Three-dimensional space geometric element calculation method and device and storage medium

Abstract

The invention discloses a three-dimensional space geometric element calculation method and device and a storage medium, and relates to the technical field of three-dimensional model data processing, and the method comprises the steps: obtaining a three-dimensional model file and three-dimensional space planning information of a to-be-recognized region; splitting geometric object instances in the three-dimensional model file, and constructing a same geometric object set according to the obtained geometric object instances; storing the compressed same geometric object set in a persistent memory, and constructing a bounding box of the compressed same geometric object set; constructing a geometric object spatial index of the same geometric object set according to the bounding box; and controlling the calculation node to perform geometric element calculation based on the geometric object spatial index to obtain spatial position information between the to-be-identified region and the three-dimensional space planning information. The geometric objects with similar features are aggregated, so that the number of the geometric objects for storage compression and spatial index establishment is reduced, and the calculation efficiency of the geometric element task is improved.

Description

Technical Field

[0001] The present application relates to the technical field of three-dimensional model data processing, and in particular to a calculation method, a calculation device and a storage medium for three-dimensional space geometric elements. Background Art

[0002] In the calculation method of related three-dimensional model geometric data, the redundant vertex data, facet data and internal geometric details in the three-dimensional model geometric data are usually simplified by simplifying the number of triangles, retaining the main geometric features and boundary information. This simplified geometric data is used for spatial calculations to improve calculation efficiency.

[0003] However, the current geometry compression and simplification strategy has a significant flaw: it only simplifies a single object without merging similar features between objects. This practice leads to repeated calculations of similar feature data, which in turn reduces the overall computational efficiency when performing 3D model geometry data calculation tasks.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a calculation method, a calculation device and a storage medium for three-dimensional space geometric elements, aiming to solve the technical problem that the current geometric model processing strategy is difficult to avoid repeated calculation of similar features, resulting in low calculation efficiency when calculating three-dimensional model geometric data.

[0006] To achieve the above purpose, the present application proposes a method for calculating three-dimensional space geometric elements, the method comprising: Obtain the 3D model file and 3D space planning information of the area to be identified; Splitting the geometric object instances in the three-dimensional model file, and constructing a set of identical geometric objects according to the split geometric object instances; Saving the compressed set of identical geometric objects into a persistent memory, and constructing a bounding box of the compressed set of identical geometric objects; Constructing a geometric object spatial index of the same set of geometric objects according to the bounding box; The control calculation node performs geometric element calculation based on the geometric object spatial index to obtain spatial position information between the area to be identified and the three-dimensional space planning information.

[0007] In one embodiment, the step of splitting the geometric object instances in the three-dimensional model file and constructing the same geometric object set according to the split geometric object instances includes: Acquire the geometric object instance of the geometric data in the three-dimensional model file; Based on the spatial position and / or subordinate relationship of the geometric data, the geometric object instance is split to obtain a primary geometric object instance and a multi-level component object instance, wherein the multi-level component geometric object instance is a component of the primary geometric object instance; The first-level geometric object set of the first-level collection object instance is determined based on the first transformation matrix after the first-level geometric object instance is aggregated, and the multi-level component geometric object set of the multi-level component geometric object instance is determined based on the second transformation matrix after the multi-level component geometric object instance is aggregated.

[0008] In one embodiment, the steps of determining the first-level geometric object set of the first-level set object instance according to the first transformation matrix after the aggregation processing of the first-level geometric object instance, and determining the multi-level component geometric object set of the multi-level component geometric object instance according to the second transformation matrix after the aggregation processing of the multi-level component geometric object instance include: Performing a same geometric object aggregation process on the first-level geometric object instances, and establishing first transformation matrices of the aggregated geometric objects and the first-level geometric object instances, wherein the number of the first transformation matrices is equal to the number of the first-level geometric object instances; Generate the first-level geometric object set according to the first transformation matrix and the first-level geometric object instance; Performing same geometric object aggregation processing on the multi-level component geometric object instances, and establishing second transformation matrices of the aggregated geometric objects and the multi-level component geometric object instances, wherein the number of the second transformation matrices is equal to the number of the multi-level component geometric object instances; Generate the multi-level component geometric object set according to the second transformation matrix and the multi-level component geometric object instance; The transformation matrix is ​​multiplied by a subset of the same set of geometric objects to obtain the separated geometric object instances.

[0009] In one embodiment, before the step of saving the compressed set of identical geometric objects into a persistent memory and constructing a bounding box of the compressed set of identical geometric objects, the step further includes: Determining a compression processing algorithm for the set of identical geometric objects, and compressing the set of identical geometric objects based on the compression processing algorithm; Determine a set of transformation matrices associated with the same set of geometric objects; The step of saving the compressed set of identical geometric objects into a persistent memory and constructing a bounding box of the compressed set of identical geometric objects comprises: Storing the compressed set of identical geometric objects and the set of transformation matrices in the persistent memory; A bounding box processing algorithm is determined, and the bounding box is constructed based on the bounding box processing algorithm.

[0010] In one embodiment, the step of constructing a geometric object spatial index of the same set of geometric objects according to the bounding box comprises: The point, line and surface feature information of the bounding box is set as the spatial index of the geometric object.

[0011] In one embodiment, the control computing node performs geometric element calculation based on the geometric object spatial index to obtain the spatial position information between the to-be-identified area and the three-dimensional space planning information, including: Determine the current task processing volume of the computing node, and determine the task weight value of each computing task in the to-be-identified area according to the computing intensity weight model; Controlling the computing intensity weight model, based on the current task processing amount and the task weight value, determining the computing node corresponding to the computing task; Controlling the computing node to perform the geometric element calculation corresponding to the geometric element calculation task based on the geometric object spatial index, and obtaining the collision calculation result fed back by the computing node; According to the collision calculation result, spatial position information of position collision detection between the area to be identified and the three-dimensional space planning information is generated.

[0012] In one embodiment, the step of determining the current task processing amount of the computing node and determining the task weight value of each computing task in the to-be-identified area according to the computing intensity weight model includes: Determine the current task processing volume of each of the computing nodes, obtain the time complexity of the collision algorithm associated with the three-dimensional space planning information, and determine the complexity and number of geometric object instances corresponding to the same set of geometric objects in the persistent memory; Obtaining a first weight associated with the number of instances and a second weight associated with the time complexity; Generate the computational intensity weight model according to the complexity, the number of instances, the first weight, the second weight, and the time complexity; According to the computing intensity weight model, a task weight value associated with the computing task is determined.

[0013] In one embodiment, after the step of obtaining the three-dimensional model file and the three-dimensional space planning information of the area to be identified, the method further includes: Acquire the separated data after preprocessing the three-dimensional model file, wherein the separated data at least includes geometric data, material data, texture data, hierarchical structure data and business attribute data; Determine the geometric object instance of the geometric data, and based on the geometric object instance, perform the steps of splitting the geometric object instance in the three-dimensional model file, and constructing the same geometric object set according to the split geometric object instance.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a computing device for three-dimensional space geometric elements, wherein the computing device for three-dimensional space geometric elements includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for calculating three-dimensional space geometric elements as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for calculating the three-dimensional space geometric elements as described above are implemented.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: When processing the three-dimensional model file of the area to be identified, the geometric object instances in the three-dimensional model file are split, and a geometric object set is constructed based on similar geometric objects of the split geometric object instances, so as to statistically associate the instances with the same geometric objects and reduce the number of geometric objects that are repeatedly calculated. Then, the geometric object set is compressed and stored in the persistent memory, so that it is convenient to directly access these data through the memory during spatial calculation, thereby improving data access efficiency. Subsequently, the bounding box of the compressed geometric object is constructed to reduce the spatial calculation vertex and patch information, and the spatial index of the same geometric object set is constructed based on the geometric object bounding box, further improving the retrieval query efficiency of the geometric object during spatial calculation. Finally, the computing node is controlled to perform geometric element calculation based on the geometric object spatial index, thereby obtaining the spatial position information between the area to be identified and the three-dimensional space planning information. Based on this, by aggregating geometric objects with similar features, the number of geometric objects that are stored, compressed and spatially indexed is reduced, and the computational efficiency of the geometric element calculation task of the area to be identified and the three-dimensional space planning information is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the method for calculating three-dimensional space geometric elements of the present application; Figure 2 A schematic diagram of a flow chart provided for a second embodiment of a method for calculating three-dimensional space geometric elements of the present application; Figure 3 A task processing diagram of a calculation intensity weight model provided in the second embodiment of the present application; Figure 4 This is an optional flowchart of the method for calculating three-dimensional space geometric elements of this application; Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the method for calculating three-dimensional spatial geometric elements in the embodiment of the present application.

[0020] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] The main solution of the embodiment of the present application is: obtaining a three-dimensional model file and three-dimensional space planning information of the area to be identified; Splitting the geometric object instances in the three-dimensional model file, and constructing a set of identical geometric objects according to the split geometric object instances; Saving the compressed set of identical geometric objects into a persistent memory, and constructing a bounding box of the compressed set of identical geometric objects; Constructing a geometric object spatial index of the same set of geometric objects according to the bounding box; The control calculation node performs geometric element calculation based on the geometric object spatial index to obtain spatial position information between the area to be identified and the three-dimensional space planning information.

[0023] In the calculation method of the relevant 3D model geometric data, the redundant vertex data, patch data and internal geometric details in the 3D model geometric data are usually simplified by simplifying the number of triangles, retaining the main geometric features and boundary information. This simplified geometric data is used for spatial calculations to improve calculation efficiency. However, this geometric compression simplification strategy only simplifies a single object and does not merge similar features between objects, resulting in repeated calculations of similar feature data, reducing the overall task execution efficiency.

[0024] The present application provides a solution. When processing the three-dimensional model file of the area to be identified, by splitting the geometric object instances in the geometric data and aggregating the same geometric objects, the geometric object instances with similar geometric objects can be statistically associated to reduce the number of geometric objects that are repeatedly calculated. Then, the same geometric object set is compressed and stored in the persistent memory, which is convenient for directly accessing these data through the memory during spatial calculation, thereby improving data access efficiency. Subsequently, the bounding box of the compressed geometric object is constructed to reduce the spatial calculation vertex and patch information, and a spatial index is constructed based on the geometric object bounding box to further improve the retrieval and query efficiency of the geometric object during spatial calculation. Finally, each computing node is controlled to perform geometric element calculation based on the geometric object spatial index, thereby obtaining the spatial position information between the area to be identified and the three-dimensional space planning information. Therefore, by merging similar objects, the number of geometric object spatial indexes that need to be constructed is reduced, and the computational efficiency of the geometric element calculation task is improved.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, calculation of three-dimensional space geometric elements, etc. The following takes the calculation of three-dimensional space geometric elements as an example to illustrate this embodiment and the following embodiments.

[0026] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0027] The present application embodiment provides a method for calculating three-dimensional space geometric elements, referring to Figure 1 , Figure 1 This is a schematic flow chart of the first embodiment of the method for calculating three-dimensional space geometric elements of the present application.

[0028] In this embodiment, the method for calculating the three-dimensional space geometric elements includes steps S10 to S50: Step S10, obtaining a three-dimensional model file and three-dimensional space planning information of the area to be identified.

[0029] It should be noted that the area to be identified refers to a specific area that requires three-dimensional modeling and three-dimensional space planning, and the three-dimensional model information can be determined through the three-dimensional model file of the area. The three-dimensional model file contains data on complete geometric information in a real three-dimensional scene. It is usually obtained through three-dimensional scanning, measurement or calculation, and contains information such as the three-dimensional coordinates, shape, size, texture, etc. of the object, that is, it contains the object's geometric data, material data, texture data, hierarchical data, and business attribute data. Three-dimensional space planning information refers to information on the layout, configuration and utilization of three-dimensional space related to the area to be identified, including flight route laying information, building layout, road traffic planning, terrain analysis and other information.

[0030] When performing three-dimensional spatial planning on the identified area, such as constructing a low-altitude flight route on a street in City A, it is necessary to calculate the spatial relationship between the route and the architectural geometry of the street, and find its boundary points to improve the flight safety and efficiency of the route. Based on this, it is necessary to accurately calculate the spatial position relationship between the route and the building to ensure that the route avoids all obstacles, while optimizing the flight path, reducing unnecessary flight altitudes and turns, thereby reducing flight costs and improving overall operational efficiency. It is understandable that in addition to low-altitude flight routes, there is also the design of drone flight routes, the planning of urban smart transportation systems, environmental protection and planning, and planning information related to urban areas can all be applied to the calculation scenarios of spatial position information.

[0031] Therefore, in this embodiment, during the calculation of the three-dimensional space geometric elements, the information of the area to be identified can be automatically input from the existing regional three-dimensional model file in response to the instruction of the area to be identified, so as to obtain the three-dimensional model file of the area to be identified that needs to be identified and the three-dimensional space planning information of the area. For example, the user inputs area A and uploads the three-dimensional space planning information of area A at the same time. At this time, the computing device reads the three-dimensional space planning information and obtains the three-dimensional model file of area A from the existing database so as to perform calculation processing based on the three-dimensional model file.

[0032] Step S20, splitting the geometric object instances in the three-dimensional model file, and constructing a set of identical geometric objects based on the split geometric object instances.

[0033] The 3D model file contains the geometric data, material data, texture data, hierarchical data, business attribute data, etc. of the buildings in the area to be identified. When calculating the geometric elements of the 3D space, it is only necessary to determine the geometric data of the buildings. Therefore, after step S10, it is necessary to obtain the separated data after preprocessing the 3D model file, and the separated data at least includes geometric data, material data, texture data, hierarchical data and business attribute data, and then perform the processing action of step S20 based on the separated geometric data.

[0034] A geometric object instance is a geometric object instance with a specific location and attributes created based on geometric data. Each building, vehicle, terrain, and other geometric data in the area to be identified can be considered as a geometric object instance. In three-dimensional space, a geometric object can be a basic element such as a point, line, surface, or body, or a combination of these basic elements or a more complex polyhedron structure.

[0035] In this embodiment, after obtaining the geometric data corresponding to the three-dimensional model file, the geometric object instance of the geometric data in the three-dimensional model file can be obtained, and then the geometric object instance in the three-dimensional model file is split according to the data characteristics and hierarchical information of the geometric data. Among them, the data characteristics and hierarchical information at least include the subordinate relationship and spatial position between the data, that is, based on the spatial position and / or subordinate relationship of the geometric data, the geometric object instance is split and processed to obtain a first-level geometric object instance and a multi-level geometric object instance, wherein the multi-level component geometric object instance is a component of the first-level geometric object instance. The first-level geometric object instance usually represents a relatively simple and basic geometric shape, such as the building frame geometry, which mainly describes the overall structure and outline of the building, while the multi-level component geometric object instance contains more complex geometric details and functions, such as building doors and windows, layered and household divisions, etc. The multi-level component geometric object instance can be a component of the first-level geometric object instance.

[0036] As an optional implementation method of splitting geometric object instances in a three-dimensional model file based on spatial position, assuming that there is a building model, this building model can be regarded as a first-level geometric object instance. At the same time, according to the spatial position of the building, it is split into multiple parts, such as roofs, walls, doors and windows, etc. The instances constructed by geometric objects are regarded as multi-level component object instances.

[0037] As an optional implementation method of splitting geometric object instances in a 3D model file based on a subordinate relationship, taking a car as an example, the entire car model can be regarded as a first-level geometric object instance. At this time, according to the subordinate relationship of the car, it is split into multiple parts such as the body, lights, and tires. These parts can be further subdivided into smaller components. For example, the body can be split into instances constructed by geometric objects such as the roof, doors, and windows, which are regarded as multi-level component object instances.

[0038] As an optional implementation method of splitting geometric object instances in a three-dimensional model file based on spatial position and subordinate relationships, taking a bridge model as an example, the entire bridge can be regarded as a first-level geometric object instance. When splitting the bridge model, it is necessary to consider both spatial position and subordinate relationships. Among them, the bridge can be split into multiple parts such as piers, bridge decks, and guardrails. The piers are the key parts supporting the bridge deck, and the spatial position relationship between them is clear. At the same time, the bridge deck is composed of multiple plates, and these plates have both spatial position relationships and subordinate relationships. Therefore, instances constructed by geometric objects such as piers, bridge deck blocks, and guardrails can be regarded as multi-level component object instances.

[0039] Therefore, the geometric object instance can be split based on the spatial position and / or subordinate relationship of the geometric data to obtain a first-level geometric object instance and a multi-level component object instance. It can be understood that the split components can remove redundant data and only retain necessary geometric information and attributes, thereby optimizing data storage. At the same time, the split components can be processed in parallel, thereby improving the overall processing efficiency. It can significantly shorten the processing time and improve the computing efficiency. Splitting the three-dimensional model data into a first-level geometric object instance and a multi-level component geometric object instance can significantly improve data processing efficiency, optimize data storage and management, enhance the reusability and flexibility of the model, and support more complex analysis and applications.

[0040] Furthermore, in this embodiment, after the geometric object instance is split, it is also necessary to construct a set of identical geometric objects based on the split geometric object instances to reduce the number of geometric objects that need to be compressed and stored.

[0041] Specifically, the first-level geometric object instance split out contains 10 buildings, and the geometric objects corresponding to these 10 buildings include cuboids, cylinders, and cones. In the conventional processing method, the 10 geometric objects corresponding to the geometric object instances of the 10 buildings are usually compressed and stored separately. At this time, since there are multiple cuboid buildings with the same shape and size, repeatedly compressing and storing them as independent objects will lead to a waste of storage resources. At the same time, if each similar geometric object is compressed separately, the compression algorithm will not be able to effectively use the similarity between these objects to further reduce the storage space. Based on this, the present application constructs the same geometric object geometry by splitting the geometric object instances. For example, in the geometric object instances of 10 highly similar buildings, the buildings numbered 1-3 are in the shape of a cuboid, the buildings numbered 4-6 are in the shape of a cone, and the buildings numbered 7-10 are in the shape of a cylinder. At this time, the same geometric object geometry containing only three types of geometric objects, namely, a cuboid, a cone, and a cylinder, can be constructed, and these geometric object sets can be compressed for storage. By improving storage efficiency, the loading and processing speed of data can be accelerated, thereby improving overall performance. Among them, the process of determining similar geometric objects of geometric object instances is called the aggregation processing process of geometric object instances, and geometric object instances with different spatial positions but the same geometric shapes are aggregated.

[0042] It is understandable that after the same geometric object set is stored, it is necessary to ensure which geometric object instance it corresponds to when the geometric object is read, such as knowing the building instance corresponding to the conical building that is compared with the current low-altitude flight path. Therefore, an instantiation relationship between the geometric object instance and the geometric object to be stored can be constructed, and the instantiation relationship is the transformation matrix between the geometric object and the geometric object instance, that is, the geometric object * transformation matrix = geometric object instance. Continuing with the above example, the geometric object instance of the building numbered 1 can be transformed with the geometric object of the cuboid through the transformation matrix. Among them, the transformation matrix is ​​multiplied by one of the subsets of the same geometric object set to obtain the split geometric object instance, and the transformation matrix is ​​associated with the only subset in the same geometric object set. Although there are similar geometric objects corresponding to different geometric object instances, there are still differences, so each geometric object instance corresponds to an independent transformation matrix. When the same geometric object set is compressed and stored, in order to maintain the accurate position and posture of each geometric object in space, these transformation matrices are also stored in the data table. It should be noted that transformation matrices are essentially just matrix data formats, and they do not contain the complex shape information of the geometric objects themselves. From the perspective of storage space, the storage space required by transformation matrices is almost negligible. Compared with the geometric data of the geometric objects themselves (such as vertex coordinates, normals, texture coordinates, etc.), the space occupied by transformation matrices is very small.

[0043] Therefore, when constructing the same geometric object set based on the split geometric object instances, the first-level geometric object set of the first-level geometric object instance can be determined based on the first transformation matrix after the first-level geometric object instance is aggregated, and the multi-level component geometric object set of the multi-level component geometric object instance can be determined based on the second transformation matrix after the multi-level component geometric object instance is aggregated.

[0044] As an optional implementation method of determining a first-level geometric object set of a first-level geometric object instance, the first-level geometric object instance may be subjected to a same geometric object aggregation process, and a first transformation matrix of each geometric object after the aggregation process and the first-level geometric object instance is established, the number of first transformation matrices is equal to the number of first-level geometric object instances, and the first transformation matrix and the geometric object are uniquely determined. Subsequently, a first-level geometric object set is generated according to the first transformation matrix and the first-level geometric object instance.

[0045] As an optional implementation of determining a multi-level component geometric object set of a multi-level component geometric object instance, the multi-level component geometric object instance may be subjected to a same geometric object aggregation process, and a second transformation matrix of the aggregated geometric objects and the multi-level component geometric object instance is established, wherein the number of the second transformation matrices is equal to the number of the multi-level component geometric object instance. Subsequently, a multi-level component geometric object set is generated according to the second transformation matrix and the multi-level component geometric object instance.

[0046] Step S30, saving the compressed set of identical geometric objects into a persistent memory, and constructing a bounding box of the compressed set of identical geometric objects.

[0047] It should be noted that persistent memory (PMEM) is a new storage technology that combines the characteristics of traditional memory (DRAM, Dynamic Random Access Memory) and persistent storage (such as hard disk or SSD, Solid State Drive). Persistent memory allows data to be retained after power failure, which is different from DRAM (data in DRAM will be lost after power failure), but similar to hard disk or SSD. At the same time, the access speed of persistent memory is much faster than hard disk or SSD, close to the speed of DRAM. By storing the same set of geometric objects in persistent memory, it is convenient to directly access through memory during spatial calculations, thereby improving data access efficiency.

[0048] In this embodiment, before storing the geometric objects in the persistent memory, in order to reduce the redundant information of the three-dimensional geometric data and realize the compression of the single geometric data, it is also necessary to compress these geometric objects. Therefore, before step S30, it is also necessary to determine the compression processing algorithm of the geometric objects, and compress the geometric objects based on the compression processing algorithm. Among them, the compression processing algorithm can be a method such as Draco compression, binary serialization compression method and OpenCTM compression, which is not limited in this application.

[0049] When the geometric objects are compressed and stored in persistent memory, the geometric objects and transformation matrices need to be stored in persistent memory together to ensure the uniqueness of the geometric object instances corresponding to the geometric objects in the three-dimensional space information. Therefore, after compressing the same set of geometric objects, the transformation matrix geometry associated with the same geometric objects needs to be determined. Finally, the compressed set of the same geometric objects and the transformation matrix are stored in persistent memory.

[0050] Furthermore, geometric objects are usually composed of a large number of vertices and patches, and these detailed information may be very time-consuming when performing spatial calculations. Therefore, when constructing a geometric object spatial index, it is necessary to reduce the spatial calculation vertex and patch information based on a bounding box processing algorithm. For example, the original geometric object space vertices may have hundreds of points, such as a four-sided cone. At this time, only the spatial relationship between the four vertices needs to be calculated for subsequent calculations, so the information of other points needs to be ignored.

[0051] Therefore, after storing the compressed set of identical geometric objects and the set of transformation matrices in persistent memory, it is necessary to determine a bounding box processing algorithm, and then construct a bounding box based on the bounding box processing algorithm, so as to construct a geometric object spatial index of the identical geometric object based on the bounding box.

[0052] Step S40: constructing a geometric object spatial index of the same geometric object set according to the bounding box.

[0053] In this embodiment, point, line, and surface feature information of the bounding box may be set as a geometric object spatial index.

[0054] For example, an oriented bounding box (OBB) can be used to approximate the position and size of a geometric object, thereby reducing the reliance on original vertex and patch information. In practical applications, the boundary information of the OBB (such as the minimum and maximum coordinate values) is usually used as a key or index item for a spatial index, so the bounding box can be set as a spatial index for a geometric object. A spatial index is constructed based on the bounding box of a geometric object to further improve the efficiency of retrieval and query of geometric objects during spatial calculations.

[0055] Optionally, in addition to using the oriented bounding box OBB, the axis-aligned bounding box AABB (Axis-Aligned Bounding Box) can also be used to represent the spatial extent of the object to build a spatial index tree. By organizing geometric objects into a tree structure (such as an R-tree, quadtree, octree, etc.), spatial data can be efficiently managed and accessed, thereby improving data access efficiency.

[0056] Step S50: Control the computing node to perform geometric element calculation based on the geometric object spatial index to obtain spatial position information between the area to be identified and the three-dimensional space planning information.

[0057] It should be noted that the geometric element calculation task is usually issued by a computing device, and the calculation task can be determined based on the number of geometric object instances. For example, if there are 100 geometric object instances, 100 task comparison processes are required.

[0058] The geometric element calculation task is used to compare the information position between the area to be identified and the three-dimensional space planning information, such as calculating whether the low-altitude flight path will collide with the buildings in the area to be identified. The computing node can be a device that performs three-dimensional space geometric element calculations, usually a computer, or a cloud server.

[0059] In this embodiment, after obtaining the spatial index of the geometric object, each computing node can be controlled to perform the task of calculating the geometric elements of the three-dimensional spatial information of the area to be identified, thereby performing geometric element calculations based on the spatial index of the geometric object, and finally calculating the spatial position information between the area to be identified and the three-dimensional spatial planning information, thereby improving the computational efficiency during spatial planning and the effectiveness of spatial planning.

[0060] As an optional implementation, when controlling each computing node to perform geometric element calculation tasks, the geometric calculation amount corresponding to the complexity of different geometric objects will be different, which will cause some cluster nodes to be overloaded, while some computing nodes have a small amount of calculation and complete in advance, and are in an empty waiting state, resulting in low utilization of the overall cluster resources. Therefore, the calculation intensity corresponding to each geometric object (including the time required for calculation) can be calculated first, and the current calculation amount of each computing node can be analyzed at the same time. Finally, the geometric element calculation tasks are dynamically allocated based on the calculation intensity and the current calculation amount, thereby improving the overall calculation efficiency.

[0061] This embodiment provides a method for calculating geometric elements in three-dimensional space. By performing the same geometric object aggregation processing on the geometric object instances in the geometric data, the repeated geometric objects are statistically associated and the number of geometric objects that are repeatedly calculated is reduced. Then, after the same geometric objects are compressed, the geometric objects are stored together with the transformation matrix in the persistent memory to ensure accurate calculations. At the same time, it is convenient to directly access these data through the memory during spatial calculations, thereby improving data access efficiency. Subsequently, the bounding box of the compressed geometric object is constructed to reduce the spatial calculation vertex and patch information. A spatial index is constructed based on the geometric object bounding box to further improve the retrieval and query efficiency of the geometric object during spatial calculations. Finally, each computing node is controlled to perform the geometric element calculation task based on the geometric object spatial index, thereby obtaining the spatial position information between the area to be identified and the three-dimensional space planning information. Based on this, the compression processing amount is reduced and the indexing efficiency of the three-dimensional data is improved, thereby improving the overall computing efficiency when performing the calculation and comparison of the three-dimensional space geometric elements.

[0062] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction, and no further description will be given later. The three-dimensional space planning information is the low-altitude flight path planning information of the area to be identified. On this basis, please refer to Figure 2 , step S50 includes steps S51 to S54: Step S51, determining the current task processing amount of the computing node, and determining the task weight value of each computing task in the to-be-identified area according to a computing intensity weight model.

[0063] When calculating spatial geometric elements, different computing nodes have different computing efficiencies, and different geometric objects have different complexity, which results in different geometric element calculation times. Therefore, it is necessary to flexibly allocate the processing tasks of computing nodes so that all computing nodes are in operation to avoid the situation where some computing nodes are overloaded while others are under-calculated.

[0064] In this embodiment, the task processing information of the computing node can be directly read to determine its current task processing volume, and the task processing includes the task execution time. Then, the time complexity of the collision algorithm associated with the three-dimensional space planning information is obtained, and the complexity and number of geometric object instances corresponding to the same geometric object geometry in the persistent memory are determined. Among them, the three-dimensional space planning information is to set a low-altitude flight route in the area to be identified. At this time, the calculation algorithm associated with the planning information needs to calculate the intersection relationship of the space, such as the collision calculation algorithm between the route and the building. The complexity is related to the complexity of the vertex patches of the geometric object after the spatial index is constructed. The number of instances is the current task processing volume involved.

[0065] In this embodiment, the geometric calculation amount corresponding to different geometric complexities will be different, which will cause some cluster nodes to be overloaded, and some nodes to have a small amount of calculation, which will be completed in advance and be in an empty waiting state, resulting in low utilization of the overall cluster resources. Therefore, it is necessary to construct a calculation intensity weight model that takes into account parameters such as geometric object complexity, number of geometric object instances, and time complexity of the calculation algorithm, so as to perform real-time scheduling and dynamic distribution of geometric element calculation tasks based on the model, maximize the utilization of computing resources, and improve the computing efficiency of the overall task. When constructing the calculation intensity weight model, it is also necessary to obtain the first weight associated with the number of instances and the second weight associated with the time complexity. The first weight and the second weight can be preset weight information. Then, based on the complexity, number of instances, first weight, second weight, and time complexity, a calculation intensity weight model is generated.

[0066] Exemplarily, the calculation intensity weight model is constructed based on the following formula:

[0067] , where W 1 is the first weight, W 2 is the second weight, N is the number of geometric object instances, C type is the geometric complexity of each geometric object, T matrix is the time complexity of the calculation algorithm, and Q is the task complexity.

[0068] After the computing intensity weight model is generated, the task weight value associated with the computing task, that is, the task complexity corresponding to the task, is determined based on the computing intensity weight model, so as to dynamically distribute tasks based on the task complexity and improve the task allocation processing effect.

[0069] Step S52: Control the computing intensity weight model to determine the computing node corresponding to the computing task based on the current task processing amount and the task weight value.

[0070] In this embodiment, in addition to calculating the task weight value, the computing intensity weight model can issue tasks based on the weight value of each computing task to ensure the computing balance of each computing node.

[0071] Step S53: Control the computing node to perform the geometric element calculation corresponding to the geometric element calculation task based on the geometric object spatial index, and obtain the collision calculation result fed back by the computing node.

[0072] Step S54: generating spatial position information of position collision detection between the area to be identified and the three-dimensional space planning information according to the collision calculation result.

[0073] After completing the task issuance, the computing node performs collision detection calculations on each geometric object in three-dimensional space through the geometric object spatial index, and finally generates collision detection results for all geometric objects that the flight path passes through. In order to generate spatial location information of collision detection based on the collision detection results, it provides data support for the subsequent low-altitude flight path planning.

[0074] It is understandable that when each computing node is in an idle state, the corresponding geometric element computing task is determined directly according to the current task processing state of each computing node, that is, the tasks are sequentially allocated to each computing node.

[0075] For example, please refer to Figure 3 , in the design of the computational intensity weight model, Q=W 1 *N*C type +W 2 *T matrix , where W 1 and W 2 represents the weight ratio of each item, namely the first weight and the second weight, N represents the number of geometric object instances, C type Represents the geometric object complexity, that is, the geometric complexity of each geometric object, T matrix Represents the time complexity of the computational algorithm. After calculating the complexity of each processing task through the computational intensity weight model, the computational tasks are dynamically allocated according to the task processing complexity and the task processing amount of each computing node. That is, in the task complexity Q sum =0.2, computing node 1 processes tasks 1, 2, 3, and 1, while computing node 2 processes tasks 5 and 6, computing node 3 processes tasks 7, 8, 9, 10, and 11, computing node 4 executes task 12, and computing node 5 executes tasks 13, 14, and 15. Based on this, computing tasks are dynamically allocated through the computing intensity weight model to improve task processing efficiency.

[0076] This embodiment provides a method for calculating three-dimensional space geometric elements. By setting a computing intensity weight model, the tasks to be performed by each computing node are dynamically allocated based on the computing intensity weight model, thereby avoiding computing redundancy of the computing nodes and improving the overall computing efficiency.

[0077] For example, in order to help understand the implementation process of the method for calculating the three-dimensional space geometric elements obtained by combining the above-mentioned first embodiments, please refer to Figure 4 , Figure 4 An optional flow chart of a method for calculating three-dimensional space geometric elements is provided, specifically: After obtaining the 3D model file, the material data, geometric data, texture data and business data of the 3D model file are extracted by data extraction, and then the geometric object instance data in the geometric data is determined, and then the geometric object instance data is decomposed and divided into primary geometric objects and multi-level component geometric objects, and repeated geometric object data is statistically associated to construct a transformation matrix between geometric objects and geometric instances, so as to achieve hierarchical classification and simplification of geometric data. Among them, the primary geometric object instance can be decomposed into primary geometric multinomials and transformation matrix A, and the multi-level component geometric object instance can be decomposed into multi-level component geometric objects and transformation matrix C, and the transformation matrix B is the conversion relationship between the primary geometric object and the multi-level geometric object. It can be understood that the relationship between the primary geometric object instance and the multi-level instance is "multi-level geometric object instance + transformation matrix A + transformation matrix B + transformation matrix C = primary geometric object instance".

[0078] Based on this, after obtaining the first-level geometric objects and multi-level component geometric objects, the first-level geometric objects and multi-level component geometric objects are compressed. The Draco compression algorithm is used during the compression process, including the use of geometric compression technology and entropy coding technology to reduce the redundant information of three-dimensional geometric data, thereby realizing geometric data compression. Subsequently, the compressed geometric objects and the transformation matrix of the geometric objects (not shown in the figure) are stored in persistent memory so that they can be directly accessed through memory in spatial calculations, thereby improving data access efficiency.

[0079] Furthermore, the bounding box in the persistent memory is calculated so as to build a spatial index based on the bounding box. After building the spatial index, a computing intensity weight model is set based on the complexity of the geometric objects in the persistent memory, the number of geometric object instances, and the complexity of the computing algorithm such as the collision detection algorithm. Then, based on the computing task, the weight value is obtained through the computing intensity weight model, and the computing task is dynamically assigned to the computing nodes of the distributed processing according to the weight value, and finally the computing result is output. Based on this, by using GIS, spatial computing, big data and other technologies, the three-dimensional data storage space is reduced, the network transmission volume is reduced, the computing resource utilization efficiency is improved, and the three-dimensional space computing efficiency is improved.

[0080] The present application provides a device for calculating three-dimensional space geometric elements, and the device for calculating three-dimensional space geometric elements includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for calculating three-dimensional space geometric elements in the first embodiment mentioned above.

[0081] Reference below Figure 5, which shows a schematic diagram of the structure of a computing device suitable for implementing three-dimensional spatial geometric elements of an embodiment of the present application. Figure 5 The computing device for the three-dimensional space geometric elements shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0082] like Figure 5 As shown, the computing device of the three-dimensional space geometric elements may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM) 1004. In the random access memory 1004, various programs and data required for the operation of the computing device of the three-dimensional space geometric elements are also stored. The processing device 1001, the read-only memory 1002 and the random access memory 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the computing device of three-dimensional space geometric elements to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a computing device of three-dimensional space geometric elements with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0083] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0084] The three-dimensional space geometric element calculation device provided by the present application adopts the three-dimensional space geometric element calculation method in the above-mentioned embodiment, which can solve the technical problem that the current geometric model processing strategy is difficult to avoid repeated calculation of similar features, resulting in low calculation efficiency when calculating the three-dimensional model geometric data. Compared with the prior art, the beneficial effects of the three-dimensional space geometric element calculation device provided by the present application are the same as the beneficial effects of the three-dimensional space geometric element calculation method provided by the above-mentioned embodiment, and the other technical features in the three-dimensional space geometric element calculation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0085] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0086] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0087] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for calculating the three-dimensional space geometric elements in the above-mentioned embodiment.

[0088] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM, Erasable Programmable ReadOnly Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM, CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, radio frequencies (RF, Radio Frequency), etc., or any suitable combination of the above.

[0089] The above-mentioned computer-readable storage medium may be included in the computing device of the three-dimensional space geometric elements; or it may exist independently without being assembled into the computing device of the three-dimensional space geometric elements.

[0090] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by a computing device for three-dimensional space geometric elements, the computing device for three-dimensional space geometric elements: Obtain the 3D model file and 3D space planning information of the area to be identified; Splitting the geometric object instances in the three-dimensional model file, and constructing a set of identical geometric objects according to the split geometric object instances; Saving the compressed set of identical geometric objects into a persistent memory, and constructing a bounding box of the compressed set of identical geometric objects; Constructing a geometric object spatial index of the same set of geometric objects according to the bounding box; The control calculation node performs geometric element calculation based on the geometric object spatial index to obtain spatial position information between the area to be identified and the three-dimensional space planning information.

[0091] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0094] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for calculating three-dimensional space geometric elements, and can solve the technical problem that the current geometric model processing strategy is difficult to avoid repeated calculations of similar features, resulting in low calculation efficiency when calculating three-dimensional model geometric data. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the method for calculating three-dimensional space geometric elements provided by the above-mentioned embodiment, and will not be repeated here.

[0095] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for calculating three-dimensional space geometric elements, characterized in that: The calculation method of the three-dimensional space geometric elements includes: Obtain the 3D model file and 3D space planning information of the area to be identified; Splitting the geometric object instances in the three-dimensional model file, and constructing a set of identical geometric objects according to the split geometric object instances; Saving the compressed set of identical geometric objects into a persistent memory, and constructing a bounding box of the compressed set of identical geometric objects; Constructing a geometric object spatial index of the same set of geometric objects according to the bounding box; The control calculation node performs geometric element calculation based on the geometric object spatial index to obtain spatial position information between the area to be identified and the three-dimensional space planning information.

2. The method for calculating three-dimensional space geometric elements according to claim 1, characterized in that: The step of splitting the geometric object instances in the three-dimensional model file and constructing the same geometric object set according to the split geometric object instances comprises: Acquire the geometric object instance of the geometric data in the three-dimensional model file; Based on the spatial position and / or subordinate relationship of the geometric data, the geometric object instance is split to obtain a first-level geometric object instance and a multi-level component object instance, wherein the multi-level component geometric object instance is a component of the first-level geometric object instance; The first-level geometric object set of the first-level collection object instance is determined based on the first transformation matrix after the first-level geometric object instance is aggregated, and the multi-level component geometric object set of the multi-level component geometric object instance is determined based on the second transformation matrix after the multi-level component geometric object instance is aggregated.

3. The method for calculating three-dimensional space geometric elements according to claim 2, characterized in that: The steps of determining the first-level geometric object set of the first-level set object instance according to the first transformation matrix after the aggregation processing of the first-level geometric object instance, and determining the multi-level component geometric object set of the multi-level component geometric object instance according to the second transformation matrix after the aggregation processing of the multi-level component geometric object instance include: Performing a same geometric object aggregation process on the first-level geometric object instances, and establishing first transformation matrices of the aggregated geometric objects and the first-level geometric object instances, wherein the number of the first transformation matrices is equal to the number of the first-level geometric object instances; Generate the first-level geometric object set according to the first transformation matrix and the first-level geometric object instance; Performing same geometric object aggregation processing on the multi-level component geometric object instances, and establishing second transformation matrices of the aggregated geometric objects and the multi-level component geometric object instances, wherein the number of the second transformation matrices is equal to the number of the multi-level component geometric object instances; Generate the multi-level component geometric object set according to the second transformation matrix and the multi-level component geometric object instance; The transformation matrix is ​​multiplied by a subset of the same set of geometric objects to obtain the separated geometric object instances.

4. The method for calculating three-dimensional space geometric elements according to claim 1, characterized in that: Before the step of saving the compressed set of identical geometric objects into a persistent memory and constructing a bounding box of the compressed set of identical geometric objects, the method further includes: Determining a compression processing algorithm for the set of identical geometric objects, and compressing the set of identical geometric objects based on the compression processing algorithm; Determine a set of transformation matrices associated with the same set of geometric objects; The step of saving the compressed set of identical geometric objects into a persistent memory and constructing a bounding box of the compressed set of identical geometric objects comprises: Storing the compressed set of identical geometric objects and the set of transformation matrices in the persistent memory; A bounding box processing algorithm is determined, and the bounding box is constructed based on the bounding box processing algorithm.

5. The method for calculating three-dimensional space geometric elements according to claim 4, characterized in that: The step of constructing a geometric object spatial index of the same set of geometric objects according to the bounding box comprises: The point, line and surface feature information of the bounding box is set as the spatial index of the geometric object.

6. The method for calculating three-dimensional space geometric elements according to claim 1, characterized in that: The step of controlling the computing node to calculate geometric elements based on the geometric object spatial index to obtain spatial position information between the area to be identified and the three-dimensional space planning information includes: Determine the current task processing volume of the computing node, and determine the task weight value of each computing task in the to-be-identified area according to the computing intensity weight model; Controlling the computing intensity weight model, based on the current task processing amount and the task weight value, determining the computing node corresponding to the computing task; Controlling the computing node to perform the geometric element calculation corresponding to the geometric element calculation task based on the geometric object spatial index, and obtaining the collision calculation result fed back by the computing node; According to the collision calculation result, spatial position information of position collision detection between the area to be identified and the three-dimensional space planning information is generated.

7. The method for calculating three-dimensional space geometric elements according to claim 6, characterized in that: The step of determining the current task processing amount of the computing node and determining the task weight value of each computing task in the to-be-identified area according to the computing intensity weight model comprises: Determine the current task processing volume of each of the computing nodes, obtain the time complexity of the collision algorithm associated with the three-dimensional space planning information, and determine the complexity and number of geometric object instances corresponding to the same set of geometric objects in the persistent memory; Obtaining a first weight associated with the number of instances and a second weight associated with the time complexity; Generate the computational intensity weight model according to the complexity, the number of instances, the first weight, the second weight, and the time complexity; According to the computing intensity weight model, a task weight value associated with the computing task is determined.

8. The method for calculating three-dimensional space geometric elements according to claim 1, characterized in that: After the step of obtaining the three-dimensional model file and the three-dimensional space planning information of the area to be identified, the method further includes: Acquire the separated data after preprocessing the three-dimensional model file, wherein the separated data at least includes geometric data, material data, texture data, hierarchical structure data and business attribute data; Determine the geometric object instance of the geometric data, and based on the geometric object instance, perform the steps of splitting the geometric object instance in the three-dimensional model file, and constructing the same geometric object set according to the split geometric object instance.

9. A device for calculating geometric elements of three-dimensional space, characterized in that: The device for calculating geometric elements of three-dimensional space includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method for calculating geometric elements of three-dimensional space as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for calculating three-dimensional space geometric elements as described in any one of claims 1 to 8.

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