A GIS spatial data processing system and method

Abstract

The present invention discloses a GIS spatial data processing system and method, which relates to the field of geographic information systems and includes: a data acquisition module that acquires raw vector map data from multiple data sources and performs format conversion; a data cleaning module that uses geometric analysis to detect and remove abnormal data points; a multi-scale processing module that performs multi-level processing of data through Laplace multi-scale decomposition; a geometric optimization module that optimizes the geometric structure of vector data based on curvature analysis; a topology analysis module that uses algebraic topology to extract topological features and perform continuous homology analysis on data; and a data storage module that compresses and stores the optimized data and constructs a query index. The present invention is used to solve the quality and efficiency problems of manual processing of complex geographic data, greatly improving the efficiency and quality of GIS spatial data processing, and is suitable for complex GIS data processing scenarios.

Description

Technical Field

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

[0002] In the field of existing geographic information systems (GIS), processing and analyzing map data, especially vector map data, is a fundamental and critical task. These systems often need to handle large amounts of spatial data, including the collection, storage, management, calculation, and presentation of geographic information. Although GIS provides powerful map view construction and geographic database access capabilities, integrating map visualization and geographic analysis functions with database operations, several challenges remain when processing complex geographic data, particularly in areas such as data quality control, multi-scale representation, and maintaining topological consistency.

[0003] Traditional GIS data processing methods often rely on manual intervention to address data errors and inconsistencies. This reliance not only consumes significant time and labor, but also limits processing efficiency and data quality. Furthermore, as geographic data grows in size and complexity, traditional methods are increasingly lacking in their ability to handle multi-scale data representations and optimize data storage structures. These limitations are particularly acute in modern, large-scale geographic data applications, such as urban planning, environmental monitoring, and real-time location-based services.

[0004] Therefore, a system that can automatically process complex vector map data is needed. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a GIS spatial data processing system and method to improve the automation, efficiency and quality of vector map data processing, while ensuring the multi-scale representation and topological integrity of the data to meet the complex needs of modern GIS applications.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] A GIS spatial data processing system, comprising:

[0008] A data acquisition module is configured to acquire original vector map data from multiple data sources and perform format conversion;

[0009] 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;

[0010] The multi-scale processing module receives the output of the data cleaning module and performs multi-level processing on the data through Laplace multi-scale decomposition, decomposing the data to facilitate finer-grained geometric and topological analysis;

[0011] The geometry 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 representation and analysis accuracy of the data;

[0012] The topology analysis module is connected to the geometry 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;

[0013] The data storage module is used to compress and store the optimized data and build query indexes to improve data retrieval efficiency and storage efficiency.

[0014] Preferably, the data cleaning module includes:

[0015] A local curvature calculation unit is used to calculate the local curvature of each vector data point. The local curvature is determined by the distance and angle difference between adjacent vector points.

[0016] an outlier identification unit, configured to detect and identify outlier data points based on a local curvature value and a set outlier threshold;

[0017] The data correction unit is used to automatically correct or delete detected abnormal data points to maintain the geometric continuity and spatial consistency of the data.

[0018] Preferably, the multi-scale processing module includes:

[0019] Scale selection unit, 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;

[0020] 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;

[0021] The optimized aggregation unit is used to aggregate adjacent 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.

[0022] Preferably, the geometry optimization module includes:

[0023] Geometric refinement elements are used to remove unnecessary points in vector data using the Douglas-Peucker algorithm to reduce data complexity;

[0024] A shape correction unit that applies Bezier curve technology to smooth vector lines, optimizing the visual presentation of vector maps while preserving key structural features;

[0025] 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.

[0026] Preferably, the topology analysis module includes:

[0027] A topology building unit is used to generate a simplicial complex based on vector data, representing the vertices, edges, and faces in the data to capture the complex topology structure in the data;

[0028] Homology group calculation unit, used to calculate homology groups of different orders based on simplicial complexes constructed based on topological structures ,extract topological features from the data, including connected components, rings, and cavities;

[0029] A persistent coherence analysis unit 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;

[0030] 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, persistent topological structures.

[0031] Preferably, the coherence group calculation unit is configured to perform the following steps:

[0032] (1) Extract geometric topological entities from vector data and establish simple complexes , which includes the vertex set , edge set Noodle set ;

[0033] (2) Applying boundary operators Compute the boundary of a simplicial complex where , used for mapping -WeChat to -Dimension Chain;

[0034] (3) Calculation of homology groups As a business group ,in yes -Dimension Chain Space The core, yes -Image of the dimension chain mapping.

[0035] Preferably, the continuous coherence analysis unit is configured to perform the following steps:

[0036] (1) Gradually constructing simplicial complexes of vector data at different scales Sequence, which records the evolution of topological features at each scale as the scale changes;

[0037] (2) For each scale simplicial complex Computational homology groups , and track the generation and extinction moments of the same topological feature;

[0038] (3) Generate a persistent barcode , whose barcode is represented as a set of intervals ,in It is a topological feature The moment of generation, is the moment of its demise, the length of the barcode This reflects the persistence of this topological feature;

[0039] (4) Based on the length and position of persistent barcodes, we identify and retain those topological features with longer duration to reflect the stable geometric and topological structures in the data.

[0040] Preferably, the data storage module includes:

[0041] A data compression unit, used for compressing the processed vector data using a simplified algorithm based on topological features;

[0042] A data encoding unit is used to encode the compressed vector data by generating a data representation in the form of a sparse matrix;

[0043] Index building unit, used to build data query index based on topological features, using multi-dimensional spatial index structure to achieve fast topological query and retrieval operations;

[0044] The data synchronization unit is used to store the compressed and encoded data and topological feature information synchronously into the spatial database.

[0045] The present invention also provides a GIS spatial data processing method, comprising the following steps:

[0046] S1. Obtain original vector map data from multiple data sources and convert the data into different formats;

[0047] S2, using geometric analysis to detect and remove abnormal data points;

[0048] S3, multi-level processing of data based on Laplace multi-scale decomposition;

[0049] S4. Optimize the geometric structure of vector data based on curvature analysis;

[0050] S5. Use algebraic topology to extract topological features and perform continuous homology analysis on data;

[0051] S6. Compress and store the optimized data, and build a query index at the same time.

[0052] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method when executing the computer program.

[0053] 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 described above is implemented.

[0054] Beneficial effects:

[0055] 1. This invention reduces reliance on manual intervention and improves the speed and quality of data processing through automated geometric analysis and removal of abnormal data points. The collaborative operation of the data acquisition module and the data cleaning module ensures high-quality data input from the source, providing more accurate foundational data for subsequent steps.

[0056] 2. This invention utilizes Laplace multi-scale decomposition to process different levels of data detail according to different analysis requirements, thereby providing more flexible data analysis capabilities. The multi-scale processing module works in conjunction with the geometric optimization module to optimize the representation of data at different scales while maintaining accuracy.

[0057] 3. By applying the Douglas-Peucker algorithm and Bezier curve technology, this invention significantly improves the visual presentation of vector data, making it smoother and more geometrically structured. The close collaboration between the geometry optimization module and the topology analysis module ensures that the visual optimization does not compromise the topological integrity of the data.

[0058] 4. This invention utilizes algebraic topology methods to accurately extract topological features from data and continuously tracks changes in these features through continuous homology analysis, which helps identify and preserve key structural information in the data. The topology analysis module strengthens the maintenance of data structural integrity and also improves the efficiency of the data storage module during compression and index construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of the system architecture of the present invention;

[0060] Figure 2 Schematic diagram of the method flow of the present invention;

[0061] Figure 3 Schematic diagram of the computer device structure of the present invention.

[0062] Among them, 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] Please see the attached Figure 1 The embodiment of the present invention provides a GIS spatial data processing system, including:

[0065] Data acquisition module

[0066] The GIS spatial data processing system of this invention first ensures the consistency and high quality of raw vector map data acquired from multiple sources. This is the foundation for the smooth execution of subsequent steps such as data cleaning, multi-scale processing, geometric optimization, and topological analysis. The core function of the data acquisition module includes not only data collection but also format conversion of data from different sources to ensure unified processing and data reliability.

[0067] In this embodiment, the specific implementation steps of the data acquisition module are as follows:

[0068] 1. Data Source Integration: The data acquisition module extracts raw vector map data from multiple data sources (such as geographic information databases, remote sensing image databases, and online map services). Each data source contains data in different formats (such as Shapefile, GeoJSON, and KML), often with different coordinate systems and resolutions. To ensure unified system processing, the data acquisition module first performs data source integration, collecting data from different sources through standardized interfaces and recording data metadata (such as coordinate system, resolution, and data timestamp) to enable effective analysis and processing by subsequent processing modules.

[0069] 2. Data Format Conversion: After data integration is complete, the data acquisition module converts the raw vector map data in various formats into a unified format. Specifically, it converts various data formats into a predefined standard format, such as GeoJSON. This format offers excellent compatibility and scalability, making it suitable for subsequent geometric and topological analysis operations.

[0070] 3. Coordinate system conversion: Since 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 in the same reference system.

[0071] 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 metadata (such as the number of data points and boundary range) before and after the data conversion.

[0072] 5. Data Preprocessing and Storage: After format and coordinate system conversion, the data is temporarily stored in the system's memory or cache for subsequent processing modules. This preprocessing and storage module is also responsible for generating a unique identifier for each dataset for subsequent data tracking and management.

[0073] Through the aforementioned functions of the data acquisition module, the GIS spatial data processing system of the present invention ensures the format consistency and quality reliability of raw data acquired 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.

[0074] Data cleaning module

[0075] After the data acquisition module acquires and converts data, ensuring the quality of the input data is crucial for smooth subsequent processing in the GIS spatial data processing system of this invention. The data cleaning module is responsible for preliminary geometric analysis and outlier detection, ensuring 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 topology analysis modules.

[0076] In this embodiment, the data cleaning module includes:

[0077] 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 changes 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 before and after it and , calculate the curvature of the polyline formed by these points.

[0078] local curvature The calculation can be done by the following formula:

[0079]

[0080] in, Indicated by point 、 and The area of ​​the triangle formed, The formula calculates the distance between adjacent points. The curvature of the surrounding area reflects the local geometric characteristics of the point.

[0081] 2. Outlier Identification Unit: After calculating the local curvature, the outlier identification unit detects anomalous data points by comparing the local curvature value with a preset anomaly threshold. Outliers are typically those with excessively large or small curvature values. These points may be caused by acquisition errors or data noise and do not conform to the geometric regularity of the overall data.

[0082] Set threshold range , the outlier recognition unit performs outlier detection according to the following rules:

[0083]

[0084] in, for point The local curvature of and are the lower and upper thresholds of the curvature, respectively. Detected outliers will be marked for subsequent correction.

[0085] 3. Data Correction Unit: For points marked as outliers, 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 uses a variety of correction strategies, including interpolation, smoothing, and deletion.

[0086] Correction methods include:

[0087] Interpolation correction: When the number of detected abnormal points is small and the distribution is relatively isolated, the interpolation method is used to correct the abnormal points. and is the control point, and the corrected point is calculated by linear interpolation :

[0088]

[0089] Smoothing correction: If the abnormal points are concentrated in a certain section of the curve, the curve smoothing technology is used to smooth the entire curve to eliminate local abnormal mutations.

[0090] Deletion: When outliers are too discrete and cannot be corrected, they are directly deleted from the dataset. The deletion operation needs to maintain the topological structure of the data, so the local topological relationship needs to be rebuilt after deleting the points.

[0091] 4. Data Verification and Output: After completing data correction, the data cleaning module verifies the corrected data to ensure that it meets the predetermined geometric continuity and spatial consistency requirements. Once verified, the data cleaning module outputs the processed data to the multi-scale processing module in the next step.

[0092] Through the above functions of the data cleaning module, the GIS spatial data processing system of the present invention can effectively detect and correct abnormal points in the original vector data, reduce noise and errors in the data, and ensure data quality in subsequent processing steps.

[0093] Multi-scale processing module

[0094] The GIS spatial data processing system of this invention is designed to refine vector data through multi-scale processing technology to meet the needs of different levels of analysis. By selecting different spatial resolutions, this module can analyze data layer by layer, from large-scale macro structures to microscopic details.

[0095] In this embodiment, the multi-scale processing module includes:

[0096] 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. Different application scenarios require different map details. For example, urban planning may require higher precision, while regional analysis may focus on large-scale features. This unit automatically selects the optimal spatial resolution by calculating the detail requirements for each scenario.

[0097] Select the processing scale according to application requirements and accuracy requirements The formula is:

[0098]

[0099] in, is the scale of the target area, is the required detail resolution. The scale selection unit calculates the appropriate processing scale based on this formula , thereby ensuring that sufficient details are retained while avoiding excessive calculation in specific application scenarios.

[0100] 2. Scaling Unit: After selecting a processing scale, the scaling unit is responsible for dynamically adjusting the data granularity based on the selected scale. This process requires striking a balance between the data's geometric structure and topological relationships, simplifying the data's complexity while preserving its geometric features. To this end, the data is scaled using the Thiessen polygon (Voronoi diagram) method. This method divides the space into multiple regions, preserving the distance relationships between data points and their neighbors within each region.

[0101] The Thiessen polygon method divides the plane area into a set of polygons. The corresponding Thiessen polygon is defined as:

[0102]

[0103] in, Indicates a point Arrive The Euclidean distance is 0.000. The Thiessen polygon partitioning ensures that the geometric characteristics and spatial distribution relationship of the data are preserved during the scale conversion process.

[0104] 3. Optimized Aggregation Unit: After completing the scale conversion, the optimized aggregation unit further aggregates adjacent vector objects using geometric and topological optimization techniques. This unit's primary function is to reduce data complexity while maintaining spatial integrity and topological consistency. This process merges adjacent vector objects using an algorithm based on geometric similarity and topological structure, thereby reducing the number of data points and edges.

[0105] Optimizing aggregation units through geometric similarity metrics and topology preserving functions For data points and The polymerization conditions are:

[0106]

[0107] in, is the geometric similarity threshold This method ensures that the aggregated data not only simplifies the geometric structure but also preserves the topological relationship.

[0108] 4. Data Output: After multi-scale processing is complete, the module outputs the processed data to the Geometry Optimization or Topology Analysis module. The output data reduces unnecessary details while retaining key geometric and topological information, making it suitable for further processing and analysis.

[0109] Through the aforementioned functions of the multi-scale processing module, the GIS spatial data processing system of the present invention can flexibly adjust data resolution and optimize data structure according to specific application requirements. By employing the Thiessen polygon method and geometric and topological aggregation techniques, it can reduce data complexity while ensuring that the core characteristics of the data are preserved at different scales. Ultimately, it provides a simpler and more efficient data input for subsequent processing modules, thereby improving the processing efficiency and accuracy of the entire system.

[0110] Geometry Optimization Module

[0111] The GIS spatial data processing system of this invention aims to improve the accuracy and presentation of vector data through geometric optimization techniques. The geometric optimization module further optimizes the geometric structure of the vector data output from the multi-scale processing module, ensuring that the visual quality and spatial relationship integrity of the data are maintained while reducing data complexity. The geometric optimization module simplifies, smoothes, and topologically reconstructs the data using a series of geometric algorithms, providing high-quality data input for the topology analysis module.

[0112] In this embodiment, the geometry optimization module includes:

[0113] 1. Geometric Reduction Element: This element performs geometric simplification on vector data using the Douglas-Peucker algorithm. Its core task is to remove unnecessary points from vector data, reducing data complexity while preserving the data's geometric characteristics as much as possible. The Douglas-Peucker algorithm uses recursive partitioning to find a simplified representation of a polyline. While meeting accuracy requirements, it removes points that have little impact on the overall geometry.

[0114] Given a Polyline composed of points The core idea of ​​the Douglas-Peucker algorithm is to use a specified tolerance threshold To simplify the polyline. Set the tolerance , be on point To polyline Vertical distance Less than , remove the point:

[0115]

[0116] Through this algorithm, the system can effectively reduce the number of data points and reduce the complexity of data processing.

[0117] 2. Shape Correction Unit: After completing geometric simplification, the shape correction unit applies Bezier curve technology to smooth vector lines. Bezier curves provide a smooth representation of complex vector lines while maintaining key structural features. By performing Bezier fitting on each line segment, irregularities introduced by geometric simplification are eliminated, optimizing the visual presentation of the vector map.

[0118] For a line with control points The parametric equation of the Bezier curve defined is:

[0119]

[0120] in, is the binomial coefficient, 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 improve the fluency of the geometric representation.

[0121] 3. Topology Reconstruction Unit: Geometric simplification and shape correction operations may affect the topological relationships between vector objects. Therefore, the topology reconstruction unit is responsible for redefining the spatial relationships between simplified vector objects. Based on geometric topology theory, this unit calculates adjacency, inclusion, and overlap relationships between objects to ensure that the original topological structure is preserved or properly reconstructed in the simplified and corrected data.

[0122] This unit calculates the adjacency matrix between vector objects , and combines geometric similarity and spatial distance to redefine the topological relationship between objects:

[0123]

[0124] In addition, the unit will correct the topological structure that is broken due to geometric simplification based on the spatial adjacency relationship to ensure the consistency and connectivity of spatial data.

[0125] 4. Data Verification and Output: After completing geometry optimization and topology reconstruction, the geometry optimization module verifies the processed data to ensure that the geometric continuity, smoothness, and topological consistency meet the requirements. Once verified, the data is transferred to the topology analysis module for further topological feature extraction and analysis.

[0126] The geometry optimization module significantly improves the performance and processing efficiency of vector data through geometric simplification, shape correction, and topology reconstruction techniques. The Douglas-Peucker algorithm effectively reduces the number of data points and reduces data complexity; Bezier curve fitting improves data smoothness and visualization; and topological relationship reconstruction ensures spatial consistency of the data. Overall, the geometry optimization module provides high-quality geometric input for the system's subsequent topological analysis, improving the overall processing performance and accuracy of the system.

[0127] Topology Analysis Module

[0128] The GIS spatial data processing system of the present invention is designed to reveal the underlying geometric and topological features in data through in-depth topological analysis techniques, particularly those not easily captured by traditional geometric methods. Building on the foundation of the geometric optimization module, the topological analysis module performs multi-level topological analysis on data at different scales by constructing simplicial complexes, calculating homology groups, and performing continuous homology analysis, thereby achieving a comprehensive understanding and simplified processing of the data structure. Ultimately, through topological simplification techniques, the system is able to remove transient, unimportant topological features while retaining critical structural information.

[0129] In this embodiment, the topology analysis module includes:

[0130] 1. Topology Construction Unit: The main function of the Topology Construction Unit is to generate simplicial complexes from vector data to capture the complex topological structure in the data. A simplicial complex is a mathematical object used to represent multidimensional topological structures, consisting of geometric entities such as vertices, edges, and faces. By converting vector data into simplicial complexes, this unit lays the foundation for subsequent topological analysis.

[0131] Vector data contains vertex sets , edge set Noodle set , a simplicial complex It can be expressed as:

[0132]

[0133] Every one -dimensional simplices (vertices, edges, faces) are topological representations of the geometric structure of data. Based on simplicial complexes, topological building blocks can capture complex relationships in data, such as connectivity, loops, and cavities.

[0134] 2. Homology Group Computation Unit: After constructing the simplicial complex, the Homology Group Computation Unit is responsible for computing homology groups of varying orders based on this structure. By computing homology groups, the system can extract topological features of the data, such as connected components, loops, and cavities. Homology groups transform geometric objects into topological invariants through algebraic methods, effectively revealing the topological structure in the data.

[0135] For the established simplicial complex , define the boundary operator , which is to -Dimension chain mapping to -Dimension Chain:

[0136]

[0137] homology group is the quotient group of the kernel and the image, which is defined as:

[0138]

[0139] in, yes -Dimension Chain Space The core, yes -dimensional chain mapping. Calculations of homology groups reveal topological structures in data, such as represents the connected component, Represents a ring structure, Represents a cavity.

[0140] 3. Continuous Homology Analysis Unit: Based on the calculation of homology groups, the continuous homology analysis unit further analyzes the topological changes of data at different scales. This unit gradually constructs a simplicial complex sequence of data. , and calculates homology groups at each scale, tracking the generation and extinction of topological features. By generating persistent barcodes, the continuous homology analysis unit can identify stable topological features at different scales, providing a deep understanding of the evolution of data structure.

[0141] For each scale The simplicial complex Computational homology groups , and record the generation and extinction moments of topological feature ii:

[0142]

[0143] in, It is a feature The moment of generation, is the moment of its demise. The length of the persistent barcode Indicates the persistence of topological features. 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.

[0144] 4. Topology Simplification Unit: Based on the results of the persistent homology analysis, the topology simplification unit is responsible for simplifying the topology. This unit removes short-lived topological features in the persistent barcode while retaining key, long-lasting topological structures. This process ensures that the simplified topology is not only concise but also preserves the key topological features of the data, avoiding unnecessary complexity.

[0145] The topology simplification process includes:

[0146] By analyzing the persistent barcodes, the topology simplification unit removes topological features with shorter lengths (i.e., lower persistence) and retains those with longer lengths (i.e., higher persistence). This process significantly simplifies the topological structure while maintaining the key topological information of the data.

[0147] Through the aforementioned functions of the topology analysis module, the GIS spatial data processing system of the present invention can deeply explore the complex topological structures in vector data and effectively identify stable topological features at different scales. Through the construction of topological structures, the calculation of homology groups, continuous homology analysis, and topological simplification, the system can provide comprehensive topological analysis of data.

[0148] Data storage module

[0149] After completing geometric optimization and topological analysis of data, the GIS spatial data processing system of the present invention requires efficient storage of the processed data to enable rapid retrieval and query in subsequent applications. The data storage module compresses and encodes the optimized vector data, constructs a multidimensional index structure suitable for spatial data, and ultimately stores the data synchronously in a spatial database. This module uses data compression and encoding techniques to reduce storage space usage, while improving data query efficiency through index construction, ensuring that the data's topological features are efficiently preserved and quickly accessed.

[0150] In this embodiment, the data storage module includes:

[0151] 1. Data Compression Unit: This unit compresses processed vector data using a topology-based simplification algorithm. This unit leverages the results of the previous topology simplification unit to remove less persistent topological features, thereby simplifying the data structure and reducing the data size. During the compression process, the algorithm prioritizes preserving highly persistent topological structures that have a significant impact on the overall data, ensuring that data compression does not lose important geometric and topological information.

[0152] Importance index of data compression unit based on topological features , selectively compress data. The compression strategy is implemented through the following rules:

[0153]

[0154] in, is the preset compression threshold, Features An importance index is defined based on the persistence or other geometric properties of the feature. Through this algorithm, the system can effectively reduce the volume of data storage while retaining important topological features in the data.

[0155] 2. Data Encoding Unit: After data compression, the data encoding unit encodes the compressed vector data. This unit converts the vector data into a sparse matrix format, reducing data storage redundancy and improving storage and transmission efficiency. Sparse matrix encoding is a well-suited representation for spatial data, effectively compressing unnecessary blank areas or duplicate data in large-scale spatial data.

[0156] The data encoding unit converts vector data Convert to a sparse matrix ,in:

[0157]

[0158] Through this sparse matrix representation, the system can only store the non-zero elements in the matrix, thereby significantly reducing the storage space occupied. Sparse matrix encoding is particularly suitable for the sparse features that exist in large quantities in geographic information data.

[0159] 3. Index Building Unit: To enable fast topological queries and retrieval operations, the index building unit builds query indexes based on topological features. This unit uses multidimensional spatial index structures, such as R-tree or KD-tree, to ensure efficient access and query of spatial data. Based on the geometric and topological properties of the data, the index building unit constructs a multidimensional index suitable for spatial queries, improving retrieval performance.

[0160] For each topological feature in the vector data , the index building unit creates index nodes in the multidimensional space:

[0161]

[0162] in, Representation characteristics The minimum bounding rectangle of Represents the topological attributes of the feature. By building this multidimensional index, the system can quickly perform range-based spatial queries and retrieval operations based on topological features.

[0163] 4. Data Synchronization Unit: After data compression, encoding, and indexing, the data synchronization unit is responsible for synchronizing the processed data and topological feature information into the spatial database. This unit ensures that the consistency and integrity of all data are maintained during storage, and that topological features are stored synchronously with the compressed vector data.

[0164] The data synchronization unit first stores the compressed and encoded vector data in the spatial database, ensuring that each data record contains relevant topological feature information. The system then synchronizes the generated index to the database's index structure to support efficient query operations.

[0165] Through the data storage module, the GIS spatial data processing system of the present invention significantly reduces storage space usage and improves data retrieval efficiency while maintaining data integrity. The data compression unit's compression algorithm removes unnecessary topological features, effectively reducing the size of stored data. The data encoding unit converts data into a sparse matrix format, further optimizing storage efficiency. The index construction unit utilizes a multidimensional index structure to enable rapid query and retrieval of topological features. Finally, the data synchronization unit stores the processed data in a spatial database, ensuring efficient access and use of the data in subsequent applications.

[0166] This invention provides a GIS spatial data processing system that, through the collaborative work of modules such as data acquisition, cleaning, multi-scale processing, geometric optimization, topological analysis, and data storage, addresses the low efficiency, low quality, and manual reliance of traditional vector map data processing. The system optimizes the geometric structure and topological relationships of data through automated data processing procedures. It extracts and simplifies topological features using homology group calculations and continuous homology analysis. Furthermore, it achieves efficient data storage and query through data compression, encoding, and multidimensional index construction. The system improves the efficiency, accuracy, and stability of GIS data processing overall and has broad application value.

[0167] Please see the attached Figure 2 The present invention also provides a GIS spatial data processing method, comprising the following steps:

[0168] S1. Obtain original vector map data from multiple data sources and convert the format:

[0169] Initial vector map data is collected through multiple data sources, and data in different formats are standardized into a unified format that can be processed by the system, laying the foundation for subsequent processing steps.

[0170] S2. Use geometric analysis to detect and remove abnormal data points:

[0171] Through geometric analysis technology, the local curvature value of each vector point is calculated, and outliers in the data are automatically detected and removed according to the set threshold, ensuring data quality while providing reliable input for subsequent geometric optimization and topological analysis.

[0172] S3. Multi-level processing of data based on Laplace multi-scale decomposition:

[0173] According to the needs of the target application, the Laplace multi-scale decomposition technology is used to perform multi-level data processing, decomposing the vector data into different scales to ensure that the geometric and topological features at different levels can be properly processed and support fine-grained or coarse-grained analysis.

[0174] S4. Optimize the geometric structure of vector data based on curvature analysis:

[0175] Through curvature analysis, the Douglas-Peucker algorithm is applied to simplify the data geometry and remove unnecessary points. At the same time, Bezier curve technology is used to smooth the data to ensure that the data is visually optimized while maintaining its structural characteristics.

[0176] S5. Use algebraic topology to extract topological features and perform continuous homology analysis on data:

[0177] Based on algebraic topology, we construct a simplicial complex of vector data and extract topological features through homology group calculations. We also use persistent homology analysis techniques to track the changes in topological features at different scales, generate persistent barcodes to identify stable topological features, and simplify the topological structure.

[0178] S6. Compress and store the optimized data, and build a query index:

[0179] The optimized data is compressed using a topological feature compression algorithm to generate a sparse matrix encoding representation, and a multidimensional spatial index is constructed based on the topological features. Finally, the data is synchronously stored in a spatial database to achieve efficient data query and retrieval.

[0180] The method of the present invention effectively improves the efficiency and quality of GIS spatial data processing. Through the collaborative work of various steps, it solves the problems of traditional vector data processing such as dependence on manual labor, time consumption, and high labor costs. It is suitable for complex GIS data processing scenarios.

[0181] Please see the attached Figure 3 The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is performed.

[0182] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41 , the above method is executed.

[0183] 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 disk.

[0184] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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 is 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 performs multi-level processing on the data 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 geometric structure of the vector data based on curvature analysis to improve the visual representation and analysis accuracy of the data; The topology analysis module is connected to the geometry 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 query indexes to improve data retrieval efficiency and storage efficiency; The data cleaning module includes: A local curvature calculation unit is used to calculate the local curvature of each vector data point. The local curvature is determined by the distance and angle difference between adjacent vector points. an outlier identification unit, configured to detect and identify outlier data points based on a local curvature value and a set outlier threshold; Data correction unit, used to automatically correct or delete detected abnormal data points to maintain the geometric continuity and spatial consistency of the data; The multi-scale processing module includes: Scale selection unit, 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; Optimized aggregation unit, used to aggregate adjacent vector objects at a selected scale through geometric and topological optimization techniques, reducing the complexity of the data while maintaining the spatial integrity and topological consistency of the data; 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; A shape correction unit that applies Bezier curve technology to smooth vector lines, optimizing the visual presentation of vector maps while preserving 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.

2. A GIS spatial data processing system according to claim 1, characterized in that: The topology analysis module includes: A topology building unit is used to generate a simplicial complex based on vector data, representing the vertices, edges, and faces in the data 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 ,extract topological features from the data, including connected components, rings, and cavities; A persistent coherence analysis unit 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, persistent topological structures.

3. A GIS spatial data processing system according to claim 2, characterized in that: The homology group calculation unit is configured to perform the following steps: (1) Extract geometric topological entities from vector data and establish simple complexes , which includes the vertex set , edge set Noodle set ; (2) Applying boundary operators Compute the boundary of a simplicial complex where , used for mapping -WeChat to -Dimension Chain; (3) Calculation of homology groups As a business group ,in yes -Dimension Chain Space The core, yes -Image of the dimension chain mapping.

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

5. A GIS spatial data processing system according to claim 1, characterized in that: The data storage module includes: A data compression unit, used for compressing the processed vector data using a simplified algorithm based on topological features; A data encoding unit is used to encode the compressed vector data by generating a data representation in the form of a sparse matrix; Index building unit, used to build data query index based on topological features, using 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.

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