Geographic data model construction method, system and equipment and storage medium

Abstract

The invention discloses a geographic data model construction method, system and device and a storage medium, and the key points of the technical scheme are that the fitting hierarchy of each geographic space element is determined from a multi-hierarchy geographic grid system according to a data object, and the data object refers to geographic data supporting various geographic space analysis; determining all target grid units corresponding to the spatial position features of the geographic space elements according to the fitting hierarchy, wherein all the target grid units form grid objects of the geographic space elements; creating attribute information for the grid object and the target grid unit; and establishing a first data relationship between the data object and the grid object, establishing a second data relationship between the grid object and the target grid unit, and constructing a geographic data model according to the first data relationship and the second data relationship. According to the method, cross-task, cross-scale and high-efficiency dynamic utilization can be carried out by using large-scale geospatial data, so that remodeling and isomorphism of the overall content of the multi-source heterogeneous geospatial data are realized.

Description

Technical Field

[0001] The present invention belongs to the field of image processing technology, and in particular relates to a method, system, device and storage medium for constructing a geographic data model. Background Art

[0002] Geographic data is an information carrier that describes the characteristics, attributes, relationships, and spatiotemporal changes of geographic features. Geographic data typically organizes and describes the spatial, attribute, and temporal characteristics of geographic features in a specific format. Spatial location characteristics describe the location of features, such as geographic coordinates, routes, and administrative boundaries. Attribute information describes qualitative or quantitative characteristics of features, such as their name, type, and size. Temporal characteristics refer to the time or period of time when geographic data is collected or when a geographic phenomenon occurs, such as the real-time location of a typhoon or the NDVI index of vegetation in different seasons. Spatial location characteristics are the core of geographic data, while attribute and temporal characteristics are closely linked to specific geographic features. For example, geographic features in fields such as ecology, meteorology, and transportation all have their own attributes and temporal characteristics. Geographic data comes from a wide range of sources and is rich in content, resulting in diverse data structures and storage formats. To ensure data integration and utilization, existing GIS applications typically use various vector and raster data structures to record spatial data in the form of files. They employ a "database + file system" model to store, organize, manage, and utilize large-scale geographic data. This method can not only ensure the original details of the data to the greatest extent, but also adapt to the needs of collecting, managing, organizing, sharing and applying various types of geographic data.

[0003] However, traditional GIS applications typically load the entire data file into memory when processing geographic data. This makes it difficult to meet the requirements of dynamic interactive data analysis when processing large amounts of data. Existing technologies use raster or vector tiling to process raw data files in layers and blocks. While this can address the issue of dynamic data loading, it does not fundamentally reorganize the data content. Therefore, the efficiency of computational analysis is still limited by the diversity and complexity of geographic features. For example, to calculate the area of ​​irregular vector polygons, existing methods essentially require first decomposing the polygon into smaller parts and then summing the areas of each part to obtain the result. For more complex shapes, this decomposition process can significantly negatively impact efficiency. Furthermore, even across different analytical tasks, the analysis process still shares some common requirements, and the results are valuable for comparison and further integration. However, due to differences in data accuracy, task boundaries, spatial references, and projection methods across tasks, cross-task data alignment, comparison, integration, and utilization are often difficult to achieve directly.

[0004] In summary, for the analysis of geospatial data, the existing organizational form of data content cannot unify the data organization, operation and application modes of different tasks at the analysis and application level, resulting in the inability to maximize the computing efficiency and analytical value of each task, and the inability to achieve optimal configuration of computing resources as a whole. This greatly limits the dynamic and efficient use of large-scale geospatial data across tasks and scales. Summary of the Invention

[0005] The purpose of the present invention is to provide a geographic data model construction method, system, device and storage medium, which can provide support for the cross-task, cross-scale and high-efficiency dynamic utilization of large-scale geographic spatial data, thereby realizing the reshaping and isomorphism of the overall content of multi-source heterogeneous geographic spatial data.

[0006] A first aspect of the present invention provides a method for constructing a geographic data model, comprising:

[0007] Determining the fitting level of each geospatial element from a multi-level geographic grid system based on data objects, wherein the data objects refer to geographic data supporting various geospatial analyses;

[0008] Determine all target grid cells corresponding to the spatial position characteristics of the geographic space element according to the fitting level, wherein all target grid cells constitute a grid object of the geographic space element;

[0009] Creating attribute information for the grid object and the target grid cell;

[0010] A first data relationship is established between the data object and the grid object, and a second data relationship is established between the grid object and the target grid unit.

[0011] In some embodiments, determining the fitting level of each geospatial element from a multi-level geographic grid system according to the data object includes:

[0012] In the case where the data object is vector data, the target accuracy is determined according to the application requirements of each geographic spatial element;

[0013] A level closest to each target accuracy is selected from the multi-level geographic grid system as the fitting level for each geographic spatial element.

[0014] In some embodiments, determining a fitting level from a multi-level geographic grid system according to the data object comprises:

[0015] In the case where the data object is raster data, a level having a grid aperture not less than the spatial scale of a pixel is selected from a multi-level geographic grid system as a fitting level;

[0016] In the case that the data object is text data, the data object is converted into vector data, and the fitting level is determined using the fitting level determination step for vector data.

[0017] In some embodiments, determining all target grid cells corresponding to the spatial location features of the geographic spatial elements according to the fitting level includes:

[0018] Determine whether the data object is vector data;

[0019] If so, determining all target grid cells corresponding to the spatial location features from a multi-level geographic grid system according to the type and fitting level of the geographic spatial element;

[0020] If not, the data object is converted into vector data, and the target grid unit is determined using the target grid unit determination step of the vector data.

[0021] In some embodiments, determining all target grid cells corresponding to the spatial location features from a multi-level geographic grid system according to the type and fitting level of the geographic spatial element includes:

[0022] In a case where the type of the geographic spatial element is a point element, selecting a grid cell corresponding to the point element from the fitting level as a target grid cell;

[0023] In a case where the type of the geographic spatial element is a line element, a grid cell corresponding to the line element is selected from the fitting hierarchy as a basic grid cell, a mapping grid cell is determined from an upper hierarchy according to a mapping relationship between the basic grid cell and grid cells of each hierarchy, a replacement grid cell is determined from the mapping grid cell according to the basic grid cell, and the basic grid cell is updated according to the replacement grid cell until at least one lower-level grid cell corresponding to each mapped grid cell is found to be a non-basic grid cell, and the basic grid cell at this time is used as a target grid cell, wherein the upper hierarchy is a hierarchy above the hierarchy corresponding to the basic grid cell.

[0024] In some embodiments, determining all target grid cells corresponding to the spatial location features from a multi-level geographic grid system according to the type and fitting level of the geographic spatial element further includes:

[0025] In a case where the type of the geographic spatial element is a surface element, determining whether the spatial scale of the surface element is greater than a preset scale threshold;

[0026] If so, extract the smallest grid unit that completely covers the geographic space elements in space from the multi-level geographic grid system as the basic grid unit, determine the intersecting grid unit from the lower level according to the mapping relationship between the basic grid unit and the grid units of each level, determine the replacement grid unit from the basic grid unit according to the intersecting grid unit, update the intersecting grid unit according to the replacement grid unit, and use the updated intersecting grid unit as the basic grid unit until the target level is reached to obtain the final basic grid unit, and use the final basic grid unit as the target grid unit, wherein the lower level is the level below the level corresponding to the basic grid unit, and the target level is pre-set, and the target level is not greater than the simulated level;

[0027] If not, the target grid cell is determined using the target grid cell determination step for the line element.

[0028] In some embodiments, the first data relationship includes a one-to-one or one-to-many data relationship between a data object and a grid object, and the second data relationship includes a one-to-one, one-to-many or many-to-many data relationship between a grid object and a target grid unit.

[0029] A second aspect of the present invention provides a geographic data processing system, comprising:

[0030] a level determination module for determining the fitting level of each geographic spatial element from a multi-level geographic grid system based on data objects, wherein the data objects refer to geographic data supporting various types of geographic spatial analysis;

[0031] A grid determination module, configured to determine all target grid cells corresponding to the spatial position characteristics of the geographic spatial element according to the fitting level, wherein all target grid cells constitute a grid object of the geographic spatial element;

[0032] An attribute creation module, configured to create attribute information for the grid object and the target grid unit;

[0033] The relationship establishing module is used to establish a first data relationship between the data object and the grid object, and to establish a second data relationship between the grid object and the target grid unit.

[0034] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0036] The technical solution provided by the present invention has the following advantages and effects: by converting various types of geospatial data into a geographic data model constructed from three interrelated concepts: "data object - grid object - grid unit", it can support the cross-task, cross-scale, and high-efficiency dynamic utilization of large-scale geospatial data, thereby achieving the reshaping and isomorphism of the overall content of multi-source heterogeneous geospatial data. It has good versatility and can provide broad support for the integration and utilization of various types of geospatial data. At the same time, the gridded isomorphism of data can also significantly improve the access and computing efficiency of large-scale geospatial data, effectively balancing the performance, accuracy, and versatility requirements of large-scale geospatial data computing. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of the method for constructing a geographic data model provided by the present invention;

[0038] Figure 2 It is a schematic diagram of the multi-level geographic grid system provided by the present invention and the spatial fitting of the same polygon using different levels;

[0039] Figure 3 Schematic diagrams of two types of mesh degradation in the surface element fitting process provided by the present invention;

[0040] Figure 4 It is a schematic diagram of converting a geographic vector data object into a gridded data object provided by the present invention;

[0041] Figure 5 This is a schematic diagram of the data relationship of "data object-grid object-grid unit" provided by the present invention;

[0042] Figure 6 It is a structural diagram of the geographic data model construction system provided by the present invention;

[0043] Figure 7 It is a diagram of the internal structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To facilitate understanding of the present invention, specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0045] Unless otherwise specified or defined, the "first, second..." used in this article is only used to distinguish names and does not represent a specific quantity or order.

[0046] Unless stated otherwise or defined otherwise, the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] It should be noted that, in this document, “fixed to” or “connected to” may mean directly fixing or connecting to an element, or indirectly fixing or connecting to an element.

[0048] like Figure 1 As shown, this embodiment provides a method for constructing a geographic data model, including the following steps S1 to S5:

[0049] Step S1: Determine the fitting level of each geographic spatial element from a multi-level geographic grid system according to the data object.

[0050] In practical applications, such as Figure 2 As shown, the multi-level geographic grid system refers to a global or regional geographic subdivision grid system with multi-level and structured characteristics. The geographic subdivision grid system will divide the earth's surface into a multi-level geographic grid composed of grid cells according to specific rules, wherein the larger the level of the grid, the smaller the spatial scale of the grid cells that make up the level; there will be a definite mathematical mapping relationship between the grid cells of different levels, such as the grid cells of the previous level will cover (or approximately cover) a certain number of grid cells of the next level, such as the grid cells of the first level will cover specific 4 grid cells in the second level. Multi-level geographic grid systems include but are not limited to various geographic grid systems constructed using pyramid models, such as Figure 2 The following examples are shown in Figure 1; GeoSot, a global latitude and longitude grid based on a quadtree grid with equal degrees, equal divisions, and equal seconds; and the H3 geographic grid system with a hexagonal grid. Multi-level geographic grid systems can provide grid cells ranging from global to meter or centimeter levels, thus meeting the accuracy requirements for fitting various spatial location features.

[0051] Specifically, data objects refer to the geographic data that supports various geospatial analyses. They can come from specially collected data, such as plant distribution records from a botanical garden, or from existing data, such as publicly available remote sensing imagery. Data objects are often driven by specific tasks or goals, such as the regularly updated WorldClim data on global bioclimatic variables or the Landsat series covering the global land surface. Data objects often have clear task attributes and application characteristics, making them suitable as basic data collection and update targets.

[0052] A geospatial feature refers to something or an object with a specific location, shape, size, and spatial distribution characteristics on the surface of the earth. Specifically, the data types of data objects include: vector data, raster data, and text data. For vector data, a geospatial feature refers to any geospatial object composed of one or more single vector elements (points, lines, and surfaces), such as the tree distribution data of a botanical garden composed of multiple point elements, or a national highway composed of a line element. For raster data, a geospatial feature refers to any raster object composed of one or more pixels. It can be the entire raster data or a part of the raster data. In practical applications, raster data is often cut into multiple non-overlapping slices for storage. A geospatial feature can be composed of one or more raster slices, which can be continuous or discontinuous in space. Furthermore, a data object can typically consist of one or more geospatial elements. For example, a park map can be composed of multiple geospatial elements of various categories, such as roads, buildings, and green spaces. A large remote sensing raster image can also be abstracted into different geospatial elements based on zoning or task requirements. Geospatial elements belonging to the same data object can be spatially non-overlapping or overlapping.

[0053] The fitting level refers to the maximum grid level that can be used to ensure fitting accuracy when using grid cells in a multi-level geographic grid system to fit the spatial location characteristics of geographic spatial elements. Figure 2 II, III, IV, and V in the figure use level 1, level 2, level 3, and level 4 grids to perform spatial fitting on the same polygon respectively. By determining the fitting level of each geographic spatial element, the fitting accuracy can be guaranteed.

[0054] Furthermore, determining the fitting level from the multi-level geographic grid system according to the data object includes:

[0055] In the case where the data object is vector data, the target accuracy is determined according to the application requirements of each geographic spatial element;

[0056] A level closest to each target accuracy is selected from the multi-level geographic grid system as the fitting level for each geographic spatial element.

[0057] In actual applications, when the data object is vector data, according to the application requirements, for example, if the application requirement is to simulate the error of geographic spatial elements to a certain extent, the data error in the data object is used as the target accuracy, and then the level closest to the target accuracy is selected from the multi-level geographic grid system as the fitting level of the geographic spatial element. When the application requirement is to restore the original distribution style, contour details and other high-precision requirements of the geographic spatial elements, the first accuracy is selected as the target accuracy. When the application requirement is to restore the general direction, general contour and other low-precision requirements of the geographic spatial elements, the second accuracy is selected as the target accuracy, and the second accuracy is less than the first accuracy. Specifically, when the spatial location characteristics of geographic space elements are point elements, such as the geographic location data of wild plants, the point elements are randomly distributed, the absolute error of longitude and latitude is 10m, and the numerical accuracy is about 10cm (for example, the longitude and latitude coordinates are: 113.359276, 23.190215). When the application requirement is to simulate the errors of point elements to a certain extent, 10m is selected as the target accuracy, and the grid level with a grid aperture closest to 10m is selected from the multi-level geographic grid system as the fitting level of the element; when the application requirement is to restore the original distribution pattern of point elements in the data, 10cm is selected as the target accuracy, and the grid level with a grid aperture closest to 10cm is selected from the multi-level geographic grid system as the fitting level. If the spatial location feature of a geographic spatial element is a line element, for example, the line element represents a national highway with a width of 8m. If the application requirement is not only to fit the general direction of the road, but also to allow the actual road surface width to be covered by one or two grid units as much as possible, then the target accuracy can be selected as an accuracy greater than 8m, and the layer with a grid aperture greater than or equal to 8m and the closest to 8m is selected from the multi-level geographic grid system as the fitting layer. Then, the grid unit of this layer can not only fit the general direction of the road, but also allow the actual road surface width to be covered by one or two grid units as much as possible. If the application requirement is to further distinguish different lanes on the road on the grid, then the target accuracy can be selected as an accuracy less than 4m, and the layer with a grid aperture less than 4m and the closest to 4m is selected from the multi-level geographic grid system as the fitting layer. If the spatial location feature of a geographic spatial element is a surface element, such as a building, and the application requirement is to fit the general outline of the building and the orientation of the entrances and exits, then the meter level is selected as the target accuracy, and the level with the grid aperture closest to the meter level is selected from the multi-level geographic grid system as the fitting level; if the application requirement is to fit the outline details of the building, then the centimeter level is selected as the target accuracy, and the level with the grid aperture reaching the centimeter level is selected from the multi-level geographic grid system as the fitting level.

[0058] Furthermore, the determining of the fitting level from the multi-level geographic grid system according to the data object further includes:

[0059] In the case where the data object is raster data, a level having a grid aperture not less than the spatial scale of a pixel is selected from a multi-level geographic grid system as a fitting level;

[0060] In the case that the data object is text data, the data object is converted into vector data, and the fitting level is determined using the fitting level determination step for vector data.

[0061] In practical applications, data objects include not only vector data but also raster data and text data. Therefore, it is also necessary to determine the fitting level for the geospatial features of data objects whose data types are raster data and text data. Specifically, for raster data, such as remote sensing images with a spatial resolution of 30m for a single pixel, a fitting level with a grid aperture of at least 30m should be selected. By selecting a level with a grid aperture no smaller than the spatial scale of the pixel as the fitting level, the original data resolution can be retained to the greatest extent possible, avoiding redundant calculations.

[0062] For the spatial location characteristics of geographic spatial elements in text data, they can be converted into vector data first, and then the fitting level can be determined according to the logic of vector data, that is, the fitting level can be determined according to the steps of determining the fitting level of vector data. For example, for plant species distribution records only up to the county level, the text records of the county-level administrative districts can be converted into corresponding county vector polygons first, and then the target accuracy can be determined according to the actual application requirements of the data, and the fitting level can be determined according to the target accuracy. In actual applications, the distribution of plants can neither cover the entire county nor be completely restricted by the county boundaries. Therefore, according to the research characteristics of the flora, grid units with an aperture close to 1km or 10km can be used as the basic unit for measuring the plant distribution area. If 1km or 10km is selected as the target accuracy, the level with a grid aperture close to 1km or 10km can be selected as the fitting level of each county vector polygon.

[0063] In practical applications, the grid unit in the fitting level is the smallest unit for fitting specific spatial position features. The fitting level controls the fitting details of the spatial position features and usually determines the data size after the spatial position is converted into grid unit data. In this application, the fitting level is determined by the application requirements and the actual situation of the data to achieve a balance between the actual situation of the data and the general needs of the application.

[0064] Step S2: determining all target grid cells corresponding to the spatial position characteristics of the geographic space element according to the fitting level, and all target grid cells constitute a grid object of the geographic space element.

[0065] In practical applications, the target grid cells in the grid object can include not only the grid cells of the fitting level, but also the grid cells of other different levels. The grid cells of the fitting level in the grid object at least intersect with the spatial position of the geographic spatial elements to ensure the boundary fitting accuracy.

[0066] Furthermore, determining all target grid cells corresponding to the spatial position characteristics of the geographic space elements according to the fitting level includes:

[0067] Determine whether the data object is vector data;

[0068] If so, determining all target grid cells corresponding to the spatial location features from a multi-level geographic grid system according to the type and fitting level of the geographic spatial element;

[0069] If not, the data object is converted into vector data, and the target grid unit is determined using the target grid unit determination step of the vector data.

[0070] In actual applications, after step S1, the text data will be converted into vector data, but the raster data is not converted into vector data. Therefore, it is necessary to first determine whether the data object is vector data. If the data object is vector data, all target grid cells corresponding to the geographic spatial element are determined from the vector data according to the type and fitting level of the geographic spatial element. If the data object is raster data, the geographic range covered by the raster data is first converted into vector data, and then all target grid cells corresponding to the geographic spatial element are determined from the vector data according to the type and fitting level of the geographic spatial element.

[0071] Furthermore, determining all target grid cells corresponding to the spatial location features from the multi-level geographic grid system according to the type and fitting level of the geographic spatial element includes:

[0072] In a case where the type of the geographic spatial element is a point element, selecting a grid cell corresponding to the point element from the fitting level as a target grid cell;

[0073] When the type of the geographic spatial element is a line element, a grid cell corresponding to the line element is selected from the fitting hierarchy as a basic grid cell, a mapping grid cell is determined from an upper hierarchy according to a mapping relationship between the basic grid cell and grid cells of each hierarchy, a replacement grid cell is determined from the mapping grid cell according to the basic grid cell, and the basic grid cell is updated according to the replacement grid cell until at least one lower-level grid cell corresponding to each mapped grid cell is found to be a non-basic grid cell, and the basic grid cell at this time is used as a target grid cell, wherein the upper hierarchy is a hierarchy above the hierarchy corresponding to the basic grid cell;

[0074] In the case where the type of the geographic spatial element is a surface element, it is determined whether the spatial scale of the surface element is greater than a preset scale threshold. If so, the smallest grid unit that completely covers the geographic spatial element in space is extracted from the multi-level geographic grid system as the basic grid unit. According to the mapping relationship between the basic grid unit and the grid units of each level, the intersecting grid unit is determined from the lower level. The replacement grid unit is determined from the basic grid unit according to the intersecting grid unit. The intersecting grid unit is updated according to the replacement grid unit. The updated intersecting grid unit is used as the basic grid unit until the target level is reached to obtain the final basic grid unit. The final basic grid unit is used as the target grid unit, wherein the lower level is the level below the level corresponding to the basic grid unit, the target level is pre-set, and the target level is not greater than the simulated level. If not, the target grid unit is determined using the target grid unit determination step of the line element.

[0075] In practical applications, if the geographic spatial element is composed of point elements, for example, if the planting distribution map of plants needs to be converted into a grid object, it is necessary to convert the point of each plant into a grid unit that can spatially cover the point as the target grid unit. Usually, the grid points that can cover each plant point are selected from the fitting level as the target grid unit, and all target grid cells constitute the grid object of the element.

[0076] If a geographic spatial feature is composed of line elements, such as converting a line element representing a road into a grid object, the conversion can begin by first extracting all intersecting grid cells from the feature's fitting hierarchy as basic grid cells. Then, based on the mapping relationships between grid cells at each level of the target geographic grid system, these basic grid cells are subjected to grid degeneration. This means that the basic grid cells are mapped onto grid cells at the upper level to obtain mapped grid cells. In this case, the mapped grid cells represent the grid cells at the upper level that have a mapping relationship with the basic grid cells. If all subordinate grid cells corresponding to a mapped grid cell are basic grid cells, the mapped grid cell is used as a replacement grid cell. The subordinate grid cells represent the grid cells at the level below the corresponding level of the mapped grid cell. The basic grid cell corresponding to the replacement grid cell is used as the grid cell to be replaced. The replacement grid cell is then used to replace the grid cell to update the basic grid cell. This process continues upwards until at least one subordinate grid cell corresponding to each mapped grid cell is found to be a non-basic grid cell. The basic grid cell at this point is then used as the target grid cell.

[0077] like Figure 3As shown in , if the geographic spatial element is composed of surface elements and the spatial scale of the surface elements is not greater than the preset scale threshold, the spatial scale of the surface elements is small and can be converted in the same way as the line elements, such as Figure 3 As shown in Ⅰ, the spatial position features of the surface elements are first converted into grid units of the fitting level, and then degenerated into target grid units. The target grid units are as follows: Figure 3 As shown in the rightmost figure of Figure Ⅰ, it includes: some grid cells in the fitting level (level 4), level 3, and level 2; However, when converting large-scale surface elements, that is, when converting surface elements whose spatial scale is greater than the preset scale threshold, in order to reduce the processing of a large amount of small-scale grid cell information during the conversion process and improve the conversion efficiency, as shown in the figure, Figure 3 As shown in II, a minimum grid cell that can completely cover the geographic spatial elements can be extracted from the multi-level geographic grid system as a basic grid cell. Then, based on the mapping relationship of the hierarchical structure of the multi-level geographic grid system, the basic grid cell is mapped to the grid cells of the lower level to obtain a mapped grid cell. The mapped grid cell in this case represents the grid cell in the lower level that has a mapping relationship with the basic grid cell. The grid cells that intersect with the geographic spatial elements are selected from the mapped grid cells to obtain an intersecting grid cell. When all the lower level grid cells corresponding to a basic grid cell are intersecting grid cells, the basic grid cell is used as a replacement grid cell, and the intersecting grid cell corresponding to the basic grid cell is used as the grid cell to be replaced. The replacement grid cell is used to replace the grid cell to be replaced to obtain an updated intersecting grid cell. The updated intersecting grid cell is used as the basic grid cell, and the sub-level is continued to be explored until the target level is reached. After the replacement grid cell replaces the corresponding grid cell to be replaced in the target level, the final basic grid cell is obtained and the final basic grid cell is used as the target grid cell. The target level cannot be larger than the fitting level. In addition, in some practical applications, the target grid unit may be directly or indirectly limited to grid units from a specific level. For example, the aperture of the target grid unit must be as close to 1 km as possible but not more than 10 km. Among them, the grid level closest to the 1 km aperture can be used as the fitting level of the spatial element, and the remaining grid levels cannot be used as target levels. The target grid unit can be limited by pre-setting the target level.

[0078] In practical applications, such as Figure 4 As shown, a geographic vector data object is converted into a grid data object, where Figure 4 The I in the figure is the original data object, including a lake composed of surface elements, a road composed of line elements, and two types of spatial elements composed of point elements ( Figure 4 II), convert them into mesh objects ( Figure 4 III), and finally a new data object can be obtained logically ( Figure 4 The target grid cell in a grid object is essentially a degenerate version of the grid cell in the fitting level. Therefore, the grid object not only maximizes the accuracy of the boundary fitting of geographic spatial features, but also reduces the number of grid cells in the grid object, thereby significantly simplifying the spatial position expression of various geographic features and improving the access efficiency of spatial data.

[0079] Step S3: Create attribute information for the grid object and the target grid unit.

[0080] In practical applications, a grid object corresponds to one or more target grid cells. In addition to including the pointer to the data object and the grid object's identifier, the grid object's attribute information can also directly inherit or write the attribute information possessed or contained by the geographic spatial element. For example, if the geographic spatial element represents a lake, the lake's water storage capacity, surface area, maximum water depth, and data collection time can be written into the grid object's attributes. If it represents a forest, the number of trees, average diameter at breast height, average tree height, average crown width, and data collection time can be written into the grid object's attributes. If it represents the temperature conditions at a specific time and space, the minimum temperature, maximum temperature, average temperature, and data collection time of the region at that time and space can be written into the grid object's attributes. In addition, the grid object's attribute information also needs to include some information related to the target grid cell, such as the grid object's fitting level, the number of grid cells covered in the fitting level, the maximum and minimum row and column numbers of the covered grid cells in the fitting level, and the maximum and minimum levels of the target grid cells included. This grid object's attribute information can support the grid object's participation in corresponding spatial operations.

[0081] Specifically, the target grid cell is the basic unit that makes up the grid object. The attributes of each target grid cell can include the orientation of the grid object to which it belongs, the level of the target geographic grid system to which it belongs, the geographic grid code of the target grid cell, the actual spatial area of ​​the target grid cell, the length, width, and aperture of the grid cell, and other information. In addition, if the geographic spatial feature is composed of simple point elements, the attributes of the target grid cell can also inherit the existing attributes of the point elements. For example, if the geographic spatial feature is a tree distribution map composed of point elements, the attribute information of each tree, such as tree height, diameter at breast height, and tree age, can be written into the attribute information of the corresponding target grid cell. Discrete, regular, and hierarchical target grid cells can significantly improve the access and computation efficiency of spatial data. For example, geographic spatial metrics such as area and distance can be converted into the sum of the area or aperture of geographic grid cells. Topological relationships such as spatial inclusion, intersection, adjacency, and separation, as well as the calculation of spatial intersection, union, difference, and complement can be converted into set operations based on geographic grid codes. Discrete grid cells also better support distributed access and parallel computing, thus further meeting the needs of dynamic interactive computing and analysis of large-scale data.

[0082] Step S4: establishing a first data relationship between the data object and the grid object, establishing a second data relationship between the grid object and the target grid unit, and constructing a geographic data model based on the first data relationship and the second data relationship.

[0083] Furthermore, if Figure 5 As shown, the first data relationship includes a one-to-one or one-to-many data relationship between a data object and a grid object, and the second data relationship includes a one-to-one, one-to-many or many-to-many data relationship between a grid object and a target grid unit.

[0084] In practical applications, the design of data relationships should be determined based on the specific relationships of business data, in order to simplify data relationships as much as possible and improve the efficiency of data access and computational analysis. For example, in some businesses, if the data object and grid object have a one-to-one relationship, the data object can be combined with the grid object, that is, one object includes both the attributes of the data object and the attributes of the grid object. In some businesses, if the grid object and the target grid cell have a one-to-one relationship, the grid object can also be combined with the target grid cell. If, in actual business, a target grid cell can be associated with multiple grid objects, then the decision to adopt a one-to-many or many-to-many relationship can be made based on the specific application design requirements. The former can achieve more efficient single-table queries, while the latter can provide more flexible relationship creation for the business. In addition, if each data object is a point element, then the data object, grid object, and target grid cell can be integrated into one, that is, a target grid cell can represent a data object, and the target grid cell has the triple attributes of data object, grid object, and target grid cell. In summary, the "data object-grid object-grid unit" model based on a multi-level structured geographic grid system can provide a unified integrated isomorphic framework for heterogeneous geographic spatial data, and at the same time provide support for achieving optimal balance in terms of accuracy, efficiency, and versatility for the computational analysis of large-scale geographic spatial data.

[0085] The geographic data model construction method of the present invention transforms various types of geospatial data into gridded data composed of three interrelated conceptual models: "data object-grid object-grid unit." This method achieves the overall reshaping and isomorphism of multi-source heterogeneous geospatial data. This method has excellent versatility and can provide broad support for the integration and utilization of various geospatial data. Furthermore, the gridded isomorphism of data can significantly improve the access and computational efficiency of large-scale geospatial data, effectively balancing the performance, accuracy, and versatility requirements of large-scale geospatial data computation.

[0086] like Figure 6 As shown, an embodiment of the present invention further provides a geographic data model construction system, including:

[0087] A level determination module 10 is configured to determine the fitting level of each geographic spatial element from a multi-level geographic grid system based on data objects, wherein the data objects refer to geographic data supporting various types of geographic spatial analysis;

[0088] A grid determination module 20 is configured to determine all target grid cells corresponding to the spatial position characteristics of the geographic spatial element according to the fitting level, wherein all target grid cells constitute a grid object of the geographic spatial element;

[0089] An attribute creation module 30 is used to create attribute information for the grid object and the target grid unit;

[0090] The model building module 40 is used to establish a first data relationship between the data object and the grid object, establish a second data relationship between the grid object and the target grid unit, and build a geographic data model based on the first data relationship and the second data relationship.

[0091] Each module of the geographic data model construction system described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each of the modules and units described above may be embedded in or independent of a processor within a computer device in the form of hardware, or may be stored in a memory within the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0092] like Figure 7 As shown, an embodiment of the present invention discloses a computer device, including a memory and a processor, wherein the memory stores a computer program;

[0093] The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements the geographic data model construction method described in the above embodiments.

[0094] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0095] An embodiment of the present invention further discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the geographic data model construction method described in the above embodiments.

[0096] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0097] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for constructing a geographic data model, characterized in that: include: Determining the fitting level of each geospatial element from a multi-level geographic grid system based on data objects, wherein the data objects refer to geographic data supporting various geospatial analyses; Determine all target grid cells corresponding to the spatial position characteristics of the geographic space element according to the fitting level, wherein all target grid cells constitute a grid object of the geographic space element; Creating attribute information for the grid object and the target grid cell; A first data relationship is established between the data object and the grid object, a second data relationship is established between the grid object and the target grid unit, and a geographic data model is constructed according to the first data relationship and the second data relationship.

2. The method for constructing a geographic data model according to claim 1, wherein: Determining the fitting level of each geographic spatial element from the multi-level geographic grid system according to the data object includes: In the case where the data object is vector data, the target accuracy is determined according to the application requirements of each geographic spatial element; A level closest to each target accuracy is selected from the multi-level geographic grid system as the fitting level for each geographic spatial element.

3. The method for constructing a geographic data model according to claim 2, wherein: Determining the fitting level from the multi-level geographic grid system according to the data object includes: In the case where the data object is raster data, a level having a grid aperture not less than the spatial scale of a pixel is selected from a multi-level geographic grid system as a fitting level; In the case that the data object is text data, the data object is converted into vector data, and the fitting level is determined using the fitting level determination step for vector data.

4. The method for constructing a geographic data model according to claim 1, wherein: The step of determining all target grid cells corresponding to the spatial position characteristics of the geographic spatial elements according to the fitting level includes: Determine whether the data object is vector data; If so, determining all target grid cells corresponding to the spatial location features from a multi-level geographic grid system according to the type and fitting level of the geographic spatial element; If not, the data object is converted into vector data, and the target grid unit is determined using the target grid unit determination step of the vector data.

5. The method for constructing a geographic data model according to claim 4, wherein: The step of determining all target grid cells corresponding to spatial location features from a multi-level geographic grid system according to the type and fitting level of the geographic spatial elements includes: In a case where the type of the geographic spatial element is a point element, selecting a grid cell corresponding to the point element from the fitting level as a target grid cell; In a case where the type of the geographic spatial element is a line element, a grid cell corresponding to the line element is selected from the fitting hierarchy as a basic grid cell, a mapping grid cell is determined from an upper hierarchy according to a mapping relationship between the basic grid cell and grid cells of each hierarchy, a replacement grid cell is determined from the mapping grid cell according to the basic grid cell, and the basic grid cell is updated according to the replacement grid cell until at least one lower-level grid cell corresponding to each mapped grid cell is found to be a non-basic grid cell, and the basic grid cell at this time is used as a target grid cell, wherein the upper hierarchy is a hierarchy above the hierarchy corresponding to the basic grid cell.

6. The method for constructing a geographic data model according to claim 1, wherein: The step of determining all target grid cells corresponding to spatial location features from a multi-level geographic grid system according to the type and fitting level of the geographic spatial elements further includes: In a case where the type of the geographic spatial element is a surface element, determining whether the spatial scale of the surface element is greater than a preset scale threshold; If so, the smallest grid unit that completely covers the geographic space elements in space is extracted from the multi-level geographic grid system as the basic grid unit, and the intersecting grid unit is determined from the lower level according to the mapping relationship between the basic grid unit and the grid units of each level, and the replacement grid unit is determined from the basic grid unit according to the intersecting grid unit, and the intersecting grid unit is updated according to the replacement grid unit, and the updated intersecting grid unit is used as the basic grid unit until the final basic grid unit is obtained at the target level, and the final basic grid unit is used as the target grid unit, wherein the lower level is the next level of the level corresponding to the basic grid unit, and the target level is pre-set, and the target level is not greater than the simulated level. If not, the target grid cell is determined using the target grid cell determination step for the line element.

7. The method for constructing a geographic data model according to claim 1, wherein: The first data relationship includes a one-to-one or one-to-many data relationship between a data object and a grid object, and the second data relationship includes a one-to-one, one-to-many or many-to-many data relationship between a grid object and a target grid unit.

8. A geographic data processing system, characterized in that include: a level determination module for determining the fitting level of each geographic spatial element from a multi-level geographic grid system based on data objects, wherein the data objects refer to geographic data supporting various types of geographic spatial analysis; A grid determination module, configured to determine all target grid cells corresponding to the spatial position characteristics of the geographic spatial element according to the fitting level, wherein all target grid cells constitute a grid object of the geographic spatial element; An attribute creation module, configured to create attribute information for the grid object and the target grid unit; The model building module is used to establish a first data relationship between the data object and the grid object, establish a second data relationship between the grid object and the target grid unit, and build a geographic data model based on the first data relationship and the second data relationship.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images