A Digital Asset Security Verification and Information Monitoring Method and System

Abstract

The present application provides a digital asset security verification and information monitoring method and system, including: classifying digital assets using a multi-dimensional classification model according to the type attributes of digital assets to obtain classification labels and feature vector representations of digital assets, and establishing a digital asset feature database; obtaining transaction historical data of digital assets from a digital asset association graph, and using time series pattern mining technology to discover association rules and abnormal patterns of digital asset transactions to form a transaction verification rule database; according to the data structure of the algorithm adaptation layer, using intelligent data parsing and conversion technology to map heterogeneous digital asset data to a unified feature space, generating standardized digital asset data, and forming a digital asset feature database; using the digital asset feature database to perform in-depth verification analysis to obtain security attribute information of digital assets, and using a multi-factor risk assessment model to dynamically evaluate the security level of digital assets to generate a digital asset security rating.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to a method and system for digital asset security verification and information monitoring. Background Art

[0002] In the process of security verification of digital asset portfolios, since the portfolio contains various types of digital assets, each with its unique attributes and verification methods, it is necessary to design a comprehensive verification system that can uniformly verify the legality and ownership of different digital assets. At the same time, due to the possible correlations between digital assets, for example, some digital assets may be derived from other digital assets, or there are trading relationships between digital assets, the verification system needs to be able to identify and process these correlations, perform weighted verification on related digital assets to ensure the integrity and credibility of the entire portfolio. In addition, different digital assets may adopt different encryption algorithms, consensus mechanisms, and data structures. The verification system needs to be compatible with these differences and be able to track the changes and updates of digital assets in real time. At the same time, due to the high value of digital assets, the verification system also needs to have a high level of security and anti-tampering capabilities, be able to effectively resist various forgery, tampering, and attack behaviors, and ensure the authenticity and reliability of the portfolio. Therefore, designing a comprehensive, reliable, and secure digital asset portfolio verification system requires comprehensively considering multiple dimensions such as digital asset attributes, correlations, and dynamics, adopting advanced cryptography, consensus algorithms, and data analysis technologies, and constructing a highly intelligent and adaptive verification framework to provide solid technical support for the management and trading of digital asset portfolios. Summary of the Invention

[0003] The present invention provides a method for digital asset security verification and information monitoring, mainly including:

[0004] According to the type attributes of digital assets, a multi-dimensional classification model is used to classify digital assets, obtain the classification labels and feature vector representations of digital assets, and establish a digital asset feature database;

[0005] For the classified digital asset feature database, an adaptive verification algorithm is designed. By dynamically selecting matching verification rules and threshold parameters, comprehensive verification of the legality and ownership of digital assets is realized, and digital asset verification results are generated;

[0006] Based on the digital asset verification results and the correlations between digital assets, a digital asset association graph is constructed. The graph neural network algorithm is used to jointly model related digital assets. Through message passing and feature aggregation, the verification weights of digital asset nodes are calculated;

[0007] Obtain the transaction history data of digital assets from the digital asset association graph, use time series pattern mining technology to discover the association rules and abnormal patterns of digital asset transactions, and form a transaction verification rule library;

[0008] Apply the transaction verification rule library to the digital asset verification process, combine the encryption algorithm and consensus mechanism of digital assets, design a multi-algorithm adaptation layer, and achieve compatible processing of different types of digital assets through algorithm registration and dynamic scheduling;

[0009] According to the data structure of the algorithm adaptation layer, adopt intelligent data parsing and conversion technology to map heterogeneous digital asset data to a unified feature space, generate standardized digital asset data, and form a digital asset feature database;

[0010] Utilize the digital asset feature database to conduct in-depth verification analysis, obtain the security attribute information of digital assets, adopt a multi-factor risk assessment model to dynamically evaluate the security level of digital assets, and generate digital asset security rating results;

[0011] Based on the digital asset security rating results and verification results, construct a blockchain verification proof mechanism, and form a hash proof chain for digital asset verification by generating a hash value containing the verification process, results, and timestamp, ensuring the anti-tampering and traceability of the verification results.

[0012] The present invention provides a digital asset verification and information monitoring system, mainly including:

[0013] A digital asset classification module for classifying digital assets in multiple dimensions;

[0014] A digital asset verification module for verifying the legality and ownership of digital assets;

[0015] A digital asset association modeling module for constructing a digital asset association graph and calculating weights;

[0016] A transaction verification rule mining module for discovering the association rules and anomalies of digital asset transactions;

[0017] A multi-algorithm adaptation processing module for compatibly processing different types of digital assets;

[0018] A digital asset security assessment module for evaluating the security level of digital assets.

[0019] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0020] The present invention discloses a method for digital asset security verification and information monitoring. The present invention classifies digital assets in multiple dimensions, establishes a digital asset feature database, and designs an adaptive verification algorithm to comprehensively verify the legality and ownership of digital assets. On this basis, the present invention constructs a digital asset association graph, uses the graph neural network algorithm to calculate the verification weights of digital asset nodes, and discovers the association rules and abnormal patterns of digital asset transactions through time series pattern mining technology to form a transaction verification rule library. The present invention also designs a multi-algorithm adaptation layer to achieve compatible processing of different types of digital assets, and adopts intelligent data parsing and conversion technology to generate standardized digital asset data. Finally, the present invention uses in-depth verification analysis and a multi-factor risk assessment model to dynamically evaluate the security level of digital assets, and constructs a blockchain verification proof mechanism to ensure the tamper-proof and traceability of verification results. The present invention provides a comprehensive, intelligent, and scalable digital asset security assessment solution, effectively improving the security and credibility of digital assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a method for digital asset security verification and information monitoring according to the present invention.

[0022] Figure 2 It is a schematic diagram of a method and system for digital asset security verification and information monitoring according to the present invention.

[0023] Figure 3 It is another schematic diagram of a method and system for digital asset security verification and information monitoring according to the present invention.

[0024] Figure 4 It is a schematic structural diagram of a system for digital asset security verification and information monitoring according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] As Figures 1-4 , a method and system for digital asset security verification and information monitoring in this embodiment may specifically include:

[0027] Step S101, according to the type attributes of digital assets, classify the digital assets using a multi-dimensional classification model to obtain the classification labels and feature vector representations of the digital assets, and establish a digital asset feature database.

[0028] Obtain the multi-dimensional attribute information of digital assets, use the K-means clustering algorithm to cluster the digital assets, and obtain the coarse-grained classification labels of the digital assets; for each coarse-grained classification, extract the common features of the digital assets in this category, and construct a TF-IDF feature vector representation model for the digital assets in this category; use the support vector machine SVM classification algorithm to classify the feature vectors of the digital assets to obtain the fine-grained classification labels of the digital assets; store the classification labels and corresponding feature vectors of the digital assets in the MongoDB database to establish the mapping relationship between the digital asset labels and features; when receiving a digital asset retrieval request from the user, obtain the digital asset classification label input by the user, and obtain the digital asset feature vector corresponding to the digital asset classification label from the MongoDB database; calculate the cosine similarity between the user's portrait feature vector and the digital asset feature vector, and recommend the Top-N digital assets with the highest similarity to the user; when there are new digital assets, extract the multi-dimensional attribute information of the new digital assets, obtain their feature vectors through the TF-IDF feature vector representation model, use the SVM classification model algorithm to judge their categories, and insert the new digital asset classification label and feature vector information into the MongoDB database; regularly maintain and update the digital asset feature data in the MongoDB database, update its classification labels and feature vector representations according to the dynamic changes of the digital assets, reduce the update frequency for digital assets with no changes for a long time, and increase the update frequency for digital assets with frequent changes.

[0029] Specifically, digital assets can have many dimensions, such as the issuer, issuance time, total quantity, circulation quantity, consensus mechanism, application scenarios, and so on. These dimensions can be quantified. For example, the issuance time can be represented by a timestamp, and the consensus mechanism can be represented by a one-hot encoding. Suppose the multi-dimensional attribute information of 1000 digital assets is collected. The K-means algorithm is used to cluster these digital assets. Set the number of clusters K to 5, and the algorithm will divide the 1000 digital assets into 5 clusters. Suppose one cluster contains mainstream cryptocurrencies such as Digital Currency A, another cluster contains various decentralized tokens, and there is also a cluster containing NFT-related digital assets. In this way, coarse-grained classification labels for digital assets are obtained, such as "mainstream cryptocurrencies", "decentralized tokens", "NFT assets", etc. The reason for performing coarse-grained classification is that directly performing fine-grained classification will increase the computational complexity, while coarse-grained classification can help narrow down the scope and improve efficiency. The TF-IDF algorithm can be used to construct a feature vector representation model. The TF-IDF algorithm can evaluate the importance of a word for a document set or a single document in a corpus. Here, each digital asset can be regarded as a "document", and its attributes can be regarded as "words". The support vector machine (SVM) algorithm is used to classify the feature vectors of digital assets to obtain the fine-grained classification labels of digital assets. For example, under the coarse-grained classification of "mainstream cryptocurrencies", it can be further subdivided into "POW consensus mechanism", "POS consensus mechanism", etc. Through the SVM algorithm, "Digital Currency A" can be classified into the fine-grained classification of "POW consensus mechanism". The classification labels (coarse-grained and fine-grained) of digital assets and the corresponding feature vectors are stored in the MongoDB database. MongoDB is a non-relational database that can flexibly store various data types. A collection can be created to store the information of digital assets, including the name, classification labels, feature vectors, etc. For example, the name of "Digital Currency A", the two classification labels of "mainstream cryptocurrency" and "POW consensus mechanism", and its TF-IDF feature vector can be stored in the MongoDB database. The advantage of doing this is to facilitate subsequent retrieval and update. When the user enters the classification label of "POW consensus mechanism" for retrieval, the system will obtain the feature vectors of all digital assets belonging to the "POW consensus mechanism" from the MongoDB database. Suppose the user portrait feature vector contains features such as "high security" and "high degree of decentralization". The system will calculate the cosine similarity between the user portrait feature vector and each digital asset feature vector. The higher the cosine similarity, the higher the matching degree between the user and the digital asset. The system will recommend the top-N digital assets with the highest similarity to the user. When a new digital asset named "ABC Token" is added, its multi-dimensional attribute information will be extracted.Through the TF-IDF feature vector representation model constructed previously, the feature vector of "ABC token" can be obtained. Then, using the trained SVM classification model, determine the category to which "ABC token" belongs, such as "mainstream cryptocurrency" and "POS consensus mechanism". Finally, insert the classification label and feature vector information of "ABC token" into the MongoDB database. Regularly maintain and update the digital asset feature data in the MongoDB database. For digital assets with stable long-term attributes like A digital currency, the update frequency can be reduced. For some decentralized tokens with more frequent changes, the update frequency needs to be increased. This can ensure the accuracy and timeliness of the data in the database, thereby improving the effectiveness of the recommendation system.

[0030] Step S102, for the classified digital asset feature database, design an adaptive verification algorithm. By dynamically selecting matching verification rules and threshold parameters, achieve the comprehensive verification of the legality and ownership of digital assets, and generate digital asset verification results.

[0031] Obtain the digital asset feature database, which contains the feature information of various digital assets; according to the coarse-grained classification label of the digital asset's classification information, obtain the preset candidate verification rule set and threshold parameter range; use the decision tree algorithm to select the subset of verification rules most suitable for the digital asset to be verified from the candidate verification rule set. The feature selection of the decision tree is based on the feature information of the digital asset. By calculating the information gain ratio of each feature, select the feature with the largest information gain ratio as the splitting node, and recursively construct the decision tree until the preset stop condition is met; use the random forest algorithm to dynamically adjust the threshold parameters of the verification rule subset. The random forest constructs multiple decision trees by randomly selecting sample subsets and feature subsets, and combines the prediction results of multiple decision trees to obtain the adjusted value of the threshold parameters; use the adjusted threshold parameters to determine whether the digital asset to be verified meets the preset ownership verification rules. If it meets, judge and verify the legality of the digital asset to be verified; use the weighted average method to calculate the comprehensive verification score of the digital asset to be verified. The weight coefficients of the weighted average are preset according to the importance of ownership verification and legality verification; compare the comprehensive verification score with the preset threshold to obtain the final verification result of the digital asset to be verified; write the final verification result, the features of the digital asset to be verified, the ownership verification result, and the legality verification result information into the digital asset verification result database.

[0032] Specifically, obtain the digital asset feature database, which contains the feature information of various digital assets. This information constitutes the features of digital assets and can be used in subsequent verification processes. According to the coarse-grained classification labels of digital assets (such as "mainstream cryptocurrencies", "decentralized tokens", "NFT assets", etc.), obtain the preset candidate verification rule set and threshold parameter range. The threshold parameter range can be the lower limit of market value, the lower limit of the number of trading platforms, and so on. Use the decision tree algorithm to select the subset of verification rules that is most suitable for the digital asset to be verified from the candidate verification rule set. Suppose we want to verify a new digital asset named "ABC Token", and its coarse-grained classification label is "mainstream cryptocurrency". The reason for using the decision tree algorithm is that it can select the most appropriate verification rules based on the different features of digital assets, improving the efficiency and accuracy of verification. Use the random forest algorithm to dynamically adjust the threshold parameters of the verification rule subset. Suppose the decision tree algorithm selects the rule "the number of trading platforms is greater than 50", and the initial threshold is 50. The random forest algorithm will dynamically adjust this threshold by analyzing historical data. For example, if historical data shows that "mainstream cryptocurrencies" with more than 80 trading platforms are more reliable, then the random forest algorithm will adjust the threshold to 80. The reason for using the random forest algorithm is that it can integrate the prediction results of multiple decision trees, improving the accuracy and stability of threshold parameters. Use the adjusted threshold parameters to determine whether the digital asset to be verified meets the preset ownership verification rules. Suppose "ABC Token" is listed on 85 trading platforms and meets the adjusted threshold parameters, then it is considered to have passed the ownership verification. The purpose of ownership verification is to confirm the authenticity and credibility of digital assets and prevent forgery and fraud. If the ownership verification rules are met, then judge and verify the legality of the digital asset to be verified. Legality verification mainly examines whether the digital asset complies with relevant laws, regulations, and regulatory policies. For example, verify whether the issuance of "ABC Token" is compliant and whether it is involved in illegal activities. Use the weighted average method to calculate the comprehensive verification score of the digital asset to be verified. Suppose the weight of ownership verification is 7 and the weight of legality verification is 3. The ownership verification score of "ABC Token" is 100 points, and the legality verification score is 90 points, then its comprehensive verification score is 7*100 + 3*90 = 97 points. Using the weighted average method can comprehensively consider the importance of different verification dimensions and obtain a more comprehensive verification result. Compare the comprehensive verification score with the preset threshold to obtain the final verification result of the digital asset to be verified. Suppose the preset threshold is 90 points and the comprehensive verification score of "ABC Token" is 97 points, then the final verification result is "passed". Write the final verification result, the features of "ABC Token", the ownership verification result, and the legality verification result information into the digital asset verification result database. The advantage of doing this is to facilitate subsequent queries and analyses, and it is also convenient for regulatory agencies to conduct supervision and management.The information stored in the database can be used to track the history of digital assets and identify potential risks and issues.

[0033] Step S103: Based on the digital asset verification results and the correlation between digital assets, construct a digital asset association graph, use the graph neural network algorithm to jointly model the associated digital assets, and calculate the verification weights of digital asset nodes through message passing and feature aggregation.

[0034] Obtain the digital asset verification results and the correlation between digital assets, and construct a digital asset association graph. The nodes in the digital asset association graph represent digital assets, and the edges represent the association relationships between digital assets; preprocess the digital asset association graph, fill in missing values, filter outlier values, and extract the attribute features and structural features of the nodes; use the graph convolutional neural network algorithm to model the preprocessed digital asset association graph to obtain a graph convolutional neural network model; at each layer of the graph convolutional neural network model, update the feature representation of the nodes through the message passing mechanism according to the adjacency relationship between the nodes; at the last layer of the graph convolutional neural network model, obtain the feature representation of the digital asset nodes; calculate the correlation weights between the nodes through the attention mechanism, and the attention mechanism includes converting the feature representation of the nodes into attention weights and aggregating the features of adjacent nodes through weighted summation; splice the feature representation of the digital asset nodes and the attention weights as the input of the fully connected layer, and obtain the verification weights of each digital asset node through the fully connected layer; judge the importance of the digital asset nodes according to the verification weights, mark the digital asset nodes higher than the threshold as important nodes, and mark the digital asset nodes lower than the threshold as suspicious nodes; conduct key verification on the important nodes and suspicious nodes, and add the verification results as new node attributes to the digital asset association graph; dynamically update the feature representation and verification weights of the digital asset nodes according to the verification results, and retrain the graph convolutional neural network model to obtain continuously optimized digital asset verification results.

[0035] Specifically, the digital asset association graph aims to depict the relationships between digital assets, providing a more comprehensive perspective for risk assessment and verification. Nodes in the graph represent individual digital assets, such as digital currency A, a specific NFT collection, and so on. Edges represent the associations between them, such as being based on the same underlying technology, having a common development team, being listed and traded on the same trading platform, etc. After constructing the graph, preprocessing is required to improve the accuracy of the model. For example, if the market value data of a digital asset is missing, it can be filled with the historical market value data of the asset or estimated using the average market value of assets in the same category. In addition, it is crucial to extract the attribute features and structural features of the nodes. Attribute features can be trading volume, issue time, etc., while structural features can be the degree of the node (the number of edges connected to the node), centrality, etc. These features will be used for subsequent graph convolutional neural network modeling. The graph convolutional neural network is a deep learning model specifically designed for processing graph data. In each layer, nodes exchange information with their neighboring nodes and update their own feature representations. For example, if an NFT project is associated with a well-reputed development team, the credibility of the NFT project may increase, which is reflected in the change of its feature representation. The attention mechanism can capture the important relationships between nodes. The attention mechanism assigns different weights to different relationships, thus highlighting key information. The fully connected layer combines the feature representations of the nodes and the attention weights to generate the verification weight for each node. This weight reflects the importance of the node and its potential risks. Nodes above the threshold are marked as important nodes, while nodes below the threshold are marked as suspicious nodes. Key verification is performed on important nodes and suspicious nodes. For example, for a token marked as suspicious, its transaction records, smart contract code, etc. can be investigated in depth to determine whether there is a fraud risk. The verification results will be added to the graph as new node attributes. The model will be dynamically updated according to the new verification results. For example, if a token previously considered important is found to have a security vulnerability, its verification weight will be reduced, and the model will adjust its parameters accordingly to improve the accuracy of subsequent verification. This continuous optimization process ensures that the model can adapt to the ever-changing digital asset environment. Through the digital asset association graph and the graph convolutional neural network, high-risk and suspicious digital assets can be identified more effectively. For example, a newly launched decentralized trading platform is associated with several suspicious project parties, and these project parties have previously issued tokens with asset security risks. Through the association graph and model analysis, the potential risks of this trading platform can be discovered in a timely manner and a warning can be issued.

[0036] Step S104, obtain the transaction history data of digital assets from the digital asset association graph, and use time series pattern mining technology to discover the association rules and abnormal patterns of digital asset transactions, forming a transaction verification rule library.

[0037] Obtain the digital asset association graph, and obtain the target digital asset node and its attribute information from the digital asset association graph; according to the preset association relationship, obtain the associated digital assets related to the target digital asset; obtain the transaction history data of the target digital asset and the associated digital assets, where the transaction history data includes transaction time, transaction amount, and transaction counterparty; preprocess the transaction history data, and fill in the missing values using the preset data filling rules; align the transaction times of the target digital asset and the associated digital assets to generate a normalized time-series transaction data set; use the Apriori algorithm to mine frequent transaction patterns from the time-series transaction data set; compare the frequent transaction patterns with the preset normal transaction pattern rules to determine whether the frequent transaction patterns are abnormal transaction patterns; if the frequent transaction patterns do not conform to the normal transaction pattern rules, mark the frequent transaction patterns as abnormal transaction patterns; conduct manual review on the abnormal transaction patterns, and add the manually confirmed abnormal transaction patterns to the pre-built abnormal transaction pattern library; obtain new digital asset transaction data, and obtain the digital asset information associated with the new digital asset transaction data based on the digital asset association graph; match the new digital asset transaction data with the abnormal transaction patterns in the abnormal transaction pattern library; if the new digital asset transaction data matches the abnormal transaction patterns in the abnormal transaction pattern library, generate an abnormal transaction warning message.

[0038] Specifically, the digital asset association graph is like a huge network of relationships. Each node represents a digital asset, such as a digital currency A or a certain NFT collection. The connections between nodes represent the associations between them, such as being based on the same underlying technology, having a common development team, being listed and traded on the same trading platform, and so on. Obtaining the digital asset association graph is to draw such a network in order to better understand the relationships between digital assets, so as to conduct risk assessment and verification. For example, if you want to investigate a new NFT project named "XYZ", you can obtain the node and its attribute information of this project from the graph, such as the release time, total quantity, release price, etc. To more comprehensively evaluate the risks of the "XYZ" project, it is necessary to obtain the digital assets associated with it. Suppose the graph shows that the "XYZ" project is associated with a development team named "ABC", and the "ABC" team has previously developed another NFT project "DEF". Then, the "DEF" project is associated with the "XYZ" project, and the "DEF" project can also be included in the scope of investigation. Obtain the historical transaction data of "XYZ" and "DEF", including the transaction time, transaction amount, and counterparty. For example, "XYZ" sold 1 NFT at a price of 1 ETH on January 1, 2024, and the buyer's address is 0x123. "DEF" sold 1 NFT at a price of 5 ETH on December 1, 2023, and the buyer's address is 0x456. These transaction data will be used for subsequent analysis. There may be missing values in the transaction data. For example, the amount of some transactions is not recorded. At this time, it is necessary to fill in the missing values according to the preset data filling rules. For example, the historical average transaction price of the asset can be used to fill in the missing transaction amount. Since the transaction times of different digital assets may be different, it is necessary to align the transaction times to generate a normalized time-series transaction data set. For example, summarize the transaction data of "XYZ" and "DEF" by hour for subsequent analysis. Use the Apriori algorithm to mine frequent transaction patterns from the time-series transaction data set. For example, it may be found that the trading volumes of "XYZ" and "DEF" often increase significantly in the same time period, which may imply some associated transactions between them. Compare the mined frequent transaction patterns with the preset normal transaction pattern rules. The normal transaction pattern rules can be formulated based on historical data and market experience. For example, the trading volume of an NFT project is usually related to factors such as its market popularity and community activity. If the trading volume of a project suddenly skyrockets, but its market popularity and community activity do not increase significantly, there may be an anomaly. If the frequent transaction pattern does not conform to the normal transaction pattern rules, it is marked as an abnormal transaction pattern. For example, if the trading volumes of "XYZ" and "DEF" both skyrocket synchronously, but neither of them has released any positive news nor obvious market driving factors, it can be marked as an abnormal transaction pattern. Conduct a manual review of the abnormal transaction pattern to confirm whether there is indeed an anomaly.For example, analysts can conduct in-depth investigations into the transaction records, fund flows, etc. of "XYZ" and "DEF" to determine whether there is market manipulation or other violations. Add the manually confirmed abnormal transaction patterns to the pre-built abnormal transaction pattern library. For example, add the pattern of a sharp increase in the synchronized trading volume of "XYZ" and "DEF" to the abnormal transaction pattern library for subsequent monitoring. Suppose new digital asset transaction data is generated and needs to be matched with the patterns in the abnormal transaction pattern library. For example, for a new NFT project named "GHI", its trading volume also surges synchronously with that of "XYZ" and "DEF". If the new digital asset transaction data matches the patterns in the abnormal transaction pattern library, generate an abnormal transaction warning message. For example, the system will issue a warning: The trading pattern of "GHI" matches the synchronized trading pattern of "XYZ" and "DEF" in the known abnormal transaction pattern library, and there may be risks. In this way, potential abnormal transaction behaviors can be discovered in a timely manner, protecting the interests of investors and maintaining the healthy development of the digital asset market.

[0039] Step S105, apply the transaction verification rule library to the digital asset verification process, combine the encryption algorithm and consensus mechanism of the digital asset, design a multi-algorithm adaptation layer, and achieve compatible processing of different types of digital assets through algorithm registration and dynamic scheduling.

[0040] According to the digital asset type, obtain the corresponding transaction verification rules from the digital asset registration center; use smart contract technology to convert the transaction verification rules into executable code logic and deploy it to the blockchain network; determine the consensus mechanism adopted by the blockchain network according to the blockchain network to which the digital asset belongs; when receiving a digital asset transaction request, call the smart contract to verify the digital asset transaction request, and the verification includes checking the legality of the format, signature, and attributes of the digital asset transaction request; if the digital asset transaction request passes the verification, broadcast the digital asset transaction request to the consensus nodes in the blockchain network; the consensus nodes conduct transaction verification and confirmation on the digital asset transaction request according to the consensus mechanism; use cryptographic technology to protect the transaction verification process and verify the legality of the digital asset transaction request without disclosing the identity information of both parties to the transaction; for cross-chain digital asset transactions, introduce a cross-chain protocol and achieve atomic exchange of digital assets in different blockchain networks through a transaction proof mechanism and a digital asset mapping mechanism; blockchain nodes achieve the dissemination, synchronization, and verification of blocks and transactions through the P2P network, ensuring the decentralization and fault tolerance of the network; according to the business rules and processes of digital assets, use smart contract technology to solidify the business rules and processes into code to achieve automated management of the entire life cycle of digital assets; after the smart contract passes security audits and tests, it is deployed to the blockchain network and jointly executed and verified by multiple nodes.

[0041] Specifically, the digital asset registration center is like a household registry for digital assets, recording information about various digital assets, including asset type, issuer, total quantity, etc. For example, for an NFT collection named "Art Coin", its registration information will include its name, issuer (such as a certain art studio), total circulation (e.g., 10,000), and information about the artworks corresponding to each NFT. Obtaining the corresponding transaction verification rules from the digital asset registration center means finding the corresponding transaction rules for different types of digital assets. For example, for an NFT collection like "Art Coin", its transaction rule might be: each transaction can only transfer an integer number of NFTs and cannot be split. A smart contract is like an automatically executed contract that converts the transaction verification rules into code logic and deploys it onto the blockchain. For example, for the transaction rule of "Art Coin", the smart contract will contain a piece of code to check whether the number of NFTs in each transaction is an integer. If someone tries to trade 0.5 "Art Coins", the smart contract will automatically block this transaction. The blockchain network is like a distributed ledger, and all transaction records are recorded on the blockchain. Different blockchain networks adopt different consensus mechanisms, such as Proof of Work (PoW) or Proof of Stake (PoS). PoS determines who has the right to verify transactions based on the amount of digital assets held by nodes. Suppose "Art Coin" is issued on a blockchain using the PoS consensus mechanism, then nodes holding a larger quantity of "Art Coins" will be more likely to obtain the right to verify transactions. When someone wants to trade "Art Coin", they will send a transaction request. This transaction request will be sent to the smart contract for verification. The smart contract will check the format, signature, and legality of the transaction attributes of the transaction request. For example, the smart contract will check whether the signature of the transaction request matches the private key of the sender and whether the number of NFTs in the transaction is an integer. If the transaction request passes the verification of the smart contract, it will be broadcast to the consensus nodes in the blockchain network. The consensus nodes will verify and confirm the transaction according to the consensus mechanism of the blockchain. For example, under the PoS mechanism, nodes with a larger amount of coins will vote on the transaction, and if the majority of nodes agree, the transaction will be confirmed and added to the blockchain. Cryptography technology is like a lock, protecting the security of transactions. It can verify the legality of transactions without disclosing the identity information of both parties to the transaction. For example, zero-knowledge proof technology can prove the legality of transactions without revealing the specific content of the transaction. For cross-chain transactions, such as when someone wants to exchange A digital currency for "Art Coin", a cross-chain protocol needs to be introduced. The cross-chain protocol is like a bridge connecting different blockchain networks. Through the transaction proof mechanism and the digital asset mapping mechanism, atomic exchanges of digital assets in different blockchain networks can be achieved. For example, a user can exchange 1 A digital currency for 10 "Art Coins", and the cross-chain protocol will ensure that this transaction is either completed simultaneously or fails simultaneously.Blockchain nodes communicate through a P2P network, just like a decentralized network where each node can connect to each other. This network structure can ensure the decentralization and fault tolerance of the network. Even if some nodes fail, the network can still operate normally. Smart contracts can also be used to manage the entire life cycle of digital assets, such as the issuance, trading, and destruction of "Art Coins". These business rules and processes can be solidified into the code of smart contracts to achieve automated management. For example, a smart contract can set the total issuance of "Art Coins" and automatically control the NFT issuance process. Before being deployed to the blockchain network, smart contracts need to undergo security audits and tests to ensure their security. Multiple nodes will jointly execute and verify smart contracts to further enhance security. For example, in the smart contract of "Art Coins", a security audit process may be set, and only after passing the audit can it be officially deployed to the blockchain.

[0042] Step S106, according to the data structure of the algorithm adaptation layer, adopt intelligent data parsing and conversion technology to map heterogeneous digital asset data to a unified feature space, generate standardized digital asset data, and form a digital asset feature database.

[0043] Obtain the original data of heterogeneous digital assets, where the original data includes the attributes, parameters, and configuration information of digital assets; use an XML parser to parse the original data, extract the key attribute fields of digital assets, and construct a digital asset attribute feature vector; according to the pre-defined unified feature space, perform normalization processing on the digital asset attribute feature vector to map attribute values of different scales and types to the interval [0, 1], obtaining a standardized digital asset attribute feature vector; adopt the One-Hot encoding data conversion method to convert digital asset attributes that cannot be directly mapped to the unified feature space into the feature representation form of the unified feature space; fill the standardized digital asset attribute feature vector into the corresponding dimensions of the unified feature space to obtain the feature vector representation of digital assets in the unified feature space; according to the feature vector of digital assets in the unified feature space, generate standardized digital asset data records according to the pre-defined data format and field names; import the standardized digital asset data records into the database to establish a digital asset feature data table.

[0044] Specifically, the data formats and fields of heterogeneous digital assets vary. To uniformly process and analyze this data, it needs to be converted into a standardized form. First, the original data of heterogeneous digital assets needs to be obtained. These data are usually stored in different formats, such as database records, XML files, JSON files, etc. Taking art NFTs and game props as examples, the data of art NFTs may include fields such as name, author, creation time, work description, image hash value, etc., while the data of game props may include fields such as name, type, level, attributes, durability, etc. After obtaining the original data, the data needs to be parsed and extracted. For XML-formatted data, an XML parser can be used, such as a DOM parser or a SAX parser. For example, the data of art NFTs may be stored in an XML file, where the information of each NFT is represented by an XML element. The XML parser can traverse the XML file and extract the key attribute fields of each NFT, such as name, author, image hash value, etc. After extracting the key attribute fields, a digital asset attribute feature vector needs to be constructed. For example, the feature vector of an art NFT can be represented as: [name, author, creation time, work description, image hash value]. Since the attribute values of different digital assets may have different scales and types, such as creation time being a numerical type and name being a string type, the feature vector needs to be normalized. Normalization can map attribute values of different scales and types into a unified interval, such as [0, 1]. For example, the creation time can be converted into a ratio value relative to the earliest creation time, and the name can be converted into a normalized value of the string hash value. In this way, a standardized digital asset attribute feature vector can be obtained. For some digital asset attributes that cannot be directly mapped to a unified feature space, such as the type of game props, One-Hot encoding can be used for conversion. Suppose there are three types of game props: weapons, armors, and consumables. Then a three-dimensional vector can be used to represent the type of the prop. For example, a weapon is represented as [1, 0, 0], an armor is represented as [0, 1, 0], and a consumable is represented as [0, 0, 1]. By filling the standardized digital asset attribute feature vector and the feature vector after One-Hot encoding into the corresponding dimensions of the unified feature space, the feature vector representation of the digital asset in the unified feature space can be obtained. For example, the feature vector of an art NFT can be represented as: [normalized value of name, normalized value of author, normalized value of creation time, normalized value of work description, normalized value of image hash value]. The feature vector of a game prop can be represented as: [normalized value of name, normalized value of level, normalized value of attributes, normalized value of durability, 1, 0, 0] (assuming the prop is a weapon). According to the feature vector of the digital asset in the unified feature space, standardized digital asset data records can be generated according to the predefined data format and field names.For example, the feature vectors of art NFTs and game props can both be converted into data records containing the following fields: asset ID, asset type, and feature vector. Finally, the standardized digital asset data records are imported into a database to establish a digital asset feature data table. In this way, different types of digital assets can be uniformly stored, managed, and analyzed. The advantage of this is that operations such as data retrieval, statistical analysis, and machine learning can be conveniently carried out.

[0045] In step S107, using the digital asset feature database, perform in-depth verification analysis to obtain the security attribute information of the digital asset. Adopt a multi-factor risk assessment model to dynamically evaluate the security level of the digital asset and generate a digital asset security rating.

[0046] Obtain the digital asset feature information in the digital asset feature database to get the multi-dimensional feature vector of the digital asset to be evaluated; construct a training data set using the digital asset feature vector and train the training data set through a deep neural network model to obtain the deep representation of the digital asset feature; input the deep representation of the digital asset feature into a pre-constructed security attribute discrimination model, and infer the security attribute information of the digital asset through the security attribute discrimination model. The security attribute information includes confidentiality, integrity, and availability levels; according to the obtained digital asset security attribute information, use the analytic hierarchy process to construct a pairwise comparison matrix, calculate the eigenvector corresponding to the largest eigenvalue of the pairwise comparison matrix, and perform normalization processing to obtain the weights of each evaluation factor; perform weighted summation of the digital asset security attribute information and the evaluation factor weights to obtain the risk scores of the digital asset in each evaluation dimension; regularly and dynamically track the digital asset risk scores through a scheduled task module. When the score exceeds the preset threshold, trigger the digital asset security level adjustment process and update the digital asset security rating database; read the real-time security rating information of the digital asset from the digital asset security rating database and automatically generate a digital asset security assessment report. The report includes digital asset basic information, security attribute levels, risk scores, and security rating change record content.

[0047] Specifically, the digital asset feature database stores multi-dimensional feature vectors of each digital asset. For example, for an art NFT, its feature vector may include image hash value, author, creation time, art style, etc. For a game item, its feature vector may include name, type, attributes, rarity, etc. Obtaining these multi-dimensional features of the digital asset to be evaluated forms the feature vector of the asset. For example, for a game item "Blazing Sword", its feature vector can be ["Blazing Sword", "Weapon", "Attack +10", "Rare"]. Using the existing digital asset feature vectors, a training data set can be constructed. For example, collecting a large amount of art NFT data, including their feature vectors and corresponding market prices, can form a training data set. Then, a deep neural network model is built through the TensorFlow framework to train the training data set, and a deep representation of the digital asset features is obtained for training this data set. The deep neural network can learn the complex relationship between the feature vector and the price to obtain a deep representation of the digital asset features. This is equivalent to mapping the original feature vector to a new space, where similar digital assets will be closer. For example, after being processed by the deep neural network, the original feature vector of "Blazing Sword" may become a high-dimensional vector, such as [2, 8, 1, 9,...], which can more effectively express the features of "Blazing Sword". Using historical data, a security attribute discrimination model of digital assets is constructed through a logistic regression model. Inputting the deep representation of the digital asset features into the security attribute discrimination model can infer the confidentiality, integrity, and availability levels of the digital asset. The security attribute discrimination model can judge the risk level of being stolen, tampered with, or inaccessible according to the deep features of the digital asset. For example, if the image hash value of an art NFT is easily tampered with, its integrity level will be lower. If a game item is easily copied, its confidentiality level will also be lower. Suppose the deep features of "Blazing Sword" are input into the model, and the model infers that its confidentiality level is "high", integrity level is "medium", and availability level is "high". Next, the analytic hierarchy process is used to construct a pairwise comparison matrix, calculate the eigenvector corresponding to the largest eigenvalue of the pairwise comparison matrix, and perform normalization processing to obtain the weights of each evaluation factor. For example, for game items, more attention may be paid to their availability and rarity, so the weights of availability and rarity will be higher. Suppose after calculation by the analytic hierarchy process, the weights of confidentiality, integrity, and availability are 2, 3, and 5 respectively. The security attribute information of the digital asset is weighted and summed with the evaluation factor weights to obtain the risk scores of the digital asset under each evaluation dimension. The confidentiality, integrity, and availability levels of "Blazing Sword" are "high", "medium", and "high" respectively, and the corresponding scores can be set to 1, 5, and 1. Then the risk score of "Blazing Sword" = 2*1 + 3*5 + 5*1 = 85.Regularly (e.g., daily) dynamically track digital assets through the system's scheduled task module. If the risk score of "Blazing Sword" exceeds the preset threshold (e.g., 9), trigger the digital asset security level adjustment process and update the digital asset security rating database. For example, the system may downgrade the integrity level of "Blazing Sword" to "low". The digital asset security rating database records the real-time security rating information of digital assets. These information can be read from the database to automatically generate a digital asset security assessment report. For example, the assessment report of "Blazing Sword" will include its basic information (name, type, etc.), security attribute level (high, medium, low), risk score (85), and security rating change record (e.g., the integrity level is downgraded from "medium" to "low"). Such a report can help users understand the security status of digital assets and make corresponding decisions.

[0048] Step S108, based on the digital asset security rating and verification result, construct a blockchain verification proof mechanism. By generating a hash value containing the verification process, result, and timestamp, form a hash proof chain for digital asset verification to ensure the anti-tampering and traceability of the verification result.

[0049] Obtain the security rating result and verification result of the digital asset, and use them as the original data for the verification proof; for the original verification data, add a description of the verification process and the current timestamp to generate complete verification proof data; perform SHA256 hashing on the verification proof data to obtain a unique hash value as the fingerprint of this verification; splice the current verification hash value with the previous verification hash value to construct a hash proof chain and form a continuous verification sequence; write the verification hash value and the hash proof chain into the blockchain through an Ethereum smart contract; define verification rules and reward and punishment mechanisms through the smart contract to encourage multiple nodes to participate in the verification and reach a consensus on the verification result; use a blockchain browser to query and trace the verification data to obtain the historical verification information of the digital asset; verify the authenticity of the current verification data by comparing the historical verification hash values; synchronize the verification data in the blockchain to the local database, and use SQL to perform statistical analysis on the verification data, calculate the security indicators of the digital asset, evaluate the historical security status of the digital asset, and generate a security analysis report.

[0050] Specifically, the digital asset security rating results, such as the confidentiality of "Blazing Sword" being "high", integrity being "medium", availability being "high", risk score being 85, and the verification results, such as "verification passed", will serve as the original data for the verification proof. Add a description of the verification process, such as "Conducted a multi-factor risk assessment on the eigenvector of 'Blazing Sword'", and the current timestamp, such as "October 17, 2024 10:00:00", to generate the complete verification proof data. Perform a SHA256 hash operation on the complete verification proof data to obtain a unique hash value, such as "e4b58eb4642304a2270307021b3f42f3", as the fingerprint of this verification. This hash value can be used to uniquely identify this verification. Concatenate the current verification hash value "e4b58eb4642304a2270307021b3f42f3" with the previous verification hash value, such as "a1b2c3d4e5f678901234567890abcdef", for example, "e4b58eb4642304a2270307021b3f42f3|a1b2c3d4e5f678901234567890abcdef", to construct a hash proof chain and form a continuous verification sequence. The hash proof chain can trace the historical records of the verification. Write the verification hash value "e4b58eb4642304a2270307021b3f42f3" and the hash proof chain "e4b58eb4642304a2270307021b3f42f3|a1b2c3d4e5f678901234567890abcdef" into the blockchain through an Ethereum smart contract to ensure the immutability and traceability of the data. The smart contract defines verification rules, such as the format of the verification data, verification frequency, etc., and reward and punishment mechanisms, such as rewarding nodes with correct verification and punishing nodes with incorrect verification. These mechanisms can encourage multiple nodes to participate in the verification and reach a consensus on the verification results. For example, it is stipulated that each node participating in the verification needs to pledge a certain amount of Ether. If the verification result is correct, the node can receive a reward; if the verification result is incorrect, the pledged Ether will be deducted. Use a blockchain browser to query and trace the verification data, such as querying the verification records of "Blazing Sword" in the past month, to obtain the historical verification information of the digital asset. This information includes the timestamp, verification hash value, security rating results, etc. for each verification. By comparing the historical verification hash values, such as comparing the current hash value "e4b58eb4642304a2270307021b3f42f3" with its previous hash value stored in the blockchain, the authenticity of the current verification data can be verified. If the hash values do not match, it indicates that the data may have been tampered with. Synchronize the verification data in the blockchain, such as the historical verification records of "Blazing Sword", to the local database.Use SQL to perform statistical analysis on the verification data, such as calculating the average risk score of "Blazing Sword" in the past month, the trend of security rating changes, etc., to evaluate the historical security status of digital assets. Generate a security analysis report based on the results of the statistical analysis. For example, the security analysis report of "Blazing Sword" can include the trend chart of its security rating changes in the past month, the average risk score, and possible security risks, etc. These information can help users understand the historical security status of digital assets and make corresponding decisions. For example, if the risk score of "Blazing Sword" continues to rise, the user may need to take measures to improve its security.

[0051] The above are only embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A digital asset security verification and information monitoring method, characterized in that: The method comprises: According to the type attributes of digital assets, a multi-dimensional classification model is used to classify digital assets, obtain the classification labels and feature vector representations of digital assets, and establish a digital asset feature database; Design an adaptive verification algorithm for the classified digital asset feature database, dynamically select matching verification rules and threshold parameters to achieve comprehensive verification of the legitimacy and ownership of digital assets, and generate digital asset verification results; Based on the digital asset verification results and the correlation between digital assets, a digital asset correlation graph is constructed, and the graph neural network algorithm is used to jointly model the related digital assets. Through message transmission and feature aggregation, the verification weight of the digital asset node is calculated; Obtain the transaction history data of digital assets from the digital asset association graph, use time series pattern mining technology to discover the association rules and abnormal patterns of digital asset transactions, and form a transaction verification rule base; Apply the transaction verification rule base to the digital asset verification process, combine the encryption algorithm and consensus mechanism of digital assets, design a multi-algorithm adaptation layer, and achieve compatible processing of different types of digital assets through algorithm registration and dynamic scheduling; According to the data structure of the algorithm adaptation layer, intelligent data analysis and conversion technology is used to map heterogeneous digital asset data into a unified feature space, generate standardized digital asset data, and form a digital asset feature database; Utilize the digital asset feature database to conduct in-depth verification and analysis, obtain the security attribute information of digital assets, adopt a multi-factor risk assessment model to dynamically assess the security level of digital assets, and generate digital asset security rating results; Based on the digital asset security rating and verification results, a blockchain verification and proof mechanism is constructed. By generating a hash value containing the verification process, results and timestamp, a hash proof chain for digital asset verification is formed to ensure the tamper-proof and traceability of the verification results.

2. The method according to claim 1, characterized in that The digital assets are classified using a multi-dimensional classification model according to the type attributes of the digital assets, the classification labels and feature vector representations of the digital assets are obtained, and a digital asset feature database is established, including: Obtain multi-dimensional attribute information of digital assets, cluster digital assets using K-means clustering algorithm, and obtain coarse-grained classification labels of digital assets; For each coarse-grained classification, extract the common features of this type of digital assets and build a TF-IDF feature vector representation model for this type of digital assets; The support vector machine (SVM) classification algorithm is used to classify the feature vectors of digital assets to obtain fine-grained classification labels of digital assets; The classification labels and corresponding feature vectors of digital assets are stored in the MongoDB database, and a mapping relationship between digital asset labels and features is established; When receiving a digital asset search request from a user, obtaining the digital asset classification label input by the user, and obtaining the digital asset feature vector corresponding to the digital asset classification label from the MongoDB database; Calculate the cosine similarity between the user's portrait feature vector and the digital asset feature vector, and recommend the top-N digital assets with the highest similarity to the user; When there are new digital assets, extract the multi-dimensional attribute information of the new digital assets, obtain their feature vectors through the TF-IDF feature vector representation model, use the SVM classification model algorithm to determine their categories, and insert the new digital asset classification labels and feature vector information into the MongoDB database; Regularly maintain and update the digital asset feature data in the MongoDB database, and update its classification labels and feature vector representations based on the dynamic changes of digital assets. For digital assets that have not changed for a long time, reduce their update frequency, and for digital assets that change frequently, increase their update frequency.

3. The method according to claim 2, characterized in that The adaptive verification algorithm is designed for the classified digital asset feature database, and the digital asset legitimacy and ownership are comprehensively verified by dynamically selecting matching verification rules and threshold parameters, and the digital asset verification results are generated, including: Obtaining a digital asset feature database, which contains feature information of various digital assets; According to the coarse-grained classification labels of the classification information of the digital assets, a preset candidate verification rule set and a threshold parameter range are obtained; A decision tree algorithm is used to select the most suitable verification rule subset for the digital asset to be verified from the candidate verification rule set. The feature selection of the decision tree is based on the feature information of the digital asset. By calculating the information gain ratio of each feature, the feature with the largest information gain ratio is selected as the split node, and the decision tree is recursively constructed until the preset stop condition is met; The random forest algorithm is used to dynamically adjust the threshold parameters of the validation rule subset. The random forest constructs multiple decision trees by randomly selecting sample subsets and feature subsets, and then integrates the prediction results of multiple decision trees to obtain the adjustment value of the threshold parameter. Adopt the adjusted threshold parameters to determine whether the digital asset to be verified meets the preset ownership verification rules. If so, the legitimacy of the digital asset to be verified is determined and verified; The comprehensive verification score of the digital asset to be verified is calculated using a weighted average method, and the weight coefficient of the weighted average is preset according to the importance of ownership verification and legality verification; Compare the comprehensive verification score with the preset threshold to obtain the final verification result of the digital asset to be verified; The final verification results, the characteristics of the digital assets to be verified, the ownership verification results and the legality verification results are written into the digital asset verification result database.

4. The method according to claim 1, characterized in that: Based on the digital asset verification results and the correlation between digital assets, a digital asset correlation graph is constructed, and a graph neural network algorithm is used to jointly model the associated digital assets. Through message transmission and feature aggregation, the verification weight of the digital asset node is calculated, including: Obtaining the digital asset verification results and the correlation between digital assets, and constructing a digital asset correlation graph, wherein nodes in the digital asset correlation graph represent digital assets, and edges represent correlation relationships between digital assets; Preprocess the digital asset association graph, fill in missing values, filter outliers, and extract the attribute features and structural features of nodes; The graph convolutional neural network algorithm is used to model the preprocessed digital asset association graph to obtain a graph convolutional neural network model; In each layer of the graph convolutional neural network model, the feature representation of the nodes is updated through the message passing mechanism according to the adjacency relationship between the nodes; In the last layer of the graph convolutional neural network model, the feature representation of the digital asset node is obtained; The relevance weights between nodes are calculated through the attention mechanism, which includes converting the feature representation of the node into attention weights and aggregating the features of adjacent nodes by weighted summation; The feature representation and attention weight of the digital asset node are concatenated as the input of the fully connected layer, and the verification weight of each digital asset node is obtained through the fully connected layer; The importance of digital asset nodes is judged according to the verification weight, and digital asset nodes above the threshold are marked as important nodes, and digital asset nodes below the threshold are marked as suspicious nodes; Focus on verifying important nodes and suspicious nodes, and add the verification results as new node attributes to the digital asset association map; The feature representation and verification weight of the digital asset node are dynamically updated according to the verification results, and the graph convolutional neural network model is retrained to obtain continuously optimized digital asset verification results.

5. The method according to claim 1, characterized in that The digital asset transaction history data is obtained from the digital asset association graph, and the association rules and abnormal patterns of digital asset transactions are discovered using time series pattern mining technology to form a transaction verification rule base, including: Obtain the digital asset association map, and obtain the target digital asset node and its attribute information from the digital asset association map; According to the preset association relationship, obtain the associated digital assets associated with the target digital assets; Obtain the transaction history data of the target digital asset and the associated digital assets, including transaction time, transaction amount and transaction counterparty; Preprocess the transaction history data and fill in the missing values ​​using the preset data filling rules; Align the transaction time of the target digital asset and the associated digital asset to generate a standardized time-series transaction data set; Apriori algorithm is used to mine frequent trading patterns from time series trading data sets; Compare the frequent transaction pattern with the preset normal transaction pattern rules to determine whether the frequent transaction pattern is an abnormal transaction pattern; If the frequent trading pattern does not conform to the normal trading pattern rules, the frequent trading pattern is marked as an abnormal trading pattern; Manually review abnormal transaction patterns and add manually confirmed abnormal transaction patterns to the pre-built abnormal transaction pattern library; Acquire new digital asset transaction data, and acquire digital asset information associated with the new digital asset transaction data based on the digital asset association graph; Match new digital asset transaction data with abnormal transaction patterns in the abnormal transaction pattern library; If the new digital asset transaction data matches the abnormal transaction pattern in the abnormal transaction pattern library, an abnormal transaction alarm message is generated.

6. The method according to claim 1, characterized in that The transaction verification rule base is applied to the digital asset verification process, combined with the encryption algorithm and consensus mechanism of digital assets, and a multi-algorithm adaptation layer is designed to achieve compatible processing of different types of digital assets through algorithm registration and dynamic scheduling, including: According to the digital asset type, obtain the corresponding transaction verification rules from the digital asset registration center; Use smart contract technology to convert transaction verification rules into executable code logic and deploy it to the blockchain network; Determine the consensus mechanism adopted by the blockchain network to which the digital asset belongs; When receiving a digital asset transaction request, the smart contract is called to verify the digital asset transaction request. The verification includes checking the legitimacy of the format, signature and attributes of the digital asset transaction request. If the digital asset transaction request is verified, the digital asset transaction request will be broadcast to the consensus node in the blockchain network; Consensus nodes verify and confirm digital asset transaction requests based on the consensus mechanism; Cryptography technology is used to protect the transaction verification process, verifying the legitimacy of digital asset transaction requests without disclosing the identity information of both parties to the transaction; For cross-chain digital asset transactions, a cross-chain protocol is introduced to achieve atomic exchange of digital assets in different blockchain networks through transaction proof mechanism and digital asset mapping mechanism; Blockchain nodes use P2P networks to propagate, synchronize, and verify blocks and transactions, ensuring the decentralization and fault tolerance of the network. According to the business rules and processes of digital assets, smart contract technology is used to solidify the business rules and processes into the code to achieve automated management of the entire life cycle of digital assets; After security audit and testing, smart contracts are deployed to the blockchain network and jointly executed and verified by multiple nodes.

7. The method according to claim 1, characterized in that According to the data structure of the algorithm adaptation layer, intelligent data parsing and conversion technology is used to map heterogeneous digital asset data to a unified feature space, generate standardized digital asset data, and form a digital asset feature database, including: Obtaining the original data of heterogeneous digital assets, including the attributes, parameters and configuration information of digital assets; Use XML parser to parse the original data, extract the key attribute fields of digital assets, and construct the digital asset attribute feature vector; According to the pre-defined unified feature space, the digital asset attribute feature vector is normalized, and the attribute values ​​of different scales and types are mapped to the interval [0, 1] to obtain the standardized digital asset attribute feature vector; One-Hot encoding data conversion method is used to convert digital asset attributes that cannot be directly mapped to a unified feature space into a feature representation of the unified feature space; Fill the standardized digital asset attribute feature vector into the corresponding dimension of the unified feature space to obtain the feature vector representation of the digital asset in the unified feature space; Generate standardized digital asset data records based on the feature vectors of digital assets in a unified feature space and in accordance with predefined data formats and field names; Import standardized digital asset data records into the database and create a digital asset feature data table.

8. The method according to claim 1, characterized in that The digital asset feature database is used to conduct in-depth verification and analysis, obtain security attribute information of digital assets, adopt a multi-factor risk assessment model, dynamically assess the security level of digital assets, and generate digital asset security rating results, including: Acquire digital asset feature information in a digital asset feature database to obtain a multi-dimensional feature vector of the digital asset to be evaluated; The digital asset feature vector is used to construct a training data set, and the training data set is trained through a deep neural network model to obtain a deep representation of the digital asset features; Inputting the deep representation of digital asset features into a pre-built discriminant model, and obtaining security attribute information of the digital asset through reasoning of the security attribute discriminant model, wherein the security attribute information includes confidentiality, integrity, and availability levels; Based on the acquired digital asset security attribute information, a pairwise comparison matrix is ​​constructed using the hierarchical analysis method, the eigenvector corresponding to the maximum eigenvalue of the pairwise comparison matrix is ​​calculated, and normalized to obtain the weight of each evaluation factor; The security attribute information of the digital asset is weighted and summed with the evaluation factor weights to obtain the risk score of the digital asset in each evaluation dimension; The system's scheduled task module is used to regularly and dynamically track the risk scores of digital assets. When the score exceeds the preset threshold, the digital asset security level adjustment process is triggered and the digital asset security rating database is updated; The real-time security rating results of digital assets are read from the digital asset security rating database, and a digital asset security assessment report is automatically generated. The report includes basic information of digital assets, security attribute level, risk score and security rating result change record.

9. The method according to claim 1, characterized in that: Based on the digital asset security rating results and verification results, a blockchain verification and certification mechanism is constructed to form a hash certification chain for digital asset verification by generating a hash value containing the verification process, results and timestamp, thereby ensuring the tamper-proof and traceability of the verification results, including: Obtain the security rating and verification results of digital assets and use them as the original data for verification proof; For the original verification data, add the verification process description and current timestamp to generate complete verification proof data; Perform SHA256 hash operation on the verification proof data to obtain a unique hash value as the fingerprint of this verification; Concatenate the current verification hash value with the previous verification hash value to build a hash proof chain to form a continuous verification sequence; The verification hash value and hash proof chain are written to the blockchain through the Ethereum smart contract; Defining verification rules and reward and punishment mechanisms through smart contracts encourages multiple nodes to participate in verification and reach consensus on the verification results; Use blockchain browsers to query and trace verification data and obtain historical verification information of digital assets; Verify the authenticity of the current verification data by comparing the historical verification hash values; Synchronize the verification data in the blockchain to the local database, use SQL to perform statistical analysis on the verification data, calculate the security indicators of digital assets, evaluate the historical security status of digital assets, and generate a security analysis report.

10. A digital asset security verification and information monitoring system for use in any one of the methods of claims 1-9, characterized in that: The system comprises: Digital asset classification module, used to classify digital assets in multiple dimensions; Digital asset verification module, used to verify the legitimacy and ownership of digital assets; Digital asset association modeling module, used to build digital asset association graphs and weight calculations; Transaction verification rule mining module, used to discover association rules and anomalies of digital asset transactions; Multi-algorithm adaptation processing module, used to process different types of digital assets compatibly; The digital asset security assessment module is used to assess the security level of digital assets.

Images