A compliant public chain system for transaction verification business

Abstract

The present invention belongs to the field of blockchain security, and in particular, relates to a compliant public chain system applied to transaction verification business. The system integrates multiple modules such as registration and authentication, smart contracts, compliance verification, behavior monitoring and cross-chain evaluation to ensure the security, compliance and efficient execution of on-chain transactions. The system first implements strict identity registration and address chaining, combined with smart contracts to achieve effective verification and de-anonymization of data, ensure the authenticity and legality of transactions, monitor transaction behaviors in real time, respond to anomalies quickly, and use a traceability and positioning mechanism to accurately lock in violators, implement reputation management and risk warning. In cross-chain transactions, the reputation score is used to select the main node, ensure the quality of cross-chain connections, synchronously monitor cross-chain data transactions, disconnect and trace in case of anomalies, and effectively prevent cross-chain risks.

Description

Technical Field

[0001] The present invention belongs to the field of blockchain security, and in particular relates to a compliant public chain system applied to transaction verification business. Background Art

[0002] With the widespread application of blockchain technology, it has shown great potential in data sharing and collaboration in multiple fields such as finance, government affairs, and medical care. However, how to ensure that data and transaction activities in blockchain systems comply with the requirements of laws and regulations has become an important issue. Existing technologies, such as the Chinese patent with authorization announcement number CN112784307B, achieve secure transmission and verification of KYC information by building a consortium chain. Although it improves the security and compliance of information, it is mainly limited to banking business scenarios, and the cost of building and maintaining the consortium chain is high and the scalability is poor. In addition, the Chinese patent with publication number CN114499944B achieves privacy compliance management by building an APP compliance information consortium chain, which also faces the problems of limited scope of application, data sharing difficulties and complex technical implementation. Although these methods are effective in specific scenarios, they still have limitations, poor scalability, insufficient user privacy protection and regulatory consistency in a wider range of business applications. Therefore, it is necessary to develop a more widely applicable, easy to expand, privacy-oriented and compliant public chain system that meets regulatory requirements to meet the needs of different business scenarios. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention proposes a compliant public chain system for transaction verification business, which integrates multiple modules such as registration and authentication, smart contracts, compliance verification, behavior monitoring and cross-chain evaluation to ensure the security, compliance and efficient execution of on-chain transactions. The system first implements strict identity registration and address chaining, combined with smart contracts to achieve effective verification and de-anonymization of data, to ensure the authenticity and legality of transactions, monitor transaction behavior in real time, respond quickly to anomalies, and use the traceability and positioning mechanism to accurately lock in violators, implement reputation management and risk warning. In cross-chain transactions, the reputation score is used to select the main node to ensure the quality of cross-chain connections, and cross-chain data transactions are monitored synchronously. In case of anomalies, the connection is disconnected and traced back to effectively prevent cross-chain risks.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A compliant public chain system applied to transaction verification business, including: a registration module, an on-chain verification module and a cross-chain interaction module;

[0006] The registration module includes a registration unit and a behavior monitoring unit; the on-chain verification module includes a compliance verification unit, a validity verification unit, a traceability positioning unit, and an on-chain authorization unit;

[0007] The on-chain traders register, bind and store the on-chain trader address information on the chain through the registration unit. When the registration is completed, the on-chain traders upload the corresponding on-chain transaction data to the data storage block node corresponding to the public chain. At the same time, the smart contract calls the distributed central compliance verification library built by the compliance verification unit to verify the compliance of the uploaded data, and transmits the compliance verification data to the validity verification unit for secondary verification of validity and de-anonymization. When the secondary verification passes, the verification result is fed back to the on-chain authorization unit, and the on-chain transaction authorization is performed on the data storage block node that has passed the verification, otherwise the authorization fails;

[0008] When the authorized data storage block nodes conduct transactions on the public chain, the transaction behaviors between nodes are monitored in real time through the behavior monitoring unit. When abnormal transactions are detected, the monitoring results are fed back to the tracing and positioning unit to trace and locate the abnormal on-chain traders, and the positioning nodes are fed back to the on-chain authorization unit to mark abnormal risks and reduce the credit score of the corresponding blockchain node of the on-chain traders;

[0009] The cross-chain interaction module includes a cross-chain evaluation unit

[0010] When the on-chain trader conducts a cross-chain transaction, the cross-chain evaluation unit will evaluate the reputation of each node of the target chain of the cross-chain interaction, obtain a reputation score, and use the two blockchain nodes with the highest scores in the cross-chain operation as the cross-chain connection master nodes for cross-chain connection;

[0011] The cross-chain interaction module also includes a cross-chain synchronization unit;

[0012] When the link is successful, the smart contract calls the behavior monitoring unit to monitor the data transactions between the cross-chain connection master nodes and the data transactions within the target chain, and synchronizes the on-chain transaction data from the public chain where the current on-chain trader is located to the target chain through the cross-chain synchronization unit;

[0013] When an anomaly is detected during the data synchronization process or in the transaction process of the target chain after synchronization, the cross-chain connection master node is disconnected synchronously, and the anomaly is located and traced according to the cross-chain connection master node through the tracing positioning unit, and the tracing result is fed back to the on-chain authorization unit, and the corresponding abnormal user is marked as abnormal cross-chain. When the corresponding user performs a secondary cross-chain operation, a cross-chain abnormality alarm is issued;

[0014] A1. Obtain and pre-process data on laws and regulations, industry standards, and corporate policies to obtain a compliance verification standard data set;

[0015] A2. Build an entity-relation extraction model based on the Chinese pre-trained Bert model, input the compliance verification standard dataset into the entity-relation extraction model, and obtain compliance keywords and relationship triples;

[0016] A3. Input the compliance keywords and relationship triples into the business classification model constructed by the support vector machine to obtain the classified compliance keywords and relationship triples;

[0017] A4. Input the classified compliance keywords and relationship triples into the graph database to obtain a business compliance verification graph;

[0018] A5. Build a compliance reasoning model based on the reasoning algorithm, input the standard compliance data chain corresponding to each type of business in the business compliance verification map into the compliance reasoning model for training, and embed the compliance reasoning model into the upper-level nodes corresponding to all business type nodes in the business compliance verification map to obtain the reasoning business compliance verification map, and embed the reasoning business compliance verification map into the authorization block node in the upper chain authorization unit;

[0019] A6. Construct a federated compliance verification topology network based on federated learning algorithm and reinforcement learning, and embed the compliance verification main model in the federated compliance verification topology network into the authorized block node and integrate it with the compliance reasoning model, and embed M compliance verification sub-models into M corresponding data storage block nodes;

[0020] A7. Set the verification accuracy threshold, analyze the business data uploaded to each data storage block node through the compliance verification sub-model, and obtain the corresponding business type;

[0021] A8. When only one business type is obtained, the corresponding business type is fed back to the compliance verification main model, the standard compliance data chain of the corresponding business type in the inference business compliance verification graph is called for verification, and the verification result and the corresponding verification accuracy are output;

[0022] A9. When more than one business type is obtained, the compliance verification main model calls the compliance reasoning model and the standard compliance data chain corresponding to the business type to verify the uploaded business data, and outputs the verification result and the corresponding verification accuracy;

[0023] A10. When the verification accuracy rate corresponding to the data storage block node is greater than the verification accuracy threshold, the business data stored in the corresponding data storage block node passes the compliance verification, otherwise it fails the compliance verification;

[0024] A11. Set a risk warning threshold for on-chain transactions. When a data storage block node fails compliance verification, it will be marked as non-compliant through an automatic marking algorithm. When the number of non-compliant markings is greater than the risk warning threshold for on-chain transactions, a risk warning will be issued to the on-chain transaction corresponding to the data storage block node, and the credit score of the on-chain transaction will be reduced according to the number of non-compliant markings.

[0025] Specifically, the steps of secondary verification include:

[0026] B1. When the corresponding business passes the compliance verification, the business data of the corresponding data storage block node is input into the rule verification library of the authorization block node built into the on-chain authorization unit for validity verification. If the validity verification fails, the verification result is fed back to the authorization block node and the invalid marking is verified through the automatic marking algorithm of A11.

[0027] B2. When the validity verification is passed, the identity information in the data storage block node is matched and verified with the address information of the on-chain trader stored on the registration unit chain through the zero-knowledge proof algorithm. When the matching verification is passed, the secondary verification is passed, and the verification information is fed back to the authorization block node for on-chain authorization;

[0028] B3. When the matching verification fails, the verification failure information will be fed back to the authorization block node to reject the on-chain authorization. When the on-chain trader receives the blockchain Gas service fee certificate for the public chain address where the transaction is conducted, the on-chain transaction will be rejected and a warning will be issued for unauthenticated identity verification.

[0029] Specifically, the steps for the on-chain authorization unit to obtain the authorization block node include:

[0030] C1. Obtain the operation data, verification data and abnormal monitoring data of each on-chain trader in the business verification and transaction process on the public chain, and obtain the evaluation score of the corresponding data storage block node of each on-chain trader through the evaluation algorithm;

[0031] C2. Input the data storage block node information corresponding to the on-chain trader into the clustering algorithm to obtain the node set corresponding to each business type;

[0032] C3. Setting identity score threshold and , according to the corresponding evaluation score and identity score threshold of each data storage block node set and Compare and compare the evaluation score greater than The node is set as the initial authorization block node, and the evaluation score belongs to the interval Nodes with evaluation scores less than Set as a normal node;

[0033] C4. Based on the initialized identity nodes and identity score thresholds, the consensus algorithm is used to update the node identities in each data storage block node set in real time, and based on the update results, the continuous p The candidate nodes with decreased scores corresponding to the secondary identity nodes are converted into ordinary nodes, and are prohibited from participating in the selection of centralized authorized block nodes of the corresponding data storage block nodes.

[0034] Specifically, the steps of the cross-chain evaluation unit to perform cross-chain connection include:

[0035] D1. Obtain the verification and transaction node status information of the data storage block node set corresponding to the target chain, and input the obtained node status information into the evaluation algorithm to obtain the reputation score of each node in the data storage block node set;

[0036] D2. If the highest credit score of the corresponding business type in the target chain in the data storage block node set is greater than , a cross-chain connection is established based on the node with the highest reputation score in the data storage block node set in the public chain corresponding to the on-chain trader and the node with the highest reputation score in the data storage block node set in the target chain. Otherwise, a cross-chain connection cannot be established;

[0037] D3. After the cross-chain connection is established, the reputation score of each node in the data storage block node set is updated according to the real-time transaction node status information, and the master node of the cross-chain connection is updated according to the reputation score;

[0038] D4. When the behavior monitoring unit detects malicious or joint malicious anomalies in the master node or target node, the cross-chain connection is directly disconnected, and the authorization block node cancels the chain authorization qualification of the corresponding chain trader, and marks the malicious anomaly.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] In view of the deficiencies of the prior art, the present invention realizes the trusted binding of traders' identities, double verification of data on the chain and security of cross-chain transactions by integrating modules such as registration, compliance verification, behavior monitoring, cross-chain evaluation and synchronization. The system effectively prevents illegal transactions and data tampering, and quickly identifies and handles abnormal behaviors through real-time monitoring and traceability positioning mechanisms, thereby maintaining the purity and stability of the on-chain ecology. At the same time, the intelligent evaluation and synchronization mechanism of the cross-chain interaction module ensures the efficient and secure circulation of cross-chain data, laying a solid foundation for the application of blockchain technology in a wider range of business fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a module diagram of a compliant public chain system applied to transaction verification business in Example 1 of the present invention;

[0042] Figure 2 This is a flowchart of the corresponding unit of a compliant public chain system applied to transaction verification business in Example 2 of the present invention. DETAILED DESCRIPTION

[0043] Example 1

[0044] See also Figure 1 , an embodiment provided by the present invention: a compliant public chain system applied to transaction verification business, comprising: a registration module, an on-chain verification module and a cross-chain interaction module;

[0045] The registration module is used for the registration and abnormal monitoring of the information of on-chain traders; the registration module includes a registration unit and a behavior monitoring unit;

[0046] The registration unit is used to register the corresponding identity information of the on-chain trader, and bind the registered on-chain transaction address information to the corresponding public chain through a smart contract, and save the corresponding binding relationship on the chain to the blockchain node corresponding to the public chain;

[0047] The behavior monitoring unit is used to monitor the data transaction behavior of on-chain traders in the public chain in real time through a distributed anomaly monitoring model, and to issue abnormal warnings for malicious or joint malicious behavior, and to feed back the monitored abnormal malicious behavior to the tracing and positioning unit;

[0048] Furthermore, in this embodiment, the distributed anomaly monitoring model is constructed through a random forest algorithm; wherein the anomaly monitoring model is integrated with the compliance verification main model. When the compliance verification main model performs compliance verification on each data storage block node, it monitors the transaction data of the node through the compliance verification sub-model, and feeds back the monitoring results to the compliance verification main model. The compliance verification main model calls the integrated anomaly monitoring model, performs transaction anomaly monitoring on the corresponding data storage block node, obtains the anomaly monitoring results, and feeds back the monitoring results to the authorization block node.

[0049] The on-chain verification module is used for on-chain verification and location tracing of transaction data; the on-chain verification module includes a compliance verification unit, a validity verification unit, a tracing location unit, and an on-chain authorization unit;

[0050] The compliance verification unit is used to build a distributed central compliance verification library and perform compliance verification on data uploaded to the public chain data storage block node or data synchronized with the private chain through smart contracts;

[0051] Furthermore, in this embodiment, the steps of constructing a distributed central compliance verification library include:

[0052] A1. Obtain and pre-process data on laws and regulations, industry standards, and corporate policies to obtain a compliance verification standard data set;

[0053] A2. Build an entity-relation extraction model based on the Chinese pre-trained Bert model, input the compliance verification standard dataset into the entity-relation extraction model, and obtain compliance keywords and relationship triples;

[0054] A3. Input the compliance keywords and relationship triples into the business classification model constructed by the support vector machine to obtain the classified compliance keywords and relationship triples;

[0055] A4. Input the classified compliance keywords and relationship triples into the graph database to obtain a business compliance verification graph;

[0056] A5. Build a compliance reasoning model based on the reasoning algorithm, input the standard compliance data chain corresponding to each type of business in the business compliance verification map into the compliance reasoning model for training, and embed the compliance reasoning model into the upper-level nodes corresponding to all business type nodes in the business compliance verification map to obtain the reasoning business compliance verification map, and embed the reasoning business compliance verification map into the authorization block node in the upper chain authorization unit;

[0057] A6. Construct a federated compliance verification topology network based on federated learning algorithm and reinforcement learning, and embed the compliance verification main model in the federated compliance verification topology network into the authorized block node and integrate it with the compliance reasoning model, and embed M compliance verification sub-models into M corresponding data storage block nodes;

[0058] A7. Set the verification accuracy threshold, analyze the business data uploaded to each data storage block node through the compliance verification sub-model, and obtain the corresponding business type;

[0059] A8. When only one business type is obtained, the corresponding business type is fed back to the compliance verification main model, the standard compliance data chain of the corresponding business type in the inference business compliance verification graph is called for verification, and the verification result and the corresponding verification accuracy are output;

[0060] A9. When more than one business type is obtained, the compliance verification main model calls the compliance reasoning model and the standard compliance data chain corresponding to the business type to verify the uploaded business data, and outputs the verification result and the corresponding verification accuracy;

[0061] A10. When the verification accuracy rate corresponding to the data storage block node is greater than the verification accuracy threshold, the business data stored in the corresponding data storage block node passes the compliance verification, otherwise it fails the compliance verification;

[0062] A11. Set a risk warning threshold for on-chain transactions. When a data storage block node fails compliance verification, it will be marked as non-compliant through an automatic marking algorithm. When the number of non-compliant markings is greater than the risk warning threshold for on-chain transactions, a risk warning will be issued to the on-chain transaction corresponding to the data storage block node, and the credit score of the on-chain transaction will be reduced according to the number of non-compliant markings.

[0063] This process pre-processes laws and regulations, industry standards and corporate policy data to form a compliance verification standard data set, and uses the entity-relationship extraction model and business classification model to generate classified compliance keywords and relationship triplets to construct a business compliance verification map. Secondly, the verification model is further optimized through reasoning algorithms and federated learning algorithms to ensure the accuracy and real-time nature of the verification. Finally, by identifying and verifying the business type of the uploaded data and setting verification accuracy thresholds and risk warning thresholds, accurate compliance verification and risk warning of the data are achieved. This process not only improves the compliance and security of the data, but also enhances the intelligence and credibility of the system, thereby greatly reducing the risk of illegal transactions.

[0064] The validity verification unit is used to perform secondary verification of the validity and de-anonymization of the data that has passed the compliance verification, and feed back the verification results to the blockchain node corresponding to the public chain;

[0065] Furthermore, the validity verification in this embodiment is verified by a rule verification library constructed by the on-chain trader, and the rule verification library is added to the node corresponding to the on-chain authorization unit through a smart contract.

[0066] Furthermore, the steps of the secondary verification in this embodiment include:

[0067] B1. When the corresponding business passes the compliance verification, the business data of the corresponding data storage block node is input into the rule verification library of the authorization block node built into the on-chain authorization unit for validity verification. If the validity verification fails, the verification result is fed back to the authorization block node and the invalid marking is verified through the automatic marking algorithm of A11.

[0068] B2. When the validity verification is passed, the identity information in the data storage block node is matched and verified with the address information of the on-chain trader stored on the registration unit chain through the zero-knowledge proof algorithm. When the matching verification is passed, the secondary verification is passed, and the verification information is fed back to the authorization block node for on-chain authorization;

[0069] B3. When the matching verification fails, the verification failure information will be fed back to the authorization block node to reject the on-chain authorization. When the on-chain trader receives the blockchain Gas service fee certificate for the public chain address where the transaction is conducted, the on-chain transaction will be rejected and a warning will be issued for unauthenticated identity verification.

[0070] This process achieves dual protection of the validity and de-anonymization of compliant data by integrating the rule verification library and the zero-knowledge proof algorithm. This process not only ensures the authenticity and validity of the data on the blockchain, but also protects the privacy of traders through encryption technology. At the same time, timely marking and feedback of data that has not passed the verification effectively prevents non-compliant data from being uploaded to the chain, and improves the security and credibility of the blockchain ecosystem.

[0071] The tracing and positioning unit is used to locate and trace the on-chain traders who have detected abnormalities or failed compliance verification by the behavior monitoring unit and the compliance verification unit through the registration information bound to the public chain;

[0072] The on-chain authorization unit, through the authorization block node selected by the PL-BFT consensus algorithm, authorizes on-chain transactions for the data storage block nodes that have passed the on-chain verification, and marks the abnormal risk of the abnormal on-chain trader information traced by the tracing and positioning unit, and reduces the reputation score of the blockchain node corresponding to the abnormal on-chain trader.

[0073] Furthermore, in this embodiment, the step of the on-chain authorization unit obtaining the authorization block node includes:

[0074] C1. Obtain the operation data, verification data and abnormal monitoring data of each on-chain trader in the business verification and transaction process on the public chain, and obtain the evaluation score of the corresponding data storage block node of each on-chain trader through the evaluation algorithm;

[0075] C2. Input the data storage block node information corresponding to the on-chain trader into the clustering algorithm to obtain the node set corresponding to each business type;

[0076] C3. Setting identity score threshold and , according to the corresponding evaluation score and identity score threshold of each data storage block node set and Compare and compare the evaluation score greater than The node is set as the initial authorization block node, and the evaluation score belongs to the interval Nodes with evaluation scores less than Set as a normal node;

[0077] C4. Based on the initialized identity nodes and identity score thresholds, the consensus algorithm is used to update the node identities in each data storage block node set in real time, and based on the update results, the continuous p The candidate nodes with decreased scores corresponding to the secondary identity nodes are converted into ordinary nodes, and are prohibited from participating in the selection of centralized authorized block nodes of the corresponding data storage block nodes.

[0078] This process improves the security and reliability of the blockchain system through a sophisticated role division and dynamic adjustment mechanism. Specifically, first, the identity of the node is dynamically adjusted based on the evaluation score and identity score threshold, so that only nodes with good reputation and stable operation can become authorized block nodes, which helps prevent malicious nodes from affecting the normal operation of the system. Secondly, through continuous monitoring of node scores, once a node score is found to have dropped continuously, it will be downgraded to an ordinary node, thereby effectively reducing the risks posed by abnormal nodes to the system. This process not only enhances the system's self-protection capabilities, but also promotes healthy competition between nodes, ensuring the efficient and stable operation of the blockchain network.

[0079] Cross-chain interaction module, used for cross-chain data interaction and verification between different blockchains; the cross-chain interaction module includes a cross-chain evaluation unit and a cross-chain synchronization unit;

[0080] The cross-chain evaluation unit is used to evaluate the reputation of the target chain according to the target chain transaction data information corresponding to the cross-chain interaction through the reputation evaluation algorithm, and determine whether to conduct cross-chain interaction according to the evaluation results;

[0081] Furthermore, the steps of performing cross-chain connection by the cross-chain evaluation unit in this embodiment include:

[0082] D1. Obtain the verification and transaction node status information of the data storage block node set corresponding to the target chain, and input the obtained node status information into the evaluation algorithm to obtain the reputation score of each node in the data storage block node set;

[0083] D2. If the highest credit score of the corresponding business type in the target chain in the data storage block node set is greater than , a cross-chain connection is established based on the node with the highest reputation score in the data storage block node set in the public chain corresponding to the on-chain trader and the node with the highest reputation score in the data storage block node set in the target chain. Otherwise, a cross-chain connection cannot be established;

[0084] D3. After the cross-chain connection is established, the reputation score of each node in the data storage block node set is updated according to the real-time transaction node status information, and the master node of the cross-chain connection is updated according to the reputation score;

[0085] D4. When the behavior monitoring unit detects malicious or joint malicious anomalies in the master node or target node, the cross-chain connection is directly disconnected, and the authorization block node cancels the chain authorization qualification of the corresponding chain trader, and marks the malicious anomaly.

[0086] This process not only dynamically adjusts cross-chain connections based on node status information to ensure connection stability and security, but also flexibly responds to potential risks through real-time monitoring and reputation score updates. Once malicious or joint malicious behavior is discovered, the cross-chain connection is immediately disconnected and the authorization of the relevant on-chain traders is revoked, effectively curbing malicious behavior.

[0087] The cross-chain synchronization unit is used to synchronize data between different chains through data synchronization algorithms for blockchains that have passed the evaluation.

[0088] Example 2

[0089] See also Figure 2 Another embodiment provided by the present invention: A corresponding unit workflow of a compliant public chain system applied to a transaction verification business includes:

[0090] First, the corresponding identity information of the on-chain trader is registered through the registration unit, and the registered address in the on-chain transaction is saved on the chain through the smart contract. When the on-chain trader conducts on-chain transactions, the compliance verification unit verifies the compliance of the data storage block node of the data uploaded by the on-chain trader, and the data in the data storage block node that passes the compliance verification is verified for validity and de-anonymization through the validity verification unit, and the verification result is fed back to the on-chain authorization unit. When the verification is passed, the on-chain authorization unit authorizes the on-chain transaction for the data storage block node that passes the verification;

[0091] Second, when the authorized data storage block node conducts transactions on the public chain, the behavior monitoring unit monitors the transmission and transactions in real time, obtains the abnormal monitoring results, inputs the abnormal monitoring results or the on-chain traders who fail the verification of the compliance verification unit into the traceability positioning unit to trace the corresponding on-chain traders, and feeds back the traceability positioning results to the on-chain authorization unit, marks the traced on-chain traders with abnormal risks and reduces their credit scores;

[0092] Third, when the on-chain trader conducts a cross-chain transaction, the cross-chain evaluation unit performs a reputation evaluation on each node of the target chain of the cross-chain interaction to obtain a reputation score, and uses the two blockchain nodes with the highest scores in the cross-chain operation as the cross-chain connection master nodes to perform a cross-chain connection. When the link is successful, the smart contract calls the behavior monitoring unit to monitor the data transactions between the cross-chain connection master nodes and the transactions of the data in the target chain, and synchronizes the on-chain transaction data from the public chain where the current on-chain trader is located to the target chain through the cross-chain synchronization unit. When an abnormality is detected during the data synchronization process or after the synchronization in the transaction process of the target chain, the cross-chain connection master node is synchronously disconnected, and the tracing positioning unit is used to locate and trace the abnormality through the cross-chain connection master node, and the tracing result is fed back to the on-chain authorization unit, and the corresponding abnormal user traced is marked as an abnormal cross-chain. When the corresponding user performs a secondary cross-chain operation, a cross-chain abnormality alarm is issued.

[0093] During the data upload stage, the process ensures the legitimacy and reliability of the uploaded data by performing dual verification of compliance and validity on the data storage block nodes, reducing the risk of illegal data flowing into the public chain. Secondly, during the transaction monitoring stage, the behavior monitoring unit monitors transaction behavior in real time. Once abnormal behavior is found, it is immediately traced and located through the traceability unit, and abnormal users are marked as risky and credit points are deducted, enhancing the system's self-protection capabilities. Finally, during the cross-chain transaction stage, the node with the highest credit score is selected as the cross-chain connection master node through credit evaluation, and the behavior monitoring unit is called through the smart contract to monitor cross-chain transactions. Once an abnormality is detected, the connection is immediately disconnected and traced, ensuring the security of cross-chain transactions.

[0094] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

[0095] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A compliant public chain system applied to transaction verification business, characterized in that: include: Registration module, on-chain verification module and cross-chain interaction module; The registration module includes a registration unit and a behavior monitoring unit; the on-chain verification module includes a compliance verification unit, a validity verification unit, a traceability positioning unit and an on-chain authorization unit; The on-chain trader registers, binds and stores the on-chain trader's address information on the chain through the registration unit. When the registration is completed, the on-chain trader uploads the corresponding on-chain transaction data to the data storage block node corresponding to the public chain, and at the same time calls the distributed central compliance verification library constructed by the compliance verification unit through the smart contract to verify the compliance of the uploaded data, and transmits the compliance verification data to the validity verification unit for secondary verification of validity and de-anonymization. When the secondary verification passes, the verification result is fed back to the on-chain authorization unit, and the on-chain transaction authorization is performed on the data storage block node that passes the verification, otherwise the authorization fails; When the authorized data storage block node conducts transactions on the public chain, the transaction behavior between the nodes is monitored in real time through the behavior monitoring unit. When an abnormal transaction is detected, the monitoring result is fed back to the tracing and positioning unit to trace and locate the abnormal on-chain trader, and the positioning node is fed back to the on-chain authorization unit to mark the abnormal risk and reduce the reputation score of the blockchain node corresponding to the on-chain trader; The cross-chain interaction module includes a cross-chain evaluation unit; When the on-chain trader conducts a cross-chain transaction, the cross-chain evaluation unit performs a reputation evaluation on each node of the target chain of the cross-chain interaction to obtain a reputation score, and uses the two blockchain nodes with the highest scores in the cross-chain operation as the cross-chain connection master nodes for cross-chain connection; The cross-chain interaction module also includes a cross-chain synchronization unit; When the link is successful, the behavior monitoring unit is called through the smart contract to monitor the data transactions between the cross-chain connection master nodes and the transactions of the data in the target chain, and the on-chain transaction data is synchronized from the public chain where the current on-chain trader is located to the target chain through the cross-chain synchronization unit; When an abnormality is detected during the data synchronization process or in the transaction process of the target chain after synchronization, the cross-chain connection master node is disconnected synchronously, and the abnormality is located and traced according to the cross-chain connection master node through the tracing and positioning unit, and the tracing result is fed back to the on-chain authorization unit, and the corresponding abnormal user traced is marked as abnormal cross-chain, and when the corresponding user performs a secondary cross-chain operation, a cross-chain abnormality alarm is issued; The steps of constructing the distributed central compliance verification library include: A1. Obtain and pre-process data on laws and regulations, industry standards, and corporate policies to obtain a compliance verification standard data set; A2. Build an entity-relation extraction model based on the Chinese pre-trained Bert model, input the compliance verification standard dataset into the entity-relation extraction model, and obtain compliance keywords and relationship triples; A3. Input the compliance keywords and relationship triples into the business classification model constructed by the support vector machine to obtain the classified compliance keywords and relationship triples; A4. Input the classified compliance keywords and relationship triples into the graph database to obtain a business compliance verification graph; A5. Build a compliance reasoning model based on the reasoning algorithm, input the standard compliance data chain corresponding to each type of business in the business compliance verification map into the compliance reasoning model for training, and embed the compliance reasoning model into the upper-level nodes corresponding to all business type nodes in the business compliance verification map to obtain the reasoning business compliance verification map, and embed the reasoning business compliance verification map into the authorization block node in the upper chain authorization unit; A6. Build a federated compliance verification topology network based on federated learning algorithm and reinforcement learning, and build the compliance verification main model in the federated compliance verification topology network into the authorized block node and integrate it with the compliance reasoning model. M Compliance verification sub-models are built into M corresponding data storage block nodes; A7. Set the verification accuracy threshold, analyze the business data uploaded to each data storage block node through the compliance verification sub-model, and obtain the corresponding business type; A8. When only one business type is obtained, the corresponding business type is fed back to the compliance verification main model, the standard compliance data chain of the corresponding business type in the inference business compliance verification graph is called for verification, and the verification result and the corresponding verification accuracy are output; A9. When more than one business type is obtained, the compliance verification main model calls the compliance reasoning model and the standard compliance data chain corresponding to the business type to verify the uploaded business data, and outputs the verification result and the corresponding verification accuracy; A10. When the verification accuracy rate corresponding to the data storage block node is greater than the verification accuracy threshold, the business data stored in the corresponding data storage block node passes the compliance verification, otherwise it fails the compliance verification; A11. Set a risk warning threshold for on-chain transactions. When a data storage block node fails compliance verification, it will be marked as non-compliant through an automatic marking algorithm. When the number of non-compliant markings is greater than the risk warning threshold for on-chain transactions, a risk warning will be issued to the on-chain transaction corresponding to the data storage block node, and the credit score of the on-chain transaction will be reduced according to the number of non-compliant markings.

2. A compliant public chain system for transaction verification business as claimed in claim 1, characterized in that: The steps of the secondary verification include: B1. When the corresponding business passes the compliance verification, the business data of the corresponding data storage block node is input into the rule verification library of the authorization block node built into the on-chain authorization unit for validity verification. If the validity verification fails, the verification result is fed back to the authorization block node and the invalid marking is verified through the automatic marking algorithm of A11. B2. When the validity verification is passed, the identity information in the data storage block node is matched and verified with the address information of the on-chain trader stored on the registration unit chain through the zero-knowledge proof algorithm. When the matching verification is passed, the secondary verification is passed, and the verification information is fed back to the authorization block node for on-chain authorization; B3. When the matching verification fails, the verification failure information will be fed back to the authorization block node to reject the on-chain authorization. When the on-chain trader receives the blockchain Gas service fee certificate for the public chain address where the transaction is conducted, the on-chain transaction will be rejected and a warning will be issued for unauthenticated identity verification.

3. A compliant public chain system for transaction verification business as claimed in claim 2, characterized in that: The step of obtaining the authorization block node by the on-chain authorization unit includes: C1. Obtain the operation data, verification data and abnormal monitoring data of each on-chain trader in the business verification and transaction process on the public chain, and obtain the evaluation score of the corresponding data storage block node of each on-chain trader through the evaluation algorithm; C2. Input the data storage block node information corresponding to the on-chain trader into the clustering algorithm to obtain the node set corresponding to each business type; C3. Setting identity score threshold and , according to the corresponding evaluation score and identity score threshold of each data storage block node set and Compare and compare the evaluation score greater than The node is set as the initial authorization block node, and the evaluation score belongs to the interval Nodes with evaluation scores less than Set as a normal node; C4. Based on the initialized identity nodes and identity score thresholds, the consensus algorithm is used to update the node identities in each data storage block node set in real time, and based on the update results, the continuous p The candidate nodes with decreased scores corresponding to the secondary identity nodes are converted into ordinary nodes, and are prohibited from participating in the selection of centralized authorized block nodes of the corresponding data storage block nodes.

4. A compliant public chain system for transaction verification business as claimed in claim 3, characterized in that: The steps of the cross-chain evaluation unit performing cross-chain connection include: D1. Obtain the verification and transaction node status information of the data storage block node set corresponding to the target chain, and input the obtained node status information into the evaluation algorithm to obtain the reputation score of each node in the data storage block node set; D2. If the highest credit score of the corresponding business type in the target chain in the data storage block node set is greater than , a cross-chain connection is established based on the node with the highest reputation score in the data storage block node set in the public chain corresponding to the on-chain trader and the node with the highest reputation score in the data storage block node set in the target chain. Otherwise, a cross-chain connection cannot be established; D3. After the cross-chain connection is established, the reputation score of each node in the data storage block node set is updated according to the real-time transaction node status information, and the master node of the cross-chain connection is updated according to the reputation score; D4. When the behavior monitoring unit detects malicious or joint malicious anomalies in the master node or target node, the cross-chain connection is directly disconnected, and the authorization block node cancels the chain authorization qualification of the corresponding chain trader, and marks the malicious anomaly.

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