Global scale geospatial data processing method and device, medium and equipment

Abstract

The invention discloses a global scale geospatial data processing method and device, a medium and equipment. The invention belongs to the technical field of computers. The method comprises the following steps: constructing a virtual earth based on a preset global scaling factor, and constructing a mapping relation between geographic coordinates and virtual coordinates of the virtual earth; mapping the multi-source geographic data to the virtual earth according to the mapping relation to obtain virtual coordinates of each geographic entity; extracting cross-source geographic data to be processed from the virtual coordinate space; unifying geometric types and attribute field formats of the cross-source geographic data; and based on the unified reference of the virtual coordinate space, carrying out geometric edge matching calculation on adjacent boundaries of the cross-source data, and eliminating boundary gaps or overlapping. By adopting the technical scheme, unified processing of multi-source geographic data under a global scale and seamless connection of adjacent boundaries of cross-source data can be realized, and the problems that processing is difficult under a unified view and gaps or overlapping exist in the boundaries are solved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a global-scale geospatial data processing method, apparatus, medium and device. Background Technology

[0002] With the rapid development of applications such as Digital Earth and Global Situational Awareness, the integration and efficient processing of global-scale geospatial data has become a core requirement in the field of Geographic Information Systems (GIS). Currently, existing GIS technologies, when processing global or large-scale geospatial data, mainly employ specific projected coordinate systems to divide the data into zones, then attempt to geometrically connect the zoned data, and simultaneously perform operations such as symbolic display of point, line, and polygon features and collection of POIs (Points of Interest) directly under the geographic coordinate system.

[0003] However, existing technologies have significant limitations: First, it is difficult to achieve lossless geometric edge joining and editing of cross-projection zone data under a unified view. The difference in coordinate references between different projection zones makes it easy for gaps or overlaps to appear at the boundaries, failing to meet the requirements for globally seamless processing. That is, when facing large-scale data, especially global data resources, there may be nearly or more than thousands of edge joining operations. If each operation is projected and then rotated back in the conventional manner, the error per operation is small, but after thousands of calculations, the offset may become large. Second, in the geographic coordinate system, the latitude and longitude coordinate values ​​are too small and the unit is degrees, which leads to unstable calculation of point feature icon size, inconsistent line width display of line features, and distortion of fill patterns for polygon features, resulting in poor symbolization effects. Third, when collecting POIs under a global view, the latitude and longitude values ​​corresponding to mouse clicks are tiny, and pixel deviations can easily cause huge coordinate errors, making it difficult to achieve accurate marking. These problems make it impossible for existing solutions to simultaneously meet the core requirements of globally seamless, high-precision editing, and stable symbolization, seriously restricting the application and development of geospatial data. Summary of the Invention

[0004] This application proposes a global-scale geospatial data processing method, apparatus, medium, and equipment to solve the technical problems in the prior art where cross-projection zone geographic data is difficult to process under a unified view and where there are gaps or overlaps at adjacent boundaries of cross-source data. It achieves the technical effect of unified processing of global-scale multi-source geographic data and seamless connection of cross-source data boundaries. In particular, for the problem of multiple edge connections, this solution allows different factors to be selected for different regions, and after being enlarged by a certain factor, it can be scaled back by the same factor to achieve the effect of zero error loss.

[0005] This application provides a global-scale geospatial data processing method, the method comprising:

[0006] A virtual globe is constructed based on a pre-defined global scaling factor, and a mapping relationship is established between geographic coordinates and the virtual coordinates of the virtual globe.

[0007] Based on the mapping relationship, multi-source geographic data is mapped onto a virtual globe to obtain the virtual coordinates of each geographic entity;

[0008] Extract cross-source geographic data to be processed from virtual coordinate space;

[0009] Unify the geometric type and attribute field format of the cross-source geographic data;

[0010] Based on a unified benchmark in virtual coordinate space, geometric edge calculations are performed on adjacent boundaries of cross-source data to eliminate boundary gaps or overlaps.

[0011] Furthermore, the method also includes:

[0012] During the geometric edge calculation process, a topology consistency check algorithm is used to detect whether there are topology errors in cross-source data. If they are found, a topology error report is generated.

[0013] Furthermore, the method also includes:

[0014] If the topology error meets the preset conditions, the feature coordinates will be automatically adjusted through the topology consistency check algorithm for automatic repair.

[0015] If the topology error does not meet the preset conditions, a prompt message for manual repair will be generated, and the topology check will be re-executed after manual repair until there are no topology errors.

[0016] Furthermore, the method also includes:

[0017] Receive user interaction events on the screen, including one or more of click, drag, and drawing;

[0018] Convert the geographic coordinates corresponding to the interactive event to virtual coordinates;

[0019] Based on the virtual coordinates, POI acquisition, feature selection, and geometric editing operations are completed.

[0020] Transform back to geographic coordinates and record the interactive results.

[0021] Furthermore, a virtual Earth is constructed based on a pre-defined global scaling factor, including:

[0022] Information on operational scenarios for acquiring geographic data;

[0023] Based on the operational scenario information, from the pre-built 10 5 -10 7Within the range of values, determine a global scaling factor that matches the current operation scenario information, and construct a virtual Earth.

[0024] Furthermore, the method also includes:

[0025] Import a pre-defined geographic feature symbolization rule library. The rule library contains one or more configuration information such as symbol styles, size parameters, and display priorities for point features, line features, and polygon features. Specifically, for point features, calculate the screen size and anchor point position of the POI icon based on virtual coordinates; for line features, calculate the line width, dashed line mode, arrow style, and position parameters based on virtual coordinate distance; for polygon features, calculate the density of the polygon fill pattern and the width and style of the outline edge in virtual coordinate space.

[0026] The obtained symbolic parameters are applied to geographic features in the virtual coordinate space, and rendering operations are performed to generate a visual image.

[0027] Furthermore, the method also includes:

[0028] Receive user adjustment operations, customize the geographic feature symbolization rules, apply and synchronously store the customized rules to the geographic feature symbolization rule library.

[0029] This application embodiment also provides a global-scale geospatial data processing apparatus, the apparatus comprising:

[0030] The mapping relationship construction module is used to construct a virtual globe based on a pre-set global scaling factor, and to construct the mapping relationship between geographic coordinates and the virtual coordinates of the virtual globe;

[0031] The virtual coordinate determination module is used to map multi-source geographic data onto a virtual globe according to the mapping relationship, so as to obtain the virtual coordinates of each geographic entity.

[0032] The data extraction module is used to extract cross-source geographic data to be processed from the virtual coordinate space;

[0033] The format unification module is used to unify the geometric type and attribute field format of the cross-source geographic data.

[0034] The edge processing module is used to perform geometric edge calculations on adjacent boundaries of cross-source data based on a unified benchmark in virtual coordinate space, eliminating boundary gaps or overlaps.

[0035] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the global-scale geospatial data processing method described above.

[0036] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the global-scale geospatial data processing method described above.

[0037] The embodiments of this application adopt the following technical solution: constructing a virtual globe based on a pre-set global scaling factor, and constructing a mapping relationship between geographic coordinates and the virtual coordinates of the virtual globe; mapping multi-source geographic data onto the virtual globe according to the mapping relationship to obtain the virtual coordinates of each geographic entity; extracting cross-source geographic data to be processed from the virtual coordinate space; unifying the geometric type and attribute field format of the cross-source geographic data; and performing geometric edge calculation on the adjacent boundaries of the cross-source data based on the unified benchmark of the virtual coordinate space to eliminate boundary gaps or overlaps.

[0038] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0039] This solution proposes a global-scale geospatial data processing method. By constructing a virtual globe based on a preset global scaling factor and the mapping relationship between geographic coordinates and virtual coordinates, multi-source geographic data is mapped to the virtual globe, and the geometric types and attribute field formats of cross-source geographic data are unified. Then, based on the unified benchmark of virtual coordinate space, geometric edge calculation is performed on the adjacent boundaries of cross-source data. This achieves unified processing and seamless boundary connection of global-scale cross-source geographic data, solving the problems of lossless processing of cross-projection zone data under a unified view and the existence of gaps or overlaps in boundaries in traditional GIS technology. This provides an accurate data foundation for the subsequent processing and application of global-scale geospatial data. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 A flowchart illustrating the global-scale geospatial data processing method provided in Embodiment 1 of this application;

[0042] Figure 2 This is a comparative illustration of the edge joining effect provided in Embodiment 2 of this application;

[0043] Figure 3 This is a schematic diagram of the structure of the global-scale geospatial data processing device provided in Embodiment 3 of this application;

[0044] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0047] Example 1

[0048] Figure 1 This is a flowchart illustrating the global-scale geospatial data processing method provided in Embodiment 1 of this application. Figure 1 As shown, the method includes:

[0049] S11, Construct a virtual globe based on a pre-set global scaling factor, and construct a mapping relationship between geographic coordinates and the virtual coordinates of the virtual globe;

[0050] The global scaling factor is a numerical value used to linearly scale up the original geographic coordinate system, such as the geodetic coordinate system, to a virtual space. Its value typically ranges from 10. 5 Up to 10 7 Between, for example, in the Global Geographic Information Labelling System, it can be set to 1000000.

[0051] A virtual Earth can be a virtual geographic coordinate space constructed through affine scaling, providing a unified and stable basic platform for geospatial data processing, symbolic computation, and human-computer interaction.

[0052] Geographic coordinates can be coordinates that represent the location of a point on the Earth's surface, usually presented in the form of latitude and longitude, such as the longitude and latitude values ​​in the WGS84 coordinate system.

[0053] Virtual coordinates refer to the coordinates obtained by linearly transforming geographic coordinates through a global scaling factor. The numerical range is suitable for calculation and can reduce errors in subsequent data processing.

[0054] This solution can be implemented using an affine scaling virtual globe engine. The engine loads a pre-defined global scaling factor and linearly scales the original geographic coordinate system according to the scaling factor to generate the coordinate space of the virtual globe. A bidirectional linear transformation formula between geographic coordinates and virtual coordinates is established to ensure the real-time performance and accuracy of subsequent coordinate transformations. For example, if the geographic coordinates are (Lon, Lat) and the global scaling factor is k, then the virtual coordinates can be represented as (Lon×k, Lat×k).

[0055] S12, Based on the mapping relationship, the multi-source geographic data is mapped onto the virtual earth to obtain the virtual coordinates of each geographic entity;

[0056] Multi-source geographic data can be geospatial data from different channels and in different formats, including vector data such as global vector maps in .esdb format, remote sensing image data, etc., covering various geographic entity information such as terrain, land features, and transportation routes.

[0057] Geographic entities can be geographical features on the Earth's surface that have a clear spatial location and attribute characteristics, such as buildings, roads, rivers, and lakes.

[0058] This solution can call the coordinate transformation function of the affine scaling virtual earth engine to convert the geographic coordinates corresponding to each geographic entity in the loaded multi-source geographic data, the mapping relationship constructed above, into virtual coordinates in batches, and then load the converted data into the coordinate space of the virtual earth.

[0059] S13, extract all cross-source geographic data to be processed from the virtual coordinate space;

[0060] Virtual coordinate space can be the coordinate range corresponding to a virtual Earth. All geographic data is stored in this space after conversion, providing a unified computing environment for data processing.

[0061] Cross-source geographic data can be geographic data from different data sources, such as vector map data of the same area collected by different departments, or remote sensing image data of the same area taken by different satellites.

[0062] In this technical solution, cross-source geographic data that needs to be geometrically connected and topologically processed can be filtered and obtained from the virtual coordinate space. During the extraction process, a data association index will be established to clarify the spatial relationship between geographic data from different data sources.

[0063] S14, unify the geometric type and attribute field format of the cross-source geographic data;

[0064] Geometric types can be the spatial morphology of geographic entities, mainly including three types: points, lines, and surfaces. For example, POI (Point of Interest) belongs to the point type, roads belong to the line type, and lakes belong to the surface type.

[0065] Attribute field format can be used to describe the field structure of geographic entity attribute information, including field name, data type (e.g., character, numeric, date), field length, etc. For example, attribute fields of road data may include road name, grade, and width.

[0066] In this solution, the geometric types of geographic entities in the extracted cross-source geographic data are standardized. For example, data from different data sources that represent the same road but have inconsistent geometric type definitions, such as some being defined as line features and some as polygon features, are uniformly adjusted to the line feature type.

[0067] Unify the attribute field format. Standardize the attribute fields of cross-source geographic data according to the preset attribute field standard. For example, convert the data types of road width fields in different data sources, which are partly character type and partly numeric type, to numeric type and unify the field length to 8 digits.

[0068] S15, based on a unified benchmark in virtual coordinate space, performs geometric edge calculations on adjacent boundaries of cross-source data to eliminate boundary gaps or overlaps.

[0069] Among them, the unified benchmark is a unified coordinate reference standard provided by the virtual coordinate space, which ensures that cross-source geographic data are processed under the same coordinate system and avoids edge-connection errors caused by different coordinate benchmarks.

[0070] Adjacent boundaries refer to the spatial boundaries between adjacent areas of geographic data from different data sources, such as the boundary lines between adjacent areas of vector map data from two different projection zones.

[0071] Geometric edge calculation can be a calculation process that matches and adjusts the coordinates of adjacent boundaries across cross-source data, with the aim of achieving seamless connection between adjacent boundaries.

[0072] This solution can use a geometric edge-matching algorithm to compare the coordinates of adjacent boundaries of cross-source data based on a unified benchmark in virtual coordinate space, calculate the coordinate deviation of boundary points, and then adjust the virtual coordinates of boundary points according to the deviation value, so as to make the geometric shape of adjacent boundaries continuous and eliminate gaps or overlaps at the boundaries, such as blank areas between adjacent map boundaries or repeated display of some areas of adjacent map boundaries.

[0073] The technical solution provided in this embodiment provides a unified processing platform for multi-source geographic data by constructing a virtual earth and mapping relationships between geographic coordinates and virtual coordinates. This solves the problem in traditional GIS technology that cross-projection zone data cannot be processed under a unified view. By unifying the geometric types and attribute field formats of cross-source geographic data, it lays the foundation for subsequent geometric edge calculations. Geometric edge calculations based on a unified benchmark in virtual coordinate space achieve seamless connection of adjacent boundaries of cross-source data, eliminating boundary gaps or overlaps, and achieving the goal of geometric seamlessness of cross-projection zone data. This provides technical support for the overall processing of global-scale geospatial data. For example, in a global geographic information labeling system, after loading vector maps and remote sensing image base maps of different regions, this method can achieve seamless stitching of maps in adjacent regions, ensuring the complete display of geographic data under a global view.

[0074] In this embodiment, optionally, the method further includes:

[0075] During the geometric edge calculation process, a topology consistency check algorithm is used to detect whether there are topology errors in cross-source data. If they are found, a topology error report is generated.

[0076] Among them, topology consistency check algorithm refers to the algorithm used to detect whether the topological relationship of geographic data conforms to the preset rules. Topological relationship includes adjacency relationship, such as whether two polygon features are adjacent; containment relationship, such as whether a point feature is inside a polygon feature; overlap relationship, such as whether a line feature partially overlaps with another line feature. Common topology consistency check algorithms include rule-based topology check algorithm and graph-based topology check algorithm.

[0077] Topological errors can be spatial relationships in geographic data that do not conform to topological rules, such as overlapping polygon features (two adjacent administrative division polygon features partially overlap), self-intersecting line features (a road line feature intersects itself at a certain point), duplicate point features (two points with the same coordinates exist in the same POI in the data), dangling nodes (the endpoint of a river line feature is not connected to another river line feature, forming an isolated endpoint), and so on.

[0078] A topology error report is a document that records information related to detected topology errors. The content includes the error type, such as face overlap or line self-intersection, the error location, the corresponding virtual or geographic coordinates, the name and number of the geographic entities involved in the error, and the error description, such as the area of ​​the overlapping area, the number of self-intersection points, etc.

[0079] In this solution, during the geometric edge calculation process, a topology consistency check module is synchronously invoked. This module loads preset topology rules, such as polygon features cannot overlap and line features cannot self-intersect. It then runs a topology consistency check algorithm to traverse and detect cross-source data. The algorithm compares the spatial relationships of geographic entities in the cross-source data with the preset topology rules to determine if there are any topology errors. For example, for polygon features, the algorithm calculates the spatial extent of each polygon feature. If the intersection of the spatial extents of two polygon features exceeds a preset threshold, it is determined to be a polygon overlap error.

[0080] When a topology error is detected, the system automatically collects relevant information about the error, organizes it into a topology error report according to a preset report format, such as a table or text, stores it in a path specified by the system, and displays the access point to the report in the user interface.

[0081] This technical solution introduces a topology consistency check algorithm during the geometric edge calculation process. This algorithm can promptly detect topology errors in cross-source data, preventing subsequent data processing, such as symbolic rendering and human-computer interaction editing, from being affected by topology errors. The generated topology error report provides staff with clear error information, facilitating quick location and understanding of topology problems. This reduces the time and difficulty of manually troubleshooting topology errors and improves the efficiency and accuracy of cross-source geographic data processing. For example, when processing global transportation route data, this method can detect errors such as line self-intersections and dangling nodes in transportation routes from different data sources and generate detailed reports. Staff can then make targeted repairs based on these reports to ensure the topological integrity of the transportation route data.

[0082] In this embodiment, optionally, the method further includes:

[0083] If the topology error meets the preset conditions, the feature coordinates will be automatically adjusted through the topology consistency check algorithm for automatic repair.

[0084] If the topology error does not meet the preset conditions, a prompt message for manual repair will be generated, and the topology check will be re-executed after manual repair until there are no topology errors.

[0085] The preset conditions refer to the criteria for judging whether a topology error can be automatically repaired. They are usually set according to the type, severity and scope of the topology error. For example, minor overlap of polygon features, such as an overlap area of ​​less than 10 square meters, and minor deviation of line feature endpoints, such as a deviation distance of less than 0.5 meters, are considered topology errors that meet the preset conditions. On the other hand, large-area overlap of polygon features, such as an overlap area of ​​more than 1000 square meters, and complex self-intersection of line features, such as more than 10 self-intersection points, are considered topology errors that do not meet the preset conditions.

[0086] Automatic repair refers to a processing method that uses algorithms to automatically adjust the coordinates of geographic entities and eliminate topological errors without human intervention.

[0087] The prompt message for manual repair can be a notification sent to the user, indicating that manual intervention is needed to repair topology errors. The content includes the error type, error location, and suggested repair methods, such as adjusting the boundary coordinates of a face feature or the self-intersection of a line feature. It can be displayed on the user interface in the form of pop-up windows, message prompts, etc.

[0088] In this scheme, when the detected topology error meets the preset conditions, the topology consistency check algorithm generates a corresponding coordinate adjustment scheme according to the error type. For example, for minor facet overlap errors, the algorithm calculates the center coordinates of the overlap area and then automatically adjusts the boundary coordinates of one of the facet features to eliminate the overlap area. For minor deviation errors at the endpoints of line features, the algorithm adjusts the coordinates of the deviated endpoints to the correct connection position to achieve seamless connection of line features.

[0089] Generating manual repair prompts refers to the system analyzing the specific circumstances of a topology error and generating a prompt containing detailed error information and repair suggestions when the topology error does not meet preset conditions. This prompt is then displayed in the user interface to guide the user through manual repair. After the user completes the manual repair, the system re-invokes the topology consistency check algorithm to re-detect topology errors in the repaired cross-source data. If topology errors still exist, manual repair prompts are generated again until the detection result indicates no topology errors.

[0090] This technical solution implements hierarchical processing of topology errors. Simple topology errors that meet preset conditions are automatically repaired, reducing manual operation costs and improving repair efficiency. For complex topology errors that do not meet preset conditions, prompts are generated to guide manual repair, ensuring accuracy and preventing new problems caused by improper automatic repair. The mechanism of re-executing topology checks after repair ensures that cross-source data is ultimately error-free, further improving the quality of geographic data and providing a reliable data foundation for subsequent symbolic rendering and high-precision interactive processing. For example, when processing global administrative division data, minor overlaps in administrative boundaries can be automatically repaired. For large-area boundary deviations caused by data acquisition errors, staff are prompted to manually adjust boundary coordinates based on actual geographic information. After repair, a second check ensures the topological correctness of the administrative division data.

[0091] In this embodiment, optionally, the method further includes:

[0092] Receive user interaction events on the screen, including one or more of click, drag, and drawing;

[0093] Convert the geographic coordinates corresponding to the interactive event to virtual coordinates;

[0094] Based on the virtual coordinates, POI acquisition, feature selection, and geometric editing operations are completed.

[0095] Transform back to geographic coordinates and record the interactive results.

[0096] Interaction events refer to events triggered by user actions on the system interface. Click events are actions where a user clicks a point on the screen using a mouse or touch device, such as clicking on a building location on the screen. Drag events are actions where a user presses and holds a geographic feature or view on the screen and moves it, such as dragging a POI icon to a new location or dragging a view to view different areas. Drawing events are actions where a user draws graphics on the screen, such as drawing polygons to select geographic features in a specific area.

[0097] POI collection refers to the process of acquiring the spatial location and attribute information of POIs. POIs include geographical points with specific functions, such as buildings, shopping malls, schools, or bus stops.

[0098] Feature selection refers to the operation of filtering out target geographic features from a large number of geographic features, such as selecting a highway from global road data or selecting buildings in a residential area from urban building data.

[0099] Geometric editing operations refer to operations that modify the geometric shape of geographic features, including adjusting the direction of line features, such as modifying the curvature of a road, expanding or shrinking the extent of polygon features, such as adjusting the boundary of a lake, and moving the position of point features, such as adjusting the coordinates of a Point of Interest (POI).

[0100] The reverse transformation refers to the process of converting virtual coordinates back to the original geographic coordinates, which is the inverse operation of the transformation from geographic coordinates to virtual coordinates in S101.

[0101] Interaction results refer to the result data generated after the user completes the interaction operation, including POI coordinates and attribute information obtained from POI collection, target feature list obtained from feature selection, and new coordinate information of geographic features after geometric editing, etc.

[0102] In this solution, the user's operations on the screen are monitored in real time. Through input devices such as mouse, keyboard, touch screen, etc., the interactive events triggered by the user are obtained and the corresponding screen pixel coordinates at the time of the interactive event are recorded.

[0103] Converting the coordinates of interactive events can be done by combining parameters such as the zoom level and translation offset of the current view to convert the screen pixel coordinates corresponding to the interactive event into geographic coordinates, and then converting the geographic coordinates into virtual coordinates based on the mapping relationship.

[0104] Based on the converted virtual coordinates, the system performs corresponding interactive operations. For example, during POI acquisition, the system determines the precise location of the POI using virtual coordinates and records its attribute information, such as name, type, and address. During feature selection, the system determines the selection range based on virtual coordinates, such as the click location or the drawn polygon area, and filters out geographic features within that range. During geometric editing, the system adjusts the geometric shape of geographic features based on virtual coordinates, such as moving the virtual coordinates of point features, modifying the virtual coordinates of the vertices of line features, and adjusting the virtual coordinates of the boundaries of polygon features.

[0105] After the interactive operation is completed, the interactive results based on virtual coordinates, such as the virtual coordinates of POIs and the virtual coordinates of edited geographic features, are converted into the original geographic coordinates through reverse transformation. Then, the converted geographic coordinates and corresponding attribute information are stored in the database or file system to complete the recording of the interactive results.

[0106] This technical solution enables high-precision POI acquisition, feature selection, and geometric editing through human-computer interaction in a virtual coordinate space. Leveraging the suitable range of virtual coordinate values, it reduces the impact of screen pixel errors on interaction accuracy. The interactive results are then inversely transformed back to geographic coordinates and recorded, ensuring consistency between the interactive data and the original geographic coordinate system, facilitating subsequent data storage, sharing, and application. Support for multiple interactive events meets diverse user needs in global-scale geospatial data processing, overcoming the bottleneck of low interactive accuracy in traditional GIS global views. For example, in the global geographic information marking system, when an operator clicks on a building corner on the screen to acquire a POI, the large virtual coordinate values ​​result in high calculation accuracy, with the final recorded geographic coordinates having an error of less than 0.1 meters compared to the actual values, fully meeting high-precision acquisition requirements. Users can also adjust road directions through drag-and-drop operations; the edited road coordinates are then inversely transformed and saved, ensuring data accuracy and usability.

[0107] In this embodiment, optionally, constructing a virtual Earth based on a pre-set global scaling factor includes:

[0108] Information on operational scenarios for acquiring geographic data;

[0109] Based on the operational scenario information, from the pre-built 10 5 -10 7 Within the range of values, determine a global scaling factor that matches the current operation scenario information, and construct a virtual Earth.

[0110] Operation scenario information can describe the specific application scenario of geographic data processing, including the application field, such as geographic information labeling, the type of data processed, such as vector maps, remote sensing images, and POI data, the processing accuracy requirements, such as sub-meter accuracy, meter accuracy, and ten-meter accuracy, and the display range, such as global view, national view, and city view. For example, the operation scenario information of the global geographic information labeling system is that the application field is global geographic labeling, the processing accuracy requirement is sub-meter, and the display range is global view.

[0111] 10 pre-built 5 -10 7 The range of values ​​can be a pre-set set of global scaling factor candidate values ​​that can be selected for different operating scenarios, covering scaling factors from a small scale of 10... 5 Up to a larger scaling factor of 10 7 The values ​​are determined to meet the requirements for the size and calculation accuracy of the virtual coordinate space in different scenarios.

[0112] In this solution, the current geographic data processing operation scenario information can be obtained through user input, such as when the user selects an application scenario at startup, or when the user enters the accuracy requirements and display range in the settings interface, or through automatic recognition, such as judging the operation scenario based on the data type and data range loaded.

[0113] Determining the global scaling factor involves using a built-in rule base that matches scenes with scaling factors. This rule base stores recommended scaling factors for different operational scenarios. For example, a recommended scaling factor of 10 is used for global views and sub-meter level precision scenes. 6 (i.e., 1,000,000), for city views and meter-level precision scenes, a scaling factor of 10 is recommended. 7 (i.e., 100,000). Specifically, based on the acquired operation scenario information, a matching query can be performed in the rule base, starting from 10. 5 -10 7 Within a certain range, select the global scaling factor that best suits the current scene.

[0114] The virtual globe is constructed by determining the global scaling factor, then starting the affine scaling virtual globe engine, loading the scaling factor, and linearly scaling the original geographic coordinate system according to the scaling factor to construct a virtual globe coordinate space that matches the current operating scenario.

[0115] This technical solution achieves scenario-based adaptation of the global scaling factor. It selects an appropriate scaling factor based on different operational scenario information, ensuring that the constructed virtual globe meets both the accuracy requirements of data processing in the current scenario and the appropriate range of coordinate values ​​for calculation. This avoids the problems of increased computation due to an excessively large scaling factor or insufficient accuracy due to an excessively small scaling factor. By pre-constructing candidate value ranges, the scaling factor selection process is simplified, improving the system's usability and flexibility. It provides a more scenario-appropriate foundational platform for subsequent multi-source geographic data mapping, symbolic calculation, and human-computer interaction processing. For example, in a land resource survey scenario, the data processing accuracy requirement is at the meter level, and the display range is a global view. In this case, selecting a scaling factor of 10... 5 The global scaling factor is used to construct the virtual globe, which can meet the data accuracy requirements of land resource surveys without causing excessive computational burden on the system due to an excessively large scaling factor. In the global geographic information labeling scenario, a scaling factor of 10 is selected. 6 The scaling factor ensures sub-meter accuracy in POI acquisition.

[0116] In this embodiment, optionally, the method further includes:

[0117] Import a pre-defined geographic feature symbolization rule library. The rule library contains one or more configuration information such as symbol styles, size parameters, and display priorities for point features, line features, and polygon features. Specifically, for point features, calculate the screen size and anchor point position of the POI icon based on virtual coordinates; for line features, calculate the line width, dashed line mode, arrow style, and position parameters based on virtual coordinate distance; for polygon features, calculate the density of the polygon fill pattern and the width and style of the outline edge in virtual coordinate space.

[0118] The obtained symbolic parameters are applied to geographic features in the virtual coordinate space, and rendering operations are performed to generate a visual image.

[0119] Among them, the geographic element symbolization rule base is a database that stores the configuration information of geographic element symbolization. It is used to standardize the display style of geographic elements such as points, lines, and polygons, and ensure the consistency and professionalism of geographic data visualization effects.

[0120] Symbol styles refer to the display form of geographic features on the screen. The symbol styles of point features include the shape of the POI icon, such as circle, square, custom icon, and color, such as red and blue. The symbol styles of line features include solid lines, dashed lines, dotted lines, and double solid lines. The symbol styles of polygon features include fill patterns, such as diagonal lines, grids, solid colors, and fill colors.

[0121] Size parameters can be parameters used to describe the size of symbols. For point features, size parameters include the width and height of the POI icon; for line features, size parameters include the line width; and for polygon features, size parameters include the width of the outline edge.

[0122] Display priority can be a rule for determining the display order of multiple geographic features when they are displayed overlapping on the screen. Features with higher priority will be displayed above features with lower priority. For example, road features have a higher display priority than terrain features, and POI features have a higher display priority than road features.

[0123] Symbolization parameters can be specific parameters calculated according to symbolization rules to determine the display effect of geographic features, including screen size and anchor point position of point features, line width and dashed line mode of line features, and fill pattern density and outline edge style of polygon features.

[0124] Rendering operations can be the process of applying symbolic parameters to geographic features and converting geographic data into visualized images. This is achieved through computer graphics technologies, such as graphics rendering interfaces like OpenGL and DirectX.

[0125] Visualized images refer to images generated from rendered geographic data that can be displayed on a screen. They can intuitively present the spatial distribution and attribute characteristics of geographic elements, such as global topographic images generated from rendered global vector maps and traffic route images generated from rendered road data.

[0126] In this solution, importing a symbolization rule base involves reading configuration information such as symbol styles, size parameters, and display priorities from the rule base into system memory, providing a basis for subsequent symbolization calculations. The screen size of the POI icon is calculated based on virtual coordinates, and combined with the display device's resolution, such as 1920×1080 pixels, the pixel size of the POI icon on the screen is determined to ensure the icon is clearly displayed without being too large or too small. Anchor point positions are calculated, typically setting the center point or lower left corner of the POI icon as the anchor point, ensuring that the anchor point coordinates correspond to the POI's virtual coordinates and preventing icon display position offset.

[0127] This solution can calculate the distance between two points on a line feature based on virtual coordinates. According to the preset size parameter ratio, such as 1 pixel line width for every 1000 units of virtual coordinate distance, the line width is determined. According to the dashed line pattern rules, such as a dot-dash interval of 5 pixels × 3 pixels, the length of the dots and dashes in the virtual coordinate space is calculated in combination with the virtual coordinate distance to determine the dot distribution of the dashed line. According to the arrow style, such as the shape and size of the arrow and the direction of the line feature, the position parameters of the arrow in the virtual coordinate space are calculated to ensure that the arrow is accurately displayed at the endpoint or specified position of the line feature.

[0128] The calculation of the symbolization parameters for surface features can be done in virtual coordinate space by calculating the density of the fill pattern based on the area size of the surface feature and preset fill pattern density rules. The width of the contour line is determined based on preset contour line size parameters, and the style of the contour line is determined by combining these with edge line style rules.

[0129] Finally, the symbolic parameters of the calculated point, line, and polygon features are applied to the corresponding geographic features in the virtual coordinate space. The system calls the graphics rendering module to draw the geographic features according to the symbolic parameters, performs the rendering operation, converts the geographic data into a visual image, and presents it in the display area of ​​the user interface.

[0130] This technical solution avoids the problems of unstable symbol size calculation and distorted display effects caused by the small latitude and longitude coordinate values ​​and inconsistent dimensions in traditional geographic coordinate systems by calculating the symbolization parameters of geographic elements in virtual coordinate space. Furthermore, the symbolization parameters of point, line, and polygon elements are calculated separately, ensuring the accuracy and professionalism of the symbolization effects for different types of geographic elements. Importing a preset symbolization rule library ensures the consistency of geographic data visualization effects, meeting professional cartographic requirements. The rendered visualization image can intuitively present the spatial distribution of global-scale geographic data, achieving a "what you see is what you get" visualization effect. This solves the industry pain point of distorted and unstable symbolization of geographic elements at a global scale. For example, a complex line representing the boundary of two regions maintains a constant line width and dot-dash interval based on virtual coordinates, with uniform spacing and no distortion, ensuring the correct and stable display of the boundary of the two regions in the global view.

[0131] In this embodiment, optionally, the method further includes:

[0132] Receive user adjustment operations, customize the geographic feature symbolization rules, apply and synchronously store the customized rules to the geographic feature symbolization rule library.

[0133] The adjustment operation is the user's operation to modify the symbolization rules of geographic features. This includes modifying the symbol style, such as changing the circular icon of point features to a square icon, changing the solid line of line features to a dashed line, and changing the solid color fill of polygon features to a grid fill; adjusting the size parameters, such as increasing the size of POI icons, widening the line width of line features, and thickening the outline of polygon features; and modifying the display priority, such as increasing the display priority of POI features so that they are displayed above road features.

[0134] Custom rules are symbolic rules that differ from preset rules, formed by users through adjustments, reflecting users' personalized needs for the visualization of geographic elements.

[0135] This solution can monitor user actions in the symbolization settings interface in real time, such as clicking the symbol style selection box to change icons, dragging the size adjustment slider to adjust parameters, and moving the position of features up and down in the priority sorting list. These adjustments are captured and recorded. Based on the user's actions, the system modifies the preset rules in the geographic feature symbolization rule library. For example, if the user adjusts the line width of a line feature from 2 pixels to 3 pixels, the system updates the line width value in the line feature's size parameters to 3 pixels. If the user changes the fill pattern of a polygon feature from diagonal lines to a grid, the system updates the fill pattern in the polygon feature's symbol style to a grid, forming a custom rule.

[0136] The modified custom rules can be immediately applied to geographic features in the current virtual coordinate space, the symbolization parameters can be recalculated, and the rendering operation can be performed to update the visualization image, so that users can see the adjusted display effect in real time.

[0137] While applying custom rules, the system synchronously saves the custom rules to the geographic feature symbolization rule library, overwriting the original preset rules or storing them as new rule entries, ensuring that the custom symbolization rules can be used directly when loading geographic data later.

[0138] This technical solution allows users to customize the symbolization rules for geographic elements, meeting their personalized needs for geographic data visualization in different application scenarios and improving the system's flexibility and ease of use. Real-time application and synchronous storage of custom rules eliminate the need for repeated settings; users can directly call these rules for subsequent use, improving work efficiency. Simultaneously, the storage of custom rules facilitates team collaboration. Multiple users can share custom symbolization rules, ensuring consistency in geographic data visualization within the team. For example, in urban planning projects, planners can adjust the line width of road elements to 5 pixels and change the fill color of park area elements to green, based on project requirements. The adjusted rules can be applied and their effects can be viewed in real time, and are synchronously stored in the rule library. Subsequent project members can use these custom rules without resetting them when loading urban planning data, ensuring the uniformity of the visualization effects on planning drawings.

[0139] Understandably, the technical solution provided in this embodiment can be uniformly processed as long as the geographic coordinate system stores latitude and longitude values ​​on the same ellipsoid. This solution can avoid coordinate error loss caused by dynamic frame projection. Furthermore, since different software, whether through direct conversion or dynamic projection, inherently contains errors, even small errors in some scenarios can be detrimental to edge-joining operations, especially when summarizing global or large-scale data. Additionally, because coordinates can be enlarged to different sizes based on different latitudes to approximate their true projection size, the symbolization effect, especially for large parks, large vegetation areas, and green spaces, can be made closer to the standard effect. The symbol display density can be automatically switched based on different display scales, achieving a more standardized symbol pattern when browsing data at a 1:1 scale.

[0140] As an extended solution, the topology consistency check and repair synchronously invokes the topology consistency check module during the geometric edge calculation process, loads preset topology rules (such as non-overlapping face features and non-self-intersecting line features), and traverses and detects cross-source data through the topology consistency check algorithm. If a topology error is detected (such as face overlap, line self-intersecting, dangling nodes, etc.), a topology error report containing the error type, location, and involved entities is generated. For simple errors that meet preset conditions (such as overlap area < 10㎡, endpoint deviation < 0.5m), the algorithm automatically adjusts the coordinates for repair. For complex errors (such as large-area overlap, multiple node self-intersecting), a manual repair prompt is generated, and after repair, the check is repeated until no topology errors are found.

[0141] The high-precision human-computer interaction operation monitors user interaction events such as clicks, drags, and drawings in real time, converts screen pixel coordinates into geographic coordinates, and then maps them to virtual coordinates. Based on the virtual coordinates, it completes operations such as POI collection (error can be <0.1 meters), feature selection, and geometric editing (such as adjusting road direction and lake boundaries), and then converts the results back to geographic coordinates and stores them to ensure that the interaction accuracy is consistent with the original coordinate system.

[0142] Contextualized global scaling factor adaptation first obtains the operation scenario information (such as the application domain being 3D construction, the accuracy requirement being sub-meter level, and the display range being a nationwide view), and then, based on the built-in matching rule library, adjusts the scaling factor from 10... 5 -10 7 Select the appropriate scaling factor within the range (e.g., select 10 for meter-level precision in a nationwide view). 5 Global sub-meter accuracy selection 10 6 To construct a virtual earth that meets the needs of the scenario, and to balance computational accuracy and system load.

[0143] Import the geographic feature symbolization rule library (including symbol styles, sizes, and priorities for point / line / polygon features) into the stable symbolization rendering of geographic features, and calculate the symbolization parameters in the virtual coordinate space:

[0144] Point elements: Calculate the POI icon screen size and anchor point position based on the display device resolution to avoid icon drift;

[0145] Line elements: Calculate line width, dashed line spacing, and arrow position based on virtual coordinate distance to ensure visual consistency across the entire image;

[0146] For polygonal features, such as large areas of vegetation, the symbol spacing is dynamically adjusted based on the area of ​​a single map at the corresponding scale (e.g., 62,500 square meters for a 1:500 scale map). The spacing increases by 10% for every doubling of the area, conforming to standards (spacing can be increased 1-3 times), thus resolving the issue of overly dense symbols causing stuttering. The graphics rendering module is then invoked to apply the symbolization parameters to the geographic features, generating and presenting a visualized image. User-defined symbol rules are also supported, and adjustments are synchronously stored in the rule base to meet personalized mapping needs.

[0147] As an alternative extension, this solution can be implemented in data edge connections across large regions using the following process:

[0148] System initialization: Start the affine scaling virtual globe engine, set the global scaling factor to 1,000,000, and initialize the edge boundary line tool;

[0149] Data loading: Loads geographic coordinate system results data in .esdb format for regions A, B, and C, and automatically converts them into virtual coordinates;

[0150] Boundary delineation: The boundary lines between regions A and B, and B and C are used as boundary lines, and the data extraction range is determined by extending 1km outward. Only the ground features within this region are loaded.

[0151] Unified format and edge connection: Unify the geometric types and attribute formats of multi-region data, complete seamless edge connection in virtual coordinate space, and simultaneously complete topology checks and repairs;

[0152] Output of CRUD (Create, Read, Update) records: Generate CRUD records for elements in the Zhejiang-Shanghai and Shanghai-Jiangsu border areas, and send them back to the multi-regional data management platform in .esdb format;

[0153] Autonomous construction of entity relationships: After receiving records from multiple regional platforms, entity semantic relationships are reconstructed in proprietary software to form regional-level real-world 3D results, achieving seamless connection and data collaboration across software and platforms.

[0154] The core of this solution is to perform a uniform affine scaling (e.g., coordinate values) on all loaded geographic coordinates in memory. 100000). After scaling, the latitude and longitude values ​​are magnified into a large number, similar in magnitude to the metric unit, thus:

[0155] a) It avoids parameter issues when converting between different projection zones.

[0156] b) Improved the precision of floating-point operations and reduced rounding errors in edge concatenation operations.

[0157] c) Visually and operationally, a continuous "large ellipsoid" plane is simulated.

[0158] The entire system architecture is divided into three core layers:

[0159] Data persistence layer: Responsible for storing data in the original geographic coordinate system (ESDB / GDB), ensuring the originality and accuracy of the data.

[0160] Affine Transformation Edge Engine (Core): Performs coordinate scaling, inverse scaling, and all edge geometry operations in memory.

[0161] User interface layer: Provides users with a unified edge view and operation interface, and dynamically displays the affine coordinates.

[0162] The detailed design of the functional modules is as follows:

[0163] 1. Data preparation and import module;

[0164] Input: Production has been completed in separate zones and converted to a geographic coordinate system. .esdb or .gdb data.

[0165] Function:

[0166] Lossless import: Geographic coordinate data is imported into the workspace via the existing EPS data interface. Each ESDB file serves as an independent project data source.

[0167] Metadata Recording: The system automatically records the original coordinate system information (EPSG code, such as 4490) for each data source and specifies a global scaling factor (e.g., 100000) for the entire workspace.

[0168] 2. Affine transformation and dynamic display module;

[0169] Function:

[0170] Real-time scaling: As data is displayed in the workspace, the graphics engine automatically calculates the (longitude, latitude) of each vertex in real time. SF, latitude SF).

[0171] Simulated coordinate system definition: Define a virtual projected coordinate system (such as USER_DEFINED_SCALED_MERCATOR), whose linear unit is defined as "meter", but is actually a scaled-down degree. The coordinates and measured distances displayed in the UI are all scaled-down values.

[0172] View synchronization: Ensures that all loaded data is aligned and displayed in this unified, scaled coordinate space for seamless visualization.

[0173] 3. Edge editing and processing module;

[0174] Function:

[0175] Cross-source editing: Users can set any ESDB data source as the "current editing project" but can simultaneously capture and edit elements in adjacent data sources that are connected to it.

[0176] High-precision calculation: All geometric algorithms (such as snapping, intersection, clipping, and moving) are performed in a scaled coordinate space, taking advantage of the high floating-point precision of large values ​​to ensure the accuracy of edge processing.

[0177] Transaction management: Editing operations are recorded in the transaction stack. When saving, the system automatically handles coordinate transformation.

[0178] 4. Data saving and export module;

[0179] Function:

[0180] Inverse transformation and storage:

[0181] When a user saves an ESDB project, the system performs an inverse transformation (X / SF, Y / SF) on all edited geometries within that project to obtain the correct geographic coordinates.

[0182] Only the edited, inversely transformed coordinates and attributes are written back to the original ESDB file. Unedited data remains unchanged.

[0183] Batch export:

[0184] Provides a batch export tool to losslessly convert multiple processed ESDB data back to the target format (such as GDB).

[0185] It supports exporting to a geographic coordinate system, or reprojecting to a unified target projection coordinate system (such as the Albers projection) as specified by the user, to generate the final result.

[0186] This solution first proposes a method for selecting and managing scaling factors, and suggests providing a default factor (e.g., 10). 5 This factor must be persistently saved in the workspace file to ensure consistency upon repeated openings.

[0187] In addition, this solution provides a boundary management method, offering a function that allows users to predefine or automatically generate "boundary lines". When an editing operation occurs within the boundary line buffer, the system automatically enables feature capture and linked editing across data sources.

[0188] In addition, this solution integrates scaling / inverse transformation calculations into the data access layer of the graphics engine, implemented using high-level languages ​​such as C++. Spatial indexes (such as R-Tree) are used to accelerate cross-source queries in the scaled coordinate space.

[0189] The implementation of this solution mainly includes the following steps:

[0190] Phase 1 (Prototype Validation): Implement the core affine transformation engine. Create a test workspace, load two cross-band ESDB datasets, and verify that they can be displayed correctly and seamlessly after scaling.

[0191] Phase 2 (Core Functionality Development): Develop the edge editing module to enable cross-data source capture and editing, and complete the saving function with inverse transformation.

[0192] Phase 3 (UI / UX Integration): Integrate the above functions into the EPS workspace UI, and develop dialog boxes for "affine edge mode" switch, scaling factor settings, edge management, etc.

[0193] Phase 4 (Testing and Optimization): Conduct comprehensive testing using real, large-scale, cross-band project data to optimize performance and stability, and improve batch export tools.

[0194] Phase 5 (Documentation and Release): Compile user manuals and training materials, and officially release the feature.

[0195] This solution proposes a pragmatic and efficient technical approach, cleverly combining affine transformation with the existing capabilities of the EPS platform to successfully address the industry pain point of cross-projection zone data processing. It not only maintains the highest accuracy during the data production stage (zonal Gaussian projection), but also provides unparalleled convenience and accuracy for the data integration and quality inspection stages, making it highly valuable for product development and competitive in the market.

[0196] In addition, this solution also provides a symbolic display technology for large-area vegetation elements.

[0197] Topographic map data often contains large areas of filled symbols, especially when dealing with global data production and edge computing. If physical merging occurs, the symbols need to be reconstructed and re-symbolized according to the new area. Because the symbols are too dense, zooming in and out on the display can cause stuttering, and the display effect cannot be simply set according to the map dimensions. Browsing or exporting the map will also result in a high density of symbols. These symbols are only for illustrative purposes and do not have locational significance; they are automatically generated within the area of ​​the polygon symbol. Furthermore, based on the following description in the standard:

[0198] Symbol configuration;

[0199] Soil and vegetation symbols can be categorized into three types based on their arrangement:

[0200] a) Row-and-column arrangement: Arranged in a certain number of rows and columns, such as nurseries, grasslands, and economic forests;

[0201] b) Hash-style: Not arranged in a fixed row and column pattern, such as small grassy hills, shrublands, or rocky areas:

[0202] c) Corresponding symbols: Symbols are used to indicate the density or location of trees in the field, such as sparse forest, scattered trees, etc. When using symbols, attention should be paid to showing their distribution characteristics.

[0203] In a linear arrangement, symbols are generally arranged at intervals as represented by the diagram. When the area is large, the symbol intervals can be increased by 1 to 3 times. Annotations can be used to represent the symbols, provided they are clear enough. Alternatively, the most frequent symbol in the diagram can be omitted and explained in an external annotation, but this should be consistent across a single diagram or a batch of diagrams.

[0204] Note: "Configuration" refers to the use of descriptive symbols that do not have locating significance. Symbols are arranged in a scattered or orderly fashion within the distribution area of ​​land features to indicate the category of areal features.

[0205] This solution can appropriately enlarge the display spacing by 1 to 3 times. While the concept of appropriateness can be manually adjusted based on visual perception during editing, dynamic adjustment is not conducive to frequent operation. Therefore, this technology was designed.

[0206] To enhance the persuasiveness of the automatic symbolic display principle, we use 1:500, 1:1000, and 1:2000 scales for comparison. At a scale of 1:500, a 1mm map spacing represents a physical dimension of 0.5 meters. Similarly, at scales of 1:1000 and 1:2000, a 1mm map spacing represents 1 meter and 2 meters in reality, respectively. Based solely on the display effect, we can infer that if a feature area is fully covered, the spacing doubles for every four-fold increase in area (i.e., a three-fold increase in area corresponds to a one-fold increase in spacing, 1 ÷ 3 = 1 / 3). If the area increases sixteenfold, the spacing quadruples (i.e., a fifteen-fold increase in area corresponds to a three-fold increase in spacing, 3 ÷ 15 = 1 / 5). If we interpret 1 / 3 and 1 / 5 as step distances, we find that the larger the area increase, the smaller the step distance. Therefore, the software uses 1 / 10 as the base step distance and the area of ​​one map at each scale as the base area, using this as the display principle.

[0207] Taking a 50cm x 50cm map as an example, and using a 1:500 scale map as an example (the area of ​​a 1:500 scale map is 62,500 square meters, the area of ​​a 1:1000 scale map is 250,000 square meters, and the area of ​​a 1:2000 scale map is 1,000,000 square meters), 1 is the standard spacing (less than or equal to 62,500 square meters). For every doubling of the area of ​​the terrain features, the spacing increases by 10%.

[0208] The description uses a coded sand and mudflat (the most densely symbolized surface feature besides vegetation) at a 1:500 scale. The dimensions on the map can include the values ​​1.5 and 1.5, where 1.5mm and 1.5mm represent the horizontal and vertical dimensions respectively. When the area of ​​a symbol-filled surface is less than or equal to 50,000 square meters, this standard spacing is used; when the area is greater than 62,500 square meters but less than 125,000 square meters, 1.5 is used. The dimensions on the map are (1+10%)=1.65mm, and the spacing is adjusted accordingly. When the area is increased 16 times, reaching the area of ​​a 1:2000 scale map, 1.5... (1+10%) 15) = 3.75mm, and 3.75 ÷ 1.5 = 2.5 times, which is less than the 3 times specified in the drawing, and also does not exceed the 4 times value of the standard symbol display effect. Therefore, it has a certain effect within the standard range.

[0209] It can dynamically display the size of symbols according to the area, and it also has a certain degree of conformity with mainstream standards. Most importantly, it solves the problem that large-area symbols are too dense and cannot be dynamically adjusted.

[0210] Example 2

[0211] To enable those skilled in the art to more clearly understand the technical solution provided in this embodiment, a preferred implementation method is also provided as a specific solution. It should be noted that this embodiment is merely an example and does not limit the specific scope of protection of this solution.

[0212] This invention provides a method and system designed to simultaneously address three major challenges in global-scale geospatial data processing: seamless geometric transformation across projection zones, stability of spherical feature symbolization, and accurate global point of interest (POI) acquisition. This solution constructs an affine-scaled virtual geographic coordinate space, providing a unified, stable, and high-precision computational and display foundation for upper-level applications.

[0213] This solution creates an affine-scaled virtual globe as the foundational platform for data processing. This platform not only solves the geometric edge-matching problem, but more importantly, it provides a virtual environment with stable metrics and appropriate numerical ranges for the symbolic computation and human-computer interaction of all geographic features at a global scale.

[0214] First, a geospatial data processing system supporting seamless visualization and editing at a global scale is provided, including:

[0215] The affine scaling virtual globe engine defines a global scaling factor (e.g., 10). 5 Up to 10 7 This engine linearly scales up the original geographic coordinate system into a virtual space. It handles all bidirectional real-time conversions between geographic and virtual coordinates.

[0216] The stable symbolic computation module is used to calculate all symbolic parameters in a virtual coordinate space.

[0217] For point elements: the screen size and anchor point position of the POI icon are calculated based on magnified virtual coordinates, avoiding icon flickering or drifting caused by slight jitter of the underlying coordinates.

[0218] For line elements, the calculation of symbol parameters such as line width, dashed line mode, and arrows is based on stable virtual coordinate distances, ensuring visual consistency of symbols across the entire map.

[0219] For surface elements: the density of the fill pattern and the calculation of the outline are also performed in virtual space, ensuring the uniformity and stability of the fill effect.

[0220] High-precision human-computer interaction module: This module receives user interaction operations on the screen (such as clicking and dragging) and converts them into virtual coordinate space for calculation.

[0221] Precise POI acquisition: When a user clicks on a pixel, its coordinates in the virtual coordinate space are a large value. When transforming it back to geographic coordinates, the relative error caused by converting screen pixels to integer coordinates is greatly reduced, achieving acquisition accuracy at the sub-meter or even centimeter level.

[0222] Cross-source seamless processing module: Inherits and enhances the original edge-joining function, allowing seamless geometric editing of data from different sources in virtual space.

[0223] In addition, this solution also provides a geospatial data processing method that supports seamless visualization and editing at a global scale, including the following steps:

[0224] Step 1: Construct a virtual globe: Set a global scaling factor and establish a mapping relationship between geographic coordinates and virtual coordinates.

[0225] Step 2: Data loading and transformation: Load multi-source geographic data into the system and transform its coordinates to a unified virtual coordinate space.

[0226] Step 3: Stabilized symbolic rendering: In the virtual coordinate space, the symbols of point, line, and surface elements are calculated and rendered according to the symbolic rules.

[0227] Step 4: High-precision interactive processing: Receive user interaction events and perform precise POI collection, feature selection, and geometric editing in virtual coordinate space.

[0228] Step 5: Seamless Geometric Processing: In virtual coordinate space, perform edge matching, topology checks, and editing across data sources.

[0229] Step 6: Data persistence: After the processing results are reverse-transformed, they are saved back to the original geographic coordinate system.

[0230] The technical effects of this invention are multi-layered and disruptive:

[0231] At the geometric level: it achieves true seamlessness of data across projection zones with no loss of accuracy.

[0232] At the visualization level: it solves the industry pain point of distorted and unstable symbolization of geographical elements at a global scale, and achieves a professional mapping effect of "what you see is what you get".

[0233] At the interaction level: it breaks through the bottleneck of low interaction accuracy in the global view of traditional GIS, and provides a reliable technical basis for high-precision global POI collection and annotation.

[0234] At the architecture level: it provides a unified underlying framework, so that when developing global GIS applications, there is no need to worry about issues such as projection selection, accuracy loss and symbolization distortion, which greatly reduces the development complexity.

[0235] The specific process for applying this scheme in the global geographic information labeling system is as follows:

[0236] The system starts up, initializes the virtual globe, and sets the scaling factor to 1,000,000.

[0237] Load the global vector map (geographic coordinate system .esdb) and remote sensing imagery base map. All data is automatically transformed.

[0238] Symbolic display: A complex line (dotted line with symbolic features) representing the boundaries of different regions. In the display from the equator to high latitudes, its line width and dot-dotted intervals remain constant. The spacing of this symbolic feature is uniform and there is no distortion.

[0239] POI (Point of Interest) Acquisition: The operator clicks and marks a specific target (such as a building corner) on the screen. Due to the large virtual coordinate values ​​and high calculation accuracy, the error between the final recorded geographic coordinate values ​​and the actual values ​​is less than 0.1 meters (at the corresponding scale), fully meeting the requirements for high-precision acquisition.

[0240] Data output: All collected POIs are stored in high-precision geographic coordinates and can be exported to any other system without loss of quality.

[0241] The difference between this invention and simple edge-joining solutions is that this invention elevates "affine scaling" from a tool for solving local geometric edge-joining to a fundamental framework that supports stable visualization and high-precision interaction of all global elements.

[0242] The difference from graphics scaling: Instead of scaling the final rendered image, it performs a linear transformation on the underlying geometric coordinate system, thereby affecting all upstream computations (symbolization, interaction, analysis).

[0243] This technical solution simultaneously achieves three goals that were previously difficult to balance: geometric seamlessness, symbolic stability, and precise interaction, providing a core technical foundation for building the next generation of global GIS applications.

[0244] In the field of geographic information data processing, geographic coordinate systems need to be converted into planar coordinate systems through projection to adapt to engineering applications. A commonly used method is the 3-degree zone projection (e.g., zone number 40 corresponds to the central meridian 120°E, and zone number 41 corresponds to 123°E). In existing technologies, when loading data from different projection zones, software such as ArcGIS uses the projection of the first loaded data as the data frame coordinate system and performs dynamic projection transformations on subsequent data; or, multiple data projections can be aligned by manually setting the data frame coordinate system.

[0245] However, map projection is essentially the process of "flattening" the unwrapable ellipsoid of the Earth into a plane, which inevitably involves deformation:

[0246] Mathematical approximation: The conversion from geographic coordinates (sphere) to projected coordinates (plane) is a lossy process. The forward / inverse projection formulas are approximate fits to the surface, not absolute inverses.

[0247] Iteration error: Projection back calculation often requires numerical iteration methods (such as the Newton-Raphson method) to approximate the solution, which is limited by tolerance, and the result is only "close enough" to the original value;

[0248] Cumulative error: When switching frequently between multiple software / projection zones, errors accumulate, causing data (such as linear features) to have "hanging" problems (such as line features that should be connected appearing "unconnected" or "out of place") - even if the accuracy of a single data point meets the requirements, geometric misalignment will still occur after splicing across regions / software.

[0249] Scene description;

[0250] Scenario 1: Dynamic projection with multiple projections and data loading;

[0251] Data A: A certain coordinate system, 3-degree zone number 40 (central meridian 120°E);

[0252] Data B: A coordinate system, 3-degree zone number 41 (central meridian 123°E).

[0253] Scenario 1: Load data A first;

[0254] Create a new empty project with the data frame coordinate system set to empty / default.

[0255] When loading data A, the coordinate system of the data frame can be set automatically;

[0256] Load data B, and the software starts dynamic projection, converting data B from the 123° zone to the 120° zone for display—the position of data B is distorted due to forced adaptation to a non-central meridian.

[0257] Case 2: The process of loading data B is the reverse of Case 1. The coordinate system of the data frame is set to 123° zone, and data A is dynamically projected to the 123° zone for display.

[0258] Scenario 3: After manually intervening in the data frame coordinate system loading data, the target projection zone (such as the 123° zone) can be manually set through the data frame properties. All data (including the loaded A and B) will be dynamically projected to this zone for display.

[0259] Figure 2 This is a schematic diagram comparing the edge joining effect provided in Embodiment 2 of this application. For example... Figure 2 As shown, this illustrates the geometric misalignment of linear features caused by projection transformation:

[0260] The above shows the connection relationship between the original linear features (diagonal lines) and the horizontal features (AB lines);

[0261] The middle and bottom sections respectively indicate that after projection transformation, the endpoints of the diagonal line elements deviate from the AB line due to coordinate errors, resulting in "hanging" (the endpoints of the diagonal line do not fall on the AB line), which intuitively reflects the geometric deformation problem caused by projection transformation.

[0262] This solution achieves zero-loss coordinate transformation from a numerical storage perspective by performing a "decimal point movement" operation on latitude and longitude (e.g., converting 123.456789012 to 12345678.9012 and then reverting back to its original value). This avoids approximation and cumulative errors in projection transformation and solves the problem of geometric misalignment of data across projection zones / software.

[0263] Example 3

[0264] Figure 3 This is a schematic diagram of the structure of the global-scale geospatial data processing device provided in Embodiment 3 of this application. Figure 3 As shown, the device includes:

[0265] The mapping relationship construction module 31 is used to construct a virtual earth based on a pre-set global scaling factor, and to construct a mapping relationship between geographic coordinates and virtual coordinates of the virtual earth;

[0266] The virtual coordinate determination module 32 is used to map multi-source geographic data onto a virtual globe according to the mapping relationship to obtain the virtual coordinates of each geographic entity;

[0267] Data extraction module 33 is used to extract all cross-source geographic data to be processed from the virtual coordinate space;

[0268] The format unification module 34 is used to unify the geometric type and attribute field format of the cross-source geographic data.

[0269] The edge processing module 35 is used to perform geometric edge calculations on adjacent boundaries of cross-source data based on a unified benchmark in virtual coordinate space, eliminating boundary gaps or overlaps.

[0270] This device can execute the global-scale geospatial data processing methods provided in the above embodiments, and has corresponding functional units and beneficial effects. Further details are omitted here.

[0271] Example 4

[0272] Those skilled in the art will understand that embodiments of this solution can provide methods, systems, or computer program products. Therefore, this solution can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this solution can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0273] Therefore, this application also proposes a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any embodiment of this application.

[0274] This solution is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this solution. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0275] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0276] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0277] Furthermore, Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this application. Figure 4As shown, this application also proposes an electronic device (or computing device) including a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor 41, wherein the processor 41 executes the computer program to implement the method described in any embodiment of this application.

[0278] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent storage in computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media. Computer-readable media includes both permanent and non-persistent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by the computing device. As defined in this article, computer-readable media do not include transient media, such as modulated data signals and carrier waves.

[0279] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0280] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A global-scale geospatial data processing method, characterized in that, Includes the following steps: A virtual globe is constructed based on a pre-defined global scaling factor, and a mapping relationship is established between geographic coordinates and the virtual coordinates of the virtual globe. Based on the mapping relationship, multi-source geographic data is mapped onto a virtual globe to obtain the virtual coordinates of each geographic entity; Extract cross-source geographic data to be processed from virtual coordinate space; Unify the geometric type and attribute field format of the cross-source geographic data; Based on a unified benchmark in virtual coordinate space, geometric edge calculations are performed on adjacent boundaries of cross-source data to eliminate boundary gaps or overlaps.

2. The global-scale geospatial data processing method according to claim 1, characterized in that, Also includes: During the geometric edge calculation process, a topology consistency check algorithm is used to detect whether there are topology errors in cross-source data. If they are found, a topology error report is generated.

3. The global-scale geospatial data processing method according to claim 2, characterized in that, Also includes: If the topology error meets the preset conditions, the feature coordinates will be automatically adjusted through the topology consistency check algorithm for automatic repair. If the topology error does not meet the preset conditions, a prompt message for manual repair will be generated, and the topology check will be re-executed after manual repair until there are no topology errors.

4. The global-scale geospatial data processing method according to claim 1, characterized in that, Also includes: Receive user interaction events on the screen, including one or more of click, drag, and drawing; Convert the geographic coordinates corresponding to the interactive event to virtual coordinates; Based on the virtual coordinates, POI acquisition, feature selection, and geometric editing operations are completed. Transform back to geographic coordinates and record the interactive results.

5. The global-scale geospatial data processing method according to claim 1, characterized in that, A virtual Earth is constructed based on a pre-defined global scaling factor, including: Information on operational scenarios for acquiring geographic data; Based on the operational scenario information, from the pre-built 10 5 -10 7 Within the range of values, determine a global scaling factor that matches the current operation scenario information, and construct a virtual Earth.

6. The global-scale geospatial data processing method according to claim 1, characterized in that, Also includes: Import a pre-defined geographic feature symbolization rule library. The rule library contains one or more configuration information such as symbol styles, size parameters, and display priorities for point features, line features, and polygon features. Specifically, for point features, calculate the screen size and anchor point position of the POI icon based on virtual coordinates; for line features, calculate the line width, dashed line mode, arrow style, and position parameters based on virtual coordinate distance; for polygon features, calculate the density of the polygon fill pattern and the width and style of the outline edge in virtual coordinate space. The obtained symbolic parameters are applied to geographic features in the virtual coordinate space, and rendering operations are performed to generate a visual image.

7. The global-scale geospatial data processing method according to claim 6, characterized in that, Also includes: Receive user adjustment operations, customize the geographic feature symbolization rules, apply and synchronously store the customized rules to the geographic feature symbolization rule library.

8. A global-scale geospatial data processing apparatus, used to implement the method described in any one of claims 1 to 7, characterized in that, include: The mapping relationship construction module is used to construct a virtual globe based on a pre-set global scaling factor, and to construct the mapping relationship between geographic coordinates and the virtual coordinates of the virtual globe; The virtual coordinate determination module is used to map multi-source geographic data onto a virtual globe according to the mapping relationship, so as to obtain the virtual coordinates of each geographic entity. The data extraction module is used to extract cross-source geographic data to be processed from the virtual coordinate space; The format unification module is used to unify the geometric type and attribute field format of the cross-source geographic data. The edge processing module is used to perform geometric edge calculations on adjacent boundaries of cross-source data based on a unified benchmark in virtual coordinate space, eliminating boundary gaps or overlaps.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-7.

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