Data desensitization analysis method and system based on space-time grid

Abstract

The invention relates to the technical field of spatial geographic data desensitization and processing, and discloses a data desensitization analysis method and system based on a space-time grid, and the method comprises the steps: generating a grid unit based on a preset geocoding rule; carrying out association processing on the grid units and administrative region hierarchy information to generate basic grid units containing administrative region hierarchy identifiers; performing intersection space operation on the basic grid units and the space data to obtain grid units with an intersection relation; expanding source element attribute fields for the grid units with the intersection relationship, and storing source element attribute data of the spatial data to the source element attribute fields corresponding to the grid units; performing desensitization processing on the grid units storing the source element attribute data to obtain desensitized grid units; and performing multi-scale data aggregation and analysis on the desensitized grid units based on the administrative level identifiers of the basic grid units. According to the method, the security, flexibility and efficiency of a spatial geographic information data analysis technology are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of spatial geographic data desensitization and processing, and more particularly to a data desensitization analysis method and system based on a space-time grid. BACKGROUND

[0002] Spatial geographic information analysis in the field of urban and rural planning has long relied on traditional GIS technology. However, in actual applications, high-precision sensitive data (such as ecological protection zone boundaries and pollution monitoring points) must be processed in a closed network and special equipment due to security management requirements, resulting in low efficiency of data sharing and cross-scene application. At the same time, the widespread existence of multi-source heterogeneous data formats (such as Shapefile, GeoJSON, GeoTIFF, etc.) makes it necessary to frequently rely on manual operations for format conversion and feature extraction before analysis, resulting in a cumbersome process and a lack of standardization, which seriously hinders the automation and intelligence level of data analysis.

[0003] Existing technologies generally control classified data through physical isolation, rely on manual operation of professional GIS tools to complete data conversion and basic analysis, or use customized scripts to perform partial standardized processing on some data. However, such methods require the use of original coordinate data in a closed environment, making it difficult to achieve safe flow after desensitization. Manual processing steps are prone to errors and low efficiency, and scattered data standards and processing tools further result in poor algorithm reusability and weak cross-platform analysis capabilities. The above methods generally have the defects of prominent contradiction between safety controllability and application flexibility, and low fusion efficiency of multi-source heterogeneous data, making it difficult to meet the needs of urban and rural planning businesses for efficient, flexible, and secure data analysis. SUMMARY

[0004] To overcome the defects of safety, flexibility, and low efficiency in existing spatial geographic information data analysis technologies, the present application proposes the following technical solutions: In a first aspect, the present application proposes a data desensitization analysis method based on a space-time grid, comprising: Generating a grid cell based on a preset geographic coding rule.

[0005] Associating the grid cell with administrative district level information to generate a basic grid cell containing administrative district level identifiers.

[0006] Obtaining spatial data.

[0007] Performing intersection spatial operations on the basic grid cell and the spatial data to obtain grid cells with intersection relationships.

[0008] Extending the source feature attribute field of the grid cell with an intersection relationship, and storing the source feature attribute data of the spatial data to the source feature attribute field corresponding to the grid cell.

[0009] A desensitization process including at least one of grid data aggregation, spatial disturbance and temporal disturbance is performed on the grid unit storing the active feature attribute data to obtain a desensitized grid unit.

[0010] Based on the administrative level identification of the basic grid unit, multi-scale data aggregation and analysis are performed on the desensitized grid units.

[0011] As a preferred technical solution, grid cells are generated based on preset geocoding rules, including: Obtain multi-level administrative boundary data including spatial boundaries at the provincial, municipal, district, county, town, and village levels.

[0012] Based on the multi-level administrative division boundary data, a multi-level spatial grid unit is generated using Geohash encoding rules.

[0013] The administrative division code and name of each multi-level spatial grid unit are associated with the data attributes of the multi-level spatial grid unit to generate a basic grid unit containing the administrative division level identifier.

[0014] As a preferred technical solution, the source element attribute data includes one or more of cultivated land area, slope, temperature and precipitation.

[0015] As a preferred technical solution, grid data aggregation processing is performed on the grid cells storing active feature attribute data, including: The source element attribute data of the intersecting grid cells are grouped and aggregated according to a preset statistical function to generate aggregated data. The preset statistical function includes at least one of a sum function, a maximum function, a minimum function, and an average function.

[0016] The aggregated data is used to replace the source feature attribute data stored in the intersecting grid cells to generate desensitized grid cells containing the aggregated data.

[0017] As a preferred technical solution, spatial perturbation processing is performed on the grid cells storing active feature attribute data, including: A preset disturbance radius is set, and the disturbance radius is converted into a latitude change and a longitude change based on the radius of the earth.

[0018] A random disturbance value is generated according to the latitude change and the longitude change, and the random disturbance value is superimposed on the original coordinate data of the grid unit to generate a disturbed spatial coordinate.

[0019] The disturbed spatial coordinates are used to replace the original coordinate data of the grid unit to obtain a desensitized grid unit that has undergone spatial disturbance processing.

[0020] As a preferred technical solution, the method further comprises: Replacing the point-like or boundary element in the desensitized grid cell subjected to spatial disturbance processing with a grid coverage area containing the location of the point-like or boundary element, the grid coverage area comprising at least one of the following forms: A single grid cell centered at the location of the point-like or boundary element.

[0021] A multi-ring coverage area formed by extending outward n layers of grids centered at the location of the point-like or boundary element, where n≥1.

[0022] As a preferred technical solution, the grid cell storing the source element attribute data is subjected to time disturbance processing, comprising: Setting a preset time offset range and converting the time offset range into a maximum time offset in seconds.

[0023] Based on the maximum time offset in seconds, a random disturbance value is generated, the value range of the random disturbance value being [-maximum time offset in seconds, +maximum time offset in seconds].

[0024] Superimposing the random disturbance value with the original timestamp data of the grid cell to generate a disturbed timestamp.

[0025] Replacing the original time data of the grid cell with the disturbed timestamp to obtain a desensitized grid cell subjected to time disturbance processing.

[0026] As a preferred technical solution, based on the administrative level identifier of the basic grid cell, the desensitized grid cell is subjected to multi-scale data aggregation and analysis, comprising: Grouping by grid code, and counting the summary indicators of desensitized grid data in each grid.

[0027] Grouping by village-level administrative division code and name, and counting the summary indicators of desensitized grid data in each village.

[0028] Grouping by town-level administrative division code and name, and counting the summary indicators of desensitized grid data in each town.

[0029] Grouping by county-level administrative division code and name, and counting the summary indicators of desensitized grid data in each county.

[0030] Grouping by city-level administrative division code and name, and counting the summary indicators of desensitized grid data in each city.

[0031] Based on the summary indicators, planning decision support information in different levels is generated.

[0032] As a preferred technical solution, the basic grid unit is subjected to intersection spatial operation with the spatial data to obtain a grid unit having an intersection relationship, including: The GeoHash code of the basic grid unit is converted into a geometric spatial data type to generate a bounding box spatial graph of the basic grid unit.

[0033] The intersection operation is performed on the bounding box spatial graph and the spatial data to obtain an intersection of the bounding box spatial graph and the geometric graph of the spatial data.

[0034] If the intersection is a non-empty geometric graph, it is determined that the basic grid unit has an intersection relationship with the spatial data. If the intersection is empty, the basic grid unit is marked as having no intersection relationship with the spatial data.

[0035] In a second aspect, the present application further provides a data desensitization analysis system based on a space-time grid, which is applied to the data desensitization analysis method based on the space-time grid according to any one of the first aspect, and includes: A generation module generates a grid unit based on a preset geographic coding rule.

[0036] An association module associates the grid unit with administrative region level information to generate a basic grid unit containing an administrative region level identifier.

[0037] An acquisition module is configured to acquire spatial data.

[0038] An intersection spatial operation module is configured to perform intersection spatial operation on the basic grid unit and the spatial data to obtain a grid unit having an intersection relationship.

[0039] An extension storage module is configured to extend a source element attribute field of the grid unit having the intersection relationship, and store source element attribute data of the spatial data into the source element attribute field corresponding to the grid unit.

[0040] A desensitization module is configured to perform at least one desensitization processing including grid data aggregation, spatial disturbance and temporal disturbance on the grid unit storing the source element attribute data to obtain a desensitized grid unit.

[0041] An aggregation analysis module is configured to perform multi-scale data aggregation and analysis on the desensitized grid unit based on the administrative level identifier of the basic grid unit.

[0042] In a third aspect, the present application further provides an electronic device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor performs the operations of the data desensitization analysis method based on the space-time grid according to any one of the first aspect when executing the program.

[0043] The beneficial effects of the present application at least include: The present application generates multi-level spatio-temporal grid cells by presetting geocoding rules, combines intersection space operation and source feature attribute field extension technology, and uniformly maps multi-source heterogeneous data into standardized grids, eliminating the interference of format differences on analysis and significantly reducing the cost of manual conversion, providing a unified data basis for automated and intelligent analysis. At the same time, by using grid data aggregation, spatial disturbance and time disturbance, etc. Desensitization means, high-precision coordinates and sensitive attribute information (such as ecological protection area boundaries, pollution monitoring points) are converted into grid-level statistical results or disturbed data, blocking the reverse restoration path of the original sensitive information, ensuring that the data meets the security management requirements in cross-department sharing, open analysis, etc., balancing data usability and privacy protection, effectively solving the security and application flexibility of sensitive spatial geographic information in urban and rural planning.

[0044] The present application further realizes multi-scale aggregation from grid-level refinement analysis to city-county-level macro statistics by associating grid cells with administrative area level identifiers. Based on the desensitized grid data, it can flexibly support village-level cultivated land distribution statistics, town-level water quality evaluation, city-level ecological protection area aggregation, and other multi-granularity business requirements, meeting the precision requirements of different planning scenarios, and avoiding exposure of sensitive details. This "grid-administrative area" dual-dimension fusion mechanism not only solves the data fragmentation and process redundancy bottleneck in traditional GIS technology, but also improves the query and analysis efficiency of large-scale spatial data through a unified grid data model, providing efficient and compliant decision support for urban and rural planning, and promoting the digital and fine transformation of planning management. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The flowchart of the data desensitization analysis method based on spatio-temporal grid provided in Embodiment 1.

[0046] Figure 2 The architecture diagram of the data desensitization analysis system based on spatio-temporal grid provided in Embodiment 3.

[0047] Figure 3 The structural diagram of the electronic device provided in Embodiment 4. DETAILED DESCRIPTION

[0048] The embodiments of the present application will be described below with reference to the accompanying drawings and preferred technical solutions. Those skilled in the art can easily understand other advantages and effects of the present application from the disclosure in this specification. The present application can also be implemented or applied by different specific embodiments, and the details in this specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be understood that the preferred technical solutions are only for illustration of the present application, but not for limiting the protection scope of the present application.

[0049] It is to be understood that the figures provided in the following embodiments only schematically illustrate the basic concept of the present application, and only the components related to the present application are shown in the figures, rather than being drawn according to the number, shape and size of the components in actual implementation. The actual implementation of each component can be a random change, and the component layout pattern can be more complex.

[0050] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in the form of details, so as not to make the embodiments of the present application difficult to understand.

[0051] Embodiment 1 The present embodiment proposes a data desensitization analysis method based on a space-time grid, as shown in Figure 1 The present embodiment proposes a data desensitization analysis method based on a space-time grid, as shown in Figure 1 The present embodiment proposes a data desensitization analysis method based on a space-time grid, as shown in S1: generating a grid unit based on a preset geographic coding rule.

[0052] S2: associating the grid unit with administrative area level information to generate a basic grid unit containing administrative area level identification.

[0053] S3: obtaining spatial data.

[0054] S4: performing intersection space operation on the basic grid unit and the spatial data to obtain grid units with intersection relationship.

[0055] S5: extending the source element attribute field of the grid unit with intersection relationship, and storing the source element attribute data of the spatial data to the source element attribute field corresponding to the grid unit.

[0056] In the present embodiment, the source element attribute data includes one or more of cultivated land area, slope, temperature and precipitation.

[0057] Table 1 shows example data of grid units storing source element attribute data

[0058] As shown in Table 1, taking the grid cell encoded as `wstimgxy` as an example, after the grid is intersected with the cultivated land plot vector data, the system automatically extends the source element attribute fields such as "cultivated land area" and "slope", and fills the field values according to the intersected source plot data. For example, a certain grid covers multiple cultivated land plots, and its "cultivated land area" field stores the sum of the intersected plots (such as 2342.44353 mu), and the "slope" field calculates the weighted average value (such as 12.2°), while recording environmental data such as temperature (22.9°C) and precipitation (10.3mm). For grids that do not associate with monitoring points (such as the "water pollution monitoring point" and "air monitoring range" field values in the table are 0.0), the system automatically marks them as sensitive element-free areas. Through this process, the source element attribute data (cultivated land, slope, etc.) of all intersected grids are standardized and stored, forming a structured data set as shown in Table 1, providing uniform input for subsequent aggregation analysis. For example, based on the slope value (13.1°) of the `wstimgxz` grid, steep slope cultivated land distribution can be quickly identified to support village-level land remediation decisions without exposing original plot boundary coordinates.

[0059] S6: At least one desensitization processing including grid data aggregation, spatial disturbance and temporal disturbance is performed on the grid cell in which the source element attribute data is stored, to obtain a desensitized grid cell.

[0060] S7: Based on the administrative level identifier of the basic grid cell, multi-scale data aggregation and analysis are performed on the desensitized grid cell.

[0061] As an exemplary description, in the specific implementation process, the environmental protection department of a certain city needs to perform desensitization analysis on the ecological protection area boundary data. First, based on the Geohash encoding rule, a 7-level grid cell (about 153m precision) is generated, and is associated with the town and village administrative information to form a basic grid containing administrative code. Then, the original protection area boundary vector data is intersected with the grid, and the grid cells covering the protection area are selected, and attribute fields such as "protection area type" and "area ratio" are added in these grids to store the business information of the original boundary. For sensitive coordinates, spatial disturbance technology is used to apply a random offset of ±100m to the boundary coordinates in the grid, and the total area of the protection area is aggregated according to the grid to generate a desensitized grid data set, ensuring that the true position cannot be restored.

[0062] Based on the desensitized data, the environmental protection department aggregates and counts the proportion of protected area grid area and type distribution in each region at the town and county level, and generates a town-level ecological evaluation report and county-level protection planning suggestions. For example, a town found 3 high-density protected area grids through village-level grid aggregation, guiding local ecological restoration. At the municipal level, the layout of the city's protection network is analyzed through the fusion of county data to support macro decision-making. This method not only ensures the security of sensitive data in cross-department collaboration, but also improves the efficiency of multi-scale analysis through a unified grid model, realizing seamless connection from fine grid management to regional strategic planning.

[0063] It can be understood that when dealing with ecological protection area boundary data, the environmental protection department of a certain city first generates 7-level grid units (about 153 meters precision) based on the Geohash coding rule, and associates them with town and village administrative division information to form basic grids containing administrative codes. This operation not only realizes the standardized mapping of multi-source heterogeneous data (such as vector boundary files), but also lays the foundation for subsequent multi-scale analysis through the binding of grids and administrative levels, directly corresponding to the technical advantages of "eliminating the interference of format differences on analysis" and "unified grid data model". Subsequently, through intersection spatial operation, grid units covering protected areas are selected, and attribute fields such as "protected area type" and "area proportion" are extended, and the original boundary information is stored in the grid, significantly reducing the complexity of traditional manual format conversion and attribute association, reflecting the core value of "efficient fusion of multi-source data".

[0064] Further, for the spatial disturbance (±100 meters random offset) of sensitive coordinates and grid-level area aggregation, the real location of the protected area boundary is hidden, and the regional distribution characteristics are preserved through statistical aggregation, perfectly balancing data security and usability. For example, a town found 3 high-density protected area grids through village-level grid aggregation, guiding local ecological restoration. At the municipal level, the layout of the city's protection network is analyzed through the fusion of county data. This seamless connection from grid-level fine management to municipal-level macro decision-making is a direct manifestation of the "grid-administrative area" dual-dimension fusion mechanism. It not only solves the problem of data fragmentation and complex process in traditional GIS technology, but also improves the analysis efficiency through a unified data model, so that desensitized data can not only support fine-grained needs such as village-level farmland distribution statistics, but also serve the city-level ecological protection strategic planning, promoting the digital and fine transformation of urban and rural planning management.

[0065] Embodiment 2 This embodiment improves the data desensitization analysis method based on spatio-temporal grid proposed in Embodiment 1.

[0066] In this embodiment, based on a preset geographic coding rule, a grid unit is generated, including: Multi-level administrative division boundary data including spatial boundaries of province, city, county, town, and village levels are acquired.

[0067] Based on the multi-level administrative division boundary data, multi-level spatial grid cells are generated using Geohash coding rules.

[0068] Administrative division codes and names to which each multi-level spatial grid cell belongs are associated to data attributes of the multi-level spatial grid cell, and basic grid cells containing administrative area level identifiers are generated.

[0069] Table 2: Example data of basic grid cell basic information

[0070] In the specific implementation process, as shown in Table 2, the urban and rural planning department of a certain city takes Shiba Town Julong Village of Boluo County, Huizhou City, Guangdong Province as an example. First, five-level administrative division boundary data of province (440000), city (441300), county (441322), town (441322100000), and village (441322119203) are acquired, and 7-level grid cells (for example, geohash code ws1qp35 corresponds to about 153m x 153m grid) are generated based on Geohash coding rules. Subsequently, the administrative division codes and names (for example, “Guangdong Province-Huizhou City-Boluo County-Shiba Town-Julong Village”) to which each grid cell belongs are written into its attribute table, and basic grid cells containing complete administrative level identifiers are formed. For example, the grid with geohash code ws1qp35 in Table 1 has shjdm (provincial code) of 440000 and cjmc (village-level name) of “Julong Village”. Through this process, all grid cells not only retain the uniqueness of geographical spatial coding, but also are strictly associated with administrative management levels, providing structured data support for subsequent screening of grid cells at the town, village, and other levels (such as quickly locating all cultivated land grids within Shiba Town), ensuring seamless connection between desensitization analysis and business needs.

[0071] In this embodiment, the basic grid cells and the spatial data are subjected to intersection spatial operation to obtain grid cells having intersection relationship, including: The GeoHash code of the basic grid cell is converted into geometric spatial data type to generate a bounding box spatial graph of the basic grid cell.

[0072] Intersection operation is performed on the bounding box spatial graph and the spatial data to obtain the intersection of the geometric graphs of the bounding box spatial graph and the spatial data.

[0073] If the intersection is a non-empty geometric graph, it is determined that the basic grid cell has intersection relationship with the spatial data. If the intersection is empty, the basic grid cell is marked as having no intersection relationship with the spatial data.

[0074] In the implementation process, first, for the GeoHash encoding of each base grid cell, the ST_Box2dFromGeoHash function of the PostGIS library is called to convert it into a boundary box spatial figure of the geometry type. Then, the ST_Intersection(geometryl, geometry2) function of the library is used to perform intersection operation between this boundary box figure (geometryl) and the source spatial data (for example, the polygon geometry2 of a certain building) to obtain the intersection figure. Finally, the ST_IsEmpty(intersection_result) function is used to judge whether the intersection is empty. If true is returned, it means that there is intersection, and the intersection figure is saved as the result of the grid cell with intersection relationship. If false is returned, it is marked that the grid cell has no intersection relationship, thereby completing the entire intersection space operation process.

[0075] In the embodiment, the grid data aggregation processing is performed on the grid cell storing the source feature attribute data, including: According to a preset statistical function, the source feature attribute data of the intersecting grid cell is grouped and aggregated to generate aggregated data. The preset statistical function includes at least one of a summation function, a maximum value function, a minimum value function and an average value function.

[0076] The aggregated data is replaced with the source feature attribute data stored in the intersecting grid cell to generate a desensitized grid cell containing the aggregated data.

[0077] In the implementation process, taking the desensitization analysis of the cultivated land resources in a certain area as an example, after the source feature attribute fields (such as the cultivated land area and the slope) of the intersecting grid cell are stored, the system groups and aggregates the source data of the same grid cell based on the preset statistical function. For example, for a grid covering multiple cultivated land plots, a summation function is used to calculate the total area of the cultivated land in the grid, and an average value function is used to calculate the average slope of the grid, and the aggregation result is replaced with the original plot-level data to generate a desensitized grid cell containing only the fields of “total area of grid cultivated land” and “average slope of grid”, which not only blurs the property boundary information of a single plot, but also retains the grid-level cultivated land distribution and topographic features, significantly compresses the data size and eliminates sensitive details, so that the desensitized grid data can support business needs such as village-level cultivated land density statistics and town-level slope classification evaluation, and avoid leakage of original high-precision information, providing security for cross-department data sharing and compliance analysis In practical application, this aggregation desensitization mechanism is automatically implemented through SQL, for example: Aggregate the area of multiple farmland plots within a grid, SQL syntax: select sum (farmland intersection area) as farmland area within grid from grid farmland intersection result table group by geohash.

[0078] Aggregate the maximum slope value within a grid, SQL syntax: select max (slope) as maximum slope within grid from grid slope intersection result table group by geohash.

[0079] Aggregate the minimum temperature value within a grid, SQL syntax: select min (temperature) as minimum temperature within grid from grid temperature intersection result table group by geohash.

[0080] Aggregate the average precipitation within a grid, SQL syntax: select avg (precipitation) as average precipitation within grid from grid slope intersection result table group by geohash.

[0081] In this embodiment, the grid cells storing source element attribute data are subjected to spatial perturbation processing, including: A preset perturbation radius is set, and the perturbation radius is converted into a latitude change amount and a longitude change amount based on the Earth's radius.

[0082] According to the latitude change amount and the longitude change amount, a random perturbation value is generated. The random perturbation value is subject to a uniform distribution in the latitude direction and the longitude direction, respectively.

[0083] The random perturbation value is superimposed with the original coordinate data of the grid cell to generate a perturbed spatial coordinate.

[0084] The perturbed spatial coordinate replaces the original coordinate data of the grid cell to obtain a desensitized grid cell subjected to spatial perturbation processing.

[0085] In this embodiment, the point or boundary element in the desensitized grid cell subjected to spatial perturbation processing is replaced by a grid coverage area containing the position of the point or boundary element, and the grid coverage area includes at least one of the following forms: A single grid cell centered on the position of the point or boundary element.

[0086] A multi-ring coverage area formed by extending n layers of grids outward from the position of the point or boundary element, where n ≥ 1.

[0087] In the specific implementation process, the environmental protection department of a certain city takes the sensitive coordinate data of the ecological protection area boundary as an example. First, a perturbation radius of 100 meters is set, and the perturbation range is converted into a latitude change amount based on the Earth's radius, and the calculation formula is: Latitude change amount = 100 / (6371000 * π / 180) And according to the latitude of the protected area center point, the longitude change amount is calculated, and the calculation formula is: Longitude change amount = Latitude change amount / cos(radians(latitude)) Subsequently, a random function is used to generate a disturbance value uniformly distributed in the range of [-latitude change amount, +latitude change amount], and the boundary coordinate point is superimposed. For example, the original coordinates of a protected area boundary point are (30.5°N, 120.3°E), and after random disturbance, they are adjusted to (30.5°±0.0012°, 120.3°±0.0015°). The offset is controlled within about 100 meters, which hides the true boundary line and retains the regional distribution trend, so that the desensitized grid cell cannot restore the original geographic information through coordinate reverse.

[0088] Further, the sensitive point after disturbance is processed by regional blurring. Taking a water pollution monitoring station as an example, its accurate coordinates (such as 31.2°N, 121.4°E) are replaced by the center point (calculated by the PostGIS function `ST_Centroid(ST_GeomFromGeohash(geohash encoding))`) of the 153m×153m grid cell where it is located, or expanded outward by 2 layers of grid to form a 3×3 coverage area (a total of 9 grids), marked as “monitoring range grid”. For example, the original 200-meter radius influence range of a monitoring station is replaced by an expanded grid to form a 459m×459m fuzzy area, which eliminates the accuracy of the point and retains the pollution diffusion trend through grid association. This “disturbance + blur” dual mechanism ensures the security of sensitive data such as ecological protection areas and monitoring stations, supports town-level pollution heat analysis and city-level protection network planning, and realizes the balance between data security and business efficiency.

[0089] In this embodiment, the grid cell storing the source element attribute data is subjected to time disturbance processing, including: A preset time offset range is set, and the time offset range is converted into a maximum time offset second.

[0090] Based on the maximum time offset second, a uniformly distributed random disturbance value is generated, and the value range of the random disturbance value is [-maximum time offset second, +maximum time offset second].

[0091] The random disturbance value is superimposed with the original timestamp data of the grid cell to generate a disturbed timestamp.

[0092] The disturbed timestamp replaces the original time data of the grid cell to obtain a desensitized grid cell subjected to time disturbance processing.

[0093] Table 3 Example data of desensitized grid cells after time perturbation

[0094] In the specific implementation process, as shown in Table 3, a city's environmental protection department desensitized the pollution monitoring time data within grid cells, set the time offset range to ±2 hours (i.e., ±7200 seconds), and generated uniformly distributed random perturbations that were superimposed on the original timestamps. For example, in Table 3, the grid cell with the geohash `wsimz6d` had its original monitoring time randomly perturbed to the range "2025-04-20T08:09:12Z" to "T17:35:13Z." This blurred the accuracy of individual time points (e.g., a monitoring time might shift from "14:30:00" to "12:45:15"). However, the daily average pollution discharge pattern (e.g., peak hours from afternoon to evening) was still preserved through aggregate analysis. In the desensitized data, the timestamp maintains a logical association with other attributes (such as "plural number (mgU)" and "total limit (mgU)") to support regional-level pollution trend analysis. For example, the multi-period data of the `wsimz6d` grid in Table 3 is used to generate a pollution discharge change curve for the region, guiding the optimization of law enforcement inspection periods while avoiding exposing the specific production times of enterprises, ensuring the compliance and security of data sharing.

[0095] In this embodiment, based on the administrative level identifier of the basic grid unit, multi-scale data aggregation and analysis are performed on the desensitized grid unit, including: Group by grid code and calculate the summary indicators of desensitized grid data within each grid. Group by village-level administrative division code and name and calculate the summary indicators of desensitized grid data within each village. Group by town-level administrative division code and name and calculate the summary indicators of desensitized grid data within each town. Group by district-level administrative division code and name and calculate the summary indicators of desensitized grid data within each district and county. Group by city-level administrative division code and name and calculate the summary indicators of desensitized grid data within each city. Based on the summary indicators, generate planning decision support information at different levels.

[0096] In actual applications, SQL automated queries (such as groupby statements) are used, for example: (1) Grid dimension data analysis, for example, analyzing the distribution of water pollution monitoring stations in a city, SQL syntax: select geohash, geohash_center, sum (water pollution monitoring site) as water pollution monitoring site from grid data table where water pollution monitoring site is not null; (2) Rural dimension data analysis, such as analyzing the distribution of nature reserves at the village level, SQL syntax: select cjdmas village code, cjmcas village name, count(nature reserve area) as nature reserve area from grid data table group by cjdm, cjmc; (3) Town-level dimensional data analysis, such as analyzing the drinking water quality assessment of the town level, sql syntax: select zjdmas town code, zjmcas town name, avg(water quality measurement) as water quality measurement from grid data table group by zjdm, zjmc; (4) District-level dimensional data analysis, such as analyzing the distribution of nature reserves at the district level, sql syntax: select xjdmas county code, xjmcas county name, sum(nature reserve area) as nature reserve area from grid data table group by xjdm, xjmc; (5) City-level dimensional data analysis, such as analyzing the distribution of nature reserves at the city level, sql syntax: select sjdmas city code, sjmcas city name, sum(nature reserve area) as nature reserve area from grid data table group by sjdm, sjmc; Convert the grid-level desensitized data into different levels of statistical indicators (such as the total area of nature reserves in the district and the pollution grid density in the city), output visual heat maps and structured reports, support the development of village ecological restoration plans, and provide data for city-level land space planning, realizing the decision-making closed loop from micro to macro.

[0097] Table 4: Example data based on grid city-level data statistical analysis results

[0098] In the implementation process, a provincial environmental protection department based on the desensitized grid data carries out city-level ecological protection evaluation, for example, by grouping according to the city-level administrative division code (such as Huizhou 441300), the field of "natural protection area" in the grid unit is summed up (sum (natural protection area)) to generate the city-level summary results as shown in Table 4. The total area of natural protection in Huizhou City in Table 4 is 1.109 billion square meters, which clearly reflects the distribution of regional ecological resources, and because the data has been desensitized by grid, the original protection area boundary coordinates and plot information have been hidden. Combined with the data of Shaoguan City (440200) 29.01 billion square meters, Qingyuan City (441800) 22.54 billion square meters, etc., the system automatically generates a provincial ecological protection heat map and ranking report, which intuitively displays the differences in ecological carrying capacity of each city and provides quantitative basis for provincial land space planning. For example, the high protection area ratio of Shaoguan City can guide the construction of ecological barrier in the north of Guangdong, and the data of 3.69 million square meters in Shenzhen City (440300) reveals the ecological protection pressure in the urbanization area, which promotes the formulation of differentiated policies. This "grid desensitization-hierarchical aggregation-visual decision" closed loop not only ensures the safety of sensitive geographic information, but also realizes the scientific allocation of provincial planning resources through standardized statistical models.

[0099] It can be understood that the present application solves the problems of low efficiency of manual conversion and fragmentation of multi-source data in traditional GIS technology through automatic analysis and adaptive processing of compatible multiple spatial file formats. Based on the Geohash spatio-temporal grid, sensitive coordinate information is converted into desensitized grid data, which eliminates the risk of privacy leakage while supporting the safe copying, transmission and application of data, greatly expanding the coverage of urban and rural planning business scenarios. By integrating multi-source heterogeneous data through a unified grid model, the spatio-temporal dynamic characteristics (such as urban expansion and environmental change) are retained, and fine spatial resolution is provided, so that business such as farmland distribution statistics and pollution monitoring analysis can be aggregated across levels through standardized interfaces, significantly improving query efficiency and decision accuracy.

[0100] Further, the spatio-temporal grid technology converts sensitive information such as ecological protection area boundaries and infrastructure points into grid-level statistical indicators or perturbed data through the "desensitization-aggregation-analysis" closed loop, which meets the differentiated needs of village-level repair scheme formulation and city-level strategic planning, and promotes the digital transformation of planning management through dynamic time series analysis. For example, grid desensitized data can support town-level water quality evaluation and city-level ecological network analysis, providing scientific basis for land space optimization, environmental governance and other scenarios while ensuring data compliance, realizing full-chain empowerment from micro-governance to macro-decision.

[0101] Embodiment 3 As Figure 2As shown, the embodiment provides a data desensitization analysis system based on a space-time grid, which is applied to the data desensitization analysis method based on a space-time grid as described in the above embodiment, and includes a generation module 100, an association module 200, an acquisition module 300, an intersection space operation module 400, an extended storage module 500, a desensitization module 600, and an aggregation analysis module 700.

[0102] The generation module 100 generates grid cells based on a preset geocoding rule. The association module 200 associates the grid cells with administrative area level information to generate basic grid cells containing administrative area level identifiers. The acquisition module 300 is configured to acquire spatial data. The intersection space operation module 400 is configured to perform intersection space operation on the basic grid cells and the spatial data to obtain grid cells having intersection relationship. The extended storage module 500 is configured to extend a source feature attribute field of the grid cells having intersection relationship, and store source feature attribute data of the spatial data to the source feature attribute field corresponding to the grid cells. The desensitization module 600 is configured to perform at least one desensitization processing including grid data aggregation, spatial disturbance, and temporal disturbance on the grid cells storing the source feature attribute data to obtain desensitized grid cells. The aggregation analysis module 700 is configured to perform multi-scale data aggregation and analysis on the desensitized grid cells based on the administrative area level identifiers of the basic grid cells.

[0103] It should be noted that the above explanation of the embodiment of the data desensitization analysis method based on a space-time grid is also applicable to the data desensitization analysis system based on a space-time grid of the embodiment, which will not be described here.

[0104] Embodiment 4 Figure 3 The structure schematic diagram of the electronic device 800 provided in the embodiment is shown. The electronic device 800 includes a memory 801, a processor 802, and a computer program stored in the memory 801 and executable on the processor 802.

[0105] The processor 802 implements the data desensitization analysis method based on a space-time grid provided in the above embodiment when executing the program.

[0106] Further, the electronic device 800 further includes a communication interface 803 for communication between the memory 801 and the processor 802.

[0107] The memory 801 can include a high-speed RAM (Random Access Memory, random access memory) memory, and can also include a non-volatile memory such as at least one disk memory.

[0108] If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0109] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0110] The processor 802 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0111] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned data desensitization analysis method based on the spatiotemporal grid.

[0112] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0113] In addition, the terms "first", "second", etc. are used herein only to describe different instances, and do not imply or suggest relative importance or a quantity of the indicated technical features. Thus, features defined with "first", "second", etc. can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise explicitly and specifically limited.

[0114] Any process or method descriptions or descriptions of the flow diagrams described herein can be understood as representing modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions (or steps) described by that module, segment, or portion of code. The various embodiments of the present application can include additional or fewer steps or methods as described in the examples provided herein, and the order of the steps or methods can be changed, including the use of concurrent steps or methods, unless otherwise specifically limited. The various embodiments of the present application can be implemented in hardware, software, firmware, or a combination thereof.

[0115] It should be understood that various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, as in another embodiment, any of the following technologies or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application-specific integrated circuits having appropriate combinational logic gates, programmable gate arrays, field-programmable gate arrays, etc.

[0116] Those skilled in the art can understand that all or part of the steps carried out by the above-described embodiments can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, they include one or a combination of the steps of the method embodiments.

[0117] Obviously, the above-described embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the embodiments of the present application. Based on the above description, other different forms of changes or modifications can be made by those skilled in the art. Here, all embodiments need not be exhaustively listed. Any modifications, equivalent replacements, and improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A data desensitization analysis method based on spatiotemporal grid, characterized in that: include: Generate grid cells based on preset geocoding rules; Associating the grid unit with the administrative district level information to generate a basic grid unit containing the administrative district level identifier; Get spatial data; Performing an intersection spatial operation on the basic grid unit and the spatial data to obtain grid units having an intersection relationship; Expand the source feature attribute fields for the grid cells that have an intersecting relationship, and store the source feature attribute data of the spatial data into the source feature attribute fields corresponding to the grid cells; Performing a desensitization process on a grid unit storing active feature attribute data, including at least one of grid data aggregation, spatial perturbation, and temporal perturbation, to obtain a desensitized grid unit; Based on the administrative level identification of the basic grid unit, multi-scale data aggregation and analysis are performed on the desensitized grid units.

2. The data desensitization analysis method based on spatiotemporal grid according to claim 1 is characterized in that: Generate grid cells based on preset geocoding rules, including: Obtain multi-level administrative boundary data including spatial boundaries at the provincial, municipal, district, county, town, and village levels; Based on the multi-level administrative division boundary data, a multi-level spatial grid unit is generated using a Geohash encoding rule; The administrative division code and name of each multi-level spatial grid unit are associated with the data attributes of the multi-level spatial grid unit to generate a basic grid unit containing the administrative division level identifier.

3. The data desensitization analysis method based on spatiotemporal grid according to claim 1 is characterized in that: The source element attribute data includes one or more of cultivated land area, slope, temperature and precipitation.

4. The data desensitization analysis method based on spatiotemporal grid according to claim 3 is characterized in that: Perform grid data aggregation processing on the grid cells storing active feature attribute data, including: Performing grouping and aggregation calculations on the source feature attribute data of the intersecting grid cells according to a preset statistical function to generate aggregated data; the preset statistical function includes at least one of a sum function, a maximum function, a minimum function, and an average function; The aggregated data is used to replace the source feature attribute data stored in the intersecting grid cells to generate desensitized grid cells containing the aggregated data.

5. The data desensitization analysis method based on spatiotemporal grid according to claim 3 is characterized in that: Perform spatial perturbation on the grid cells storing active feature attribute data, including: Setting a preset disturbance radius, and converting the disturbance radius into a latitude change and a longitude change based on the radius of the earth; generating a random disturbance value according to the latitude change and the longitude change; Superimposing the random disturbance value with the original coordinate data of the grid unit to generate disturbed spatial coordinates; The disturbed spatial coordinates are used to replace the original coordinate data of the grid unit to obtain a desensitized grid unit that has undergone spatial disturbance processing.

6. The data desensitization analysis method based on spatiotemporal grid according to claim 5 is characterized in that: The method further comprises: The point-shaped or boundary elements in the desensitized grid cells that have undergone spatial perturbation processing are replaced with a grid coverage area containing the location of the point-shaped or boundary elements. The grid coverage area includes at least one of the following forms: A single grid cell centered at the location of the point or boundary feature; A multi-circle coverage area is formed by expanding n layers of grids outward from the location of the point or boundary element as the center, where n≥1.

7. The data desensitization analysis method based on spatiotemporal grid according to claim 3 is characterized in that: Perform time perturbation processing on the grid cells storing active feature attribute data, including: Setting a preset time offset range and converting the time offset range into a maximum time offset in seconds; Generate a random disturbance value based on the maximum time offset in seconds, where the range of the random disturbance value is [-maximum time offset in seconds, +maximum time offset in seconds]; Superimposing the random disturbance value with the original timestamp data of the grid unit to generate a disturbed timestamp; The disturbed timestamp is used to replace the original time data of the grid unit to obtain a desensitized grid unit that has undergone time disturbance processing.

8. The data desensitization analysis method based on spatiotemporal grid according to any one of claims 1 to 7, characterized in that: Based on the administrative level identification of the basic grid unit, multi-scale data aggregation and analysis are performed on the desensitized grid unit, including: Group by grid code and calculate the summary indicators of desensitized grid data in each grid; Group by village-level administrative division code and name, and calculate summary indicators of desensitized grid data within each village; Group by town-level administrative division code and name, and calculate summary indicators of desensitized grid data within each town; Group by district and county-level administrative division code and name, and collect summary indicators of desensitized grid data within each district and county; Group by city-level administrative division code and name, and collect summary indicators of desensitized grid data within each city; Based on summary indicators, planning decision support information at different levels is generated.

9. The data desensitization analysis method based on spatiotemporal grid according to claim 1 is characterized in that: Performing an intersection spatial operation on the basic grid unit and the spatial data to obtain grid units having an intersection relationship includes: Converting the GeoHash code of the basic grid cell into a geometric spatial data type to generate a bounding box spatial graphic of the basic grid cell; performing an intersection operation on the bounding box spatial graphic and the spatial data to obtain an intersection of the geometric figures of the bounding box spatial graphic and the spatial data; If the intersection is a non-empty geometric figure, it is determined that the basic grid unit and the spatial data have an intersection relationship; if the intersection is empty, it is marked that the basic grid unit and the spatial data have no intersection relationship.

10. A data desensitization analysis system based on spatiotemporal grid, characterized in that: include: A generation module generates grid cells based on preset geocoding rules; an association module, which associates the grid unit with the administrative district level information to generate a basic grid unit containing the administrative district level identifier; Acquisition module, used to obtain spatial data; An intersection space operation module, configured to perform an intersection space operation on the basic grid unit and the spatial data to obtain grid units having an intersection relationship; An extended storage module is used to expand the source feature attribute fields of the grid cells that have an intersecting relationship, and store the source feature attribute data of the spatial data into the source feature attribute fields corresponding to the grid cells; a desensitization module, configured to perform at least one desensitization process of grid cells storing active feature attribute data, including grid data aggregation, spatial perturbation, and temporal perturbation, to obtain desensitized grid cells; The aggregation analysis module is used to aggregate and analyze multi-scale data of desensitized grid cells based on the administrative level identification of the basic grid cells.

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