprising program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0128] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for spatial gridding based on time of flight, characterized in that: The method comprises the following steps: S100, obtaining a flight path RE of the target UAV; wherein RE includes the take-off point, landing point, and several waypoints of the target UAV; S200, determining the level L of the airspace grid corresponding to the RE based on the take-off point and landing point corresponding to the RE; S300, obtain the target airspace grid of each L level corresponding to RE to obtain the airspace grid list A=(A1, A2, ..., A i ,…,A n ), i=1, 2,...,n; where, A i is the i-th L-level target airspace grid corresponding to RE, n is the number of L-level target airspace grids corresponding to RE; the target airspace grid is the airspace grid that RE passes through; S400, according to A, determine the L-level code of each target airspace grid to obtain the target airspace grid code list G L =(G L 1, G L 2,…,G L i ,…,G L n ); where G L i A i The corresponding target spatial grid code; G L i =(G L i，P , G L i，H );G L i，P is the latitude and longitude code of the target airspace grid of the i-th L level corresponding to RE, G L i，H is the height code of the target airspace grid of level i L corresponding to RE; S500, according to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid to obtain the time list to be encoded t=(t1, t2, ..., t i ,…,t n ); where t i Fly the target drone to A i Time of hour; S600, encode each time to be encoded in t to obtain an L-level time encoding list T L =(T L 1. T L 2,…,T L i ,…,T L n ); where T L i t i The corresponding time code; S700, T L i Add to G L i In order to get A i The corresponding target airspace grid coding GA including flight time L i =(G L i，P , G L i，H , T L i ).

2. The method for spatial gridding based on time of flight according to claim 1, characterized in that: Step S600 includes the following steps: S610, obtain t i Corresponding hour i 、minutesmin i and seconds i ; S620, for hour i 、min i and second i Convert binary integer to get hour i 、min i and second i The corresponding binary integer hour' i 、min' i and second' i ; Among them, hour' i =hour i ×32 / 24;min' i =min i ×64 / 60;second' i =second i ×64 / 60; S630, will hour' i 、min' i and second' i Convert to binary code to get T L i .

3. The method for spatial gridding based on time of flight according to claim 2, characterized in that: Before step S610, the method further includes the following steps: S601, if the hour corresponding to t1 is the same as t n If the corresponding hours are different, then go to S610; otherwise, go to S602; S602, obtain t i Corresponding minutes min i and seconds i ; S603, for min i and second i Perform binary integer conversion to get the min i and second i The corresponding binary integer min' i and second' i ; Among them, min' i =min i ×64 / 60;second' i =second i ×64 / 60; S604, min' i and second' i Convert to binary code to get T L i .

4. The method for spatial gridding based on time of flight according to claim 1, characterized in that: G L i It is obtained by dividing it using the preset three-dimensional geographic spatial reference system GeoSOT-3D.

5. The method for spatial gridding based on time of flight according to claim 1, characterized in that: Step S200 includes the following steps: S210, obtaining the straight-line distance S between the take-off point and the landing point corresponding to RE RE ; S220, according to S RE And the preset straight-line distance route type mapping table QR, determine S RE The corresponding route type; where QR includes several rows, each row corresponds to a route type, and each route type corresponds to a straight-line distance range and an airspace grid level; S230, according to S RE The corresponding route type determines the level L of the airspace grid corresponding to RE.

6. The method for spatial gridding based on time of flight according to claim 1 or 5, characterized in that: L＝21。 7. The method for spatial gridding based on time of flight according to claim 1, characterized in that: A i Including the take-off point, landing point or passing point corresponding to RE.

8. The method for spatial gridding based on time of flight according to claim 1, characterized in that: After step S700, the method further includes the following steps: S800, when the target drone flies into A i When the preset time is reached, get A i The corresponding occupation time code is used to obtain the occupation time code list ZT i =(ZT i，1 , ZT i，2 ,…,ZT i，j ,…,ZT i，f(i) ), j = 1, 2, ..., f(i); where ZT i，j A i The corresponding j-th occupied time code, f(i) is A i The number of corresponding occupied time codes; the time corresponding to the occupied time code is A i Time occupied; S810, traverse ZT i , if ZT i There exists any L i The occupation time code that meets the preset occupation judgment condition is determined by A i is the airspace grid to be avoided; otherwise, determine A i It is the non-obstacle avoidance airspace grid.

9. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the time-of-flight-based spatial gridding method as described in any one of claims 1-8.

10. An electronic device, characterized in that: The device comprises a processor and the non-transitory computer-readable storage medium of claim 9.

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