Grid data protection method and system

Abstract

The invention relates to the technical field of data processing, and provides a grid data protection method and system, and the method comprises the steps: inputting grid basic data of a provider into a preset service platform for conversion, and obtaining service data; when a demand side sends a grid data access request, querying and matching the grid data access request to obtain corresponding service data; and returning the service data to the demand side through the service platform. The grid basic data of the provider is input into the service platform for conversion to generate the service data, so that the problems of data diffusion and leakage caused by direct transmission of the grid basic data are effectively prevented; the service platform is used as a data sharing bridge, so that effective protection and safe sharing are realized, diversified requirements of a demand side on grid information are met, direct exposure of grid basic data is avoided, direct transmission of grid coordinate data is improved, and the propagation range of the grid basic data cannot be effectively controlled; and the problems of data leakage risk and insufficient security exist.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a method and system for protecting grid data. Background Art

[0002] Grid management is a grassroots management model widely used in many industries. Its core is to divide the jurisdiction into multiple small management units (i.e. grids). These grids are based on territorial management and are divided by comprehensively considering factors such as location, population size, area size, and management difficulty. In industries with strong physical space correlation, such as power management, social governance, and logistics, grid management is widely used for refined operations and resource allocation. In addition, regional service industries such as bank branches, convenience stores, and chain stores also widely use grid management to optimize service scope and management efficiency.

[0003] Among the relevant technical means, the traditional grid division and data transmission methods include using existing grid data provided by other industries or companies or optimizing and adjusting the grid data of other industries or companies to generate new grid data that meets their own needs. The grid data is usually shared in a direct transmission manner, which realizes the efficient transmission of grid data, facilitates the demand side to quickly use the grid information in its business system for positioning, regional coverage and other needs, and meets the diverse grid management scenarios.

[0004] Regarding the above technical solution, although the sharing needs of grid information can be met by directly transmitting grid basic data, due to the direct transmission of grid coordinate data, the provider cannot effectively control the dissemination scope of the grid basic data, which can easily lead to the spread and abuse of grid data, especially in sensitive data or important business scenarios, there are risks of data leakage and insufficient security. Summary of the invention

[0005] In order to improve the direct transmission of grid coordinate data, which cannot effectively control the propagation range of grid basic data, and has the problems of data leakage risk and insufficient security, the present application provides a grid data protection method and system.

[0006] The present invention provides a method for protecting grid data, comprising: inputting grid basic data of a provider into a preset service platform for conversion to obtain service data; when a demander issues a grid data access request, querying and matching the grid data access request to obtain corresponding service data; and returning the service data to the demander through the service platform.

[0007] As a preferred solution, the step of inputting the provider's grid basic data into a preset service platform for conversion to obtain service data includes: encrypting the provider's grid basic data through a symmetric encryption algorithm to obtain encrypted grid basic data; and fuzzifying the encrypted grid basic data through preset data conversion rules to generate corresponding service data.

[0008] As a preferred solution, the data conversion rules include: based on the geographic location information of the grid basic data, randomly perturbing the longitude and latitude coordinates through Gaussian noise to generate a fuzzy area identification; grading the fuzzy area identification through preset grading rules to generate service data of different levels; wherein the grading rules divide the levels according to the size of the geographic boundaries, population density and business characteristics.

[0009] As a preferred solution, the step of querying and matching the grid data access request to obtain corresponding service data includes: using the service platform to receive the grid data access request submitted by the demander, and parsing the grid data access request to obtain query conditions; screening and matching the service data in the service platform according to the query conditions to obtain service data related to the query conditions, and generating corresponding service data based on the service data related to the query conditions.

[0010] As a preferred scheme, the step of generating corresponding service data based on the service data related to the query conditions includes: obtaining the identity authentication information of the demander, and determining the access permission level of the demander based on the identity authentication information; according to the access permission level, screening the service data related to the query conditions to obtain corresponding service data to ensure that the demander can only access service data within the scope of the access permission level.

[0011] As a preferred solution, the step of returning the service data to the demander through the service platform includes: performing dynamic fuzzy processing on the service data to obtain response data that does not contain the original grid coordinates; and returning the response data to the demander in the form of an API interface through the service platform.

[0012] As a preferred solution, the dynamic fuzzy processing includes: using a GIS algorithm to blur the geographic boundary information of the service data to generate a simplified boundary suitable for public access, and shielding specific attribute information in the service data to hide the sensitive data of the provider, thereby obtaining shielded attribute data; wherein the specific attribute information includes the latitude and longitude coordinates of the grid, specific boundary points and core data related to the business; and compressing the simplified boundary and the shielded attribute data through a data compression algorithm to obtain response data that does not contain the original grid coordinates.

[0013] The present application also provides a grid data protection system, including: a conversion unit, used to input the provider's grid basic data into a preset service platform for conversion to obtain service data; a matching unit, used to query and match the grid data access request when the demander issues a grid data access request to obtain corresponding service data; a transmission unit, used to return the service data to the demander through the service platform.

[0014] Compared with the prior art, the present application has the following beneficial effects: low risk of leakage and high security. By inputting the grid basic data of the provider into the service platform for encryption, fuzzification and hierarchical processing, service data is generated, which effectively prevents the data diffusion and leakage problems caused by the direct transmission of grid basic data; the service platform parses the grid data access request of the demander and screens and matches the service data, ensuring that the demander can obtain service data that meets its query conditions; by dynamically fuzzifying the returned data and shielding sensitive information, the security of data sharing is further improved; in the whole process, the service platform acts as a bridge for data sharing, realizing effective protection and safe sharing of data, which not only meets the diversified needs of the demander for grid information, but also avoids the direct exposure of grid basic data, improves the direct transmission of grid coordinate data, and cannot effectively control the dissemination range of grid basic data, and has the problems of data leakage risk and insufficient security. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0017] Figure 1 It is a flowchart of a method for protecting grid data provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of a grid data protection system provided by an embodiment of the present invention.

[0018] Description of reference numerals: 10. Grid data protection system; 11. Conversion unit; 12. Matching unit; 13. Transmission unit. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0021] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0022] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0024] Embodiment 1: like Figure 1 As shown, the present application provides a method for protecting grid data, including steps S100 to S300.

[0025] Step S100: input the grid basic data of the provider into a preset service platform for conversion to obtain service data.

[0026] In this step, firstly, the grid basic data of the provider is input into the service platform through the preset data import interface; specifically, the grid basic data includes the boundary coordinate information of the grid, the regional service scope and related attribute data, and the grid basic data is encrypted through the preset encryption algorithm to ensure the security of the data. Then, the coordinate information in the grid basic data is fuzzified using geographic information processing technology to generate fuzzy regional identification. Finally, the fuzzy regional identification is graded based on the preset rules, and the processed data is stored as service data in the service platform.

[0027] For example, the power management department of a city uploads the basic coordinate data of its urban grid to the service platform. The platform protects data security through AES encryption, adds Gaussian noise perturbations to the boundary coordinates of each area, and divides the urban area into three levels: high, medium, and low according to the size of the grid, generating service data that can be used by the demand side.

[0028] Step S200: When the demander issues a grid data access request, the grid data access request is queried and matched to obtain corresponding service data.

[0029] In this step, the service platform receives and parses the grid data access request submitted by the demander; specifically, the demander's request usually contains query conditions, such as a specified geographical location range, grid service range, or regional attribute requirements. The service platform uses the query parsing module to parse these conditions and generate query parameters, and then filters and matches the service data according to the query parameters to obtain the service data that meets the demander's request.

[0030] For example, a logistics company submits a query request to obtain the distribution area information of a certain city. After the service platform analyzes the geographical scope conditions of the request, it selects the corresponding grid information from the service data.

[0031] Step S300: Return the service data to the demander through the service platform.

[0032] In this step, the service platform further processes the service data obtained by query matching to ensure that the returned data meets the privacy protection requirements; specifically, the sensitive information in the filtered service data (such as the specific coordinate points of the grid boundary) is shielded, and the Douglas-Peucker algorithm is used to simplify the grid boundary information to generate response data that does not contain the original grid basic data. Finally, the processed response data is returned to the demander through the API interface for its use.

[0033] For example, after a logistics company obtains urban distribution area information, the service data returned by the platform only contains blurred distribution boundaries and removes the original specific longitude and latitude coordinate information, which meets the company's needs while protecting the provider's grid basic data.

[0034] In this embodiment, the grid basic data of the provider is input into a preset service platform, and the grid basic data is converted and processed by the service platform to generate service data; then, when the demander issues a grid data access request, the service platform parses the content of the grid data access request, queries and matches the service data according to the parsing result, and screens out the service data related to the demander's query condition; finally, the screened service data is returned to the demander through the service platform to meet the demander's usage requirements. The security issues caused by directly transmitting grid basic data in traditional technologies are avoided, and the protection of the grid basic data of the provider in the data sharing process is realized. The direct transmission of grid coordinate data cannot effectively control the dissemination range of grid basic data, and there are data leakage risks and insufficient security issues.

[0035] Embodiment 2: In step S100, the grid basic data of the provider is encrypted by a symmetric encryption algorithm to obtain encrypted grid basic data.

[0036] The grid basic data of the provider is encrypted by using the AES symmetric encryption algorithm; specifically, the grid basic data of the provider, including the boundary coordinate information, geographic location description and regional attribute data of the grid, is imported into the encryption module, and the data is encrypted using the encryption key of the AES algorithm to generate encrypted data in ciphertext form. The encrypted grid basic data exists in ciphertext form during transmission and storage, ensuring the security of the data and preventing unauthorized access and leakage.

[0037] For example, in the power management system, after a power supply company imports the latitude and longitude boundaries of the regional grid and the number of users covered into the service platform, the platform uses the AES algorithm to encrypt these basic data and generate corresponding ciphertext data. The ciphertext data can be directly stored in the database of the service platform, avoiding the risk of leakage of plaintext data.

[0038] The encrypted grid basic data is fuzzified through preset data conversion rules to generate corresponding service data.

[0039] The encrypted grid basic data is fuzzified; specifically, the fuzzification process uses a Gaussian noise injection algorithm to randomly offset the longitude and latitude coordinates of the grid, so that the generated service data can fuzzily reflect the geographic scope of the grid without leaking the original precise boundary information. In addition, the key fields in the regional attribute data are fuzzy processed, for example, the specific number of users is ranged into interval values, further reducing the sensitivity of the original data.

[0040] For example, in the management of logistics grids, for the boundary coordinates of a distribution area, the platform adds random noise of ±0.01 degrees to its longitude and latitude coordinates, and blurs the population coverage of the area from a specific number (such as 2,345 people) to an interval range (such as 2,000-2,500 people), generating fuzzy service data that can protect privacy.

[0041] The data conversion rules include: based on the geographic location information of the grid basic data, randomly disturbing the longitude and latitude coordinates through Gaussian noise to generate a fuzzy area identification.

[0042] The longitude and latitude coordinates are randomly disturbed by Gaussian noise to generate fuzzy regional identification. Specifically, for the boundary coordinate points of each grid, they are disturbed by adding random values ​​that conform to the Gaussian distribution, while ensuring that the coordinate points after disturbance can still roughly form the shape of the original grid. The fuzzy regional identification not only effectively protects the privacy of the grid basic data, but also meets the application needs of the demand side for regional data.

[0043] For example, in the processing of administrative division data of a certain city, a longitude and latitude coordinate point of the original boundary (such as longitude 121.12345, latitude 31.54321) is changed to longitude 121.12401, latitude 31.54289 by adding Gaussian noise (such as a disturbance range of ±0.001). The generated fuzzy area identification can still be used for urban planning applications.

[0044] The fuzzy area identification is graded according to preset classification rules to generate service data of different levels; the classification rules divide the levels according to the size of geographical boundaries, population density and business characteristics.

[0045] The fuzzy area identification is graded through preset classification rules; specifically, the grids are divided into large, medium and small levels according to the size of the geographical boundary; the area is divided into high, medium and low density grids according to the population density; the grids are divided into priority areas and ordinary areas according to the business characteristics. Each division rule adopts a standardized classification method and provides different service data versions in combination with the access rights level of the demander.

[0046] For example, in bank branch planning, for a commercial area with a high population density, the platform divides its area into "high-priority small grids" and provides high-precision service data; while for suburban areas with a low population density, it is divided into "low-priority large grids" and only provides low-precision fuzzy data.

[0047] In step S200, the step of querying and matching the grid data access request to obtain the corresponding service data includes: using the service platform to receive the grid data access request submitted by the demander, and parsing the grid data access request to obtain the query condition.

[0048] By parsing the grid data access request submitted by the demander, specific query conditions are generated; specifically, the demander's request usually contains information such as geographical scope, service attributes and grid priority. The platform parses the request content and converts it into a query condition format that the system can recognize for retrieval of service data.

[0049] For example, a logistics company requests "obtaining the distribution grid information within a 5km radius of Area A". After parsing the request, the platform generates query conditions, including "geographic scope = Area A" and "service radius ≤ 5km", and uses them to query service data.

[0050] The service data in the service platform are screened and matched according to the query conditions to obtain the service data related to the query conditions, and corresponding service data are generated based on the service data related to the query conditions.

[0051] By screening and matching the service data; specifically, the platform uses the query conditions to retrieve relevant grid records in the stored service data, and further determines the data range that the demander can access through the demander's identity authentication information. Based on the screening results, the platform optimizes the format of the service data that meets the conditions and shields sensitive information, and generates the final response data returned.

[0052] For example, the identity authentication of a demander shows that his authority is limited to low-precision data. After matching the distribution grid of Area A, the platform further simplifies the original fuzzy data, removes the precise boundary information of the area coverage, and returns only general range data.

[0053] The step of generating corresponding service data based on service data related to the query condition includes: obtaining identity authentication information of the demander, and determining the access authority level of the demander based on the identity authentication information.

[0054] The authentication module obtains the authentication information of the demander; specifically, the authentication information includes the demander's user ID, authorization key, and access level identifier. The service platform compares the authentication information submitted by the demander with the preset permission list in the background to determine the demander's access permission level. According to the access permission level, the demander will be assigned service data access permissions of different precisions and ranges to ensure the security and hierarchical nature of data sharing.

[0055] For example, a user of a logistics company submits an access request and attaches the user ID "12345" and authorization key. The service platform compares the user's permission level record and determines that the user has medium-level permissions and can only access blurred service data, but cannot obtain high-precision grid information.

[0056] According to the access permission level, the service data related to the query condition is filtered to obtain the corresponding service data to ensure that the demander can only access the service data within the scope of the access permission level.

[0057] The service data related to the query conditions are filtered through the permission filtering module; specifically, firstly, the filtered service data is accurately matched according to the permission level of the demander, for example, only the fuzzy area identification and attribute information that meet the permission range is returned. Then, the high-authority data is dynamically masked, such as shielding the high-precision coordinate points of the boundary and hiding sensitive attribute data, and finally the service data that meets the permission range of the demander is generated.

[0058] For example, if a demander's permission level is "low-precision permission", when querying "distribution grid information of a certain urban area", the platform will downgrade the filtered high-precision grid boundary data and only return the blurred grid boundary information and a simplified distribution range description.

[0059] In step S300, dynamic fuzzy processing is performed on the service data to obtain response data that does not contain the original grid coordinates.

[0060] Data privacy is further protected through dynamic fuzzy processing; specifically, the platform uses GIS algorithms to dynamically simplify the geographic boundaries of service data, and at the same time masks and compresses the attribute information of the region to ensure that the generated response data not only meets the use requirements of the demander, but also avoids leaking sensitive information of the original grid basic data.

[0061] For example, for the grid data of a commercial area, the platform compresses the boundary data into the smallest polygon through a dynamic simplification algorithm, and performs interval processing on the attributes of the area (such as the number of covered users), returning only the fuzzy area range and attribute description.

[0062] The response data is returned to the demander in the form of an API interface through the service platform.

[0063] The filtered response data is returned to the demander through the API interface; specifically, the response data includes fuzzy grid boundary information and desensitized regional attribute information. The service platform encapsulates the response data into a standard API response format (such as JSON or XML) based on the query parameters of the demander, and transmits the data to the demander through a network protocol (such as HTTPS) to ensure security and integrity during the transmission process.

[0064] For example, after a logistics company requests distribution area data from the platform, the platform generates response data in JSON format containing the fuzzy grid range, and transmits it to the company's business system through a secure connection for regional planning and distribution scheduling.

[0065] Among them, dynamic fuzzy processing includes: using GIS algorithms to blur the geographic boundary information of service data, generating simplified boundaries suitable for public access, and shielding specific attribute information in the service data to hide the provider's sensitive data and obtain shielded attribute data; among them, specific attribute information includes the latitude and longitude coordinates of the grid, specific boundary points and core data related to the business.

[0066] The geographic boundary information of the service data is dynamically fuzzified through GIS algorithms; specifically, the Douglas-Peucker algorithm is used to simplify the boundary information, compressing the complex boundary point set into a small number of key points while maintaining the overall shape of the boundary. In addition, specific attribute information in the service data (such as the number of covered users and service type) is shielded, hiding the latitude and longitude coordinates, specific boundary points and business-related core data, and generating shielded attribute data.

[0067] For example, the original boundary of a grid contains 100 coordinate points. The platform compresses it into 10 key points through the Douglas-Peucker algorithm, hides the specific number of covered users, and only retains the approximate range and service type description.

[0068] The simplified boundary and masked attribute data are compressed by a data compression algorithm to obtain response data that does not contain the original grid coordinates.

[0069] The simplified boundary and masked attribute data are optimized through data compression algorithms; specifically, the simplified boundary data and masked attribute information are encoded and compressed using a combination algorithm based on RLE (Run Length Encoding) and ZLIB compression to reduce the amount of data and improve transmission efficiency. The compressed data format is compact, which is convenient for further reducing resource consumption during network transmission and storage.

[0070] For example, the distribution grid information of a certain urban area contains 50 coordinate points and 5 attribute fields. The platform uses the RLE algorithm to compress the repeated parts of the coordinate data, and then combines ZLIB to optimize the encoding of the entire data stream, compressing the original size of 10KB of data to 3KB before returning it.

[0071] In this embodiment, the grid basic data of the provider is input into the service platform and encrypted, and the grid basic data is securely encrypted using the AES symmetric encryption algorithm, effectively preventing the data from being stolen or tampered with during transmission and storage; then, the longitude and latitude coordinates in the grid basic data are randomly disturbed using the Gaussian noise algorithm to generate fuzzy area identifiers, ensuring that the service data protects the privacy of the provider's precise grid boundary data while meeting the query needs of the demander. Based on the preset classification rules, the fuzzy area identifiers are graded according to multiple dimensions such as geographic boundary size, population density, and business characteristics to generate multi-level service data, providing adaptive precision data services for different authority demanders.

[0072] After receiving the grid data access request from the demander, the service platform parses the query conditions, verifies the identity of the demander using the permission authentication module, and screens and controls the accuracy of the query matching data according to the permission level to ensure that the demander can only access the service data within the permission scope. Before returning the data to the demander, the service platform further protects the privacy of the service data through dynamic fuzzy processing, such as using the Douglas-Peucker algorithm to simplify the grid boundary information and shield the attribute information (such as longitude and latitude coordinates and specific boundary points). Finally, the processed service data is optimized using a data compression algorithm to generate small-volume response data that does not contain the original grid coordinates, and is securely transmitted to the demander through the API interface.

[0073] Embodiment 3: like Figure 2 As shown, the present application also provides a grid data protection system 10 , including a conversion unit 11 , a matching unit 12 and a transmission unit 13 .

[0074] The conversion unit 11 is used to input the grid basic data of the provider into a preset service platform for conversion to obtain service data.

[0075] The grid basic data of the provider is processed by the conversion unit 11, which inputs the grid basic data of the provider into the preset service platform, and encrypts and protects the data through an encryption algorithm, and at the same time randomly perturbs and fuzzifies the boundary coordinates and attribute information of the grid in combination with data fuzzification technology to generate service data that does not contain sensitive information. The conversion unit 11 also classifies the fuzzified service data through hierarchical rules, and divides it into different levels of data according to multiple dimensions such as geographic boundary size, population density and business characteristics, so as to meet the authority and accuracy requirements of different demand parties.

[0076] The matching unit 12 is used to query and match the grid data access request when the demander issues a grid data access request, and obtain corresponding service data.

[0077] The matching unit 12 is responsible for processing the grid data access request of the demander. By parsing the query conditions of the demander, the matching unit 12 can quickly retrieve the service data related to the query conditions in the service data, and determine the access permission level of the demander according to the identity authentication information of the demander, and further screen and adjust the matching results to ensure that the demander can only access the data within the scope of the access permission. This process realizes the refined management of data access, avoids the leakage of sensitive data, and meets the actual needs of the demander.

[0078] The transmission unit 13 is used to return the service data to the demander through the service platform.

[0079] The transmission unit 13 is responsible for returning the service data to the demander. The unit further simplifies the returned data through dynamic fuzzy processing technology, such as using GIS algorithms to dynamically simplify grid boundary information, shielding sensitive attribute data (such as specific latitude and longitude coordinates and core business data), and optimizing the data format through data compression algorithms to generate response data that does not contain the original grid basic data. The transmission unit 13 transmits the processed data to the demander in a secure manner through the API interface to ensure data integrity and security during the transmission process.

[0080] In this embodiment, by designing a grid data protection system including a conversion unit 11, a matching unit 12 and a transmission unit 13, the secure sharing and privacy protection of grid data are effectively realized, and the security and reliability of grid data sharing are significantly improved through encryption, fuzzification, hierarchical processing, authority matching, dynamic fuzzification and data compression and other technical means. This system not only solves the privacy leakage problem caused by the direct transmission of grid basic data in traditional technologies, but also ensures the data security of the provider while meeting the data needs of the demander, and has wide applicability and promotion value.

[0081] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the system and each unit described above can refer to the corresponding process in the aforementioned grid data protection method and system embodiment, and will not be repeated here.

[0082] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for protecting grid data, characterized in that: include: Input the grid basic data of the provider into the preset service platform for conversion to obtain service data; When the demander issues a grid data access request, the grid data access request is queried and matched to obtain corresponding service data; The service data is returned to the demander through the service platform.

2. The method for protecting grid data according to claim 1, characterized in that: The step of inputting the grid basic data of the provider into the preset service platform for conversion to obtain the service data comprises: Encrypting the grid basic data of the provider through a symmetric encryption algorithm to obtain encrypted grid basic data; The encrypted grid basic data is fuzzified by using preset data conversion rules to generate corresponding service data.

3. The method for protecting grid data according to claim 2, characterized in that: The data conversion rules include: Based on the geographic location information of the grid basic data, the longitude and latitude coordinates are randomly disturbed by Gaussian noise to generate fuzzy area identification; The fuzzy area identification is graded according to preset grading rules to generate service data of different levels; wherein the grading rules divide the levels according to the size of geographical boundaries, population density and business characteristics.

4. The method for protecting grid data according to claim 1, characterized in that: The step of querying and matching the grid data access request to obtain corresponding service data includes: Utilizing the service platform to receive a grid data access request submitted by a demander, and parsing the grid data access request to obtain a query condition; The service data in the service platform are screened and matched according to the query conditions to obtain service data related to the query conditions, and corresponding service data are generated based on the service data related to the query conditions.

5. The method for protecting grid data according to claim 4, characterized in that: The step of generating corresponding service data based on the service data related to the query condition comprises: Obtaining identity authentication information of the requester, and determining the access permission level of the requester based on the identity authentication information; According to the access permission level, the service data related to the query condition is screened to obtain corresponding service data, so as to ensure that the demander can only access the service data within the scope of the access permission level.

6. The method for protecting grid data according to claim 1, characterized in that: The step of returning the service data to the demander through the service platform includes: Performing dynamic fuzzy processing on the service data to obtain response data that does not include original grid coordinates; The response data is returned to the demander in the form of an API interface through the service platform.

7. The method for protecting grid data according to claim 6, characterized in that: The dynamic blur processing includes: Using GIS algorithm to blur the geographic boundary information of the service data, generate simplified boundaries suitable for public access, and shield specific attribute information in the service data to hide the sensitive data of the provider, and obtain shielded attribute data; wherein the specific attribute information includes the latitude and longitude coordinates of the grid, specific boundary points and core data related to the business; The simplified boundary and the shielded attribute data are compressed by a data compression algorithm to obtain response data that does not contain the original grid coordinates.

8. A grid data protection system, characterized in that: include: A conversion unit, used for inputting the grid basic data of the provider into a preset service platform for conversion to obtain service data; A matching unit, configured to query and match the grid data access request when the demander issues a grid data access request, and obtain corresponding service data; The transmission unit is used to return the service data to the demander through the service platform.

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