A space analysis method and system based on a Beidou grid code and a three-dimensional engine

Abstract

This invention discloses a spatial analysis method and system based on BeiDou grid codes and a 3D engine. The method includes encoding geospatial data using BeiDou grid codes; loading and rendering the geospatial data using a 3D engine; rewriting the encoded geospatial data according to different accessed 3D engines; and uniformly encapsulating the interfaces of different 3D engines to form a unified map API for providing a unified API interface to the outside world. This invention utilizes BeiDou grid code technology to lightweight, discretize, and grid 3D geospatial data. Simultaneously, it achieves efficient association and integration of 3D data and spatial sensing data based on grid coding, which can significantly improve the computational efficiency of massive amounts of 3D geospatial data without consuming excessive memory or disk space.

Description

Technical Field

[0001] This application relates to the field of spatial analysis, and in particular to a spatial analysis method and system based on BeiDou grid codes and a 3D engine. Background Technology

[0002] In recent years, with the rise of smart city construction, spatial information, with its important spatiotemporal attributes, has become the core and foundation of smart city construction, and geospatial information and visualization are playing an increasingly important role. 3D geographic information platform software engines, serving digital city construction, possess comprehensive geographic feature geometric models, can easily manage multi-type, large-scale geographic feature data, and provide accurate spatial analysis and computation capabilities. However, in the spatial analysis of massive amounts of data, the spatial retrieval, analysis, and computation capabilities provided by the engine are all vector matrices composed of point strings, requiring complex floating-point operations. This method of computational processing is computationally intensive and inefficient, and requires long waiting times during spatial analysis and computation, resulting in a very poor user experience. Currently, in smart city applications, the amount of geospatial information data in 3D scenes is becoming increasingly larger, and this drawback is becoming more and more apparent.

[0003] Currently, most 3D spatial analysis engines on the market use vector matrices composed of point strings, requiring complex floating-point operations. While this is perfectly efficient for spatial analysis in two-dimensional planes, the computation time increases exponentially when the dimension is increased to three. Furthermore, when dealing with time-sensitive 3D spatial data, the computation time increases again, reaching another level. Therefore, it's clear that traditional methods struggle with massive amounts of 3D geospatial information. This demonstrates that traditional spatial analysis and computation methods are relatively inefficient when handling large volumes of 3D geospatial data. In 3D application scenarios, this manifests as significant lag and stuttering when performing complex spatial analyses such as spatial indexing and calculations on massive datasets, resulting in a very unfriendly and poor user experience.

[0004] To address the relatively low efficiency when dealing with massive amounts of 3D geospatial data, the first step is to improve the computational efficiency of spatial analysis when handling multi-dimensional spatial data. Only by resolving the efficiency issue of multi-dimensional spatial analysis can the overall spatial analysis capabilities of massive 3D geospatial data be improved, reducing analysis lag and resulting in a better user experience. A common approach is to trade space for time, adding redundant fields, spreading data across multiple tables, using indexes, or optimizing by constructing new data structures that consume significant memory. However, these methods are extremely memory and disk space consuming. When dealing with massive amounts of data, this space consumption significantly increases implementation costs and difficulties, leading to less than ideal results. In conclusion, traditional spatial analysis and computation methods are relatively inefficient when dealing with massive amounts of 3D geospatial data. In 3D application scenarios, this manifests as lag during spatial analysis, which is very unfriendly and provides a poor user experience. The solution involves creating spatial indexes for spatial data and caching strategies for logical structures. However, the logic of existing data structures needs to be rebuilt according to business needs, and there are also issues with memory and disk space consumption. But when faced with massive amounts of 3D geospatial data, the cost of this solution is clearly unacceptable. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a spatial analysis method and system based on BeiDou grid codes and a 3D engine. It utilizes a 3D rendering engine to load massive amounts of geospatial information data. Furthermore, when the 3D engine requires 3D data retrieval and spatial analysis calculations, the BeiDou grid code, using a three-dimensional grid as the smallest unit, offers significant advantages in speed and complexity compared to point-string vector matrices during computation.

[0006] In a first aspect, the present invention provides a spatial analysis method based on BeiDou grid codes and a 3D engine, the method comprising:

[0007] Geospatial data is encoded using the BeiDou grid code;

[0008] Use a 3D engine to load and render geospatial data;

[0009] The encoded geospatial data is rewritten based on the different 3D engines accessed.

[0010] The interfaces of different 3D engines are uniformly encapsulated to form a unified map API, which is used to provide a unified API interface to the outside world.

[0011] In some embodiments of the present invention, geospatial data includes spatial feature data, attribute feature data, and temporal data;

[0012] Spatial feature data is the location of a feature in geographic space, including the geographical location of the feature itself, as well as the positional or spatial relationships between multiple features.

[0013] Attribute feature data are qualitative / quantitative indicator data that describe the natural or human attributes of land features;

[0014] Temporal characteristic data refers to the time / period when geospatial data is collected or when geographical phenomena occur.

[0015] In some embodiments of the present invention, encoding geospatial data according to the BeiDou grid code specifically includes lightweighting, discretizing, gridding, fusing, and coding the data;

[0016] Data lightweighting includes data compression and LOD processing;

[0017] Data discretization involves grouping continuous data into discrete interval data.

[0018] Data gridding includes processing data into a grid according to the rules of BeiDou grid codes;

[0019] Data fusion includes the integration of data from multiple information sources;

[0020] Data encoding involves encoding the gridded data.

[0021] In some embodiments of the present invention, encoding geospatial data according to the BeiDou grid code also includes using the encoding rules of the BeiDou grid code to perform spatial analysis and query retrieval on the encoded data.

[0022] A second aspect of the present invention provides a spatial analysis system based on BeiDou grid codes and a 3D engine, comprising:

[0023] The data encoding module is used to encode geospatial data according to the BeiDou grid code;

[0024] The data loading module is used to load and render geospatial data using the 3D engine;

[0025] The data rewrite module is used to rewrite the encoded geospatial data according to the different 3D engines accessed.

[0026] The Unified Map API module is used to encapsulate the interfaces of different 3D engines in a unified manner to provide a unified API interface to the outside world.

[0027] In some embodiments of the present invention, the system further includes a memory for storing geospatial data, including spatial feature data, attribute feature data, and temporal feature data;

[0028] Spatial feature data is the location of a feature in geographic space, including the geographical location of the feature itself, as well as the positional or spatial relationships between multiple features.

[0029] Attribute feature data are qualitative / quantitative indicator data that describe the natural or human attributes of land features;

[0030] Temporal characteristic data refers to the time / period when geospatial data is collected or when geographical phenomena occur.

[0031] In some embodiments of the present invention, the encoding of geospatial data according to the BeiDou grid code in the data encoding module specifically includes data lightweighting, discretization, gridding, fusion, and coding;

[0032] Data lightweighting includes data compression and LOD processing;

[0033] Data discretization involves grouping continuous data into discrete interval data.

[0034] Data gridding includes processing data into a grid according to the rules of BeiDou grid codes;

[0035] Data fusion includes the integration of data from multiple information sources;

[0036] Data encoding involves encoding the gridded data.

[0037] In some embodiments of the present invention, the data encoding module for encoding geospatial data according to the BeiDou grid code also includes using the encoding rules of the BeiDou grid code to perform spatial analysis and query retrieval on the encoded data.

[0038] This invention provides a spatial analysis method and system based on BeiDou grid codes and a 3D engine. By managing and applying data using a lightweight 3D model based on BeiDou grid codes, complex spatial analysis calculations are performed. The method integrates basic attribute data with spatial location, enabling efficient querying of multi-attribute data. The 3D structural location design of BeiDou grid codes improves computational efficiency and significantly simplifies the identification, expression, and calculation of location information. It effectively addresses the organization problems of massive, multi-source, and heterogeneous spatial information in terms of information processing speed, indexing efficiency, information exchange and integration. Furthermore, it can be easily converted to existing information systems under various technological frameworks, enabling rapid encoding and digitization of existing data, effectively improving the spatial analysis efficiency of massive 3D geospatial data. The ultimate beneficial effects are:

[0039] 1. Perform BeiDou gridding, lightweighting, discretization, fusion, and encoding on geospatial data, and use BeiDou grid code rules to provide the 3D engine with efficient spatial analysis and data retrieval capabilities.

[0040] 2. By using a unified API fusion technology, the internal spatial analysis-related interfaces have been reconstructed. Even when faced with massive amounts of data and multiple engine switching, statistical analysis can still be performed smoothly without the additional complexity and difficulty brought by the engine.

[0041] 3. A unified API integration technology has been implemented for various 3D engines, providing a unified interface to achieve seamless engine switching without incurring unnecessary development costs. Attached Figure Description

[0042] Figure 1 This is a flowchart of a spatial analysis method based on BeiDou grid codes and a 3D engine according to an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the spatial analysis system based on Beidou grid code and 3D engine according to an embodiment of the present invention. Detailed Implementation

[0044] To further illustrate the technical solution of the present invention in detail, this embodiment is implemented based on the technical solution of the present invention, and provides detailed implementation methods and specific steps.

[0045] like Figure 1 As shown in the figure, the spatial analysis method based on BeiDou grid codes and a 3D engine provided by this invention firstly uses BeiDou grid code technology to lightweight, discretize, and grid the 3D geospatial data. After fusion, the data is coded and stored in a database, effectively providing efficient spatial analysis capabilities. Simultaneously, the geospatial data is directly loaded using the 3D engine's large-scene rendering, and basic spatial information acquisition, such as simple coordinate acquisition, is provided. A new unified map API interface is encapsulated to provide a new spatial analysis interface for the data encoded by the 3D engine and BeiDou grid codes, allowing map developers to directly perform time-consuming big data spatial analysis. The method specifically includes steps A1 to A4:

[0046] A1. Encode geospatial data according to the BeiDou grid code;

[0047] Geospatial data includes spatial feature data, attribute feature data, and temporal feature data. Spatial feature data refers to the location of a feature in geographic space, including the geographical location of the feature itself and the positional or spatial relationships between multiple features. Geospatial data needs to include the clear location of features in geographic space; this information is called spatial feature data. Spatial feature data includes: 1. The geographical location of the feature itself, which is usually expressed using geographic coordinates, such as x, y, z, or longitude, latitude, elevation, or a combination thereof; it can also be described relative to other reference frames or features, such as a feature next to Xinhua Bookstore being "120m west of Xinhua Bookstore," etc.; 2. The positional or spatial relationships between multiple features, such as the distance, adjacency, connection, and association between features. Most spatial feature data have obvious geometric characteristics. Attribute characteristic data refers to qualitative / quantitative indicator data describing the natural or human attributes of geographical features. Besides spatial characteristic data, geospatial data also includes qualitative or quantitative indicator data describing the natural or human attributes of geographical features. This part of the data is attribute characteristic data. For example, to describe a city settlement, if only location coordinates (x, y) are available, it is merely a geometric point. To constitute geospatial data for a settlement, regional data such as economic data (population, output, social employment rate), resource data (pollution index), etc., are also needed. Temporal characteristic data refers to the time / period of geospatial data collection or the occurrence of geographical phenomena. Multiple time-period data for the same geographical feature can dynamically represent its development and changes. Temporal characteristic data can be divided according to time scale into short-term (e.g., earthquakes, floods, frosts), medium-term (e.g., land use, crop yield estimation), long-term (e.g., urbanization, soil erosion), and ultra-long-term (e.g., crustal movement, climate change).

[0048] Furthermore, encoding geospatial data based on the BeiDou grid code specifically includes data lightweighting, discretization, gridding, fusion, and coding.

[0049] Data lightweighting includes data compression and LOD processing, mainly involving lightweighting of massive BIM and CIM models and single-instance processing of models used in multiple places.

[0050] Data discretization involves grouping continuous data into discrete interval data, mapping a finite number of individuals in an infinite space to a finite space, thereby improving the time and space efficiency of the algorithm. Specifically, discretization reduces the size of the data without changing its relative size, mainly referring to grouping continuous data into a discrete interval.

[0051] Data gridding involves processing data into grids according to the rules of the BeiDou grid code. It mainly involves converting spatially unevenly distributed data into representative / trend values ​​in a regular grid using numerical calculation methods. These numerical calculation methods include, but are not limited to, the moving average method and the Kriging method.

[0052] Data fusion involves integrating data from multiple information sources. It plays a crucial role in processing and coordinating data across multiple information sources, platforms, and user systems, ensuring connectivity and correlation between the data processing component and geospatial data.

[0053] Data encoding involves encoding the gridded data and then storing it in a geospatial data storage device.

[0054] Furthermore, encoding geospatial data using BeiDou grid codes also includes spatial analysis and query retrieval of the encoded data using the BeiDou grid code encoding rules. Specifically, the spatial analysis method utilizes the BeiDou grid location coding system to globally identify the spatial data, effectively improving data sharing and computational efficiency. The query retrieval method utilizes the binary integer encoding of the BeiDou grid location code, employing binary bitwise operations for calculation. This enables highly efficient encoded query retrieval operations, significantly improving the computational speed compared to latitude and longitude methods. By utilizing the BeiDou grid code encoding rules, the efficiency of spatial analysis and query retrieval can be increased by several times, tens of times, or even hundreds of times.

[0055] A2. Use the CityMaker 3D engine to load and render geospatial data;

[0056] A3. Based on the different 3D engines accessed, including but not limited to CityMaker, SkyLine, Cesium, and UnrealEngine4, the calls to the encoded geospatial data are rewritten; the spatial analysis methods for each type of 3D engine are rewritten, the internal spatial analysis class is changed, and the interface is intercepted and replaced when different 3D engines need to perform spatial type operations. A unified API is used to uniformly handle the calls, and the complex and time-consuming processing is handled by BeiDou grid data encoding.

[0057] A4. Unify the interfaces of different 3D engines to form a unified map API, which can be used to provide a unified API interface to the outside world.

[0058] The following is for reference Figure 2 To describe the relationship with embodiments of this disclosure Figure 1The system corresponding to the method shown is a spatial analysis system based on BeiDou grid codes and a 3D engine. The system 100 includes: a data encoding module 101 for encoding geospatial data according to BeiDou grid codes; a data loading module 102 for loading and rendering geospatial data using a 3D engine; a data rewriting module 103 for rewriting the encoded geospatial data according to different accessed 3D engines; and a unified map API module 104 for uniformly encapsulating the interfaces of different 3D engines to provide a unified API interface. In addition to these four modules, the system 100 also includes a memory 105 for storing geospatial data, including spatial feature data, attribute feature data, and temporal data. Spatial feature data refers to the location of land features in geospatial space, including the geographical location of the land feature itself and the positional or spatial relationships between multiple land features. Attribute feature data refers to qualitative / quantitative indicator data describing the natural or human attributes of land features. Temporal data refers to the time / period of geospatial data collection / geographical phenomenon occurrence. In addition, system 100 may include other components; however, since these components are not relevant to the content of this disclosure, their illustrations and descriptions are omitted herein.

[0059] Furthermore, the data encoding module 101 encodes geospatial data according to the BeiDou grid code, specifically including data lightweighting, discretization, gridding, fusion, and coding. Data lightweighting includes data compression and LOD processing; data discretization includes grouping continuous data to transform it into discrete interval data; data gridding includes gridding the data according to the rules of the BeiDou grid code; data fusion includes fusing data from multiple information sources; and data coding includes encoding the gridded data.

[0060] Furthermore, the data encoding module 101 also includes encoding geospatial data according to the BeiDou grid code, and performing spatial analysis and query retrieval on the encoded data using the encoding rules of the BeiDou grid code.

[0061] The specific working process of a spatial analysis system 100 based on Beidou grid code and 3D engine is described in the above description of a spatial analysis method based on Beidou grid code and 3D engine, and will not be repeated here.

[0062] The most similar solution to this invention for improving the spatial analysis of massive amounts of data using 3D engines is to build an index based on a spatial database. This involves indexing each field of key information in each piece of feature data to improve efficiency. Simultaneously, it uses a new data structure to cache the massive 3D geospatial data, accelerating processing. However, these solutions require restructuring and reprocessing the data, consuming significant memory and disk space resources, and requiring corresponding logic development for the applications. This invention combines a single 3D map engine with BeiDou grid location codes. In complex and time-consuming spatial analyses, BeiDou grid location codes are used to construct different levels of 3D block data, achieving dimensionality reduction and improved spatial computation efficiency. With permissible accuracy, the BeiDou grid code achieves a maximum precision of 1.5 centimeters. This dimensionality reduction significantly improves the speed and accuracy of data queries in spatial analysis and navigation calculations, enabling users to obtain faster and more accurate results in analyzing massive amounts of 3D spatial data, reducing time consumption and increasing efficiency.

[0063] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a step or method that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such a step or method.

[0064] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A spatial analysis method based on BeiDou grid codes and a 3D engine, characterized in that, The method includes: Geospatial data is encoded using the BeiDou grid code. Use a 3D engine to load and render geospatial data; The encoded geospatial data is rewritten based on the different 3D engines accessed. The interfaces of different 3D engines are uniformly encapsulated to form a unified map API, which is used to provide a unified API interface to the outside world; Encoding geospatial data based on the BeiDou grid code specifically includes data lightweighting, discretization, gridding, fusion, and coding. Data lightweighting includes data compression and LOD processing; Data discretization involves grouping continuous data into discrete interval data. Data gridding includes processing data into a grid according to the rules of BeiDou grid codes; Data fusion includes the integration of data from multiple information sources; Data encoding involves encoding the gridded data; Using the encoding rules of the BeiDou grid code, spatial analysis and query retrieval are performed on the encoded data. The spatial analysis method includes: using the BeiDou grid location coding system to perform global location identification on the spatial data; the query retrieval method includes: using the binary integer encoding of the BeiDou grid location code and performing calculations using binary bit operations.

2. The spatial analysis method based on BeiDou grid code and 3D engine according to claim 1, characterized in that, Geospatial data includes spatial feature data, attribute feature data, and temporal feature data; Spatial feature data is the location of a feature in geographic space, including the geographical location of the feature itself, as well as the positional or spatial relationships between multiple features. Attribute feature data are qualitative / quantitative indicator data that describe the natural or human attributes of land features; Temporal characteristic data refers to the time / period when geospatial data is collected or when geographical phenomena occur.

3. A spatial analysis system based on BeiDou grid codes and a 3D engine, characterized in that, include: The data encoding module is used to encode geospatial data according to the BeiDou grid code; The data loading module is used to load and render geospatial data using the 3D engine; The data rewrite module is used to rewrite the encoded geospatial data according to the different 3D engines accessed. The Unified Map API module is used to encapsulate the interfaces of different 3D engines in a unified manner to provide a unified API interface to the outside world. The data encoding module encodes geospatial data according to the BeiDou grid code, specifically including data lightweighting, discretization, gridding, fusion, and coding. Data lightweighting includes data compression and LOD processing; Data discretization involves grouping continuous data into discrete interval data. Data gridding includes processing data into a grid according to the rules of BeiDou grid codes; Data fusion includes the integration of data from multiple information sources; Data encoding involves encoding the gridded data; Encoding geospatial data using the BeiDou grid code also includes using the BeiDou grid code encoding rules to perform spatial analysis and query retrieval on the encoded data. The spatial analysis methods include: using the BeiDou grid location coding system to perform global location identification on the spatial data; the query retrieval methods include: using the binary integer encoding of the BeiDou grid location code and performing calculations using binary bit operations.

4. A spatial analysis system based on BeiDou grid codes and a 3D engine according to claim 3, characterized in that, The system also includes a memory for storing geospatial data, including spatial feature data, attribute feature data, and temporal feature data; Spatial feature data is the location of a feature in geographic space, including the geographical location of the feature itself, as well as the positional or spatial relationships between multiple features. Attribute feature data are qualitative / quantitative indicator data that describe the natural or human attributes of land features; Temporal characteristic data refers to the time / period when geospatial data is collected or when geographical phenomena occur.

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