Visual large-screen map data loading optimization method and device

Abstract

The invention relates to the technical field of data processing, and particularly provides a visual large-screen map data loading optimization method and device.The method comprises the two parts of layered and partitioned optimization storage, attribute data integration and index optimization, and the layered and partitioned optimization storage is divided into geographic hierarchy division, spatial partitioning and related data association; geographic hierarchy division: performing hierarchical storage on the map data according to geographic hierarchies; in the spatial partitioning, map data is further partitioned and stored according to regions in each geographic hierarchy; related data association realizes data connection by establishing an association relationship between each hierarchy and each block; the attribute data integration and index optimization comprises attribute data integration and multi-dimensional index construction; in the attribute data integration step, various attribute data related to the geographic entity are integrated and stored; and constructing a multi-dimensional index according to common query requirements of the visual large screen. Compared with the prior art, the method has the advantage that the loading efficiency of the visual large-screen map data can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically provides a method and device for optimizing data loading of a visualized large-screen map. Background Art

[0002] In today's digital age, visualization screens are widely used in various fields, such as smart cities, traffic monitoring, enterprise data analysis, etc. Among them, maps, as an important visualization element, can intuitively display geographic information and related data.

[0003] However, traditional database tables used for large-screen visualization map data may have structural characteristics such as data storage redundancy and lack of index optimization. For example, when storing data related to geographic areas, basic information such as the name and coordinates of the geographic areas may be repeatedly stored in multiple tables, increasing the amount of data and query complexity. In addition, appropriate indexes are not set according to common query requirements of large-screen visualization (such as querying data by area, filtering data by specific attributes, etc.), resulting in slow query speeds.

[0004] Therefore, when loading map data, we often face problems such as large data volume, long loading time, and performance bottlenecks, which not only affects the user experience, but may also cause slow system response or even crash. Summary of the invention

[0005] The present invention aims at solving the above-mentioned deficiencies of the prior art and provides a highly practical method for optimizing data loading for a large-screen visualized map.

[0006] A further technical task of the present invention is to provide a reasonably designed, safe and applicable visual large-screen map data loading optimization device.

[0007] The technical solution adopted by the present invention to solve its technical problem is:

[0008] A method for optimizing data loading of a large-screen visualization map, including two parts: hierarchical and block optimized storage, and attribute data integration and index optimization. The hierarchical and block optimized storage is divided into geographic level division, spatial block division and related data association;

[0009] The geographical level division stores the map data in layers according to the geographical levels;

[0010] The spatial block further stores the map data in blocks according to regions within each geographical level;

[0011] The related data association realizes data connection by establishing association relationships between various levels and blocks;

[0012] The attribute data integration and index optimization include attribute data integration and building a multi-dimensional index;

[0013] The attribute data integration integrates and stores various attribute data related to geographic entities;

[0014] The multi-dimensional index is constructed according to the common query requirements of the visualization large screen.

[0015] Furthermore, in the geographical layer division, the layers are stored as global layer, continent layer, country layer, province or state layer and city layer, and the data table of each layer contains geographical information specific to the layer.

[0016] Furthermore, in the spatial segmentation, at the city layer, the city map is divided into a number of square or hexagonal blocks, and the data table of each block stores detailed geographic information within the block. The block storage enables the data of the target block to be obtained when loading data in a local area of ​​the visualization large screen.

[0017] Furthermore, in the related data association, each country record in the country-level data table establishes a one-to-many association with the province or state-level data table within the country, each record in the province or state-level data table establishes an association with the corresponding city in the city-level data table, and each city in the city-level data table establishes an association with the block data table within the city.

[0018] Furthermore, in the attribute data integration, for a city's geographical area, its population, economic indicators and environmental data attributes are integrated into a special attribute data table, and associated with the geographic information data table through geocoding.

[0019] Furthermore, the multi-dimensional index is constructed according to the common query requirements of the visualization large screen, and an index is established for frequently queried attribute values. Considering the spatial characteristics of map data, a spatial index is also constructed to support queries based on geographic area ranges.

[0020] Furthermore, after constructing the two parts of layered and block optimized storage, attribute data integration and index optimization, the loading optimization process is as follows:

[0021] S1. The visualization screen issues a map data loading request based on user operations;

[0022] S2, determining whether the request information contains a geographic area range and a zoom level;

[0023] S3, determining the target geographic level and tile according to the geographic area range and zoom level in the request;

[0024] S4, query the data table of the target geographical level to obtain the basic geographical information of the level;

[0025] S5. Determine whether detailed attribute data is needed;

[0026] S6. Query a special attribute data table according to the geographic code to obtain detailed attribute data;

[0027] S7. Merge the acquired geographic information and attribute data to form complete map data, and return it to the visualization screen.

[0028] Further, in step S2, if the request includes geographic area range and zoom level information, the process proceeds to step S3 to achieve data positioning and loading;

[0029] Otherwise, proceed to step S5 to obtain data according to the default loading strategy.

[0030] Further, in step S5, it is determined whether the visualization screen needs detailed attribute data. If so, the process proceeds to step S6 of querying the attribute data table.

[0031] Otherwise, the acquired geographic information data is directly returned to the visualization large screen step S7.

[0032] A visual large-screen map data loading optimization device, comprising: at least one memory and at least one processor;

[0033] The at least one memory is used to store a machine-readable program;

[0034] The at least one processor is used to call the machine-readable program to execute a method for optimizing data loading for a visual large-screen map.

[0035] Compared with the prior art, the method and device for optimizing data loading of a large-screen visualized map of the present invention have the following outstanding beneficial effects:

[0036] (1) Deep optimization of hierarchical and block storage: Different from traditional single-level storage, the hierarchical storage structure of this technology accurately defines the data granularity of each level according to the logical hierarchy of the geographical area and the actual needs of the visualization large screen.

[0037] (2) Spatial optimization of the block strategy: Spatial block division is not just a simple area division, but a comprehensive consideration of the distribution patterns of geographical elements and the display characteristics of the visualization large screen.

[0038] (3) Attribute data integration and index construction: In the process of attribute data integration, it is not just a simple merger of various attribute data into one table, but a deep mining of the semantic associations between attributes. And intelligent index combination is performed according to the query mode and data characteristics of the visualization screen.

[0039] (4) Data loading process optimization: The data loading process can accurately locate the target geographic level and block based on the geographic area range and zoom level information contained in the request issued by the visualization screen. This makes data loading highly targeted and avoids the reading and transmission of a large amount of irrelevant data that may occur in traditional methods. It also provides a flexible switching mechanism between the default loading strategy and the customized loading strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Attached Figure 1 The invention is a flowchart of a method for optimizing data loading of a visualized large-screen map. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] A best embodiment is given below:

[0044] like Figure 1 As shown, a visualization large-screen map data loading optimization method in this embodiment includes two parts: hierarchical and block optimized storage, attribute data integration and index optimization;

[0045] The implementation of the layered and block optimized storage structure method is divided into three aspects:

[0046] (1) Geographical hierarchical division: Map data is stored in layers according to geographical levels, such as global layer, continental layer, country layer, province / state layer, city layer, etc. The data table of each level contains geographic information specific to that level. For example, the global layer stores the outlines and basic information of each continent, and the continental layer stores the relative position and general information of each country within the continent.

[0047] This hierarchical structure helps to quickly locate the data at the required level according to the zoom level of the visualization screen, avoiding unnecessary data reading.

[0048] (2) Spatial segmentation: Within each geographic level, map data is further segmented and stored according to spatial regions. At the city level, the city map can be divided into several square or hexagonal blocks. The data table of each block stores detailed geographic information within the block, such as streets, buildings, etc. Block storage enables accurate acquisition of target block data when loading data in a local area of ​​the visualization screen, thus reducing the amount of data transmission.

[0049] (3) Relevant data association: By establishing associations between various levels and blocks, seamless data connection is achieved. Each country record in the country-level data table establishes a one-to-many association with the province / state-level data table within the country. Each record in the province / state-level data table is associated with the corresponding city in the city-level data table. Each city in the city-level data table is associated with the block data table within the city. This association is achieved through a unique identifier (such as geocoding) to ensure data integrity and traceability.

[0050] The implementation of attribute data integration and index optimization methods is divided into two aspects:

[0051] (1) Attribute data integration: various attribute data related to geographic entities are integrated and stored. For a city’s geographic area, its population, economic indicators, environmental data and other attributes are integrated into a special attribute data table and associated with the geographic information data table through geocoding. This integration avoids the scattered storage of attribute data and reduces data redundancy.

[0052] (2) Construct multi-dimensional indexes. Construct multi-dimensional indexes based on common query requirements of visualization large screens. In addition to traditional geocoding-based indexes, indexes are also established for frequently queried attribute values, such as population ranges, economic indicator levels, etc. In addition, considering the spatial characteristics of map data, spatial indexes such as R-Tree indexes or Quad-Tree indexes are also constructed to support fast queries based on geographic area ranges.

[0053] After constructing the two parts of layered and block optimized storage, attribute data integration and index optimization, the loading optimization process is as follows:

[0054] S1. The visualization screen sends a request to load map data

[0055] The visualization screen issues a map data loading request based on user operations (such as map zooming, panning, specific area query, etc.). The request may contain information such as geographic area range and zoom level, or it may be a default request without specific conditions.

[0056] S2. Determine whether the requested information contains a geographic area range and a zoom level

[0057] If the request contains geographic area range and zoom level information, the process enters step S3 to achieve accurate data positioning and loading.

[0058] Otherwise, proceed to step S5 to obtain data according to the default loading strategy.

[0059] S3. Target data positioning

[0060] The target geographic layer and tile are determined based on the geographic area range and zoom level in the request. For example, if the request is to view a local area of ​​a city and the zoom level is high, the target geographic layer may be the city layer and the tile may be the tile corresponding to the local area.

[0061] S4. Basic geographic information query

[0062] Query the data table of the target geographic level to obtain the basic geographic information of the level, such as geographic outlines, major geographic elements, etc. Due to the use of a hierarchical and block storage structure and effective indexes, this query can quickly locate and obtain data.

[0063] S5. Determine whether detailed attribute data is needed

[0064] Determine whether the visualization screen needs detailed attribute data, such as population, economic indicators, etc. If necessary, enter step S6 of querying the attribute data table;

[0065] Otherwise, the acquired geographic information data is directly returned to the visualization large screen step S7.

[0066] S6. Attribute data query

[0067] Query the dedicated attribute data table based on the geocode to obtain detailed attribute data. The existence of multi-dimensional indexes ensures the efficiency of the query.

[0068] S7. Data merging and returning

[0069] The acquired geographic information and attribute data are merged to form complete map data and returned to the visualization screen.

[0070] Based on the above method, a visualization large-screen map data loading optimization device in this embodiment includes: at least one memory and at least one processor;

[0071] The at least one memory is used to store a machine-readable program;

[0072] The at least one processor is used to call the machine-readable program to execute a method for optimizing data loading for a visual large-screen map.

[0073] The above-mentioned specific implementations are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above-mentioned specific implementations. Any technical solutions that conform to the above-mentioned specific implementations of the present invention and any appropriate changes or substitutions made by ordinary technicians in the relevant technical field shall fall within the patent protection scope of the present invention.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing data loading for a large-screen visualization map, characterized in that: It includes two parts: hierarchical block optimization storage and attribute data integration and index optimization. The hierarchical block optimization storage is divided into geographical level division, spatial block and related data association; The geographical level division stores the map data in layers according to the geographical levels; The spatial block further stores the map data in blocks according to regions within each geographical level; The related data association realizes data connection by establishing association relationships between various levels and blocks; The attribute data integration and index optimization include attribute data integration and building a multi-dimensional index; The attribute data integration integrates and stores various attribute data related to geographic entities; The multi-dimensional index is constructed according to the common query requirements of the visualization large screen.

2. A method for optimizing data loading of a large-screen visualization map according to claim 1, characterized in that: In the geographical layer division, the layers are stored as a global layer, a continent layer, a country layer, a province or state layer, and a city layer, and the data table of each layer contains geographical information specific to the layer.

3. A method for optimizing data loading of a large-screen visualization map according to claim 2, characterized in that: In the spatial segmentation, at the city layer, the city map is divided into a number of square or hexagonal blocks, and the data table of each block stores the detailed geographic information within the block. The segmented storage enables the data of the target block to be obtained when loading data in a local area of ​​the visualization large screen.

4. A method for optimizing data loading of a large-screen visualization map according to claim 3, characterized in that: In the related data association, each country record in the country-level data table establishes a one-to-many association with the province or state-level data table within the country, each record in the province or state-level data table establishes an association with the corresponding city in the city-level data table, and each city in the city-level data table establishes an association with the block data table within the city.

5. A method for optimizing data loading of a large-screen visualization map according to claim 4, characterized in that: In the attribute data integration, for a city's geographical area, its population, economic indicators and environmental data attributes are integrated into a special attribute data table, and associated with the geographic information data table through geocoding.

6. A method for optimizing data loading of a large-screen visualization map according to claim 5, characterized in that: The multi-dimensional index is constructed according to the common query requirements of the visualization large screen, and an index is established for frequently queried attribute values. Considering the spatial characteristics of map data, a spatial index is also constructed to support queries based on geographic area ranges.

7. A method for optimizing data loading of a large-screen visualization map according to claim 6, characterized in that: After constructing the two parts of layered and block optimized storage, attribute data integration and index optimization, the loading optimization process is as follows: S1. The visualization screen issues a map data loading request based on user operations; S2, determining whether the request information contains a geographic area range and a zoom level; S3, determining the target geographic level and tile according to the geographic area range and zoom level in the request; S4, query the data table of the target geographical level to obtain the basic geographical information of the level; S5. Determine whether detailed attribute data is needed; S6. Query a special attribute data table according to the geographic code to obtain detailed attribute data; S7. Merge the acquired geographic information and attribute data to form complete map data, and return it to the visualization screen.

8. A method for optimizing data loading of a large-screen visualization map according to claim 7, characterized in that: In step S2, if the request includes geographic area range and zoom level information, the process proceeds to step S3 to achieve data positioning and loading; Otherwise, proceed to step S5 to obtain data according to the default loading strategy.

9. A method for optimizing data loading of a large-screen visualization map according to claim 8, characterized in that: In step S5, it is determined whether the visualization screen needs detailed attribute data. If so, the process proceeds to step S6 of querying the attribute data table. Otherwise, the acquired geographic information data is directly returned to the visualization large screen step S7.

10. A visual large-screen map data loading optimization device, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to call the machine-readable program to execute the method according to any one of claims 1 to 9.

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