CN118171498A - Grid technology-based computable airspace modeling method - Google Patents
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Abstract
The invention provides a computational airspace modeling method based on a grid technology. The method comprises the following steps: s1, selecting a planning airspace, and setting grids and the number of layers; s2, constructing a parameter comparison table for representing precision and the number of corresponding layers; s3, acquiring an airspace data value distributed in a planning airspace to obtain corresponding precision, and looking up a table in a parameter comparison table to obtain a level L; s4, calculating the level difference delta L between the level L and the lowest level, judging whether the delta L is 0, if so, performing layer-by-layer up downsampling, otherwise, calculating the level difference delta L' between the highest level and the level L; s5, judging whether delta L 'is 0, if so, carrying out up-sampling downwards layer by layer, otherwise, respectively carrying out up-down sampling according to delta L and delta L'; s6, the up-sampling and down-sampling are combined to form a three-dimensional grid space data matrix, and a computable airspace is obtained. The invention expresses the low-altitude characteristic which is difficult to calculate by digitalization, is convenient for parallel calculation and has stronger universality.
Description
Technical Field
The invention relates to the technical field of airspace modeling, in particular to a computational airspace modeling method based on a grid technology.
Background
In recent years, research and study on a low-altitude airspace are never interrupted, and the research directions are different, for example, an unmanned aerial vehicle traffic management system avoids large-scale collision accidents of unmanned aerial vehicle flight through route planning of low-altitude traffic. Or the unmanned aerial vehicle airspace blueprint project, aiming at seamlessly integrating civil and military unmanned aerial vehicles into the existing airspace environment, so that the unmanned aerial vehicle and a manned aircraft can safely and effectively fly at the same time. Or the unmanned aerial vehicle information service system is used for monitoring the air flight state of the unmanned aerial vehicle and providing low-altitude flight safety assistance by providing airspace information inquiry, flight plan application, flight prompt and other services. These are still in the research and development or experimental stage, but are not separated from data processing calculation, and the realization of the calculation of the low-altitude airspace generally involves the following key technologies:
digital map and Geographic Information System (GIS): by using digital map and GIS technology, the information of the terrain, building, road and the like on the ground surface can be accurately recorded, displayed and analyzed. The aircraft can carry out path planning and flight calculation through the data, so that the aircraft is ensured not to collide with ground obstacles in the flight process of a low-altitude airspace.
Flight control system: the flight control system can calculate and adjust the flight path of the aircraft in real time according to the current position, speed, heading and other information of the aircraft. By calculating and controlling the dynamic parameters of the aircraft, the aircraft can be ensured to fly safely and stably in a low-altitude airspace.
Collision avoidance technique: by using sensor technologies such as radar, infrared sensor, laser radar and the like, the aircraft can monitor obstacles in the surrounding environment in real time and take corresponding avoidance measures so as to avoid collision.
Unmanned aerial vehicle track planning algorithm: by adopting an advanced flight path planning algorithm, the optimal flight path can be calculated according to factors such as flight tasks, environmental conditions, performance of the aircraft and the like, and the aircraft can be ensured to efficiently and safely execute the tasks in a low-altitude airspace.
Artificial intelligence technology: by combining artificial intelligence technology such as machine learning and deep learning, the aircraft can have autonomous decision making and intelligent capabilities, and can make proper reactions and adjustments according to real-time environmental information, so that the operability and safety of the aircraft in a low-altitude space are improved.
By using digital map and GIS technology, the information of the terrain, building, road and the like on the ground surface can be accurately recorded, displayed and analyzed. The aircraft can carry out path planning and flight calculation through the data, so that the aircraft is ensured not to collide with ground obstacles in the flight process of a low-altitude airspace.
However, the existing low-altitude space calculation has the following problems:
1. The calculation complexity is high: processing and analyzing large-scale and complex geographic information data consumes a large amount of computing resources, so that computing time is long, and instantaneity is affected.
2. The data quality is different: the quality and accuracy of the geographic information data varies, resulting in the data being calculated may not be accurate enough.
3. Artificial intelligence is difficult to apply: the manual work can not accurately learn due to the large data volume, high calculation complexity and other reasons of the existing geographic information technology.
Thus, the safety of large-scale fusion low-altitude activities characterized by "heterogeneous, high density, high frequency, and high complexity" is currently not fully supported. From the technical point of view, the initially set targets are difficult to achieve completely, because the precise management of the low-altitude airspace cannot be realized at the present stage, and the precise airspace management technology and means are lacking.
Disclosure of Invention
Based on the above, it is necessary to provide a computational airspace modeling method based on a grid technology, aiming at the problems of high computational complexity and difficult application in the existing low-altitude airspace computation.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a computational airspace modeling method based on a grid technology comprises the following steps:
S1, selecting a target position in a low-altitude airspace, selecting a space region as a planning airspace according to a preset size at the target position, establishing a three-dimensional coordinate system in the planning airspace by taking a selected airspace central point as an origin, and setting grids and corresponding level numbers;
s2, determining an accuracy interval according to the required accuracy requirement, and dividing the accuracy interval according to the layer number of the planning airspace to obtain a parameter comparison table for representing accuracy and a scale value represented by the accuracy interval;
S3, acquiring spatial domain data values distributed in a planning spatial domain to obtain three-dimensional rasterized data, obtaining precision corresponding to the three-dimensional rasterized data, and looking up a table in a parameter comparison table according to the precision of the data to obtain a level L;
s4, calculating the level difference delta L=L-L 1 between the level L and the lowest level L 1, judging whether the level difference delta L is 0, and making the following decision according to the judging result:
(1) When Δl=0; determining the level L as the lowest level, and performing downsampling on the grids of the current level layer by layer according to the level number of the planning airspace;
(2) When Δl is not equal to 0; determining that level L is not the lowest level, calculating the level difference between the highest level L max and level L
ΔL’=Lmax-L；
S5, judging whether the fault level difference delta L' is 0, and making the following decision according to a judging result:
(1) When Δl' =0; determining the level L as the highest level, and performing up-sampling on the grids of the current level layer by layer downwards according to the level number of the planning airspace;
(2) When Δl' noteq0; the judgment level L is an intermediate level, up-sampling is performed layer by layer according to the level difference delta L, and down-sampling is performed layer by layer according to the level difference delta L';
S6, forming a group of three-dimensional grid space data matrixes by grids obtained by up-sampling and down-sampling and corresponding airspace data values, and obtaining the computable airspace.
Further, the specific steps of performing downsampling layer by layer according to the layer number of the planning airspace are as follows:
Acquiring grid coordinates (x, y, z) in the current level L i;
Computing a Gaussian filter kernel ; Wherein/>Expresses the whole deformation variable of the downsampled Gaussian filter kernel,/>Expressing the standard deviation of a Gaussian function, wherein pi represents the circumference ratio;
Up-sampling the current level L i by Gaussian pyramid filtering to obtain an upper layer L i+1 three-dimensional grid space data matrix: ; wherein/> A three-dimensional raster spatial data matrix representing the i-th layer of the pyramid, ∗ representing a convolution operation; /(I)Represents a Gaussian filter kernel, which is/>And (5) calculating a three-dimensional matrix.
Further, the specific steps of performing up-sampling downward layer by layer according to the layer number of the planning airspace are as follows:
The space coordinate to be calculated is obtained, the coordinate is taken as the center, 2 times of the unit length of the coordinate system is taken as the side length, the cube is set, and the sitting marks of the vertexes of the cube are used as Further, the original data value/>, of each vertex is determined；
Up-sampling is carried out through three-dimensional bilinear interpolation to obtain a matrix value of the space data of the next layer of three-dimensional grid:
; wherein/> Expressing the deviation of the corresponding linearity,/>、/>、/>Expressing the deviation of the corresponding linearity in each dimension,/>Expressing up-sampling interpolation integral deformation variable,/>Representation/>Normalized value of/>Representation/>Normalized value of/>Representation/>Is included in the above formula (c).
Further, the normalized value、/>、/>The calculation mode of (a) is the same, and the normalized value/>The calculation formula of (2) is as follows:
; wherein/> Representing the normalized value of the corresponding coordinate axis.
Compared with the prior art, the invention has the beneficial effects that:
1. The computable airspace of the invention expresses complex low-altitude characteristics (information such as position, topography, obstacle, weather and the like) which are difficult to calculate in a digital form, can be stored, issued and inquired in the digital form to form the digitalization of the computable airspace, and adopts regular grid cells, so that the computable airspace is easy to understand and realize; in some cases, the rasterized computation may provide simple and intuitive analysis results;
2. the rasterization calculation of the computable airspace is convenient for parallel calculation, and the calculation resource is utilized more efficiently; through spatial rasterization, spatial relationships between geospatial data, such as adjacent relationships, coverage relationships and the like, can be more conveniently represented, spatial analysis and calculation are facilitated, and the method is suitable for various geographic data, including remote sensing images, topographic data, demographic data and the like, and has strong universality.
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The disclosure of the present invention is described with reference to the accompanying drawings. It is to be understood that the drawings are designed solely for the purposes of illustration and not as a definition of the limits of the invention. Wherein:
FIG. 1 is a flow chart of a computational airspace modeling method based on grid technology according to the present invention;
FIG. 2 is a diagram of a reachable airspace-computable airspace mapping;
FIG. 3 is a diagram of a computable airspace pyramid model;
FIG. 4 is a logic diagram of a computable airspace modeling method based on the grid-based technique of FIG. 1.
Detailed Description
It is to be understood that, according to the technical solution of the present invention, those skilled in the art may propose various alternative structural modes and implementation modes without changing the true spirit of the present invention. Accordingly, the following detailed description and drawings are merely illustrative of the invention and are not intended to be exhaustive or to limit the invention to the precise form disclosed.
Referring to fig. 1, the present embodiment describes a computational airspace modeling method based on grid technology, which includes the following steps:
S1, selecting a target position in a low-altitude airspace, selecting a space region as a planning airspace according to a preset size at the target position, establishing a three-dimensional coordinate system in the planning airspace by taking a selected airspace central point as an origin, and setting grids and corresponding level numbers; the grid is a regular topological homoembryo body, and the topological homoembryo body adopts a regular tetrahedron in order to facilitate calculation or modeling.
S2, determining an accuracy interval according to the required accuracy requirement, and dividing the accuracy interval according to the layer number of the planning airspace to obtain a parameter comparison table for representing accuracy and a scale value represented by the accuracy interval;
S3, acquiring airspace data values distributed in the planning airspace to obtain three-dimensional rasterization data, obtaining precision corresponding to the three-dimensional rasterization data, and looking up a table in the parameter comparison table according to the precision of the data to obtain a level L; the airspace data value is determined by an application scene, comprises geographic information data, historical data, environment data, sensor data and the like, is distributed into different grids according to different precision, and can be obtained by one or more data operations of maximum value, minimum value, variance, standard deviation, median and the like.
S4, calculating the level difference delta L=L-L 1 between the level L and the lowest level L 1, judging whether the level difference delta L is 0, and making the following decision according to the judging result:
(1) When Δl=0; determining the level L as the lowest level, and performing downsampling on the grids of the current level layer by layer according to the level number of the planning airspace; the operation formula of downsampling by Gaussian filtering:
；
Wherein, Three-dimensional grid space data matrix representing pyramid layer i+1,/>A three-dimensional raster spatial data matrix representing the i-th layer of the pyramid, ∗ representing a convolution operation; /(I)Represents a Gaussian filter kernel, is/>And (5) calculating a three-dimensional matrix.
In the three-dimensional space of the computable airspace, the Gaussian filter kernel is a three-dimensional matrix, and the designed calculation formula is as follows:
；
Expresses the whole deformation variable of the downsampled Gaussian filter kernel,/> And expressing the standard deviation of a Gaussian function, wherein pi represents the circumference ratio, x, y and z represent the offset of the current point and a central point, and the central point is any point in a planning space domain.
(2) When Δl is not equal to 0; determining that level L is not the lowest level, calculating the level difference between the highest level L max and level L
ΔL’=Lmax-L；
S5, judging whether the fault level difference delta L' is 0, and making the following decision according to a judging result:
(1) When Δl' =0; determining the level L as the highest level, and performing up-sampling on the grids of the current level layer by layer downwards according to the level number of the planning airspace;
There are various ways of upsampling two-dimensional data, including nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, etc., and according to the characteristics of three-dimensional space data and the complexity of calculation, three-dimensional variations of bilinear interpolation are adopted, and the bilinear interpolation is used for upsampling.
Setting the required calculated coordinates asThe eight vertices of the coordinate-centric cube need to be fetched as the primitive data,/>、/>、/>Expression/>, respectivelyIs calculated at interpolation using the following formula:
；
For/> 、/>、/>Is commonly referred to as/>Representing the normalized value of the corresponding coordinate axis.
Setting a cube with the space coordinate to be calculated as the center and 2 times of the unit length of the coordinate system as the side length, wherein each vertex sitting mark of the cube is used as，/>0 Or 1; setting the use of raw data values for each vertexAnd (5) expression. Find/>The formula for three-dimensional bilinear interpolation used is as follows:
； Refer to/> ；/>、/>、/>Each dimension normalized value is represented. /(I)、/>、/>Expressing the deviation of the corresponding linearity in each dimension,/>And expressing the upsampling interpolation integral deformation variable.
(2) When Δl' noteq0; the judgment level L is an intermediate level, up-sampling is performed layer by layer according to the level difference delta L, and down-sampling is performed layer by layer according to the level difference delta L';
S6, forming a group of three-dimensional grid space data matrixes by grids obtained by up-sampling and down-sampling and corresponding airspace data values, and obtaining the computable airspace.
This makes direct airspace computation difficult, as airspace typically involves large amounts of spatial data, dynamically changing environments, and complex interactions. Therefore, rasterizing the airspace is an effective solution.
Rasterization modeling is the division of a continuous space into a series of discrete grid cells, each having specific properties or information. By the method, the complex airspace problem can be simplified into processing and analyzing the grid unit, so that the computational complexity is reduced.
Based on the rasterization modeling, artificial intelligence can be more easily applied to airspace. Specifically, the grid units can be classified, identified or predicted by using a machine learning algorithm, so that intelligent processing of the airspace is realized. For example, in unmanned aerial vehicle path planning, target tracking or environmental monitoring applications, rapid analysis and decision-making of airspace can be achieved through the combination of rasterization modeling and artificial intelligence techniques.
The design of the computable airspace model mentioned in this embodiment is based on three-dimensional rasterization of space, which is a method of converting continuous spatial information into discrete grids, as shown in fig. 2. The three-dimensional grid space coordinate system adopts cubes as grid units, and a comprehensive three-dimensional grid space data set is constructed, so that the flexibility requirement of the system under different tasks and scenes can be met, for example, a flight path of an unmanned aerial vehicle often needs to span a plurality of grid spaces, and the grid space data are converged together to completely reflect the flight situation of the unmanned aerial vehicle.
The airspace is rasterized, the size of the grids is proper, the processing efficiency of the data is high when the size of the grids is too large, but the resolution is low, so that the calculation result is inaccurate, the airspace cannot be fully utilized, the complicated data processing efficiency is low when the size of the grids is too small, but the resolution is high, and the calculation result is more accurate. The grid cells can be scaled down or up appropriately, as conditions allow. In order to meet different demands of different tasks and application scenes on data processing efficiency and resolution, taking reference to a slice and pyramid digital model of raster data in a GIS, downsampling is carried out layer by layer from high-resolution raster data to low-resolution raster data, an image is generated into three-dimensional raster spaces with different levels of resolution, and the images are organized into a pyramid-shaped structure as shown in fig. 3.
The pyramid structure is explained below. The pyramid is built progressively from the bottom level to the high level, k levels being built in fig. 3. The concept of gaussian pyramid filtering is applied in the construction of various levels. Specifically, for example, the most basic layer is a spatial grid space artwork, which is downsampled using a gaussian pyramid. The image resolution of each layer is half that of the previous layer, which is obtained by performing gaussian filtering and downsampling operations on the previous layer. The bottom layer is an original airspace of 1 cubic meter per resolution, downsampling is performed up layer by layer, and layers 2, 3 and 4 … are grid spaces of 2, 4 and 8. 8 … 128 cubic meters per resolution respectively. In this process, the space is divided into regular topological homoembryoids, which are usually regular tetrahedrons for ease of calculation or modeling.
In the embodiment, the data is sampled at the same resolution level, and then a group of three-dimensional grid space data matrixes with different resolutions are generated through up-sampling and down-sampling, so that the support for high-precision operation requirements is reserved, and the speed and timeliness of the system in processing large-scale data are effectively improved. The modeling mode is mainly used for calculating the data per se, and in the actual use process, the data of each resolution often also needs to keep a maximum value, a minimum value, a variance, a standard deviation, a median and the like so as to meet the operation requirements of different data. In practical application, based on the computable airspace pyramid model, reliability calculation is performed on the spatial data acquired through the direction-finding detection sensors, and then fusion calculation is performed on reliability calculation results of a plurality of direction-finding detection sensors, so that the position and the height of a detection target can be predicted.
The following describes a specific example of a setting.
A space region of 64 cubic meters is selected as a planning airspace, a data grid is set to 32 cubic meters, a hierarchy is set to 3 levels, a downward hierarchy is set to 64 cubic meters, and an upward hierarchy is set to 16 cubic meters. A grid airspace of 16 cubic meters with a level of 4 x 4, a grid airspace of 32 cubic meters with a level of 2 x 2, and a grid airspace of 64 cubic meters with a level of 1 x 1 are formed.
And measuring the temperature in the planning space domain as basic data, selecting a space domain center point as an origin, and constructing an (X, Y, Z) three-dimensional coordinate system. The measurement is carried out at intervals of 32 meters on the X, Y and Z axes, 8 groups of airspace data values are finally obtained, and 32 cubic meters of grid airspace data values of 2X 2 are obtained through grid data distribution as shown in the following tables 1 and 2:
table 1: data table with Z axis of 0
Table 2: data with Z axis of 1
And processing the basic data through downsampling, setting the overall deformation variable to be 1, and obtaining a space domain data value of 64 cubic meters of 1 x1 by a formula to be 15.68.
Processing the basic data by upsampling, setting、/>、/>All take values of 0, and the grid airspace data values of 16 cubic meters of 4 x 4 are obtained by a formula as shown in the following tables 3-6:
Table 3: data table with Z axis of 0
Table 4: data table with Z axis being 1
Table 5: data table with Z axis being 2
Table 6: data table with Z axis being 3
As can be seen from tables 3-6, 64 sets of spatial data values are ultimately obtained.
The method comprises the steps of calculating a grid space domain, expressing complex low-altitude characteristics (position, topography, obstacle, weather and other information) which are difficult to calculate in a digital form, digitally calculating and processing the calculated area, and storing, publishing and inquiring in the digital form to form the digitization of the calculated space domain.
Simple and visual: the space rasterization calculation adopts regular grid cells, and is easy to understand and realize. In some cases, the rasterized computation may provide simple and intuitive analysis results.
Parallel computing: the rasterized computation can conveniently perform parallel computation, and the utilization of computing resources is more efficient. This is of great importance for the processing and analysis of large-scale geographical data.
Spatial relationship expression: by spatial rasterization, spatial relationships between geospatial data, such as proximity relationships, coverage relationships, and the like, can be conveniently represented, facilitating spatial analysis and computation.
The applicability is wide: the space rasterization calculation method is suitable for various geographic data, including remote sensing images, topographic data, demographic data and the like, and has strong universality. The technical scope of the present invention is not limited to the above description, and those skilled in the art may make various changes and modifications to the above-described embodiments without departing from the technical spirit of the present invention, and these changes and modifications should be included in the scope of the present invention.
Claims (9)
1. A computational airspace modeling method based on a grid technology is characterized by comprising the following steps:
S1, selecting a target position in a low-altitude airspace, selecting a space region as a planning airspace according to a preset size at the target position, establishing a three-dimensional coordinate system in the planning airspace by taking a selected airspace central point as an origin, and setting grids and corresponding level numbers;
s2, determining an accuracy interval according to the required accuracy requirement, and dividing the accuracy interval according to the layer number of the planning airspace to obtain a parameter comparison table for representing accuracy and a scale value represented by the accuracy interval;
S3, acquiring airspace data values distributed in the planning airspace to obtain three-dimensional rasterization data, obtaining precision corresponding to the three-dimensional rasterization data, and looking up a table in the parameter comparison table according to the precision of the data to obtain a level L;
s4, calculating the level difference delta L=L-L 1 between the level L and the lowest level L 1, judging whether the level difference delta L is 0, and making the following decision according to the judging result:
(1) When Δl=0; determining the level L as the lowest level, and performing downsampling on the grids of the current level layer by layer according to the level number of the planning airspace;
(2) When Δl is not equal to 0; determining that level L is not the lowest level, calculating the level difference between the highest level L max and level L
ΔL’=Lmax-L；
S5, judging whether the fault level difference delta L' is 0, and making the following decision according to a judging result:
(1) When Δl' =0; determining the level L as the highest level, and performing up-sampling on the grids of the current level layer by layer downwards according to the level number of the planning airspace;
(2) When Δl' noteq0; the judgment level L is an intermediate level, up-sampling is performed layer by layer according to the level difference delta L, and down-sampling is performed layer by layer according to the level difference delta L';
S6, forming a group of three-dimensional grid space data matrixes by grids obtained by up-sampling and down-sampling and corresponding airspace data values, and obtaining the computable airspace.
2. The method for modeling a computable airspace based on a grid technique according to claim 1, wherein the specific step of performing downsampling layer by layer according to the number of layers of the planned airspace is as follows:
Acquiring grid coordinates (x, y, z) in the current level L i;
Computing a Gaussian filter kernel ; Wherein/>Expresses the whole deformation variable of the downsampled Gaussian filter kernel,/>Expressing the standard deviation of a Gaussian function, wherein pi represents the circumference ratio;
Up-sampling the current level L i by Gaussian pyramid filtering to obtain an upper layer L i+1 three-dimensional grid space data matrix: ; wherein/> A three-dimensional raster spatial data matrix representing the i-th layer of the pyramid, ∗ representing a convolution operation; /(I)Represents a Gaussian filter kernel, which is/>And (5) calculating a three-dimensional matrix.
3. The method for modeling a computable airspace based on a grid technique according to claim 2, wherein the specific step of performing up-sampling layer by layer down according to the number of layers of the planned airspace is as follows:
The space coordinate to be calculated is obtained, the coordinate is taken as the center, 2 times of the unit length of the coordinate system is taken as the side length, the cube is set, and the sitting marks of the vertexes of the cube are used as Further, the original data value/>, of each vertex is determined；
Up-sampling is carried out through three-dimensional bilinear interpolation to obtain a matrix value of the space data of the next layer of three-dimensional grid:
; wherein, 、/>、/>Expressing the deviation of the corresponding linearity in each dimension,/>Expressing up-sampling interpolation integral deformation variable,/>Representation/>Normalized value of/>Representation/>Normalized value of/>Representation/>Is included in the above formula (c).
4. A grid-technique-based computable spatial modeling method according to claim 3, wherein the normalized values、/>、/>The calculation mode of (a) is the same, and the normalized value/>The calculation formula of (2) is as follows:
; wherein/> Representing the normalized value of the corresponding coordinate axis.
5. The method of grid-based computational airspace modeling according to claim 1, wherein the grid of planned airspace settings is a regular topological homoidiosome.
6. The grid-technology-based computable spatial modeling method of claim 5, wherein the topological hypocotyl samples a regular tetrahedron.
7. The grid technology based computable airspace modeling method according to claim 1, wherein the airspace data value is determined by an application scene, including geographic information data, environment data, sensor data.
8. The grid-technology-based computable spatial modeling method of claim 6, wherein the spatial data values are assigned into different grids according to different accuracies.
9. The grid technology based computable spatial modeling method of claim 8, wherein prior to spatial data value allocation, adaptive spatial data values under different precision grids are obtained through corresponding data operations, wherein the data operations comprise one or more of maximum value, minimum value, variance, standard deviation and median.
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