GIS spatial data processing system and method

Abstract

The invention discloses a GIS spatial data processing system and method, and relates to the field of geographic information systems, and the system comprises a data obtaining module which obtains original vector map data from a plurality of data sources and carries out format conversion; the data cleaning module is used for detecting and removing abnormal data points by utilizing geometric analysis; the multi-scale processing module is used for performing multi-level processing on the data through Laplace multi-scale decomposition; the geometric optimization module is used for carrying out geometric structure optimization on the vector data based on curvature analysis; the topology analysis module is used for carrying out topological feature extraction and continuous coherence analysis on the data by utilizing algebraic topology; and the data storage module is used for compressing and storing the optimized data and constructing a query index at the same time. The method is used for solving the problem of low quality and efficiency of manual processing of complex geographic data, greatly improves the efficiency and quality of GIS spatial data processing, and is suitable for complex GIS data processing scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information systems, and particularly to a GIS spatial data processing system and method. Background Art

[0002] In the existing field of geographic information systems (GIS), processing and analyzing map data, especially vector map data, is a fundamental and crucial task. These systems typically need to process a large amount of spatial data, including collecting, storing, managing, calculating, and displaying geographic information. Although GIS provides powerful map view construction and geographic database access functions, integrating map visualization effects and geographic analysis functions with database operations, there are still some challenges in processing complex geographic data, especially in aspects such as data quality control, multi-scale representation, and topological consistency maintenance.

[0003] Most traditional GIS data processing methods rely on manual intervention to handle data errors and inconsistencies. This reliance not only consumes a large amount of time and labor but also limits the efficiency of the processing flow and the improvement of data quality. In addition, with the increase in the scale and complexity of geographic data, the ability of traditional methods in processing multi-scale data representation and optimizing data storage structures has gradually shown deficiencies. These limitations are particularly prominent in modern large-scale geographic data application scenarios, such as urban planning, environmental monitoring, and real-time location services.

[0004] Therefore, a system capable of automatically processing complex vector map data is needed. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a GIS spatial data processing system and method, which improves the automation degree, efficiency, and quality of vector map data processing, while ensuring the multi-scale representation and topological integrity of data to meet the complex requirements of modern GIS applications.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A GIS spatial data processing system, comprising: A data acquisition module configured to acquire original vector map data from multiple data sources and perform format conversion; A data cleaning module connected to the data acquisition module, for detecting and removing abnormal data points using geometric analysis to ensure data quality and providing accurate input for the geometric optimization module; A multi-scale processing module that receives the output of the data cleaning module and performs multi-level processing on the data through Laplacian multi-scale decomposition to decompose the data for finer-grained geometric and topological analysis; The geometric optimization module receives the output of the multi-scale processing module and optimizes the geometric structure of the vector data based on curvature analysis to improve the visual performance and analysis accuracy of the data; The topological analysis module is connected to the geometric optimization module and uses algebraic topology to extract topological features and perform persistent homology analysis on the data for analyzing and maintaining the structural integrity of the data; The data storage module is used to compress and store the optimized data and build a query index at the same time to improve the retrieval efficiency and storage efficiency of the data.

[0007] Preferably, the data cleaning module includes: The local curvature calculation unit is used to calculate the local curvature of each vector data point, and the local curvature is determined by the distance and angle difference between adjacent vector points; The outlier identification unit is used to detect and identify outlier data points according to the local curvature value and the set outlier threshold; The data correction unit is used to automatically correct or delete the detected outlier data points to maintain the geometric continuity and spatial consistency of the data.

[0008] Preferably, the multi-scale processing module includes: The scale selection unit is used to determine the processing scale based on the target application requirements and analysis accuracy, and adapt to different map detail requirements by selecting different spatial resolutions; The scale conversion unit is used to dynamically adjust the granularity of the vector data according to the selected scale, and uses the Thiessen polygon method to ensure the retention of geometric features during the conversion process; The optimization aggregation unit is used to aggregate adjacent vector objects through geometric and topological optimization techniques at the selected scale, reduce the data complexity while maintaining the spatial integrity and topological consistency of the data.

[0009] Preferably, the geometric optimization module includes: The geometric simplification unit is used to remove unnecessary points in the vector data through the Douglas-Peucker algorithm to reduce the data complexity; The shape correction unit is used to smooth the vector lines using the Bezier curve technique, optimize the visual presentation of the vector map while maintaining the key structural features; The topological relationship reconstruction unit is used to redefine the spatial relationship between vector objects after geometric simplification to ensure the topological consistency of the data.

[0010] Preferably, the topological analysis module includes: The topological structure construction unit is used to generate a simplicial complex based on the vector data to represent the vertices, edges and faces in the data to capture the complex topological structure in the data; Homology group calculation unit, configured to calculate homology groups of different orders based on the simplicial complex constructed from the topological structure , and extract topological features from the data, including connected components, loops, and cavities; Persistent homology analysis unit, configured to perform topological change analysis on data at multiple scales, generate persistent barcodes, and identify and track the generation and disappearance of topological features of different scales in the data; Topological simplification unit, configured to simplify the topological structure based on the results of persistent homology analysis, remove transient topological features, and retain key and persistent topological structures.

[0011] Preferably, the homology group calculation unit is configured to perform the following steps: (1) Extract geometric topological entities from the vector data and establish a simplicial complex , which includes a vertex set , an edge set , and a face set ; (2) Apply the boundary operator to calculate the boundary of the simplicial complex, where is used to map -dimensional chains to -dimensional chains; (3) Calculate the homology group as the quotient group , where is the kernel in -dimensional chain space , and is the image of -dimensional chain mapping.

[0012] Preferably, the persistent homology analysis unit is configured to perform the following steps: (1) At different scales, gradually construct a sequence of simplicial complexes of the vector data , and record the evolution process of topological features at each scale with the change of scale; (2) For the simplicial complex at each scale calculate the homology group , and track the generation and disappearance moments of the same topological feature; (3) Generate a persistent barcode , whose barcode is represented as a set of intervals , where is the generation moment of the topological feature , is its disappearance moment, and the length of the barcode reflects the persistence of this topological feature; ​​​(4) Identify and retain those topological features with longer durations based on the length and position of the persistent barcode to reflect the stable geometric and topological structures in the data.

[0013] Preferably, the data storage module includes: A data compression unit for compressing the processed vector data based on a simplification algorithm for topological features; A data encoding unit for encoding the compressed vector data by generating a data representation in the form of a sparse matrix; An index construction unit for constructing a query index for the data based on topological features and implementing fast topological query and retrieval operations using a multi-dimensional space index structure; A data synchronization unit for synchronously storing the compressed and encoded data and topological feature information into a spatial database.

[0014] The present invention also provides a GIS spatial data processing method, including the following steps: S1. Obtain original vector map data from multiple data sources and perform format conversion; S2. Detect and remove abnormal data points using geometric analysis; S3. Perform multi-level processing on the data based on Laplacian multi-scale decomposition; S4. Optimize the geometric structure of the vector data based on curvature analysis; S5. Extract topological features and perform persistent homology analysis on the data using algebraic topology; S6. Compress and store the optimized data and construct a query index at the same time.

[0015] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method as described above is implemented.

[0016] The present invention also provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method as described above is implemented.

[0017] Advantageous effects: 1. Through automated geometric analysis and the elimination of abnormal data points, the present invention reduces the dependence on manual intervention and improves the speed and quality of data processing. The collaborative operation of the data acquisition module and the data cleaning module ensures high-quality data input from the source, providing more accurate basic data for subsequent steps.

[0018] 2. The present invention utilizes Laplace multi-scale decomposition, which can process data details at different levels according to different analysis requirements, thereby providing a more flexible data analysis function. The cooperation between the multi-scale processing module and the geometric optimization module enables the data to maintain accuracy while optimizing its representation effects at different scales.

[0019] 3. By applying the Douglas-Peucker algorithm and Bessel curve technology, the present invention significantly improves the visual rendering of vector data, making it smoother and having a good geometric structure. The close cooperation between the geometric optimization module and the topological analysis module ensures that the visual optimization does not damage the topological integrity of the data.

[0020] 4. Using algebraic topology methods, the present invention can accurately extract the topological features of data and continuously track the changes of these features through persistent homology analysis, which helps to identify and retain the key structural information in the data. The function of the topological analysis module strengthens the maintenance of the data structure integrity and also promotes the efficiency of the data storage module in compression and index construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of the system architecture of the present invention; Figure 2 is a schematic diagram of the method flow of the present invention; Figure 3 is a schematic diagram of the structure of a computer device of the present invention.

[0022] Among them, 40, computer device; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] Please refer to the attached Figure 1 , the embodiments of the present invention provide a GIS spatial data processing system, including: Data acquisition module The GIS spatial data processing system of the present invention first ensures that the original vector map data obtained from multiple sources has consistency and high quality, which is the basis for the subsequent smooth progress of data cleaning, multi-scale processing, geometric optimization, and topological analysis. The core function of the data acquisition module not only includes data collection, but also needs to perform format conversion on data from different sources for unified processing and ensure the reliability of the data.

[0025] In this embodiment, the specific implementation steps of the data acquisition module are as follows: 1. Data source integration: The data acquisition module extracts the original vector map data from multiple data sources (such as geographic information databases, remote sensing image databases, online map services, etc.). Each data source contains data in different formats (such as Shapefile, GeoJSON, KML, etc.), and these data usually have different coordinate systems and resolutions. To ensure the unified processing of the system, the data acquisition module first performs the data source integration operation, collects data from different sources through a standardized interface, and records the metadata of the data (such as coordinate system, resolution, data timestamp, etc.), so that the subsequent processing module can perform effective analysis and processing.

[0026] 2. Data format conversion: After the data integration is completed, the data acquisition module then performs unified format conversion on the original vector map data in different formats. Specifically, it converts multiple data formats into a standard format predefined by the system, such as the GeoJSON format. This format has good compatibility and scalability and is suitable for subsequent geometric and topological analysis operations.

[0027] 3. Coordinate system conversion: Since the data from different data sources may use different coordinate systems, the data acquisition module also needs to perform coordinate system conversion. The coordinate system conversion operation ensures that all data is processed under the same reference system.

[0028] 4. Data integrity verification: The data acquisition module also includes a data integrity verification step to ensure that no information is lost during the data conversion process. This step verifies the integrity of the data by comparing the metadata before and after the data conversion (such as the number of data points, boundary range, etc.).

[0029] 5. Preprocessing and storage of data: The data after format and coordinate system conversion will be temporarily stored in the system's memory or cache for use by the subsequent processing module. This preprocessing storage module is also responsible for generating a unique identifier for each data set for subsequent data tracking and management.

[0030] Through the above functions of the data acquisition module, the GIS spatial data processing system of the present invention ensures the format consistency and quality reliability of the original data obtained from multiple data sources. This provides a solid foundation for subsequent data cleaning, multi-scale processing, geometric optimization, and topological analysis, avoiding processing errors caused by inconsistent data formats and coordinate systems, thereby improving the processing efficiency and accuracy of the entire system.

[0031] Data cleaning module After the GIS spatial data processing system of the present invention acquires and converts data in the data acquisition module, ensuring the quality of the input data is the key to the smooth progress of subsequent processing. The data cleaning module is responsible for preliminary geometric analysis and outlier detection to ensure that errors, redundancies, or abnormal information in the data are effectively identified and corrected, thereby providing clean and consistent input data for the multi-scale processing, geometric optimization, and topological analysis modules.

[0032] In this embodiment, the data cleaning module includes: 1. Local curvature calculation unit: The core function of the local curvature calculation unit is to calculate the local curvature of each vector data point. The local curvature measures the geometric change around the point, which is determined by the distance and angle differences between adjacent points. Specifically, for a given vector point , select two adjacent points and before and after it, and calculate the curvature of the broken line formed by these points.

[0033] Local curvature can be calculated through the following formula: where represents the area of the triangle formed by points , and , and etc. are the distances between adjacent points. This formula calculates the curvature around point , reflecting the local geometric characteristics of this point.

[0034] 2. Outlier identification unit: After calculating the local curvature, the outlier identification unit detects outlier data points by comparing the local curvature value with a preset outlier threshold. Outlier points are usually those with overly large or small curvature values, which may be caused by acquisition errors or data noise and do not conform to the geometric law of the overall data.

[0035] Set the threshold interval , and the outlier identification unit detects outlier points through the following rules: where is the local curvature of point , and and are the lower and upper threshold values of the curvature respectively. The detected outlier points will be marked for subsequent correction processing.

[0036] 3. Data correction unit: For the points marked as abnormal, the data correction unit is responsible for correcting or deleting these points to ensure the geometric continuity and spatial consistency of the data. This unit adopts a variety of correction strategies, including operations such as interpolation, smoothing, and deletion.

[0037] The correction methods include: Interpolation correction: When the number of detected abnormal points is small and their distribution is relatively isolated, the interpolation method is used to correct the abnormal points. Let the adjacent normal points and be control points, and the corrected point is calculated through linear interpolation: Smoothing correction: If the abnormal points are concentrated in a certain curve segment, the curve smoothing technique is used to smooth the entire curve segment to eliminate local abnormal mutations.

[0038] Deletion processing: When the abnormal points are too discrete and cannot be corrected, they are directly deleted from the dataset. The deletion operation needs to keep the topological structure of the data unchanged. Therefore, after deleting the points, the local topological relationship needs to be reconstructed.

[0039] 4. Data verification and output: After completing the data correction, the data cleaning module verifies the corrected data to ensure that it meets the predetermined geometric continuity and spatial consistency requirements. After the verification is completed, the data cleaning module outputs the processed data to the next multi-scale processing module.

[0040] Through the above functions of the data cleaning module, the GIS spatial data processing system of the present invention can effectively detect and correct the abnormal points in the original vector data, reduce the noise and errors in the data, and ensure the data quality in the subsequent processing steps.

[0041] Multi-scale processing module The GIS spatial data processing system of the present invention aims to finely process vector data through multi-scale processing technology to meet the analysis requirements at different levels. By selecting different spatial resolutions, this module can analyze data layer by layer from the large-scale macroscopic structure to the microscopic detailed features.

[0042] In this embodiment, the multi-scale processing module includes: 1. Scale selection unit: The scale selection unit is the starting point of the multi-scale processing module. It is responsible for determining the processing scale based on the target application requirements and analysis accuracy. The requirements for map details are different in different application scenarios. For example, higher accuracy may be required in urban planning, while large-scale features may be emphasized in regional analysis. This unit automatically selects the optimal spatial resolution by calculating the detail requirements of each scenario.

[0043] Select the processing scale according to the application requirements and accuracy requirements The formula is: Among them, is the scale size of the target area, is the required detail resolution. The scale selection unit calculates the appropriate processing scale according to this formula , so as to ensure that enough details are retained and excessive calculation is avoided in a specific application scenario.

[0044] 2. Scale conversion unit: After selecting the processing scale, the scale conversion unit is responsible for dynamically adjusting the granularity of the data according to the selected scale. This process needs to find a balance between the geometric structure and topological relationship of the data, simplify the complexity of the data while ensuring the retention of geometric features. For this purpose, the Voronoi diagram method is used to perform scale conversion on the data. This method divides the space into multiple regions, and the distance relationship between the data points in each region and their neighboring points is retained.

[0045] The Voronoi diagram method divides the planar region into a set of polygons, and the Voronoi polygon corresponding to any point is defined as: Among them, represents the Euclidean distance from point to point . Through the Voronoi diagram division, it is ensured that the geometric features and spatial distribution relationships of the data are retained during the scale conversion process.

[0046] 3. Optimization aggregation unit: After completing the scale conversion, the optimization aggregation unit further aggregates adjacent vector objects through geometric and topological optimization techniques. The main function of this unit is to reduce the complexity of the data while maintaining the integrity of the space and topological consistency. This process merges adjacent vector objects through an algorithm based on geometric similarity and topological structure, thereby reducing the number of data points and edges.

[0047] The optimization aggregation unit aggregates the data points and through the geometric similarity metric and the topology-preserving function . The aggregation condition is: Among them, is the geometric similarity threshold indicates that the topological structure remains unchanged. Through this method, it is ensured that the aggregated data not only simplifies the geometric structure but also retains the topological relationship.

[0048] 4. Data output: After the multi-scale processing is completed, the module outputs the processed data to the geometric optimization module or the topological analysis module. The output data reduces unnecessary details while retaining key geometric and topological information, making it suitable for further processing and analysis.

[0049] Through the above functions of the multi-scale processing module, the GIS spatial data processing system of the present invention can flexibly adjust the resolution of the data according to specific application requirements and optimize the data structure. By using the Thiessen polygon method and geometric and topological aggregation techniques, it can not only reduce the complexity of the data but also ensure that the core features of the data are retained at different scales. Ultimately, it provides a more concise and efficient data input for the subsequent processing module, thereby improving the processing efficiency and accuracy of the entire system.

[0050] Geometric optimization module The GIS spatial data processing system of the present invention aims to improve the accuracy and presentation effect of vector data through geometric optimization techniques. The geometric optimization module mainly targets the vector data output from the multi-scale processing module and further optimizes its geometric structure to ensure that while reducing the data complexity, the visual presentation quality of the data and the integrity of the spatial relationship are maintained. The geometric optimization module simplifies, smooths, and topologically reconstructs the data through a series of geometric algorithms, thereby providing high-quality data input for the topological analysis module.

[0051] In this embodiment, the geometric optimization module includes: 1. Geometric simplification element: The geometric simplification element is responsible for geometrically simplifying the vector data through the Douglas-Peucker Algorithm. The core task of this unit is to remove unnecessary points in the vector data, reduce the data complexity, and at the same time retain the geometric features of the data as much as possible. The Douglas-Peucker algorithm finds a simplified representation of a polyline through recursive partitioning and removes those points that have less impact on the overall geometric shape while meeting the accuracy requirements.

[0052] Given a polyline composed of points , the core idea of the Douglas-Peucker algorithm is to simplify the polyline according to a specified tolerance threshold . Set the tolerance . When the perpendicular distance from point to the polyline is less than , remove this point: Through this algorithm, the system can effectively reduce the number of data points and the complexity of data processing.

[0053] 2. Shape correction unit: After completing the geometric simplification, the shape correction unit applies Bezier curve technology to smooth the vector lines. Bezier curves can provide a smooth representation for complex vector lines while maintaining key structural features. By performing Bezier fitting on each line segment, the irregular mutations caused by geometric simplification are eliminated, optimizing the visual rendering effect of the vector map.

[0054] For a Bezier curve defined by control points its parametric equations are: where is the binomial coefficient, and

[0055] are the control points. By adjusting the positions and weights of the control points, the shape correction unit can smooth the lines, reduce the roughness of the data, and enhance the smoothness of the geometric representation.

[0056] 3. Topological relationship reconstruction unit: The geometric simplification and shape correction operations may affect the topological relationships between vector objects. Therefore, the topological relationship reconstruction unit is responsible for redefining the spatial relationships between the simplified vector objects. Based on geometric topology theory, this unit ensures that the original topological structure is retained or reasonably reconstructed in the data after simplification and correction by calculating relationships such as adjacency, inclusion, and overlap between objects. This unit redefines the topological relationships between objects by calculating the adjacency matrix between vector objects and combining geometric similarity and spatial distance:

[0057] In addition, this unit also corrects the topological structures broken due to geometric simplification according to the spatial adjacency relationship, ensuring the consistency and connectivity of the spatial data.

[0058] The geometric optimization module significantly improves the performance and processing efficiency of vector data through geometric simplification, shape correction, and topological reconstruction techniques. The Douglas-Peucker algorithm effectively reduces the number of data points and decreases data complexity; the Bezier curve fitting technique improves data smoothness and visualization effects; while topological relationship reconstruction ensures data spatial consistency. Overall, the geometric optimization module provides high-quality geometric input for subsequent topological analysis in the system, enhancing the overall processing effect and accuracy of the system.

[0059] Topological analysis module The GIS spatial data processing system of the present invention aims to reveal potential geometric and topological features in the data through in-depth topological analysis techniques, especially those that are not easily captured by traditional geometric methods. The topological analysis module is based on the geometric optimization module and performs multi-level topological analysis on data at different scales through constructing simplicial complexes, calculating homology groups, and persistent homology analysis, thereby achieving a comprehensive understanding and simplified processing of the data structure. Finally, through topological simplification techniques, the system can remove transient and unimportant topological features and retain key structural information.

[0060] In this embodiment, the topological analysis module includes: 1. Topological structure construction unit: The main function of the topological structure construction unit is to generate a simplicial complex based on vector data to capture the complex topological structure in the data. A simplicial complex is a mathematical object used to represent multi-dimensional topological structures, including geometric entities such as vertices, edges, and faces. By converting vector data into a simplicial complex, this unit lays the foundation for subsequent topological analysis.

[0061] The vector data contains a vertex set , an edge set and a face set , and the simplicial complex can be represented as: Each -dimensional simplex (vertex, edge, face) is a topological representation of the data geometric structure. Based on the simplicial complex, the topological structure construction unit can capture complex relationships in the data, such as connectivity, cycles, and cavities.

[0062] 2. Homology group calculation unit: After constructing the simplicial complex, the homology group calculation unit is responsible for calculating homology groups of different orders based on this structure. By calculating homology groups, the system can extract topological features of the data, such as connected components, loops, and cavities. Homology groups convert geometric objects into topological invariants through algebraic methods, effectively revealing the topological structure in the data.

[0063] For the established simplicial complex , define the boundary operator , whose role is to map -dimensional chains to -dimensional chains: The homology group is the quotient group of the kernel and the image, and its definition is: where is the kernel in -dimensional chain space , is the image of the -dimensional chain map. The calculation of the homology group reveals the topological structure in the data. For example represents connected components, represents loop structures, represents cavities.

[0064] 3. Persistent homology analysis unit: Based on the calculation of the homology group, the persistent homology analysis unit further analyzes the topological changes of the data at different scales. This unit constructs a sequence of simplicial complexes of the data step by step, calculates the homology groups at each scale, and tracks the generation and disappearance of topological features. By generating persistent barcodes, the persistent homology analysis unit can identify the topological features that are stable at different scales and provide an in-depth understanding of the evolution of the data structure.

[0065] For each scale of the simplicial complex calculate the homology group , and record the generation and disappearance times of topological feature ii: where is the generation time of feature , is its disappearance time. The length of the persistent barcode represents the persistence of the topological feature. The longer the barcode, the more stable the topological feature. Persistent homology analysis can identify important and stable geometric and topological structures in the data.

[0066] 4. Topological simplification unit: Based on the results of persistent homology analysis, the topological simplification unit is responsible for simplifying the topological structure. This unit removes those topological features with short persistence times in the persistent barcode and retains the key topological structures with long persistence times. This process ensures that the simplified topological structure is not only concise but also retains the main topological features of the data, avoiding unnecessary complexity.

[0067] The topological simplification process includes: By analyzing the persistence barcode, the topological simplification unit deletes those topological features with shorter lengths (i.e., lower persistence) and retains those with longer lengths (i.e., higher persistence). This process can significantly simplify the topological structure while preserving the key topological information of the data.

[0068] Through the above functions of the topological analysis module, the GIS spatial data processing system of the present invention can deeply mine the complex topological structure in vector data and effectively identify stable topological features at different scales. Through the construction of topological structures, the calculation of homology groups, persistent homology analysis, and topological simplification, the system can provide a comprehensive topological analysis of the data.

[0069] Data storage module After the GIS spatial data processing system of the present invention completes the geometric optimization and topological analysis of the data, it is necessary to efficiently store the processed data for fast retrieval and query in subsequent applications. The data storage module is responsible for compressing, encoding the optimized vector data, and constructing a multi-dimensional index structure suitable for spatial data, and finally synchronously storing the data in the spatial database. This module reduces the occupancy of storage space through data compression and encoding technologies, and at the same time improves the query efficiency of the data through index construction, ensuring that the topological features of the data are efficiently retained and quickly accessed.

[0070] In this embodiment, the data storage module includes: 1. Data compression unit: The data compression unit is responsible for compressing the processed vector data based on the simplification algorithm of topological features. This unit uses the results of the previous topological simplification unit to remove topological features with lower persistence, thereby simplifying the data structure and reducing the data volume. During the compression process, the compression algorithm will preferentially retain those topological structures with high persistence and great influence on the overall data, ensuring that important geometric and topological information is not lost during data compression.

[0071] The data compression unit is based on the importance index of topological features , and selectively compresses the data. The compression strategy is implemented through the following rules: Among them, is a preset compression threshold, is the importance index of feature , and this index can be defined based on the persistence of the feature or other geometric attributes. Through this algorithm, the system can effectively reduce the volume of data storage while retaining important topological features in the data.

[0072] 2. Data Encoding Unit: After data compression is completed, the data encoding unit encodes the compressed vector data. This unit reduces the storage redundancy of the data and improves the storage and transmission efficiency by converting the vector data into a sparse matrix form. Sparse matrix encoding is a representation form suitable for spatial data, which can effectively compress unnecessary blank areas or duplicate data in large-scale spatial data.

[0073] The data encoding unit converts the vector data into a sparse matrix , where: Through this sparse matrix representation, the system can store only the non-zero elements in the matrix, thus greatly reducing the occupation of storage space. Sparse matrix encoding is particularly suitable for the sparsity characteristics widely existing in geographic information data.

[0074] 3. Index Construction Unit: To achieve fast topological query and retrieval operations, the index construction unit constructs a query index for the data based on topological features. This unit adopts a multi-dimensional spatial index structure, such as R-tree or KD-tree, to ensure efficient access and query of spatial data. The index construction unit will construct a multi-dimensional index suitable for spatial queries according to the geometric and topological attributes of the data to improve the retrieval performance.

[0075] For each topological feature in the vector data , the index construction unit establishes index nodes in the multi-dimensional space: where, represents the minimum bounding rectangle of feature , and represents the topological attribute of this feature. By constructing such a multi-dimensional index, the system can quickly execute range-based spatial queries and retrieval operations based on topological features.

[0076] 4. Data Synchronization Unit: After data compression, encoding, and index construction are completed, the data synchronization unit is responsible for synchronously storing the processed data and topological feature information into the spatial database. This unit ensures that during the storage process, the consistency and integrity of all data are guaranteed, and the topological features are synchronously stored with the compressed vector data.

[0077] The data synchronization unit first stores the compressed and encoded vector data into the spatial database and ensures that each data record contains relevant topological feature information. Then, the system synchronously stores the generated index into the index structure of the database to support efficient query operations.

[0078] ​Through the data storage module, the GIS spatial data processing system of the present invention can significantly reduce the storage space occupancy while maintaining data integrity, and improve the data retrieval efficiency. Through the compression algorithm of the data compression unit, the system removes unnecessary topological features, effectively reducing the scale of the stored data; the data encoding unit converts the data into a sparse matrix form, further optimizing the storage efficiency; the index construction unit realizes fast query and retrieval of topological features through a multi-dimensional index structure. Finally, the data synchronization unit stores the processed data in the spatial database to ensure efficient access and use of the data in subsequent applications.

[0079] The present invention provides a GIS spatial data processing system. Through the collaborative work of modules such as data acquisition, cleaning, multi-scale processing, geometric optimization, topological analysis, and data storage, it solves the problems of low efficiency, low quality, and dependence on manual work in traditional vector map data processing. The system optimizes the geometric structure and topological relationship of the data through an automated data processing process, extracts and simplifies topological features using homology group calculation and persistent homology analysis, and at the same time realizes efficient data storage and query through data compression, encoding, and multi-dimensional index construction. The system as a whole improves the efficiency, accuracy, and stability of GIS data processing and has wide application value.

[0080] Please refer to the attached Figure 2 , the present invention also provides a GIS spatial data processing method, including the following steps: S1. Obtain the original vector map data from multiple data sources and perform format conversion: Collect the initial vector map data through multiple data sources and standardize the data in different formats into a unified format that can be processed by the system, laying a foundation for subsequent processing steps.

[0081] S2. Use geometric analysis to detect and remove abnormal data points: Through geometric analysis technology, calculate the local curvature value of each vector point, and automatically detect and remove abnormal points in the data according to the set threshold, ensuring the data quality and providing reliable input for subsequent geometric optimization and topological analysis.

[0082] S3. Perform multi-level processing on the data based on Laplacian multi-scale decomposition: According to the requirements of the target application, use Laplacian multi-scale decomposition technology to perform multi-level processing on the data, decompose the vector data into different scales, ensure that geometric and topological features at different levels can be properly processed, and support fine-grained or coarse-grained analysis.

[0083] S4. Optimize the geometric structure of the vector data based on curvature analysis: Through curvature analysis, the Douglas-Peucker algorithm is applied for geometric data reduction to remove unnecessary points. Meanwhile, the Bezier curve technique is used to smooth the data, ensuring optimized visual effects while maintaining the structural features of the data.

[0084] S5. Use algebraic topology to extract topological features and perform persistent homology analysis on the data: Based on algebraic topology, a simplicial complex of vector data is constructed, and topological features are extracted through homology group calculations. At the same time, persistent homology analysis technology is used to track the changes of topological features at different scales, generate persistent barcodes to identify stable topological features, and simplify the topological structure.

[0085] S6. Compress and store the optimized data, and construct a query index at the same time: The optimized data is compressed through a topological feature compression algorithm to generate a coded representation in the form of a sparse matrix, and a multi-dimensional space index is constructed based on topological features. Finally, the data is synchronously stored in a spatial database to achieve efficient data query and retrieval.

[0086] The method of the present invention effectively improves the efficiency and quality of GIS spatial data processing. Through the collaborative work of each step, it solves the problems of traditional vector data processing relying on manual labor, time-consuming, and high labor costs, and is applicable to complex GIS data processing scenarios.

[0087] Please refer to the appendix Figure 3 , the present invention also provides a computer device 40, including: a processor 41 and a memory 42. The memory 42 stores a computer program executable by the processor. When the computer program is executed by the processor, the above method is executed.

[0088] The present invention also provides a storage medium 43. A computer program is stored on the storage medium 43. When the computer program is run by the processor 41, the above method is executed.

[0089] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0090] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A GIS spatial data processing system, characterized in that: include: A data acquisition module, configured to acquire original vector map data from multiple data sources and perform format conversion; The data cleaning module is connected to the data acquisition module and is used to detect and remove abnormal data points using geometric analysis to ensure data quality and provide accurate input for the geometric optimization module; The multi-scale processing module receives the output of the data cleaning module and processes the data at multiple levels through Laplace multi-scale decomposition, decomposing the data to facilitate finer-grained geometric and topological analysis; The geometry optimization module receives the output of the multi-scale processing module and optimizes the geometry structure of the vector data based on curvature analysis to improve the visual representation and analysis accuracy of the data; The topological analysis module is connected to the geometric optimization module and uses algebraic topology to extract topological features and perform continuous homology analysis on the data to analyze and maintain the structural integrity of the data; The data storage module is used to compress and store the optimized data and build a query index to improve the retrieval efficiency and storage efficiency of the data.

2. A GIS spatial data processing system according to claim 1, characterized in that: The data cleaning module includes: A local curvature calculation unit, used to calculate the local curvature of each vector data point, where the local curvature is determined by the distance and angle difference between adjacent vector points; An outlier identification unit, used for detecting and identifying outlier data points according to the local curvature value and a set outlier threshold; The data correction unit is used to automatically correct or delete the detected abnormal data points to maintain the geometric continuity and spatial consistency of the data.

3. A GIS spatial data processing system according to claim 1, characterized in that: The multi-scale processing module comprises: The scale selection unit is used to determine the processing scale based on the target application requirements and analysis accuracy, and to adapt to different map detail requirements by selecting different spatial resolutions; The scale conversion unit is used to dynamically adjust the granularity of the vector data according to the selected scale, and uses the Thiessen polygon method to ensure the preservation of geometric features during the conversion process; The optimization aggregation unit is used to aggregate neighboring vector objects at a selected scale through geometric and topological optimization techniques to reduce the complexity of the data while maintaining the spatial integrity and topological consistency of the data.

4. A GIS spatial data processing system according to claim 1, characterized in that: The geometry optimization module includes: Geometric refinement elements are used to remove unnecessary points in vector data using the Douglas-Peucker algorithm to reduce data complexity; Shape correction unit, which is used to smooth vector lines using Bezier curve technology to optimize the visual presentation of vector maps while maintaining key structural features; The topological relationship reconstruction unit is used to redefine the spatial relationship between vector objects after geometric simplification to ensure the topological consistency of the data.

5. A GIS spatial data processing system according to claim 1, characterized in that: The topology analysis module includes: A topology building unit, which is used to generate a simplicial complex based on vector data, representing the vertices, edges and faces in the data, so as to capture the complex topology structure in the data; Homology group calculation unit, used to calculate homology groups of different orders based on simplicial complexes constructed based on topological structures , extracting topological features in the data, including connected components, loops, and cavities; A persistent coherence analysis unit, which is used to analyze topological changes in data at multiple scales and generate persistent barcodes to identify and track the generation and disappearance of topological features at different scales in the data; The topology simplification unit is used to simplify the topology structure based on the results of continuous homology analysis, remove short-lived topological features, and retain key and persistent topological structures.

6. A GIS spatial data processing system according to claim 5, characterized in that: The homology group calculation unit is configured to perform the following steps: Step 1. Extract geometric topological entities from vector data and create simplicial complexes , which includes the vertex set , edge set Noodle set ; Step 2. Apply boundary operator Compute the boundary of a simplicial complex where , used for mapping - Link to -WeChain; Step 3. Calculate the homology group As a business group ,in yes -WeChain Space The core of yes -Image of the dimension chain mapping.

7. A GIS spatial data processing system according to claim 5, characterized in that: The continuous coherence analysis unit is configured to perform the following steps: Step 1. Gradually construct the simplicial complex of vector data at different scales Sequence, which records the evolution of topological features at each scale as the scale changes; Step 2. For each scale simplicial complex Computing homology groups , and track the creation and extinction moments of the same topological feature; Step 3. Generate a persistent barcode , whose barcode is represented as a set of intervals ,in It is a topological feature The generation time, is the moment of its demise, the length of the barcode This reflects the persistence of this topological feature; Step 4. Based on the length and position of persistent barcodes, identify and retain those topological features with longer duration to reflect stable geometric and topological structures in the data.

8. A GIS spatial data processing system according to claim 1, characterized in that: The data storage module comprises: A data compression unit, used for compressing the processed vector data based on a simplified algorithm of topological features; A data encoding unit, used for encoding the compressed vector data by generating a data representation in the form of a sparse matrix; An index building unit is used to build a query index for data based on topological features, and uses a multi-dimensional spatial index structure to achieve fast topological query and retrieval operations; The data synchronization unit is used to store the compressed and encoded data and topological feature information synchronously into the spatial database.

9. A GIS spatial data processing method, characterized in that: The following steps are involved: S1. Obtaining original vector map data from multiple data sources and converting the data into different formats; S2, using geometric analysis to detect and remove abnormal data points; S3, multi-level processing of data based on Laplace multi-scale decomposition; S4, optimizing the geometric structure of vector data based on curvature analysis; S5. Use algebraic topology to extract topological features and perform continuous homology analysis on data; S6. Compress and store the optimized data, and build a query index at the same time.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 9 is implemented.

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