A multi-level transaction security authentication method based on supply chain finance

Abstract

The present invention discloses a multi-level transaction security authentication method based on supply chain finance, which relates to the technical field of financial services, including: when a transaction is initiated, reading encrypted data stored in a blockchain, and calculating a dynamic credit score in combination with real-time data in a transaction request; dividing the risk level of the transaction according to the dynamic credit score, transaction amount and transaction frequency; dynamically triggering a corresponding authentication level according to the transaction risk level, and performing privacy protection verification on sensitive data related to the transaction in combination with zero-knowledge proof technology; after the verification is passed, selecting an adapted hierarchical consensus mechanism according to the transaction risk level to perform a consensus operation, and storing the consensus result in the blockchain; by dynamically dividing the transaction risk level, triggering the corresponding authentication level for different risk levels, both the high efficiency of low-risk transactions and fine-grained privacy protection are guaranteed for high-risk transactions.

Description

Technical Field

[0001] The present invention relates to the technical field of financial services, and in particular to a multi-level transaction security authentication method based on supply chain finance. Background Art

[0002] With the rapid development of supply chain finance, traditional financial services are gradually expanding in the direction of digitalization and intelligence, and blockchain technology has become an important technical support in the field of supply chain finance due to its decentralization, immutability and transparency. However, in practical applications, supply chain finance faces many challenges. For example, trust issues between participants, privacy protection of transaction data, accuracy of risk assessment, and efficient transaction authentication and consensus mechanisms. These problems make it difficult for the existing supply chain finance system to balance security, efficiency and privacy protection needs.

[0003] In the existing technology, the risk assessment and authentication mechanism of supply chain finance often relies on static credit scoring models and centralized transaction verification methods. These methods are insufficient in terms of data privacy protection and dynamic analysis, and are difficult to cope with complex and changing transaction scenarios. For example, the traditional risk assessment mechanism ignores the dynamic interaction between historical data and real-time data, resulting in low accuracy in risk level classification; in addition, the existing authentication mechanism is difficult to achieve fine-grained privacy protection for high-risk transactions, and cannot meet the dual needs of transaction security and privacy protection. Therefore, how to build a dynamic, efficient and secure authentication and consensus mechanism based on blockchain technology has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a multi-level transaction security authentication method based on supply chain finance to solve the problems of dynamic credit scoring, transaction risk level classification, and multi-level authentication and hierarchical consensus mechanism based on privacy protection in supply chain finance.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a multi-level transaction security authentication method based on supply chain finance, which includes collecting historical transaction behavior data of supply chain finance participants, encrypting it, and storing it in a blockchain;

[0008] When a transaction is initiated, the encrypted data stored in the blockchain is read and combined with the real-time data in the transaction request to calculate the dynamic credit score;

[0009] Transaction risk levels are classified based on dynamic credit scores, transaction amounts, and transaction frequency;

[0010] Dynamically trigger the corresponding authentication level according to the transaction risk level, and use zero-knowledge proof technology to verify the privacy protection of sensitive data related to the transaction;

[0011] After verification, the appropriate hierarchical consensus mechanism is selected according to the transaction risk level to perform consensus operations, and the consensus results are stored in the blockchain;

[0012] Based on the consensus result, the final transaction is confirmed and a hash mark of the transaction completion status is generated;

[0013] According to the completion status of the transaction, the traceability query function is triggered to form a complete traceability log and store it in the blockchain.

[0014] As a preferred solution of the multi-level transaction security authentication method based on supply chain finance described in the present invention, wherein: the historical transaction behavior data includes transaction amount, transaction frequency, transaction success rate and default record;

[0015] The encryption process and storage in the blockchain include the following steps:

[0016] Perform deduplication, formatting, outlier removal and data standardization on the collected historical transaction behavior data;

[0017] The processed data is encrypted using an elliptic curve algorithm to generate a unique pair of public and private keys for each participant;

[0018] Use lattice cryptography to generate the identity private key through the SampleMat algorithm and calculate the identity index;

[0019] Each field data is encrypted using the public key of the participant, and the identity index and encrypted field data are uploaded to the blockchain.

[0020] As a preferred solution of the multi-level transaction security authentication method based on supply chain finance described in the present invention, when a transaction is initiated, the encrypted data stored in the blockchain is read, and the dynamic credit score is calculated in combination with the real-time data in the transaction request, including the following steps:

[0021] Read the encrypted transaction data stored in the blockchain, decrypt it with the private key of the participant to get the original field data, receive the real-time data in the transaction request, construct a new dynamic feature set, and introduce a dynamic weight adjustment function to adjust the impact of each feature. The expression is:

[0022] ;

[0023] in, express Dynamically adjust feature weights, Indicates The transaction frequency of a feature, Parameters representing the magnitude of the adjustment weights, a tuning parameter representing the sensitivity of the tuning frequency;

[0024] Based on the dynamic adjustment of feature weights and time decay factors, the dynamic credit score is calculated as follows:

[0025] ;

[0026] in, represents a dynamic credit score, represents the activation function, represents the bias term, Indicates The weight parameter of each feature, Represents the first Features, represents the time attenuation coefficient, Indicates the difference between the current time and the historical record time. represents the feature index, Represents the total number of features.

[0027] As a preferred solution of the multi-level transaction security authentication method based on supply chain finance described in the present invention, the risk level classification of transactions based on dynamic credit score, transaction amount and transaction frequency includes the following steps:

[0028] The behavioral deviation correction function is used to adjust the credibility of the dynamic credit score, and the expression is:

[0029] ;

[0030] in, Indicates the difference between the current score and the previous score. represents the behavioral deviation correction factor, represents the hyperbolic tangent function;

[0031] Obtain transaction amount data from real-time transactions and extract transaction frequency data from historical transaction behavior data;

[0032] Combining the dynamic credit score, transaction amount, transaction frequency and behavioral deviation correction factor, the transaction risk factor is calculated as follows:

[0033] ;

[0034] in, Represents the transaction risk factor, represents the transaction frequency, Indicates the transaction amount;

[0035] Set risk factor thresholds based on historical transaction data distribution and divide risk levels based on transaction risk factors;

[0036] when When , it indicates low risk, when When , indicating high risk;

[0037] in, Indicates a low risk threshold, Indicates a high risk threshold;

[0038] For the current transaction, dynamically determine the risk level based on the calculated transaction risk factor;

[0039] The risk level of the transaction and its related data are encrypted at the field level and stored in the blockchain.

[0040] As a preferred solution of the multi-level transaction security authentication method based on supply chain finance described in the present invention, the corresponding authentication level is dynamically triggered according to the transaction risk level, and the privacy protection verification of sensitive data related to the transaction is performed in combination with zero-knowledge proof technology, including the following steps:

[0041] When the risk transaction is low, basic authentication is performed to verify the integrity of the transaction data;

[0042] When the risk transaction is medium risk, additional verification is performed to verify whether the transaction amount range and dynamic credit score meet the business rules;

[0043] When the risky transaction is high risk, advanced verification is performed to verify the compliance of the transaction amount, credit score and default record, while using zero-knowledge proof technology for privacy protection of sensitive information;

[0044] When the risky transaction is a cross-domain transaction, the transaction initiator generates an authentication request through the identity authentication server of the domain, using the identity index and private key signature of the participant;

[0045] After receiving the authentication request, the external domain identity authentication server verifies the initiator's identity through the participant's identity index and private key signature;

[0046] When all triggered authentication levels and corresponding verifications are passed, it means the authentication is passed. When any authentication verification fails, it means it fails.

[0047] As a preferred solution of the multi-level transaction security authentication method based on supply chain finance described in the present invention, wherein: after verification, selecting an adapted hierarchical consensus mechanism according to the transaction risk level to perform a consensus operation, and storing the consensus result in the blockchain includes the following steps:

[0048] When the authentication result is passed, the transaction amount, transaction frequency and dynamic credit score are read from the blockchain;

[0049] For low-risk transactions, a fast and simplified consensus mechanism is used to generate committee signatures through pre-selected nodes and multiple rounds of message interactions;

[0050] For medium-risk transactions, an efficient fault-tolerant consensus mechanism is used to select super nodes through equity agents and generate super node signatures;

[0051] For high-risk transactions, a consensus mechanism that combines proof of work with the Byzantine Fault Tolerance protocol is used. First, the legitimate working nodes are identified through proof of work, and then the final consensus is reached through the Byzantine Fault Tolerance protocol to generate signatures of all nodes in the network.

[0052] The expression that defines the consensus completion of the hierarchical consensus mechanism is:

[0053] ;

[0054] in, Indicates the degree of consensus completion, Indicates the start time of consensus, represents the current time of consensus, Representation Node The weight distribution of represents the consensus efficiency factor, Indicates the time during the consensus process;

[0055] Using the blockchain’s smart contract interface, the consensus results are encrypted and stored in the blockchain.

[0056] As a preferred solution of the multi-level transaction security authentication method based on supply chain finance described in the present invention, the final transaction is confirmed based on the consensus result, and the hash mark of the transaction completion status is generated, including the following steps:

[0057] Obtain transaction data and consensus result data from the consensus result;

[0058] Organize the transaction completion status and acquired data into a structured JSON object;

[0059] Serialize structured JSON objects into strings using a fixed field order;

[0060] Perform hash calculation on the serialized string to generate a unique hash identifier;

[0061] Organize the transaction completion status and hash identifier into a new JSON object, and encrypt the new JSON object using a public key based on elliptic curve encryption;

[0062] The encrypted transaction completion status is stored in the blockchain and its hash value is recorded.

[0063] As a preferred solution of the multi-level transaction security authentication method based on supply chain finance described in the present invention, wherein: according to the status of transaction completion, triggering the traceability query function, forming a complete traceability log and storing it in the blockchain includes the following steps:

[0064] Extract the unique identifier of the transaction completion status and the unique identifier of the transaction from the generated hash identifier of the transaction completion status, and extract the data set related to the transaction from the data stored in the blockchain;

[0065] Using the unique identifier of the transaction completion status, find the storage location of the associated transaction completion status data in the blockchain;

[0066] Dynamically query key node data of the transaction's entire life cycle based on the transaction's unique identifier and associated blockchain storage location;

[0067] Organize the queried transaction life cycle data into a structured traceability log;

[0068] Recalculate the hash value of the data at each stage and compare it with the hash value stored in the blockchain to determine the integrity of the data;

[0069] Use a symmetric encryption algorithm to encrypt the integrity-verified source tracing log to generate encrypted data;

[0070] Encrypt the encryption key using an asymmetric algorithm;

[0071] Call the blockchain smart contract interface to store the encrypted traceability log and encryption key on the blockchain.

[0072] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the multi-level transaction security authentication method based on supply chain finance as described in the first aspect of the present invention.

[0073] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the multi-level transaction security authentication method based on supply chain finance as described in the first aspect of the present invention.

[0074] The beneficial effects of the present invention are as follows: by combining historical transaction behavior data with real-time transaction data and adopting a dynamic scoring framework based on weighted logistic regression, the dynamic behavior patterns of the participants can be accurately captured, thereby improving the accuracy of risk assessment; by dynamically dividing transaction risk levels and triggering corresponding authentication levels for different risk levels, the efficiency of low-risk transactions is guaranteed, and fine-grained privacy protection is provided for high-risk transactions, especially after combining with zero-knowledge proof technology, the security verification of sensitive data is realized; by designing a hierarchical consensus mechanism, differentiated consensus strategies are adopted for transactions of different risk levels, such as fast and simplified consensus, efficient fault-tolerant consensus, and hybrid consensus of proof of work and Byzantine fault-tolerant protocol, which significantly improves the consensus efficiency and ensures the security of high-risk transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0076] Figure 1 This is a flow chart of the multi-level transaction security authentication method based on supply chain finance in Example 1.

[0077] Figure 2 This is a schematic diagram of risk level classification in Example 1. DETAILED DESCRIPTION

[0078] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0079] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0080] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0081] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a multi-level transaction security authentication method based on supply chain finance, including the following steps:

[0082] S1. Collect historical transaction behavior data of supply chain finance participants, encrypt it, and store it in the blockchain.

[0083] S1.1. Historical transaction behavior data includes transaction amount, transaction frequency, transaction success rate and default record.

[0084] Specifically, the transaction amount refers to the amount of each transaction, in currency units (such as CNY);

[0085] Transaction frequency refers to the total number of transactions within a specified period, measured in times / month;

[0086] The transaction success rate refers to the ratio of successful transactions to the total number of transactions within a specified period, and the expression is:

[0087] ;

[0088] in, Indicates the transaction success rate, Indicates the number of successful transactions. Indicates the total number of transactions.

[0089] Default record refers to the number of historical defaults by a participant.

[0090] S1.2. Encrypt historical transaction behavior data and store it in the blockchain.

[0091] S1.2.1 Perform deduplication, formatting, outlier removal and data standardization on the collected historical transaction behavior data.

[0092] Furthermore, deduplication refers to deleting duplicate transaction records from the collected data to ensure uniqueness.

[0093] Formatting refers to standardizing the data format. For example, the date format is unified as YYYY-MM-DD, and the amount is rounded to two decimal places.

[0094] Outlier removal refers to detecting and removing abnormal data (such as negative amounts, extreme transaction frequencies) to improve data quality.

[0095] Data standardization refers to the normalization of data so that data with different numerical characteristics remain at the same level to facilitate subsequent analysis and encryption.

[0096] It should be noted that eliminating duplicate and abnormal data improves data quality and accuracy; unifying data formats facilitates subsequent encryption and storage operations; data standardization eliminates the impact of numerical magnitude differences on the algorithm and improves the stability of analysis results.

[0097] S1.2.2. Use the elliptic curve encryption algorithm for the processed data to generate a unique pair of public and private keys for each participant; use lattice cryptography technology to generate the identity private key through the SampleMat algorithm and calculate the identity index; encrypt each field data using the public key of the participant, and upload the identity index and encrypted field data to the blockchain.

[0098] Specifically, the expression for generating the identity private key through the SampleMat algorithm is:

[0099] ;

[0100] in, Represents a short private key, represents the public key matrix, represents a modular lattice with A as the base, represents the identity vector, represents the modulus, Indicates the modulus The value of the following;

[0101] The expression for calculating the identity index is:

[0102] ;

[0103] where represents the identity index, Represents a collision-resistant hash representation that maps the result to a fixed-length identity index.

[0104] S2. When a transaction is initiated, the encrypted data stored in the blockchain is read and combined with the real-time data in the transaction request to calculate the dynamic credit score.

[0105] S2.1. Query the encrypted data stored in the blockchain, use the private key of the transaction initiator to decrypt the read encrypted data, and receive real-time transaction data from the transaction request; construct the interaction features between historical transaction behavior data and real-time transaction data to capture dynamic behavior patterns.

[0106] Specifically, the interactive features include transaction amount interactive features, transaction frequency and transaction time interactive features, success rate adjustment features and default impact features.

[0107] Furthermore, the transaction amount interaction feature refers to the difference between the average and standard deviation of historical transaction amounts and the current transaction amount (for example, calculating the deviation rate and capturing abnormal large transactions); the transaction frequency and transaction time interaction feature refers to the degree of match between the historical transaction frequency and the current transaction time (such as whether the current transaction occurs in the user's regular transaction time period); the success rate adjustment feature refers to adjusting the trust of the current transaction based on the historical transaction success rate (such as when the historical success rate is low, it has a negative impact on the real-time transaction score); the default impact feature refers to combining indicators such as the number and severity of historical default records to evaluate the potential risk of default behavior to the current transaction.

[0108] It should be noted that through the construction of interactive features, the impact of historical behavior patterns on current transaction behaviors is captured, which helps to improve the dynamic adaptability of the scoring model. The interactive features of transaction amount and time can identify abnormal transactions (such as excessive amounts or abnormal transaction times), thereby improving the risk control capabilities of the scoring model.

[0109] S2.2. Read the encrypted transaction data stored in the blockchain, decrypt it with the private key of the participant to obtain the original field data, receive the real-time data in the transaction request, construct a new dynamic feature set, and introduce a dynamic weight adjustment function to adjust the impact of each feature. The expression is:

[0110] ;

[0111] in, express Dynamically adjust feature weights, Indicates The transaction frequency of a feature, Parameters representing the magnitude of the adjustment weights, a tuning parameter representing the sensitivity of the tuning frequency;

[0112] Based on the dynamic adjustment of feature weights and time decay factors, the dynamic credit score is calculated as follows:

[0113] ;

[0114] in, represents a dynamic credit score, represents the activation function, represents the bias term, Indicates The weight parameter of each feature, Represents the first Features, represents the time attenuation coefficient, Indicates the difference between the current time and the historical record time. represents the feature index, Represents the total number of features.

[0115] S3. Transaction risk levels are classified based on dynamic credit scores, transaction amounts and transaction frequencies.

[0116] S3.1. Obtain transaction amount data from real-time transactions and extract transaction frequency data from historical transaction behavior data;

[0117] The behavioral deviation correction function is used to adjust the credibility of the dynamic credit score, and the expression is:

[0118] ;

[0119] in, Indicates the difference between the current score and the previous score. represents the behavioral deviation correction factor, represents the hyperbolic tangent function;

[0120] Obtain transaction amount data from real-time transactions and extract transaction frequency data from historical transaction behavior data;

[0121] Combining the dynamic credit score, transaction amount, transaction frequency and behavioral deviation correction factor, the transaction risk factor is calculated as follows:

[0122] ;

[0123] in, Represents the transaction risk factor, represents the transaction frequency, Indicates the transaction amount;

[0124] Set risk factor thresholds based on historical transaction data distribution, and divide risk levels based on transaction risk factors;

[0125] when When , it indicates low risk, low transaction amount, normal frequency and good credit of the initiator; when When , it means medium risk, medium transaction amount or low credit score; when When , it indicates high risk, high transaction amount, abnormal frequency or poor credit score;

[0126] in, Indicates a low risk threshold, Indicates a high risk threshold;

[0127] For the current transaction, the risk level is dynamically determined based on the calculated transaction risk factor; the transaction risk level and its related data are encrypted at the field level and stored in the blockchain.

[0128] It should be noted that blockchain technology ensures the transparency of risk level classification results, facilitates subsequent audits, and the tamper-proof nature of transaction risk data improves the credibility of the risk control system. Field-level encryption is used to protect sensitive data and ensure user privacy. Only authorized parties can decrypt and view risk levels and related data to ensure information security. Dynamic risk level classification can help risk control systems quickly respond to high-risk transactions (such as setting up automatic interception or manual review) and reduce potential losses.

[0129] S4. Dynamically trigger the corresponding authentication level according to the transaction risk level, and use zero-knowledge proof technology to verify the privacy protection of sensitive data related to the transaction.

[0130] S4.1. When the risk transaction is low, basic authentication is performed to verify the integrity of the transaction data.

[0131] Specifically, for authentication of low-risk transactions:

[0132] Compare the hash value in the transaction data with the hash value stored in the blockchain to ensure that the data has not been tampered with. If the data integrity verification passes, the authentication result is returned as passed; otherwise, failure is returned.

[0133] S4.2. When the risk transaction is medium risk, additional verification is performed to verify whether the transaction amount range and dynamic credit score meet the business rules.

[0134] Specifically, authentication of medium-risk transactions:

[0135] Use the zero-knowledge proof range proof protocol to verify whether the transaction amount is within the set range without exposing the specific amount; use the zero-knowledge proof protocol to verify whether the dynamic credit score meets the business rules.

[0136] If both verifications pass, the authentication result is returned as passed; otherwise, it returns failed.

[0137] S4.3. When the risk transaction is high risk, advanced verification is performed to verify the compliance of the transaction amount, credit score and default record, while using zero-knowledge proof technology to protect the privacy of sensitive information.

[0138] Specifically, authentication of high-risk transactions:

[0139] First, use the medium-risk verification method for verification. After verification, use zero-knowledge set proof to verify whether the default record of the trading party belongs to a compliant set. If the verification passes, the authentication result is returned as passed; otherwise, it returns failure.

[0140] S4.4. When the risk transaction is a cross-domain transaction, the transaction initiator generates an authentication request through the identity authentication server of the domain, using the identity index and private key signature of the participant;

[0141] After receiving the authentication request, the external domain identity authentication server verifies the identity of the initiator through the identity index and private key signature of the participant; when all triggered authentication levels and corresponding zero-knowledge proof verifications are passed, it means that the authentication is successful, and when any authentication verification fails, it is a failure.

[0142] S5. After verification, select the appropriate hierarchical consensus mechanism according to the transaction risk level to perform consensus operations, and store the consensus results in the blockchain.

[0143] S5.1. When the authentication result is passed, the transaction amount, transaction frequency and dynamic credit score are read from the blockchain; for low-risk transactions, a fast and simplified consensus mechanism is adopted to obtain the transaction confirmation status and committee signature through pre-selected nodes and multiple rounds of message interaction.

[0144] Specifically, the operation process of the quick and simplified consensus mechanism is as follows:

[0145] Broadcast transaction data to pre-selected Consensus nodes, of which: , Indicates the number of consensus nodes, Indicates the number of possible Byzantine fault nodes (such as =1, then ≥4); The master node proposes the hash value of the transaction and broadcasts it to all replica nodes; The replica node verifies the integrity of the transaction data and sends a prepare message to other nodes; If the node receives Prepare message, then send a commit message. When the master node receives When a message is submitted, consensus is considered reached.

[0146] S5.2. For medium-risk transactions, an efficient fault-tolerant consensus mechanism is adopted to select super nodes through equity agents, and output transaction confirmation status and super node signatures.

[0147] Specifically, the operation process of the efficient fault-tolerant consensus mechanism is as follows:

[0148] Based on historical reputation and dynamic credit score, select Super nodes participate in the consensus and broadcast the transaction to Super nodes; each super node verifies the legitimacy of the transaction data and votes on the transaction. If the vote is passed, consensus is achieved.

[0149] S5.3. For high-risk transactions, a consensus mechanism that combines proof of work with the Byzantine Fault Tolerance protocol is adopted. First, the legitimate working nodes are identified through proof of work, and then the final consensus is reached through the Byzantine Fault Tolerance protocol to obtain the transaction confirmation status and the signatures of all nodes in the network; the consensus result is encrypted and stored in the blockchain using the blockchain's smart contract interface.

[0150] Specifically, the operation process of the consensus mechanism that combines proof of work with the Byzantine fault tolerance protocol is as follows:

[0151] Each node competes to solve the proof-of-work calculation problem. The first node to find a solution becomes a candidate working node and broadcasts its result. The Byzantine fault-tolerant protocol mechanism is used to confirm the legitimacy of the candidate working node. The candidate working node broadcasts the transaction data to the entire network, and other nodes verify the data and sign to confirm.

[0152] The expression that defines the consensus completion of the hierarchical consensus mechanism is:

[0153] ;

[0154] in, Indicates the degree of consensus completion, Indicates the start time of consensus, represents the current time of consensus, Representation Node The weight distribution of represents the consensus efficiency factor, Indicates the time during the consensus process.

[0155] S6. Based on the consensus result, the final transaction is confirmed and a hash mark of the transaction completion status is generated.

[0156] S6.1. Obtain transaction data and consensus result data from the consensus result.

[0157] Specifically, transaction data includes transaction ID, participant information, transaction amount and transaction timestamp.

[0158] The consensus result data includes consensus status, consensus mechanism and consensus node signature.

[0159] S6.2. Organize the transaction completion status and the acquired data into a structured JSON object; serialize the structured JSON object into a string using a fixed field order; perform hash calculation on the serialized string to generate a unique hash identifier; organize the transaction completion status and hash identifier into a new JSON object, and encrypt the new JSON object using a public key based on elliptic curve encryption; call the blockchain smart contract interface, store the encrypted transaction completion status in the blockchain, and record its hash value.

[0160] Specifically, the elliptic curve encryption algorithm has higher encryption strength and computational efficiency, and is suitable for high-performance scenarios such as blockchain. It encrypts JSON objects into ciphertext to ensure that only the corresponding private key holder can decrypt and view the data.

[0161] It should be noted that elliptic curve cryptography (ECC) protects the privacy of the transaction completion status, ensuring that only authorized parties can decrypt and view the data; segment-level encryption is combined with hash generation to improve the security of data storage and transmission; the use of fixed field order and efficient hash algorithm ensures the unique identification of the transaction completion status, while improving processing efficiency.

[0162] S7. According to the status of transaction completion, the traceability query function is triggered to form a complete traceability log and store it in the blockchain.

[0163] S7.1. Extract the unique identifier of the transaction completion status and the unique identifier of the transaction from the generated hash identifier of the transaction completion status, and extract the data set related to the transaction from the data stored in the blockchain.

[0164] Specifically, the transaction-related data sets include credit data collection records, risk assessment records, authentication records, consensus records and transaction confirmation records.

[0165] Credit data records the source of credit data and assessment results; risk assessment records the risk level assessment results and assessment time of the transaction location; authentication records the verification level, authentication results and timestamp of the transaction authentication location; consensus records the transaction consensus mechanism, participating nodes and timestamp; transaction confirmation records the transaction completion status and completion time.

[0166] S7.2. Use the unique identifier of the transaction completion status to find the storage location of the associated transaction completion status data in the blockchain.

[0167] It should be noted that storage location-based queries optimize data positioning within the blockchain, improve query efficiency, and the precise association of storage locations ensures dynamic query capabilities for data throughout its life cycle.

[0168] S7.3. Dynamically query key node data throughout the life cycle of a transaction based on the transaction’s unique identifier and associated blockchain storage location.

[0169] Specifically, key node data include transaction initiation stage data: such as transaction creation time, amount, and initiator information; transaction processing stage data: such as consensus node signature, transaction processing status; transaction completion stage data: such as completion status, completion time, and result summary.

[0170] S7.4. Organize the queried transaction life cycle data into a structured traceability log; recalculate the hash value of the data at each stage and compare it with the hash value stored in the blockchain to determine the integrity of the data; use a symmetric encryption algorithm to encrypt the traceability log after integrity verification to generate encrypted data; use an asymmetric algorithm to encrypt the encryption key; call the blockchain smart contract interface to store the encrypted traceability log and encryption key on the blockchain.

[0171] It should be noted that structured logs provide standardized and clear records for the traceability function, and the stage division of the log facilitates subsequent analysis and verification; verifying data integrity ensures the credibility and security of the traceability log, and comparing the hash values ​​stored in the blockchain ensures that the data cannot be tampered with.

[0172] This embodiment also provides a computer device, which is suitable for the case of a multi-level transaction security authentication method based on supply chain finance, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the multi-level transaction security authentication method based on supply chain finance as proposed in the above embodiment.

[0173] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0174] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the multi-level transaction security authentication method in supply chain finance proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0175] In summary, the present invention combines historical transaction behavior data with real-time transaction data, and adopts a dynamic scoring framework based on weighted logistic regression, which can accurately capture the dynamic behavior patterns of participants, thereby improving the accuracy of risk assessment; by dynamically dividing transaction risk levels and triggering corresponding authentication levels for different risk levels, it not only ensures the efficiency of low-risk transactions, but also provides fine-grained privacy protection for high-risk transactions, especially after combining with zero-knowledge proof technology, it realizes the security verification of sensitive data; by designing a layered consensus mechanism, differentiated consensus strategies are adopted for transactions of different risk levels, such as fast and simplified consensus, efficient fault-tolerant consensus, and hybrid consensus of proof of work and Byzantine fault-tolerant protocol, which significantly improves the consensus efficiency and ensures the security of high-risk transactions.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-level transaction security authentication method based on supply chain finance, characterized by: including Collecting the historical transaction behavior data of the participants in supply chain finance, encrypting the data, and storing it in the blockchain; When a transaction is initiated, reading the encrypted data stored in the blockchain, combining with the real-time data in the transaction request, and calculating the dynamic credit score; Dividing the risk level of the transaction according to the dynamic credit score, transaction amount, and transaction frequency; Dynamically triggering the corresponding authentication level according to the transaction risk level, and combining with zero-knowledge proof technology to perform privacy protection verification on the sensitive data related to the transaction; After the verification is passed, selecting an appropriate hierarchical consensus mechanism according to the transaction risk level to perform the consensus operation, and storing the consensus result in the blockchain; Based on the consensus result, confirming the final transaction, and generating a hash identifier of the transaction completion status; According to the transaction completion status, triggering the traceability query function, forming a complete traceability log and storing it in the blockchain; Dividing the risk level of the transaction according to the dynamic credit score, transaction amount, and transaction frequency includes the following steps Using a behavior deviation correction function to adjust the credibility of the dynamic credit score, and the expression is: where ΔS represents the difference between the current score and the previous score, ζ(ΔS) represents the behavior deviation correction factor, and tanh(·) represents the hyperbolic tangent function; Obtaining the transaction amount data from the real-time transaction and extracting the transaction frequency data from the historical transaction behavior data; Combining the dynamic credit score, transaction amount, transaction frequency, and behavior deviation correction factor to calculate the transaction risk factor, and the expression is: where R represents the transaction risk factor, T represents the transaction frequency, and A represents the transaction amount; Setting the risk factor threshold according to the historical transaction data distribution, and dividing the risk level based on the transaction risk factor; When R ≤ θ1, it represents low risk. When θ1 < R ≤ θ2, it represents medium risk. When R > θ2, it represents high risk; where θ1 represents the low risk threshold and θ2 represents the high risk threshold; For the current transaction, dynamically judging the所属 risk level based on the calculated transaction risk factor; Encrypting the risk level of the transaction and its related data at the field level and storing it in the blockchain; After the verification is passed, selecting an appropriate hierarchical consensus mechanism according to the transaction risk level to perform the consensus operation, and storing the consensus result in the blockchain includes the following steps When the authentication result is passed, reading the transaction amount, transaction frequency, and dynamic credit score from the blockchain; For low-risk transactions, using a fast and simplified consensus mechanism to generate a committee signature through preselected nodes and multiple rounds of message interaction; For medium-risk transactions, using an efficient fault-tolerant consensus mechanism to select super nodes through rights and interests agents and generate super node signatures; For high-risk transactions, using a consensus mechanism that combines proof-of-work and Byzantine fault tolerance protocol. First, identify legal working nodes through proof-of-work, and then reach a final consensus through the Byzantine fault tolerance protocol to generate signatures of all network nodes; Defining the expression of the consensus completion degree of the hierarchical consensus mechanism as: where C represents the consensus completion degree, t0 represents the start time of the consensus, t represents the current time of the consensus, P(v) represents the weight distribution of node v, μ represents the consensus efficiency factor, and x represents the time in the consensus process; Using the blockchain’s smart contract interface, the consensus results are encrypted and stored in the blockchain.

2. The multi-level transaction security authentication method based on supply chain finance according to claim 1, characterized in that: The historical transaction behavior data includes transaction amount, transaction frequency, transaction success rate and default record; The encryption process and storage in the blockchain include the following steps: Perform deduplication, formatting, outlier removal and data standardization on the collected historical transaction behavior data; The processed data is encrypted using an elliptic curve algorithm to generate a unique pair of public and private keys for each participant; Each field data is encrypted using the public key of the participant, and the encrypted field data is uploaded to the blockchain.

3. The multi-level transaction security authentication method based on supply chain finance as claimed in claim 2 is characterized by: When a transaction is initiated, the encrypted data stored in the blockchain is read and combined with the real-time data in the transaction request to calculate the dynamic credit score. The following steps are included: Read the encrypted transaction data stored in the blockchain, decrypt it with the private key of the participant to get the original field data, receive the real-time data in the transaction request, construct a new dynamic feature set, and introduce a dynamic weight adjustment function to adjust the impact of each feature. The expression is: Among them, ψ(T i ) indicates T i Dynamically adjust feature weights, T i represents the transaction frequency of the ith feature, α represents the parameter for adjusting the magnitude of the weight, and β represents the adjustment parameter for adjusting the sensitivity of the frequency; Based on the dynamic adjustment of feature weights and time decay factors, the dynamic credit score is calculated as follows: Among them, S represents the dynamic credit score, σ represents the activation function, w0 represents the bias term, and w i represents the weight parameter of the i-th feature, F i It represents the i-th feature in the feature set, λ represents the time decay coefficient, Δt represents the difference between the current time and the historical record time, i represents the feature index, and n represents the total number of features.

4. The multi-level transaction security authentication method based on supply chain finance as claimed in claim 3 is characterized by: The corresponding authentication level is dynamically triggered according to the transaction risk level, and the privacy protection verification of sensitive data related to the transaction is carried out in combination with zero-knowledge proof technology. The following steps are included: When the risk transaction is low, basic authentication is performed to verify the integrity of the transaction data; When the risk transaction is medium risk, additional verification is performed to verify whether the transaction amount range and dynamic credit score meet the business rules; When the risky transaction is high risk, advanced verification is performed to verify the compliance of the transaction amount, credit score and default record, while using zero-knowledge proof technology for privacy protection of sensitive information; When all triggered authentication levels and corresponding zero-knowledge proof verifications are passed, it means the authentication is passed. When any authentication verification fails, it means it has failed.

5. The multi-level transaction security authentication method based on supply chain finance as claimed in claim 4 is characterized by: Based on the consensus result, the final transaction is confirmed and the hash mark of the transaction completion status is generated, including the following steps: Obtain transaction data and consensus result data from the consensus result; Organize the transaction completion status and acquired data into a structured JSON object; Serialize structured JSON objects into strings using a fixed field order; Perform hash calculation on the serialized string to generate a unique hash identifier; Organize the transaction completion status and hash identifier into a new JSON object, and encrypt the new JSON object using a public key based on elliptic curve encryption; The encrypted transaction completion status is stored in the blockchain and its hash value is recorded.

6. The multi-level transaction security authentication method based on supply chain finance as claimed in claim 5, characterized in that: According to the status of transaction completion, the traceability query function is triggered to form a complete traceability log and store it in the blockchain, including the following steps: Extract the unique identifier of the transaction completion status and the unique identifier of the transaction from the generated hash identifier of the transaction completion status, and extract the data set related to the transaction from the data stored in the blockchain; Using the unique identifier of the transaction completion status, find the storage location of the associated transaction completion status data in the blockchain; Dynamically query key node data of the transaction's entire life cycle based on the transaction's unique identifier and associated blockchain storage location; Organize the queried transaction life cycle data into a structured traceability log; Recalculate the hash value of the data at each stage and compare it with the hash value stored in the blockchain to determine the integrity of the data; Use a symmetric encryption algorithm to encrypt the integrity-verified source tracing log to generate encrypted data; Encrypt the encryption key using an asymmetric algorithm; Call the blockchain smart contract interface to store the encrypted traceability log and encryption key on the blockchain.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-level transaction security authentication method based on supply chain finance according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-level transaction security authentication method based on supply chain finance according to any one of claims 1 to 6 are implemented.

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