Adaptive cube indexing method and system

Abstract

The present invention relates to an adaptive cube indexing method and system. The method comprises: for the topological relationship between adaptive cube tiles and geometries, setting topological type coding rules for segmenting and calibrating geometry indexes which are generated on the basis of the present file; on the basis of a multi-level tree structure of tiled adaptive cubes and the topological type coding rules, segmenting geometry identifiers of various geometric types such as multi-point, line, surface, solid or grid geometries which express earth phenomena, to generate a multi-level topological index, and storing the multi-level topological index; defining a request tile area on the basis of an access request to obtain access topological type coding rules; and acquiring the geometry indexes within the tile area range on the basis of the topological type coding rules, filtering redundant geometry indexes within the tile area range, and accessing corresponding target geometry data on the basis of the filtered geometry indexes. The present invention improves the access efficiency of target geometries of adaptive cubes, and improves the performance of an adaptive cube-based earth phenomenon instant-analysis application.

Description

An adaptive cube indexing method and system Technical Field

[0001] The present invention belongs to the field of big data technology, and in particular relates to an adaptive cube indexing method and system. Background Art

[0002] We have entered the era of big data, especially with the continuous development of geographic information applications. The corresponding online, on-the-go analysis applications are placing new demands on indexing and accessing multidimensional geometric cubes, resulting in exponential growth in the volume of multidimensional geometric cube data. Single sources of geometric data are increasingly unable to meet the growing real-time or near-real-time demands of online, on-the-go analysis for geographic information applications. Data of various geometric types is often distributed across the databases of major data collection and management institutions, both domestically and internationally. Each data center typically has a relatively independent set of data indexing and access methods, resulting in varying data provision methods. While standardized GIS cloud computing technologies and WebGIS data services can connect multiple distributed resources through standard protocols to support collaborative operations, mainstream standardization organizations such as ISO TC211 and the Open Geospatial Consortium (OGC) have, through standardization activities, provided a series of data catalogs or data interface service specifications for collaborative discovery of geographic information data. For example, the OGC CSW specification is a web-based catalog service specification that lacks detailed indexing of geometric data. While the OGC WCS and WFS provide detailed geometric data, they lack effective tiled indexing mechanisms. It is difficult for standardized Web protocols to directly penetrate the internal data indexing level, especially the tiled indexing for big data and the geometric data indexing and access methods for large earth scene data. Further exploration is needed to support analytical computing between database clusters.

[0003] Furthermore, in scenarios where an immediate analysis application only requires partial data, traditional full data access methods waste bandwidth and fail to meet the performance requirements of immediate analysis applications. With the continued development and maturity of NoSQL databases, they can better address the application needs of massive storage and immediate analysis. NoSQL databases can support the storage of data of different geometric types. For geometric data, multiple indexing methods may be used to improve the speed and efficiency of data queries. Indexing can be based on various geometric attributes, such as shape, size, and dimension. However, when the amount of earth scene data is extremely large, effective index segmentation is also required.

[0004] The BeiDou grid code is a discrete, multi-scale regional location identification system developed based on the GeoSOT theory of global equal latitude and longitude grids. It can easily establish intrinsic connections with any entity object and various data within the same area, and can meet the needs of data index management at different levels from macro to fine. Therefore, it can be considered for indexing the geometric body identification of different geometric body types in earth phenomenon scenes. Technical issues

[0005] Because Earth phenomena often exhibit a degree of continuity, geometries often span partitioned regions. Index redundancy often varies under different topological conditions. Filtering redundant indexes can prevent duplicate access to geometry data. Compared to single indexes, multi-level tree-structured indexing allows for multiple indexing schemes, and redundant indexes are often higher within large tile retrieval areas. Selecting an appropriate index hierarchy minimizes redundant indexes, enabling access to the relevant geometries for the desired cube tile. This reduces bandwidth consumption and index retrieval computing power during analysis, significantly improving performance for large-scale online analysis of geometry data. Technical Solutions

[0006] The purpose of the present invention is to solve the above problems and provide an adaptive cube indexing method and system, which improves the access efficiency of adaptive cube target geometry and enhances the performance of adaptive cube-based earth phenomenon analysis applications.

[0007] To achieve the above-mentioned purpose, the technical solution of the present invention is: an adaptive cube indexing method, comprising: setting a topological type coding rule based on the topological relationship between adaptive cube tiles and geometric bodies, for segmenting and calibrating the geometric body index generated by this file; geometric body identifiers of different geometric types such as multiple points, lines, surfaces, bodies or grid geometric bodies expressing earth phenomena are segmented according to the multi-level tree structure and topological type coding rules based on the tiled adaptive cube, generating a multi-level topological index and storing it; based on the access request, defining the requested tile area, obtaining the access topological type coding rule, obtaining the tile area range geometric body index based on the topological type coding rule, filtering the redundant geometric body indexes in the tile area range, and accessing the corresponding target geometric body data based on the filtered geometric body index to improve the adaptive cube access performance.

[0008] In one embodiment of the present invention, the topology type coding rule is as follows: each topology type code is composed of 3 binary digits, each digit represents whether the intersection of the geometric body and the interior, boundary and exterior of the adaptive cube tile is empty, 0 for empty and 1 for non-empty; a topology type code set is composed of the topology type code as the basic unit t, which is expressed as {t i}; When setting the topological type encoding set for subdivision, the geometric bodies in the earth phenomenon theme data are subdivided according to the topological type encoding set, and any geometric body that satisfies any {t i} element is indexed by the corresponding tile, which is used to calibrate the geometric body index generated according to the rules of this document; when setting the topological type encoding set for access, it is used to select the geometric body index in the retrieved data that satisfies any {t i} element.

[0009] In an embodiment of the present invention, the multi-level tree structure of the tiled adaptive cube is that the tile is an adaptive cube composed of the spatial dimension X, the spatial dimension Y, and the adaptive dimension V, and the tiles are divided by levels. Each parent node tile is the union of all child node tiles. Each tile is an adaptive cube tile, each tile contains multiple data slices, and each data slice contains a type of geometric body; let the root tile node be R, which has n child node tiles, namely T1, T2,..., T n , and each child node tile contains its own child node tiles, expressed as the parent node tile = ∪(child node tiles), where ∪ represents the union; a hierarchical tree structure is formed, and this tree structure is represented recursively. For T i (0 < i < n + 1), the next level has m child nodes, namely T i1 , T i2 ,..., T im ; let the tile be T i , which consists of k data slices, namely P1, P2,..., P k ; each data slice includes a set of geometric bodies of different geometric types, namely G1, G2,..., G k , and the geometric bodies in each geometric body set have the same geometric type, respectively expressed as {g1}, {g2},..., {g k}; i, j, k, m, and n are all positive integers.

[0010] In an embodiment of the present invention, the adaptive cube composed of the spatial dimension X, the spatial dimension Y, and the adaptive dimension V is a cube in which the variable information of the data on the adaptive dimension, including elevation, time, and variables, is arranged in an orderly manner.

[0011] In an embodiment of the present invention, the geometric body identifiers of different geometric types expressing multi-points, lines, surfaces, bodies, or grid geometric bodies of earth phenomena are segmented according to the multi-level tree structure of the tiled adaptive cube and the given topological type encoding rules, and the satisfied topological type encoding is calibrated in the geometric body index to generate a multi-level tree structure index of the earth phenomenon theme data.

[0012] In one embodiment of the present invention, an access request includes a request range given according to a coordinate system compatible with the adaptive cube space, and an access topology type encoding rule given; the request range is divided according to the multi-level tree structure of the tiled adaptive cube, and the tile level corresponding to the minimum tile area covering the request range is selected, and the corresponding tile area is the defined request tile area.

[0013] In one embodiment of the present invention, the defined tile area range geometry index is obtained based on the topology type encoding rule in the access request, and according to the rule of tile area geometry index = ∪ (geometry index of tile in tile area), where ∪ represents union, the redundant geometry indexes of the tile area range are filtered, and based on the filtered geometry index, the corresponding target geometry data is accessed.

[0014] The present invention also provides an adaptive cube indexing system, comprising: an indexing module carried on an adaptive cube storage system, generating a multi-level topological index according to the multi-level tree structure of a given tiled adaptive cube; a request proxy module carried on an adaptive cube application service platform, accessing the multi-level topological index in the index library, defining the requested tile area according to the cube request range in the client's analysis application; a geometry access module carried on an online adaptive cube application service platform, obtaining a tile area geometry index according to the index range, obtaining the corresponding geometry from the adaptive cube storage system, and returning it to the client to complete the access.

[0015] In one embodiment of the present invention, the method steps described above are performed. Beneficial effects

[0016] Compared to existing technologies, this invention offers the following advantages: It provides an efficient solution for indexing and accessing geometric data in NoSQL databases using adaptive cube indexing and access technology for geospatial phenomena. It flexibly adjusts storage and indexing strategies based on actual needs, reduces the transmission of redundant geometric data and the consumption of computing power for fusion of corresponding tile regions, and improves the performance of geospatial information analysis applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG1 is a schematic diagram of the module structure of the present invention. Modes for Carrying Out the Invention

[0018] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] As shown in Figure 1, an embodiment of the present invention provides an adaptive cube indexing method for earth phenomena, and the main processing steps include: setting a topological type coding rule based on the topological relationship between adaptive cube tiles and geometric bodies, which is used to segment and calibrate the geometric body index generated by this file; segmenting geometric body identifiers of different geometric types such as multiple points, lines, surfaces, bodies or grid geometric bodies that express earth phenomena according to a multi-level tree structure and topological type coding rules based on tiled adaptive cubes, generating and storing multi-level topological indexes; based on access requests, defining the requested tile area and obtaining access topological type coding rules; obtaining the tile area range geometric body index based on the topological type coding rules, filtering the redundant geometric body indexes in the tile area range, and accessing the corresponding target geometric body data based on the filtered geometric body index to improve the adaptive cube access efficiency.

[0020] This embodiment also provides an example of topology type encoding rules, such as {001, 010, 011, 110, 111}, which respectively represent that the geometric body is inside the adaptive cube tile, the geometric body is at the boundary of the adaptive cube tile, the geometric body is inside and at the boundary of the adaptive cube tile, the geometric body is at the boundary and outside the adaptive cube tile, and the geometric body is inside, at the boundary, and outside the adaptive cube tile.

[0021] This embodiment also provides an example of segmentation based on the multi-level tree structure and topology type coding rules of the tiled adaptive cube to generate a multi-level topological index. A multi-level tree model of the tiled adaptive cube is established on the spatial plane according to the "Beidou Grid Position Code" (GB / T 39409-2020) and on the variable axis according to different themes (such as time, elevation, and time-elevation combination). MongoDB is used to store geometric bodies of different geometric types such as multiple points, lines, surfaces, bodies, or grid geometries of earth phenomena, and their geometric bodies are identified and marked. They are segmented according to the constructed multi-level tree structure of the tiled adaptive cube and the set topology type coding rules to generate a multi-level topological index and store it in the MySQL index library.

[0022] This embodiment also provides an adaptive cube request example for earth phenomena, in which the request includes a request range (lower limit of longitude, upper limit of longitude; lower limit of latitude, upper limit of latitude; lower limit of time; upper limit of time) in the WGS84 space-time coordinate system, and an access topology type encoding rule {001, 010, 011, 110, 111}.

[0023] The request proxy module installed on the adaptive cube application service platform receives the cube request initiated by the client, reads the corresponding scene index, and calculates the tile level and tile area range corresponding to the minimum tile area covering the request range according to the multi-level tree structure of the tiled adaptive cube. Based on the topological type encoding rule in the cube request, the geometric index of the defined tile area range is obtained, and then according to the rule of tile area geometric index = ∪ (geometric index of tile area tile), the index of redundant geometric identifiers of the tile area range is filtered through MySQL DISTINCT, and the filtered geometric index and the analysis application handle are sent to the geometric access module of the adaptive cube storage system.

[0024] The geometry access module of the adaptive cube storage system reads the corresponding target geometry data of MongoDB according to the received geometry index, and sends the data to the instant analysis application client according to the instant analysis application handle.

[0025] An embodiment of the present invention also provides an adaptive cube indexing system for earth phenomena, including: an indexing module carried by an adaptive cube storage system based on MongoDB, generating a multi-level topological index according to the multi-level tree structure of a given tiled adaptive cube; a request proxy module carried by an adaptive cube application service platform, accessing the multi-level topological index in the index library, defining the requested tile area according to the cube request range in the client's analysis application; a geometry access module carried by an online adaptive cube application service platform, obtaining a tile area geometry index according to the index range, obtaining the corresponding geometry from the adaptive cube storage system based on MongoDB, and returning it to the client to complete the access.

[0026] In summary, the present invention involves an adaptive cube indexing method and system for earth phenomena that utilizes topological type encoding rules to partition and store geographic information geometric indexes for earth phenomena in a multi-level tree structure of tiled adaptive cubes, determines the appropriate index level and the minimum tile area covering the request range, filters the redundant geometric indexes within the tile area range, and accesses the relevant geometries of the required cube tiles on this basis, thereby reducing the bandwidth and index retrieval computing power consumption during the analysis process, and significantly improving the performance of large-scale online analysis applications of geometric data.

[0027] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions and effects do not exceed the scope of the technical solution of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. An adaptive cube indexing method, It is characterized in that include: Aiming at the topological relationship between adaptive cube tiles and geometric bodies, topological type coding rules are set to segment and calibrate the geometric body index generated by this file; geometric body identifiers of different geometric types of multi-point, line, surface, body or grid geometric bodies expressing earth phenomena are segmented according to the multi-level tree structure based on tiled adaptive cube and topological type coding rules, and multi-level topological indexes are generated and stored; Based on the access request, the requested tile area is defined, the access topology type encoding rule is obtained, the tile area range geometry index is obtained based on the topology type encoding rule, the redundant geometry indexes of the tile area range are filtered, and the corresponding target geometry data is accessed based on the filtered geometry index to improve the adaptive cube access performance.

2. The adaptive cube indexing method according to claim 1, It is characterized in that The topological type encoding rule is as follows: Each topological type encoding consists of 3 - bit binary numbers, and each digit represents whether the intersection of the geometric body with the inside, boundary, and outside of the adaptive cube tile is empty. If it is empty, it is 0; if it is not empty, it is 1. Using the topological type encoding as the basic unit t to form a topological type encoding set, which is expressed as {t i}; When setting the topological type encoding set for subdivision, the geometric bodies in the earth phenomenon theme data are subdivided according to the topological type encoding set. The geometric body that satisfies any {t i} element is indexed by the corresponding tile, which is used to calibrate the geometric body index generated according to the rules of this document; When setting the topological type encoding set for access, it is used to select the geometric body index in the retrieved data that satisfies any {t i} element.

3. The adaptive cube indexing method according to claim 1, It is characterized in that The multi-level tree structure of the tiled adaptive cube is as follows: the tiled adaptive cube consists of the spatial dimension X, the spatial dimension Y, and the adaptive dimension V, and the tiles are divided hierarchically. Each parent node tile is the union of all its child node tiles. Each tile is an adaptive cube tile, each tile contains multiple data slices, and each data slice contains a type of geometric body. Let the root tile node be R, which has n child node tiles, namely T 1 , T 2 , …, T n . Each child node tile contains its own child node tiles, expressed as parent node tile = ∪(child node tiles), where ∪ represents the union. It forms a hierarchical tree structure, which is represented recursively. The next level of T i (0 < i < n + 1) has m child node tiles, namely T i1 , T i2 , …, T im . Let the tile be T i , which consists of k data slices, namely P 1 , P 2 , …, P k . Each data slice includes a set of geometric bodies of different geometric types, namely G 1 , G 2 , …, G k . The geometric types of the geometric bodies in each geometric body set are the same, expressed as {g 1}, {g 2}, …, {g k}. i, j, k, m, and n are all positive integers.

4. The adaptive cube indexing method according to claim 3, It is characterized in that The adaptive cube composed of the spatial dimension X, the spatial dimension Y, and the adaptive dimension V is a cube that arranges the variable information of the data, including elevation, time, and variables, in an orderly manner on the adaptive dimension.

5. An adaptive cube indexing method according to any one of claims 1 to 4, It is characterized in that The geometric body identifiers of different geometric types such as multiple points, lines, surfaces, volumes or grid geometries expressing earth phenomena are segmented according to the multi-level tree structure based on tiled adaptive cubes and the given topological type coding rules, and the topological type codes that meet the requirements are marked in the geometric body index to generate a multi-level tree structure earth phenomenon subject data index.

6. An adaptive cube indexing method according to any one of claims 1 to 4, It is characterized in that The access request includes a request range given in a coordinate system compatible with the adaptive cube space and an access topology type encoding rule; the request range is divided according to the multi-level tree structure of the tiled adaptive cube, and the tile level corresponding to the minimum tile area covering the request range is selected, and the corresponding tile area is the defined request tile area.

7. An adaptive cube indexing method according to any one of claims 1 to 4, It is characterized in that Based on the topology type encoding rules in the access request, the bounded tile area range geometry index is obtained, and according to the rule of tile area geometry index = ∪ (geometry index of tile in tile area), where ∪ represents union, the redundant geometry indexes of the tile area range are filtered out, and based on the filtered geometry index, the corresponding target geometry data is accessed.

8. An adaptive cube indexing system, It is characterized in that include: The index module installed in the adaptive cube storage system generates a multi-level topological index according to the multi-level tree structure of the given tiled adaptive cube; The request proxy module mounted on the adaptive cube application service platform accesses the multi-level topology index in the index library, and defines the requested tile area according to the cube request range of the client in the analysis application; the geometry access module mounted on the online adaptive cube application service platform obtains the tile area geometry index according to the index range, obtains the corresponding geometry from the adaptive cube storage system, and returns it to the client to complete the access.

9. An adaptive cube index system according to claim 8, wherein, it performs the method steps as described in any one of claims 1-7.

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