Air movement trajectory grid modeling method and device based on spatial stereo grid

Abstract

The application provides an air movement trajectory grid modeling method and device based on a space domain three-dimensional grid. The air movement trajectory grid modeling method based on the space domain three-dimensional grid comprises the following steps: obtaining a space domain boundary grid code set, the boundary grid code set is closed; randomly selecting a grid in a space domain space defined by the space domain boundary grid code set; judging whether the selected grid meets a set condition, and if yes, filling the selected grid; on the basis of the selected grid, expanding the neighborhood of the selected grid, and recursively filling each grid in the neighborhood. The purpose of space domain fine partition is achieved.

Description

Technical Field

[0001] This invention belongs to the field of airspace management technology, and in particular relates to a method and apparatus for modeling aerial motion trajectory grids based on airspace three-dimensional grids. Background Technology

[0002] Airspace is a vital strategic resource for a nation, possessing immense economic, defense, and social value. It plays an irreplaceable and crucial role in national economic and social development and is the primary area for general aviation activities. The rational use of low-altitude resources is an essential condition for the development and prosperity of the general aviation economy.

[0003] The current technology does not delineate airspace types scientifically or precisely enough, and cannot flexibly release idle airspace, resulting in a large amount of high-quality airspace resources being idle and extremely wasteful. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention provides a method and apparatus for modeling aerial motion trajectory grid based on airspace three-dimensional grid, which at least partially solves the problem of insufficient airspace management in the prior art.

[0005] In a first aspect, embodiments of this disclosure provide a method for modeling aerial motion trajectories based on a spatial three-dimensional grid, including:

[0006] Obtain the spatial boundary grid code set, which is closed;

[0007] Randomly select grids within the spatial space defined by the spatial boundary grid encoding set;

[0008] Determine if the selected grid meets the set conditions; if so, fill the selected grid.

[0009] Based on the selected grid, expand the neighborhood of the selected grid and recursively fill each grid in the neighborhood.

[0010] Secondly, embodiments of this disclosure also provide an aerial motion trajectory mesh modeling device based on a spatial three-dimensional mesh, comprising:

[0011] The acquisition module is used to acquire the spatial boundary grid code set, which is closed.

[0012] The selection module is used to randomly select a grid in the spatial space defined by the spatial boundary grid coding set;

[0013] The judgment module is used to determine whether the selected grid meets the set conditions; if it does, the selected grid is filled.

[0014] The fill module is used to expand the neighborhood of the selected grid based on the selected grid, and recursively fill each grid in the neighborhood.

[0015] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0016] At least one processor; and,

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the aerial motion trajectory mesh modeling method based on spatial three-dimensional mesh as described in any of the first aspects.

[0019] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing computer instructions for causing a computer to execute the aerial motion trajectory mesh modeling method based on a spatial three-dimensional mesh as described in any of the first aspects.

[0020] The present invention provides a method and apparatus for modeling aerial motion trajectories based on a three-dimensional airspace grid. By filling the grid in the airspace boundary, the motion trajectory in the airspace is modeled by grid coding. Due to the high accuracy of grid coding, the purpose of fine airspace partitioning is achieved, and fine situation evolution can be performed. Attached Figure Description

[0021] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0022] Figure 1 A schematic block diagram of the coding verification test provided in the embodiments of this disclosure;

[0023] Figure 2 A flowchart illustrating the aerial motion trajectory mesh modeling method based on a spatial three-dimensional mesh provided in this disclosure embodiment;

[0024] Figure 3 This is a schematic diagram illustrating the effect of the mesh modeling algorithm provided in the embodiments of this disclosure;

[0025] Figures 4a to 4c This is a schematic diagram illustrating the boundary mesh modeling effect provided in an embodiment of this disclosure.

[0026] Figure 5 This is a schematic diagram of low-level mesh modeling provided in an embodiment of the present disclosure;

[0027] Figure 6 A schematic diagram of high-level mesh modeling provided in an embodiment of this disclosure;

[0028] Figure 7 This is a schematic diagram of low-level mesh internal modeling provided in an embodiment of this disclosure;

[0029] Figure 8 This is a schematic diagram of low-level mesh internal modeling provided in an embodiment of this disclosure;

[0030] Figure 9 This is a schematic diagram illustrating the simulation effect of the pentagonal training area provided in an embodiment of this disclosure;

[0031] Figure 10 This is a schematic diagram illustrating the simulation effect of the triangular training region provided in an embodiment of this disclosure;

[0032] Figure 11 This is a schematic diagram illustrating the simulation effect of a quadrilateral training area provided in an embodiment of the present disclosure;

[0033] Figure 12 A schematic diagram of historical flight track data for one day across the country provided in this embodiment of the disclosure;

[0034] Figure 13 A 3D view of flight sortie status provided in the embodiments of this disclosure;

[0035] Figure 14 A 2D view of flight sortie status provided in an embodiment of this disclosure;

[0036] Figure 15 A spatial region operation status diagram provided for embodiments of this disclosure;

[0037] Figure 16 Spatial region operation extraction surface situation diagram provided in the embodiments of this disclosure;

[0038] Figure 17 A highly layered planar situational 2D view provided for embodiments of this disclosure;

[0039] Figure 18 A highly layered 3D situation map provided for embodiments of this disclosure;

[0040] Figure 19 A size quantization view of airspace busy status provided in embodiments of this disclosure;

[0041] Figure 20 A time-division 2D diagram of the airspace operational status provided in this embodiment of the disclosure;

[0042] Figure 21 A 3D view of the airspace operational status provided in the embodiments of this disclosure;

[0043] Figure 22 This is a schematic block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0044] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0045] The exponent for the mesh modeling of aerial motion trajectories in this embodiment,

[0046] ①Supports gridded discrete modeling of trajectories for aircraft of different types and speeds;

[0047] ② The airspace shape types used for training airspace experiments shall include at least triangular, quadrilateral, pentagonal, hexagonal, circular, and sector-shaped shapes;

[0048] ③ The flight track data used for airspace grid situation evolution should include at least one day's historical airspace operation data for the entire country;

[0049] ④ The spatial grid coding implies spatial location relationships and can be directly used as a spatial location identifier.

[0050] ⑤ Spatial grid coding supports efficient spatial computation;

[0051] ⑥ Supports gridded discrete modeling for different training spatial domains.

[0052] Based on spatial 3D grid coding and combined with air traffic control operational requirements, the feasibility verification of grid coding for the training airspace is mainly divided into two parts. For example... Figure 1 As shown, the first aspect is the training of gridded modeling of the spatial domain, which involves assigning values ​​to the grids in the restricted spatial domain separately; the second aspect is the analysis of the modeling fitting accuracy and patterns for different application scenarios.

[0053] like Figure 2 As shown, this embodiment discloses a method for modeling aerial motion trajectories based on a spatial three-dimensional grid, including:

[0054] Obtain the spatial boundary grid code set, which is closed;

[0055] Randomly select grids within the spatial space defined by the spatial boundary grid encoding set;

[0056] Determine if the selected grid meets the set conditions; if so, fill the selected grid.

[0057] Based on the selected grid, expand the neighborhood of the selected grid and recursively fill each grid in the neighborhood.

[0058] Optionally, the determination that the selected grid meets the set conditions, and if so, filling the selected grid, includes:

[0059] Determine whether the current grid is within the boundary of the spatial boundary grid encoding set and whether the grid occupancy attribute is 0. If the conditions are met, fill the occupancy attribute; otherwise, return the set of grids with an attribute of 1.

[0060] Optionally, the step of expanding the neighborhood of the selected grid based on the selected grid, and recursively filling each grid within the neighborhood, includes:

[0061] By using a stack or queue to store unfilled areas, the grids above, below, to the left, and to the right of the grids existing in the filling space are added to the stack or queue in turn, filling them in turn until the stack or queue is empty.

[0062] In a specific application scenario, the modeling method is as follows:

[0063] Step S1: Input the boundary binary grid code set. The boundary grid code set must be closed.

[0064] Step S2: Specify a random grid.

[0065] Step S3: Determine if the current mesh is within the boundary and if the mesh occupancy attribute is 0. If the conditions are met, fill the occupancy attribute. If the conditions are not met, return the set of meshes with an attribute of 1.

[0066] Step S4: Based on the current grid, expand the eight-neighborhood by recursively calling this method for each grid within the eight-neighborhood. Return to step S3.

[0067] Mesh modeling algorithms utilize arbitrary closed mesh boundaries to fill the interior with a mesh-like structure. The effect is as follows. Figure 3 As shown.

[0068] In this diagram, the dark areas represent the airspace boundary grid, while the light areas represent the internal airspace grid. It can be observed that, in scenarios involving the division of irregular regions, the grid structure can cover irregularly shaped airspace delineations, providing strong support for the scheduling of various aircraft.

[0069] The filling process in step S4 described above is very memory-intensive, and if there are many grids to be filled, it can lead to overflow. Therefore, this embodiment uses the following filling method.

[0070] By using a stack or queue to store unfilled areas, the grids above, below, to the left, and to the right of the grids existing in the filling space are added to the stack or queue in turn, filling them in turn until the stack or queue is empty.

[0071] The difference in accuracy between meshed modeling of the training region lies primarily in the meshed fitting of the boundary region, such as... Figure 4a , Figure 4b and Figure 4cAs shown, this illustrates the results for the same set of boundary points at different grid granularities.

[0072] according to Figure 4a , Figure 4b and Figure 4c Three patterns can be observed:

[0073] (1) When the sampling granularity of the boundary encoding set is small, the boundary fitting accuracy is low; when the sampling granularity is large, the boundary fitting accuracy is high.

[0074] (2) When the grid level of the boundary encoding set is low, the boundary fitting effect is poor; when the grid level is high, the boundary fitting effect is good.

[0075] (3) A larger sampling granularity of the boundary set will increase the space complexity of the algorithm, while selecting a higher level of the boundary set will increase the time complexity of the algorithm.

[0076] Due to the multi-scale nature of grids, the accuracy of grid modeling inevitably increases with the increase of grid level. However, in the field of airspace management, unnecessary accuracy improvements are often undesirable; the higher the grid level, the larger the number of grids, and the greater the storage required. Grid modeling effectiveness evaluation methods, starting from the different accuracy requirements of different types of airspace, find the optimal combination of grid number and fitting accuracy through the design of evaluation functions.

[0077] For a given spatial domain, the optimal combinational mathematical expression for its gridded representation is:

[0078] AirSpace grid =Boundary grid ∪Inner grid ,

[0079]

[0080]

[0081] Among them, Boundary grid For the boundary set, Inner grid AirSpace determines the storage capacity of the grid. grid Represents a spatial grid. i Represents a grid, layer k Indicates the grid level.

[0082] Boundary grid The boundary set determines the overall accuracy of mesh modeling, and when the boundaries are defined, the Inner... grid This determines the amount of storage space required for the mesh. Therefore, evaluating the effectiveness of mesh modeling requires overall constraints on both the boundary mesh set and the internal mesh set.

[0083] The accuracy constraint of grid modeling mainly acts on the boundary region of the spatial grid expression. According to the design of the multi-scale grid filling algorithm, the boundary set of the grid can be expressed as the set formed by the grids:

[0084]

[0085] The ultimate purpose of spatial modeling is to serve air traffic management. Different types of airspaces have different speeds of aircraft in their regions, and the accuracy requirements for refined modeling are also different. It is necessary to design a reasonable constraint function to control the accuracy of grid modeling, so as to prevent unnecessary accuracy loss while ensuring flight safety. Taking the 2D planar grid shown in Figure 5 and Figure 6 as an example, the necessity of grid modeling accuracy constraint is specifically explained.

[0086] The black line boundary is the top view of the current modeled airspace, and the grayscale grid blocks are the airspace range of grid modeling fitted with low-level grid blocks. It can be seen that the low-level grid airspace modeling has low accuracy and there are a large number of redundant areas. If it is applied to the modeling of no-fly zones, it will lead to waste of airspace resources and affect the efficiency of refined management. As shown in Figure 5 and Figure 6 , high-level grids can well reduce the redundant space of fitting and carry out more refined modeling. The grid modeling accuracy constraint method is:

[0087] Accuracyconstraint airspace =f(Boundary grid ,Original)<threshold,

[0088] Accuracyconstraint airspace is the modeling accuracy constraint, Original represents the starting point, threshold represents the threshold, and f is the error function,

[0089] Through the set error function f, the error between the grid modeling and the original airspace volume is found, the application accuracy is guaranteed through the threshold threshold, and the grid modeling accuracy is constrained by minimizing Accuracyconstraint airspace .

[0090] Optionally, the error function f includes:

[0091] Difference function: f(x1,x2)=|x1-x2|, which is used to measure the volume difference after grid modeling. When f(x1,x2)<threshold, the accuracy is within the acceptable range, and x1 and x2 are error parameters;

[0092] or,

[0093] Ratio function: is used to measure the ratio of the volume after grid modeling. When |f(x1,x2)-1|<threshold, the accuracy is within the acceptable range.

[0094] The storage constraint of grid modeling mainly acts on the internal area of the airspace gridded representation. According to the design of the multi-scale grid filling algorithm, the internal set of grids can be expressed as a set composed of grids of the same scale:

[0095]

[0096] The purpose of internal domain gridding is to serve the refined management of airspace. Different types of airspace face different airspace users and have different speed limits, so their demands for refined management are different. It is necessary to design a reasonable constraint function to control the representation accuracy of the grids by controlling the number of grids. Taking the two-dimensional plane grid as an example, we illustrate the necessity of the quantity constraint for grid modeling.

[0097] As shown in Figure 7 and Figure 8 , the number of grids required for airspace modeling with low-level grids is small, but their granularity is large, which may not meet the accuracy requirements in specific application scenarios; the number of grids required for high-level grids is large, the granularity is small, but the consumed storage space is large. The storage constraint method for grid modeling is:

[0098] Quantityconstraint airspace =N inner *memory grid

[0099] Quantityconstraint airspace is the modeling storage constraint, N inner is the internal constraint coefficient, memory grid is the grid storage space,

[0100] By minimizing Quantityconstraint airspace , the grid modeling result is evaluated in terms of storage capacity.

[0101] According to the accuracy and storage constraints, the evaluation method for airspace gridding modeling requires reasonable configuration between the accuracy of airspace gridding modeling and the number of airspace grids. The designed objective function is as follows:

[0102] Cost=(1-α)Accuracyconstraint airspace +αQuantityconstraintairspace

[0103] The evaluation problem of spatial domain modeling is transformed into a problem of minimizing the objective function loss by using an objective function. Cost is the objective value, and α is a configuration parameter used to adjust the weights of accuracy and storage. When the evaluation function tends to use low-level modeling, it indicates that the current type of spatial domain has low precision requirements and strong constraints on storage; when At that time, the evaluation function tends to use high-level modeling, indicating that the current type of spatial domain has high precision requirements but low constraints on storage.

[0104] The simulation data encoding verification of the modeling method is as follows:

[0105] The simulation experiments include simulated data for static airspace regions such as triangles, quadrilaterals, pentagons, hexagons, circles, and sectors. The experimental data range is automatically generated airspace data for the Chinese region. Data characteristics are summarized in Table 1.

[0106] Table 1. Explanation of Experimental Data

[0107]

[0108] The verification experiment consists of two parts. First, it verifies whether the proposed airspace management grid model and grid filling algorithm can perform unified gridding modeling of data with different shapes. Second, it verifies whether the modeling method of the airspace management grid model can achieve the accuracy of the original data, and, while satisfying the accuracy requirement, finds the optimal combination of grid accuracy and storage capacity.

[0109] Spatial grid encoding was trained using polygons generated from simulated boundary data. Due to limited sampling data, the experimental data consisted only of the corner points of the polygons, and a boundary dataset was generated based on a grid boundary fitting algorithm. The results are as follows: Figure 9 , Figure 10 and Figure 11 As shown:

[0110] Accuracy analysis of experimental results:

[0111] To find the optimal accuracy level for different types of airspace, the optimal level was determined using a gridded modeling effect evaluation method. Experimental results are shown in Table 2.

[0112] Table 2. Numerical Table of Experimental Results

[0113]

[0114]

[0115] The following conclusions were drawn from the experiment:

[0116] (1) The terminal area airspace is relatively complex and the simulated shape is irregular. Therefore, the number of sampling points is highly correlated with the fitting accuracy. The more sampling points there are, the greater the fitting accuracy of the region. The flight area airspace is mostly regular, and the number of sampling points has little impact on the accuracy when it reaches a certain threshold.

[0117] (2) Based on different α coefficients, the fitting accuracy can be screened according to the cost criterion, thereby determining the best fitting level.

[0118] Experimental conclusion:

[0119] Grid-based spatial domain fitting algorithms can fit restricted spatial domains well. The modeling method in this embodiment can effectively filter the generated grid spatial domains, thereby determining the optimal level in practical applications.

[0120] The verification of the airspace management grid situation evolution diagram based on historical flight tracks is as follows:

[0121] Experimental data:

[0122] The original ADS-B data format includes information elements such as sequence number, track ID, track number, flight number, secondary code, departure airport, arrival airport, aircraft type, longitude, latitude, altitude (10m), CFL, heading, speed (km / h), and time. For ADS-B data, only the latitude, longitude, and time data features are needed. Therefore, for each flight with a unique flight number, we intend to extract the geographic information (latitude, longitude, altitude, coordinates, and time) of several features as the transfer data processing format. The conversion result is as follows... Figure 12 As shown.

[0123] Based on historical flight track data from around the world and across the country for one day, the flight routes across the country are visualized, allowing for a general observation of the direction of flight routes.

[0124] Experimental procedure:

[0125] Based on historical flight track data from across the country for one day, we statistically analyzed the airspace utilization parameters at different levels of grid and generated a three-dimensional dynamic operation status map of the grid airspace.

[0126] Specific statistical parameters may include indicators such as flight count and number of takeoffs and landings. Based on the Cesium visualization platform, we input the statistical parameters into the visualization platform, and the different colors can be used to determine the local aviation busyness and compare the aviation economic development of different regions.

[0127] Number of rises and falls:

[0128] The number of takeoffs and landings within a statistical area is an important indicator for measuring airspace capacity, similar to throughput. If the number of takeoffs is greater than the number of landings, the airspace is considered busy, and more aircraft should be refused entry. Similarly, if the number of takeoffs is less than the number of landings, the airspace is considered idle, and aircraft can be allowed to operate there.

[0129] Flights:

[0130] The number of flights within a statistical area is also an important indicator for measuring airspace capacity. If the number of flights exceeds the reference capacity, the airspace is considered busy, and more aircraft should be refused entry. Similarly, if the number of flights is less than the reference capacity, it can be considered idle, and aircraft can be allowed to operate there.

[0131] Experimental results:

[0132] Nationwide situation map display:

[0133] The 3D globe display is implemented using the Cesium.js JavaScript library, enabling the creation of a world-class 3D globe and a range of map operations. Cesium.js commonly uses the WGS84 geographic coordinate system and the Cartesian spatial coordinate system.

[0134] Based on the Cesium visualization platform, statistical parameters are input into the platform, and the local air traffic volume can be determined by different colors, which can be used to compare the air economy development of different regions.

[0135] By selecting a grid with a scale consistent with the airway width, approximately 10-20km, and performing visualization operations on the national airways for a day, the basic framework of the national airway network can be obtained.

[0136] Regional-scale situation map display:

[0137] Extracting the location points of air routes and flight paths in Jiangsu Province and developing a smaller-scale situational map allows for a more detailed observation and comparison of air traffic in different regions, enabling the establishment of inferences about the aviation economic development of these regions. For example... Figure 13 , Figure 14 and Figure 15 As shown. Figure 13 , Figure 14 and Figure 15 The text in the example text is for illustrative purposes only.

[0138] This shows that busy air traffic locations are mainly found in small and medium-sized cities in Jiangsu Province, while they are relatively less frequent in other places. This is roughly consistent with the distribution of airports, thus confirming the accuracy of the situation map.

[0139] A portion of the airspace was extracted for the situation map display. The selected airspace ranged from 104° to 113° longitude and from 25° to 34° latitude, with altitudes ranging from 3000m to 6000m. The time frame included was from 00:00:00 to 12:00:00 of the current day. The resulting airspace situation evolution map is shown below. Figures 16 to 21 As shown.

[0140] Among them, the Figure 19 By quantifying the traffic situation of the corresponding airspace according to the grid size, busy areas of the airspace can be identified intuitively and early warnings can be broadcast to the outside world.

[0141] This embodiment also discloses an aerial motion trajectory mesh modeling device based on a spatial three-dimensional mesh, including:

[0142] The acquisition module is used to acquire the spatial boundary grid code set, which is closed.

[0143] The selection module is used to randomly select a grid in the spatial space defined by the spatial boundary grid coding set;

[0144] The judgment module is used to determine whether the selected grid meets the set conditions; if it does, the selected grid is filled.

[0145] The fill module is used to expand the neighborhood of the selected grid based on the selected grid, and recursively fill each grid in the neighborhood.

[0146] The electronic device disclosed in this embodiment includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0147] The processor may be a central processing unit (CPU) or other processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to run the computer-readable instructions stored in the memory, causing the electronic device to perform all or part of the steps of the aerial motion trajectory mesh modeling method based on spatial three-dimensional mesh of the foregoing embodiments of this disclosure.

[0148] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0149] like Figure 22 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the electronic device in the embodiment of the present disclosure. Figure 22 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0150] like Figure 22 As shown, an electronic device may include a processing unit (such as a central processing unit, graphics processing unit, etc.) that can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from a storage device into random access memory (RAM). The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0151] Typically, the following devices can be connected to the I / O interface: input devices, such as sensors or visual information acquisition devices; output devices, such as displays; storage devices, such as magnetic tapes or hard drives; and communication devices. Communication devices allow electronic devices to communicate wirelessly or wiredly with other devices (such as edge computing devices) to exchange data. Although Figure 22 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead.

[0152] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, all or part of the steps of the aerial motion trajectory mesh modeling method based on a spatial three-dimensional mesh according to embodiments of this disclosure are performed.

[0153] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0154] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the aerial motion trajectory mesh modeling method based on a spatial three-dimensional mesh according to the foregoing embodiments of the present disclosure are performed.

[0155] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0156] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0157] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0158] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for modeling aerial motion trajectories based on a spatial three-dimensional grid, characterized in that, include: Obtain the spatial boundary grid code set, which is closed; Randomly select grids within the spatial space defined by the spatial boundary grid encoding set; Determine if the selected grid meets the set conditions; if so, fill the selected grid. Based on the selected grid, expand the neighborhood of the selected grid and recursively fill each grid in the neighborhood; The modeling effect is evaluated, and the evaluation is used to find the optimal combination of grid number and fitting accuracy based on the different accuracy requirements of different types of spatial domains through the evaluation function. The evaluation of the modeling effect includes configuring the accuracy and number of spatial grids in the spatial gridding modeling based on modeling accuracy constraints and modeling storage constraints. The objective function for the configuration is as follows: For the target value, To constrain modeling accuracy, To model storage constraints, For configuration parameters, Used for weighting precision and storage capacity, when When the evaluation function tends to use low-level modeling, it indicates that the current type of spatial domain has low precision requirements and strong constraints on storage; when At that time, the evaluation function tends to use high-level modeling, indicating that the current type of spatial domain has high precision requirements but low constraints on storage.

2. The method for modeling aerial motion trajectories based on spatial three-dimensional meshes according to claim 1, characterized in that, The determination that the selected grid meets the set conditions, and if so, filling the selected grid, includes: Determine whether the current grid is within the boundary of the spatial boundary grid encoding set and whether the grid occupancy attribute is 0. If the conditions are met, fill the occupancy attribute; otherwise, return the set of grids with an attribute of 1.

3. The method for modeling aerial motion trajectories based on spatial three-dimensional meshes according to claim 1, characterized in that, The step of expanding the neighborhood of the selected grid and recursively filling each grid within the neighborhood includes: By using a stack or queue to store unfilled areas, the grids above, below, to the left, and to the right of the grids existing in the filling space are added to the stack or queue in turn, filling them in turn until the stack or queue is empty.

4. The method for modeling aerial motion trajectories based on spatial three-dimensional meshes according to claim 1, characterized in that, The evaluation of the modeling effect includes: For a given spatial domain, the optimal combination expression for the lattice representation is: ， ， ， in, For the boundary set, Determine the storage capacity of the grid. Represents the spatial grid. Represents a grid. Indicates the grid level.

5. The aerial motion trajectory mesh modeling method based on spatial three-dimensional mesh according to claim 4, characterized in that, The evaluation of modeling performance includes: modeling accuracy constraints and modeling storage constraints; The modeling accuracy constraint is: ， To constrain modeling accuracy, Indicates the starting point. Indicates the threshold. Let be the error function. By setting an error function Find the error between the mesh model and the original spatial volume, and use a threshold. To ensure application accuracy, by minimizing To constrain the accuracy of mesh modeling.

6. The method for modeling aerial motion trajectories based on spatial three-dimensional meshes according to claim 5, characterized in that, Error function ,include: Difference function: This is used to measure the difference in volume after mesh modeling. At that time, the accuracy was within an acceptable range. and For error parameters; or, Ratio function: This is used to measure the ratio of the volume after mesh modeling. At that time, the accuracy was within an acceptable range.

7. The method for modeling aerial motion trajectories based on spatial three-dimensional meshes according to claim 6, characterized in that, The modeling and storage constraints include: To model storage constraints, For internal constraint coefficients, For grid storage space, By minimizing The storage requirements of the mesh modeling results are evaluated.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the aerial motion trajectory mesh modeling method based on a spatial three-dimensional mesh as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the aerial motion trajectory mesh modeling method based on a spatial three-dimensional mesh as described in any one of claims 1-7.

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