An agricultural ecological member data security management method and system

Abstract

The application provides an agricultural ecological member data security management method and system, which is applied to the technical field of data security management, and effectively solves the integrity and non-tampering verification problem of agricultural ecological member data in a multi-subject, long-period and dynamic updating environment through the introduction of client data digest, server data digest comparison, chain structure storage, multi-independent subject confirmation certificate and multiple security verification mechanisms, and ensures the authenticity and reliability of the data.

Description

Technical Field

[0001] This application relates to the field of data security management technology, and in particular to a method and system for managing the data security of agricultural ecosystem members. Background Technology

[0002] In large agricultural cooperatives, multiple farms, processing plants, and distributors participate in the production and distribution of agricultural products as members. The cooperative has established a data management platform to aggregate key business data from each member, such as planting batches, fertilization records, pesticide usage, and harvest dates from farms; warehousing inspections, processing batches, and quality inspection reports from processing plants; and logistics tracking and sales records from distributors. This data is crucial for ensuring the traceability of agricultural products from source to consumer. The platform receives and stores data from each member through standardized interfaces. When consumers scan the QR code on the product packaging, they can access the product's traceability information. The cooperative's internal quality management department also regularly reviews this data to ensure that products meet established quality standards and regulatory requirements.

[0003] However, in actual operation, agricultural production has a long cycle, involves many links, and data flows between multiple independent members, making it easy for data tampering to occur.

[0004] Traditional technologies address this issue by employing post-submission verification mechanisms. While the platform performs initial data format and logic checks when a member submits data, this only ensures the apparent correctness of the data at the time of submission. It fails to effectively prevent data tampering before submission or guarantee against subsequent modifications by members on their local systems. This leads to inconsistencies between the platform-stored data and the member's "real" data, requiring significant manpower and resources for periodic or surprise checks. This inefficiency and limited coverage make it difficult to achieve real-time, comprehensive integrity verification of massive, dynamically updated data. Furthermore, this approach lacks a trusted third party to centrally manage all members' raw data. Existing data security management methods struggle to establish a reliable mechanism capable of automatically verifying data integrity and mitigating tampering risks while ensuring the independence of each member's data.

[0005] In summary, in agricultural cooperative data management platforms comprised of multiple independent operating entities such as farms, processing plants, and distributors, existing data security management methods struggle to establish a mechanism that, while ensuring the independence of each member's data, can reliably record and verify the integrity of agricultural ecosystem member data throughout its entire lifecycle—from generation and submission to any subsequent modifications—in real time, and effectively resist traceability tampering. This would be impossible while maintaining the data independence of each member.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] In view of the shortcomings of the prior art, this application provides a method and system for the security management of agricultural ecological member data, which has the advantages of effectively solving the problem of integrity and tamper-proof verification of agricultural ecological member data in a multi-subject, long-cycle, and dynamically updated environment, and ensuring the authenticity and credibility of the data.

[0008] Firstly, a method for secure management of agricultural ecosystem member data, the method comprising the following steps:

[0009] S1: Obtain the original business data uploaded by the user on the client, and extract the first data digest based on the original business data;

[0010] S2: Obtain the current business data received by the server, and extract the second data digest based on the current business data;

[0011] S3: Compare the first data digest with the second data digest. If the comparison results are consistent, obtain the third data digest of the preceding business data associated with the original business data, and store the first data digest and the second data digest in a chain structure with the third data digest.

[0012] S4: Obtain specified data and a fifth data digest corresponding to the specified data, distribute the fifth data digest to multiple independent entities, and receive confirmation credentials from the multiple independent entities;

[0013] S5: When performing security verification of the original business data, the current business data is re-extracted to obtain a fourth data digest. When the fourth data digest is consistent with the second data digest, the chain structure is not modified, and the confirmation credential is valid, the original business data is determined to be secure.

[0014] This application proposes a data security management method for agricultural ecosystem members. By extracting and comparing data summaries at both the data generation and receiving ends, and combining this with a chain structure to record data change history, a distributed verification mechanism involving multiple independent entities is introduced. Ultimately, during data security verification, the consistency of data summaries, the integrity of the chain structure, and the validity of multi-party confirmation credentials are comprehensively considered. This constructs a multi-dimensional, full-lifecycle data integrity and tamper-proof verification system, effectively solving the problem of ensuring the authenticity and credibility of agricultural ecosystem member data in environments with multi-party collaboration, dynamic updates, and the risk of tampering. It has the advantage of ensuring the authenticity and credibility of data.

[0015] Furthermore, step S1 includes:

[0016] S11: On the client side, when the original business data is generated for the first time or modified, the first data digest is immediately calculated on the original business data;

[0017] S12 writes the original business data, the first data summary, the generation time of the original business data, and the modification time of the original business data into the local data log; the local data log only supports append write operations.

[0018] S13: When the client establishes a connection with the server, it obtains the latest version of the original business data and its corresponding first data summary from the local data log and uploads it to the server.

[0019] The proposed method for managing the security of agricultural ecosystem member data ensures that original business data is recorded and summarized when generated or modified on the client side, and is promptly uploaded to the server, thereby guaranteeing the integrity and traceability of the data source.

[0020] Furthermore, step S2 includes:

[0021] S21: When the server receives the current service data, it parses the current service data;

[0022] S22: Based on the parsing results, identify and extract a preset set of key fields;

[0023] S23: Perform a summary calculation on the set of key fields to obtain the second data summary.

[0024] This application proposes a method for managing the security of agricultural ecological member data, which can improve the accuracy and efficiency of data summaries and reduce unnecessary data processing volume by performing summary calculations on key fields.

[0025] Furthermore, step S3 includes:

[0026] S31: Compare the first data digest with the second data digest. If the comparison results are consistent, determine the preceding business data type used for chain association based on the business type of the current business data and the corresponding business entity identifier.

[0027] S32: Based on the business entity identifier and the preceding business data type, obtain the historical records associated with the current business data;

[0028] S33: Extract the second data digest of the historical record as the third data digest;

[0029] S34: Store the first data digest, the second data digest, and the third data digest as a chain structure.

[0030] This application proposes a method for managing the security of agricultural ecosystem member data, which can construct a complete data traceability chain by linking preceding business data in a chain, thereby enhancing the immutability of historical data records.

[0031] Furthermore, step S4 includes:

[0032] S41: Obtain specified data and a fifth data digest corresponding to the specified data;

[0033] S42: Obtain the business type and associated entity of the specified data, and select multiple independent entities with verification permissions according to the business type and the associated entity;

[0034] S43: Send the fifth data digest to the independent entity, and after the independent data has been verified, receive the verification result containing the timestamp and entity representation returned by the independent entity;

[0035] S44: Summarize and verify the received verification results to obtain the confirmation credential.

[0036] Furthermore, step S42 includes:

[0037] S421: A pre-configured independent entity permission verification table is provided, which records the business types and associated entity scope that the independent entity can verify;

[0038] S422: Query the independent entity permission verification table based on the business type and associated entity of the specified data to obtain the query result;

[0039] S423: Based on the query results, select multiple independent entities with verification permissions.

[0040] Further, in step S5, the step in which the chain structure is not modified includes:

[0041] S51: Starting from the chain structure corresponding to the current business data, traverse the data change log of the chain structure in reverse according to the third data digest;

[0042] S52: Recalculate the summary for each historical record traversed;

[0043] S53: Compare the recalculated summary with the second data summary stored in the historical record;

[0044] S54: If all the comparison results are consistent, it is determined that the chain structure has not been modified.

[0045] Furthermore, in step S5, the step of confirming the validity of the credential includes:

[0046] S55: Verify the digital signatures of the confirmation credentials returned by the multiple independent entities to obtain the digital signature verification result;

[0047] S56: Check the validity period of the confirmation certificate and obtain the validity period check result;

[0048] S57: Check the current state of the independent entity and obtain the entity state check result;

[0049] S58: Based on the digital signature verification result, the validity period check result, and the subject status check result, determine whether the confirmation credential is a valid credential;

[0050] S59: Count the number of valid vouchers. When the number of valid vouchers reaches a preset threshold for the number of valid vouchers, determine that the confirmation voucher is valid.

[0051] Furthermore, step S58 includes:

[0052] S581: If the digital signature verification result is issued by the claimed independent entity and has not been tampered with during transmission and storage, then the digital verification result is deemed valid.

[0053] S582: If the validity period check result shows that the timestamp of the confirmation voucher is within the validity period, then the validity period check result is determined to be valid;

[0054] S853: When the entity status check result shows that the corresponding independent entity has the verification qualification and its operation is normal, the entity status check result is determined to be valid.

[0055] S854: If the digital signature verification result, the validity period check result, and the subject status check result are all valid, then the confirmation credential is determined to be a valid credential; otherwise, the confirmation credential is determined to be an invalid credential.

[0056] Secondly, an agricultural ecosystem member data security management system is provided for implementing the method described in any of the above claims, the system comprising:

[0057] First acquisition module: Acquires the original business data uploaded by the user on the client, and extracts a first data digest based on the original business data;

[0058] The second acquisition module acquires the current business data received by the server and extracts a second data digest based on the current business data.

[0059] Chain-structured storage module: Compare the first data digest with the second data digest. If the comparison results are consistent, obtain the third data digest of the preceding business data associated with the original business data, and store the first data digest and the second data digest in a chain structure with the third data digest.

[0060] Confirmation credential receiving module: acquires specified data and a fifth data digest corresponding to the specified data, distributes the fifth data digest to multiple independent entities, and receives confirmation credentials from multiple independent entities;

[0061] Security verification module: When performing security verification of the original business data, the current business data is re-extracted to obtain a fourth data digest. When the fourth data digest is consistent with the second data digest, the chain structure is not modified, and the confirmation credential is valid, the original business data is determined to be secure.

[0062] Beneficial Effects: The agricultural ecosystem member data security management method and system proposed in this application effectively solves the problem of verifying the integrity and non-tamperability of agricultural ecosystem member data in a multi-entity, long-cycle, and dynamically updated environment by introducing client-side data digests, server-side data digest comparison, chain-structured storage, multi-independent entity confirmation credentials, and multiple security verification mechanisms. This ensures the authenticity and credibility of the data. Attached Figure Description

[0063] Figure 1 This is a flowchart of a method for managing the security of agricultural ecosystem member data proposed in this application.

[0064] Figure 2 This is a structural diagram of an agricultural ecological member data security management system proposed in this application.

[0065] Figure 3This is an architecture diagram of an agricultural ecological member data security management system proposed in this application.

[0066] Labeling Explanation: 201, First Acquisition Module; 202, Second Acquisition Module; 203, Chain Structure Storage Module; 204, Confirmation Certificate Receiving Module; 205, Security Verification Module. Detailed Implementation

[0067] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and marked in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0068] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] Please refer to Figure 1 A method for managing the security of agricultural ecosystem member data, the method includes the following steps:

[0070] S1: Obtain the original business data uploaded by the user on the client, and extract the first data summary based on the original business data;

[0071] S2: Obtain the current business data received by the server and extract the second data digest based on the current business data;

[0072] S3: Compare the first data digest with the second data digest. If the comparison results are consistent, obtain the third data digest of the preceding business data associated with the original business data, and store the first data digest, the second data digest, and the third data digest in a chain structure.

[0073] S4: Obtain the specified data and the fifth data digest corresponding to the specified data, distribute the fifth data digest to multiple independent entities, and receive confirmation credentials from multiple independent entities;

[0074] S5: When performing security verification of the original business data, the current business data is re-extracted to obtain the fourth data digest. When the fourth data digest is consistent with the second data digest, the chain structure is not modified, and the confirmation credential is valid, the original business data is determined to be secure.

[0075] Clients include, but are not limited to, farm terminals, processing plant terminals, and distribution terminals.

[0076] Among them, the first data digest, the second data digest, the third data digest, the fourth data digest, and the fifth data digest refer to unique "data fingerprints" calculated or recalculated based on the corresponding original business data, current business data, previous business data, current business data, and specified data, which are used to quickly and accurately verify the integrity and tamper-proof nature of the data.

[0077] Taking the first data digest as an example, the specific calculation process is as follows:

[0078] First, all keys (i.e., field names, such as "pesticide application rate", "daily sunshine duration", and "location number"—these are just examples; in actual applications, the actual fields in the original business data should prevail) in the original business data are sorted according to Unicode dictionary order. Then, the sorted key-value pairs are stringified in the form of 'key': 'value', and commas are used as separators between key-value pairs to form a continuous string. For example, for the original business data {"pesticide application rate": "50.0", "daily sunshine duration": "8.5", "location number": "A001"}, after sorting in Unicode dictionary order, its logical order becomes {"location number": "A001", "pesticide application rate": "50.0", "daily sunshine duration": "8.5"}. The concatenated string is: '“Lot Number”:“A001”,“Pesticide Application Amount”:“50.0”,“Daily Sunlight Duration”:“8.5”'. The SHA-256 hash value of this concatenated string is calculated, and the result is the first data digest, serving as the unique “data fingerprint” of the original business data.

[0079] Understandably, the calculation principles for the second, third, fourth, and fifth data digests are the same as above.

[0080] A chain structure refers to a structure that connects multiple data summaries in chronological or business logic order. For example, the first data summary of the original business data, the second data summary of the current business data, and the third data summary of the preceding business data are stored together. The main purpose is to record the historical change trajectory of the data and ensure that the data is difficult to be retroactively modified once it is recorded.

[0081] Independent entities refer to different participants with independent status and verification capabilities in the business ecosystem. They can be entities such as third-party auditing firms, alliance members, or regulatory authorities, such as farms, processing plants, or distributors in agricultural cooperatives. The main purpose is to introduce a multi-party consensus mechanism, enhance the credibility of data verification, and avoid the risks of single-centralization.

[0082] A confirmation credential refers to a legally or business-valid certificate issued by an independent entity after verifying specific data. It can be implemented in the form of digital signatures, timestamps, or multi-party signed certificates. For example, a verification result with its digital signature is generated after an independent entity verifies a data digest. Its main purpose is to provide external parties with recognition of the authenticity of the data and serve as an important basis for determining data security.

[0083] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0084] On the client side, when a user uploads agricultural product batch data, the data is preprocessed, and then the hash value of the data is calculated using the SHA-256 algorithm as the first data digest. This data is compressed into a data packet and uploaded to the server along with the first data digest. After receiving the data packet and the first data digest, the server parses the data packet, extracts the agricultural product batch data, and independently calculates its hash value again using the SHA-256 algorithm as the second data digest. The system automatically compares these two digests. If they match, the preceding business data is determined based on the business type and entity identifier of the batch data, and the digest of the preceding business data is obtained as the third data digest. Subsequently, the first, second, and third data digests are stored in the form of a hash linked list to ensure the traceability of data changes. When critical verification of a batch of agricultural product data is required, such as for organic certification application, the system distributes the second data digest of the batch data to multiple independent entities with verification permissions, such as the quality management department of the cooperative, third-party testing agencies, and regulatory agencies. After completing the verification, these independent entities digitally sign the verification results using their respective digital certificates, attaching a timestamp, and sending the resulting confirmation credential back to the server. During the final agricultural product data security verification, the system recalculates the hash value of the currently stored planting batch data as the fourth data digest and checks its consistency with the second data digest. Simultaneously, the system traverses the hash chain corresponding to the planting batch data in reverse order to verify the chain's integrity. Furthermore, the system verifies the validity of all received digitally signed confirmation credentials from independent entities. Only when all these verifications pass is the agricultural product planting batch data deemed secure and trustworthy.

[0085] If the comparison results between the first and second data digests are inconsistent during the above process, it indicates that the original business data has been modified during transmission or before being submitted to the server. In this case, the server will mark the original business data as anomalous data for processing. This processing method aims to prevent tampered data from entering the data management platform, thereby maintaining the authenticity and integrity of data throughout the agricultural ecosystem.

[0086] Furthermore, step S1 includes:

[0087] S11: On the client side, when the original business data is generated for the first time or modified, the first data digest is immediately calculated on the original business data;

[0088] S12: Write the original business data, the first data summary, the generation time of the original business data, and the modification time of the modification time to the local data log; the local data log only supports append write operations.

[0089] S13: When the client establishes a connection with the server, it retrieves the latest version of the original business data and its corresponding first data summary from the local data log and uploads it to the server.

[0090] Local data logs refer to a data storage structure maintained on client devices, used to record the generation and modification of original business data, as well as their corresponding summary information and timestamps.

[0091] The fact that local data logs only support append-only write operations means that the data writing method of local data logs is strictly limited to adding new records to the end of existing records, and no existing historical records can be modified or deleted, in order to ensure the immutability of the logs.

[0092] The generation time refers to the timestamp when the original business data was first created and recorded, which can be recorded using the system clock or the time synchronized by the network time protocol.

[0093] Modification time refers to the timestamp of the most recent update or change of the original business data, which can be recorded using the system clock or network time protocol synchronization time.

[0094] The latest version refers to the record with the latest timestamp in the local data log for a specific original business data. It represents the final state of the data on the client side and can be determined by querying the record with the largest timestamp in the log.

[0095] This application effectively solves the problem of untraceable data tampering on the client side by instantly recording and summarizing the generation and modification of original business data on the client side, and establishing an immutable local log. This ensures that the data uploaded to the server is the authentic, tamper-free, latest version, significantly improving the accuracy of the first data summary and the reliability of overall data security verification. By establishing a traceable local data change chain, even if the data is modified multiple times locally, each change can be definitively recorded, thereby greatly enhancing the credibility of the client-side local data and providing a reliable data source for the entire data security management process.

[0096] Furthermore, step S2 includes:

[0097] S21: When receiving current business data on the server side, parse the current business data;

[0098] S22: Based on the parsing results, identify and extract the preset set of key fields;

[0099] S23: Perform a summary calculation on the set of key fields to obtain a second data summary.

[0100] In this process, raw business data is typically compressed into data packets and uploaded by the client. The system receives these compressed data packets (current business data), parses them, and reconstructs the original business data. However, since the client sends raw business data in JSON (or other) format, the server-side system may not recognize the same data format. Therefore, a recognizable conversion of the raw business data format is necessary to unify it into a format that the server-side system can recognize and process. This recognizable conversion may include identifying field boundaries, data types, and encoding formats, aiming to provide a clear and operable foundation for subsequent data processing and key information extraction.

[0101] After data parsing, the server-side system obtains the current business data that can be recognized. Then, based on pre-set business rules on the server, it identifies and extracts the set of key fields from the current business data. For example, for agricultural product batch data, the pre-set business rules extract data including "batch number," "product type," "production date," and "quantity." In the actual key field extraction, according to the pre-set business rules, the corresponding key fields and their corresponding values ​​are extracted. For example, if the received data contains {"batch number: A123," "product type: organic vegetables," "production date: 2023-09-01," "quantity: 500kg"} and other non-key fields such as {"remarks: first harvest"}, the system will only extract the key fields "batch number," "product type," "production date," and "quantity," as well as the corresponding values ​​"A123," "organic vegetables," "2023-09-01," and "500kg."

[0102] Finally, a digest is calculated for these extracted key field sets. Specifically, the values ​​of these key fields can be concatenated in a predetermined order to form a string, such as '"Batch Number": "A123", "Product Type": "Organic Vegetables", "Production Date": "2023-09-01", "Quantity": "500kg"'. Then, the SHA-256 algorithm is used to hash this string, thereby generating a fixed-length second data digest.

[0103] Furthermore, step S3 includes:

[0104] S31: Compare the first data digest with the second data digest. If the comparison results are consistent, determine the preceding business data type used for chain association based on the business type of the current business data and the corresponding business entity identifier.

[0105] S32: Based on the business entity identifier and the preceding business data type, retrieve the historical records associated with the current business data;

[0106] S33: Extract the second data summary of the historical records as the third data summary;

[0107] S34: Store the first data digest, the second data digest, and the third data digest as a linked structure.

[0108] Since the first data digest contains key-value pairs of all fields, while the second data digest only contains key-value pairs of key fields, for the sake of accuracy, the comparison is made between the key-value pairs of key fields in the first and second data digests. The consistency of the comparison results means that the key-value pairs of key fields in the first and second data digests are the same.

[0109] Business type refers to the category to which the business data belongs, such as planting, harvesting, processing, or distribution.

[0110] A business entity identifier is a unique code or name that identifies a specific entity (such as a farm, processing plant, distributor, etc.) to which business data belongs. It can be implemented using a globally unique identifier, a company registration number, or an internal coding system.

[0111] To ensure the security of agricultural data, it is not stored in a simple time sequence, but rather in a chain structure that utilizes the logical relationships between various business data, thereby enabling effective tracking and tracing against any tampering.

[0112] Business data stored in the same chain structure share the same business logic, which can be determined based on the business type and the business entity identifier associated with that business type. For example, suppose for the same plot of land (each plot has its own specific plot number, let's say A001), the current business data's business type is "fertilizing," and the specific crop batch number for the current agricultural product batch is B001. When determining the preceding business data associated with it, it's not the previous record of any type, but rather the preceding business data type in the plot with plot number A001, prior to the business type "fertilizing" with crop batch number B001, such as "planting." Here, the plot number and crop batch number are the business entity identifiers, and "planting" and "fertilizing" are the business types.

[0113] Based on this storage principle, in this solution, after obtaining the current business data, the preceding business data type associated with the current business data can be determined through specific semantic queries within the historical records of all preceding business data, based on the business type and business entity identifier of the current business data. This dual-qualification acquisition mechanism of "consistent business entity identifier" and "business type association" ensures that the associated historical records are truly logically continuous with the current business data and belong to the same business entity, thus constructing a genuine, reliable, and uninterrupted traceability chain.

[0114] In this context, the second data summary of historical records specifically refers to the data summary calculated by the server based on the key field set of the historical business data when receiving and processing it. This is the same method used to extract the second data summary of the current business data, but the content differs. Therefore, the second data summary of the historical records is used as the third data summary for easy distinction.

[0115] The purpose of obtaining the third data digest is that the third data digest is the data digest generated when the history is received and processed by the server. This means that the preceding data digest referenced in the chain structure has been verified and confirmed by the server, rather than just the original digest from the client, which further enhances the reliability of the chain structure.

[0116] Finally, the first data digest verified by the client, the second data digest received and verified by the server, and the third data digest representing the server-side verification status of the preceding business data are stored together in a chain structure, forming an immutable and traceable data chain. In this way, this application solves the problem of accurately establishing data chain relationships in complex agricultural ecosystems while ensuring data transmission consistency. It significantly improves the integrity and traceability of the data chain, providing a solid foundation for subsequent data security verification. Any tampering with historical data will result in digest mismatches, which will be detected by the system in a timely manner, effectively guaranteeing the integrity and immutability of agricultural ecosystem member data.

[0117] Furthermore, step S4 includes:

[0118] S41: Obtain the specified data and the fifth data digest corresponding to the specified data;

[0119] S42: Obtain the business type and associated entity of the specified data, and select multiple independent entities with verification permissions based on the business type and associated entity;

[0120] S43: Send the fifth data digest to the independent entity, and after the independent data has been verified, receive the verification result returned by the independent entity, which includes a timestamp and the entity's representation;

[0121] S44: Summarize and verify the received verification results to obtain confirmation credentials.

[0122] The designated data refers to business data that requires external verification. This data can take various forms, such as planting records for agricultural products, processing batch information, sales order details, or quality inspection reports. In practice, the key fields of the designated data are manually selected by technical personnel based on the received current business data.

[0123] When the specified data is current business data, the fifth data digest is the same as the second data digest; when the specified data is a portion of the data selected from the current business data, the fifth data digest refers to the unique digital fingerprint obtained by hashing the specified data.

[0124] Business type refers to the business category to which the specified data belongs, which may include planting, processing, logistics, sales or quality traceability, etc.

[0125] Related entities refer to entities or participants that are directly related to the specified data, including but not limited to farms, plots of land, crop batches, processing plants, distributors, or logistics companies.

[0126] An independent entity refers to a third-party entity that is independent of the data generator and the platform provider and has professional knowledge and verification capabilities. It may include government regulatory agencies, industry associations, third-party certification bodies, professional testing laboratories, or credible auditing institutions.

[0127] To address the shortcomings of existing solutions in selecting independent entities and generating confirmation vouchers, this solution refines the processes for distributing data summaries and generating confirmation vouchers. First, in step S41, the specified data and its corresponding fifth data summary are obtained, ensuring that all subsequent operations revolve around a clearly defined data target.

[0128] Subsequently, in step S42, the system does not blindly distribute data digests, but intelligently obtains the business type and associated entities of the specified data. Based on this contextual information, the system can select multiple independent entities with genuine verification permissions according to preset rules or permission configurations. This selection mechanism ensures that data digests are distributed to third parties with professional capabilities and credibility, thereby significantly enhancing the authority of subsequent verification results.

[0129] Once independent entities are selected, in step S43, the system sends a second data digest to these entities. After completing their own verification work, these independent entities will return verification results containing a timestamp and entity representation. The timestamp records when the verification occurred, providing a timeliness basis for the entity representation, while the entity representation clarifies the source of the verification, providing a foundation for subsequent traceability and accountability. This structured method of receiving verification results ensures the integrity and traceability of the original information.

[0130] Finally, in step S44, the system summarizes and verifies the multiple received verification results. The summarization process may involve statistical analysis of majority opinions or aggregation based on specific rules, while verification may include verifying digital signatures, checking the validity of timestamps, and checking the identity of the subject to ensure the authenticity and tamper-proof nature of these results. Through comprehensive processing of independent verification results from multiple parties, this scheme effectively reduces the risk of errors or malicious behavior from a single subject, ultimately generating a highly credible confirmation credential.

[0131] In the above process, it is mentioned that the system can select multiple independent entities with genuine verification permissions based on preset rules or permission configurations. The specific operation method is as follows:

[0132] Furthermore, step S42 includes:

[0133] S421: Pre-configure an independent entity permission verification table, which records the business types that an independent entity can verify and the scope of associated entities;

[0134] S422: Query the independent entity permission verification table based on the business type and associated entity of the specified data to obtain the query results;

[0135] S423: Based on the query results, select multiple independent entities with verification permissions.

[0136] The independent entity authorization verification table is a pre-established data structure used to store and manage the mapping relationship between independent entities and their authorized business data. It can be implemented using tables in a relational database.

[0137] Business type refers to the business category to which the specified data belongs, which may include planting, processing, logistics, sales or quality traceability, etc.

[0138] The scope of related subjects refers to the set of entities or participants that are related to the specified data.

[0139] After obtaining the business type and associated entity of the specified data, the system uses the "business type of the specified data" to be verified and its "associated entity" (e.g., quality data of a batch of agricultural products, with the associated entity being the batch of agricultural products), and matches and filters it from the independent entity permission verification table to obtain query results. The query results are divided into independent entities with verification permissions and independent entities without verification permissions. Multiple independent entities with verification permissions are selected from the query results to ensure that the selected independent entities not only have verification permissions, but their verification capabilities are also highly consistent with the current business scenario of the data. This improves the targeting of data verification, avoids resource waste, and enhances the authority and credibility of the verification results, thereby achieving accurate selection of independent entities.

[0140] Furthermore, in step S5, the steps where the chain structure is not modified include:

[0141] S51: Starting from the chain structure corresponding to the current business data, traverse the data change log of the storage chain structure in reverse according to the third data digest;

[0142] S52: Recalculate the summary for each historical record traversed;

[0143] S53: Compare the recalculated summary with the second data summary stored in the historical record;

[0144] S54: If all comparison results are consistent, the chain structure is determined to have not been modified.

[0145] This application provides a mechanism for verifying the integrity of a chain-like structure to ensure that the historical change records of agricultural ecosystem member data have not been tampered with. The operating logic of this mechanism is as follows:

[0146] First, step S51 establishes the starting point and path for verification. Choosing to start with the current business data ensures the real-time nature and specificity of the verification, meaning that the verification verifies the integrity of the data chain in the current state.

[0147] Reverse traversal based on the third data digest leverages the inherent encrypted linking characteristics of the chain structure. Since the third data digest is the second data digest of the preceding business data, this linking mechanism makes the traversal process non-jumpable, thus enabling the traversal to trace all historical change records since the data was generated, covering the entire data lifecycle and laying the foundation for subsequent integrity verification.

[0148] Then, in step S52, a "digital fingerprint" of the current content can be generated by independently calculating a summary for each historical record. In step S53, this newly generated data summary will be used as a benchmark for comparison with the second data summary. Any slight modification to the content of the historical record will cause the recalculated summary to be inconsistent with the second data summary, thereby exposing the tampering behavior.

[0149] Finally, in step S54, if all comparison results are consistent, the chain structure is determined to be unmodified. This step provides the final judgment criterion. Only when the recalculated digests of all traversed historical records in the chain structure are completely consistent with their original stored second data digests can the entire chain structure be ultimately determined to be complete and unmodified. It is precisely because of this reverse verification mechanism that this application can effectively confirm whether the chain structure carrying data history changes has been tampered with.

[0150] Furthermore, in step S5, the step of confirming the validity of the voucher includes:

[0151] S55: Verify the digital signatures of confirmation credentials returned by multiple independent entities to obtain the digital signature verification result;

[0152] S56: Check the validity period of the confirmation document and obtain the validity period check result;

[0153] S57: Check the current state of the independent entity and obtain the entity state check result;

[0154] S58: Determine whether the confirmation credential is a valid credential based on the digital signature verification result, the validity period check result, and the subject status check result;

[0155] S59: Count the number of valid vouchers. When the number of valid vouchers reaches the preset threshold for the number of valid vouchers, the voucher is deemed valid.

[0156] In this context, a confirmation credential refers to an electronic certificate generated and returned by an independent entity after verifying a specific data digest. It serves to demonstrate that the independent entity acknowledges the integrity and authenticity of the data. It may include information such as a data digest, an issuing entity identifier, a timestamp, and a digital signature.

[0157] Digital signatures refer to an electronic signature technology based on cryptographic principles, used to verify the authenticity of data sources, the integrity of data content, and the non-repudiation of the issuer. It can employ asymmetric encryption algorithms, using a private key to encrypt and generate a data digest, and then using the corresponding public key for decryption and verification.

[0158] The valid voucher number threshold refers to the minimum number of vouchers that must be deemed valid among confirmation vouchers submitted by multiple independent entities. It can be a fixed value, such as 3 or 5, or a percentage, such as exceeding half of the total number of vouchers, or it can be dynamically adjusted based on business importance.

[0159] This solution addresses the vulnerability in data security assessments caused by the potential for forged, expired, or untrusted credentials to be issued by untrusted entities within the agricultural ecosystem. By introducing a multi-dimensional verification mechanism for the validity of verification credentials and ensuring the rigor of the verification process, this solution aims to improve the effectiveness of verification.

[0160] Specifically, verifying the digital signature of a certificate can prevent the risk of forgery and ensure the authenticity and integrity of the certificate.

[0161] By checking and confirming the validity period of the voucher, expired or invalid vouchers can be excluded, ensuring that the verification results used are currently valid and avoiding the risk of using outdated information for data security judgment.

[0162] By checking the current state of an independent entity, the credibility of the credential source can be ensured, preventing security issues caused by changes in the entity's state.

[0163] By comprehensively evaluating the digital signature verification results, validity period check results, and entity status check results, the reliability of a single confirmation credential can be improved, avoiding judgment biases that may be caused by a single verification dimension.

[0164] By counting the number of valid credentials and comparing them with a preset threshold, a consensus-based verification mechanism can be introduced. Even if a few independent entities exhibit abnormal or malicious behavior, as long as the preset threshold is reached, it indicates that most independent entities have reached a consensus on the integrity of the data. This enhances the robustness and anti-attack capability of the overall confirmation credentials, ensures the accuracy and credibility of the final data security judgment, and addresses the risk of a single node failure or attack in a distributed environment.

[0165] Therefore, this solution can provide a comprehensive and reliable basis for determining the security of agricultural ecosystem member data, thereby ensuring the integrity and tamper-proof nature of the data, and enhancing the trust foundation and product quality and safety of the entire agricultural ecosystem.

[0166] Furthermore, step S58 includes:

[0167] S581: If the digital signature verification result is issued by the claimed independent entity and has not been tampered with during transmission and storage, the digital verification result is deemed valid.

[0168] S582: If the validity period check result shows that the timestamp of the confirmation voucher is within the validity period, then the validity period check result is deemed valid;

[0169] S853: When the entity status check result shows that the corresponding independent entity has the qualification for verification and there are no abnormalities in its operation, the entity status check result is deemed valid.

[0170] S854: If the digital signature verification result, validity period check result, and subject status check result are all valid, the confirmation certificate is determined to be a valid certificate; otherwise, the confirmation certificate is determined to be an invalid certificate.

[0171] The digital signature verification result, which confirms that the document was issued by the claimed independent entity and has not been tampered with during transmission and storage, refers to verifying the digital signature of the confirmation credential using cryptographic algorithms. This ensures that the signature matches the identity of the claimed independent entity and that the content has not been modified at any stage from the generation of the credential to its verification. This can be achieved using digital certificate verification technology under the Public Key Infrastructure (PKI) system. This digital certificate verification technology is existing technology and is applied in this application as follows: After receiving the confirmation credential returned by a third-party testing agency A (an independent entity), the server first extracts the public key using A's X.509 digital certificate. Then, it uses this public key to decrypt the digital signature within the credential using RSA-PSS and compares it with the locally recalculated SHA-256 digest. If the two match, it can be confirmed that the credential was indeed issued by A and has not been tampered with at any stage since its issuance, thus completing the digital signature validity verification.

[0172] The validity check result showing that the timestamp of the confirmation voucher is within the validity period means that the timestamp information contained in the confirmation voucher is checked to determine whether the voucher is within the preset valid timestamp. This can be achieved by comparing it with the current system time, or with the start and end times set when the voucher was issued.

[0173] The entity status check result showing that the corresponding independent entity has verification qualifications and operates without abnormalities refers to a real-time or near-real-time query of the current qualifications, reputation, and operational status of the independent entity that issued the confirmation certificate. This verifies whether it still possesses legal and valid verification authority and whether there are any abnormalities such as bankruptcy, license revocation, or serious violations that could affect the credibility of its certificate. This can be achieved by querying the independent entity registration information database, calling third-party credit reporting interfaces, or through a pre-set blacklist / whitelist mechanism.

[0174] In summary, this solution refines the steps for determining the validity of confirmation credentials, clarifies the validity criteria for three key verification results—digital signature, validity period, and independent entity status—and further specifies the logic for comprehensively judging the final validity of confirmation credentials. This resolves the issues of ambiguity or inconsistency in determining the validity of confirmation credentials, ensuring the authenticity of the credential's source, the completeness of its content, its timeliness, and the reliability of the issuing entity. Through this multi-dimensional cross-verification mechanism, its rigor enables the system to accurately identify and exclude forged, tampered, expired, or untrusted credentials, thereby improving the reliability and rigor of the entire data security verification process. This provides a solid and reliable foundation for determining the security of original business data, effectively resisting potential data tampering risks.

[0175] Please refer to Figure 2 , Figure 3 An agricultural ecological member data security management system, used to implement any of the above methods, the system comprising:

[0176] First Acquisition Module 201: Acquires the original business data uploaded by the user on the client, and extracts the first data digest based on the original business data;

[0177] Second acquisition module 202: Acquires the current business data received by the server and extracts a second data digest based on the current business data;

[0178] Chain structure storage module 203: compares the first data digest with the second data digest. If the comparison results are consistent, it obtains the third data digest of the preceding business data associated with the original business data and stores the first data digest and the second data digest in a chain structure with the third data digest.

[0179] Confirmation credential receiving module 204: acquires specified data and the fifth data digest corresponding to the specified data, distributes the fifth data digest to multiple independent entities, and receives confirmation credentials from multiple independent entities;

[0180] Security verification module 205: When performing security verification of original business data, the current business data is re-extracted to obtain a fourth data digest. When the fourth data digest is consistent with the second data digest, the chain structure is not modified, and the confirmation credential is valid, the original business data is determined to be secure.

[0181] The first acquisition module 201 refers to the component responsible for capturing and processing raw business data at the data source end, which can be implemented by a software agent or data acquisition service deployed on the client device.

[0182] The second acquisition module 202 refers to the component responsible for processing the received business data at the data receiving end, which can be implemented using a data receiving service or API interface deployed on a server cluster or data center.

[0183] The chain-structured storage module 205 refers to the component responsible for managing and maintaining the historical integrity chain of business data, which can be implemented using storage components based on blockchain technology or an immutable log system.

[0184] The confirmation credential receiving module 204 is a component responsible for coordinating multiple parties to verify data integrity and collecting verification results. It can be implemented using a coordination service deployed in a central platform or a distributed network.

[0185] The security verification module 205 is a component responsible for making the final determination of the security of business data. It can be implemented using verification services deployed on a data management platform or an independent auditing system.

[0186] This system, through modular design, concretizes the abstract method of agricultural ecosystem member data security management into executable system components, thereby solving the problem of how to efficiently, automatically, and reliably implement such methods in a distributed agricultural ecosystem. Its overall operational logic is as follows:

[0187] First, when a user generates or modifies raw business data on the client side, the first acquisition module 201 actively captures this data and immediately extracts a first data digest based on its content. This process ensures that the data is marked at the source, laying the foundation for subsequent integrity verification.

[0188] Subsequently, when this raw business data is uploaded to the server, the second acquisition module 202 receives it and independently processes the current business data to extract a second data digest. By generating digests on both the client and server sides, the system can perform preliminary consistency comparisons during data transmission.

[0189] Next, the chain-structured storage module 203 compares the first data digest with the second data digest. If the comparison results match, it indicates that the data has not been tampered with during transmission. The module then further obtains the third data digest of the preceding business data associated with the current business data and stores the first, second, and third data digests in a chain structure. This chain-structured storage mechanism constructs an immutable historical data record. Any modification to the historical data will cause the chain to break, thereby effectively detecting tampering and providing a complete trajectory of business data changes.

[0190] Based on this, the confirmation credential receiving module 204 obtains the specified data and its corresponding fifth data digest, and distributes the digest to multiple independent entities with verification permissions. After completing the verification, these independent entities return confirmation credentials containing a timestamp and entity identifier via digital signature. This mechanism introduces multi-party collaborative verification, greatly enhancing the credibility and non-repudiation of data integrity, and avoiding the risks of single centralized verification.

[0191] Finally, when security verification of the original business data is required, the security verification module 205 will be activated. It will re-extract the current business data to obtain a fourth data digest and compare it with the stored second data digest. Simultaneously, this module will also check whether the chain structure has been modified and whether the confirmation credentials received from multiple independent entities are valid. Only when the fourth data digest matches the second data digest, the chain structure is complete, and the confirmation credentials are valid will the system determine that the original business data is secure.

[0192] Through the close collaboration of the aforementioned modules, this system achieves full automation and standardization of the data process, from acquisition, summary generation, chained storage, multi-party verification to final security determination. This systematic approach not only transforms abstract methods and steps into operable entities but also significantly improves the efficiency and reliability of data integrity verification, effectively mitigating the risk of data tampering. It ensures that the massive, dynamically updated data distributed across multiple independent entities in the agricultural ecosystem can be systematically managed and protected, thus fully leveraging the advantages of data integrity verification and anti-tampering methods.

[0193] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0194] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An agricultural eco-member data security management method, characterized by, The method comprises the steps of: S1: obtaining original business data uploaded by a user on a client, and extracting a first data digest from the original business data; S2: obtaining current business data received by a server, and extracting a second data digest from the current business data; S3: comparing the first data digest with the second data digest, if the comparison result is consistent, obtaining a third data digest of previous business data associated with the original business data, and storing the first data digest, the second data digest and the third data digest as a chain structure; S4: obtaining specified data and a fifth data digest corresponding to the specified data, and distributing the fifth data digest to a plurality of independent subjects, and receiving confirmation credentials fed back by the plurality of independent subjects; wherein the specified data refers to business data that needs to be externally verified; Step S4 comprises: S41: obtaining specified data and a fifth data digest corresponding to the specified data; S42: obtaining a business type and an associated subject of the specified data, and selecting a plurality of independent subjects with verification authority according to the business type and the associated subject; S43: sending the fifth data digest to the independent subjects, and receiving a verification result containing a timestamp and a subject representation returned by the independent subjects after the independent subjects complete the verification; S44: summarizing and checking the received verification result to obtain the confirmation credentials; S5: when performing security verification on the original business data, re-extracting the current business data to obtain a fourth data digest, and when the fourth data digest is consistent with the second data digest, the chain structure is not modified, and the confirmation credentials are valid, determining that the original business data is secure.

2. The method of claim 1, wherein the method further comprises: Step S1 comprises: S11: when the original business data is first generated or modified on the client, immediately calculating the first data digest for the original business data; S12: writing the original business data, the first data digest, the generation time and the modification time of the original business data into a local data log; the local data log only supports append write operation; S13: when the client establishes a connection with the server, obtaining the latest version of the original business data and its corresponding first data digest from the local data log, and uploading to the server.

3. The method of claim 1, wherein the method further comprises: Step S2 comprises: S21: when the server receives the current business data, parsing the current business data; S22: identifying and extracting a preset key field set according to the parsing result; S23: calculating a digest of the key field set to obtain the second data digest.

4. The method of claim 1, wherein the method further comprises: Step S3 comprises: S31: comparing the first data digest with the second data digest, if the comparison result is consistent, determining the type of previous business data used for chain association according to the business type of the current business data and the corresponding business entity identifier; S32: obtaining historical records associated with the current business data according to the business entity identifier and the type of previous business data. S33: extracting a second data digest of the historical record as the third data digest; S34: storing the first data digest, the second data digest and the third data digest as a chain structure.

5. The method of claim 1, wherein the method further comprises: Step S42 comprises: S421: pre-configuring an independent subject permission verification table, which records the business types and associated subject ranges that the independent subject can verify; S422: querying the independent subject permission verification table according to the business type and the associated subject of the specified data to obtain a query result; S423: selecting a plurality of independent subjects with verification permissions according to the query result.

6. The method of claim 1, wherein the method further comprises: In step S5, the step of determining that the chain structure is not modified comprises: S51: starting from the chain structure corresponding to the current business data, traversing the data change log stored in the chain structure in reverse according to the third data digest; S52: recalculating the digest of each historical record traversed; S53: comparing the recalculated digest with the second data digest stored in the historical record; S54: if all comparison results are consistent, determining that the chain structure is not modified.

7. The method of claim 6, wherein the method further comprises: In step S5, the step of confirming the validity of the certificate comprises: S55: verifying the digital signature of the confirmation certificate fed back by the plurality of independent subjects to obtain a digital signature verification result; S56: checking the validity period of the confirmation certificate to obtain a validity period checking result; S57: checking the current state of the independent subject to obtain a subject state checking result; S58: determining whether the confirmation certificate is a valid certificate according to the digital signature verification result, the validity period checking result and the subject state checking result; S59: counting the number of valid certificates, and determining that the confirmation certificate is valid when the number of valid certificates reaches a preset valid certificate number threshold.

8. The method of claim 7, wherein the method further comprises: Step S58 comprises: S581: when the digital signature verification result is that the independent subject claims to issue and is not tampered with in the transmission and storage process, determining that the digital verification result is valid; S582: when the validity period checking result shows that the timestamp of the confirmation certificate is within the validity period, determining that the validity period checking result is valid; S853: when the subject state checking result shows that the corresponding independent subject has verification qualifications and the business is normal, determining that the subject state checking result is valid; S854: when the digital signature verification result, the validity period checking result and the subject state checking result are all valid, determining that the confirmation certificate is a valid certificate, otherwise, determining that the confirmation certificate is an invalid certificate.

9. An agricultural eco-member data security management system, characterized by, The system for implementing the method of any one of claims 1-8 comprises: A first acquisition module: acquiring original business data uploaded by a user on a client, and extracting a first data digest according to the original business data; A second acquisition module: acquiring current business data received by a server, and extracting a second data digest according to the current business data; The chain structure storage module: comparing the first data digest with the second data digest, if the comparison result is consistent, obtaining a third data digest of the previous business data associated with the original business data, and storing the first data digest, the second data digest and the third data digest as a chain structure; The confirmation voucher receiving module: obtaining specified data and a fifth data digest corresponding to the specified data, and distributing the fifth data digest to a plurality of independent subjects, and receiving the confirmation vouchers fed back by the independent subjects; wherein the specified data refers to business data that needs to be externally verified; The confirmation voucher receiving module is also used to obtain specified data and a fifth data digest corresponding to the specified data; obtain the business type and the associated subject of the specified data, and select a plurality of independent subjects with verification authority according to the business type and the associated subject; send the fifth data digest to the independent subjects, receive the verification results containing time stamps and subject representations returned by the independent subjects after the independent subjects complete the verification; and summarize and check the received verification results to obtain the confirmation voucher; The security verification module: when performing security verification on the original business data, re-extracting the current business data to obtain a fourth data digest, and when the fourth data digest is consistent with the second data digest, the chain structure is not modified, and the confirmation voucher is valid, determining that the original business data is secure.

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