A multi-scale grid-based spatial data de-encryption method and device
By constructing mapping relationships using multi-scale grid technology and replacing the boundary feature point coordinates of vector data objects, the problems of insufficient scale adaptability and privacy protection in existing technologies are solved, achieving efficient spatial data declassification and improved data availability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING GEOWAY INFORMATION TECH
- Filing Date
- 2026-06-10
- Publication Date
- 2026-07-14
AI Technical Summary
Existing spatial data declassification technologies struggle to balance scale adaptability, privacy protection, and data availability. Furthermore, traditional methods are inadequate in terms of adjusting protection intensity and preserving spatial correlations, resulting in poor performance of data in sharing and analysis applications.
By employing multi-scale grid technology, the boundary feature points of vector data objects are acquired, mapping relationships are constructed, and a multi-scale grid set is generated. Based on the correlation between sensitivity level and spatial range, the coordinates of the boundary feature points are replaced to generate a replacement point set, while preserving the topological connectivity and reconstructing the de-encrypted vector data objects.
It achieves the effective preservation of the topological integrity of spatial data while protecting privacy, improves declassification efficiency and data availability, and adapts to the security needs of different sensitive areas.
Smart Images
Figure CN122389081A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of spatial data security and geographic information processing technology, specifically to a spatial data decryption method and apparatus based on multi-scale grids. Background Technology
[0002] With the rapid development and widespread application of Geographic Information System (GIS) technology, the contradiction between secure sharing and efficient utilization of spatial data has become increasingly prominent. In promoting data openness and facilitating the development of the digital economy, how to achieve secure declassification and compliant circulation of spatial data has become a critical issue urgently needing to be addressed in the field of geographic information science. Spatial data declassification technology aims to reduce the sensitivity of raw data while preserving its usable value as much as possible through a series of transformation processes, in order to meet diverse application needs such as data sharing, spatial analysis, and visualization.
[0003] Currently, various technical approaches have emerged in the field of spatial data declassification. For example, coordinate offset methods change the data location by applying fixed or random offsets to the original coordinates, and are simple and easy to implement. Differential privacy perturbation techniques quantify the protection strength by adding controllable noise to spatial data, providing a theoretical guarantee for privacy protection. Geographic masking techniques hide precise location information through aggregation or blurring. In addition, multi-scale grid technology is widely used in the field of geographic information for feature extraction and spatial index optimization, improving data processing efficiency by constructing grids of different granularities.
[0004] However, existing technologies still have many shortcomings when applied to spatial data declassification. First, regarding scale adaptability, traditional gridded declassification often uses a single-scale uniform grid partitioning with fixed grid granularity that cannot be dynamically adjusted. This makes it difficult to adapt to different regional sensitivity levels, resulting in insufficient protection for highly sensitive areas or excessive loss of data usability in low-sensitivity areas. Second, balancing privacy protection and data usability is difficult. While differential privacy perturbation technology can quantify protection strength, adding noise can easily disrupt the topological relationships and local patterns of spatial data, leading to larger errors in spatial analysis and trend prediction after declassification. Geographic masking technology, on the other hand, is difficult to quantify in terms of protection effectiveness and is prone to over-blurring or under-protection, failing to flexibly adapt to the security requirements of different scenarios. Third, traditional coordinate offset methods use fixed or random offset strategies, which are simple to operate but lack security. Attackers may be able to reverse-engineer the original coordinates through background knowledge or data correlation. Furthermore, these methods lack flexible protection strength adjustment mechanisms, making it difficult to meet diverse spatial data sharing needs. Finally, most declassification techniques destroy the spatial correlation of the original data during the processing, and have a weak ability to preserve spatial relationships, which greatly reduces the practical value of the declassified data in subsequent spatial analysis, decision support and other application scenarios. Summary of the Invention
[0005] To address the aforementioned problems in the existing technology, this application provides a spatial data decryption method and apparatus based on multi-scale grids. The technical problem to be solved by this application is achieved through the following technical solution: Firstly, this application provides a spatial data decryption method based on multi-scale grids, including: S100: Obtain the vector data object to be decrypted, extract the boundary feature points in the vector data object, and construct the first mapping relationship between the vector data object and the boundary feature points; S200 generates a multi-scale grid set based on the preset correlation between sensitivity level and spatial range; S300, based on the first mapping relationship, obtain the boundary feature points of each vector data object, and perform spatial matching between the obtained boundary feature points and the multi-scale grid set to establish a second mapping relationship between each boundary feature point and its corresponding grid unit; S400, according to the preset substitution rule, based on the second mapping relationship, the original coordinates of each boundary feature point are replaced with the coordinates of the substitution points in their respective grid cells to generate the final substitution boundary feature point set; based on the final substitution boundary feature point set, and retaining the topological connection relationship of the original vector data object, the decrypted vector data object is reconstructed and generated.
[0006] Secondly, this application provides a spatial data decryption device based on a multi-scale grid, comprising: The vector data object input and boundary feature point extraction module is configured to acquire the vector data object to be decrypted, extract the boundary feature points in the vector data object, and construct a first mapping relationship between the vector data object and the boundary feature points. The multi-scale mesh generation and configuration module is configured to generate a multi-scale mesh set based on the preset correlation between sensitivity level and spatial range; The boundary feature point-mesh matching module is configured to obtain the boundary feature points of each vector data object based on the first mapping relationship, and to perform spatial matching between the obtained boundary feature points and the multi-scale mesh set to establish a second mapping relationship between each boundary feature point and its corresponding mesh unit. The grid center point replacement and data reconstruction module is configured to replace the original coordinates of each boundary feature point with the coordinates of the replacement point in its grid cell according to the second mapping relationship based on the preset replacement rules, thereby generating a final replacement boundary feature point set; and based on the final replacement boundary feature point set, while retaining the topological connection relationship of the original vector data object, reconstruct and generate a de-encrypted vector data object.
[0007] Beneficial effects: This application discloses a spatial data declassification method and apparatus based on multi-scale grids, belonging to the field of spatial data security and geographic information processing technology. The method includes: acquiring vector data objects to be declassified, extracting boundary feature points, and constructing a first mapping relationship between the vector data objects and the boundary feature points; determining the sensitivity level of each vector data object based on the correlation between sensitivity level and spatial range, and generating a multi-scale grid set of corresponding scales based on the sensitivity level; acquiring the boundary feature points of each vector data object based on the first mapping relationship, spatially matching the boundary feature points with the multi-scale grid set, and establishing a second mapping relationship between the boundary feature points and their respective grid cells; replacing the original coordinates of the boundary feature points with the coordinates of substitute points within their respective grid cells based on a preset substitution rule and the second mapping relationship, generating a set of substituted boundary feature points; and reconstructing and generating declassified vector data objects based on the set of substituted boundary feature points, while preserving the topological connectivity of the original vector data objects. This application, through dynamic adaptation of multi-scale grids and sensitivity levels, effectively preserves the topological integrity of spatial data while protecting privacy, thus improving declassification efficiency and data usability.
[0008] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0009] Figure 1 This is a flowchart illustrating a spatial data decryption method based on multi-scale grids provided in this application; Figure 2 This is a schematic diagram illustrating the matching of boundary feature points with multi-scale mesh sets provided in this application; Figure 3 This is a schematic diagram of the generation of replacement boundary feature points provided in this application; Figure 4 This is a schematic diagram of the generated decrypted vector data object provided in this application. Detailed Implementation
[0010] The present application will be described in further detail below with reference to specific embodiments, but the implementation of the present application is not limited thereto.
[0011] like Figure 1 As shown, this application provides a spatial data decryption method based on multi-scale grids, including: S100: Obtain the vector data object to be decrypted, extract the boundary feature points in the vector data object, and construct the first mapping relationship between the vector data object and the boundary feature points; The function of S100 in this application is to read the vector data object O to be decrypted. i And automatically extract boundary feature points {v} from the data. i , v i+1 , ..., vi+n}, construct the mapping relationship between vector data objects and boundary feature points {O i , (v i , v i+1 ,..., v i+n )}, where v i For the i-th boundary feature point, O i This refers to the i-th vector data object. Here, n is the number of boundary feature points; in this application, there are a total of n+1 boundary feature points, from i to i+n.
[0012] S200 generates a multi-scale grid set based on the preset correlation between sensitivity level and spatial range; S300, based on the first mapping relationship, obtain the boundary feature points of each vector data object, and perform spatial matching between the obtained boundary feature points and the multi-scale grid set to establish a second mapping relationship between each boundary feature point and its corresponding grid unit; The core function of this step is to establish the correspondence between boundary feature points and grid cells, thereby enabling the grid affiliation and location of boundary feature points.
[0013] S400, according to the preset substitution rule, based on the second mapping relationship, the original coordinates of each boundary feature point are replaced with the coordinates of the substitution points in their respective grid cells to generate the final substitution boundary feature point set; based on the final substitution boundary feature point set, and retaining the topological connection relationship of the original vector data object, the decrypted vector data object is reconstructed and generated.
[0014] The core function of this step is to replace the coordinates of the original boundary feature points with the center point of the grid or any point within the grid range, and to reconstruct the de-dense vector data object.
[0015] In one specific embodiment of this application, S100 includes: S110, Obtain the vector data object to be decrypted, and determine the type of the vector data object; S120, if the type of the vector data object is a point vector, then the point position is directly used as the boundary feature point; S130, if the type of the vector data object is a linear or planar vector, then the vertices on its inner and outer contour boundaries are extracted as boundary feature points by a topology analysis algorithm; S140, using the extracted boundary feature points, construct the first mapping relationship between the vector data object and the boundary feature points.
[0016] In this implementation, for point vectors, the points are directly used as boundary feature points; for line and surface vectors, vertices on the inner and outer contour boundaries are extracted as boundary feature points through topological analysis algorithms, thereby constructing the first mapping relationship between vector data objects and boundary feature points.
[0017] In one specific embodiment of this application, S200 includes: S210 defines the set of relationships between sensitivity levels and spatial extent in vector data objects, as well as the set of mapping relationships between sensitivity levels and gridding levels; Define the set of relationships between sensitivity levels and spatial extent in the data as L = {r i , l i ), (r i+1 , l i+1 ),..., (r i+h , l i+h )}; Define the relationship between different sensitivity levels and the grid levels required for declassification processing {(l i G j ), (l i+1 G j+a ..., (l i+b G j+b )}, where L is the set of relationships between data sensitivity levels and spatial scope, representing the entire set of mapping relationships. i Let G be the i-th sensitivity level, representing the level of sensitivity of the data. j is the j-th grid level, representing the scale or granularity of the grid division. i This represents the spatial extent corresponding to the i-th sensitivity level, i.e., the geographical area covered by that sensitivity level. h is the number of sensitivity levels, ranging from i to i+h, for a total of h+1 levels. a is the grid level offset, representing the step size for grid level adjustment when the sensitivity level changes.
[0018] S220, Based on the spatial range of each vector data object, calculate its corresponding sensitivity level in the association set using a preset function, and construct a mapping relationship between vector data objects and sensitivity levels; S230, Based on the mapping relationship set between the sensitivity level and the meshing level, construct the mapping relationship between vector data objects and meshing levels; S240, Based on the mapping relationship between the vector data object and the sensitivity level, and the mapping relationship between the vector data object and the meshing level, generate the corresponding multi-scale mesh set.
[0019] This application can calculate O using the f(r) function. i Within the sensitivity level range of L, construct a mapping relationship between vector data objects and sensitivity levels {(Oi , l i )}, where r is the spatial extent. Then, based on the relationship between sensitivity level and grid level, construct the relationship between vector data objects and gridding level {(O i , l j The process generates a multi-scale mesh set (including the coordinate range, center point coordinates, and spatial encoding of each mesh) and outputs it. Furthermore, this application can receive privacy protection strength instructions (user-defined scale requirements) to dynamically adjust the parameters of the mesh cells.
[0020] In one specific embodiment of this application, S300 includes: S310, based on the first mapping relationship, obtain the boundary feature points of each vector data object, and use a spatial coordinate algorithm to match the original coordinates of each boundary feature point with the multi-scale grid set to determine the grid cell to which it belongs; wherein, for boundary feature points that cross grid boundaries, the principle of proximity is adopted to match them to the nearest grid cell. S320: Record the original coordinates of each boundary feature point, the code of the grid cell to which it belongs, and the coordinates of the grid center point to generate the second mapping relationship.
[0021] This implementation method employs a spatial coordinate algorithm for mesh generation and calculation. g Establish a mapping relationship (x, y, m, l), where x and y are the original coordinates of the boundary feature points, m represents the mesh method, and l is the mesh level; the original coordinates of each boundary feature point {(O i , {v i , v i+1 , ..., v i+n})}, matching with multi-scale grid sets {(O i , l i )}, using f g Calculate O from (x,y,m,l) i All feature points in the grid at level l i The set of grid cells at time, and record the corresponding grid space, encoding and center point (O). i , {(g i e i , c i For special points that cross grid boundaries, the "nearest associativity" principle is used to match them to the nearest grid cell. Then, the second mapping relationship between "boundary feature point and grid cell" is output, which includes the original coordinates of the boundary feature point, its grid code, and the coordinates of the grid center point. i The mesh cell matched for a boundary feature point, e represents the mesh cell to which the boundary feature point belongs. iThis represents the code of a grid cell, used to uniquely identify the spatial location and level of the grid, supporting multi-scale grid retrieval and merging operations. i These are the coordinates of the center point of the grid cell, which will serve as the optional replacement point location in subsequent replacement operations.
[0022] refer to Figure 2 , Figure 2 The red dots in the leftmost sub-image represent boundary feature points, the pink boxes in the middle sub-image represent grid cells, and the rightmost sub-image shows the relationship between boundary feature points and grid cells. The boundary feature points in the rightmost sub-image are represented by green dots.
[0023] In one specific embodiment of this application, S400 includes: S410, a substitution rule is preset, wherein the substitution rule is to replace the coordinates of the boundary feature point with any point within the range of its grid cell; S420, read the second mapping relationship, and replace the original coordinates of each boundary feature point with the alternative point coordinates calculated according to the substitution rule; the alternative point coordinates can be the center point coordinates of the grid cell, or any point coordinates in the grid cell other than the center point coordinates.
[0024] This application defines the replacement rule as f. t (g i The vector data object O can be specified as any point within the grid area, such as the center point; according to the second mapping relationship, the vector data object O... i The original coordinates of each boundary feature point {v i , v i+1 , ..., v i+n Replace} with f in its corresponding mesh cell. t (g i The calculated substitution point {v} ti , v ti+1 , ..., v ti+n}. Where, v ti+n This is the replacement point for the (i+n)th boundary feature point, where n represents the number of boundary feature points. The entire sequence contains a total of n+1 boundary feature points.
[0025] S430, the coordinates of all replacement points after the replacement are completed are used to form the final replacement boundary feature point set; S440, based on the final alternative boundary feature point set and retaining the topological connection relationship of the original vector data object, reconstruct and generate the decrypted vector data object.
[0026] In one specific embodiment of this application, S430 includes: S431, based on the first mapping relationship, the coordinates of the substitute points belonging to the same vector data object are collected to form a preliminary set of substitute boundary feature points; S432, when there are grid cells of different scales in adjacent areas, for the boundary feature points in the preliminary alternative boundary feature point set that belong to the small-scale grid cell, if their spatial range is completely covered by the large-scale grid cell, then by comparing the prefix matching relationship of the grid codes, the coordinates of the alternative points of the boundary feature points are remapped to the coordinates of the alternative points in the large-scale grid cell, and the preliminary alternative boundary feature point set is updated to generate the final alternative boundary feature point set.
[0027] Adjacent regions with different scales of grid cells (multi-scale merging) are encoded using a large-scale grid. i (The encoding has s bits) to retrieve the small-scale trellis encoding e i+p The first s bits, for the retrieved small-scale encoded e i+p The corresponding boundary feature points are remapped to e i Within the grid range corresponding to the encoding.
[0028] refer to Figure 3 , Figure 3 The leftmost subplot shows grid cells of different scales in adjacent regions, the middle subplot shows the relationship between boundary feature points and grid cells, and the rightmost subplot shows the specific process of remapping the alternative point coordinates of boundary feature points to the alternative point coordinates within large-scale grid cells.
[0029] In one specific embodiment of this application, S440 includes: S441 preserves the connection order, contour closure relationship and topological connection relationship between points in a vector data object; S442, Based on the topological connection relationship, the final alternative boundary feature point set is reconstructed into a de-encrypted vector data object.
[0030] The decrypted vector data object has the same data type as the vector data object to be decrypted, and each replacement point retains its corresponding grid cell code for data traceability and secondary verification.
[0031] Based on the final replacement boundary feature point set, the topological connectivity of the original vector data objects (such as the point order of line vectors and the contour closure relationship of surface vectors) is preserved, and the de-encrypted vector data objects are reconstructed, while retaining each v ti The grid code e corresponding to the point i This facilitates subsequent data traceability and secondary verification. (Reference) Figure 4 , Figure 4The process from the final replacement of the boundary feature point set to the reconstruction of the decrypted vector data object is shown.
[0032] In one specific embodiment of this application, after S400, the spatial data decryption method based on multi-scale grids further includes: S500 performs topological integrity verification and privacy protection strength verification on the decrypted vector data object and generates verification results. S600 If the verification result shows that the verification is successful, the declassified vector data object and a declassification report containing the grid scale, verification result and privacy protection level will be output.
[0033] The core function of this implementation is to verify the topological integrity and privacy protection strength of the declassified data and output the final results. Topological integrity verification ensures data usability by comparing the similarity of boundary contours and the consistency of topological relationships (e.g., no intersections, no breaks) between the original and declassified data. After successful verification, it supports outputting declassified data in various formats (compatible with existing GIS software formats) and generates a declassification report (including grid scale, verification results, and privacy protection level).
[0034] This application provides a spatial data decryption device based on a multi-scale grid, comprising: The vector data object input and boundary feature point extraction module is configured to acquire the vector data object to be decrypted, extract the boundary feature points in the vector data object, and construct a first mapping relationship between the vector data object and the boundary feature points. The multi-scale mesh generation and configuration module is configured to generate a multi-scale mesh set based on the preset correlation between sensitivity level and spatial range; The boundary feature point-mesh matching module is configured to obtain the boundary feature points of each vector data object based on the first mapping relationship, and to perform spatial matching between the obtained boundary feature points and the multi-scale mesh set to establish a second mapping relationship between each boundary feature point and its corresponding mesh unit. The grid center point replacement and data reconstruction module is configured to replace the original coordinates of each boundary feature point with the coordinates of the replacement point in its grid cell according to the second mapping relationship based on the preset replacement rules, thereby generating a final replacement boundary feature point set; and based on the final replacement boundary feature point set, while retaining the topological connection relationship of the original vector data object, reconstruct and generate a de-encrypted vector data object.
[0035] This application discloses a spatial data declassification method and apparatus based on multi-scale grids, belonging to the field of spatial data security and geographic information processing technology. The method includes: acquiring vector data objects to be declassified, extracting boundary feature points, and constructing a first mapping relationship between the vector data objects and the boundary feature points; determining the sensitivity level of each vector data object based on the correlation between sensitivity level and spatial range, and generating a multi-scale grid set of corresponding scales based on the sensitivity level; acquiring the boundary feature points of each vector data object based on the first mapping relationship, spatially matching the boundary feature points with the multi-scale grid set, and establishing a second mapping relationship between the boundary feature points and their respective grid cells; replacing the original coordinates of the boundary feature points with the coordinates of substitute points within their respective grid cells based on a preset substitution rule and the second mapping relationship, generating a set of substituted boundary feature points; and reconstructing and generating declassified vector data objects based on the set of substituted boundary feature points, while preserving the topological connectivity of the original vector data objects. This application, through dynamic adaptation of multi-scale grids and sensitivity levels, effectively preserves the topological integrity of spatial data while protecting privacy, thus improving declassification efficiency and data usability.
[0036] It is worth noting that the terms "first" and "second" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0037] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of this application and should not be construed as limiting the specific implementation of this application to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of this application, and all such modifications or substitutions should be considered within the scope of protection of this application.
Claims
1. A spatial data decryption method based on multi-scale grids, characterized in that, include: S100: Obtain the vector data object to be decrypted, extract the boundary feature points in the vector data object, and construct the first mapping relationship between the vector data object and the boundary feature points; S200 generates a multi-scale grid set based on the preset correlation between sensitivity level and spatial range; S300, based on the first mapping relationship, obtain the boundary feature points of each vector data object, and perform spatial matching between the obtained boundary feature points and the multi-scale grid set to establish a second mapping relationship between each boundary feature point and its corresponding grid unit; S400, according to the preset substitution rule, based on the second mapping relationship, the original coordinates of each boundary feature point are replaced with the coordinates of the substitution points in their respective grid cells to generate the final substitution boundary feature point set; based on the final substitution boundary feature point set, and retaining the topological connection relationship of the original vector data object, the decrypted vector data object is reconstructed and generated.
2. The spatial data decryption method based on multi-scale grids according to claim 1, characterized in that, S100 includes: S110, Obtain the vector data object to be decrypted, and determine the type of the vector data object; S120, if the type of the vector data object is a point vector, then the point position is directly used as the boundary feature point; S130, if the type of the vector data object is a linear or planar vector, then the vertices on its inner and outer contour boundaries are extracted as boundary feature points by a topology analysis algorithm; S140, using the extracted boundary feature points, construct the first mapping relationship between the vector data object and the boundary feature points.
3. The spatial data decryption method based on multi-scale grids according to claim 1, characterized in that, S200 includes: S210 defines the set of relationships between sensitivity levels and spatial extent in vector data objects, as well as the set of mapping relationships between sensitivity levels and gridding levels; S220, Based on the spatial range of each vector data object, calculate its corresponding sensitivity level in the association set using a preset function, and construct a mapping relationship between vector data objects and sensitivity levels; S230, Based on the mapping relationship set between the sensitivity level and the meshing level, construct the mapping relationship between vector data objects and meshing levels; S240, Based on the mapping relationship between the vector data object and the sensitivity level, and the mapping relationship between the vector data object and the meshing level, generate the corresponding multi-scale mesh set.
4. The spatial data decryption method based on multi-scale grids according to claim 1, characterized in that, The S300 includes: S310, based on the first mapping relationship, obtain the boundary feature points of each vector data object, and use a spatial coordinate algorithm to match the original coordinates of each boundary feature point with the multi-scale grid set to determine the grid cell to which it belongs; wherein, for boundary feature points that cross grid boundaries, the principle of proximity is adopted to match them to the nearest grid cell. S320: Record the original coordinates of each boundary feature point, the code of the grid cell to which it belongs, and the coordinates of the grid center point to generate the second mapping relationship.
5. The spatial data decryption method based on multi-scale grids according to claim 1, characterized in that, The S400 includes: S410, a substitution rule is preset, wherein the substitution rule is to replace the coordinates of the boundary feature point with any point within the range of its grid cell; S420, Read the second mapping relationship and replace the original coordinates of each boundary feature point with the coordinates of the alternative point calculated according to the substitution rule; S430, the coordinates of all replacement points after the replacement are completed are used to form the final replacement boundary feature point set; S440, based on the final alternative boundary feature point set and retaining the topological connection relationship of the original vector data object, reconstruct and generate the decrypted vector data object.
6. The spatial data decryption method based on multi-scale grids according to claim 5, characterized in that, The S430 includes: S431, based on the first mapping relationship, the coordinates of the substitute points belonging to the same vector data object are collected to form a preliminary set of substitute boundary feature points; S432, when there are grid cells of different scales in adjacent areas, for the boundary feature points in the preliminary alternative boundary feature point set that belong to the small-scale grid cell, if their spatial range is completely covered by the large-scale grid cell, then by comparing the prefix matching relationship of the grid codes, the coordinates of the alternative points of the boundary feature points are remapped to the coordinates of the alternative points in the large-scale grid cell, and the preliminary alternative boundary feature point set is updated to generate the final alternative boundary feature point set.
7. The spatial data decryption method based on multi-scale grids according to claim 5, characterized in that, The S440 includes: S441 preserves the connection order, contour closure relationship and topological connection relationship between points in a vector data object; S442, Based on the topological connection relationship, the final alternative boundary feature point set is reconstructed into a de-encrypted vector data object.
8. The spatial data decryption method based on multi-scale grids according to claim 7, characterized in that, The decrypted vector data object has the same data type as the vector data object to be decrypted, and each replacement point retains its corresponding grid cell code for data traceability and secondary verification.
9. The spatial data decryption method based on multi-scale grids according to claim 1, characterized in that, Following S400, the spatial data decryption method based on multi-scale grids further includes: S500 performs topological integrity verification and privacy protection strength verification on the decrypted vector data object and generates verification results. S600 If the verification result shows that the verification is successful, the declassified vector data object and a declassification report containing the grid scale, verification result and privacy protection level will be output.
10. A spatial data decryption device based on multi-scale grids, characterized in that, include: The vector data object input and boundary feature point extraction module is configured to acquire the vector data object to be decrypted, extract the boundary feature points in the vector data object, and construct a first mapping relationship between the vector data object and the boundary feature points. The multi-scale mesh generation and configuration module is configured to generate a multi-scale mesh set based on the preset correlation between sensitivity level and spatial range; The boundary feature point-mesh matching module is configured to obtain the boundary feature points of each vector data object based on the first mapping relationship, and to perform spatial matching between the obtained boundary feature points and the multi-scale mesh set to establish a second mapping relationship between each boundary feature point and its corresponding mesh unit. The grid center point replacement and data reconstruction module is configured to replace the original coordinates of each boundary feature point with the coordinates of the replacement point in its grid cell according to the second mapping relationship based on the preset replacement rules, thereby generating a final replacement boundary feature point set; and based on the final replacement boundary feature point set, while retaining the topological connection relationship of the original vector data object, reconstruct and generate a de-encrypted vector data object.