A blockchain-based credit evaluation method, device and system
By building a multi-party governance credit environment through consortium blockchains and oracles, the problems of data silos and privacy protection in cross-regional transactions are solved, and dynamic updates and efficient mutual recognition of credit assessments are achieved, thereby improving the security and accuracy of credit assessments.
Patent Information
- Application Number
- CN202610797922.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-04
- Publication Date
- 2026-08-25
AI Technical Summary
Existing technologies suffer from problems such as regional data silos, insufficient mutual trust among multiple platforms, privacy protection risks, and the inability to dynamically update credit scores, making it difficult to achieve efficient and secure credit mutual recognition in cross-regional transaction credit assessments.
By adopting a consortium blockchain to build a credit environment governed by multiple parties, on-chain and off-chain data are verified and standardized through oracles to generate a multi-source integrated credit dataset. Smart contracts are used to dynamically update credit assessment results, and privacy protection mechanisms are combined to conduct cross-institutional credit verification and cross-regional mutual recognition.
It enables secure, reliable, dynamically updated, and efficiently mutually recognized cross-regional credit assessments, improving the comprehensiveness and accuracy of credit assessments, reducing transaction costs, and ensuring data privacy and real-time synchronization of credit results.
Smart Images
Figure CN122636201A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of e-commerce payment and credit management technology, specifically relating to a blockchain-based credit assessment method, device, and system. Background Technology
[0002] With the rapid development of the market economy and digital commerce, credit assessment of trading entities has become a crucial support for ensuring transaction security, improving financing efficiency, and enhancing transaction convenience. Currently, cross-regional and cross-platform transaction credit assessment in China mainly adopts two types of technical solutions: the first is the traditional centralized credit assessment system, which is centered on bank credit reporting systems and third-party credit rating agencies, relying on centralized databases and manual review processes to conduct credit assessments; the second is the platform-endogenous big data credit scoring model, in which commercial platforms, e-commerce platforms, etc., construct credit scoring systems based on their own internal transaction data.
[0003] The aforementioned existing technologies generally suffer from insurmountable technical flaws. First, centralized assessment models suffer from regional data silos, cross-departmental information asymmetry, and severe data update delays, making it difficult for some small and medium-sized trading entities to obtain effective cross-regional credit endorsements. Furthermore, platform-based big data scoring sources are closed, resulting in insufficient mutual trust between multiple platforms and an inability to effectively protect enterprise data privacy and security. In addition, existing solutions cannot achieve dynamic updates to credit scores, failing to meet the practical needs of cross-regional credit recognition and efficient assessment in China. Summary of the Invention
[0004] This application provides a blockchain-based credit assessment method, apparatus, and system, aiming to solve the technical problems in the prior art, such as regional data silos, insufficient mutual trust among multiple platforms, risks to privacy protection, and the inability to dynamically update credit scores. It provides a blockchain credit assessment method that takes into account multi-source data fusion, security and reliability, and efficient cross-regional mutual recognition.
[0005] In a first aspect, embodiments of this application provide a blockchain-based credit assessment method, the method comprising: Obtain the identity and permission information of participating institutions in the consortium blockchain, determine the consortium blockchain node admission list based on the identity and permission information, and deploy the consortium blockchain credit ledger based on the consortium blockchain node admission list; The system acquires on-chain native business data and off-chain third-party trusted data of the transaction entity. It then uses an oracle to verify the authenticity of the off-chain third-party trusted data and standardize its format. Based on the on-chain native business data and the processed off-chain third-party trusted data, it determines a multi-source fusion credit dataset. Credit assessment results are generated based on preset credit assessment rules and the multi-source fusion credit dataset, and the credit assessment results are stored on the blockchain in the consortium blockchain credit ledger. When the update trigger conditions are met, the latest business behavior data and external data update information of the transaction entity are obtained, and the credit assessment result is dynamically updated based on the latest business behavior data and external data update information.
[0006] Furthermore, the verification of the authenticity and standardization of the off-chain third-party trusted data using an oracle includes: Obtain the digital signature of the data source institution, the data timestamp, and the unique identifier of the data source; The legality of the data source is verified by the digital signature, the timeliness of the data is verified by the timestamp, and the data is verified not to have been reused by the unique identifier. Cross-domain field mapping standardization is performed on data that has passed authenticity verification, unifying unstructured data from different institutions into structured credit metadata that includes credit dimension labels.
[0007] Furthermore, the step of determining the multi-source fusion credit dataset based on the on-chain native business data and the processed off-chain third-party trusted data includes: Dimensional alignment and cross-validation are performed based on native on-chain business data and structured credit metadata; The aligned multi-dimensional data is aggregated into a multi-source fused credit dataset that does not contain the original sensitive plaintext.
[0008] Furthermore, after determining the multi-source fused credit dataset, the method further includes: Obtain the privacy authorization configuration information of the transaction entity, and determine the scope and target of credit data disclosure based on the privacy authorization configuration information; Construct encrypted computation rules that correspond one-to-one with the credit dimensions, and convert each credit dimension data into corresponding encrypted computation inputs; Without decrypting the original data, the dimensional weighted aggregation calculation is performed according to the ciphertext calculation rules to generate a credit feature vector, and privacy-protected credit verification information is generated based on the credit feature vector.
[0009] Furthermore, the method also includes: Cross-institutional credit verification operations are performed based on the privacy-protected credit verification information.
[0010] Furthermore, the step of generating a credit assessment result based on the preset credit assessment rules and the multi-source fusion credit dataset includes: Deploy the preset credit assessment rules as smart contracts; For the multi-source fused credit dataset, the credit score is calculated through the smart contract to determine the credit score and credit rating; The credit limit, risk control level, and transaction facilitation permissions are configured according to the credit rating, which serve as the credit assessment result.
[0011] Furthermore, the step of dynamically updating the credit assessment result based on the latest business behavior data and external data update information includes: Obtain information on transaction fulfillment, changes in tax status, and changes in bank creditworthiness; The updated information is transmitted to the blockchain system via an oracle, triggering a smart contract to re-execute the credit score calculation. The credit rating is updated based on the recalculated credit score, and the updated record is stored on the blockchain for evidence.
[0012] Furthermore, the method also includes: Obtain cross-regional credit mutual recognition request information and establish communication connections with blockchain systems in other regions through a pre-set standardized cross-link interface; The scope of credit information that can be disclosed across regions shall be determined in accordance with cross-regional data compliance rules; Cross-regional credit verification operations are performed based on privacy-preserving credit verification information.
[0013] Secondly, embodiments of this application provide a blockchain-based credit assessment device, the device comprising: The consortium blockchain deployment module is used to obtain the identity and permission information of participating institutions in the consortium blockchain, determine the consortium blockchain node admission list based on the identity and permission information, and deploy the consortium blockchain credit ledger based on the consortium blockchain node admission list. The multi-source dataset construction module is used to acquire on-chain native business data and off-chain third-party trusted data of the transaction subject. The off-chain third-party trusted data is verified for authenticity and standardized in format by an oracle. The multi-source fusion credit dataset is determined based on the on-chain native business data and the processed off-chain third-party trusted data. The credit assessment module is used to generate credit assessment results based on preset credit assessment rules and the multi-source fusion credit dataset, and to store the credit assessment results on the blockchain to the consortium blockchain credit ledger. The dynamic update module is used to obtain the latest business behavior data and external data update information of the transaction entity when the update trigger conditions are met, and to dynamically update the credit assessment results based on the latest business behavior data and external data update information.
[0014] Thirdly, embodiments of this application provide a blockchain-based credit assessment system, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of the method described in the first aspect.
[0015] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.
[0016] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.
[0017] The technical solution provided in this application constructs a cross-regional, cross-departmental, and multi-party trusted co-governance underlying credit environment through consortium blockchain node access and credit ledger deployment, effectively breaking down data silos between regions and departments. Simultaneously, this solution employs oracles to verify and standardize on-chain business data and off-chain third-party trusted data, achieving reliable fusion of multi-source data and significantly improving the comprehensiveness and accuracy of credit assessment. Furthermore, the solution can dynamically update credit results, ensuring real-time synchronization between credit status and the actual business activities of the transaction entity, greatly improving the timeliness of credit assessment and establishing a multi-party mutually recognized, secure, reliable, and efficient credit assessment mechanism for the domestic cross-regional transaction credit system. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating the blockchain-based credit assessment method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram of the structure of the blockchain-based credit assessment device provided in Embodiment 2 of this application; Figure 3 This is a schematic diagram of the structure of the blockchain-based credit assessment system provided in Embodiment 3 of this application. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.
[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.
[0021] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.
[0022] The following detailed description, in conjunction with the accompanying drawings, of the blockchain-based credit assessment method, apparatus, and system provided in this application, through specific embodiments and application scenarios, will be provided in detail.
[0023] Example 1 Figure 1 This is a flowchart illustrating the blockchain-based credit assessment method provided in Embodiment 1 of this application. Figure 1 As shown, the specific steps include the following: S11, obtain the identity and permission information of the participating institutions in the consortium blockchain, determine the consortium blockchain node admission list based on the identity and permission information, and deploy the consortium blockchain credit ledger based on the consortium blockchain node admission list.
[0024] Consortium blockchain participants refer to entities with cross-regional transaction supervision, service, or credibility. These can be regulatory agencies such as tax authorities, financial institutions such as commercial banks and factoring companies, cross-regional e-commerce platforms, international logistics companies, or third-party certification agencies.
[0025] Identity information is information used to uniquely identify an organization and prove its legal qualifications. It can be a unified social credit code, an organization registration number, or a digital identity certificate.
[0026] Permission information refers to the scope of on-chain operations allocated according to the functions of the organization, including data query permissions, consensus verification permissions, credit verification permissions, and data writing permissions.
[0027] This solution can collect data through government service interfaces, enterprise registration interfaces, and institutional authorization interfaces, or through active submission and real-name authentication by participating institutions. The authenticity of identity information, the validity of qualifications, and compliance will be verified, and institutions that pass the verification will be included in the node list.
[0028] Deploying a consortium blockchain credit ledger allows all admitted nodes to jointly build a distributed ledger, using a multi-party consensus mechanism to store credit summaries, evaluation results, and data hashes, without storing the original sensitive plaintext.
[0029] S12: Obtain the on-chain native business data and off-chain third-party trusted data of the transaction entity. Verify the authenticity and standardize the format of the off-chain third-party trusted data through an oracle. Determine the multi-source fusion credit dataset based on the on-chain native business data and the processed off-chain third-party trusted data.
[0030] Among them, the trading entity refers to the enterprise or business entity engaged in cross-regional import and export, cross-regional e-commerce, and supply chain services. It can be an export trader, a cross-regional e-commerce seller, or a supply chain service provider.
[0031] On-chain native business data can be business data generated and stored directly in the blockchain environment, such as transaction contract information, cross-regional payment records, logistics status on the blockchain, or smart contract execution results.
[0032] Trusted off-chain third-party data can be data generated by authoritative off-chain institutions with high credibility, such as tax credit ratings, bank credit reports, or third-party certification results.
[0033] An oracle (or external trusted data connector for blockchain) is an intermediate component used to connect a blockchain system with external business systems and enable the trusted transfer of off-chain data.
[0034] Specifically, relevant data can be obtained through blockchain event monitoring, platform interface integration, and authorized retrieval of government data, and then its authenticity can be verified. This includes checking the legality, integrity, and tamper-proofing of the data source. Afterward, data from different sources and with different structures are unified into a data format with the same fields, definitions, and time dimensions. Furthermore, on-chain business data is integrated with verified off-chain data to form a credit data set covering multiple dimensions such as transactions, performance, compliance, and creditworthiness.
[0035] S13, Generate a credit assessment result based on the preset credit assessment rules and the multi-source fusion credit dataset, and store the credit assessment result on the blockchain to the consortium blockchain credit ledger.
[0036] Preset credit assessment rules can be pre-defined credit scoring models, weight configurations, rating standards, compliance judgment conditions, or weights for transaction performance, compliance records, and financial creditworthiness.
[0037] Credit assessment results may include credit score, credit rating, risk label, creditworthiness reference, and transaction facilitation level.
[0038] This solution can calculate and judge the fused data according to the evaluation rules, and obtain the credit evaluation result after summarizing. Then, the credit evaluation result and the corresponding hash value are written into the consortium blockchain, which is jointly confirmed by multiple nodes and permanently stored to ensure that it cannot be tampered with.
[0039] S14, if the update triggering condition is met, obtain the latest business behavior data and external data update information of the transaction entity, and dynamically update the credit assessment result based on the latest business behavior data and external data update information.
[0040] Update trigger conditions can include transaction completion, performance confirmation, tax changes, changes in bank creditworthiness, and automatic period refresh.
[0041] The latest business activity data includes new orders, payment and receipt records, logistics receipts, and contract performance status.
[0042] External data updates, including changes in tax ratings, adjustments to bank creditworthiness, and updates to third-party certifications, allow this solution to recalculate credit scores, adjust credit ratings, and update on-chain records synchronously based on the latest data.
[0043] The technical solution provided in this embodiment constructs a multi-party, tamper-proof underlying credit environment through consortium blockchain node access and credit ledger deployment, providing fundamental support for cross-regional transaction credit mutual recognition. Oracles enable trusted access, verification, and standardized processing of on-chain business data and off-chain authoritative data, solving the problems of data silos, single sources, and insufficient authenticity. Credit assessment results are generated based on multi-source data and stored on the blockchain, ensuring the objectivity and traceability of the results. A dynamic update mechanism synchronizes credit results with actual business in real time, improving the timeliness and accuracy of credit assessments and providing secure, efficient, and reliable credit services for cross-regional transactions.
[0044] In one embodiment, optionally, the verification of the authenticity and format standardization of the off-chain third-party trusted data via an oracle includes: Obtain the digital signature of the data source institution, the data timestamp, and the unique identifier of the data source; The legality of the data source is verified by the digital signature, the timeliness of the data is verified by the timestamp, and the data is verified not to have been reused by the unique identifier. Cross-domain field mapping standardization is performed on data that has passed authenticity verification, unifying unstructured data from different institutions into structured credit metadata that includes credit dimension labels.
[0045] A digital signature from a data source institution can be an electronic signature generated by the data provider using an asymmetric encryption algorithm. It is used to prove that the data was issued by a legitimate institution and has not been tampered with. It can be a digital signature from a tax authority, or a digital signature from a bank or certification authority.
[0046] A data timestamp can be a standard time that records the generation or update of data, used to determine whether the data is within a valid period. It can be the system time accurate to the second, the time of a third-party time center, or the blockchain block time.
[0047] A unique identifier for the data source can be a non-repeatable identifier assigned to each set of data to prevent data reuse or forgery. It can be a data serial number, a business order number, or a hash code.
[0048] This scheme can be obtained synchronously by the oracle when it makes a request to an external data source and returned along with the data. It verifies whether the signature matches the institution's public key to confirm that the data was issued by a registered and legitimate institution. It also verifies whether the interval between the current time and the timestamp is within a preset range to exclude expired or invalid data. Finally, it verifies whether the unique identifier already exists in the system to prevent the same data from being counted twice in the evaluation.
[0049] Cross-domain field mapping standardization can unify the field names, data types, and units of measurement of different institutions such as taxation and banking to the system's standard fields.
[0050] Credit dimension labels can include performance dimension, compliance dimension, financial dimension, and risk dimension, and can be performance rate label, no violation label, low risk label, high credit rating label, etc.
[0051] Structured credit metadata can be standardized, tagged data that can be directly used for credit calculation. It has a unified format and can be directly read by smart contracts.
[0052] This technical solution employs a three-factor verification mechanism—digital signature, timestamp, and unique identifier—to perform multiple checks on the source, timeliness, and uniqueness of trusted off-chain third-party data. This effectively eliminates forged, expired, and duplicate data, significantly improving data authenticity and reliability. Furthermore, by standardizing cross-domain field mapping, unstructured data from multiple institutions is uniformly converted into structured metadata with credit dimension tags. This eliminates data format barriers between different systems, enabling efficient integration of multi-source data and direct use in assessment calculations. This provides high-quality, standardized data input for subsequent credit assessments, improving the accuracy and consistency of assessment results.
[0053] In one embodiment, optionally, determining the multi-source fused credit dataset based on the on-chain native business data and the processed off-chain third-party trusted data includes: Dimensional alignment and cross-validation are performed based on native on-chain business data and structured credit metadata; The aligned multi-dimensional data is aggregated into a multi-source fused credit dataset that does not contain the original sensitive plaintext.
[0054] Dimensional alignment allows matching on-chain and off-chain data by key fields such as transaction entity, business order number, and time period. Alignment can be done by enterprise unified social credit code, order number, or monthly / quarterly period.
[0055] Cross-validation can be used to verify the consistency of data from different sources. It can compare customs declaration amount with transaction amount, logistics records with performance status, and tax data with revenue data.
[0056] This solution can merge and organize multi-dimensional data by subject to form a complete credit data view. It can aggregate data by a single enterprise, by a corporate group, or by a business line.
[0057] Original sensitive plaintext, including information that should not be directly disclosed, such as specific transaction amounts, customer information, profit data, internal operating data, and core financial data.
[0058] A multi-source fusion credit dataset refers to a dataset that integrates information from multiple dimensions such as transactions, performance, compliance, and creditworthiness, retaining only the features required for assessment and not containing any original sensitive content.
[0059] This technical solution achieves precise matching and mutual verification between on-chain and off-chain data through dimensional alignment and cross-validation, reducing data conflicts, errors, and biases, and significantly improving the accuracy and credibility of credit data. By aggregating multi-dimensional data into a unified dataset, it forms a comprehensive credit profile covering all aspects of an enterprise, providing comprehensive data support for assessment. Simultaneously, the dataset does not contain raw sensitive plaintext, reducing the risk of data leakage while ensuring the usability of the assessment, balancing data integrity, usability, and security, and laying a stable foundation for subsequent credit calculation, privacy protection, and cross-institutional mutual recognition.
[0060] In one embodiment, optionally, after determining the multi-source fused credit dataset, the method further includes: Obtain the privacy authorization configuration information of the transaction entity, and determine the scope and target of credit data disclosure based on the privacy authorization configuration information; Construct encrypted computation rules that correspond one-to-one with the credit dimensions, and convert each credit dimension data into corresponding encrypted computation inputs; Without decrypting the original data, the dimensional weighted aggregation calculation is performed according to the ciphertext calculation rules to generate a credit feature vector, and privacy-protected credit verification information is generated based on the credit feature vector.
[0061] Privacy authorization configuration information can be data usage rules set independently by the transaction entity, disclosure scope configuration, disclosure target configuration, or authorization validity period configuration.
[0062] The scope of disclosure can be limited to disclosing only the credit rating, disclosing some credit dimensions, or restricting the disclosure of specific values.
[0063] The entities to be disclosed can be banks within the alliance, cooperative trading platforms, or institutions in specific other regions.
[0064] Ciphertext computation rules are rules built on zero-knowledge proofs (ZKP) or homomorphic encryption (HE) that allow computation to be performed in ciphertext without decryption.
[0065] Credit-related data can be single-dimensional values such as performance data, compliance data, financial data, and transaction data.
[0066] Dimension-weighted aggregation calculation is a process that performs summation, averaging, and grading operations on encrypted data of each dimension according to preset weights.
[0067] Credit feature vectors are high-dimensional feature data formed by weighted aggregation of multiple credit dimension values. They can represent credit level but do not expose the original information.
[0068] Privacy-protected credit verification information is proof that can be used to verify creditworthiness without disclosing the original data. It can be proof of creditworthiness, credit rating, or risk assessment.
[0069] This technical solution enables transaction entities to autonomously and controllably manage their own credit data through privacy authorization configuration, clearly defining the scope and objects of data use and meeting data compliance and trade secret protection requirements. By constructing dedicated encrypted computation rules, it achieves encrypted aggregation calculation of credit dimensions without decrypting original sensitive data throughout the process, thus preventing privacy leaks at the source. The generated credit feature vector and privacy-protected verification information can complete credit verification without exposing data details, balancing data security and credit circulation, effectively solving the technical problem of balancing privacy protection and credit mutual recognition in cross-regional transactions.
[0070] In one embodiment, optionally, the method further includes: Cross-institutional credit verification operations are performed based on the privacy-protected credit verification information.
[0071] Cross-institutional credit verification refers to the operation of different institutions within the consortium blockchain to verify and confirm the credit status of the transaction entity without obtaining the original data or accessing sensitive information.
[0072] Privacy-protected credit verification information is proof information that does not contain raw data and only represents the credit result. It can be proof of credit rating compliance, proof of risk status, proof of performance capability, or proof of compliance.
[0073] Specifically, this could be verification by banks before granting credit, by trading platforms during onboarding, or by logistics companies before cooperation. The verification process could involve institutions only verifying the validity and authenticity of the credit information, without requesting, viewing, or storing sensitive content such as original business data or financial data.
[0074] The verification results can be directly used for business decisions, and the entire process is traceable, auditable, and tamper-proof.
[0075] This technical solution enables cross-institutional credit verification based on privacy-preserving credit verification information. It allows alliance institutions such as banks, platforms, and logistics companies to confirm credit status without obtaining original business data or disclosing sensitive information. This protects corporate data privacy and trade secrets while achieving mutual recognition of credit results among institutions. This approach eliminates the need for companies to repeatedly submit materials and institutions to conduct repeated due diligence, significantly improving verification efficiency, reducing trust costs in cross-regional transactions, and meeting cross-regional data compliance requirements. It provides a secure and reliable path for a cross-regional transaction credit system based on mutual trust and efficient collaboration among multiple parties.
[0076] In one embodiment, optionally, generating a credit assessment result based on a preset credit assessment rule and the multi-source fusion credit dataset includes: Deploy the preset credit assessment rules as smart contracts; For the multi-source fused credit dataset, the credit score is calculated through the smart contract to determine the credit score and credit rating; The credit limit, risk control level, and transaction facilitation permissions are configured according to the credit rating, which serve as the credit assessment result.
[0077] Among them, smart contracts are contract code deployed on the blockchain that can be executed automatically, and can automatically complete calculations, logical judgments and output results according to preset rules.
[0078] Preset credit assessment rules can be weighted scoring models, rating classification models, or risk threshold models.
[0079] Credit score calculation is a weighted calculation based on dimensions such as performance, compliance, transactions, and creditworthiness to obtain a credit score.
[0080] Credit ratings can be AAA, AA, A, BBB, etc., and can be scores from 1 to 10. They can also be high, medium, or low risk levels.
[0081] A credit line can be a financing limit granted by a financial institution based on credit rating, or it can be an order financing limit, an accounts receivable financing limit, or a credit loan limit.
[0082] Risk control levels can be low risk, medium risk, or high risk, corresponding to different levels of review intensity.
[0083] Transaction facilitation authority can include reducing inspection rates, prioritizing customs clearance, providing green channels, and simplifying documentation.
[0084] This technical solution automates and immutably executes the credit assessment process by deploying credit assessment rules as smart contracts, avoiding distortions caused by human intervention. Based on multi-source fusion data, on-chain scoring calculations generate objective and verifiable credit scores and ratings. Credit limits, risk control levels, and transaction facilitation permissions are automatically configured according to the credit rating, enabling credit results to be directly linked to actual business operations such as financial services, customs clearance facilitation, and transaction risk control. This enhances the practicality and business support capabilities of credit assessments, promoting the efficient application of credit value in cross-regional transaction scenarios.
[0085] In one embodiment, optionally, the step of dynamically updating the credit assessment result based on the latest business behavior data and external data update information includes: Obtain information on transaction fulfillment, changes in tax status, and changes in bank creditworthiness; The updated information is transmitted to the blockchain system via an oracle, triggering a smart contract to re-execute the credit score calculation. The credit rating is updated based on the recalculated credit score, and the updated record is stored on the blockchain for evidence.
[0086] Among them, transaction fulfillment information can include order completion, goods receipt, contract fulfillment, and payment settlement information.
[0087] Tax status changes can include adjustments to taxpayer credit rating, updates to tax declaration status, and changes in tax-related risk status.
[0088] Information on changes in bank creditworthiness can include credit report updates, credit limit adjustments, changes in repayment status, and changes in account status.
[0089] When updated information is received, oracles can complete the trusted transfer of external data to the blockchain system, ensuring the data is authentic and has not been tampered with.
[0090] The smart contract is triggered to recalculate, and the credit score is automatically refreshed after new data is accessed, without the need for manual operation.
[0091] Update records are stored on the blockchain, writing the score, level, time, and reason for each update into the consortium blockchain to form a complete and traceable credit history file.
[0092] This technical solution achieves dynamic and real-time updates to credit assessment results by acquiring the latest operational and compliance information from transaction fulfillment, taxation, and banking in real time. This is combined with trusted oracle input and automatic recalculation via smart contracts, ensuring that credit status is accurately synchronized with actual corporate behavior. On-chain storage of update records ensures traceability and immutability of credit history, enhancing the credibility of the credit system. The dynamic update mechanism effectively addresses the problems of delayed, distorted, and out-of-trace credit assessment results in traditional methods, improving the timeliness and accuracy of credit assessments and enhancing the precision and reliability of cross-regional transaction risk control, financing, and customs clearance services.
[0093] In one embodiment, optionally, the method further includes: Obtain cross-regional credit mutual recognition request information and establish communication connections with blockchain systems in other regions through a pre-set standardized cross-link interface; The scope of credit information that can be disclosed across regions shall be determined in accordance with cross-regional data compliance rules; Cross-regional credit verification operations are performed based on privacy-preserving credit verification information.
[0094] Among them, cross-regional credit mutual recognition request information can be credit inquiry requests from financial institutions in other regions, or entity credit verification requests from trading platforms in other regions.
[0095] A standardized cross-link interface can be a standard communication interface used to achieve secure interoperability between different blockchain systems, supporting protocol adaptation, data encryption, and identity verification.
[0096] Other regional blockchain systems could be regional trading platform blockchain systems, international credit agency blockchain systems, or even trading credit infrastructure in other countries.
[0097] Cross-regional data compliance rules are information use and transmission standards that comply with cross-regional data regulatory requirements. These rules can include cross-regional data whitelists, the minimum necessity principle, and tiered disclosure rules.
[0098] The scope of credit information that can be disclosed across regions can be limited to disclosing only the credit rating, the risk status, restricting the disclosure of specific values, or setting a valid duration.
[0099] Cross-regional credit verification operations can be carried out by institutions in other regions to verify the credit of domestic transaction entities without transmitting the original data.
[0100] This technical solution achieves secure integration with blockchain systems in other regions through standardized cross-link interfaces. It breaks down credit data barriers between different regions and platforms, enhancing the global interoperability of cross-regional transaction credit systems. By determining the scope of disclosable information based on cross-regional compliance rules and completing cross-regional credit verification based on privacy-preserving credit verification information, it achieves cross-regional credit mutual recognition while meeting cross-regional data compliance requirements. This mechanism effectively reduces information asymmetry and trust costs in cross-regional transactions, minimizes redundant authentication and due diligence, and provides key technical support for the construction of a global digital transaction credit infrastructure.
[0101] To enable those skilled in the art to better understand this solution, this application also provides a preferred embodiment.
[0102] This application provides a blockchain-based credit assessment method, apparatus, and system, including: 1. Multi-source heterogeneous data fusion mechanism; This system supports the simultaneous access of on-chain data (transaction contracts, payment records, logistics status, smart contract execution records) and off-chain data (tax ratings, bank credit reports, third-party certifications). It achieves secure integration of on-chain and off-chain data through oracles or trusted computing nodes, forming a multi-dimensional credit profile of cross-regional trading entities.
[0103] 2. A multi-party credit system based on consortium blockchain; By adopting a consortium blockchain architecture, banks, cross-regional trading platforms, logistics companies, certification authorities, and other entities act as consortium nodes to jointly maintain the credit ledger, achieving "one-time assessment, multi-party mutual recognition," breaking down data silos, and reducing the cost of repeated due diligence in cross-regional transactions.
[0104] 3. Privacy protection and selective disclosure mechanisms; By introducing zero-knowledge proofs or homomorphic encryption technologies, credit verification can be completed without exposing original sensitive data (such as transaction amounts and customer lists). Enterprises can independently control the scope and recipients of credit data disclosure, meeting the dual requirements of cross-regional data compliance and trade secret protection.
[0105] 4. Dynamic credit assessment model; Smart contracts enable dynamic updates to credit scores. When a new transaction is completed, a performance action occurs, or an external data source is updated, the credit score can be automatically adjusted, triggering subsequent operations such as changes to the credit rating and credit limit.
[0106] 5. Cross-regional interoperability interface; The design standardizes cross-chain interaction protocols and data interfaces to support data interoperability with blockchain systems of trading platforms in other regions or international credit institutions, providing a technological foundation for building a globally trustworthy cross-regional trading credit network.
[0107] This solution can be widely applied to the following scenarios: Cross-regional e-commerce platforms: Provide credible credit scores for both buyers and sellers on the platform to assist in transaction decisions and reduce the risk of fraud.
[0108] Cross-regional supply chain finance: Banks or financial institutions can provide SMEs with services such as accounts receivable financing and order financing without collateral based on on-chain credit assessment results.
[0109] Cross-regional digital product transactions: Establish a transaction guarantee mechanism based on the credit of the main entities for cross-regional transactions of intangible products such as software, data services, and digital content.
[0110] Cross-regional investment and mergers and acquisitions: providing a reliable credit reference for due diligence in cross-regional investment.
[0111] This plan has broad market prospects: Policy-driven: The development of digital transactions is being actively promoted, with the Ministry of Commerce and other departments encouraging the exploration of a blockchain-based cross-regional transaction trust system. International agreements such as RCEP (Regional Comprehensive Economic Partnership) and DEPA (Digital Economy Partnership Agreement) have put forward new requirements for digital transactions and cross-regional data flows, making mutual recognition of credit a key infrastructure.
[0112] Market Size: According to relevant data, there are over one million cross-regional trading entities, many of which are small and medium-sized enterprises (SMEs) facing credit deficiencies and financing difficulties. If this technology can be widely adopted, it could cover a supply chain finance market worth hundreds of billions of yuan and significantly reduce trust costs in cross-regional transactions.
[0113] Platform Value: This achievement possesses the characteristics of "one-time construction, multi-party reuse" as a platform, and can serve as an important component of cross-regional transaction digital infrastructure, possessing high technical barriers and commercial scalability.
[0114] This technical solution addresses the core pain points of credit assessment in cross-regional transactions, such as "data silos, privacy concerns, and difficulties in cross-regional trust," and proposes a dynamic credit assessment system and methodology based on consortium blockchains, integrating multi-source data, and balancing privacy protection. This achievement possesses clear technological innovation and broad application market prospects, and is of great significance for promoting the high-quality development of digital transactions and building a trust infrastructure for cross-regional transactions.
[0115] This embodiment takes three types of entities—cross-regional e-commerce export enterprises, cross-regional supply chain service providers, and small and medium-sized foreign trade enterprises—as examples to fully disclose the specific implementation process, execution details, and data integration methods of the blockchain-based credit assessment method.
[0116] 1. Consortium blockchain networking and trust ledger deployment; The system first obtains the identity information, qualification certificates, and digital identity identifiers of commercial banks, cross-regional e-commerce platforms, international logistics companies, and third-party certification agencies. Based on the legitimacy of their identities and their business functions, it determines the list of admitted consortium blockchain nodes and assigns each node tiered permissions such as data query, consensus verification, credit verification, and result writing. Each consortium node uses a distributed consensus mechanism to jointly build a consortium blockchain credit ledger. The ledger only stores summary information such as credit scores, credit ratings, assessment result hash values, and update records, and does not store sensitive plaintext such as original transaction amounts, customer information, and financial data, ensuring data security and trustworthy institutional collaboration.
[0117] 2. Multi-source data acquisition and trusted data integration methods; In this embodiment, the system simultaneously acquires two types of data: 1) On-chain native business data, including transaction contracts, cross-regional payment records, logistics on-chain status, and smart contract execution records, are collected in real time through the blockchain event monitoring mechanism.
[0118] 2) Off-chain trusted third-party data, including tax credit ratings, bank credit reports, and third-party certification information, are retrieved through oracles via authorized interfaces.
[0119] The key means by which this solution achieves data integration: Oracles perform three-factor verification on off-chain data: digital signature verification, timestamp validity verification, and data uniqueness identification to prevent duplication. They also map heterogeneous data to four standard credit dimensions: performance, compliance, finance, and risk. Existing solutions rely solely on platform-owned data or simple on-chain storage, lacking authoritative signature verification mechanisms, cross-domain data standardization rules, and the ability to fuse encrypted data. This makes it impossible to achieve trusted on-chain and off-chain integration, and also hinders the fusion of multi-source data while protecting privacy—a common technical bottleneck.
[0120] 3. Data fusion and privacy-preserving computing implementation; The system aligns on-chain data with off-chain structured credit metadata based on the enterprise's unified social credit code, business order number, and time period, and improves data accuracy through cross-validation. For example, it compares customs declaration amounts with on-chain transaction amounts and logistics receipt records with performance status. Subsequently, it obtains the privacy authorization configuration of the transaction entity to limit the scope and recipients of data disclosure. The system uses homomorphic encryption or zero-knowledge proofs to construct ciphertext calculation rules, completing the weighted aggregation of credit dimensions without decrypting the original data, generating credit feature vectors and privacy-protected credit verification information, achieving "usable but invisible."
[0121] 4. Smart contract credit assessment execution process; The system deploys a pre-defined credit assessment model as a smart contract, with performance accounting for 40%, compliance records for 30%, and creditworthiness for 30%. The system inputs a multi-source fusion credit dataset, and the smart contract automatically calculates credit scores, classifies credit levels, and outputs corresponding credit limits, risk control levels, and transaction facilitation permissions based on the level. For example, a credit score of 900 or above corresponds to an AAA level, which entitles the user to order financing up to 1.5 million yuan and facilitation measures such as a 30% reduction in risk for low-risk levels. After the assessment is completed, the results and hash values are stored on the blockchain for verification and confirmed by consensus among consortium nodes.
[0122] 5. Implementation of dynamic credit updates; When the update trigger conditions are met, such as order fulfillment, tax rating adjustment, or bank credit update, the system obtains the latest data through the oracle and transmits it to the blockchain. This automatically triggers the smart contract to re-execute the scoring calculation, update the credit score, credit rating, and corresponding business strategy, and records the update on the blockchain for evidence storage, forming a complete and traceable credit profile.
[0123] 6. Implementation of cross-institutional verification and cross-regional mutual recognition; Within the alliance, institutions complete credit verification based on privacy-preserving credit verification information, without needing to obtain original data or conduct repeated due diligence. When receiving cross-regional credit mutual recognition requests from trading platforms in other regions, the system establishes a secure connection through standardized cross-link interfaces, determines the scope of information that can be disclosed according to cross-regional data compliance rules, and completes cross-regional credit verification based on privacy-preserving credit verification information, thereby achieving cross-regional credit mutual recognition.
[0124] This solution forms an overall technical solution through three-factor authentication, cross-domain data standardization, encrypted computation fusion, smart contract dynamic evaluation, and cross-chain privacy interoperability. It solves the technical problems that have long existed in existing technologies, such as data silos, inability to connect authoritative on-chain and off-chain data, weak privacy protection, difficulty in cross-regional mutual recognition, and lagging credit updates.
[0125] Example 2 Figure 2This is a schematic diagram of the structure of the blockchain-based credit assessment device provided in Embodiment 2 of this application. Figure 2 As shown, the device includes: The consortium blockchain deployment module 201 is used to obtain the identity information and permission information of the participating institutions in the consortium blockchain, determine the consortium blockchain node admission list based on the identity information and permission information, and deploy the consortium blockchain credit ledger based on the consortium blockchain node admission list. The multi-source dataset construction module 202 is used to obtain the on-chain native business data and off-chain third-party trusted data of the transaction subject, verify the authenticity of the off-chain third-party trusted data and standardize its format through an oracle, and determine the multi-source fusion credit dataset based on the on-chain native business data and the processed off-chain third-party trusted data. Credit assessment module 203 is used to generate credit assessment results based on preset credit assessment rules and the multi-source fusion credit dataset, and to store the credit assessment results on the blockchain to the consortium blockchain credit ledger; The dynamic update module 204 is used to obtain the latest business behavior data and external data update information of the transaction entity when the update triggering conditions are met, and to dynamically update the credit assessment result based on the latest business behavior data and external data update information.
[0126] The blockchain-based credit assessment device in this application embodiment can be a system, or a component, integrated circuit, or chip in a terminal.
[0127] The blockchain-based credit assessment device in this application embodiment can be integrated into a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit its application.
[0128] The blockchain-based credit assessment device provided in this application can implement the various processes of the above embodiments, and will not be described again here to avoid repetition.
[0129] Example 3 like Figure 3 As shown, this application embodiment also provides a blockchain-based credit assessment system 300, including a processor 301, a memory 302, and a program or instruction stored in the memory 302 and executable on the processor 301. When the program or instruction is executed by the processor 301, it implements the various processes of the above-described blockchain-based credit assessment method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0130] It should be noted that the blockchain-based credit assessment system in this application embodiment includes the mobile blockchain-based credit assessment system and the non-mobile blockchain-based credit assessment system as described above.
[0131] Example 4 This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described blockchain-based credit assessment method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.
[0132] The processor mentioned above is the processor in the blockchain-based credit assessment system described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0133] Example 5 This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described blockchain-based credit assessment method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.
[0134] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.
[0135] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0136] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0137] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above, which are merely illustrative and not restrictive. Those skilled in the art, under the guidance of this application, can make many modifications without departing from the spirit and scope of the claims, all of which fall within the protection scope of this application.
[0138] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.
Claims
1. A blockchain-based credit assessment method, characterized in that, The method includes: Obtain the identity and permission information of participating institutions in the consortium blockchain, determine the consortium blockchain node admission list based on the identity and permission information, and deploy the consortium blockchain credit ledger based on the consortium blockchain node admission list; The system acquires on-chain native business data and off-chain third-party trusted data of the transaction entity. It then uses an oracle to verify the authenticity of the off-chain third-party trusted data and standardize its format. Based on the on-chain native business data and the processed off-chain third-party trusted data, it determines a multi-source fusion credit dataset. Credit assessment results are generated based on preset credit assessment rules and the multi-source fusion credit dataset, and the credit assessment results are stored on the blockchain in the consortium blockchain credit ledger. When the update trigger conditions are met, the latest business behavior data and external data update information of the transaction entity are obtained, and the credit assessment result is dynamically updated based on the latest business behavior data and external data update information.
2. The method according to claim 1, characterized in that, The process of verifying the authenticity of the off-chain third-party trusted data and standardizing its format using an oracle includes: Obtain the digital signature of the data source institution, the data timestamp, and the unique identifier of the data source; The legality of the data source is verified by the digital signature, the timeliness of the data is verified by the timestamp, and the data is verified not to have been reused by the unique identifier. Cross-domain field mapping standardization is performed on data that has passed authenticity verification, unifying unstructured data from different institutions into structured credit metadata that includes credit dimension labels.
3. The method according to claim 2, characterized in that, The step of determining the multi-source fusion credit dataset based on the on-chain native business data and the processed off-chain third-party trusted data includes: Dimensional alignment and cross-validation are performed based on native on-chain business data and structured credit metadata; The aligned multi-dimensional data is aggregated into a multi-source fused credit dataset that does not contain the original sensitive plaintext.
4. The method according to claim 3, characterized in that, After determining the multi-source fused credit dataset, the method further includes: Obtain the privacy authorization configuration information of the transaction entity, and determine the scope and target of credit data disclosure based on the privacy authorization configuration information; Construct encrypted computation rules that correspond one-to-one with the credit dimensions, and convert each credit dimension data into corresponding encrypted computation inputs; Without decrypting the original data, the dimensional weighted aggregation calculation is performed according to the ciphertext calculation rules to generate a credit feature vector, and privacy-protected credit verification information is generated based on the credit feature vector.
5. The method according to claim 4, characterized in that, The method further includes: Cross-institutional credit verification operations are performed based on the privacy-protected credit verification information.
6. The method according to claim 1, characterized in that, The step of generating credit assessment results based on preset credit assessment rules and the multi-source fusion credit dataset includes: Deploy the preset credit assessment rules as smart contracts; For the multi-source fused credit dataset, the credit score is calculated through the smart contract to determine the credit score and credit rating; The credit limit, risk control level, and transaction facilitation permissions are configured according to the credit rating, which serve as the credit assessment result.
7. The method according to claim 6, characterized in that, The step of dynamically updating the credit assessment results based on the latest business behavior data and external data update information includes: Obtain information on transaction fulfillment, changes in tax status, and changes in bank creditworthiness; The updated information is transmitted to the blockchain system via an oracle, triggering a smart contract to re-execute the credit score calculation. The credit rating is updated based on the recalculated credit score, and the updated record is stored on the blockchain for evidence.
8. The method according to claim 1, characterized in that, The method further includes: Obtain cross-regional credit mutual recognition request information and establish communication connections with blockchain systems in other regions through a pre-set standardized cross-link interface; The scope of credit information that can be disclosed across regions shall be determined in accordance with cross-regional data compliance rules; Cross-regional credit verification operations are performed based on privacy-preserving credit verification information.
9. A blockchain-based credit assessment device, characterized in that, The device includes: The consortium blockchain deployment module is used to obtain the identity and permission information of participating institutions in the consortium blockchain, determine the consortium blockchain node admission list based on the identity and permission information, and deploy the consortium blockchain credit ledger based on the consortium blockchain node admission list. The multi-source dataset construction module is used to acquire on-chain native business data and off-chain third-party trusted data of the transaction subject. The off-chain third-party trusted data is verified for authenticity and standardized in format by an oracle. The multi-source fusion credit dataset is determined based on the on-chain native business data and the processed off-chain third-party trusted data. The credit assessment module is used to generate credit assessment results based on preset credit assessment rules and the multi-source fusion credit dataset, and to store the credit assessment results on the blockchain to the consortium blockchain credit ledger. The dynamic update module is used to obtain the latest business behavior data and external data update information of the transaction entity when the update trigger conditions are met, and to dynamically update the credit assessment results based on the latest business behavior data and external data update information.
10. A blockchain-based credit assessment system, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the blockchain-based credit assessment method as described in any one of claims 1-8.