A data set container storage and management method based on multi-scale space partition grid

Abstract

The application discloses a kind of based on multi-scale space section grid data set container storage and management method, comprising: first based on earth space section theory, construct the multi-scale space section grid coding system covering ground and height dimension, give each grid unique code to form unified space-time framework;Again, image, vector, non-spatial and other multi-source data are collected and transmitted to processing center, after consistency detection and normalization processing, generate uniform grid code for each type of data based on the above coding system and establish mapping;Finally, the coded data is stored and updated index, relies on unified coding to realize multi-source data dynamic scheduling, logic aggregation and visual display, realize massive heterogeneous data efficient management and cross-domain collaboration, improve data storage consistency and search, display efficiency.The application effectively solves the problems of spatial big data fragmentation, slow search response and cross-domain integration difficulty, significantly improves the management efficiency and sharing depth of spatial data.

Description

Technical Field

[0001] This invention relates to spatiotemporal data management and high-performance computing storage, and in particular to a data container storage and management method based on multi-scale spatial partitioning grids. Background Technology

[0002] With the widespread application of space technologies such as remote sensing, surveying and mapping, geographic information systems, and satellite navigation, numerous remote sensing data organization and management systems with broad coverage, large data volumes, and strong service capabilities have been established globally, covering the entire process from data acquisition, transmission, archiving to visualization services. The Earth Observing System (EOS) built by the National Aeronautics and Space Administration (NASA) is one of the most representative space remote sensing data platforms. Through the HDF-EOS (Hierarchical Data Format for the Earth Observing System) hierarchical format and the ECHO metadata exchange middleware, it achieves unified format storage, sharing, and access of multi-source remote sensing data. It employs three spatial models—point, strip, and grid—combined with structured metadata to bind Earth science remote sensing data with geographic points, making it the world's largest scientific remote sensing database system. The Google Earth platform, based on the BigTable architecture, has built a petabyte-scale data indexing service, organizing remote sensing data into variable-version, scalable sparse tables, enabling rapid access to 3D imagery and time-series management. Bing Maps uses a quadtree-structured tile partitioning and QuadKey indexing method to map remote sensing images onto fixed-size tiles and organize them hierarchically, combined with Mercator projection to support multi-level image access. China's "Tianditu" platform employs Morton coding for spatiotemporal indexing and uses multi-level pyramid-style tile technology to provide nationwide imagery, vector, and terrain data services, supporting integrated display of 2D and 3D data as well as plotting, distance, and area measurement services. Furthermore, the National Satellite Meteorological Center and the National Satellite Ocean Application Center, among others, have built remote sensing data centers for polar-orbiting, geostationary, and marine satellites, respectively employing database and magnetic tape inventory management methods to construct systems supporting high-concurrency access and large-capacity data archiving. These systems have made significant progress in spatial data management, constructing scalable, distributed, and structured remote sensing data systems; however, they still have significant shortcomings in cross-domain fusion of spatiotemporal partitioning networks, coding uniformity, and logically integrated organizational capabilities.

[0003] Existing remote sensing data storage and management systems generally adopt a multi-layered distributed structure. After data acquisition, multi-source data such as remote sensing images, vector maps, digital elevation models, and nautical charts are first transmitted to the spatial information center server through terminals in various locations. Subsequently, the system performs consistency checks and normalization processing on the data, including coordinate system conversion to CGCS2000, file format standardization, metadata extraction, and image browsing map generation, to ensure the data's semantic and structural uniformity. The preprocessed data is assigned a unique partition code based on its spatial extent and type information, establishing a spatial correspondence with the Earth's low-altitude multi-scale spatial partition grid, thereby generating logical identifiers for structured or unstructured data. In the actual data storage process, structured data is usually automatically coded by the system, while unstructured data requires manual annotation. Both types are indexed using codes and included in the data catalog. After coding, the data is allocated to online disks, near-line tapes, or offline storage devices, with different types of storage resources uniformly scheduled by a distributed resource pool. The logical organization of data is centered on partitioning and coding, replacing traditional directory path dependencies, and establishing computable spatial relationships between storage locations, scales, and data sources. Users can quickly retrieve datasets for target regions by inputting partitioning codes, and the system supports functions such as multi-source data aggregation and stitching, local rendering with spheres, and 3D display. The entire process is completed through automated interface collaboration between the storage system, indexing system, and service system, establishing a complete workflow from data catalog construction, inbound storage, labeling to visualization. In terms of technical implementation, many platforms use commercial databases such as Oracle and SQL Server to manage metadata, combined with file systems or FTP for data upload and download, achieving high reliability and system elasticity through scheduling strategies and resource redundancy.

[0004] For spatial data organization technologies, the existing QuadKey, Morton, and orbital partitioning systems coexist and are incompatible with each other, resulting in a lack of logical grid benchmarks and hindering seamless cross-platform and cross-departmental integration. Simultaneously, the low coupling between encoding and data entities and the lack of containerized encapsulation mechanisms make horizontal integration of heterogeneous data difficult, limiting the system to vertical hierarchical management and restricting regional integrated applications. Furthermore, existing platforms suffer from high energy consumption and slow response under high-concurrency scenarios, still relying on manual retrieval and downloading, lacking dynamic aggregation and on-the-fly loading display capabilities. In addition, inconsistent metadata standards and the lack of shared interfaces further impede wide-area data sharing and collaborative services. Therefore, there is an urgent need to establish a spatiotemporal integrated partitioning grid and unified encoding system, construct a data management framework supporting cross-domain integration and collaboration, and achieve unified organization, efficient sharing, and cross-domain collaborative application capabilities for spatial data.

[0005] Although current remote sensing data management platforms have formed a certain spatial organization framework and service system, significant shortcomings remain in cross-domain data fusion and unified spatiotemporal coding representation. First, existing platforms employ diverse and fragmented spatial partitioning and coding methods. For example, Google Earth uses BigTable plot indexing, Bing Maps uses QuadKey quadtree coding, and Tianditu relies on Morton coding. These systems are independently designed and lack a unified spatiotemporal benchmark and coding rules, making it difficult to achieve consistent spatial semantic positioning and association of multi-source heterogeneous data. This severely restricts the logical fusion and shared utilization of cross-platform and cross-domain data. Second, current spatial data organization largely relies on physical storage paths or database entry management, lacking a "logical container" mechanism centered on spatial partitioning and coding. This results in low efficiency and high redundancy during data migration, distribution, and sharing, making it difficult to support the distributed dynamic scheduling of large-scale data. Secondly, existing systems tend to focus on single-domain, single-level vertical data display and services, lacking the ability to horizontally integrate imagery, vector, attribute, and thematic data at the same spatial scale. This results in insufficient multi-source data collaboration, hindering the realization of comprehensive analysis, intelligent applications, and multi-scenario collaboration. Furthermore, these systems generally suffer from slow query response times, high energy consumption, inconsistent metadata structures, and a lack of code-driven event subscription and push mechanisms, making it difficult to meet the high-efficiency and real-time requirements of next-generation intelligent remote sensing and geographic information services.

[0006] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a data container storage and management method based on multi-scale spatial partitioning grids.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for storing and managing data containers based on multi-scale spatial grid partitioning includes the following steps:

[0010] S1. Construct a multi-scale spatial subdivision grid coding system: Based on the theory of Earth spatial subdivision, construct a multi-scale spatial subdivision grid covering the Earth's surface space and height dimension, assign a unique code to each grid unit, and form a unified spatiotemporal coding framework;

[0011] S2. Data Acquisition and Transmission: Acquire multi-source spatial data, including image data, vector data, and non-spatial data, and transmit the data to the data processing center;

[0012] S3. Data Consistency Detection and Normalization: Perform consistency detection and normalization on the collected data, including coordinate system conversion, file format standardization, and metadata extraction.

[0013] S4. Spatial Data Integrated Coding: Based on the multi-scale spatial grid coding system, a unified spatial grid code is generated for image data, vector data and non-spatial data respectively, and a mapping relationship between data and grid is established.

[0014] S5. Data entry and storage: Enter and store the encoded data, and update the data index;

[0015] S6. Dynamic scheduling and display of data containers: Based on the unified spatial grid coding, dynamic scheduling, logical aggregation and visualization of multi-source data are realized.

[0016] Further, in step S1, the construction of the multi-scale spatial subdivision grid coding system includes:

[0017] Constructing a planar two-dimensional spatial partitioning model: The Earth's surface is partitioned into multiple levels using a quadtree approach, generating multiple levels of grid cells, with each grid cell assigned a unique code;

[0018] Constructing a three-dimensional spatial partitioning model: Based on the existing two-dimensional spatial partitioning model, the height dimension is partitioned into multiple levels using a bisection method to generate three-dimensional mesh units, each of which is assigned a unique code;

[0019] In this process, for grid codes of different levels, the upper-level grid code is generated by the intermediate value of adjacent grid codes to avoid code duplication.

[0020] Further, in step S3, the data consistency detection and normalization process includes:

[0021] Perform a consistency check on the data, identify inconsistent data, and list the reasons;

[0022] Data normalization processing includes cataloging geospatial data, extracting metadata, converting coordinate systems to a unified coordinate system, and converting file formats to standard formats;

[0023] The system was expanded to integrate the input of new data formats.

[0024] Further, in step S4, generating a unified spatial grid code for the image data includes:

[0025] The image data is logically partitioned, and a mapping relationship between the data and the three-dimensional multi-scale spatial partitioning grid is established based on the spatial range.

[0026] Different logical partitioning methods are used for image data of different product levels: for low-level images, an outward logical partitioning method is used to extend the spatial range of the 3D partitioning grid to cover the possible geographic error range; for high-level images, a precise logical partitioning method is used to directly establish the mapping.

[0027] Generate a segmentation index file for the image data, recording the three-dimensional location code of each logical image block and its position information in the image file.

[0028] Further, in step S4, generating a unified spatial grid code for the vector data includes:

[0029] Calculate the smallest outer cube of the vector object and obtain the three-dimensional coordinates of its eight corner points;

[0030] Select the optimal partitioning level based on the volume of the smallest enclosing cube;

[0031] Calculate the encoding of all 3D mesh volumes that a vector object traverses within a selected mesh level;

[0032] Select the 3D mesh volume with the largest volume coverage as the main location identifier and generate a unique code.

[0033] Furthermore, the encoding of the vector data also includes attribute information encoding and time information encoding. The attribute information encoding includes attribute classification code and attribute sequence code. The attribute sequence code is a variable-length design, used to sort and number vector objects of the same category within the same three-dimensional mesh.

[0034] Further, in step S4, generating a unified spatial grid code for non-spatial data includes:

[0035] Obtain the spatial location coordinates of non-spatial data and convert them into latitude and longitude coordinates;

[0036] Select the appropriate subdivision level based on the spatial extent of the non-spatial data associated region;

[0037] Based on the three-dimensional mesh system, the code of the mesh blocks covered by non-spatial data is calculated, and the main covering blocks are selected as the coding reference.

[0038] Generate partition identification codes for non-spatial data, including three-dimensional partition location information coding, data type and source information coding, time information coding, and extended information coding.

[0039] Furthermore, in step S6, the dynamic scheduling and display of the data container includes:

[0040] Based on unified spatial grid coding, it enables rapid location and retrieval of multi-source heterogeneous data within any region;

[0041] Enables on-demand data aggregation, seamless stitching, and partial dynamic loading;

[0042] For large-scale image data that has not been seamlessly processed, rapid display is achieved based on logical aggregation.

[0043] Furthermore, the method also includes:

[0044] After the data is entered into the database, the large data index table is automatically updated, and a data update notification is sent to the visualization subsystem to ensure data consistency and real-time performance across all modules within the system.

[0045] A computer program product includes a computer program that, when executed by a processor, implements the data container storage and management method based on a multi-scale spatially partitioned grid.

[0046] The present invention has the following beneficial effects:

[0047] This invention proposes a data container storage and management method based on a multi-scale spatial partitioning grid. Addressing the current problems of data fragmentation, index redundancy, complex stitching, and slow query response in the organization, logical scheduling, regional querying, and distributed stitching display of spatial big data, this invention proposes a data container mechanism based on a partitioning network without altering the original data segmentation / scene division system. This enables efficient logical organization and unified management of massive, multi-source, heterogeneous data. By logically binding spatial data with partitioned patches and constructing a scalable multi-layered partitioning identifier system, flexible integration and precise scheduling of remote sensing imagery, 3D models, trajectory data, etc., in multi-scale space are achieved, significantly improving the system's overall capabilities in data storage consistency, retrieval performance, and display efficiency.

[0048] Specifically, this invention relates to a data container storage and management method based on low-altitude multi-scale spatial subdivision grids. By constructing a unified three-dimensional multi-scale spatial subdivision coding system, it achieves integrated global representation and cross-domain collaborative organization of various data types, including imagery, vector data, and non-spatial data. Driven by unified spatiotemporal coding, this method encapsulates multi-source heterogeneous data into computable and aggregable data container units, enabling cross-system and cross-platform fusion, association, and efficient sharing at the logical layer. Simultaneously, through a subdivision coding index table, a distributed storage resource pool, and a coding-driven retrieval and display mechanism, it breaks the coupling relationship between data and physical paths, significantly improving the system's scalability, cross-domain collaborative capabilities, response speed, and energy efficiency. This fundamentally solves problems such as dispersed spatial data organization, limited fusion, and inefficient retrieval, laying a technical foundation for building a unified, efficient, and intelligent spatial information service system.

[0049] Compared with the prior art, the significant technical advantages of the present invention are mainly reflected in the following aspects:

[0050] (1) Unified spatiotemporal coding system:

[0051] This invention proposes a unified coding system based on a multi-scale spatiotemporal partitioning grid. Compared to the existing technology where multiple coding systems such as QuadKey, Morton, and orbital partitioning operate in parallel and are incompatible, this system achieves unified expression and organization of spatial data across different platforms, types, and scales. By introducing a collaborative coding mechanism of temporal and spatial dimensions within the same partitioning logic, this system enables data to possess not only unique spatial location identifiers but also traceability of temporal evolution, thereby establishing a spatiotemporally integrated coding framework that is fully covered, recursively aggregated, and computationally locatable. Compared to existing technologies, this coding system overcomes the limitations of traditional indexes that rely on file paths or database fields, achieving logical association and semantic unity of multi-source spatial information. It provides a unified entry point for data retrieval, aggregation, updating, and intelligent scheduling, significantly improving the system's organizational consistency and query efficiency.

[0052] Furthermore, this coding system possesses excellent scalability and compatibility, seamlessly integrating with existing remote sensing, navigation, surveying and mapping, and geographic information systems to achieve cross-platform coding mapping and parsing capabilities. Based on this, data can be recursively aggregated and decomposed across different resolutions, time points, and geographical scales, thereby supporting multi-level and multi-dimensional spatial data computation and display, laying the foundation for building a globalized and refined spatiotemporal information service platform.

[0053] (2) Cross-domain integration mechanism:

[0054] To address the fragmentation issues in heterogeneous data fusion and cross-departmental collaboration within existing remote sensing data platforms, this invention proposes a data "logical container" mechanism driven by unified coding. This mechanism achieves integrated organization and dynamic fusion of multi-source spatial data across domains, systems, and modalities. Using unified spatiotemporal coding as the core index, this mechanism encapsulates remote sensing imagery, vector geographic features, terrain models, attribute data, and thematic results into independent data units. This ensures that data from different sources, formats, and precisions possess a consistent positioning benchmark and aggregatable characteristics at the logical level. Through the collaborative scheduling of the partitioned coding index table and the distributed storage resource pool, the system can automatically aggregate, call on demand, and display multi-source data while loading within the same spatiotemporal grid, significantly improving cross-domain collaboration efficiency and system response speed.

[0055] Compared to existing technologies that rely on physical path mapping and manual data docking, this invention achieves logical fusion and intelligent interoperability of spatial data. This enables data from multiple fields, including geographic information, meteorology, oceanography, and the environment, to be shared and served within a unified spatiotemporal framework. It breaks down industry barriers and platform fragmentation, achieving true "cross-domain integration." This mechanism not only significantly improves the management efficiency and sharing depth of spatial data but also provides a new technological path and strong technical support for building a national and even global spatial information collaborative service system.

[0056] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0057] Figure 1 This is the flowchart of the existing method.

[0058] Figure 2 This is a flowchart of the data container storage and management method based on multi-scale spatial grid partitioning of the present invention.

[0059] Figure 3 This is an example diagram of the three-dimensional segmentation and encoding process according to an embodiment of the present invention. Detailed Implementation

[0060] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0061] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0062] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0063] This invention aims to solve the problems of fragmentation, cross-domain fusion, and inefficient retrieval of spatial big data. It provides a data container storage and management method based on multi-scale spatial partitioning grids, and proposes a unified coding system based on multi-scale spatial partitioning grids to encapsulate multi-source heterogeneous data into "data containers". This enables efficient logical organization, unified management and control, and cross-domain collaborative scheduling of massive data, and significantly improves data storage consistency, retrieval and display efficiency.

[0064] See Figure 1 This invention provides a method for storing and managing data containers based on multi-scale spatial partitioning grids, comprising the following steps:

[0065] Step S1: Construct a multi-scale spatial subdivision grid coding system: Based on the theory of Earth spatial subdivision, construct a multi-scale spatial subdivision grid covering the Earth's surface space and height dimension, assign a unique code to each grid unit, and form a unified spatiotemporal coding framework.

[0066] In some embodiments, step S1, the construction of a multi-scale spatial subdivision grid coding system includes: constructing a planar two-dimensional spatial subdivision model: using a quadtree to perform multi-level subdivision of the Earth's surface, generating multiple levels of grid units, and assigning a unique code to each grid unit; constructing a three-dimensional spatial subdivision model: based on the planar two-dimensional spatial subdivision model, using a bisection method to perform multi-level subdivision of the height dimension, generating three-dimensional grid units, and assigning a unique code to each unit; wherein, for grid codes of different levels, the upper-level grid code is generated by the intermediate value of adjacent grid codes to avoid code duplication.

[0067] Step S2, Data Acquisition and Transmission: Acquire multi-source spatial data, including image data, vector data and non-spatial data, and transmit the data to the data processing center.

[0068] Step S3, Data Consistency Detection and Normalization: Perform consistency detection and normalization on the collected data, including coordinate system conversion, file format standardization, and metadata extraction.

[0069] In some embodiments, step S3, the data consistency detection and normalization process includes: performing consistency detection on the data, identifying inconsistent data and listing the reasons; performing data normalization process, including cataloging geospatial data, extracting metadata, converting the coordinate system to a unified coordinate system, and converting the file format to a standard format; and expanding the system to achieve the integration of newly added data formats into the database.

[0070] Step S4, Spatial Data Integrated Coding: Based on the multi-scale spatial grid coding system, a unified spatial grid code is generated for image data, vector data and non-spatial data respectively, and a mapping relationship between data and grid is established.

[0071] In some embodiments, step S4, generating a unified spatial grid code for the image data, includes: logically partitioning the image data and establishing a mapping relationship between the data and a three-dimensional multi-scale spatial partitioning grid based on its spatial range; employing different logical partitioning methods for image data of different product levels: for low-level images, using an outward logical partitioning method to extend the spatial range of the three-dimensional partitioning grid to cover possible geographic error ranges; for high-level images, using a precise logical partitioning method to directly establish the mapping; generating a partitioning index file for the image data, recording the three-dimensional location code of each logical image block and its position information in the image file.

[0072] In some embodiments, step S4, generating a unified spatial grid code for vector data includes: calculating the minimum enclosing cube of the vector object and obtaining the three-dimensional coordinates of its eight corner points; selecting the optimal subdivision level based on the volume of the minimum enclosing cube; calculating the codes of all three-dimensional subdivision grids that the vector object traverses within the selected subdivision level; selecting the three-dimensional subdivision grid with the largest volume coverage as the primary location identifier and generating a unique code.

[0073] In some embodiments, the encoding of the vector data further includes attribute information encoding and time information encoding, wherein the attribute information encoding includes attribute classification code and attribute sequence code, and the attribute sequence code is a variable length design, used to sort and number vector objects of the same category within the same three-dimensional mesh.

[0074] In some embodiments, step S4, generating a unified spatial grid code for non-spatial data, includes: obtaining the spatial location coordinates of the non-spatial data and converting them into latitude and longitude coordinates; selecting a suitable subdivision level based on the spatial range of the region associated with the non-spatial data; calculating the grid block code covered by the non-spatial data based on the three-dimensional subdivision grid system, and selecting the main covering block as the coding reference; generating a subdivision identifier code for the non-spatial data, including three-dimensional subdivision location information code, data type and source information code, time information code, and extended information code.

[0075] Step S5, Data Input and Storage: Input and store the encoded data into the database and update the data index.

[0076] Step S6: Dynamic scheduling and display of data containers: Based on the unified spatial grid coding, dynamic scheduling, logical aggregation and visualization of multi-source data are realized.

[0077] In some embodiments, step S6, the dynamic scheduling and display of the data container includes: quickly locating and calling multi-source heterogeneous data within any region based on unified spatial grid coding; realizing on-demand aggregation, seamless stitching and local dynamic loading of data; and achieving rapid display of large-scale image data that has not undergone seamless processing based on logical aggregation.

[0078] In some embodiments, the method further includes: automatically updating the large data index table after the data is stored in the database, and sending a data update notification to the visualization subsystem to ensure data consistency and real-time performance of each module in the system.

[0079] The data container storage and management method based on multi-scale spatial grid partitioning proposed in this invention has the following main technical advantages: It constructs a multi-scale, three-dimensional unified spatiotemporal coding system, breaking the limitations of incompatibility between existing coding systems such as QuadKey and Morton. This achieves unique identification and unified organization of spatial data across multiple platforms, types, and scales, improving data retrieval efficiency and system consistency. Through an innovative "data container" mechanism, it logically encapsulates multi-source heterogeneous data without altering the original data segmentation / scene division system, enabling efficient sharing and dynamic aggregation across systems and platforms, breaking down industry data barriers. Simultaneously, it breaks down the coupling between data and physical paths, significantly improving system scalability, cross-domain collaboration capabilities, response speed, and energy efficiency through coding-driven storage, retrieval, and scheduling. This fundamentally solves the problems of data fragmentation, limited fusion, and inefficient retrieval.

[0080] The features, principles, and advantages of specific embodiments of the present invention are further described below.

[0081] A data container storage and management method based on multi-scale spatial grid partitioning, the overall process of which is as follows: Figure 2 As shown. This method focuses on a unified encoding and organization approach for multiple data types based on 3D spatial meshing, enabling multi-scale spatial management of imagery, vector, and non-spatial data. The main steps are as follows:

[0082] 1. Construct a low-altitude spatial coding system.

[0083] Based on the theory of Earth spatial partitioning, the Earth's surface space with longitude [-180°, 180°], latitude [-90°, 90°] and altitude range of [0, 4294967296] cm is divided into 31 grid units, each assigned a unique code to form a spatial reference framework.

[0084] 1) Planar two-dimensional spatial partitioning model.

[0085] For a two-dimensional planar space, the 0th layer subdivision includes the entire Earth's surface space, with a longitude range of [-180°, 180°] and a latitude range of [-90°, 90°]. The 1st layer subdivision divides the Earth's surface into two hemispheres along the 0° meridian, resulting in two spatial grids with a span of 180° in both longitude and latitude. The 2nd layer subdivision further subdivides the two spatial grids obtained from the 1st layer subdivision using a quadtree method, resulting in four 2nd layer spatial grids for each, for a total of eight 2nd layer spatial grids. The longitude and latitude spans of each spatial grid are both 45°. The 3rd to 30th layer subdivisions further subdivides each spatial grid obtained from the 2nd layer subdivision using a quadtree method, resulting in four current layer spatial grids. The longitude and latitude spans of each spatial grid are half that of the previous layer. Based on this, the longitude and latitude spans of the Lth layer grid satisfy formula (1): The number of spatial grids in each layer satisfies formula (2):

[0086] As the location approaches the poles, the physical scale of the grid decreases. For precise calculations, accurate calculations can be performed using the chosen reference ellipsoid and the Vicente formula. Here, the approximate scale (in meters) of each grid layer in the equatorial region can be obtained using formula (3): Based on the planar two-dimensional spatial partitioning model, 31 grid layers of different scales can be obtained. The largest single grid can cover the entire Earth's surface, while the smallest grid has a side length of less than 4 cm, achieving seamless and non-overlapping multi-scale coverage of the Earth's surface. A binary encoding format is used as the basic encoding, which is then converted to decimal numbers, assigning a unique code to each grid space. The binary code has a fixed length of 63 bits, with the first 4 bits used to identify direction and the last 59 bits used to identify spatial location. For spatial region identification, longitude occupies 30 bits, and since the first layer of partitioning only partitions along the longitude direction and not the latitude direction, latitude occupies 29 bits.

[0087] Because spatial coding overlaps between different levels, the following method is used to regenerate the coding: First, multiply all 30-level grid codes by 2 to obtain the identifier code for the corresponding 30-level grid space. Then, group every four adjacent codes [E1, E2, E3, E4], and use the median value as the identifier code for the corresponding 29-level position code, i.e., E = (E2 + E3) / 2 = (E1 + E4) / 2. Figure 3Taking the first group of four grid codes at level 30 as an example, [E1,E2,E3,E4]=[0,2,4,6], E=(2+4) / 2=(0+6) / 2=3, we use 3 as the identifier code for the first grid space at level 29; similarly, the second group [E1,E2,E3,E4]=[8,10,12,14], E=11, is used as the identifier code for the second grid space at level 29; and so on, we can obtain the identifier codes for all grid spaces at level 29. Following the same principle, we can obtain the identifier codes for all grid spaces at levels 1-28. An identifier code can use a single unique code value to identify a specific grid space at a specific level.

[0088] 2) Three-dimensional spatial partitioning model.

[0089] Based on the planar two-dimensional spatial subdivision model, the height dimension will be subdivided into 30 levels from the ground surface to a height of 4294967296cm using a bisection method to obtain a height dimension grid with a scale similar to that of the planar grid.

[0090] The level 0 mesh encompasses the entire height space from 0 to 4294967296 cm; the level 1 mesh divides the height space obtained from the level 0 mesh into two level 1 spatial grids of equal height at the middle height: Regin1 [0, 2147483648) cm and Regin2 [2147483648, The height span of each grid is half that of the level 0 grid; the second level subdivision separates each height space obtained from the first level subdivision from the middle height to obtain two level 2 spatial grids with equal height, resulting in a total of 4 level 2 height-dimensional spatial grids, with a height span of 1073741824cm for each grid; the third to third level subdivision: separates each height space obtained from the previous level subdivision from the middle height to obtain two current level spatial grids with equal height, and the height span of the current level height-dimensional grid is the same as that of the previous level. The first and second levels also satisfy this condition, so the height scale relationship of each level satisfies formula (4).

[0091] (4) The grid scale of each level satisfies formula (5), and the unit is centimeters.

[0092] Based on the three-dimensional spatial partitioning model, 31 grid layers of different scales can be obtained. The largest-scale grid can cover the entire Earth's space in terms of longitude [-180°, 180°], latitude [-90°, 90°], and altitude [0, 4294967296] cm, while the smallest-scale grid has a side length of less than 4 cm, achieving seamless and non-overlapping multi-scale coverage of Earth's space. For ease of identification and calculation, each grid space needs to be assigned a unique code. This paper uses binary encoding as the basic code, which is then converted to decimal numbers for common identification and calculation. The binary code has a fixed length of 94 bits, where the first 5 bits are used to identify the direction, and the last 89 bits are used to identify the three-dimensional spatial location. For the identification of the three-dimensional spatial region, the longitude direction occupies 30 bits, and the altitude direction occupies 30 bits. Since the first layer of partitioning only partitions along the longitude direction and not the latitude direction, the latitude direction occupies 29 bits.

[0093] Because spatial coding overlaps between different levels, the following method is used to regenerate the coding: First, multiply all 30-level grid codes by 2 to obtain the identifier code for the corresponding 30-level grid space. Then, group every 8 adjacent codes [E1, E2, E3, E4, E5, E6, E7, E8], and use the median value as the identifier code for the corresponding 29-level position code, i.e., E = (E4 + E5) / 2 = (E3 + E6) / 2 = (E2 + E7) / 2 = (E1 + E8) / 2. Figure 3 Taking the first group of 8 grid codes of level 30 as an example,

[0094] [E1,E2,E3,E4,E5,E6,E7,E8]=[0,2,4,6,8,10,12,14], E=(6+8) / 2=7, so 7 is used as the identifier code for the first grid space of level 29; similarly, the second group [E1,E2,E3,E4,E5,E6,E7,E8]=[16,18,20,22,24,26,28,30], E=23, is used as the identifier code for the second grid space of level 29; and so on, the identifier codes for all grid spaces of level 29 can be obtained. Following the same principle, the identifier codes for all grid spaces of levels 1-28 can be obtained. An identifier code can use a single unique code value to identify a specific grid space at a specific level.

[0095] 2. Data acquisition and transmission stage.

[0096] Spatial information, including various types such as images, DEMs, vectors, and nautical charts, is collected by data acquisition terminals across the country and then transmitted to the spatial information center server through their respective channels to complete the data collection work and prepare for data entry into the database.

[0097] 3. Data consistency detection and normalization processing.

[0098] 1) Data consistency detection: The detection algorithm is used to detect inconsistent data. The detection results are displayed in a list and the reasons for the data inconsistency are provided.

[0099] 2) Data normalization processing:

[0100] Data standardization is an important foundation for various data grid organization. The processing includes preprocessing such as cataloging geospatial data, extracting metadata, transforming coordinates, and standardizing formats. This enables the standardization and normalization of massive, multi-source, heterogeneous data, laying the technical foundation for the integrated organization and efficient management of spatial data.

[0101] The data normalization process includes:

[0102] Step 1: For data managed by the existing database, export its metadata, including geographic coordinates, resolution, view, original image size, and storage location;

[0103] Step 2: For remote sensing image products, extract metadata such as geographic coordinates, resolution, image size, color depth, imaging time, and satellite information, and extract the browsing image;

[0104] Step 3: For geographic information data, environmental information, images, and other data, extract metadata such as geographic coordinates, resolution / scale, data time, and data source;

[0105] Step 4: Determine if the data coordinate system is CGCS2000, convert non-CGCS2000 coordinate system information to CGCS2000 coordinate system information, and generate metadata;

[0106] Step 5: Determine whether each type of data file format is a standard format, and convert non-standard format information to a standard format;

[0107] Step 6: In accordance with the interface specifications of the database design, catalog and archive all types of data. This completes the preprocessing of various types of spatial data.

[0108] 3) Expand the system to integrate new data formats. If the data format for the new data is provided, the system can be quickly integrated and developed.

[0109] 4. Integrated coding of spatial data.

[0110] For five types of data—raster data, vector data, attribute data, elevation-sensitive data, and target data—a unified spatial grid code is generated based on their geographical location or the geographical area represented by the data. Each data object is assigned a unique code identifier upon being imported into the database, and the system simultaneously creates or updates the relevant data index. When new data is imported, the database system automatically updates the index table and sends a data update notification to the global framework map reading subsystem, ensuring data consistency and real-time performance across all modules within the system. This achieves integrated organization and association of various data types on a low-altitude, multi-scale spatially partitioned grid.

[0111] 1) Image data segmentation and organization.

[0112] The basic method of image data segmentation and preprocessing is as follows: Based on the existing spatial data organization system, and taking the multi-scale spatial segmentation grid of the low-altitude Earth as a reference, the image data is logically segmented according to spatial location and the data location identification coding is performed. A new set of three-dimensional spatial location identification coding and internal logical block coding is added to the image data. On this basis, a mapping relationship between the image data and its corresponding three-dimensional segmentation grid is established to realize the efficient cataloging, storage, retrieval and application of image data.

[0113] Image logical partitioning, during image data processing, involves logically partitioning the image according to a three-dimensional multi-scale spatial grid, generating a location-coded index file for each logical image block. This file records the three-dimensional location code of each logical image block and its start and end row and column numbers in the image file, laying the foundation for subsequent image data partitioning applications.

[0114] For different product levels of remote sensing image data, taking into full account the positioning error of the image itself, differentiating processing is carried out by using the external logical partitioning method and the precise logical partitioning method respectively.

[0115] The specific method involves extracting the image thumb map and its spatial extent information for low-level (Level 1 and Level 2) remote sensing image data. A correspondence is then established between the image's spatial extent and a three-dimensional multi-scale spatial mesh, achieving spatial mapping between the remote sensing image and the three-dimensional mesh. Given the positioning errors inherent in low-level images, the spatial extent of the three-dimensional mesh is expanded outwards during spatial relationship establishment to cover potential geographic errors in the image data. Subsequently, based on the expanded mesh spatial extent, the correspondence between the four ground corner points of the mesh and the row and column numbers of the image data is determined and written into the corresponding mesh index file.

[0116] For high-level (Level 3 and above) remote sensing image data, extract the image thumb map and its spatial extent information. Establish a direct correspondence between the image's spatial extent and the 3D mesh, without needing to extend it outwards. Based on the actual spatial extent of the mesh, determine the correspondence between the four corner points of the mesh and the row and column numbers of the image data, and write this information into the corresponding mesh index file.

[0117] In the process of cataloging and organizing remote sensing data, the image data after being divided into three-dimensional blocks is identified by expanding the original image data catalog table based on the existing data organization system of remote sensing satellite ground application system, adding a three-dimensional spatial partition location identifier field, and forming a unified data organization structure based on a three-dimensional multi-scale spatial partitioning framework.

[0118] The data file location identifier encoding access method is as follows: for the segmentation identifier encoding of remote sensing image data, an aggregate encoding composed of location segmentation block encoding, the number of meridional blocks, the number of latitudinal blocks, and the number of elevation blocks is used.

[0119] 2) Vector data partitioning and organization.

[0120] The basic method for vector data subdivision preprocessing is as follows: based on the spatial location and three-dimensional region range of vector data (such as digital maps, building models, three-dimensional point clouds, etc.), calculate the corresponding three-dimensional spatial subdivision grid code of the Earth, so that it forms an intrinsic relationship with the three-dimensional subdivision grid of the Earth.

[0121] The primary function of the vector data identification model is to identify and encode vector data, associating it with the spatial, attribute, and temporal characteristics of vector objects, thus providing technical support for the efficient organization, representation, retrieval, and indexing of vector data. Locational features, attribute features, and temporal features are the three most important types of features in vector data. Therefore, the vector data partitioning and identification coding model design consists of three-dimensional partitioning location information coding, attribute information coding, temporal information coding, and extended coding.

[0122] [1] The three-dimensional partition location information encoding mainly expresses the regional location information of vector data in the multi-scale partition space, including the partition level code, the three-dimensional partition mesh volume encoding, etc.

[0123] [2] Attribute information encoding mainly expresses the individual characteristic information of vector data within a defined scale and three-dimensional location, as well as sorting information.

[0124] [3] Time information encoding mainly provides spatial-temporal record association information for vector data.

[0125] [4] The extension code provides reserved coding positions for future practical application needs, which can be added by the user as needed; for example, the security key information and department information of vector objects can be added to the extension code.

[0126] Depending on the application requirements and modes, vector data identification encoding can be divided into two modes: vector block identification encoding and vector object identification encoding. Vector block identification encoding is primarily used for the unified management of vector data organized into mesh blocks, suitable for managing large areas or volumes of vector data within three-dimensional Earth space (such as building complexes or city models). Vector object identification encoding, on the other hand, can identify individual vector objects (such as single buildings, trees, or roads), suitable for refined management and analysis of vector data. The method of vector block identification is similar to that of 3D raster data identification and will not be elaborated further.

[0127] The attribute encoding of the vector object mesh identification model consists of two parts: an attribute classification code and an attribute sequence code. The attribute classification code categorizes vector objects according to function, form, or purpose (e.g., buildings, roads, vegetation, water bodies, underground pipelines), assigning a unique code to each category. The attribute sequence code is used to sort and number multiple vector objects of the same category within the same 3D mesh. The length of the attribute sequence code is determined by the number of vector objects of the same category within the mesh. For example, if there are 5 vector objects of the same category within a 3D mesh, the attribute sequence code length is 5, which can be represented by 3 bits; if there are 130 vector objects of the same category, the attribute sequence code length is 130, which can be represented by 8 bits. Because the attribute sequence code is a variable-length design, 5 bits are reserved at the beginning of the encoding to record the actual length of the attribute sequence code. This design ensures efficient identification and indexing of individual vector objects even within large 3D meshes.

[0128] When determining the encoding of the section location information of a vector object, the system has selected a three-dimensional section level that matches the spatial size and position of the vector object. The same three-dimensional section mesh volume usually does not contain too many targets of the same type, thus avoiding coding redundancy caused by excessively long attribute label codes.

[0129] By appropriately selecting the subdivision level and grid size, attribute encoding can, in principle, support globally unique identification of any vector target on the Earth's surface and in the space above and below the surface. Based on the vector object subdivision and identification model and encoding method, the specific implementation process of vector object subdivision and identification is given:

[0130] [1] Calculate the minimum outer cube of the vector object based on the boundary coordinates (including height information) of the vector object, and obtain the three-dimensional coordinates of its eight corner points;

[0131] [2] Calculate the volume V of the smallest enclosing cube based on its three-dimensional coordinates;

[0132] [3] Compare the volume of V with the three-dimensional mesh at each level, select the optimal meshing level according to the identification level strategy, so that the outer body has the best positioning accuracy in the mesh corresponding to the level;

[0133] [4] Calculate the codes of all three-dimensional mesh volumes that the outer cube spans within the selected mesh level based on the three-dimensional coordinates of the eight corner points of the outer cube;

[0134] [5] Calculate the volume percentage of the outer cube within each spanned 3D mesh to determine the main covered mesh volume;

[0135] [6] Select the 3D mesh with the largest volume coverage as the main location identifier of the vector object; if there are parallel meshes, select the mesh with the spatial position relative to the "upper left and front";

[0136] [7] Based on the unified coding rules, calculate the unique code of the three-dimensional mesh volume determined in [6], and use it as the location identifier code of the vector object.

[0137] 3) Non-spatial data partitioning and organization.

[0138] Non-spatial data with spatial location attributes, such as text, reports, images, and videos, are assigned location identification codes based on a three-dimensional mesh according to the spatial region they are associated with. This creates a logical connection between the non-spatial data and the spatial mesh, enabling effective binding and efficient management of non-spatial data and the spatial mesh.

[0139] The generation process for non-spatial data location identifier codes is as follows:

[0140] [1] Obtain the spatial location coordinates and optional elevation information of non-spatial data;

[0141] [2] Determine the spatial location coordinate type of non-spatial data. If it is latitude and longitude coordinates, proceed to step 3. If it is not latitude and longitude coordinates, convert its coordinates to latitude and longitude coordinates.

[0142] [3] Select appropriate data segmentation levels based on the spatial range (including horizontal and vertical dimensions) of the non-spatial data associated area to achieve multi-scale spatial positioning;

[0143] [4] Based on a three-dimensional mesh system (such as GeoSOT-3D or similar systems), calculate the code of the mesh blocks covered by non-spatial data. For data with range information, the set of mesh blocks it spans can be further calculated, and the main covering blocks can be selected as the coding reference;

[0144] [5] Based on business needs, select automatic or manual methods to generate non-spatial data partitioning identifier codes. The coding structure consists of three-dimensional partitioning location information coding, data type and source information coding, time information coding, and extended information coding.

[0145] The most important aspect of integrating non-spatial data partitioning into existing information systems is the generation and management of non-spatial data partition location identifiers. The specific application process is as follows:

[0146] [1] On each data acquisition or processing machine, a three-dimensional subdivision coding generator middleware is automatically configured to collect data spatial location information in real time and generate the corresponding three-dimensional subdivision location identifier code.

[0147] [2] Configure a dedicated 3D subdivision coding management server on the server side for centralized management, version control and data consistency maintenance of non-spatial data subdivision identifiers;

[0148] [3] Deploy a partitioned coding search engine that supports three-dimensional spatial retrieval to achieve fast data location, indexing and retrieval based on three-dimensional mesh block coding;

[0149] [4] The three-dimensional subdivision identifiers generated in the distributed system are automatically collected by the subdivision coding search engine, and the non-spatial data is uniformly identified, stored and applied in the global information system through the management server.

[0150] 5. Data entry and storage.

[0151] Data management is divided into the management of existing data and the management of newly added data; and the management of structured and unstructured data. Spatial data is stored after being partitioned and encoded, and the storage confirmation information and data storage location are fed back to the spatial data partitioning and encoding subsystem so that it can complete the data index table update.

[0152] 6. Dynamic scheduling and display of data containers.

[0153] This method supports dynamic scheduling and 3D spherical display based on data containers. Through mesh encoding, users can quickly locate and retrieve multi-source heterogeneous data within any region, enabling on-demand data aggregation, seamless stitching, and local dynamic loading. Especially for large-scale image data that has not undergone seamless processing, rapid display can be achieved based on logical aggregation without physical modifications. For high-precision image products, standard sheet data can be directly generated, significantly improving the data response speed and overall system efficiency in 3D visualization scenes.

[0154] In summary, this invention proposes a data container storage and management method based on multi-scale spatial partitioning grids. The key innovative contributions and technical points of this invention include:

[0155] (1) Unified spatiotemporal coding:

[0156] This invention proposes a unified spatiotemporal coding system based on multi-scale spatial grid partitioning. By dividing geographic space into hierarchical, recursive, and seamlessly overlapping multi-level grids, it achieves a three-dimensional integrated expression of longitude, latitude, and altitude. This coding system possesses uniqueness, recursiveness, and aggregability, providing unified logical identification and spatial positioning capabilities for different types of data (including imagery, vector data, terrain data, attribute data, and thematic data) across the entire domain. Unlike existing single-dimensional or localized coding methods such as QuadKey and Morton, the spatiotemporal coding of this invention not only unifies the spatial representation of data but also establishes a unified indexing mechanism across scales, time periods, and platforms, thereby eliminating the problems of coding incompatibility and spatial logical fragmentation between different systems. This coding system supports efficient retrieval, aggregation, and mapping of spatial data, laying the foundation for collaborative management and analysis of multi-source data.

[0157] The unified spatiotemporal coding system constructed in this invention further breaks away from the traditional data organization method that relies on file paths and physical locations, achieving logical unity and independence. Through this coding, the system can standardize and semantically associate datasets from different sources using grid cells as the basic unit, enabling data to possess a computable and exchangeable unified identifier across different domains and platforms. This approach significantly improves the management accuracy and consistency of spatial data, providing a universal technical support framework for cross-domain data interoperability and globally integrated management.

[0158] (2) Cross-domain integration:

[0159] This invention proposes a cross-domain fusion mechanism driven by unified spatiotemporal coding. By constructing logical encapsulation methods of "data containers," it organizes, binds, and dynamically schedules multi-source heterogeneous spatial data within a unified spatiotemporal framework. This mechanism uses spatiotemporal coding as its core link, mapping data units from different domains, systems, and formats to the same partitioned grid unit, achieving logical alignment and semantic unification at the data layer. Through this mechanism, the system can seamlessly fuse multiple types of data, including imagery, vectors, attributes, and models, without relying on traditional format conversions or path indexing, significantly improving data sharing and collaboration capabilities across platforms and industries.

[0160] The cross-domain fusion mechanism of this invention not only achieves logically unified management of multi-source data, but also constructs a scalable distributed resource pool and an encoding-driven dynamic retrieval mechanism, enabling data to be aggregated on demand and displayed while loading. Through this encoding-based fusion and scheduling approach, the system possesses cross-domain access, intelligent association, and collaborative service capabilities, supporting comprehensive analysis and visualization in complex scenarios, and enhancing the application value of spatial information systems in multi-domain collaboration, regional integration, and integrated services.

[0161] Compared with the prior art, the significant technical advantages of the present invention are mainly reflected in the following aspects:

[0162] (1) Unified spatiotemporal coding system:

[0163] This invention proposes a unified coding system based on a multi-scale spatiotemporal partitioning grid. Compared to the existing technology where multiple coding systems such as QuadKey, Morton, and orbital partitioning operate in parallel and are incompatible, this system achieves unified expression and organization of spatial data across different platforms, types, and scales. By introducing a collaborative coding mechanism of temporal and spatial dimensions within the same partitioning logic, this system enables data to possess not only unique spatial location identifiers but also traceability of temporal evolution, thereby establishing a spatiotemporally integrated coding framework that is fully covered, recursively aggregated, and computationally locatable. Compared to existing technologies, this coding system overcomes the limitations of traditional indexes that rely on file paths or database fields, achieving logical association and semantic unity of multi-source spatial information. It provides a unified entry point for data retrieval, aggregation, updating, and intelligent scheduling, significantly improving the system's organizational consistency and query efficiency.

[0164] Furthermore, this coding system possesses excellent scalability and compatibility, seamlessly integrating with existing remote sensing, navigation, surveying and mapping, and geographic information systems to achieve cross-platform coding mapping and parsing capabilities. Based on this, data can be recursively aggregated and decomposed across different resolutions, time points, and geographical scales, thereby supporting multi-level and multi-dimensional spatial data computation and display, laying the foundation for building a globalized and refined spatiotemporal information service platform.

[0165] (2) Cross-domain integration mechanism:

[0166] To address the fragmentation issues in heterogeneous data fusion and cross-departmental collaboration within existing remote sensing data platforms, this invention proposes a data "logical container" mechanism driven by unified coding. This mechanism achieves integrated organization and dynamic fusion of multi-source spatial data across domains, systems, and modalities. Using unified spatiotemporal coding as the core index, this mechanism encapsulates remote sensing imagery, vector geographic features, terrain models, attribute data, and thematic results into independent data units. This ensures that data from different sources, formats, and precisions possess a consistent positioning benchmark and aggregatable characteristics at the logical level. Through the collaborative scheduling of the partitioned coding index table and the distributed storage resource pool, the system can automatically aggregate, call on demand, and display multi-source data while loading within the same spatiotemporal grid, significantly improving cross-domain collaboration efficiency and system response speed.

[0167] Compared to existing technologies that rely on physical path mapping and manual data docking, this invention achieves logical fusion and intelligent interoperability of spatial data. This enables data from multiple fields, including geographic information, meteorology, oceanography, and the environment, to be shared and served within a unified spatiotemporal framework. It breaks down industry barriers and platform fragmentation, achieving true "cross-domain integration." This mechanism not only significantly improves the management efficiency and sharing depth of spatial data but also provides a new technological path and strong technical support for building a national and even global spatial information collaborative service system.

[0168] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0169] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.

[0170] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0171] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc or CD-ROM; magnetic surface memory can be disk storage or magnetic tape storage. The storage media described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0172] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0173] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0174] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0175] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0176] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0177] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0178] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0179] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0180] 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 equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A method for data set container storage and management based on multi-scale spatial partitioning grid, characterized in that, Includes the following steps: S1. Construct a multi-scale spatial subdivision grid coding system: Based on the theory of Earth spatial subdivision, construct a multi-scale spatial subdivision grid covering the Earth's surface space and height dimension, assign a unique code to each grid unit, and form a unified spatiotemporal coding framework; S2. Data Acquisition and Transmission: Acquire multi-source spatial data, including image data, vector data, and non-spatial data, and transmit the data to the data processing center; S3. Data Consistency Detection and Normalization: Perform consistency detection and normalization on the collected data, including coordinate system conversion, file format standardization, and metadata extraction. S4. Spatial Data Integrated Coding: Based on the multi-scale spatial grid coding system, a unified spatial grid code is generated for image data, vector data and non-spatial data respectively, and a mapping relationship between data and grid is established. The process of generating a unified spatial grid code for image data includes: logically partitioning the image data and establishing a mapping relationship between its spatial range and a three-dimensional multi-scale spatial partitioning grid; employing different logical partitioning methods for image data of different product levels; generating a partitioning index file for the image data, recording the three-dimensional location code of each logical image block and its position information in the image file; the process of generating a unified spatial grid code for vector data includes: calculating the minimum bounding cube of the vector object and obtaining the three-dimensional coordinates of its eight corner points; selecting the optimal partitioning level based on the volume of the minimum bounding cube; calculating the codes of all three-dimensional partitioning grid bodies crossed by the vector object within the selected partitioning level; selecting the three-dimensional partitioning grid body with the largest volume coverage as the main location identifier and generating a unique code; the process of generating a unified spatial grid code for non-spatial data includes: obtaining the spatial location coordinates of the non-spatial data; selecting a suitable partitioning level based on the spatial range of the associated region of the non-spatial data; calculating the grid body block codes covered by the non-spatial data based on the three-dimensional partitioning grid system and selecting the main covering block as the coding reference; and generating the partitioning identifier code for the non-spatial data. S5. Data entry and storage: Enter and store the encoded data, and update the data index; S6. Dynamic scheduling and display of data containers: Based on the unified spatial grid coding, dynamic scheduling, logical aggregation and visualization of multi-source data are realized.

2. The multi-scale spatially partitioned grid based data set packing and managing method according to claim 1, wherein, In step S1, constructing a multi-scale spatial subdivision grid coding system includes: Constructing a planar two-dimensional spatial partitioning model: The Earth's surface is partitioned into multiple levels using a quadtree approach, generating multiple levels of grid cells, with each grid cell assigned a unique code; Constructing a three-dimensional spatial partitioning model: Based on the existing two-dimensional spatial partitioning model, the height dimension is partitioned into multiple levels using a bisection method to generate three-dimensional mesh units, each of which is assigned a unique code; In this process, for grid codes of different levels, the upper-level grid code is generated by the intermediate value of adjacent grid codes to avoid code duplication.

3. The multi-scale spatially partitioned grid based data set packing and managing method of claim 1, wherein, In step S3, the data consistency detection and normalization process includes: Perform a consistency check on the data, identify inconsistent data, and list the reasons; The data is normalized, including cataloging of geospatial data, metadata extraction, coordinate system conversion to a unified coordinate system, and file format conversion to a standard format; The system is extended to achieve integration of newly added data formats.

4. The method of claim 1, wherein the method further comprises: In step S4, for low-level images, the spatial range of the three-dimensional partition grid is expanded to cover the possible geographical error range using an outward expansion logical partition method; for high-level images, the mapping is directly established using an accurate logical partition method.

5. The method of claim 1, wherein the method further comprises: The encoding of the vector data also includes attribute information encoding and time information encoding, wherein the attribute information encoding includes attribute classification code and attribute index code, and the attribute index code is designed to be variable length, used for sorting and numbering the same type of vector objects in the same three-dimensional partition grid.

6. The multi-scale spatially partitioned grid based data set packing and managing method of claim 1, wherein, In step S4, the spatial position coordinates of the non-spatial data are converted to latitude and longitude coordinates; and the partition identification code includes three-dimensional partition location information code, data type and source information code, time information code and extension information code.

7. The multi-scale spatially partitioned grid based data set packing and managing method of claim 1, wherein, In step S6, the dynamic scheduling and display of the data container include: Based on the unified spatial grid encoding, any multi-source heterogeneous data in a certain area can be quickly located and called; The data can be aggregated, seamlessly spliced and locally dynamically loaded on demand; For large-scale image data that has not been seamlessly processed, fast display can be achieved based on logical aggregation.

8. The multi-scale spatially partitioned grid based data set packing and managing method of claim 1, wherein, The method further includes: After the data is stored, the data index table is automatically updated, and a data update notification is sent to the visualization subsystem, to ensure the data consistency and real-time performance of each module in the system.

9. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method for storing and managing data containers based on multi-scale spatial partition grids according to any one of claims 1 to 8.

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