3D Spatial Scene Management Method of Digital Earth Compatible with Beidou Grid

Abstract

The present invention proposes a method for managing three-dimensional spatial scenes of a digital earth compatible with Beidou grid codes. First, based on Beidou grid codes, a Beidou grid space bounding box is defined, and its coding method is given; then, an intuitive and convenient intersection test method for the Beidou grid space bounding box is given; finally, for each object, the smallest Beidou grid space bounding box that can contain the object is found, and a hierarchical bounding box of the Beidou grid space with an R-tree structure that takes into account the polar regions is established step by step. Based on this R-tree structure, efficient spatial search can be achieved, which is particularly suitable for the spatial positions defined by Beidou grid codes.

Description

Technical Field

[0001] The present invention belongs to the technical field of spatial scene management in computer graphics, and further relates to the technology of efficient management and query of three-dimensional spatial scenes in a large range of digital earth, and specifically relates to a method for managing three-dimensional spatial scenes of digital earth compatible with Beidou grids. Background Art

[0002] Digital earth is a virtual form of the earth. Digital earth systems have extensive applications in industries such as geographic information, virtual reality, and battlefield simulation. In particular, it provides editable, three-dimensional, and multi-level combat scenarios, replacing the previous flat combat concept maps.

[0003] Scene management algorithms are an important part of digital earth systems, involving the rendering efficiency and system operation efficiency of digital earth. Compared with ordinary scenes, digital earth scenes have particularities. On the digital earth, the spherical coordinate system is more suitable for expressing the positions of objects. The movement range of the camera on the earth is relatively large, and the quantity changes involved are large. Therefore, introducing integer coding avoids the error problem of floating-point operations to a certain extent.

[0004] Spatial indexing is an important part of scene management. The structure of the spatial index indirectly determines the system operation efficiency. Whether it is the acceleration structure for collision detection or the acceleration structure for visibility culling depends on the structure of the spatial index. Therefore, it is crucial to construct a spatial index structure suitable for digital earth.

[0005] There are a large number of specific objects on the actual earth. Everything on the actual earth is involved. What digital earth wants to achieve is to clone the actual world and create a virtual form of the earth. The digital information such as the position state of each object in the actual world is transmitted to the virtual space. The virtual space can restore the real world based on this information. Now, with the rapid development of the Internet of Things, sensors can be seen everywhere. Digital earth needs to visualize the data collected by these sensors. Moreover, the specific position information of each sensor needs to be clearly identified so that the presented data can have more specific meanings.

[0006] There are many spatial levels on the actual earth, from the deep sea to space. The radius of the earth is about 6,731 km. The farthest distance of artificial satellites from the earth is tens of thousands of kilometers, and they are active several times the radius of the earth away. Objects such as satellites, spacecraft, airplanes, cars, ships, and submarines generally do not move at the same altitude. Digital earth involves a large vertical height span and a complex spatial hierarchical structure.

[0007] The data distribution of the actual Earth is uneven. Almost most of the objects are distributed near the Earth's surface. There are few objects in the upper atmosphere and deep sea at a slightly greater distance. In the Digital Earth, this means that there are a large number of models to be drawn and managed near the Earth's surface, while the models to be drawn and managed in the upper atmosphere and deep sea at a slightly greater distance are relatively sparse.

[0008] The Beidou Grid Location Code is a national standard designed for the independently developed Beidou Navigation and Positioning System in China, which can achieve seamless nested subdivision of the space on the Earth. The Beidou Grid is a fixed ten-layer structure, and the minimum subdivision accuracy is 1.5 cm. However, there are few reports on the related work of the Beidou Grid Location Code in the three-dimensional scene management of the Digital Earth. Therefore, a compatible Beidou Grid and efficient three-dimensional space scene management method for the Digital Earth is needed. Summary of the Invention

[0009] To solve the above problems, the present invention provides a three-dimensional space scene management method for the Digital Earth compatible with the Beidou Grid, specifically including the following steps:

[0010] Based on the Beidou Grid Code, define the Beidou Grid space bounding box and give its coding method;

[0011] Give the intersection test method for the Beidou Grid space bounding box;

[0012] Find the smallest Beidou Grid space bounding box that can contain each object.

[0013] Further, the coding method of the Beidou Grid space bounding box includes the following steps:

[0014] Intercept the front several hierarchical structures of the Beidou two-dimensional code and the Beidou height code to form a hierarchical detail structure for the two-dimensional longitude and latitude range and height range;

[0015] Combine the intercepted Beidou two-dimensional code and Beidou height code to form a three-dimensional space bounding box code.

[0016] Further, the intersection test method for the Beidou Grid space bounding box includes the following steps:

[0017] Obtain the Beidou Grid space bounding box codes of the two bounding boxes to be tested for intersection;

[0018] Respectively intercept the minimum length of the heads of the two bounding box codes in longitude, latitude and height, and recombine them;

[0019] Judge whether the two recombined and intercepted bounding box codes are the same;

[0020] If they are the same, judge as intersecting, otherwise judge as non-intersecting.

[0021] Further, finding the minimum Beidou grid space bounding box that can enclose an object includes the following steps:

[0022] For each object, obtain the node position information;

[0023] Convert the node position information into Beidou grid codes;

[0024] Obtain the object bounding box data;

[0025] Gradually query the Beidou grid bounding box at this level in terms of both latitude / longitude and altitude, and perform an intersection test with the object bounding box until the Beidou grid bounding box at this level does not intersect with the object bounding box;

[0026] Output the Beidou grid bounding box code, and the three-dimensional space range represented by this Beidou grid bounding box code is the minimum Beidou grid space bounding box that can enclose the object.

[0027] Further, it also includes the following steps:

[0028] Establish an R-tree structured Beidou grid space hierarchical bounding box that takes into account the polar regions;

[0029] Implement spatial search based on this R-tree structure.

[0030] Further, establishing an R-tree structured Beidou grid space hierarchical bounding box that takes into account the polar regions includes the following steps:

[0031] Divide the digital Earth space range into three subtrees by region at the first layer, namely the Arctic region, the Antarctic region, and the normal latitude region;

[0032] Take the objects within the same region and their Beidou grid space bounding boxes as the leaf nodes of the corresponding subtrees;

[0033] For the three regions, layer by layer establish a hierarchical bounding box tree in the same bottom-up manner as establishing an R-tree; for several adjacent nodes that need to be aggregated, aggregate their Beidou grid bounding boxes upward to form the minimum circumscribed Beidou grid bounding box of a higher-level node;

[0034] Take the three R-trees formed after the intersection test as the three subtrees under a root node, and the root node represents the entire digital Earth surface space range.

[0035] Further, implementing spatial search based on this R-tree structure includes the following steps:

[0036] According to the latitude / longitude information, determine which one of the three R-trees the object for spatial search is located in;

[0037] Use the breadth-first search algorithm to search the determined R-tree.

[0038] The beneficial effects of the present invention are as follows: The digital earth three-dimensional space scene management method compatible with the Beidou grid provided by the present invention is highly efficient in compatible with the use of the Beidou grid code on the digital earth. In terms of program writing, it can be implemented only through bit operations; a R-tree structure Beidou grid space hierarchical bounding box that takes into account the polar regions is established step by step, and based on this R-tree structure, efficient spatial search can be realized, which is particularly suitable for the spatial positions defined by the Beidou grid code. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Flow chart of the present invention for finding the smallest Beidou grid space bounding box that can contain an object

[0040] Figure 2 . Flow chart of the intersection test of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The present invention will be further described below in conjunction with the drawings and embodiments. The following embodiments are only used to explain the content of the present invention and are not used to limit the protection scope of the present invention.

[0042] The present invention proposes a digital earth three-dimensional space scene management method compatible with the Beidou grid. Based on the Beidou grid code, a Beidou grid space bounding box is defined, and its coding method is given; then, according to the intersection test method of the given Beidou grid space bounding box, the smallest Beidou grid space bounding box that can contain an object is found for each object.

[0043] The definition method and coding method principle of the Beidou grid bounding box of the present application are as follows:

[0044] The definition method and coding method of the bounding box based on the Beidou grid are mainly based on the Beidou grid code. The difference is that the Beidou grid code has a fixed length and each code element has its fixed meaning. However, the length of the bounding box based on the Beidou grid may vary, and the two-dimensional Beidou grid code and the height coding are encoded separately. Therefore, the coding method of the bounding box based on the Beidou grid should be slightly different from the Beidou grid code. The present application adds a "+" between the Beidou two-dimensional coding and the height coding to distinguish the two different structures in the coding. When storing in the program, a structure is used for storage, and the two-dimensional Beidou grid coding and the height coding are distinguished in the structure.

[0045] For example, N57A73+0032 (the two-dimensional Beidou grid coding is N57A73, and the height coding is 0032). Among them, N represents the Northern Hemisphere, 57 represents the longitude of the first-level grid, A represents the latitude of the first-level grid, 7 represents the longitude of the second-level grid, and 3 represents the latitude of the second-level grid; the part after the + sign represents the grid division in the height direction, 0 represents on the ground, 03 represents the height of the first-level grid, and 2 represents the height of the second-level grid.

[0046] Then, the intersection test method is used to find the most suitable Beidou grid bounding box:

[0047] The object is positioned in three-dimensional space, and the most suitable Beidou grid bounding box is found, that is, the smallest Beidou grid bounding box that can contain the object. The method adopted in this application is to first convert the geographical location of the object into a Beidou grid code, and then query upward level by level. The axis-based bounding box and bounding sphere information are included in the obj model file - if the bounding box code representing the geographical location of the object intersects with the bounding box code obtained from the obj file, it means that this Beidou grid space bounding box cannot completely contain the object; if the bounding box code representing the geographical location of the object does not intersect with the bounding box code obtained from the obj file, it means that this Beidou grid space bounding box can completely contain the object.

[0048] In summary, referring to Figure 1 , the specific steps for this application to find the smallest Beidou grid space bounding box that can contain the object are as follows:

[0049] Step 1, input the position information of the object;

[0050] Step 2, convert it into a Beidou grid code;

[0051] Step 3, obtain the bounding box data from the obj file;

[0052] Step 4, in terms of longitude and latitude, gradually query upward whether the Beidou grid bounding box at this level can contain the object bounding box until the Beidou grid bounding box at this level can completely contain the object bounding box;

[0053] Step 5, in terms of height, gradually query upward whether the Beidou grid bounding box at this level can contain the object bounding box until the Beidou grid bounding box at this level can completely contain the object bounding box;

[0054] Step 6, output the Beidou grid bounding box code at the current level. The three-dimensional space range represented by this Beidou grid bounding box code is the Beidou grid bounding box of the object.

[0055] The intersection test of the bounding box based on the Beidou grid converts the collision of the object into the intersection between the bounding box based on the Beidou grid and the bounding box based on the Beidou grid. Since the Beidou grid is nested and seamlessly connected, as long as the names of two bounding boxes based on the Beidou grid are available, the intersection situation between the two bounding boxes can be quickly obtained.

[0056] As Figure 2 shown, the specific steps of the intersection test in this application are as follows:

[0057] Step 1, input the codes of two bounding boxes based on the Beidou grid;

[0058] Step 2: Intercept the minimum lengths of the two bounding box encodings in terms of longitude, latitude, and altitude respectively, and recombine them (for example, N000990+002111 and S0000+0011, after interception, it becomes N0009+0211 and S0000+0011);

[0059] Step 3: Determine whether the intercepted encodings are the same;

[0060] Step 4: Output the result.

[0061] Finally, based on the above digital earth three-dimensional spatial scene management method compatible with the Beidou grid, establish an R-tree scene management structure compatible with the Beidou grid.

[0062] Rectangular bounding box compatible with the Beidou grid

[0063] The Beidou grid is a grid divided according to longitude and latitude. The rectangular bounding box of the R-tree is to create a minimum bounding rectangle for each object, and then form larger minimum bounding rectangles upward for several adjacent rectangular bounding boxes. And so on, finally forming a tree-like data structure composed of minimum bounding rectangles with a hierarchical structure.

[0064] In order to be compatible with the Beidou grid, expand the matrix bounding box of the R-tree into a two-dimensional Beidou grid to establish a hierarchical structure. Each Beidou grid has its longitude and latitude range, and this longitude and latitude range is almost equivalent to the definition of the two-axis range of the rectangle. Therefore, the concept of the Beidou grid is well integrated into the R-tree. As a natural extension of the B-tree in two-dimensional space, the R-tree is a balanced tree and has outstanding performance in performing nearest neighbor searches.

[0065] Special handling for polar regions

[0066] Since the two-dimensional Beidou grid has special handling in the polar regions, to be compatible with the Beidou grid, the scene management structure in this paper also uses independent R-trees for management in the polar regions. Since the division method of the Beidou grid in the polar regions is different from that in non-polar regions. However, the differences are mainly concentrated in the division methods of the first few levels. Therefore, in the polar regions, the forms of sector and spherical bounding boxes are used to be compatible with the division method of the Beidou grid in the polar regions.

[0067] In the dissection of the two-dimensional Beidou grid code in the polar regions, at the first level, a circular area is equally divided into four regions P0, P1, P2, and P3. P0 is the central circular region at the first level, with a radius of half of the entire circular radius and an area of 1 / 4 of the circle. P1, P2, and P3 are the three equal parts of the outer sector area, each accounting for 120°. At the second level, further division is carried out on the basis of the first level. For the central circular region, the same division method as the upper-level circular region is adopted to equally divide the area. The central region is half of the circumscribed circular radius, and the outer ring is divided into three equal parts to form the remaining three regions. For the division of the outer ring sector region, two divisions are carried out in the longitude and latitude directions to form four sub-regions. There are a total of 16 sub-regions. In the national standard, by analogy, it is dissected down to the fifth level, and the same dissection method as that in non-polar regions is adopted.

[0068] In the design of the R-tree bounding box, considering the relatively few application scenarios in the polar regions and the programming efficiency issues, only at the first layer, the same division method as the second-level grid in the two-dimensional Beidou grid division in the polar regions is adopted for division. From the third-level non-central region, the same division method as that in non-polar regions is adopted.

[0069] For the central region, considering the issues of regional equal division and convenient coding, the same division method as that in the fifth-level non-polar region is adopted for the division of the central region.

[0070] Construction of the R-tree

[0071] The R-tree in this article is divided into three sub-trees at the first layer, namely the Arctic region, the Antarctic region, and the normal latitude region. After the first-layer sub-trees are distinguished, each layer is constructed from bottom to top in the same way as the R-tree construction method. The difference is that the bounding box adopts a bounding box mode that fits the Beidou grid boundary. Although this method has a certain loss in numerical accuracy, for the sake of compatibility with the Beidou grid code, such errors are within an acceptable range.

[0072] The specific process of bottom-up construction can be divided into the following stages:

[0073] 1. Create a Beidou grid bounding box for each object;

[0074] 2. Aggregate several objects upward to form the smallest circumscribed Beidou grid bounding box;

[0075] 3. The objects in the three regions are respectively built into three R-trees in this way;

[0076] 4. Take the three R-trees as the three sub-trees under a root node.

[0077] Search of the R-tree

[0078] The R-tree has excellent performance in nearest neighbor search. However, the bounding box compatible with the Beidou grid conveys the concept of distance in longitude and latitude. To convert the distance concept in longitude and latitude to the distance concept in real life, this paper approximately regards the Earth as a sphere, and the problem of the distance between two points on the Earth is converted into the problem of solving the length of the minor arc top between two points on the Earth's surface. Given that the radius of the Earth is R and ignoring the influence of the terrain on the Earth, assume that the longitude of point A is α A , and the latitude is β A ; the longitude of point B is α B , and the latitude is β B ; at the same time, it is agreed that the east longitude is positive, the west longitude is negative, the south latitude is 90° + geographical latitude value, and the north latitude is 90° - geographical latitude value.

[0079] D = R cos -1 (c) (Equation 1)

[0080] where c = sin(β A )sin(β B )cos(α A - α B ) + cos(α A )cos(α B ).

[0081] The general search method for nodes is the same as that for traditional tree structures. Commonly used ones can be divided into breadth-first search and depth-first search. Among them, breadth-first search is to continue searching for nodes in the next layer after searching for nodes at the same depth, and depth-first search is to always visit the child nodes of the current node until the node is a leaf node and then return, and visit the next child node in the same way.

[0082] When applying the R-tree nearest neighbor search, breadth-first search often has better performance. Because each node contains a range information, that is, the minimum bounding Beidou grid information, and through the approximate conversion of the longitude and latitude range and distance in Equation (1), the approximate geographical range can be obtained. At the same time, the minimum bounding Beidou grid of the upper layer is larger than that of the lower layer. When the range of the Beidou grid of the upper layer meets the range of the nearest neighbor search, there is no need to continue searching downward, saving a lot of time.

[0083] In summary, this is only a preferred embodiment of the present invention, and does not limit the protection scope of the present invention. All equivalent changes and modifications made according to the scope of the present invention patent and the content of the specification are within the scope covered by the present invention patent.

Claims

1. A method for managing a three-dimensional spatial scene of a digital earth compatible with the Beidou grid code, characterized in that, It includes the following steps: Based on the Beidou grid code, define the Beidou grid space bounding box and give its encoding method; Give the intersection test method for the Beidou grid space bounding box; For each object, find the smallest Beidou grid space bounding box that can contain the object; The encoding method of the Beidou grid space bounding box includes the following steps: Intercept the front several hierarchical structures of the Beidou two-dimensional code and the Beidou height code to form a hierarchical detail structure for the two-dimensional longitude and latitude range and the height range; Combine the intercepted Beidou two-dimensional code and Beidou height code to form a three-dimensional space bounding box code; The intersection test method for the Beidou grid space bounding box includes the following steps: Obtain the Beidou grid space bounding box codes of the two bounding boxes to be tested for intersection; Intercept the minimum length of the heads of the two bounding box codes respectively in terms of longitude, latitude and height, and recombine them; Judge whether the two recombined bounding box codes after interception are the same; If they are the same, judge as intersecting, otherwise judge as non-intersecting; Finding the smallest Beidou grid space bounding box that can contain an object includes the following steps: For each object, obtain the node position information; Convert the node position information into a Beidou grid code; Obtain the object bounding box data; Gradually query the Beidou grid space bounding box upward in terms of longitude, latitude and height, and perform an intersection test with the object bounding box until the Beidou grid space bounding box and the object bounding box do not intersect; Output the Beidou grid space bounding box code, and the three-dimensional space range represented by the Beidou grid space bounding box code is the smallest Beidou grid space bounding box that can contain the object.

2. The method for managing a three-dimensional spatial scene of a digital earth compatible with the Beidou grid code according to claim 1, characterized in that, It also includes the following steps: Establish an R-tree structured Beidou grid space hierarchical bounding box that takes into account the polar regions; Implement spatial search based on this R-tree structure.

3. The method for managing a three-dimensional spatial scene of a digital earth compatible with the Beidou grid code according to claim 2, characterized in that, Establishing an R-tree structured Beidou grid space hierarchical bounding box that takes into account the polar regions includes the following steps: Divide the digital earth space range into three subtrees by region at the first layer, namely the Arctic region, the Antarctic region and the normal latitude region; Take the objects within the same region and their Beidou grid space bounding boxes as the leaf nodes of the corresponding subtrees; For the three regions, layer by layer establish a hierarchical bounding box tree in the same way as building an R-tree from bottom to top; for several adjacent nodes that need to be aggregated, aggregate their Beidou grid space bounding boxes upward to form the smallest circumscribed Beidou grid space bounding box of a higher-level node; Take the three R-trees formed after the intersection test as the three subtrees under a root node, and the root node represents the entire digital earth surface space range.

4. The method for managing a three-dimensional spatial scene of a digital earth compatible with the Beidou grid code according to claim 3, characterized in that, Implementing spatial search based on this R-tree structure includes the following steps: According to the longitude and latitude information, determine which one of the three R-trees the object for spatial search is located in; Use the breadth-first search algorithm to search the determined R-tree.

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