Multi-risk interception and artificial intelligence error correction method in RWA asset cross-chain transfer

Abstract

The invention relates to the technical field of block chain technology and asset transfer, in particular to a multi-risk interception and artificial intelligence error correction method in RWA asset cross-chain transfer, comprising the following steps: step 1, cross-chain transfer initialization and asset mapping verification; step 2, multi-level dynamic risk interception; step 3, an artificial intelligence error correction mechanism; step 4, asset right confirmation and log auditing after cross-chain completion; according to the method, technical risks (such as contract vulnerabilities), data risks (such as information inconsistency) and behavior risks (such as abnormal transactions) in RWA asset cross-chain transfer are covered through a multi-level interception system of "pre-transaction-in-process-contract layer" in combination with AI dynamic learning ability, the risk identification rate is greatly improved compared with a traditional method, and the risk identification efficiency is greatly improved. An artificial intelligence error correction mechanism is used for automatically classifying risk types and generating strategies, so that the average error correction time is shortened, and the error correction success rate is improved.

Description

Technical Field

[0001] This invention relates to the fields of blockchain technology and asset transfer technology, specifically to a method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets. Background Technology

[0002] With the development of blockchain technology, the digitization and cross-chain transfer of RWA assets (such as real estate, art, and commodities) has become a trend. Cross-chain technology breaks down the barriers between different blockchain networks, enabling the cross-ecosystem circulation of assets, but it also faces multiple risks, such as smart contract vulnerabilities, node attacks, data inconsistencies, incorrect asset ownership confirmation, and cross-chain protocol compatibility issues.

[0003] In existing technologies, risk interception often relies on a single rule engine or static verification mechanism, which is insufficient to cope with dynamically changing attack methods and complex cross-chain scenarios. For example, traditional risk interception methods only judge whether a transaction is abnormal by setting a preset threshold, lacking the ability to identify new and covert attacks. In terms of error correction, manual intervention or simple rollback mechanisms are often used, which are inefficient and prone to asset loss. Therefore, this paper proposes a multi-risk interception and artificial intelligence error correction method for RWA asset cross-chain transfer. Summary of the Invention

[0004] In view of this, the present invention provides a method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets, so as to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option.

[0005] The technical solution of this invention is implemented as follows: a method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets, comprising the following steps:

[0006] Step 1: Cross-chain transfer initialization and asset mapping verification;

[0007] Step Two: Multi-level Dynamic Risk Interception;

[0008] Step 3: Artificial intelligence error correction mechanism;

[0009] Step 4: Asset ownership confirmation and log auditing after cross-chain completion.

[0010] In a further preferred embodiment, in step one, multi-dimensional information is collected on the RWA asset, including but not limited to asset ownership certificates (such as blockchain-based notarized hashes of property ownership certificates and ownership certificates), physical status data (such as encrypted data of property area, location, and appraised value), and on-chain registration information (the asset's TokenID and smart contract address on the original chain). Through a distributed data cleaning module, the collected information is converted into structured data in a unified format, redundant fields (such as duplicate ownership certificate numbers) and outliers (such as records with negative appraised values) are removed, and a unique asset identifier (composed of asset type code + original chain ID + timestamp hash) is generated. The mapping relationship database of the cross-chain gateway is called to verify whether the mapping contract of the RWA asset on the target chain has been registered (verifying the legality and activity of the contract address, i.e., whether there are normal transaction records in the past 72 hours). Zero-knowledge proof (ZKP) technology is used to verify the matching of the ownership of the original chain asset and the permissions of the target chain receiving address without disclosing asset privacy (e.g., for corporate assets, the legal entity's digital signature of the receiving address needs to be verified). If the verification fails, a level 1 risk interception is triggered, generating a "mapping relationship mismatch" warning report and terminating the transfer process.

[0011] Further preferably, in step two, an abnormal transaction identification model based on the Transformer architecture is constructed. Input features include: asset transfer frequency (standard deviation from the historical transfer records of the asset), transaction amount fluctuation (the proportion of deviation from the average transfer amount of similar assets), and the behavioral profile of the initiating address (whether there are related transactions with blacklisted addresses). The model updates parameters in real time through a sliding window (integrating new cross-chain transaction data every 10 minutes) and outputs a transaction risk score (0-100 points). When the score is ≥60 points, a secondary risk interception is triggered, the transaction is frozen, and a manual review channel is initiated. An improved Byzantine Fault Tolerance (BFT) algorithm is adopted, requiring that the nodes participating in cross-chain verification (≥5 nodes) reach a consensus of ≥80% on the asset transfer data. The node verification includes: hash consistency of asset metadata (comparison of asset description data hash values ​​between the original chain and the target chain), and reasonableness of cross-chain transaction fees (whether they fluctuate within a preset range). If the consensus failure count is ≥2 times, a level 3 risk interception is triggered, and a cross-chain node health check is initiated (checking the node's CPU utilization, network latency, and historical consensus accuracy). After removing abnormal nodes, consensus is re-initiated. A contract vulnerability detection tool based on symbolic execution is integrated to perform real-time scanning before cross-chain contract calls, focusing on detecting reentrancy attack risks (whether there are vulnerabilities in the function call order), access control defects (whether there is a possibility of unauthorized calls), and integer overflow (whether the asset quantity calculation exceeds the variable type range). When a high-risk vulnerability is found (such as a reentrancy vulnerability that could lead to asset loss), a level 4 risk interception is triggered, automatically generating contract patch suggestions (such as adding a reentrancy lock code snippet) and suspending the cross-chain process.

[0012] In a further preferred embodiment, in step three, a risk knowledge base is constructed, and historical interception cases are classified into: data inconsistency type (such as asset valuation difference between the original chain and the target chain > 10%), permission abnormal type (receiving address has no asset receiving permission), and technical failure type (cross-chain node network interruption). A knowledge graph-based tracing algorithm is used to determine the root cause of the risk through correlation analysis. For example, when an "asset valuation inconsistency" is detected, the system automatically traces back to the original chain's valuation data source (such as the API interface of a third-party assessment agency) to check for tampering or delays during data transmission. For data inconsistency risks, the system calls a federated learning model to correct asset parameters (e.g., merging valuation data from the original chain, target chain, and third-party data sources, and generating a corrected value using a weighted average algorithm). For permission anomaly risks, the system activates a smart permission configuration assistant, pushes a permission application template to the initiator, and automatically updates permissions through the on-chain identity authentication system (e.g., adding the receiving address to the asset whitelist). For technical failure risks, the system triggers an automatic switching mechanism (e.g., switching from an abnormal node cluster to a backup node cluster) and corrects the time record of cross-chain transactions using a timestamp synchronization algorithm. After error correction, the system simulates the cross-chain process in a sandbox environment (using mirrored data) to verify whether the corrected transaction meets the following criteria: risk score ≤ 30 points, node consensus rate ≥ 90%, and contract vulnerability scan result of "no high-risk vulnerabilities." The system feeds back the characteristic data (such as risk type, error correction strategy, and verification results) from the error correction process to the abnormal transaction identification model, and updates the model parameters through reinforcement learning (the reward factor is positively correlated with the error correction success rate).

[0013] In a further preferred embodiment, in step four, after the asset is transferred to the target chain, a certificate of ownership is generated containing the original chain hash, the target chain hash, a timestamp, and signatures of participating nodes. This certificate is simultaneously stored in IPFS (InterPlanetary File System) and the consortium chain nodes (ensuring data immutability). A smart contract is invoked to automatically execute the ownership confirmation logic (such as binding the asset token to the receiving address and triggering an ownership change event notification). Blockchain notarization technology is used to record all operation logs during the cross-chain transfer process (including risk interception time, error correction measures, and node interaction data). The log information is encrypted using SHA-256 and written to the sidechain. An AI auditing robot is developed to periodically (daily) mine abnormal patterns in the logs, identify potential systemic risks (such as a sudden increase in the cross-chain failure rate of a certain type of asset), and generate a risk trend report.

[0014] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions:

[0015] I. This invention utilizes a multi-level interception system encompassing "pre-transaction, during-transaction, and contract layer," combined with AI dynamic learning capabilities, to cover technical risks (such as contract vulnerabilities), data risks (such as information inconsistencies), and behavioral risks (such as abnormal transactions) in RWA asset cross-chain transfers. This significantly improves the risk identification rate compared to traditional methods. The AI ​​error correction mechanism automatically classifies risk types and generates strategies, shortening the average error correction time and increasing the error correction success rate.

[0016] Second, this invention introduces distributed ownership confirmation and tamper-proof log auditing to ensure clear and traceable ownership after asset transfer. Combined with zero-knowledge proof and improved BFT algorithm, the risk of asset loss is greatly reduced. The method design is compatible with mainstream cross-chain protocols and supports multiple RWA asset types. Through modular architecture, new risk features and error correction strategies can be flexibly added to adapt to the future development of the blockchain ecosystem.

[0017] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0020] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0021] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] like Figure 1 As shown, this embodiment of the invention provides a method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets, including the following steps:

[0023] Step 1: Cross-chain transfer initialization and asset mapping verification;

[0024] Step Two: Multi-level Dynamic Risk Interception;

[0025] Step 3: Artificial intelligence error correction mechanism;

[0026] Step 4: Asset ownership confirmation and log auditing after cross-chain completion.

[0027] In one embodiment, in step one, multi-dimensional information is collected on the RWA asset, including but not limited to asset ownership certificates (such as blockchain-based notarized hashes of property ownership certificates and ownership certificates), physical status data (such as encrypted data of property area, location, and appraised value), and on-chain registration information (the asset's TokenID and smart contract address on the original chain). Through a distributed data cleaning module, the collected information is converted into structured data in a unified format, redundant fields (such as duplicate ownership certificate numbers) and outliers (such as records with negative appraised values) are removed, and a unique asset identifier (composed of asset type code + original chain ID + timestamp hash) is generated. The mapping relationship database of the cross-chain gateway is called to verify whether the mapping contract of the RWA asset on the target chain has been registered (verifying the legality and activity of the contract address, i.e., whether there are normal transaction records in the past 72 hours). Zero-knowledge proof (ZKP) technology is used to verify the matching of the ownership of the original chain asset and the permissions of the target chain receiving address without disclosing asset privacy (e.g., for corporate assets, the legal entity's digital signature of the receiving address needs to be verified). If the verification fails, a level 1 risk interception is triggered, generating a "mapping relationship mismatch" warning report and terminating the transfer process.

[0028] In one embodiment, in step two, an abnormal transaction identification model based on the Transformer architecture is constructed. Input features include: asset transfer frequency (standard deviation from the historical transfer records of the asset), transaction amount fluctuation (the proportion of deviation from the average transfer amount of similar assets), and the behavioral profile of the initiating address (whether there are related transactions with blacklisted addresses). The model updates parameters in real time through a sliding window (integrating new cross-chain transaction data every 10 minutes) and outputs a transaction risk score (0-100 points). When the score is ≥60 points, a secondary risk interception is triggered, the transaction is frozen, and a manual review channel is initiated. An improved Byzantine Fault Tolerance (BFT) algorithm is used, requiring ≥5 nodes participating in cross-chain verification to reach ≥80% consensus on the asset transfer data. The node verification includes: hash consistency of asset metadata (comparison of asset description data hash values ​​between the original chain and the target chain), and reasonableness of cross-chain transaction fees (whether they fluctuate within a preset range). If the consensus failure count is ≥2 times, a level 3 risk interception is triggered, and a cross-chain node health check is initiated (checking the node's CPU utilization, network latency, and historical consensus accuracy). After removing abnormal nodes, consensus is re-initiated. A contract vulnerability detection tool based on symbolic execution is integrated to perform real-time scanning before cross-chain contract calls, focusing on detecting reentrancy attack risks (whether there are vulnerabilities in the function call order), access control defects (whether there is a possibility of unauthorized calls), and integer overflow (whether the asset quantity calculation exceeds the variable type range). When a high-risk vulnerability is found (such as a reentrancy vulnerability that could lead to asset loss), a level 4 risk interception is triggered, automatically generating contract patch suggestions (such as adding a reentrancy lock code snippet) and suspending the cross-chain process.

[0029] In one embodiment, in step three, a risk knowledge base is constructed, and historical interception cases are classified into: data inconsistency type (such as asset valuation difference between the original chain and the target chain > 10%), permission abnormal type (receiving address has no asset receiving permission), and technical failure type (cross-chain node network interruption). A knowledge graph-based tracing algorithm is used to determine the root cause of the risk through correlation analysis. For example, when an "asset valuation inconsistency" is detected, the system automatically traces back to the original chain's valuation data source (such as the API interface of a third-party assessment agency) to check for tampering or delays during data transmission. For data inconsistency risks, the system calls a federated learning model to correct asset parameters (e.g., merging valuation data from the original chain, target chain, and third-party data sources, and generating a corrected value using a weighted average algorithm). For permission anomaly risks, the system activates a smart permission configuration assistant, pushes a permission application template to the initiator, and automatically updates permissions through the on-chain identity authentication system (e.g., adding the receiving address to the asset whitelist). For technical failure risks, the system triggers an automatic switching mechanism (e.g., switching from an abnormal node cluster to a backup node cluster) and corrects the time record of cross-chain transactions using a timestamp synchronization algorithm. After error correction, the system simulates the cross-chain process in a sandbox environment (using mirrored data) to verify whether the corrected transaction meets the following criteria: risk score ≤ 30 points, node consensus rate ≥ 90%, and contract vulnerability scan result of "no high-risk vulnerabilities." The system feeds back the characteristic data (such as risk type, error correction strategy, and verification results) from the error correction process to the abnormal transaction identification model, and updates the model parameters through reinforcement learning (the reward factor is positively correlated with the error correction success rate).

[0030] In one embodiment, in step four, after the asset is transferred to the target chain, a certificate of ownership is generated containing the original chain hash, the target chain hash, a timestamp, and signatures of participating nodes. This certificate is then synchronously stored in IPFS (InterPlanetary File System) and the consortium chain nodes (ensuring data immutability). A smart contract is invoked to automatically execute the ownership confirmation logic (such as binding the asset token to the receiving address and triggering an ownership change event notification). Blockchain notarization technology is used to record all operation logs during the cross-chain transfer process (including risk interception time, error correction measures, and node interaction data). The log information is encrypted using SHA-256 and written to the sidechain. An AI auditing robot is developed to periodically (daily) mine abnormal patterns in the logs, identify potential systemic risks (such as a sudden increase in the cross-chain failure rate of a certain type of asset), and generate a risk trend report.

[0031] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-risk interception and AI error correction method for RWA asset cross-chain transfer, characterized by: Includes the following steps: Step 1: Cross-chain transfer initialization and asset mapping verification; Step Two: Multi-level Dynamic Risk Interception; Step 3: Artificial intelligence error correction mechanism; Step 4: Asset ownership confirmation and log auditing after cross-chain completion.

2. The method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets according to claim 1, characterized in that: In step one, multi-dimensional information is collected on RWA assets, including but not limited to asset ownership certificates (such as blockchain-based notarized hashes of property ownership certificates and ownership certificates), physical status data (such as encrypted data of property area, location, and appraised value), and on-chain registration information (the asset's TokenID and smart contract address on the original chain). Through a distributed data cleaning module, the collected information is converted into structured data in a unified format, removing redundant fields (such as duplicate ownership certificate numbers) and outliers (such as records with negative appraised values), and generating a unique asset identifier (composed of asset type code + original chain ID + timestamp hash). The mapping relationship database of the cross-chain gateway is called to verify whether the mapping contract of the RWA asset on the target chain has been registered (verifying the legality and activity of the contract address, i.e., whether there are normal transaction records in the past 72 hours). Zero-knowledge proof (ZKP) technology is used to verify the matching of the ownership of the original chain asset with the permissions of the target chain receiving address without disclosing asset privacy (e.g., for corporate assets, the legal entity's digital signature of the receiving address needs to be verified). If the verification fails, a level 1 risk interception is triggered, generating a "mapping relationship mismatch" warning report and terminating the transfer process.

3. The method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets according to claim 1, characterized in that: In step two, an abnormal transaction identification model based on the Transformer architecture is constructed. Input features include: asset transfer frequency (standard deviation from historical transfer records of the asset), transaction amount fluctuation (proportion of deviation from the average transfer amount of similar assets), and the behavioral profile of the initiating address (whether there are related transactions with blacklisted addresses). The model updates parameters in real time via a sliding window (integrating new cross-chain transaction data every 10 minutes) and outputs a transaction risk score (0-100 points). When the score is ≥60 points, a secondary risk interception is triggered, freezing the transaction and initiating a manual review channel. An improved Byzantine Fault Tolerance (BFT) algorithm is used, requiring ≥80% consensus among the nodes participating in cross-chain verification regarding the asset transfer data. The node verification includes: hash consistency of asset metadata (comparison of asset description data hash values ​​between the original chain and the target chain), and reasonableness of cross-chain transaction fees (whether they fluctuate within a preset range). If the consensus failure count is ≥2 times, a level 3 risk interception is triggered, and a cross-chain node health check is initiated (checking the node's CPU utilization, network latency, and historical consensus accuracy). After removing abnormal nodes, consensus is re-initiated. A contract vulnerability detection tool based on symbolic execution is integrated to perform real-time scanning before cross-chain contract calls, focusing on detecting reentrancy attack risks (whether there are vulnerabilities in the function call order), access control defects (whether there is a possibility of unauthorized calls), and integer overflow (whether the asset quantity calculation exceeds the variable type range). When a high-risk vulnerability is found (such as a reentrancy vulnerability that could lead to asset loss), a level 4 risk interception is triggered, automatically generating contract patch suggestions (such as adding a reentrancy lock code snippet) and suspending the cross-chain process.

4. The method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets according to claim 1, characterized in that: In step three, a risk knowledge base is constructed, and historical interception cases are classified into: data inconsistency type (such as asset valuation difference between the original chain and the target chain > 10%), permission abnormal type (receiving address has no asset receiving permission), and technical failure type (cross-chain node network interruption). A knowledge graph-based tracing algorithm is used to determine the root cause of the risk through correlation analysis. For example, when an "asset valuation inconsistency" is detected, the system automatically traces back to the original chain's valuation data source (such as the API interface of a third-party assessment agency) to check for tampering or delays during data transmission. For data inconsistency risks, the system calls a federated learning model to correct asset parameters (e.g., merging valuation data from the original chain, target chain, and third-party data sources, and generating a corrected value using a weighted average algorithm). For permission anomaly risks, the system activates a smart permission configuration assistant, pushes a permission application template to the initiator, and automatically updates permissions through the on-chain identity authentication system (e.g., adding the receiving address to the asset whitelist). For technical failure risks, the system triggers an automatic switching mechanism (e.g., switching from an abnormal node cluster to a backup node cluster) and corrects the time record of cross-chain transactions using a timestamp synchronization algorithm. After error correction, the system simulates the cross-chain process in a sandbox environment (using mirrored data) to verify whether the corrected transaction meets the following criteria: risk score ≤ 30 points, node consensus rate ≥ 90%, and contract vulnerability scan result of "no high-risk vulnerabilities." The system feeds back the characteristic data (such as risk type, error correction strategy, and verification results) from the error correction process to the abnormal transaction identification model, and updates the model parameters through reinforcement learning (the reward factor is positively correlated with the error correction success rate).

5. The method for multiple risk interception and artificial intelligence error correction in cross-chain transfer of RWA assets according to claim 1, characterized in that: In step four, after the assets are transferred to the target chain, a certificate of ownership is generated, containing the original chain hash, the target chain hash, a timestamp, and signatures from participating nodes. This certificate is simultaneously stored in IPFS (InterPlanetary File System) and the consortium chain nodes (ensuring data immutability). A smart contract is invoked to automatically execute the ownership confirmation logic (such as binding the asset token to the receiving address and triggering an ownership change event notification). Blockchain notarization technology is used to record all operation logs during the cross-chain transfer process (including risk interception time, error correction measures, and node interaction data). The log information is encrypted using SHA-256 and written to the sidechain. An AI auditing robot is developed to periodically (daily) mine abnormal patterns in the logs, identify potential systemic risks (such as a sudden increase in the cross-chain failure rate of a certain type of asset), and generate a risk trend report.

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