Geographic information processing method, device, equipment and storage medium

Abstract

The present application provides a geographic information processing method, apparatus, device, and storage medium, relating to the technical field of GIS data processing. The method comprises: obtaining vector geographic information data in multiple formats, wherein the vector geographic information data includes at least one vector layer containing land attributes; processing the vectors of the vector layer using a data editing and processing tool to obtain a target vector layer; obtaining the intersection area obtained by performing an intersection operation on multiple target vector layers, and using the intersection area as a first result layer; classifying and rendering the first result layer according to the layer attributes of the first result layer to obtain a second result layer; and performing map finishing on the second result layer to obtain a target map file. The technical effect of the present application is to provide an efficient and intelligent solution that meets the needs of modern geographic information processing and provides strong support for spatial decision-making and analysis in various industries.

Description

Technical Field

[0001] The present application relates to the technical field of GIS data processing, and specifically to a geographic information processing method, apparatus, device and storage medium. Background Art

[0002] With the rapid development and widespread application of Geographic Information System (GIS) technology, processing and analyzing complex geospatial data has become a critical requirement across all industries. However, in practical applications, geographic information data often comes from diverse sources and formats, and contains a large number of vector layers that require complex processing and analysis. Efficiently integrating this heterogeneous data and conducting in-depth spatial analysis and visualization has become a major technical challenge facing the GIS field.

[0003] Existing GIS software typically addresses this problem using a step-by-step, multi-tool approach. Users must first import data in various formats into the system, then use a variety of independent tools for data editing, spatial analysis, and mapping. While this approach can accomplish basic data processing and analysis tasks, it requires frequent switching between different functional modules when dealing with large numbers of complex vector layers, increasing operational complexity and the risk of errors. Summary of the Invention

[0004] This application provides a geographic information processing method, device, equipment and storage medium, which are used to provide an efficient and intelligent solution that can meet the needs of modern geographic information processing and provide strong support for spatial decision-making and analysis in various industries.

[0005] In the first aspect, the present application provides a geographic information processing method, which is applied to a desktop geographic information system. The method includes: obtaining vector geographic information data in multiple formats, the vector geographic information data including at least one vector layer containing land attributes; processing the vectors of the vector layer through a data editing and processing tool to obtain a target vector layer; obtaining the intersection area obtained after performing an intersection operation on multiple target vector layers, and using the intersection area as a first result layer; classifying and rendering the first result layer according to the layer attributes of the first result layer to obtain a second result layer; and performing map finishing on the second result layer to obtain a target map file.

[0006] By adopting the above technical solution, the problem of data source diversity is first addressed by acquiring vector geographic information data in multiple formats. Subsequently, the vector layers are processed using data editing and processing tools to obtain the target vector layers, improving data quality and consistency. By performing an intersection operation on multiple target vector layers, a first result layer is generated, enabling complex spatial analysis. Classification and rendering are performed on the first result layer to obtain a second result layer, enhancing data visualization. Finally, map finishing is performed on the second result layer to generate the target map file, completing the conversion from raw data to professional map products. This coherent workflow not only improves the efficiency of data processing and analysis, but also ensures data consistency throughout the entire process, ultimately producing high-quality map files that meet the needs of various professional applications.

[0007] Optionally, the processing of the vectors of the vector layer by a data editing and processing tool to obtain a target vector layer includes: simplifying the vectors of the vector layer to obtain a target vector; dividing the target vector into at least two sub-vectors, and adding nodes at the corners of the sub-vectors by a node tool, wherein the corners are positions on the sub-vectors where the curvature is greater than a preset curvature threshold; performing node editing on the sub-vectors after adding the nodes to obtain target sub-vectors; and merging all the target sub-vectors to obtain a target vector layer.

[0008] By adopting the above technical solution, first, the vector layer is simplified, redundant data is reduced, and processing efficiency is improved. Subsequently, the target vector is divided into sub-vectors and nodes are added at the corners. This step enhances the ability to express complex geographic features. By adding nodes at locations where the curvature is greater than the preset threshold, the accurate representation of key geographic features is ensured. The sub-vectors after adding nodes are edited to further optimize the data quality. Finally, all target sub-vectors are merged into the target vector layer, which retains details and ensures integrity. This processing method not only improves the accuracy and expressiveness of geographic data, but also lays a solid foundation for subsequent spatial analysis and visualization, making the final generated map more accurate and reliable. The entire process ensures the refined processing of data while maintaining the consistency and efficiency of operations, providing strong support for high-quality geographic information processing.

[0009] Optionally, after taking the intersecting area as the first result layer, the method further includes: performing a topological rule check on the first result layer, marking the topological errors found on the first result layer, the topological rules including no overlapping patches and no seamless patches; and using an editing tool to correct the topological errors marked on the first result layer so that the first result layer does not contain the topological errors.

[0010] By adopting the above technical solution and introducing the topology rule checking and correction steps, the data quality and spatial accuracy of the first result layer are significantly improved. Topology rule checking of the first result layer, especially the two key rules of no overlapping patches and no gaps in patches, can effectively identify potential errors in spatial data. The detected topology errors are directly marked on the first result layer, providing a clear target for subsequent corrections. The identified topology errors are accurately corrected using editing tools to ensure that the first result layer ultimately reaches a high-quality state without topology errors. This process not only improves the integrity and continuity of spatial data, but also enhances the reliability and consistency of the data. By eliminating common problems such as patch overlap and gaps, this method significantly improves the accuracy of spatial analysis and lays a solid foundation for subsequent classification rendering and map finishing. This automated error detection and correction mechanism greatly reduces the need for manual intervention, improves the efficiency of data processing, and ensures the high quality and professionalism of the final map product.

[0011] Optionally, obtaining the intersection area obtained after performing an intersection operation on multiple target vector layers and using the intersection area as the first result layer includes: selecting multiple target vector layers, setting parameters for the intersection operation, where the parameters include the path and name of the input layer and the output layer; performing overlay analysis on the multiple target vector layers according to the parameters to obtain the geometric intersection area between the target vector layers; and converting the geometric intersection area into a new vector layer as the first result layer.

[0012] By adopting the above technical solution, by selecting multiple target vector layers and setting the intersection operation parameters, clear guidance and flexibility are provided for subsequent operations. This parameterized setting not only improves the accuracy of the operation, but also enhances the controllability and repeatability of the processing process. Subsequently, an overlay analysis is performed according to the set parameters to accurately identify the geometric intersection areas between each target vector layer. This step effectively reveals the spatial associations between different geographic elements. Finally, the obtained geometric intersection areas are converted into a new vector layer as the first result layer. This conversion process not only retains the complex spatial relationship information, but also converts it into a form that can be further analyzed and visualized. The entire process realizes the efficient conversion from multiple independent layers to a comprehensive result layer, providing a high-quality data foundation for subsequent spatial analysis, classification rendering and map production, and greatly improving the efficiency and accuracy of geographic information processing.

[0013] Optionally, the first result layer is classified and rendered according to the layer properties of the first result layer to obtain a second result layer, including: extracting the rendering properties of multiple spot elements in the first result layer; matching the rendering symbol parameters corresponding to each of the spot elements in the rendering rule library according to the rendering properties; symbolically rendering each of the spot elements according to the rendering symbol parameters corresponding to each of the spot elements to generate rendered spot elements; and constructing all the rendered spot elements into a result layer after symbolic rendering to obtain a second result layer.

[0014] By adopting the above technical solution, first, by extracting the rendering attributes of multiple map elements in the first result layer, a data foundation is laid for the subsequent rendering process. Subsequently, attribute matching is performed using the rendering rule library to ensure that the selection of rendering symbol parameters meets professional standards and is consistent. This rule-based matching method not only improves the degree of automation in the rendering process, but also ensures the standardization and reliability of the rendering results. Symbolic rendering is performed on each map element according to the matching rendering symbol parameters. This step converts abstract spatial data into an intuitive visual expression. Finally, all rendered map elements are integrated into the result layer after symbolic rendering to obtain the second result layer, realizing a complete conversion from data to a visual map. The entire process not only improves the efficiency and quality of map production, but also enhances the expressiveness and interpretability of geographic information, providing a high-quality visualization foundation for subsequent map finishing and final product output.

[0015] Optionally, the map finishing of the second result layer to obtain a target map file includes: symbolizing the second result layer according to preset mapping rules to obtain a symbolized result layer; automatically generating annotation information of layer elements on the symbolized result layer according to preset annotation rules to obtain a third result layer; optimizing the layout of the third result layer and adding preset essential map elements to obtain a fourth result layer; exporting the fourth result layer as a target map file, wherein the format of the target map file is a general electronic map format of vector or raster.

[0016] By employing this technical solution, the second result layer is symbolized according to preset cartographic rules, ensuring the standardization and consistency of map symbols and enhancing the professional quality of the map. Subsequently, labeling information for the layer elements is automatically generated based on preset labeling rules, resulting in the third result layer. This step not only improves the map's information richness but also ensures the accuracy and readability of the labeling. The layout of the third result layer is optimized and essential map elements are added to form the fourth result layer, further enhancing the map's integrity and aesthetics. Finally, the fourth result layer is exported as a target map file in a universal electronic map format, ensuring the versatility of the results while meeting the needs of diverse application scenarios. The entire process automates the conversion from raw data to high-quality map products, significantly improving cartographic efficiency and product quality while ensuring that the maps meet professional standards and user needs. This automated map finishing method not only reduces manual intervention but also ensures consistency and repeatability in map production, providing high-quality visualization support for various geographic information applications.

[0017] Optionally, before obtaining vector geographic information data in multiple formats, the method further includes: receiving a data acquisition instruction input by a user, the data acquisition instruction including location information of a data source; obtaining vector geographic information data in the data source through file import or a network data interface according to the location information; performing coordinate conversion and data format conversion on the obtained vector geographic information data, converting the obtained vector geographic information data into a data format compatible with the desktop geographic information system and vector geographic information data under a unified coordinate system, and using the converted vector geographic information data as the vector geographic information data in the multiple formats.

[0018] By adopting the above technical solution, user customization of the data acquisition process is achieved by receiving data acquisition instructions input by the user, including the location information of the data source, thereby enhancing the adaptability of the system. Vector geographic information data is acquired through file import or network data interface based on the location information, which expands the diversity of data sources and enables the system to process data from different sources and formats. Subsequently, the acquired data is subjected to coordinate conversion and data format conversion, and converted into a data format and unified coordinate system compatible with the desktop geographic information system. This step solves the problem of data heterogeneity and ensures the consistency and accuracy of subsequent processing. Through these preprocessing steps, the system can efficiently integrate vector geographic information data in various formats, providing a standardized and high-quality data foundation for subsequent data editing, spatial analysis and map production. This intelligent data acquisition and preprocessing method not only improves the efficiency of data processing, but also enhances the data compatibility and scalability of the system, making the entire geographic information processing process smoother and more reliable.

[0019] In the second aspect, the present application provides a geographic information processing device, which includes: a first acquisition module, an editing module, a second acquisition module, a rendering module and an output module; wherein the first acquisition module is used to acquire vector geographic information data in multiple formats, and the vector geographic information data includes at least one vector layer containing land class attributes; the editing module is used to process the vector of the vector layer through a data editing and processing tool to obtain a target vector layer; the second acquisition module is used to obtain the intersection area obtained after performing an intersection operation on multiple target vector layers, and use the intersection area as a first result layer; the rendering module is used to classify and render the first result layer according to the layer attributes of the first result layer to obtain a second result layer; the output module is used to perform map finishing on the second result layer to obtain a target map file.

[0020] In the third aspect, the present application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any one of the above-mentioned geographic information processing methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned geographic information processing methods.

[0022] In summary, this application includes at least one of the following beneficial technical effects:

[0023] 1. By acquiring vector geographic information data in multiple formats, the problem of data source diversity was resolved. Subsequently, data editing and processing tools were used to process the vector layers to generate the target vector layers, improving data quality and consistency. Intersection operations were performed on multiple target vector layers to generate the first result layer, enabling complex spatial analysis. Classification and rendering were performed on the first result layer to generate the second result layer, enhancing data visualization. Finally, map finishing was performed on the second result layer to generate the target map file, completing the conversion from raw data to a professional map product. This coherent workflow not only improves the efficiency of data processing and analysis, but also ensures data consistency throughout the entire process, ultimately producing high-quality map files that meet the needs of various professional applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flowchart of a geographic information processing method provided by an embodiment of the present application;

[0025] Figure 2This is a schematic diagram of the structure of a geographic information processing device provided in an embodiment of the present application;

[0026] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0027] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0028] In order to enable people skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0029] In the description of the embodiments of this application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0030] The desktop geographic information system in this application is specifically the LStarGIS desktop geographic information system, which is a software application specially designed for processing, analyzing and visualizing geospatial data.

[0031] LStarGIS Desktop Geographic Information System is a comprehensive, powerful professional GIS software that integrates geographic data management, analysis, and visualization into a single desktop application, providing users with a complete geographic information processing platform. This system makes the processing and analysis of geospatial data more efficient and convenient, providing powerful tools for professionals in fields such as urban planning, resource management, and environmental monitoring.

[0032] Figure 1 This application discloses a geographic information processing method, such as Figure 1 As shown, the method includes S101-S105.

[0033] S101, obtaining vector geographic information data in multiple formats, where the vector geographic information data includes at least one vector layer containing land attributes.

[0034] Specifically, in the field of GIS, vector data is a data format that uses geometric shapes such as points, lines, and surfaces to represent geographic features. Multiple formats of vector geographic information data refer to the fact that these vector data may be stored in different file formats or data structures.

[0035] A vector layer is a collection of vector features with the same attribute structure. Land attributes are attributes that describe land use or land cover types.

[0036] In one example, the system first receives a data acquisition instruction from the user, which includes the location information of the data source. This design allows users to flexibly specify the data source, improving the system's applicability. Based on the user-provided location information, the system can then retrieve vector geographic information from the data source through file import or a network data interface. This dual acquisition approach ensures the system's adaptability to diverse data storage and transmission scenarios, enhancing the flexibility and compatibility of data acquisition.

[0037] After acquiring the data, the system performs necessary preprocessing on the vector geographic information data. This includes coordinate conversion and data format conversion. The goal is to convert the acquired vector geographic information data into a data format compatible with desktop geographic information systems and unify it into a common coordinate system. This conversion enables the system to process geographic data from different sources and formats, greatly improving the system's data compatibility and processing capabilities.

[0038] During data acquisition and conversion, the system pays special attention to vector layers containing land class attributes. These layers are particularly important for subsequent geographic information analysis, as land class information forms the foundation for many spatial analysis and mapping processes. By ensuring that at least one such layer is included, the system lays the foundation for subsequent classification rendering and thematic mapping.

[0039] S102, processing the vectors of the vector layer by using a data editing and processing tool to obtain a target vector layer.

[0040] In one example, the system first simplifies the vectors in a vector layer. This step is necessary because the original data may contain excessive redundant nodes, increasing the data volume and potentially slowing processing. The simplification process uses the Douglas-Peucker algorithm, which effectively reduces the number of nodes while preserving the overall shape of the graph. The system performs simplification based on a preset tolerance (for example, 0.1 meters), ensuring that the simplified graph does not deviate from the original by more than this threshold. This not only reduces data volume and speeds up subsequent processing, but also preserves the basic shape and topological relationships of the graph.

[0041] After simplification, the system splits the target vector into at least two sub-vectors. The purpose of this splitting is to control and edit complex graphics more finely. The selection of split points is based on the geometric characteristics of the graphics, such as the midpoint of the length or area, or the characteristic points of the graphics (such as the convex points of polygons). After splitting, the system uses the node tool to add nodes at the corners of the sub-vectors. The corners here are defined as the positions on the sub-vectors where the curvature is greater than the preset curvature threshold, for example, the threshold can be set to 30 degrees. The purpose of adding nodes is to provide more editing control points while maintaining the overall shape of the graphics, making subsequent editing more precise and flexible.

[0042] Next, the system performs node editing on the added subvectors. This step allows the operator to fine-tune the graph based on actual needs. For example, nodes can be moved to better align boundaries with actual terrain features, or unnecessary nodes can be deleted to simplify the graph. This kind of detailed editing can significantly improve data quality and accuracy, especially when working with high-precision maps or conducting detailed spatial analysis.

[0043] Finally, the system merges all the edited target sub-vectors to obtain the final target vector layer. During the merging process, the system checks and maintains the topological relationship of the layers to ensure that the merged layer remains continuous and consistent in space.

[0044] Based on the above embodiment, as an optional implementation, in S102, the vectors of the vector layer are processed by a data editing and processing tool to obtain the target vector layer, which specifically includes S201-S204:

[0045] S201, simplifying the vector of the vector layer to obtain a target vector.

[0046] In one example, the LStarGIS system first analyzes the input vector layer, including basic information such as its geometry type (point, line, or surface), number of vertices, and spatial extent. Based on this information, the system automatically determines whether simplification is necessary and recommends an appropriate simplification algorithm and parameters. For point features, simplification is generally not necessary. For line and surface features, the system offers a variety of simplification algorithms, the most commonly used of which is the Douglas-Peucker algorithm.

[0047] Before performing the simplification operation, the system prompts the user to set a simplification threshold parameter. This parameter, typically expressed in map units (such as meters) or pixels, determines the degree of simplification. A smaller threshold preserves more detail, while a larger threshold produces simpler shapes. The system recommends an initial threshold based on the characteristics of the data and the intended use case (such as map scale), which the user can adjust as needed.

[0048] Once the simplification process begins, the LStarGIS system processes each feature in the vector layer one by one. For line features, the system uses a selected algorithm (such as the Douglas-Peucker algorithm) to reduce the number of vertices that make up the line. This algorithm recursively splits the original line segment into simpler parts and removes intermediate points whose deviation from the original segment is less than a threshold. For area features, the system processes the outer boundary first, then processes internal holes (if any). During processing, the system pays special attention to maintaining topological relationships, ensuring that the simplified polygons do not intersect with themselves and that there are no overlaps or gaps between adjacent polygons.

[0049] To ensure the quality of the simplified results, the system performs a series of checks and corrections during the simplification process. For example, for polygon features, the system ensures that the area change after simplification does not exceed a preset percentage; for line features, the system ensures that the simplified line segments do not accidentally intersect with other features. If potential problems are found, the system automatically adjusts the simplification parameters or locally retains more vertices.

[0050] After simplification is complete, LStarGIS generates a new vector layer as the target vector. This new layer maintains the same attribute structure as the original layer, but its geometry is simplified. The system automatically calculates and displays statistics before and after simplification, such as the percentage reduction in vertex count and the change in file size, allowing users to intuitively understand the simplification effect.

[0051] S202 : Split the target vector into at least two sub-vectors, and add nodes at the corners of the sub-vectors using a node tool. The corners are positions on the sub-vectors where the curvature is greater than a preset curvature threshold.

[0052] In one example, the LStarGIS system first analyzes the geometric characteristics of the target vector, including its shape complexity, length (for line features), or area (for polygon features). Based on these characteristics, the system automatically determines the appropriate split method and number of splits. The purpose of splitting is to decompose complex vector features into more manageable and processable subunits while maintaining the topological relationships and attribute information of the original features.

[0053] For line features, the system may use equal-length segmentation or feature-point segmentation. Equal-length segmentation divides the line feature into several evenly spaced segments of similar length; feature-point segmentation splits the line feature at key locations (such as sharp bends). For area features, the system may determine the segmentation scheme based on area, shape complexity, or internal characteristics (such as internal boundaries). For example, for a complex polygon, the system may segment along its major axis or natural dividing line.

[0054] After segmentation, the LStarGIS system adds independent nodes to each subvector. This process first involves calculating curvature. The system calculates curvature values ​​point by point along the subvector's boundary. Curvature is a mathematical quantity that describes the curvature of a curve; it reflects the degree to which a curve deviates from a straight line near a certain point. For discrete vector data, the system typically uses a three-point method to approximate curvature, estimating the curvature by considering the current point and the two adjacent points before and after it.

[0055] The system automatically recommends a curvature threshold based on data characteristics and intended use, and users can adjust this threshold as needed. A lower threshold will result in more nodes being added, thus capturing more details; a higher threshold will produce smoother results.

[0056] After calculating curvature and determining a threshold, the system traverses the subvector's boundaries, adding new nodes where the curvature exceeds the preset threshold. This process effectively densifies the original vector locally to better describe complex geometries. When adding nodes, the system takes special care to maintain topological integrity, ensuring that the added nodes do not cause self-intersections or conflicts with adjacent features.

[0057] S203 , performing node editing on the sub-vector after adding the node to obtain a target sub-vector.

[0058] In one example, the LStarGIS system first comprehensively analyzes each subvector after adding nodes, including geometric features such as node density, changes in line segment length, and angles. Based on these features, the system automatically identifies areas that may require further editing, such as areas with excessively dense nodes, locations with unnatural sharp turns, or areas with potential conflicts with adjacent features. This intelligent identification helps users quickly locate areas requiring special attention, improving editing efficiency.

[0059] Node editing operations include moving nodes, deleting redundant nodes, adding necessary nodes, and smoothing nodes. LStarGIS provides a comprehensive suite of node editing tools, which users can select and use as needed. When moving nodes, the system supports precise positioning, allowing users to ensure accurate node placement by entering coordinates or using snap tools. When moving nodes, the system displays changes in the length and angle of adjacent line segments in real time, helping users make editing decisions.

[0060] Removing redundant nodes is an important step in optimizing vector data. LStarGIS provides an intelligent node simplification tool that automatically identifies and removes nodes that contribute little to the overall shape. This process considers factors such as the spatial distribution of nodes and the angular variations of line segments to ensure that simplification does not significantly alter the shape of features. Users can set a simplification tolerance, and the system automatically removes nodes based on this tolerance while preserving key shape features.

[0061] Necessary nodes are added to better describe complex geographic features. In some cases, the automatically added nodes may not be sufficient to accurately represent certain details. The LStarGIS system allows users to manually add nodes or use intelligent tools to automatically add nodes under specific conditions. For example, the system can automatically add nodes at the midpoint of a curve segment to better approximate the curve shape.

[0062] Node smoothing is an important means of improving the visual quality of vector data. The LStarGIS system provides a variety of smoothing algorithms, such as B-spline curves and Bezier curves. Users can select an appropriate algorithm to smooth the selected node sequence. The smoothing process reduces jagged edges while maintaining the overall shape, making the lines smoother and more natural. The system also allows users to control the degree of smoothing to strike a balance between preserving detail and achieving a smooth effect.

[0063] During node editing, the LStarGIS system performs real-time topology checks to ensure that edits do not result in topological errors, such as self-intersections or overlaps with adjacent features. If potential topological issues are detected, the system immediately alerts the user and provides correction suggestions. This real-time checking mechanism significantly reduces the risk of generating erroneous data and improves editing efficiency and accuracy.

[0064] S204: Merge all target sub-vectors to obtain a target vector layer.

[0065] S103: Obtain an intersection area obtained by performing an intersection operation on multiple target vector layers, and use the intersection area as a first result layer.

[0066] In one example, the system first needs to identify the layers involved in the intersection operation. These layers may be the target vector layer edited in the previous step, or they may be other related layers newly imported by the user. For example, in an urban planning project, an intersection operation may be required for the current land use layer, the planning control line layer, and the ecological protection zone layer. The system will ask the user to select the layers involved in the intersection operation through a graphical interface and specify the operation priority.

[0067] Next, the LStarGIS system calls the built-in spatial analysis module to perform an intersection operation. This process is actually a complex geometric operation. The system compares the geometric features in the selected layer pair by pair to determine whether they intersect spatially. To improve operational efficiency, the system first uses spatial indexing techniques (such as R-trees) to quickly screen for potentially intersecting feature pairs, and then performs detailed geometric intersection judgments on these candidate feature pairs. During this judgment, the system considers the intersection between different types of geometric features (points, lines, and surfaces), such as the containment relationship between points and surfaces and the intersection relationship between lines and surfaces.

[0068] The result of the intersection operation is a new vector layer, the first result layer. This layer contains the spatially overlapping areas of all input layers. For each overlapping area, the system not only preserves its geometry but also inherits the attribute information of all the original layers involved in the intersection. For example, if an area belongs to both "Commercial Land" and "Ecological Protection Area", the result layer will contain the "Commercial Land" attributes from the land use layer and the relevant attributes from the ecological protection area layer.

[0069] To ensure the quality and usability of the first result layer, the system performs a series of post-processing operations after the intersection operation is completed. This includes fixing possible geometric errors (such as self-intersecting polygons and overlapping nodes), merging adjacent polygons with the same attributes, and recalculating basic statistics such as area and perimeter. The system also automatically generates a new attribute field to identify the original layer combination corresponding to each result feature, which facilitates subsequent classification and analysis.

[0070] After using the intersection area as the first result layer, it also includes:

[0071] Perform a topology rule check on the first result layer and mark the topology errors found on the first result layer. The topology rules include no overlapping patches and no gaps in patches. Use editing tools to correct the topology errors marked on the first result layer to ensure that the first result layer has no topology errors.

[0072] In one example, the LStarGIS system first applies predefined topological rules to the first result layer, including non-overlapping patches and seamless patches. These two rules are fundamental requirements for ensuring the integrity of polygon feature data. The non-overlapping patch rule requires that adjacent polygons should not overlap, while the seamless patch rule requires that there should be no gaps between polygons. The application of these rules ensures the logical continuity and integrity of spatial data, which is crucial for many spatial analysis and mapping tasks.

[0073] The system uses efficient spatial indexing and geometric algorithms to perform topology checks. For the "No Overlap" rule, the system checks the boundaries of each pair of adjacent polygons, calculates their intersection, and flags any intersections with non-zero area as overlap errors. For the "No Gap" rule, the system analyzes the boundaries between adjacent polygons and flags any gaps (i.e., incomplete alignment) between the boundaries as gap errors.

[0074] During the inspection process, the LStarGIS system generates an error report in real time and graphically identifies detected topological errors on the first result layer. This visual error identification method allows users to intuitively understand the location and type of errors, greatly improving the efficiency of subsequent correction work. The system usually uses different colors or symbols to distinguish different types of topological errors. For example, red may be used to indicate overlap errors and yellow to indicate gap errors.

[0075] After detecting and identifying topological errors, LStarGIS provides a powerful set of editing tools for correcting them. These tools include, but are not limited to, automatic repair tools, manual editing tools, and advanced topological editing tools. Automatic repair tools can handle simple topological errors, such as small overlaps or narrow gaps. For more complex situations, the system provides manual editing tools that allow users to precisely adjust polygon boundaries.

[0076] During the correction process, the system updates topological relationships in real time, ensuring that each edit operation does not introduce new topological errors. For example, when a user moves the border of a polygon to eliminate overlap, the system automatically adjusts the borders of adjacent polygons to maintain boundary consistency. This intelligent editing mechanism greatly reduces the risk of human-induced errors.

[0077] Based on the above embodiment, as an optional implementation, in S103, obtaining the intersection area obtained by performing an intersection operation on multiple target vector layers and using the intersection area as the first result layer specifically includes S301-S303:

[0078] S301, select multiple target vector layers and set the parameters of the intersection operation, which include the path and name of the input layer and the output layer.

[0079] In one example, the LStarGIS system first provides an intuitive user interface that allows users to browse and select the available vector layers in the system. This interface typically includes a layer list, displaying basic information such as each layer's name, type (point, line, polygon), and coordinate system. Users can select multiple target vector layers by checking boxes or dragging. The system displays the number of selected layers in real time and highlights them in the map window, allowing users to visually confirm whether their selection is correct.

[0080] During layer selection, LStarGIS performs a series of compatibility checks. For example, the system verifies that the coordinate systems of the selected layers are consistent. If inconsistencies exist, the system alerts the user and provides options for coordinate conversion. Furthermore, the system checks whether the layer's geometry type is suitable for intersection operations. For example, at least one polygon layer must be present before a face-to-face intersection can be performed. These checks help avoid potential errors in subsequent processing and improve the reliability of the entire analysis process.

[0081] After selecting the input layer, the user needs to set the parameters for the intersection operation. The first step is to specify the path and name of the output layer. The LStarGIS system will provide a file dialog box, allowing the user to select the save location and input file name for the output file. The system will automatically check the validity of the file name to ensure that it complies with the naming rules of the operating system and avoid overwriting existing files. The user can also select the output file format, such as Shapefile, GeoJSON, etc., and the system will automatically add the correct file extension based on the selected format.

[0082] S302: performing overlay analysis on multiple target vector layers according to the parameters to obtain geometric intersection areas between the target vector layers.

[0083] In one example, the LStarGIS system first loads the selected target vector layer according to the parameters set by the user in step S301. The system then verifies the coordinate system consistency of all layers and automatically converts any inconsistencies to ensure that all layers are analyzed in the same spatial reference system. This step is crucial for ensuring the accuracy of the intersection results.

[0084] Next, the system selects the most appropriate intersection algorithm based on the layer geometry and data volume. For a simple intersection between two polygonal layers, the system might use a traditional planar scanline algorithm. For complex intersections involving multiple layers or large amounts of data, the system might choose a more efficient algorithm, such as an R-tree-based spatial indexing algorithm or a parallel processing algorithm. This intelligent algorithm selection can significantly improve the efficiency of intersection operations, especially when processing large amounts of spatial data.

[0085] When performing intersection operations, the LStarGIS system utilizes a high-precision geometric operation library to ensure accurate intersection results. The system processes each polygon individually, calculating its intersection with polygons in other layers. During this process, the system considers the user-defined spatial tolerance to account for minor geometric errors that may occur due to numerical precision limitations. This approach effectively avoids false or missed intersections caused by inconsistent data precision.

[0086] When intersecting multiple layers, the LStarGIS system employs optimized processing strategies. For example, the system may first intersect pairs of layers with significant spatial overlap before gradually incorporating additional layers. This strategy reduces the complexity of intermediate results and improves overall computational efficiency. Furthermore, the system utilizes parallel computing technology to partition large datasets into multiple subsets and perform computations simultaneously on multiple processing cores, accelerating processing.

[0087] During the intersection process, LStarGIS processes not only geometric information but also attribute information according to user settings. The system can retain all attribute fields from the original layer or, as specified by the user, retain only key fields. For the new polygons generated by the intersection, the system combines attributes from different input layers according to pre-set rules. For example, you can choose to retain the attributes of the original polygon with the largest area or create new attribute fields to store the intersection information.

[0088] To improve the efficiency of large-scale data processing, the LStarGIS system adopts a block processing strategy. The system divides the entire study area into multiple smaller spatial blocks, performs intersection operations on the data within each block, and then merges the results. This approach not only fully utilizes computing resources but also effectively controls memory usage, enabling the system to handle extremely large spatial datasets.

[0089] During the intersection operation, the LStarGIS system displays real-time progress information, including the number of layers processed, the number of intersecting polygons generated, and the estimated remaining time. This real-time feedback not only allows users to understand the processing progress but also helps to promptly identify potential problems. If the system detects an anomaly, such as an area where the intersection operation is particularly time-consuming, the user is immediately notified and can choose whether to continue processing or adjust parameters.

[0090] After the intersection operation is completed, LStarGIS generates a new vector layer containing the geometric and attribute information of all intersecting areas. The system performs a preliminary quality check on this new layer to verify geometric validity and attribute integrity. If any problems are found, the system generates a detailed error report to help users quickly locate and resolve the issues.

[0091] S303: Convert the geometric intersection area into a new vector layer as the first result layer.

[0092] S104: Classify and render the first result layer according to the layer attributes of the first result layer to obtain a second result layer.

[0093] In one example, the LStarGIS system first analyzes the attribute table structure of the first result layer. This layer contains comprehensive information obtained after the intersection operation. Its attribute fields may come from multiple original layers, so the information is often complex. The system automatically identifies key attribute fields, such as land type attributes, administrative divisions, and environmental sensitivity, and provides users with selections as the basis for classification rendering. At the same time, the system also analyzes the data types (such as text and numeric) and data distribution characteristics of these fields to provide recommendations for subsequent classification methods.

[0094] Next, the system will provide several commonly used classification methods for users to choose from based on the selected attribute fields and data characteristics. For categorical data (such as land use type), the system will use the unique value classification method by default, assigning a unique color or symbol to each category. For continuous numerical data (such as population density), the system will provide equal interval classification, natural breakpoint classification, quantile classification and other methods. Users can choose the appropriate classification method based on data characteristics and analysis purposes, and can adjust the number of categories. For example, when analyzing the urban heat island effect, the surface temperature field may be selected and divided into 5 levels using the natural breakpoint method.

[0095] Once the classification method is determined, the LStarGIS system automatically assigns a color or symbol to each category. The system includes a variety of built-in color schemes, including single-color gradients, multi-color gradients, and contrasting colors. These schemes have been carefully designed to take into account both visual aesthetics and the professional standards of map making. For example, for a temperature classification, the system might default to a gradient color scheme from blue (cold) to red (hot). Users can adjust the color scheme as needed, and the system also provides the option of customizing colors to meet special mapping needs.

[0096] In addition to color rendering, the system also supports adjusting other visual variables of features based on attribute values, such as transparency and symbol size. This multi-dimensional rendering capability allows maps to simultaneously express multiple attribute information. For example, when analyzing urban development potential, color can be used to represent land use type, while transparency can be used to indicate development intensity.

[0097] After rendering is complete, the system generates a second result layer. This layer is identical in geometry and attribute structure to the first result layer, but with additional rendering information. The system automatically generates a legend explaining the meaning of the various colors and symbols, and allows users to edit the legend title and description.

[0098] Based on the above embodiment, as an optional implementation, in S104, classifying and rendering the first result layer according to the layer attributes of the first result layer to obtain the second result layer specifically includes:

[0099] Extract rendering properties of multiple spot elements in the first result layer; match rendering symbol parameters corresponding to each spot element in the rendering rule library according to the rendering properties; perform symbolic rendering on each spot element according to the rendering symbol parameters corresponding to each spot element to generate rendered spot elements; construct all rendered spot elements into a result layer after symbolic rendering to obtain a second result layer.

[0100] In one example, the LStarGIS system first extracts the rendering attributes of each tile feature from the first result layer. These attributes may include key information such as the tile type, area, and thematic value. The system uses efficient data access methods, such as index queries or parallel processing, to quickly traverse the entire layer and extract the required attribute information. This step is the foundation of the subsequent rendering process, as these attributes will determine how to select the appropriate rendering symbol for each tile.

[0101] Next, the system will match the rendering symbol parameters corresponding to each patch feature in the pre-defined rendering rule library based on the extracted rendering attributes. The rendering rule library is a database containing various rendering styles and parameters, which defines how spatial features of different types or attributes should be presented on the map. The LStarGIS system uses an intelligent matching algorithm to consider multiple factors such as patch type, attribute value range, spatial relationship, etc., to select the most appropriate rendering symbol for each patch. This process not only considers the characteristics of a single patch, but also the visual harmony of the entire map, ensuring that the final rendering result can accurately express the data information while maintaining good readability and aesthetics.

[0102] During the matching process, the LStarGIS system also considers any custom rendering rules that users may have set. The system provides a flexible interface that allows users to modify or create rendering rules based on specific needs. For example, users can define unique color schemes for specific patch types or set gradient fill styles based on attribute values. This flexibility allows the system to adapt to the specific needs of various professional fields, resulting in more customized and specialized map presentations.

[0103] Once the rendering symbol parameters for each tile feature are determined, the LStarGIS system begins the actual symbol rendering process. The system uses a high-performance graphics rendering engine capable of rapidly processing a large number of tile features. The rendering process includes filling colors, drawing boundaries, and adding textures or patterns. The system precisely controls the visual appearance of each tile based on the rendering symbol parameters, including color, transparency, line type, and fill style. To improve rendering efficiency, the system utilizes multi-threaded processing and GPU acceleration technology, enabling parallel processing of multiple tile rendering tasks, significantly improving overall rendering speed.

[0104] S105: Perform map finishing on the second result layer to obtain a target map file.

[0105] In one example, the LStarGIS system first enters map creation mode and places the second result layer on the drawing canvas. The system automatically adjusts the map's display extent to ensure the entire study area is represented on the canvas. Next, the system automatically calculates and adds a scale bar and north arrow based on the canvas size and map scale. These two elements are crucial for readers to understand the map's spatial scale and orientation. The system offers a variety of scale bar and north arrow styles, allowing users to adjust them to suit the map's theme and personal preferences.

[0106] The system then automatically generates a legend based on the rendering information of the second result layer. The legend's layout and style are automatically adjusted based on the map's theme and rendering method to ensure clarity and readability. For example, for thematic maps with layered colors, the system generates a color-coded legend; for point features represented by symbols, the system generates corresponding symbol descriptions. Users can further edit the legend title and adjust the legend's position and size to optimize the legend's presentation.

[0107] Next, LStarGIS prompts you to add a map title. The map title is the heart of the map, concisely summarizing the map's theme and content. The system offers a variety of font and style options, intelligently recommending appropriate font size and placement based on the map's theme. Users can adjust the title's content and style as needed to ensure it accurately conveys the map's primary message.

[0108] To increase the information content and readability of the map, the system also supports adding auxiliary text information, such as map description, data source, mapping date, etc. This information is usually placed on the edge or blank space of the map, and the system will intelligently suggest appropriate locations to avoid blocking important map elements.

[0109] During the finishing process, the LStarGIS system also provides the ability to add additional layers, such as administrative boundaries, major roads, water systems, and other background information. These auxiliary layers help readers better understand the spatial context of the map content. The system automatically adjusts the display style of these layers.

[0110] Finally, the system optimizes the overall map layout. This includes adjusting the spatial relationships between elements to ensure visual balance; adding appropriate white space to enhance the map's overall aesthetic; and adding borders and grid lines as needed to improve map standardization. The system also provides a variety of pre-set map templates that users can quickly apply and then customize.

[0111] After finishing, the LStarGIS system generates a target map file. This file not only contains the visual map image but also retains all geospatial information and attribute data. The system supports multiple output formats, including common image formats (such as PNG and JPEG), vector formats (such as PDF and SVG), and specialized geographic information formats (such as GeoPDF). Users can select the appropriate output format based on their subsequent needs.

[0112] Perform map finishing on the second result layer to obtain the target map file, including:

[0113] The second result layer is symbolized according to preset mapping rules to obtain a symbolized result layer; on the symbolized result layer, labeling information of the layer elements is automatically generated according to preset labeling rules to obtain a third result layer; the layout of the third result layer is optimized, and preset necessary map elements are added to obtain a fourth result layer; the fourth result layer is exported to a target map file, and the format of the target map file is a general electronic map format of vector or raster.

[0114] In one example, the LStarGIS system further symbolizes the second result layer according to preset mapping rules. The system automatically adjusts the size, color, and style of symbols according to different map scales and theme types. For example, in small-scale maps, the system may simplify certain complex symbols to avoid visual confusion; in thematic maps, the system will highlight the elements related to the theme and weaken other background information. The LStarGIS system uses an intelligent symbolization algorithm that can automatically handle symbol conflicts, such as adjusting the position of overlapping symbols or merging similar adjacent symbols to ensure the clarity of the map. In addition, the system will also consider factors such as color harmony and visual hierarchy to create a map presentation that is both beautiful and effective.

[0115] Next, the LStarGIS system automatically generates annotation information for the layer elements on the symbolized result layer according to preset annotation rules. The system uses advanced automatic annotation algorithms to intelligently generate annotations for various spatial elements, including points, lines, and areas. During the annotation process, the system considers multiple factors, such as the position, orientation, size, and font of the annotations. To avoid overlapping and cluttered annotations, the system uses complex conflict detection and resolution algorithms that can automatically adjust the position of annotations or omit secondary annotations when necessary. For linear features (such as rivers and roads), the system can generate curved annotations along the direction of the feature; for area features, the system selects the optimal annotation position based on area size and shape. In addition, the system supports multilingual annotation and dynamic annotation, dynamically displaying or hiding annotations based on the map zoom level. The result of this process is a third result layer, which adds rich annotation information to the symbolization, further improving the map's information capacity and readability.

[0116] After obtaining the third result layer, the LStarGIS system will optimize the layout and add preset necessary map elements. The system will automatically adjust the main position of the map to ensure that important areas are properly displayed. At the same time, the system will add various necessary elements to the map, such as scale, compass, legend, latitude and longitude grid, map information, etc. The position and style of these elements will be automatically adjusted according to the main content and overall layout of the map to achieve the best visual effect. For thematic maps, the system will also add corresponding thematic legends and explanatory text. In addition, the LStarGIS system supports custom map templates. Users can design map layouts according to specific needs, and the system will automatically complete the layout according to the template. The result of this process is the fourth result layer, which is a complete and professional map product that contains all necessary map elements and information.

[0117] Finally, the LStarGIS system exports the fourth result layer as a target map file. The system supports a variety of common electronic map formats, including vector formats (such as SVG, PDF, AI) and raster formats (such as PNG, TIFF, JPEG). During the export process, the system will perform corresponding optimizations based on the selected format. For vector formats, the system will retain the editability of map elements to ensure that they can still be modified during subsequent use; for raster formats, the system will generate high-quality image files based on preset resolution and compression parameters. During the export process, the system will also perform a final quality check to ensure that all map elements are displayed correctly, without missing or errors.

[0118] Before obtaining vector geographic information data in various formats, it also includes:

[0119] Receive data acquisition instructions input by the user, the data acquisition instructions include location information of the data source; according to the location information, obtain vector geographic information data in the data source through file import or network data interface; perform coordinate conversion and data format conversion on the obtained vector geographic information data, convert the obtained vector geographic information data into a data format compatible with the desktop geographic information system and vector geographic information data under a unified coordinate system, and use the converted vector geographic information data as vector geographic information data in multiple formats.

[0120] In one example, the LStarGIS system receives a data acquisition instruction input by the user. This instruction contains the location information of the data source, which may be a local file path, a network URL, or a database connection string. The system provides an intuitive user interface that allows users to easily specify the data source location. For frequently used data sources, the system also supports saving and quickly calling data source configurations to improve user operation efficiency. This flexible data acquisition mechanism enables the LStarGIS system to adapt to various data acquisition scenarios, whether obtaining data from the local file system, a remote server, or an online geographic data service.

[0121] After receiving the data acquisition instruction, the LStarGIS system will automatically select the appropriate data acquisition method based on the location information. For local files, the system will directly read the file contents; for network data, the system will access the remote data source through HTTP, FTP or proprietary protocols. The LStarGIS system supports a variety of common geographic data formats, such as Shapefile, GeoJSON, KML, etc., as well as various spatial database formats. The system uses an efficient data reading algorithm and can quickly process large amounts of data. For large data sets, the system also supports block reading and streaming processing to effectively reduce memory usage. In addition, the LStarGIS system also implements a data caching mechanism that can cache frequently accessed remote data and improve the response speed of subsequent operations.

[0122] After successfully acquiring data, the LStarGIS system performs the critical coordinate transformation and data format conversion steps. This step aims to unify data from various sources into a standard format and coordinate system within the system, ensuring data consistency and comparability. During the coordinate transformation process, the system automatically identifies the coordinate system of the source data and converts it to a predefined unified coordinate system (such as WGS84 or a local coordinate system). LStarGIS includes a rich library of coordinate system definitions and transformation parameters, enabling it to handle a wide range of coordinate systems worldwide. The system also supports custom coordinate systems and transformation parameters to meet the needs of specialized projects.

[0123] Based on the above method, the present application also discloses a geographic information processing device, such as Figure 2As shown, Figure 2 : is a schematic diagram of the structure of a geographic information processing device provided by an embodiment of the present application, the device includes: a first acquisition module, an editing module, a second acquisition module, a rendering module and an output module; wherein,

[0124] The first acquisition module is used to obtain vector geographic information data in multiple formats, and the vector geographic information data includes at least one vector layer containing land class attributes; the editing module is used to process the vectors of the vector layer through data editing and processing tools to obtain the target vector layer; the second acquisition module is used to obtain the intersection area obtained after performing an intersection operation on multiple target vector layers, and use the intersection area as the first result layer; the rendering module is used to classify and render the first result layer according to the layer attributes of the first result layer to obtain the second result layer; the output module is used to perform map finishing on the second result layer to obtain the target map file.

[0125] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0126] See Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0127] The communication bus 1002 is used to implement the connection and communication between these components.

[0128] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0129] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0130] Processor 1001 may include one or more processing cores. Using various interfaces and circuits, processor 1001 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in memory 1005, as well as accesses data stored in memory 1005, to perform various server functions and process data. Optionally, processor 1001 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). Processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may not be integrated into processor 1001 but implemented as a separate chip.

[0131] Among them, the memory 1005 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a geographic information processing method.

[0132] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program storing a geographic information processing method in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0133] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0134] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0135] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of the devices or units can be electrical or other forms.

[0137] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0138] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0140] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A geographic information processing method, characterized in that: Applied to a desktop geographic information system, the method includes: Acquire vector geographic information data in multiple formats, wherein the vector geographic information data includes at least one vector layer containing land attributes; The vectors of the vector layer are processed using a data editing and processing tool to obtain a target vector layer; the process includes: simplifying the vectors of the vector layer to obtain a target vector; dividing the target vector into at least two sub-vectors, and adding nodes at the corners of the sub-vectors using a node tool, wherein the corners are positions on the sub-vectors where the curvature is greater than a preset curvature threshold; performing node editing on the sub-vectors after adding nodes to obtain a target sub-vector; and merging all the target sub-vectors to obtain a target vector layer. Obtaining an intersection area obtained by performing an intersection operation on the plurality of target vector layers, and using the intersection area as a first result layer; Classify and render the first result layer according to the layer properties of the first result layer to obtain a second result layer; The second result layer is subjected to map finishing to obtain a target map file.

2. The geographic information processing method according to claim 1, characterized in that: After taking the intersection area as the first result layer, the method further includes: Performing a topology rule check on the first result layer, and marking the topology errors found on the first result layer, wherein the topology rule includes no overlapping patches and no gaps between patches; The topological error identified on the first result layer is corrected using an editing tool so that the first result layer is free of the topological error.

3. The geographic information processing method according to claim 1, characterized in that: The obtaining of an intersection area obtained by performing an intersection operation on the plurality of target vector layers, and using the intersection area as a first result layer, includes: Selecting multiple target vector layers and setting parameters for the intersection operation, wherein the parameters include the paths and names of the input layers and the output layers; According to the parameters, the multiple target vector layers are overlaid and analyzed to obtain geometric intersection areas between the target vector layers; The geometric intersection area is converted into a new vector layer as the first result layer.

4. The geographic information processing method according to claim 1, wherein: The classifying and rendering the first result layer according to the layer attributes of the first result layer to obtain the second result layer includes: Extracting rendering attributes of a plurality of patch elements in the first result layer; According to the rendering attributes, matching rendering symbol parameters corresponding to each of the pattern elements in the rendering rule library; Performing symbolic rendering on each of the pattern elements according to the rendering symbol parameters corresponding to each of the pattern elements to generate rendered pattern elements; All of the rendered patch elements are constructed into a symbolically rendered result layer to obtain a second result layer.

5. The geographic information processing method according to claim 1, wherein: The step of performing map finishing on the second result layer to obtain a target map file includes: According to a preset mapping rule, symbolizing the second result layer to obtain a symbolized result layer; On the symbolized result layer, automatically generating annotation information of the layer elements according to a preset annotation rule to obtain a third result layer; Optimizing the layout of the third result layer and adding preset necessary map elements to obtain a fourth result layer; The fourth result layer is exported as a target map file, wherein the format of the target map file is a common electronic map format of vector or raster.

6. The geographic information processing method according to claim 1, wherein: Before obtaining vector geographic information data in multiple formats, the process further includes: receiving a data acquisition instruction input by a user, wherein the data acquisition instruction includes location information of a data source; According to the location information, obtaining the vector geographic information data in the data source through file import or network data interface; The obtained vector geographic information data is subjected to coordinate conversion and data format conversion, the obtained vector geographic information data is converted into a data format compatible with the desktop geographic information system and vector geographic information data under a unified coordinate system, and the converted vector geographic information data is used as the vector geographic information data in the multiple formats.

7. A geographic information processing device, characterized in that: The device includes: a first acquisition module, an editing module, a second acquisition module, a rendering module and an output module; wherein, The first acquisition module is used to acquire vector geographic information data in multiple formats, wherein the vector geographic information data includes at least one vector layer containing land attributes; The editing module is used to process the vectors of the vector layer using a data editing and processing tool to obtain a target vector layer. The editing module includes: simplifying the vectors of the vector layer to obtain a target vector; dividing the target vector into at least two sub-vectors and adding nodes at the corners of the sub-vectors using a node tool, wherein the corners are positions on the sub-vectors where the curvature is greater than a preset curvature threshold; performing node editing on the sub-vectors after adding nodes to obtain a target sub-vector; and merging all the target sub-vectors to obtain a target vector layer. The second acquisition module is used to obtain an intersection area obtained by performing an intersection operation on the plurality of target vector layers, and use the intersection area as a first result layer; The rendering module is configured to perform classification rendering on the first result layer according to the layer attributes of the first result layer to obtain a second result layer; The output module is used to perform map finishing on the second result layer to obtain a target map file.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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