Data tracing method and system based on big data and multi-dimensional features

Abstract

The present invention discloses a data tracing method and system based on big data and multi-dimensional features. The present invention relates to the field of data tracing technology, including: a shared database, which is a multi-organization trust framework, which is used to securely share tracing information between multiple organizations; a data processing subsystem, which is used to process various data, and the data processing subsystem includes an information acquisition module, an information processing module and an information transmission module, the information acquisition module is used to collect data information generated by each organization, and the information processing module is used to extract features from the processed data. The present invention establishes a multi-organization trust framework based on blockchain technology and multi-party secure computing. The method can build a trust environment for secure data sharing between multiple organizations, so it can solve the problem that information sharing between different organizations may be restricted by factors such as trust, competition and policy, affecting the comprehensiveness of tracing.

Description

Technical Field

[0001] The present invention relates to the technical field of data tracing technology, and in particular to a data tracing method and system based on big data and multi-dimensional features. Background Art

[0002] Data traceability refers to the technology and methods for tracking and verifying the source, flow and processing of data. It can help enterprises or organizations understand the generation, transmission and change of data and ensure the authenticity and reliability of data. Data traceability has important applications in many fields.

[0003] However, in practical applications, data traceability methods and systems based on big data and multi-dimensional features face a challenge: due to the trust crisis, market competition, and even related policy and legal constraints between different stakeholders, information sharing between different stakeholders is often hindered, which may affect the effectiveness and integrity of the entire traceability system. For example, in a specific industrial chain, economic competition between upstream and downstream companies may hinder each other's information sharing activities, which will undoubtedly weaken the effectiveness of the traceability system and may also increase the operating costs of the entire system. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing data tracing methods, the present invention provides the following technical solutions: a data tracing method and system based on big data and multi-dimensional features, comprising:

[0006] A shared database, which is a multi-organization trust framework used to securely share traceability information between multiple organizations;

[0007] A data processing subsystem, which is used to process various data, and the data processing subsystem includes an information acquisition module, an information processing module and an information transmission module;

[0008] The information collection module is used to collect data information generated by each organization;

[0009] The information processing module is used to extract features from the processed data;

[0010] The information transmission module is used to upload information to the shared database; the data tracing system comprises the following steps:

[0011] S1. Establish a multi-organization trust framework based on blockchain technology and multi-party secure computing to ensure the secure sharing of traceability information among multiple organizations;

[0012] S2. Collect data related to the items to be traced from the participants, and clean and format the data according to the preset data model;

[0013] S3, extracting features from the processed data to form an information set containing timestamps, geographic locations, quality attributes, and supply chain links of item identifiers;

[0014] S4. In the multi-organization trust framework, the authenticity and integrity of the information set are automatically recorded and verified based on the smart contract mechanism, and cross-organization query and collaboration are supported;

[0015] Building a multi-organization trust framework based on blockchain technology and multi-party secure computing also includes:

[0016] For each pair of parties communicating within the trust framework, encryption technology is applied to ensure the security of data transmission;

[0017] Deploy smart contracts to automatically manage data sharing rules, including:

[0018] Set the legality condition P before the execution of smart contract C1;

[0019] If the legality condition P is not met, the smart contract C1 will not be activated;

[0020] The ownership and verifiable timestamp of data are automatically updated by smart contracts O(T);

[0021] Check the node status S on each blockchain involved in data verification and ensure that the status S is consistent with the latest smart contract version;

[0022] The legality conditions P include:

[0023] The legitimacy condition P is defined as the formula P = V(F)*H(T), where V is the verification variable, F is the integrity of the participant, and H represents the credibility of the organization;

[0024] When the participant integrity F is lower than the predefined standard f0 or the organization credibility H is lower than the predefined hmin, the legitimacy condition is judged to be not met P≠true;

[0025] Mark the result that meets the legitimacy condition as R and send it to all participants of the trust framework;

[0026] If any R is equal to true, the corresponding operation in the trust framework is triggered;

[0027] The state check before the verification logic is executed by the smart contract C1 includes:

[0028] Verify each record to be written. If its integrity I differs from the standard integrity IS by more than a predetermined range r, do not write this record and mark it as non-compliant N;

[0029] If the difference ΔI of the integrity I = |I - IS| < r, add the record to the blockchain as trusted information C(TI). Otherwise, mark this record as untrusted record UN(TR) that is non-compliant;

[0030] Ensure that all records added to the smart contract comply with the consistency condition. Before adding a record to the chain, it is necessary to first determine whether the ratio condition between the overall reliability of the data and the average quality level is at least a multiple of a preset minimum amplification factor, and it is also necessary to evaluate that the quality of the original data is higher than the quality at the end point of the data life cycle;

[0031] Execute the above condition check on each record. Once it does not meet any of the above conditions, this record is regarded as abnormal data AD and not adopted;

[0032] The state management operations after verifying the records include:

[0033] When receiving the data marked with N, re-submit this record for secondary verification and record the frequency FR at which this data is re-marked as N. When FR reaches the predefined re-verification upper limit fL, it is considered as incorrect data;

[0034] Check whether FR < fL, then record this N-marked data and allow it to enter the subsequent processing flow;

[0035] Calculate the smart contract execution efficiency index E as the correct passing rate C3 of all data items / (correct passing items + abnormal items A), and ensure that E is greater than the predefined threshold e0;

[0036] Once the smart contract completes execution and the efficiency meets the standard, the smart contract will automatically generate a feedback record to the blockchain for each node to confirm the effectiveness and correctness of the execution.

[0037] A data traceability method based on big data and multi-dimensional features. The multi-organization trust framework established based on blockchain technology and multi-party secure computing includes:

[0038] Determine the trust level T of all parties in the blockchain network;

[0039] If the trust level T of any party is less than the predetermined trust threshold τ, do not grant this party the blockchain editing permission;

[0040] Use the homomorphic encryption method to encrypt the shared information provided by each organization and distribute it to each participating party;

[0041] Start a multi-party computing protocol to achieve common data access and processing in a unified secure computing environment. Calculate the weight W based on the data contribution degree C of each participating party. The formula is: if C_i ≥ average contribution degree C, then W_i = C_i * β; otherwise, W_i = C_i * α; where α ≤ β and i represents each participating party. Allocate encrypted data fragments according to the weights W of each participating party. All participating parties simultaneously execute the specified algorithm to process the allocated encrypted data fragments, and merge the intermediate results generated after processing into a final output.

[0042] Preferably, the determination of the trust level of each party in the blockchain network includes:

[0043] Collect the number of transaction records T of the participating parties in the past X time periods;

[0044] If the monthly average number of transactions M of the participating party < Y, then its initial trust value T0 is reduced by α times; otherwise, it is increased by β times;

[0045] Adjust the trust coefficient according to historical violation records. If there is no rule violation, keep the coefficient λ = 1; otherwise, λ = (1 - δ), where 0 < δ < 1, and the worse the violation event, the larger δ is;

[0046] Accumulate the adjusted trust values of all the above to calculate the total trust level: the final trust level T = T0 * M * λ.

[0047] Preferably, the use of the homomorphic encryption method to encrypt the shared information provided by each organization includes:

[0048] Use the public key PK to perform homomorphic encryption on the data submitted by each organization to form the ciphertext E(PK, data);

[0049] Confirm that the length L of each ciphertext E(PK, data) meets the security condition: if L ≥ the minimum ciphertext size L_min, it is considered valid; otherwise, it is regarded as an incorrect input and the data is returned for retry encryption;

[0050] Send the valid ciphertext to the secure computing environment;

[0051] In the secure computing environment, only specific homomorphic operation functions are allowed to decrypt.

[0052] Preferably, only allowing the use of specific homomorphic operation functions to decrypt in the secure computing environment includes:

[0053] After receiving the encrypted ciphertext E, apply the homomorphic property to decrypt to obtain the plaintext after key decoding: the decryption process D(SK, f(E)) > the data plaintext, where SK represents the key, E represents the ciphertext, and f(E) represents the decryption function;

[0054] Further verify the integrity of the data plaintext C2;

[0055] If the C2 check result shows that the probability of its integrity Pr (integrity) ≥ Z, it is considered to be legal decryption, where Z represents the probability threshold;

[0056] Completing all the above steps ensures that the valid data contained in the ciphertext can be accurately decrypted.

[0057] The present invention provides a data tracing system based on big data and multi-dimensional features. It has the following beneficial effects:

[0058] The present invention establishes a multi-organization trust framework based on blockchain technology and multi-party secure computing. This method can build a trust environment for secure data sharing among multiple organizations, thereby solving the problem that information sharing among different organizations may be restricted by factors such as trust, competition and policy, affecting the comprehensiveness of traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the present invention;

[0060] Figure 2 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0061] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0062] The present invention discloses a data tracing system based on big data and multi-dimensional features, comprising:

[0063] A shared database, which is a multi-organization trust framework used to securely share traceability information between multiple organizations;

[0064] The data processing subsystem is used to process various data. The data processing subsystem includes an information acquisition module, an information processing module and an information transmission module. The information acquisition module is used to collect data information generated by various organizations, the information processing module is used to extract features from the processed data, and the information transmission module is used to upload information to a shared database.

[0065] The data tracing methods based on big data and multi-dimensional features are as follows:

[0066] S101: By establishing a multi-organization trust framework based on blockchain technology and multi-party secure computing, this method can build a trust environment for secure data sharing among multiple organizations. Due to the interaction and information transmission between different entities, in the traditional way, the collaboration between organizations faces trust challenges: organizations are often unwilling to disclose their own data or share important transaction details due to the lack of complete trust between each other. This method proposes to use the unique characteristics of blockchain technology, which is a distributed database with the characteristics of being tamper-proof and transparent. For example, in the food safety traceability scenario, each node may be a raw material supplier, processing plant, distributor, and retailer. They will jointly participate in the trust framework and contribute information according to a unified standard protocol. This information will then be packaged into blockchain blocks and verified through a consensus mechanism. The consensus mechanism usually requires that the majority or all participants agree on the authenticity and validity of the information before new data is included in the chain. This process not only increases data transparency but also reduces the potential risk of data manipulation. In addition to blockchain as a basic platform to ensure the immutability and security of data, multi-party secure computing is also needed to solve the problem of privacy protection. Multi-party secure computation allows multiple parties to jointly calculate a value or obtain the result of a function without exposing the input value to anyone. This method enables different organizations involved in data interaction to complete computing tasks together without knowing each other's original data, effectively protecting sensitive data from illegal leakage or snooping while achieving valuable data sharing.

[0067] S102: Through the preliminary cleaning of the traceability data, key information is further extracted according to the predefined feature extraction algorithm to establish a richer and more specific description of the traceability items. This process aims to screen out the most meaningful and influential factors through data analysis. For example, in the case of food traceability, multi-dimensional key indicators such as the timestamp of when the plant was planted, harvested or manufactured, the geographical location of the plant or processing location coordinates, the quality characteristics of the nutritional content ratio, whether there are residual substances, and specific supply chain steps in the circulation process can be extracted as the main content of subsequent analysis. By collating and summarizing these metadata, a unique file for each product can be established, which can help end consumers and regulators quickly obtain complete and reliable quality and safety status, and provide scientific and strong support for subsequent quality control.

[0068] S103: With the help of the previously established multi-organization trust network, smart contracts are designed and run on this architecture to realize the automated process of managing and monitoring the extracted multi-source heterogeneous data sets; this step is an important part of ensuring the normal operation of the entire traceability system. Smart contracts are a piece of program code written on the blockchain, which automatically triggers execution actions without human intervention when the pre-set rule conditions are met. To understand this, when a batch of food needs to enter the market, the supplier only needs to upload the relevant certificate documents to the system and trigger the corresponding contract terms, such as checking whether there is an expiration risk or compliance review; then automatically check its qualification according to the program design - such as whether there are any abnormal indicators that deviate from the specified standards, whether all the necessary inspection points have been passed, etc. Smart contracts will automatically judge various standards and generate verification feedback; this not only saves manual review time and labor costs, but also greatly improves work efficiency, and also helps to enhance transparency at all stages.

[0069] Through the application and implementation of the above comprehensive solution, the inherent problem of "information sharing between different organizations is restricted by various external factors" can be better solved. Specifically:

[0070] The transparency provided by blockchain allows all parties involved to understand the specific details of each transaction, reducing the chances of fraud and misreporting.

[0071] The multi-organization trust environment encourages more partners to join the data alliance with confidence, jointly promote the development of the industrial chain and promote the overall improvement of the industry level.

[0072] Secure computing is used to ensure the security of personal or organizational business secrets and to explore in-depth communication and cooperation models.

[0073] In short, this framework not only ensures that all participants can cooperate efficiently on a trusted platform to achieve information exchange, but also promotes the optimization and upgrading of traceability and risk management efficiency in the overall value chain, laying a good foundation for the promotion of similar applications in the future.

[0074] The above are the specific steps to establish a multi-organization trust framework based on blockchain technology and multi-party secure computing:

[0075] S201: This embodiment involves determining the trust level T of each participating organization in the blockchain network. At this stage, the trust score obtained by each organization is determined by quantitative analysis of historical transaction records, participant reputation evaluation and other trust evaluation indicators. Once these trust scores are calculated, it is possible to determine which members should have the ability to edit blockchain data, such as modifying transaction status or adding new blocks to the chain, based on pre-set rules; for those participants who do not meet the threshold τ, such privileges are denied.

[0076] S202: Then, encryption algorithms such as the Paillier encryption system are needed to process shared data from different participants while ensuring privacy and to ensure that sensitive information is not accessed without authorization. The specific approach is to distribute the results expressing the encrypted state of all data among the organizations that contributed the input information - so that no party can know the original data based solely on the received information. This information processed with appropriate homomorphic encryption and decryption technology will be able to support further joint use and analysis in a unified multi-party secure computing environment without leaking confidential data.

[0077] S203: After that, a secure multi-participant computing process is initiated, such as secret sharing or differentially private algorithms to implement cross-domain query, statistical analysis, and machine learning operations on sensitive resources shared under protected conditions, and this step ensures that no individual can derive more information beyond their own input from the information they have alone. In this secure multi-party environment, each party can perform data joint access processing operations without directly accessing the original materials of others; in this way, even if one or more nodes are destroyed, it will not affect the confidentiality and integrity of the global computing results.

[0078] Through the above three-step process, the authenticity of the key asset tracking chain can be guaranteed in a multi-stakeholder collaborative scenario, while the privacy of individual business activities can be effectively protected. This framework effectively promotes the expansion and deepening of blockchain technology in the field of distributed applications, while enhancing the ability to dynamically monitor the trust of participants.

[0079] The specific steps for starting the multi-party computing protocol feature are as follows:

[0080] S301: Determine the weight value W of each party by calculating the data contribution of each party. This process is concretized according to the formula. Specifically, if the data contribution of a specific participant \C_i\ is greater than or equal to the average contribution of the entire group \C\, then the weight value obtained is \W_i=C_i*\beta\. On the contrary, if \C_i\ is less than the average contribution \C\, its weight will be assigned according to \W_i=C_i*\alpha\, where \alpha\ and \beta\ satisfy \alpha\leq\beta\, and i is used to identify each participant.

[0081] S302: Based on the calculated weights \W\ of each participant, the system distributes the encrypted data fragments according to the weights of each participant. This means that participants with higher weights may receive larger amounts of data to process or more critical parts to ensure process safety and optimize efficiency.

[0082] S303: All participants who are assigned encrypted data segments start to synchronously execute the pre-agreed algorithm to independently calculate the data segments assigned to them. This step ensures that all participants can work together based on their own data segments without direct communication to protect information security and prevent information leakage or inappropriate use.

[0083] Let's take an example to implement the above steps: suppose a data tracing plan involves five different organizations working together to analyze a set of encrypted and stored financial data. Each organization needs to first determine its own data contribution and get a relatively high or low computing task share ratio based on whether it is higher than the average contribution. Then, part of the data is allocated according to each share, and all participants then process the encrypted financial data fragments according to the preset rules. Finally, each party sends the calculation results generated by the data in their hands for aggregation, and without disclosing the original financial details, a unified conclusion is formed to track or identify the authenticity and history of the data. This method uses the principle of distributed multi-party collaboration to ensure that data processing maintains privacy without losing integrity.

[0084] The specific steps of using the homomorphic encryption method to encrypt the shared information provided by each organization are as follows:

[0085] S401: Ensure the security of the information transmission process by using secure homomorphic encryption of the information provided by each participating organization. Specifically, the data submitted by each organization is homomorphically encrypted using the public key PK that has been distributed and saved in advance, generating a ciphertext EPK,data corresponding to each batch of shared data, where PK represents the public key and data represents the shared data content that each organization needs to provide. This encryption process not only ensures the privacy and integrity of the information, but also lays the foundation for subsequent security operations;

[0086] S402: The system further ensures the validity of each ciphertext after homomorphic encryption. The system first needs to verify whether the length L of the ciphertext EPK,data meets the threshold requirement L_min of the minimum ciphertext size under the preset security conditions. That is to say, if the length of the ciphertext is greater than or equal to L_min, it is considered valid; if it does not meet this requirement, it is considered that incorrect data submission has occurred and the original data submitter is notified to correct and retry the ciphertext; this can effectively avoid security vulnerabilities and potential risks caused by maliciously constructed information or accidents;

[0087] S403: Next, the valid ciphertext will be transmitted from the client of the data uploader to a secure computing environment that is prepared in advance and guarantees privacy for processing. This is usually a space that is physically or access-protected, to ensure that the data can only be used to perform specific computing tasks under specified conditions and that the data plaintext form cannot be directly observed;

[0088] S404: Only specific types of computational operations or functions that are predefined and verified to correctly decrypt ciphertext without revealing the plaintext content are allowed to be executed in the secure computing environment. This means that even the real information contained in the ciphertext can only be partially decrypted through specific operations under strictly restricted conditions, thus completing collaborative computing while ensuring that the data of all parties is not exposed. This last step is one of the important means to ensure that all data processing links are always within the controlled range, thereby technically completing the entire process of ensuring the homomorphic encryption and processing mechanism, and ultimately achieving the goal of maintaining information security and privacy.

[0089] The specific steps to determine the trust levels of all parties in the blockchain network are as follows:

[0090] S501: The total number of transaction records T generated by the participating parties within a specific past time period X is collected as a measurement indicator. The X period here can be set according to the actual needs of the application scenario, such as 90 days or a longer time to accumulate sufficient data points. For example, for merchants in an e-commerce platform, the platform can automatically track each successful blockchain transaction of the merchant within a 90-day cycle and cumulatively calculate the total number of transactions T for a preliminary assessment of the merchant's activity level and transaction scale.

[0091] S502: Further judgment is made by referring to whether the average monthly transaction count M of the participating party within a certain period reaches a pre-specified threshold Y. If a merchant has completed fewer transactions per month on average in the past four months than the预定标准Y (for example: M < Y = 100), it is considered that its activity level is relatively low; at this time, the initial trust value T0 of the merchant needs to be reduced by a certain proportion α; assuming α = 0.1 times, the initial trust value will be reduced by 10%. If M ≥ Y, it means that its activity level meets or exceeds expectations, then the initial trust value will be increased by a factor of β (for example, β = 1.2 times); this can ensure the recognition of active participants and give them a higher basic trust score.

[0092] S503: Considering whether there are records of default or other improper behaviors in history to decide whether to adjust the temporary trust coefficient λ obtained on the existing basis. If there are no cases of bad operations such as violations in the blockchain node, λ = 1 remains unchanged during this evaluation process; but if there is evidence that a certain participant has been dishonest, punitive measures should be taken to lower λ to 1 - δ where 0 < δ < 1. For example, when the violation event is relatively serious, δ is set to a larger value of 0.5, that is, in this case, λ = 1 - 0.5 = 0.5 to reflect major doubts about the credibility of this participant. This measure ensures that only parties with good performance and reputation can obtain a fully undiminished trust score.

[0093] Through the above steps, the results of each stage are gradually implemented and summarized to finally calculate the total reliability index of each participating entity - "final trust level" T. This will be directly reflected in the formula T=T0*M*λ; that is, the comprehensive use of the revised initial score T0 and the coefficient M reflecting the frequency of behavior plus the adjusted λ based on historical records can be used to obtain a comprehensive evaluation score that can fully reflect the reliability and trustworthiness index of any party in the blockchain ecosystem.

[0094] The above specific steps of decryption that only allows the use of specific homomorphic operation functions in a secure computing environment are:

[0095] S601: By receiving the encrypted big data, i.e., ciphertext E, and processing the ciphertext using an operation function that supports homomorphic properties in a computing environment with appropriate security guarantees. On this basis, the key SK is used to execute a specific decryption function f(E), so that the operation on the ciphertext E is completed while maintaining the data encryption state, and the plaintext data in a readable form is homomorphically decrypted. The specially designed operation function in this process is carefully selected to ensure that the data can be processed correctly even in the encrypted state without directly exposing sensitive content.

[0096] S602: Then, a comprehensive integrity check C2 is performed on the received decrypted data plaintext. This step usually uses a predefined security algorithm to verify whether the decrypted content matches the data at the time of initial encryption. For example, the consistency and authenticity can be verified by comparing the original data and its hash value such as SHA-256, message authentication code and other mechanisms.

[0097] S603: Once the verification result shows that the decrypted data plaintext maintains the data structure and value of the initial state without being changed, and meets or exceeds the expected probability threshold Z, the system determines that the integrity check result is valid. That is, if the probability evaluation of the integrity verification process finds that the probability Pr integrity is greater than or equal to the preset probability threshold Z, where Z represents a value less than 1 that reasonably determines the decrypted data integrity threshold, the system determines that the ciphertext has been successfully and legally decrypted, thereby ensuring the effective credibility of the entire data transmission link. Otherwise, it indicates that the data may have been modified or forged, and corresponding measures need to be taken to troubleshoot errors.

[0098] S604: The successful completion of all the above operations ensures that the original data retains its original authenticity after being processed through encryption-transmission-homomorphic decryption, thus making safe and reliable big data tracking and tracing possible.

[0099] The above-mentioned establishment of a multi-organization trust framework based on blockchain technology and multi-party secure computing also includes the following steps:

[0100] S701: To further enhance security, for all participants who have been approved to be included in the trust framework, the system must also implement encryption mechanisms to ensure information security before they transmit data to each other. This can be achieved by selecting applicable advanced encryption technologies such as public key infrastructure (PKI), which ensures that only authorized senders and receivers can process sensitive information and outsiders cannot spy on the specific content. This encryption method can protect the transmission of data between various nodes from leakage or malicious interference.

[0101] S702: The last step is to use the characteristics of distributed ledgers combined with smart contracts to automatically execute management logic, that is, to stipulate that the relevant policies or rules for data access and sharing are the responsibility of smart contracts. This approach not only makes the agreement clearer, but also simplifies the process, improves operational efficiency, and reduces the risk of misunderstanding or human error. Whenever data is to be shared or transferred outside the scope of trust, pre-made and coded contracts will check whether these activities follow pre-established guidelines, such as permission access, payment models, or other customized agreement items.

[0102] The above are specific steps for establishing a multi-organization trust framework and using smart contracts to operate in an environment based on blockchain technology and multi-party secure computing:

[0103] S801: Setting the legality conditions of smart contracts:

[0104] Under the multi-organization trust framework, the legitimacy condition P of the smart contract C1 must be set before it is formally executed. The legitimacy condition may include but is not limited to restrictions such as participant identity confirmation and time window validity. For example, in a commodity source confirmation process that requires multiple authorizations, the legitimacy condition P can only be considered satisfied when all relevant parties such as manufacturers, distributors or logistics providers have completed the certification and uploaded the necessary certification documents within the specified time frame; if the legitimacy condition P is not met, the smart contract C1 will not be activated.

[0105] S802: The smart contract executes to update the ownership and timestamp records:

[0106] Once the set legality condition P is met, the smart contract C1 automatically starts execution and automatically completes the record of ownership transfer of the corresponding data according to the pre-set business rules, such as changing the ownership information of a specific batch of goods to the new holder; at the same time, an unalterable transaction record is automatically generated on the blockchain, and an immutable time stamp OT is assigned to the ownership change of the data and each operation, so that the information of each ownership change point can be traced back and the time authenticity of the stored data and the authenticity and reliability of the ownership history can be ensured.

[0107] S803: Check and synchronize node status to maintain consensus:

[0108] In order to maintain the consistency and credibility of the entire network, during the activation and processing of smart contracts, it is necessary to continuously monitor and verify whether the status S of each participating node N is synchronized and correctly reflects the latest version of the smart contract status and its related transaction records; this is mainly reflected in the regular or on-demand execution of on-chain data synchronization operations, verification of the validity and consistency of each block, and timely detection and resolution of any abnormalities or potential security risks. Ultimately, it ensures that all data verification nodes on the network can share a consistent data view and be consistent with the consensus mechanism, thereby effectively protecting data authenticity and improving the security and efficiency of the entire system.

[0109] The above description and steps demonstrate the basic logic and technical means of how to use blockchain technology and smart contracts based on big data and multi-dimensional features to establish a reliable data traceability system among multiple organizations.

[0110] The specific steps under the premise of setting the legality condition P in the above steps are:

[0111] S901: A legitimacy condition P is defined by the formula P=VF*HT, where V represents the verification function used to evaluate the degree of integrity of the attributes or status of the participant F, and H is an indicator used to quantify the overall credibility of the organization to which the participant belongs. When the integrity of the participant F is not higher than the preset value f0, or the credibility of the organization H is not greater than the preset threshold hmin, the condition P≠true is judged, that is, the behavior of the participant is considered to be neither legitimate nor credible.

[0112] S902: For each object being tested, after the verification conditions are determined, the algorithm will read the legality status and generate a logical result mark R. If R is True, it means that the verification has passed, and the validity condition P obtained by referring to the above calculation rules is established. Then the system will automatically broadcast the valid result R to all relevant members in the trust framework, so that they can know the verification results in real time and make an immediate evaluation of the reliability of the information.

[0113] S903: It is further stipulated that as long as there is a situation where R is equal to true, the automated management measures in the entire framework will be activated. This means that if a participant or its associated activities are verified to be legal, the system will automatically trigger the specified operation sequence in the trust framework according to the pre-configured prescribed mechanism to support data tracking and verification and protect its authenticity and integrity.

[0114] To better understand this, take blockchain as an example: Suppose a trust framework composed of multiple supply chain entities, such as manufacturers, wholesalers, retailers, logistics providers, etc., needs to trace the origin of a batch of goods. Each node must meet the corresponding legality and credibility levels to gain the trust of other entities. The system uses F integrity to evaluate the completeness of the information provided by each participant, such as recording the manufacturing time, location, materials of the product, etc. At the same time, it measures the overall trustworthiness of the organization through H credibility, such as whether there is a history of fraud. The setting of these criteria ensures the truthfulness and consistency of the information. Eventually, when the verification variable indicates that a certain link meets the established requirements, all entities in the network will be notified. In this way, any correct information path can initiate subsequent processes to ensure the truthfulness and credibility of the commodity information.

[0115] The specific steps of the above feature of the state check before the verification logic is executed by the smart contract C1:

[0116] S1001: Perform a preset standard integrity check on the newly received data record through the smart contract C1. In this process, the smart contract analyzes each content in the data packet according to the pre-set data model and compares it with the defined standard integrity IS to verify the received data. If it is found that there is a difference between the data packet integrity I and the pre-set standard integrity IS, and the difference value ΔI = |I - IS| exceeds the given threshold r, then this record is regarded as untrustworthy and is marked with non-compliance N; otherwise, the record will be considered to meet the basic consistency requirements.

[0117] S1002: Further determine whether it is written into the blockchain as part of the trustworthy information by calculating the data integrity of the record. If the above verification shows that the difference value ΔI < r, then the smart contract C will add the corresponding record CTI to the blockchain in the form of a block. These blocks that have passed the effective verification and are not marked as non-compliant contain trustworthy information TI and become an important part of the traceable record.

[0118] S1003: To ensure the consistency and integrity of the data recorded on the blockchain, there is an additional quality control process for checking the overall quality level Q of the records written, which includes a detailed comparison and evaluation of metrics such as the reliability R and the lifespan length L of the data, as well as the quality QS of the source data and the data quality QE at the end of the data. Specifically, before adding a record to the chain, it is first determined whether the ratio condition between the overall reliability of the data and the average quality level is at least a preset multiple of the minimum amplification factor, that is, the condition QR≥k*QL is satisfied, where the coefficient k is a proportional parameter greater than 1. In addition, it is also necessary to evaluate that the quality of the original data is higher than the quality at the end point of the data lifecycle, QS>QE, considering that data usually becomes stale during the process from creation to processing, so the data at the source should be more effective.

[0119] S1004: Any newly submitted record to be written that does not meet the conditional requirements is marked and prevented from being written to the blockchain. Once it is found that a newly arrived record violates the consistency check criterion, that is, it fails to meet any of the aforementioned quality assurance conditions QR<k*QL or QS≤QE, it will immediately be determined that this record belongs to the category of untrustworthy - regarded as abnormal data AD, and it is no longer allowed to be added to the record set of the smart contract, effectively maintaining the overall quality standard and traceability performance of the blockchain.

[0120] Next, refer to Figure 1 Describe the specific steps of the state management operation after verifying the records in the data traceability method based on big data and multi-dimensional features:

[0121] S1101: When a piece of data marked as N (here N represents a state that needs to be verified) is received, the reaction mechanism of the system will resubmit this record for secondary verification and count the frequency FR of this data item being relabeled as state N. If the number of times this data is relabeled as state N, that is, the frequency FR, is equal to or exceeds a preset upper limit fL of the re-verification times, it will be determined that there are potential problems or errors in this data record.

[0122] S1102: After re-verification, if the frequency FR of the data being repeatedly verified and marked as N is less than the upper limit fL of the re-verification times, it is determined that this record is data in the normal state N, and the system will further execute the processing steps for it. This includes adding this record in state N to the internal data flow of the system and making it continue to flow.

[0123] S1103: For each data involved in the entire data verification process, the system further calculates a technical parameter called efficiency index E, which is used to represent the execution efficiency of the smart contract. The value calculation method is defined as the number of correct records C in data verification divided by the total number of correct passing items C plus abnormal items A, thereby obtaining the overall efficiency level. If the calculated efficiency E exceeds the predefined efficiency threshold e0, it is considered that the system verification and state flow processing are at a good level.

[0124] S1104: After completing the data processing and state conversion work, if the execution of the smart contract achieves the expected effect and confirms that the efficiency of the smart contract meets the aforementioned performance threshold e0 requirements, the smart contract system will automatically generate a record containing all valid data flow feedback and record it in the distributed ledger of the blockchain network, so that all system nodes can confirm the effectiveness of the smart contract execution and the authenticity and validity of the data flow.

[0125] In order to more clearly understand the working mechanism of this process, for example, assume that the present invention is used in a scenario based on supply chain tracking applications. For the commodity source record whose status is to be verified twice, it is assumed that the status of the mark N is "need further investigation". When this information marked N is received for the first time S1101, if it is confirmed that the situation that requires further investigation has accumulated three times after re-examination, reaching the previously set re-examination frequency threshold of five times, where fL is set to 5 for example purposes, the system will mark the source of the information as highly suspicious and temporarily stop processing step A. For other data information in status N that does not meet the three-time further investigation frequency requirement, the system will default to continue to process them according to the conventional process S1102, and regularly count and check the verification efficiency level of the smart contract for the status data ES1103 in the entire data processing process. When the efficiency index E exceeds a specified efficiency value such as 75%, the system determines that the contract execution efficiency meets the standard and automatically generates transaction data entry feedback containing these records, such as valid data entry feedback containing the circulation details of a batch of specific commodities in each link, and uploads them to the distributed general ledger for evidence storage and verification S1104.

[0126] In actual operation, when this data traceability method and system based on big data and multi-dimensional features begins to be used, a multi-organization trust framework is first constructed to ensure that multiple different subjects can achieve effective information sharing and mutual trust environment building based on jointly formulated security rules and privacy protection measures. This framework uses the technical advantages of blockchain technology and multi-party secure computing to achieve trust building among participants and confidential data transmission. Then, the relevant information of the objects to be traced, such as goods and raw materials, from various organizations is collected uniformly, and cleaned, standardized and structured according to unified standards and models to improve the efficiency of subsequent information integration and processing. After completing the preliminary data preparation, the technical solution will further extract features and conduct multi-dimensional analysis on the data processed above, aiming to extract and combine a series of key indicators to form comprehensive, accurate and easy-to-understand and analyze time series information - including but not limited to the time stamp of product ID, geographic coordinate records of production or transportation process, quality characteristics of goods, etc.; at the same time, considering the complete supply chain process management of the product, in addition to extracting these dimensional information, it will also cover specific activity records in each link, such as raw material collection time, processing, packaging logistics and other processes. All the acquired feature values ​​are regarded as a necessary component of a single multidimensional information set, which helps to realize the traceability and tracking management of every detail in the entire chain. These feature information will be recorded in the pre-built distributed ledger, namely the blockchain system, to ensure that each data point is reliable and valid, and the consistency and traceability of the data can be verified through the preset smart contract logic, further strengthening the credibility and accuracy of the data itself. Finally, in the actual application stage, users can quickly obtain all historical transactions and processing processes related to a specific commodity through access interfaces or dedicated query interfaces according to their needs. At the same time, they can also use the collaborative characteristics of the system to complete the interactive negotiation and joint problem-solving goals with other related organizations or suppliers when necessary. The coordination effect of each part is obvious in the entire work process: the multi-party co-construction of the trust framework lays a credible foundation for data sharing; the standardized data collection method makes it possible to effectively integrate and utilize information from different sources; the application of smart contracts makes data rights confirmation transparent and controllable, thereby reducing the trust cost and management difficulty. Such a collaborative mechanism ultimately makes the entire system function more efficient and practical.

[0127] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data traceability system based on big data and multi-dimensional features, characterized by: Including: A shared database, which is a multi - organization trust framework for securely sharing traceability information among multiple organizations; A data processing subsystem for processing various data, which includes an information collection module, an information processing module, and an information transmission module; The information collection module is used to collect data information generated by each organization; The information processing module is used to extract features from the processed data; The information transmission module is used to upload information to the shared database; The data traceability system includes the following steps: S1. Establish a multi - organization trust framework based on blockchain technology and multi - party secure computing to ensure the secure sharing of traceability information among multiple organizations; S2. Collect data associated with the item to be traced from participants and clean and format the data according to a preset data model; S3. Extract features from the processed data to form an information set containing item identifiers, timestamps, geographical locations, quality attributes, and supply chain links; S4. In the multi - organization trust framework, automatically record and verify the authenticity and integrity of the information set according to the smart contract mechanism, and support cross - organization query and collaboration; Establishing a multi - organization trust framework based on blockchain technology and multi - party secure computing also includes: For each pair of parties communicating within the trust framework, apply encryption technology to ensure the security of data transmission; Deploy smart contracts to automatically manage the rules for data sharing, including: Set the legality condition P before the execution of smart contract C1; If the legality condition P is not met, the smart contract C1 will not be activated; Automatically update the data ownership and verifiable timestamp O(T) by the smart contract; Check the status S of each node on each blockchain participating in data verification and ensure that the status S is consistent with the latest smart contract version; The legality condition P includes: Define that the legality condition P is composed of the formula P = V(F)*H(T), where V is the verification variable, F is the integrity of the participant, and H represents the organizational credibility; When the participant integrity F is lower than the predefined standard f0 or the organizational credibility H is less than the predefined hmin, it is determined that the legality condition does not hold P≠true; Mark the result that meets the legality condition as R and send it to all participants of the trust framework; If any R is equal to true, trigger the corresponding operation within the trust framework; The status check before the verification logic executed by the smart contract C1 includes: Verify each record to be written. If its integrity I differs from the standard integrity IS by more than the predefined range r, do not write this record and mark it as non - compliant N; If the difference in integrity ΔI = |I - IS| < r, add the record to the blockchain as trustworthy information C(TI), otherwise, mark the record as a non - compliant untrustworthy record UN(TR); Ensure that all records added to the smart contract comply with the consistency conditions. Before adding a record to the chain, it is necessary to first determine whether the ratio between the overall reliability of the data and the average quality level is at least a multiple of a preset minimum amplification factor. It is also necessary to evaluate that the quality of the original data is higher than the quality at the end point of the data life cycle; Perform the above condition checks on each record. Once any of the above conditions is not met, the record is regarded as abnormal data AD and is not adopted; The state management operations after verifying the records include: When receiving data marked with N, perform secondary verification by resubmitting the record and record the frequency FR at which this data is re-marked as N. When FR reaches the predefined re-verification upper limit fL, it is considered incorrect data; Check whether FR < fL, then record this N-marked data and allow it to enter the subsequent processing flow; Calculate the smart contract execution efficiency index E as the correct passing rate C3 / (correct passing items + abnormal items A) for all data items, and ensure that E is greater than the predefined threshold e0; Once the smart contract completes execution and the efficiency meets the standard, the smart contract will automatically generate a feedback record to the blockchain for each node to confirm the effectiveness and correctness of the execution.

2. A data tracing method based on big data and multi-dimensional features, characterized in that: The data traceability method is applied to the data traceability system as described in claim 1. The establishment of a multi-organization trust framework based on blockchain technology and multi-party secure computing includes: Determine the trust level T of all parties in the blockchain network; If the trust level T of any party is less than the predefined trust threshold τ, then this party is not granted blockchain editing permission; Encrypt the shared information provided by each organization using the homomorphic encryption method and distribute it to each participating party; Start the multi-party computing protocol to enable common data access and processing in a unified secure computing environment. Calculate the weight W based on the data contribution degree C of each participating party. The formula is: if C_i ≥ average contribution degree C, then W_i = C_i * β; otherwise W_i = C_i * α; where α ≤ β and i represents each participating party. Distribute the encrypted data fragments according to the weights W of each participating party. All participating parties simultaneously execute the specified algorithm to process the allocated encrypted data fragments and merge the intermediate results generated after processing into a final output.

3. The data tracing method based on big data and multi-dimensional features according to claim 2 is characterized in that: The determination of the trust level of all parties in the blockchain network includes: Collect the number of transaction records T of the participating parties in the past X time period; If the monthly average transaction times M of the participating party < Y, then its initial trust value T0 is reduced by α times, otherwise it is increased by β times; Adjust the trust coefficient according to the historical violation records. If there is no violation of the rules, then keep the coefficient λ = 1, otherwise λ = (1 - δ), where 0 < δ < 1, and the worse the violation event, the larger δ; Accumulate all the above-adjusted trust values to calculate the total trust level: the final trust level T = T0 * M * λ.

4. The data tracing method based on big data and multi-dimensional features according to claim 2 is characterized in that: The use of the homomorphic encryption method to encrypt the shared information provided by each organization includes: Use the public key PK to perform homomorphic encryption on the data submitted by each organization to form the ciphertext E(PK, data); Confirm that the length L of each ciphertext E(PK, data) meets the security condition: if L ≥ the minimum ciphertext size L_min, it is considered valid, otherwise it is considered as an incorrect input and the data is returned for retry encryption; Sending valid ciphertext to a secure computing environment; In a secure computing environment, only specific homomorphic operation functions are allowed to be used for decryption.

5. The data tracing method based on big data and multi-dimensional features according to claim 4 is characterized in that: In a secure computing environment, only specific homomorphic operation functions are allowed to decrypt, including: After receiving the encrypted ciphertext E, the homomorphic property is applied to decrypt the plaintext after key decoding: the decryption process D(SK, f(E))〉data plaintext, where SK represents the key, E represents the ciphertext, and f(E) represents the decryption function; Further verify the integrity of the data plaintext C2; If the C2 check result shows that the probability of its integrity Pr (integrity) ≥ Z, it is considered to be legal decryption, where Z represents the probability threshold; Completing all the above steps ensures that the valid data contained in the ciphertext can be accurately decrypted.

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