Spatial target indexing, query method and device based on global spatial stereo subdivision

Abstract

The present invention discloses a spatial target indexing, querying method and device based on global spatial stereoscopic subdivision, providing a spatial target indexing and querying method that can support global space and realize unified, efficient and scalable global spatiotemporal data. Spatiotemporal data is obtained from global spatial data. The position information of the spatial geometric object is mapped to at least one target grid in the grid subdivision system, and the position description information and time code of the target grid are determined to form a grid code corresponding to the spatiotemporal data. The grid code, time code and grid attribute data are composed of index entries, the spatiotemporal data is organized into a multidimensional index table, the multidimensional index table is serialized into a data index table with a one-to-one mapping relationship, the global spatial data is stored based on the data index table, and an index database is obtained; the value is the index path of the corresponding spatial big data index storage information. Spatiotemporal data query is performed based on the established index database.

Description

Technical Field

[0001] The present invention relates to the technical field of global spatial data processing, and in particular to a spatial target indexing and query method and device based on global spatial stereoscopic segmentation. Background Art

[0002] In the era of big data, many application scenarios covering the entire space of land, sea, air, space, underground and underwater generate data with time and space dimensions. For example, data generated by airplanes, ships, mountains, rivers, forests, fields, lakes, grasslands and mines, urban buildings, indoor computer rooms, the management of various equipment in computer rooms, energy generation, distribution and prediction, these data with time and space dimensions can be called spatiotemporal data.

[0003] To manage spatiotemporal data, spatiotemporal databases have emerged. These databases can store spatiotemporal data and process the temporal and spatial attributes of data objects. However, with the increasing diversity of spatiotemporal data, spatiotemporal databases face the challenge of organizing and managing these diverse data sets in a unified manner.

[0004] With the rapid growth of spatiotemporal data, and considering its multi-source and heterogeneous nature, a series of solutions have been proposed for querying massive amounts of spatiotemporal data, such as the use of an index structure combining R-trees and inverted tables. However, R-tree indexes are inefficient for large amounts of data, and inverted table indexes rely on keywords as primary keys.

[0005] In view of the characteristics of multi-source, heterogeneous and massive spatiotemporal data in the global space, it is necessary to consider more efficient and flexible spatiotemporal data indexing methods based on the existing data query based on grid index and multi-level subdivision three-dimensional grid model.

[0006] Due to the multi-source, heterogeneous, and massive nature of spatiotemporal data, the system has high requirements for basic data and is difficult to obtain. The data association capabilities of existing systems are weak, making it difficult to associate spatiotemporal data.

[0007] Therefore, there is currently a lack of an indexing and query solution that can effectively associate multi-source, heterogeneous, and massive global spatial data. Summary of the Invention

[0008] In light of this, the present invention provides a spatial object indexing and query method and device based on global spatial stereoscopic segmentation. This method supports the entire spatial domain and enables unified, efficient, and scalable global spatiotemporal data. Based on this established spatial object indexing method, data queries can be performed across different spatial ranges.

[0009] To achieve the above object, the present invention provides a spatial target indexing method based on global spatial stereo subdivision, comprising the following steps:

[0010] Step 1: Obtain spatiotemporal data from the global spatial data. The spatiotemporal data contains the location and time information of spatial geometric objects.

[0011] The position information of the spatial geometric object is mapped to at least one target grid in the grid subdivision system, and the position description information and time code of the target grid are determined to form a grid code corresponding to the spatiotemporal data.

[0012] Step 2: Grid codes and grid attribute data are combined into index entries, and spatiotemporal data are organized into a multidimensional index table. The first dimension of the multidimensional index table uses the grid code as the row primary key for sorting; the second dimension is the attribute group. An attribute group is a collection of multiple attribute columns, and the attributes in an attribute group are similar; the third dimension is the attribute column. The number of attribute columns in an attribute group is not limited, and the attribute columns are named in the form of "attribute group:attribute column".

[0013] Step 3: Serialize the multidimensional index table into a data index table with a one-to-one mapping relationship. The data index table is implemented in a key-value format, where the key is represented by a grid code + grid attribute combination structure; the value is the index path of the corresponding spatial big data index storage information.

[0014] The global spatial data is stored based on the data index table to obtain an index database.

[0015] Furthermore, the global spatial data includes at least geological data, which includes at least engineering geology, hydrogeology, urban geology, agricultural geology, disaster geology, and environmental geology data of geological surveys; spatial geometric objects include mountains, water, forests, fields, lakes, grass, and minerals.

[0016] Furthermore, before obtaining spatiotemporal data from the global spatial data, data preprocessing is also included. The specific process is as follows:

[0017] S101: The global spatial data includes data managed by an existing database, remote sensing image data, geological data, geographic information data, and environmental information data, and metadata is extracted from the global spatial data.

[0018] For data managed in existing databases, metadata include geographic coordinates, resolution, browsing map, original image size, and storage location.

[0019] For remote sensing image data, its metadata includes geographic coordinates, resolution, image size, color depth, imaging time, satellite data, and browsing map.

[0020] For geological data, geographic information data and environmental information data, the metadata includes geographic coordinates, resolution / scale, data time and data source.

[0021] S102: Determine whether the data coordinate system of the metadata is CGCS2000, and convert the non-CGCS2000 coordinate system information into CGCS2000 coordinate system information.

[0022] S103: Determine whether the format of each type of data file is a standard format, and convert non-standard format data into a standard format.

[0023] S104: Comply with the interface specifications of the database design, catalog and archive various metadata, and complete the preprocessing of the global spatial data.

[0024] Furthermore, the location information of the spatial geometric object is mapped to at least one target grid in the grid subdivision system, and the location description information and time code of the target grid are determined to form the grid code corresponding to the spatiotemporal data, specifically:

[0025] According to the preset grid subdivision coding standard and grid subdivision level, the position information of the spatial geometric object is mapped to at least one target grid in the grid subdivision system, and the position description information of the target grid in the grid subdivision system is determined; the position description information includes the vertical axis code, horizontal axis code and elevation code of the target grid in the grid subdivision system.

[0026] According to the time coding specification in the grid subdivision system, the time information in the spatiotemporal data is encoded to obtain the time code.

[0027] Based on the vertical axis code, horizontal axis code, elevation code and time code of the target grid in the grid subdivision system, the grid code corresponding to the spatiotemporal data is generated.

[0028] Furthermore, the grid code also includes the target grid's segmentation standard information and segmentation level information.

[0029] Furthermore, as the amount of data grows, the data index table continues to grow. When the data index table grows to the set size, it is automatically split into two, each maintained by a different host. The split table grows independently and is split again, and this process is repeated. A global metadata table describing the sub-table index is constructed, which is responsible for storing and maintaining the indexes of all sub-tables in the system and quickly finding the host location of the sub-table.

[0030] The present invention also provides a spatial target query method based on global spatial stereoscopic segmentation, which uses the above-mentioned spatial target indexing method based on global spatial stereoscopic segmentation to construct an index database; then, spatiotemporal data query is performed based on the established index database.

[0031] Furthermore, based on the established index database, spatiotemporal data query is performed. The specific process is as follows:

[0032] The query receives a spatiotemporal data query request, which includes description information of the spatial geometric object to be queried, a target grid division standard to be used, and a target grid division level.

[0033] If the specified target meshing standard and target meshing level exist in the global metadata table, the target meshing code corresponding to the spatial geometric object to be queried is generated according to the specified target meshing standard and target meshing level and the description information of the spatial geometric object to be queried.

[0034] The spatiotemporal data corresponding to the spatial geometric object to be queried is obtained from the index database according to the target grid code.

[0035] The present invention also provides a spatial target indexing device based on global spatial stereoscopic segmentation, including a memory, a processor and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the above-mentioned spatial target indexing method based on global spatial stereoscopic segmentation is implemented, and an index database is constructed and stored.

[0036] The present invention also provides a spatial target query device based on global spatial stereoscopic segmentation, including a database engine, a memory, a processor and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the above-mentioned spatial target indexing method based on global spatial stereoscopic segmentation is implemented, an index database is constructed and stored.

[0037] The database engine receives spatiotemporal data query requests and provides spatiotemporal data query services for the index database.

[0038] Beneficial effects:

[0039] 1. The present invention provides a spatial target indexing method based on global spatial stereoscopic subdivision. First, spatiotemporal data is obtained from global spatial data, the position information of spatial geometric objects is mapped to at least one target grid in the grid subdivision system, and the position description information and time code of the target grid are determined to form a grid code corresponding to the spatiotemporal data; the grid code and grid attribute data are used to form index entries, and then the index entries form an index database model; the global spatial data is stored based on the index database model to obtain an index database, which can support the global space and realize unified, efficient and scalable spatial target indexing of global spatiotemporal data.

[0040] 2. The present invention provides a spatial target indexing method based on global spatial stereoscopic decomposition. In the process of constructing an index database model, the index table is serialized so that the "key" is represented by a grid index code + attribute combination structure, and the "value" is the index (path) of the corresponding spatial big data index storage information, thereby improving data indexing efficiency and further improving data query efficiency.

[0041] 3. The present invention provides a spatial target indexing method based on global spatial stereoscopic partitioning. During model construction, the index table is gradually split into sub-index tables. Since the split sub-tables are distributed and maintained on different hosts, operations on the large table evolve into operations on each sub-table. This significantly improves processing efficiency compared to operations on the entire large table, making spatiotemporal data queries more targeted and flexible. Furthermore, because grid index code row keys are arranged in Z-order using a quadtree traversal, they are positionally correlated. That is, adjacent grids are organized into the same or adjacent sub-tables whenever possible. This ensures both table row consistency and data access efficiency.

[0042] 4. This invention provides a spatial target indexing method based on global spatial stereoscopic subdivision. It employs space-time and spatiotemporal storage technology based on Earth grid subdivision. This spatiotemporal storage method enables unified storage of all data in key areas, as well as data within the same time period. This addresses the current issues of inefficient massive data storage and slow emergency response in key areas. The spatiotemporal storage method achieves distributed physical storage across multiple servers, organizing data using grid codes. Data can be quickly indexed using grid codes, ensuring the timeliness of data storage.

[0043] 5. The present invention provides a spatial target indexing method based on global spatial stereoscopic segmentation, which adds a data cache library to the segmentation index database and caches data that is not frequently queried in the system into the cache library, which can further improve the query speed.

[0044] 6. The present invention provides a spatial target indexing method based on global spatial stereoscopic decomposition and a data coding model based on the Beidou grid code standard, which can effectively make up for the shortcomings of inconsistent original data coding and inconsistent spatial position descriptions of multi-source data. It is specifically reflected in covering the global space of land, sea, air, space, underground, and underwater, and multi-factor urban geological survey data information; the division method of the spatial grid is fixed, less restricted by region, and not affected by regional changes. Therefore, it can be used for the stable organization and call of spatiotemporal data.

[0045] 7. This invention provides a spatial target query method based on global spatial stereoscopic segmentation. This method uses a constructed index database to query spatial targets and establishes a grid code index database. Using the grid code as the primary key for the index table, this method forms a logically segmented index table based on Earth's spatial location without overhauling or reinventing the existing system. The grid code index system manages encoded data using grids and grid codes, implementing functions such as data indexing and index updates. It provides services for grid-based rapid query and retrieval of multi-source, heterogeneous global spatial big data, supporting business applications such as grid-based computing and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is the logical structure of the index table;

[0047] Figure 2 This is a schematic diagram of the splitting of the data index table;

[0048] Figure 3 Schematic diagram of table serialization;

[0049] Figure 4 This is a diagram of the space-time storage network architecture based on the Earth grid;

[0050] Figure 5 Flowchart of a spatial target query method based on global spatial stereoscopic decomposition provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0052] The present invention adopts an indexing method based on grid codes as keywords, supports the entire space, and realizes a unified, efficient, and scalable spatial target indexing method for global spatiotemporal data. Based on the established spatial target indexing method, data queries for different spatial ranges can be realized.

[0053] The embodiment of the present invention provides a spatial target indexing method based on global spatial stereoscopic decomposition, the specific process and content of which are as follows:

[0054] Step 1) obtain spatiotemporal data from the global spatial data, where the spatiotemporal data includes the location information and time information of spatial geometric objects.

[0055] From the vast amount of data in the global space, we can obtain spatiotemporal data containing the location and time information of spatial geometric objects. The global space includes land, sea, air, space, underground, and underwater. The vast amount of data includes at least geological data, which includes engineering geology, hydrogeology, urban geology, agricultural geology, disaster geology, and environmental geology data from geological surveys. Spatial geometric objects in the geological field can include mountains, water, forests, fields, lakes, grasslands, and minerals.

[0056] In addition, before acquiring spatiotemporal data, data preprocessing can be used to normalize the data. Data normalization is an important foundation for the grid organization of various types of data. The processing content includes data cataloging, metadata extraction, coordinate conversion, format standardization, etc., which can achieve the standardization and normalization of massive multi-source heterogeneous data and lay a technical foundation for the integrated organization and efficient management of geological data. The data normalization process is as follows:

[0057] S101: For data managed in an existing database, metadata such as geographic coordinates, resolution, browsing map, original image size, and storage location are exported; for remote sensing image data, metadata such as geographic coordinates, resolution, image size, color depth, imaging time, and satellite image are extracted, and a browsing map is also extracted; for geographic information data, environmental information, images, and data, metadata such as geographic coordinates, resolution / scale, data time, and data source are extracted;

[0058] S102: Determine whether the data coordinate system is CGCS2000, convert the non-CGCS2000 coordinate system information into CGCS2000 coordinate system information, and generate metadata;

[0059] S103: Determine whether the format of each type of data file is a standard format, and convert non-standard format information into a standard format;

[0060] S104: Catalog and archive various types of data in accordance with the interface specifications of the database design, and complete the preprocessing of various types of data.

[0061] The position information of the spatial geometric object is mapped to at least one target grid in the grid subdivision system, and the position description information and time code of the target grid are determined to form a grid code corresponding to the spatiotemporal data.

[0062] According to a specified gridding encoding standard and gridding level, the location information of the spatial geometric object is mapped to at least one target grid in the gridding system, and a location description of the target grid in the gridding system is determined. The location description information includes the vertical axis code, horizontal axis code, and elevation code of the target grid in the gridding system. The time information in the spatiotemporal data is encoded according to the time encoding specification of the gridding system to obtain a time code. There are various gridding encoding standards, such as GBT 39409-2020 Beidou Grid Position Code and GBT 40087-2021 Geospatial Grid Code Rules. The specified gridding encoding standard and gridding level can be determined based on the accuracy of the spatiotemporal data resource or user-defined grid levels. For example, 17 or 18 levels are used for locating mountains, lakes, and mines, 20 or 21 levels are used for locating forests and fields, 23 or 24 levels are used for locating the trajectory of fishing vessels sailing at sea, and 28 or 29 levels are used for locating the trajectory of people walking.

[0063] Based on the target grid's vertical axis code, horizontal axis code, elevation code, and time code within the gridding system, a grid code corresponding to the spatiotemporal data is generated. A grid code uniquely identifies the data and, in addition to the grid's location and time information, also includes the gridding standard and level information. For example, a grid code can be a combination of a location grid code and a time code. The location grid code can be based on the GeoSOT-3D grid model or a BeiDou grid code.

[0064] The generated grid code is added to the grid code field in the corresponding target data record, where the target data record is a data record in the database table used to store spatiotemporal data.

[0065] Step 2: Grid codes, time codes, and grid attribute data are combined into index entries, and the spatiotemporal data is organized into a multidimensional index table. The first dimension of the multidimensional index table uses the grid code as the row primary key for sorting; the second dimension is the attribute group. An attribute group is a collection of multiple attribute columns, and the attributes in an attribute group are similar; the third dimension is the attribute column. The number of attribute columns in an attribute group is not limited, and the attribute columns are named in the form of "attribute group:attribute column".

[0066] A multidimensional index table is a "multidimensional" sparse matrix, which is logically a huge index table.

[0067] Step 3: Serialize the multidimensional index table into a data index table with a one-to-one mapping relationship. The data index table is implemented in a key-value format, where the key is represented by a grid code + grid attribute combination structure; the value is the index path of the corresponding spatial big data index storage information.

[0068] During the construction of the index database model, the index table is serialized, with the "key" represented as a grid index code + attribute structure, and the "value" as the index (path) of the corresponding spatial big data index storage information. During model construction, the index table is gradually split into sub-index tables and stored accordingly. Grid index code row keys are arranged in Z-order based on positional relationships, as in the quadtree traversal.

[0069] In the embodiment of the present invention, the specific process of constructing the index database model is as follows:

[0070] A grid code index database will be established, using the grid code as the primary key for the index table. This will create a logically segmented index table based on geospatial location, without overhauling or re-inventing the existing system. The grid code index system manages coded data using grids and grid codes, implementing data indexing and index updates. This system will provide services for rapid grid-based query and retrieval of multi-source, heterogeneous, global spatial big data, supporting business applications such as grid-based computing and analysis.

[0071] Without overhauling or rebuilding the existing system, a segmentation index table is established, with the segmentation code as the index primary key. The segmentation index data corresponding to each level of segmentation is associated. A data cache is also added to the segmentation index database to cache infrequently used data in the system, further improving query speed.

[0072] In the sparse matrix of the table model, the first dimension uses the grid index code as the row primary key for sorting; the second dimension is defined as the attribute group. An attribute group is a collection of multiple attribute columns, which generally have similar attributes. When the system stores and accesses the table, it organizes the attribute group as a unit; the third dimension is the attribute column. In theory, the number of attribute columns in an attribute group is not limited. The attribute columns are usually named in the form of "attribute group: attribute column". Figure 1 This is the logical structure diagram of the index table.

[0073] The index codes in the data index table are arranged in Z order according to the quadtree traversal, and the insertion, update, deletion, etc. of rows are all operated on the entire row, so that the consistency of the row is guaranteed.

[0074] As data volume grows (new data is constantly inserted), the data index table continues to grow. When the table reaches a certain size, it automatically splits into two, each maintained by a different host. The split table can then grow independently and be split again, repeatedly. Therefore, the data index table actually exists as a large number of "sub-tables" (tablets), each with completely equivalent properties, maintaining a portion of the data in the data index table. The data index table is split along row intervals. As a table grows, it is split into multiple row intervals within a certain row interval, with each row interval becoming a new sub-table. Because the split sub-tables are maintained on different hosts, operations on the large table evolve into operations on each sub-table, resulting in significantly higher processing efficiency than operations on the entire large table. Furthermore, because the grid index row keys are arranged in Z-order according to the quadtree traversal, adjacent grids are organized into the same or adjacent sub-tables whenever possible, ensuring efficient data access. Figure 2 Schematic diagram of splitting the data index table.

[0075] Various data attributes, such as data type, geometry, projection, time, and storage index information, are stored in the data index table in the form of attribute groups. Attribute groups are the basic unit of data storage and access control. As large tables grow, there is no limit on the number of attribute columns. New attribute columns can be created dynamically, but the corresponding attribute group must exist before creating the column. An attribute column is named in the form of "attribute group: attribute column." For each row in the table, the attribute group contains an indefinite number of attribute columns, so the partitioned index table is logically a sparse matrix.

[0076] A multidimensional table composed of index codes, attribute groups, and attribute columns is essentially a many-to-many mapping relationship table. To facilitate use and improve indexing efficiency, it is decomposed into multiple one-to-one mapping relationships. This process is called serialization of large tables. This function is implemented in the form of "key-value" (〈Key, Value〉) pairs, where the "key" is represented by a grid index code + attribute combination structure, and the "value" is the index (path) of the corresponding spatial big data index storage information. For example, the figure below is a schematic diagram of the serialized storage of subtables based on row intervals. The primary key maintains the order of the index codes externally to facilitate distributed retrieval, and internally ensures that the various attributes (essentially also 〈Key, Value〉 pairs) are arranged in lexicographic order. Figure 3 A serialization diagram.

[0077] In the data index table, index data is sorted and compressed before storage. Various attribute information is concatenated into strings and arranged in lexicographical order by attribute column. As a table grows, it splits into many subtables, and newly generated subtables can be assigned and maintained on any host. A global metadata table describing subtable indexes is designed to store and maintain indexes for all subtables in the system, enabling quick locating of subtables.

[0078] Step 4: Store the global spatial data based on the data index table to obtain an index database.

[0079] The stored procedure in constructing the index database model in the embodiment of the present invention is as follows:

[0080] By adopting space-time and space-time storage technology based on the earth's grid division, all data in key areas and data within the same time period can be stored in a unified manner, solving the problems of low efficiency in existing massive data storage and slow emergency response in key areas.

[0081] Establishing a unified data organization code provides a common data organization foundation, eliminating inconsistent data organization grids used by different departments and satellite models. It also allows for direct aggregation of data across large regions. Establishing a unified data organization code involves converting data from different departments into a unified data code, and then applying established technology to screen the coded data.

[0082] In the space-time partitioned storage system, all data entering the partitioned storage system can automatically generate partition identifiers according to unified rules based on their spatial location attributes. Based on the data identifier and the storage unit network identifier, the data is automatically distributed and stored in the storage units corresponding to its spatial area through the matching and scheduling of the system's internal data and storage resources; and then data access, resource scheduling, migration and backup and other storage management operations are performed according to the storage needs of different regions. Figure 4 The diagram shows the space-time storage network architecture based on the earth grid.

[0083] Spatiotemporal storage, on the other hand, distributes gridded data across network nodes using traditional time-series storage methods. Logical index columns are added to the data, logically distributing various types of data across different spatial grids based on time attributes and spatial location for unified organization and management. This results in a logically unified organization using grid codes. This allows for the distributed storage of various types of data across servers in a time-series manner, based on the time attributes of the gridded data, enabling physical distribution across multiple servers. Organizing data using grid codes allows for faster indexing, ensuring timely data storage.

[0084] Another embodiment of the present invention proposes a spatial target query method based on global spatial stereo segmentation. After adopting steps 1) to 4), it also includes step 5). The process is as follows: Figure 5 shown.

[0085] Step 5) Perform spatiotemporal data query based on the established index database.

[0086] The query process is as follows: receiving a spatiotemporal data query request, which includes the description information of the spatial geometric object to be queried, the specified target grid division standard and the target grid division level; if the specified target grid division standard and the target grid division level exist in the metadata table, according to the specified target grid division standard and the target grid division level, based on the description information of the spatial geometric object to be queried, generate the target grid code corresponding to the spatial geometric object to be queried; according to the target grid code, obtain the spatiotemporal data corresponding to the spatial geometric object to be queried from the index database. The construction process of the index database is as above.

[0087] In actual applications, grid data query service functions are realized, including single-grid query, multi-grid query, line query, body query, hand-drawn area query, administrative division query, combined query, etc.; select a single grid, obtain the corresponding grid code, perform data retrieval query on the grid code, and obtain the data information within the grid through the design of grid index table and code association index. Select multiple grids, obtain the corresponding grid codes, perform data retrieval queries on the grid codes, and obtain the data information within the grid group through the design of grid index table and code association index; draw any line on the map, convert the range covered by the selected area of the line into a Beidou grid cluster, and then convert it into a Beidou grid search to obtain the data information of the selected area; select a grid body, obtain the corresponding Beidou subdivision grid code, perform data retrieval queries on the grid code, and obtain the data information within the grid group through the design of grid index table and code association index; draw any area on the map, convert the range covered by the selected area into a Beidou grid cluster, and then convert it into a Beidou grid search to obtain the data information of the selected area; select any administrative area, convert the range covered by the selected administrative area into a Beidou grid cluster, and then convert it into a grid search to obtain the data information of the administrative area; realize the combination query of Beidou grid and traditional keywords, the keywords include time query conditions and attribute query conditions, and the attribute query conditions have different attributes such as scale, resolution, place name address, approval number, unit name, etc.

[0088] The process of deleting and modifying spatiotemporal data is also similar to database query and can be implemented by referring to the database query process.

[0089] Another embodiment of the present invention provides a spatial target indexing device based on global spatial stereoscopic segmentation, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the above-mentioned spatial target indexing method based on global spatial stereoscopic segmentation is implemented, and an index database is constructed and stored.

[0090] Another embodiment of the present invention adopts a spatial target query device based on global spatial stereoscopic segmentation, including a database engine, a memory, a processor and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the above-mentioned spatial target indexing method based on global spatial stereoscopic segmentation is implemented, an index database is constructed and stored; the database engine receives spatiotemporal data query requests and provides spatiotemporal data query services for the index database.

[0091] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A spatial target indexing method based on global spatial stereo subdivision, characterized in that: The steps include: Step 1: Obtain spatiotemporal data from the global spatial data. The spatiotemporal data contains the location and time information of spatial geometric objects. Mapping the position information of the spatial geometric object to at least one target grid in the grid subdivision system, and determining the position description information and time code of the target grid to form a grid code corresponding to the spatiotemporal data; Step 2: Grid codes, time codes, and grid attribute data are combined into index entries, and the spatiotemporal data is organized into a multidimensional index table. The first dimension of the multidimensional index table is sorted using the grid code as the row primary key. The second dimension is the attribute group. An attribute group is a collection of multiple attribute columns. The attributes in an attribute group are similar. The third dimension is the attribute column. There is no limit on the number of attribute columns in an attribute group. The attribute columns are named in the format of "attribute group:attribute column". Step 3: Serialize the multidimensional index table into a data index table with a one-to-one mapping relationship. The data index table is implemented in a key-value format, where the key is represented by a grid code + grid attribute combination structure; the value is the index path of the corresponding spatial big data index storage information; Step 4: storing the global spatial data based on the data index table to obtain an index database; The global spatial data includes at least geological data, which includes at least engineering geology, hydrogeology, urban geology, agricultural geology, disaster geology, and environmental geology data of geological surveys; The spatial geometric objects include mountains, water, forests, fields, lakes, grass and minerals; Before obtaining spatiotemporal data from the global spatial data, the global spatial data is preprocessed. The specific process is as follows: S101: The global spatial data includes data managed by an existing database, remote sensing image data, geological data, geographic information data, and environmental information data, and metadata is extracted from the global spatial data; For data managed in existing databases, metadata include geographic coordinates, resolution, browse map, original image size, and storage location; For remote sensing image data, its metadata includes geographic coordinates, resolution, image size, color depth, imaging time, satellite data, and browsing map; For geological data, geographic information data, and environmental information data, metadata include geographic coordinates, resolution / scale, data time, and data source; S102: Determine whether the data coordinate system of the metadata is CGCS2000, and convert the non-CGCS2000 coordinate system information into CGCS2000 coordinate system information; S103: Determine whether the format of each type of data file is a standard format, and convert the non-standard format data into a standard format; S104: Comply with the interface specifications of the database design, catalog and archive various metadata, and complete the preprocessing of the global spatial data.

2. The spatial target indexing method based on global spatial stereoscopic decomposition according to claim 1, characterized in that: The mapping of the position information of the spatial geometric object to at least one target grid in the grid subdivision system and the determination of the position description information and time code of the target grid to form the grid code corresponding to the spatiotemporal data are specifically: Mapping the position information of the spatial geometric object to at least one target grid in the gridding system according to a preset gridding coding standard and gridding hierarchy, and determining position description information of the target grid in the gridding system; the position description information includes a vertical axis code, a horizontal axis code, and an elevation code of the target grid in the gridding system; According to the time coding specification in the grid subdivision system, the time information in the spatiotemporal data is encoded to obtain the time code; Based on the vertical axis code, horizontal axis code, elevation code and time code of the target grid in the grid subdivision system, the grid code corresponding to the spatiotemporal data is generated.

3. The spatial target indexing method based on global spatial stereoscopic decomposition according to claim 2, characterized in that: The grid code also includes the subdivision standard information and subdivision level information of the target grid.

4. The spatial target indexing method based on global spatial stereoscopic decomposition according to claim 1 or 2, characterized in that: As the amount of data grows, the data index table continues to grow. When the data index table grows to a set size, it is automatically split into two, each maintained by a different host. The split table grows independently and is split again, and this process is repeated. A global metadata table describing the sub-table index is constructed, which is responsible for storing and maintaining the indexes of all sub-tables in the system and quickly finding the host location of the sub-table.

5. A spatial target query method based on global spatial stereo decomposition is characterized by: An index database is constructed using the spatial target indexing method based on global spatial stereoscopic decomposition as described in any one of claims 1 to 4; Then, spatiotemporal data queries are performed based on the established index database.

6. The spatial target query method based on global spatial stereoscopic decomposition according to claim 5, characterized in that: The spatiotemporal data query is performed based on the established index database. The specific process is as follows: receiving a spatiotemporal data query request, wherein the spatiotemporal data query request includes description information of a spatial geometric object to be queried, a target grid division standard to be used, and a target grid division level; If the specified target meshing standard and target meshing level exist in the global metadata table, the target mesh code corresponding to the spatial geometric object to be queried is generated according to the specified target meshing standard and target meshing level and the description information of the spatial geometric object to be queried; The spatiotemporal data corresponding to the spatial geometric object to be queried is obtained from the index database according to the target grid code.

7. A spatial target indexing device based on global spatial stereoscopic segmentation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the spatial target indexing method based on global spatial stereoscopic decomposition as described in any one of claims 1 to 4, constructs an index database and stores it.

8. A spatial target query device based on global spatial stereoscopic segmentation, comprising a database engine, a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the spatial target indexing method based on global spatial stereoscopic decomposition according to any one of claims 1 to 4 is implemented, and an index database is constructed and stored; The database engine receives a spatiotemporal data query request and provides a spatiotemporal data query service for the index database.

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