A method and system for digital economic conversion of assets

Abstract

The present invention relates to the field of digital economic technology, and in particular to a method and system for digital economic conversion of assets. The method comprises the following steps: establishing a digital asset trading platform; obtaining the initial physical asset data of the user; transmitting the initial physical asset data of the user to the digital asset trading platform for digital mapping of the physical assets to generate digital physical asset data; performing real-time digital asset analysis based on the digital physical asset data to generate real-time digital asset data; performing decentralized transaction monitoring processing on the digital physical asset trading platform based on the real-time digital asset data to generate transaction monitoring data; performing discriminant analysis of abnormal transactions and valid transactions on the transaction monitoring data to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmitting the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations. The present invention realizes more efficient and safe digital asset conversion.

Description

Technical Field

[0001] The present invention relates to the field of digital economic technology, and in particular to a method and system for digital economic conversion of assets. Background Art

[0002] With the rapid development of the digital economy, the digital transformation of physical assets has become an important trend in today's social and economic development. The digital economy is a new economic form that promotes the transformation of social production methods and improves production efficiency based on the innovation and integration of information and communication technology (ICT). By digitizing physical assets, efficient management, transparent transactions and rapid appreciation of assets can be achieved, which has great benefits for enterprises, individuals and the development of the entire social economy. However, the traditional digital economic conversion method of assets has large differences in the conversion standards from physical assets to digital assets, and cannot guarantee the security of digital assets, resulting in poor digital transaction efficiency and serious problems of digital asset leakage, and poor supervision of digital asset management and transaction models. Summary of the invention

[0003] Based on this, the present invention provides a method and system for digital economic conversion of assets to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a method for digital economic conversion of assets includes the following steps:

[0005] Step S1: Design a transaction mechanism smart contract based on preset digital economic transaction rules to establish a transaction mechanism smart contract; establish a digital asset trading platform based on blockchain technology and transaction mechanism smart contracts.

[0006] Step S2: Acquire the user's initial physical asset data; perform user encrypted physical asset coding analysis based on the user's initial physical asset data to generate user encrypted physical asset coding data; transmit the user encrypted physical asset coding data to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data;

[0007] Step S3: Analyze the characteristic index of digital physical asset valuation according to the digital physical asset data to generate the characteristic index of digital physical asset valuation; establish a decision mapping relationship between the digital physical asset data and the digital physical asset valuation based on the random forest algorithm and the characteristic index of digital physical asset valuation to generate a digital physical asset valuation model; perform real-time analysis of the digital physical asset valuation on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data;

[0008] Step S4: Performing real-time digital asset integration processing according to the digital physical asset data and the real-time digital physical asset valuation data to generate real-time digital asset data;

[0009] Step S5: Perform decentralized transaction monitoring and processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data; perform discrimination analysis on the transaction monitoring data for abnormal transactions and valid transactions to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations;

[0010] Step S6: Evaluate and process the potential transaction supply and demand relationship based on the effective transaction monitoring data to generate potential transaction supply and demand relationship evaluation data; perform priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data to generate priority transaction demand matching data, and perform intelligent transaction demand matching operations through the priority transaction demand matching data.

[0011] The present invention uses smart contracts, and the transaction mechanism can automatically execute preset digital economic transaction rules without human intervention, reducing human errors and operating costs. The logic and rules of smart contracts are transparent and open, and all participants can see the execution of the contract, which enhances the transparency and credibility of the transaction. Smart contracts are based on blockchain technology and are tamper-proof, ensuring the authenticity and security of transaction records. The digital asset trading platform established based on blockchain technology removes the control of centralized institutions and enhances the security and stability of the platform. Blockchain technology records transaction data through distributed ledgers, realizes rapid confirmation and settlement of transactions, improves transaction efficiency, and the tamper-proof feature ensures the authenticity of transaction data, increases the credibility of the platform and user trust. Obtain user entity asset data through standardized processes, such as extracting relevant data of user entity assets through ETL technology to ensure the integrity and accuracy of the data. Encode the user's entity assets to generate standardized asset coding data, which is convenient for data management and analysis. Generate a unique asset code through encoding to ensure the unique identification of each entity asset in the digital platform to avoid duplication and confusion. When transmitting the encoded data to the digital asset trading platform, encryption technology is used to protect the security of data transmission, prevent data leakage and tampering, and convert physical asset data into digital physical asset data through digital mapping, realize the mapping and management of physical assets in the digital platform, and facilitate subsequent transactions and evaluations. The characteristic index analysis of digital physical asset data is carried out to extract key valuation indicators, provide a scientific basis for asset evaluation, and improve the accuracy and reliability of valuation. The evaluation model is established based on the random forest algorithm, which can perform efficient feature analysis and model training in a complex data environment, realize intelligent asset evaluation, and analyze the digital physical asset data in real time through the evaluation model to generate real-time valuation data, provide the latest asset value information for transactions, and train the evaluation model through real-time data sets, so that it has self-learning and adjustment capabilities, can update the model according to real-time data, and continuously optimize the evaluation accuracy. The digital physical asset data and real-time valuation data are integrated and processed to generate comprehensive real-time digital asset data, provide all-round asset information, monitor and manage assets in real time, discover and respond to asset changes in a timely manner, and improve the flexibility and response speed of asset management. Conduct decentralized transaction monitoring on digital asset trading platforms, obtain transaction data in real time, ensure the real-time and effectiveness of transactions, analyze transaction behavior through monitoring data, identify abnormal transactions and effective transactions, and improve transaction security and compliance. Provide real-time feedback on abnormal transactions and take quick countermeasures to prevent losses and risks caused by abnormal transactions. Evaluate supply and demand relationships based on effective transaction monitoring data, identify potential transaction opportunities, promote market supply and demand balance, prioritize potential transaction supply and demand relationships, determine transaction priorities, and improve transaction matching efficiency and success rate.By matching data with priority transaction needs, we can perform intelligent transaction demand matching, automatically complete the transaction matching process, reduce manual intervention, and provide accurate transaction matching recommendations based on supply and demand relationships and priority analysis, thereby improving transaction success rates and user satisfaction.

[0012] Preferably, step S1 comprises the following steps:

[0013] Design the transaction mechanism smart contract logic according to the preset digital economic transaction rules, generate the transaction mechanism smart contract logic data, and establish the transaction mechanism smart contract based on the transaction mechanism smart contract logic data;

[0014] Perform blockchain network configuration processing based on blockchain technology, generate blockchain network configuration data, and establish a digital asset trading platform based on the blockchain network configuration data and transaction mechanism smart contracts.

[0015] The present invention performs the logic design of the transaction mechanism smart contract through the preset digital economic transaction rules, and the generated smart contract logic data can ensure that the execution of the contract complies with the predetermined rules, avoiding the uncertainty and risks brought by manual operation. Based on the generated smart contract logic data, a transaction mechanism smart contract is established to realize the automation and transparency of the transaction process, and ensure the reliability and efficiency of the transaction. Through the blockchain technology, the network configuration processing is carried out, and the generated blockchain network configuration data ensures the security and stability of the network, as well as the effectiveness of the digital asset conversion and digital transactions, providing a solid foundation for the digital asset trading platform. According to the blockchain network configuration data and the transaction mechanism smart contract, a digital asset trading platform is established, which realizes decentralized asset transactions, enhances the transparency and security of transactions, and improves the efficiency and credibility of transactions.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Obtaining the user's initial entity asset data;

[0018] Step S22: converting the user's initial entity asset data into effective entity asset data according to the ETL technology to generate user entity asset data;

[0019] Step S23: digitally encode the user entity asset data to generate user entity asset encoding data;

[0020] Step S24: encrypting the user entity asset code data to generate user encrypted entity asset code data;

[0021] Step S25: performing hash fingerprint generation processing according to the user encrypted entity asset encoding data to obtain the user entity asset hash fingerprint data;

[0022] Step S26: Through the user's physical asset hash fingerprint data, the user's encrypted physical asset encoding data is transmitted to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data.

[0023] The present invention obtains accurate initial physical asset data of the user to ensure that the basic data of subsequent data processing is true and complete. The initial physical asset data of the user is effectively converted by using ETL technology to generate user physical asset data, thereby improving the consistency and availability of the data. By digitally encoding the user physical asset data, standardized user physical asset encoding data is generated, which facilitates the subsequent transmission of data to the digital trading platform for digital asset mapping. The user physical asset encoding data is encrypted to generate user encrypted physical asset encoding data to ensure the security of data transmission and storage. A hash fingerprint is generated according to the user encrypted physical asset encoding data to obtain the user physical asset hash fingerprint data, providing data uniqueness verification and integrity verification. Through the user physical asset hash fingerprint data, the user encrypted physical asset encoding data is transmitted to the digital asset trading platform for mapping processing to generate digital physical asset data, thereby realizing the digital conversion and management of physical assets.

[0024] Preferably, step S24 includes the following steps:

[0025] Step S241: extracting user coding features according to user entity asset coding data to generate user coding feature data;

[0026] Step S242: performing user coding feature entropy generation processing on the user coding feature data to obtain user coding feature entropy data;

[0027] Step S243: performing user high entropy random number analysis and processing according to the user coding feature entropy data to obtain the user high entropy random number;

[0028] Step S244: performing high entropy key generation processing based on the user high entropy random number to obtain user high entropy key data;

[0029] Step S245: Perform asymmetric encryption processing on the user's physical asset encoding data according to the user's high entropy key data to generate user encrypted physical asset encoding data.

[0030] The present invention extracts characteristic information from user entity asset coding data, generates user coding characteristic data, provides basic data for subsequent characteristic analysis and encryption processing, and ensures the accuracy and comprehensiveness of data processing. The characteristic entropy information generation processing is performed through the user coding characteristic data to obtain the user coding characteristic entropy data, measure the complexity and randomness of the user coding information, and provide a scientific basis for generating high entropy random numbers. The high entropy random number analysis processing is performed through the user coding characteristic entropy data to obtain the user high entropy random number, which has higher unpredictability and randomness, and increases the security and strength of data encryption. The high entropy key generation processing is performed according to the user high entropy random number to obtain the user high entropy key data, which has higher security and complexity, and provides strong security protection for the asymmetric encryption of the user entity asset coding data. The user entity asset coding data is asymmetric encrypted according to the user high entropy key data to generate the user encrypted entity asset coding data. Asymmetric encryption ensures the confidentiality and integrity of the data during transmission and storage, prevents unauthorized access and tampering, and improves the security of the overall system.

[0031] Preferably, step S3 comprises the following steps:

[0032] Step S31: performing classification processing on the digitized physical asset data according to the preset physical asset types to generate classified digitized physical asset data;

[0033] Step S32: performing a digital physical asset valuation characteristic index analysis based on the classified digital physical asset data to generate a digital physical asset valuation characteristic index;

[0034] Step S33: establishing a decision mapping node between digital physical asset data and digital physical asset valuation based on the digital physical asset valuation characteristic index, and integrating the decision mapping nodes through a random forest algorithm to generate an initial digital physical asset valuation model;

[0035] Step S34: obtaining a digital physical asset valuation training set;

[0036] Step S35: transmitting the digital physical asset valuation training set to the initial digital physical asset valuation model for model training to generate a digital physical asset valuation model;

[0037] Step S36: using the digital physical asset evaluation model to perform real-time prediction of the digital physical asset valuation on the digital physical asset data to generate real-time digital physical asset valuation data;

[0038] Step S37: The digital physical asset valuation model is trained and updated in real time using real-time digital physical asset valuation data to obtain an updated digital physical asset valuation model, and the updated digital physical asset valuation model is fed back to step S35 to perform iterative updates of the digital physical asset valuation model.

[0039] The present invention classifies and processes the digital physical asset data according to the preset physical asset type, generates classified digital physical asset data, ensures the structuring and standardization of different types of asset data, helps the subsequent feature analysis and valuation processing, and improves the efficiency of data management and analysis. According to the classified digital physical asset data, feature index analysis is performed to generate digital physical asset valuation feature indexes, which can reflect the value characteristics of different assets, provide a scientific basis for the subsequent valuation model, and ensure the accuracy and reliability of the valuation process. Based on the digital physical asset valuation feature index, a decision mapping node between the digital physical asset data and the valuation is established, and these nodes are integrated through the random forest algorithm to generate an initial digital physical asset valuation model, which can effectively capture the asset value characteristics and realize accurate valuation in a complex data environment. By obtaining a variety of valuation training set data, the extensiveness and representativeness of the training data are ensured, and a rich learning sample is provided for the evaluation model, which improves the generalization ability and prediction accuracy of the model. The training set data is transmitted to the initial digital physical asset valuation model for training to generate a digital physical asset valuation model. The training process extracts the key features and patterns of asset valuation through model learning, and improves the accuracy and practicality of the model. Through the trained digital physical asset evaluation model, the digital physical asset data is predicted in real time to generate real-time digital physical asset valuation data. The real-time prediction can timely reflect market changes and provide the latest value information for asset management and transaction decisions. The evaluation model is trained and updated in real time through real-time digital physical asset valuation data to obtain an updated digital physical asset evaluation model. The iterative update of the model can continuously optimize the evaluation accuracy and ensure that the valuation model always maintains sensitivity and adaptability to market dynamics.

[0040] Preferably, step S5 comprises the following steps:

[0041] Step S51: Perform decentralized transaction monitoring processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data;

[0042] Step S52: Perform transaction behavior feature modeling based on the transaction monitoring data to generate a transaction behavior feature model;

[0043] Step S53: performing discriminant analysis of abnormal transactions and valid transactions on the transaction monitoring data according to the transaction behavior characteristic model, and obtaining abnormal transaction monitoring data and valid transaction monitoring data respectively;

[0044] Step S54: Transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations.

[0045] The present invention performs decentralized transaction monitoring and processing on the digital physical asset trading platform according to real-time digital asset data, generates transaction monitoring data, ensures the real-time and accuracy of transaction data, avoids single-point transaction monitoring omissions through decentralized monitoring, and improves the reliability and security of the system. Based on the transaction monitoring data, the transaction behavior feature modeling is performed to generate a transaction behavior feature model, which can effectively capture the laws and characteristics of transaction behavior, provide a comprehensive understanding and analysis basis for transaction behavior, and help identify abnormal behavior and assess transaction risks. According to the transaction behavior feature model, the transaction monitoring data is discriminated and analyzed, abnormal transactions are identified and marked, and abnormal transaction monitoring data is generated, which can timely discover potential transaction risks, prevent malicious transactions and fraudulent behaviors, and ensure the security of platform transactions. At the same time, through discriminant analysis, valid transactions are identified and valid transaction monitoring data is generated. Ensure the smooth progress of normal transactions, improve the credibility and efficiency of transactions, enhance users' trust in the platform, transmit abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations, and through timely feedback and processing of abnormal transactions, measures can be quickly taken to prevent the spread and influence of abnormal behaviors, protect user asset security, and improve the overall security protection capabilities of the platform.

[0046] Preferably, step S52 includes the following steps:

[0047] Step S521: extracting transaction behaviors from the transaction monitoring data to generate transaction behavior monitoring data, and performing cluster analysis on the transaction behavior monitoring data to generate clustered transaction behavior monitoring data;

[0048] Step S522: Analyze the transaction pattern based on the clustered transaction behavior monitoring data to generate transaction pattern data;

[0049] Step S523: Perform transaction behavior feature modeling processing according to the transaction pattern data to generate a transaction behavior feature model.

[0050] The present invention extracts transaction behaviors from transaction monitoring data to generate transaction behavior monitoring data, thereby ensuring the comprehensive capture of transaction behaviors, providing a rich data basis for subsequent analysis, and performing cluster analysis on transaction behavior monitoring data to generate clustered transaction behavior monitoring data. Cluster analysis can classify similar transaction behaviors, reveal the internal patterns and structures of transaction behaviors, help identify the regularity and abnormality of transactions, and improve the accuracy and depth of transaction behavior analysis. Transaction pattern analysis is performed based on clustered transaction behavior monitoring data to generate transaction pattern data. Through in-depth analysis of transaction patterns, different types of transaction patterns are identified, providing a clear pattern basis for transaction behavior feature modeling, helping to understand the diversity and complexity of transaction behaviors, and providing an effective reference for the identification of abnormal transactions. Transaction behavior feature modeling is performed based on transaction pattern data to generate a transaction behavior feature model, which can accurately characterize the characteristics of transaction behaviors, provide a comprehensive understanding and prediction capability of transaction behaviors, help identify abnormal transaction behaviors and assess transaction risks, and improve the security and stability of the platform.

[0051] Preferably, step S6 comprises the following steps:

[0052] Step S61: Analyze and process the weighted correlation characteristics of transaction flows based on the effective transaction monitoring data to generate weighted transaction flow correlation data;

[0053] Step S62: Analyze transaction supply and demand characteristics based on the effective transaction monitoring data to generate transaction supply and demand characteristic data;

[0054] Step S63: performing potential transaction supply and demand relationship evaluation processing based on the weighted transaction flow association data and the transaction supply and demand characteristic data to generate potential transaction supply and demand relationship evaluation data;

[0055] Step S64: Performing priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data, generating priority transaction demand matching data, and executing intelligent transaction demand matching operations through the priority transaction demand matching data.

[0056] The present invention performs weighted correlation feature analysis and processing of transaction flow based on effective transaction monitoring data, generates weighted transaction flow correlation data, accurately measures transaction liquidity and correlation through weighted correlation feature analysis, and weights the correlation with transaction flow, reveals the correlation and influence between transactions, and provides important reference for supply and demand analysis. Transaction supply and demand feature analysis is performed based on effective transaction monitoring data to generate transaction supply and demand feature data, and through in-depth analysis of transaction supply and demand features, the supply and demand relationship and change trend of the market are identified, accurate supply and demand information is provided, and a data foundation is laid for the evaluation of potential transaction relationships. Potential transaction supply and demand relationship evaluation is performed based on weighted transaction flow correlation data and transaction supply and demand feature data to generate potential transaction supply and demand relationship evaluation data. The evaluation of potential transaction supply and demand relationships can predict potential transaction opportunities and demands in the market, help platforms to plan and optimize transaction strategies in advance, and improve market response speed and transaction success rate. Priority transaction demand matching information analysis is performed on potential transaction supply and demand relationship evaluation data to generate priority transaction demand matching data. Through priority analysis, the most potential transaction demand is determined, the efficiency and success rate of transaction matching are improved, and the allocation of transaction resources is optimized. By performing intelligent transaction demand matching operations based on priority transaction demand matching data, accurate transaction demand matching can be achieved, human intervention and errors can be reduced, and the automation and intelligence level of the platform can be improved.

[0057] Preferably, step S61 includes the following steps:

[0058] Step S611: Designing a decentralized transaction undirected graph through effective transaction monitoring data to establish a decentralized transaction undirected graph;

[0059] Step S612: Perform transaction liquidity analysis based on the effective transaction monitoring data to generate transaction liquidity data;

[0060] Step S613: performing undirected graph edge weight analysis on the decentralized transaction undirected graph according to the transaction liquidity data to generate undirected graph edge weight data;

[0061] Step S614: Analyze transaction flow association data according to the decentralized transaction undirected graph to generate transaction flow association data;

[0062] Step S615: weighting the associated features of the transaction flow associated data according to the undirected graph edge weight data to generate weighted transaction flow associated data.

[0063] The present invention designs a decentralized transaction undirected graph through effective transaction monitoring data to establish a decentralized transaction undirected graph, which can comprehensively describe the relationship between transaction participants and the transaction flow path, provide an intuitive network structure view, and facilitate the analysis and monitoring of transaction activities. According to the effective transaction monitoring data, transaction liquidity analysis can accurately reflect the liquidity level of the transaction, identify nodes and paths with frequent transactions, and provide a basis for optimizing transaction processes and resource allocation. According to the transaction liquidity data, the decentralized transaction undirected graph is subjected to undirected graph edge weight analysis and processing, and the edge weight data can quantify the strength of the transaction relationship, reveal the important nodes and key paths in the transaction network, and help focus on monitoring and management. According to the decentralized transaction undirected graph, transaction flow association data analysis is performed to generate transaction flow association data, and the association data can show the dynamic changes of transaction activities, identify the association mode and flow characteristics in the transaction network, and provide basic data for subsequent feature weighting and risk assessment. According to the undirected graph edge weight data, the transaction flow association data is weighted by the association characteristics of the transaction flow, and the weighting processing can accurately evaluate the importance and influence of the transaction relationship, provide an in-depth understanding of the transaction network structure, and enhance the scientific nature of transaction analysis and decision-making.

[0064] The present invention provides a digital economic conversion system for assets, which is used to execute the digital economic conversion method for assets as described above. The digital economic conversion system for assets includes:

[0065] The digital asset trading platform establishment module is used to design a trading mechanism smart contract based on preset digital economic trading rules to establish a trading mechanism smart contract; a digital asset trading platform is established based on blockchain technology and a trading mechanism smart contract.

[0066] The digital physical asset conversion module is used to obtain the user's initial physical asset data; perform user encrypted physical asset coding analysis based on the user's initial physical asset data to generate user encrypted physical asset coding data; transmit the user encrypted physical asset coding data to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data;

[0067] The digital physical asset real-time valuation module is used to analyze the digital physical asset valuation characteristic indicators based on the digital physical asset data and generate the digital physical asset valuation characteristic indicators; establish the decision mapping relationship between the digital physical asset data and the digital physical asset valuation based on the random forest algorithm and the digital physical asset valuation characteristic indicators to generate a digital physical asset valuation model; perform the digital physical asset valuation real-time analysis on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data;

[0068] A digital asset integration module is used to perform real-time digital asset integration processing based on digital physical asset data and real-time digital physical asset valuation data to generate real-time digital asset data;

[0069] The abnormal transaction feedback module is used to perform decentralized transaction monitoring and processing on the digital physical asset trading platform based on real-time digital asset data to generate transaction monitoring data; perform discrimination analysis on abnormal transactions and valid transactions on the transaction monitoring data to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations;

[0070] The transaction demand intelligent matching module is used to evaluate and process the potential transaction supply and demand relationship based on the effective transaction monitoring data, and generate potential transaction supply and demand relationship evaluation data; perform priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data, generate priority transaction demand matching data, and perform intelligent transaction demand matching operations through the priority transaction demand matching data.

[0071] The beneficial effect of the present application is that a method for digital economic conversion of assets of the present invention defines digital conversion standards by analyzing immediate asset benefits, improves the fairness of conversion of physical assets to digital assets, and improves the security of digital assets by performing asymmetric encryption and hash function digital asset protection methods with designed high entropy keys. Through decentralized trading modes, digital asset management and trading mode execution monitoring are performed to ensure the processing of abnormal digital transactions. Intelligent transaction demand matching operations are designed for liquidity and transaction supply and demand characteristics during the transaction process to better promote the execution of digital transactions. Thereby realizing digital mapping, real-time valuation and intelligent transaction management of physical assets, solving the problems of data conversion, security and transaction management faced in the current asset digitization process, and promoting the development and innovation of the digital economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of the steps of an intelligent medication management method of the present invention;

[0073] Figure 2 for Figure 1 Detailed implementation steps of step S5;

[0074] Figure 3 for Figure 1 Detailed implementation steps of step S6 in the flowchart;

[0075] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0076] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0078] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0079] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for digital economic conversion of assets, comprising the following steps:

[0080] Step S1: Design a transaction mechanism smart contract based on preset digital economic transaction rules to establish a transaction mechanism smart contract; establish a digital asset trading platform based on blockchain technology and transaction mechanism smart contracts.

[0081] Step S2: Acquire the user's initial physical asset data; perform user encrypted physical asset coding analysis based on the user's initial physical asset data to generate user encrypted physical asset coding data; transmit the user encrypted physical asset coding data to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data;

[0082] Step S3: Analyze the characteristic index of digital physical asset valuation according to the digital physical asset data to generate the characteristic index of digital physical asset valuation; establish a decision mapping relationship between the digital physical asset data and the digital physical asset valuation based on the random forest algorithm and the characteristic index of digital physical asset valuation to generate a digital physical asset valuation model; perform real-time analysis of the digital physical asset valuation on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data;

[0083] Step S4: Performing real-time digital asset integration processing according to the digital physical asset data and the real-time digital physical asset valuation data to generate real-time digital asset data;

[0084] Step S5: Perform decentralized transaction monitoring and processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data; perform discrimination analysis on the transaction monitoring data for abnormal transactions and valid transactions to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations;

[0085] Step S6: Evaluate and process the potential transaction supply and demand relationship based on the effective transaction monitoring data to generate potential transaction supply and demand relationship evaluation data; perform priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data to generate priority transaction demand matching data, and perform intelligent transaction demand matching operations through the priority transaction demand matching data.

[0086] The present invention uses smart contracts, and the transaction mechanism can automatically execute preset digital economic transaction rules without human intervention, reducing human errors and operating costs. The logic and rules of smart contracts are transparent and open, and all participants can see the execution of the contract, which enhances the transparency and credibility of the transaction. Smart contracts are based on blockchain technology and are tamper-proof, ensuring the authenticity and security of transaction records. The digital asset trading platform established based on blockchain technology removes the control of centralized institutions and enhances the security and stability of the platform. Blockchain technology records transaction data through distributed ledgers, realizes rapid confirmation and settlement of transactions, improves transaction efficiency, and the tamper-proof feature ensures the authenticity of transaction data, increases the credibility of the platform and user trust. Obtain user entity asset data through standardized processes, such as extracting relevant data of user entity assets through ETL technology to ensure the integrity and accuracy of the data. Encode the user's entity assets to generate standardized asset coding data, which is convenient for data management and analysis. Generate a unique asset code through encoding to ensure the unique identification of each entity asset in the digital platform to avoid duplication and confusion. When transmitting the encoded data to the digital asset trading platform, encryption technology is used to protect the security of data transmission, prevent data leakage and tampering, and convert physical asset data into digital physical asset data through digital mapping, realize the mapping and management of physical assets in the digital platform, and facilitate subsequent transactions and evaluations. The characteristic index analysis of digital physical asset data is carried out to extract key valuation indicators, provide a scientific basis for asset evaluation, and improve the accuracy and reliability of valuation. The evaluation model is established based on the random forest algorithm, which can perform efficient feature analysis and model training in a complex data environment, realize intelligent asset evaluation, and analyze the digital physical asset data in real time through the evaluation model to generate real-time valuation data, provide the latest asset value information for transactions, and train the evaluation model through real-time data sets, so that it has self-learning and adjustment capabilities, can update the model according to real-time data, and continuously optimize the evaluation accuracy. The digital physical asset data and real-time valuation data are integrated and processed to generate comprehensive real-time digital asset data, provide all-round asset information, monitor and manage assets in real time, discover and respond to asset changes in a timely manner, and improve the flexibility and response speed of asset management. Conduct decentralized transaction monitoring on digital asset trading platforms, obtain transaction data in real time, ensure the real-time and effectiveness of transactions, analyze transaction behavior through monitoring data, identify abnormal transactions and effective transactions, and improve transaction security and compliance. Provide real-time feedback on abnormal transactions and take quick countermeasures to prevent losses and risks caused by abnormal transactions. Evaluate supply and demand relationships based on effective transaction monitoring data, identify potential transaction opportunities, promote market supply and demand balance, prioritize potential transaction supply and demand relationships, determine transaction priorities, and improve transaction matching efficiency and success rate.By matching data with priority transaction needs, we can perform intelligent transaction demand matching, automatically complete the transaction matching process, reduce manual intervention, and provide accurate transaction matching recommendations based on supply and demand relationships and priority analysis, thereby improving transaction success rates and user satisfaction.

[0087] As an embodiment of the present invention, refer to Figure 1 The above is a schematic flow chart of the steps of a digital economic conversion method of an asset of the present invention. In this embodiment, the digital economic conversion method of the asset includes the following steps:

[0088] Step S1: Design a transaction mechanism smart contract based on preset digital economic transaction rules to establish a transaction mechanism smart contract; establish a digital asset trading platform based on blockchain technology and transaction mechanism smart contracts.

[0089] In the embodiment of the present invention, the preset digital economic transaction rules are defined and analyzed, including transaction conditions, verification process, transaction consequences, etc., and the smart contract code is written according to these rules using a smart contract programming language (such as Solidity for Ethereum), and the smart contract is tested in a blockchain simulation environment (such as Remix IDE) to verify its logical correctness and performance. Select a suitable blockchain platform, such as Ethereum, EOS, etc., configure blockchain parameters according to requirements, including consensus mechanism, block size, generation speed, etc., deploy the tested smart contract to the blockchain network, ensure that its execution in the real network environment is consistent with the test environment, and initialize the basic settings of the digital asset trading platform, including user permissions, transaction restrictions, interface security, etc. Develop a front-end transaction interface to provide users with a friendly transaction operation experience, integrate smart contracts and blockchain technology in the back-end system, ensure that all transactions can be processed through smart contracts, establish a digital asset trading platform, and perform stress testing and security testing on the entire platform to ensure the stability and security of the platform.

[0090] Step S2: Acquire the user's initial physical asset data; perform user encrypted physical asset coding analysis based on the user's initial physical asset data to generate user encrypted physical asset coding data; transmit the user encrypted physical asset coding data to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data;

[0091] In an embodiment of the present invention, a data collection form is designed to list in detail the required asset information, such as asset type, proof of ownership, asset value, etc., and data is collected through online forms, API interfaces, or by direct integration with user systems to ensure the integrity and accuracy of the data. The collected physical asset data is cleaned and standardized to eliminate invalid data and format errors, and a unique digital code is assigned to each asset, which reflects the type and key attributes of the asset to facilitate subsequent processing and indexing. The encoded asset data is uploaded to the digital asset trading platform, and on the platform, the encoded data of the physical asset is converted into digital physical asset data according to preset mapping rules, including asset valuation, ownership transfer logic, etc., to ensure data security and privacy protection during the conversion process, and encryption technology is used to protect sensitive information, and finally generate digital physical asset data.

[0092] Step S3: Analyze the characteristic index of digital physical asset valuation according to the digital physical asset data to generate the characteristic index of digital physical asset valuation; establish a decision mapping relationship between the digital physical asset data and the digital physical asset valuation based on the random forest algorithm and the characteristic index of digital physical asset valuation to generate a digital physical asset valuation model; perform real-time analysis of the digital physical asset valuation on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data;

[0093] In the embodiment of the present invention, data analysis tools (such as Python's Pandas and Scikit-learn libraries) are used to conduct in-depth analysis of digital physical asset data, and key factors affecting asset value are selected as feature indicators, such as geographic location, asset size, asset type, historical transaction data, etc. Through statistical analysis and correlation analysis, the degree of influence of each feature on asset valuation is determined, and the most influential feature indicators are screened out. The random forest algorithm is applied, and the selected feature indicators are used as input variables to establish a valuation model. Random forest is an integrated learning method that improves the accuracy and stability of prediction by constructing multiple decision trees, trains the model using historical transaction data sets, adjusts model parameters such as the number of trees, the depth of the tree, etc., to optimize model performance, and performs cross-validation to test the predictive ability of the model to ensure that the model also has good performance on unseen data. The real-time acquired digital physical asset data is input into the trained evaluation model, and the model outputs the valuation results of each asset after processing, generates real-time digital physical asset valuation data, implements a feedback mechanism, feeds back real-time market data to the model, and updates the model in real time to adapt to the dynamic changes of the market.

[0094] Step S4: Performing real-time digital asset integration processing according to the digital physical asset data and the real-time digital physical asset valuation data to generate real-time digital asset data;

[0095] In an embodiment of the present invention, data integration technology, such as a data warehouse or a data lake, is used to perform real-time digital asset integration processing on digital physical asset data and real-time digital physical asset valuation data, such as performing data integration mapping according to the data sequences corresponding to the digital physical asset data and the real-time digital physical asset valuation data to ensure data correspondence between the digital physical asset data and the real-time digital physical asset valuation data, and implementing real-time data stream technology, such as Apache Kafka or AWS Kinesis, to ensure instant updating of asset data and valuation data, and ultimately generate real-time digital asset data, which is digital assets and corresponding digital values.

[0096] Step S5: Perform decentralized transaction monitoring and processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data; perform discrimination analysis on the transaction monitoring data for abnormal transactions and valid transactions to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations;

[0097] In the embodiment of the present invention, the distributed ledger technology of the real-time transaction monitoring engine is used to continuously monitor all transaction activities to ensure the integrity and real-time nature of all transaction data, including but not limited to transaction time, transaction parties, transaction amount, asset type, etc. Transaction monitoring data is analyzed using pre-defined abnormal transaction patterns (such as abnormal transaction frequency, abnormal amount, etc.) and machine learning algorithms (such as anomaly detection algorithms) to identify possible abnormal transactions, and at the same time confirm valid transactions to ensure that these transactions comply with platform rules. For transactions identified as abnormal, the system automatically generates an alarm and transmits the abnormal transaction monitoring data to the relevant terminal (such as the console of the security team). The security team conducts further analysis and processing based on the received data, such as setting user black and white names and other processing methods, blocking or revoking transactions when necessary, and notifying the relevant users.

[0098] Step S6: Evaluate and process the potential transaction supply and demand relationship based on the effective transaction monitoring data to generate potential transaction supply and demand relationship evaluation data; perform priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data to generate priority transaction demand matching data, and perform intelligent transaction demand matching operations through the priority transaction demand matching data.

[0099] In the embodiment of the present invention, effective transaction monitoring data is analyzed, and supply and demand trends and potential demands in the market are identified using data analysis and statistical methods (such as cluster analysis), and potential transaction supply and demand relationship evaluation data are generated, which reveal the supply and demand dynamics of different asset categories and market segments. Based on the potential transaction supply and demand relationship evaluation data, a priority sorting algorithm (such as rule-based sorting or machine learning sorting model) is applied to prioritize transaction demands, analyze the transaction demand and supply situation in the market, determine the priority of transaction matching, so as to maximize transaction efficiency and satisfaction, and perform intelligent transaction demand matching operations through priority transaction demand matching data, including: transmitting the priority transaction demand matching data to the transaction rule smart contract of step S1 to execute the transaction rule smart contract update, and the transaction rule smart contract automatically executes matching transactions to ensure that the needs of both parties to the transaction are best met, and realize intelligent matching mechanism, which not only improves the transaction success rate, but also reduces manual intervention, and improves the efficiency and transparency of the transaction process.

[0100] Preferably, step S1 comprises the following steps:

[0101] Design the transaction mechanism smart contract logic according to the preset digital economic transaction rules, generate the transaction mechanism smart contract logic data, and establish the transaction mechanism smart contract based on the transaction mechanism smart contract logic data;

[0102] Perform blockchain network configuration processing based on blockchain technology, generate blockchain network configuration data, and establish a digital asset trading platform based on the blockchain network configuration data and transaction mechanism smart contracts.

[0103] The present invention performs the logic design of the transaction mechanism smart contract through the preset digital economic transaction rules, and the generated smart contract logic data can ensure that the execution of the contract complies with the predetermined rules, avoiding the uncertainty and risks brought by manual operation. Based on the generated smart contract logic data, a transaction mechanism smart contract is established to realize the automation and transparency of the transaction process, and ensure the reliability and efficiency of the transaction. Through the blockchain technology, the network configuration processing is carried out, and the generated blockchain network configuration data ensures the security and stability of the network, as well as the effectiveness of the digital asset conversion and digital transactions, providing a solid foundation for the digital asset trading platform. According to the blockchain network configuration data and the transaction mechanism smart contract, a digital asset trading platform is established, which realizes decentralized asset transactions, enhances the transparency and security of transactions, and improves the efficiency and credibility of transactions.

[0104] In the embodiment of the present invention, digital economic transaction rules are predefined, including transaction conditions, asset types, user verification process, transaction restrictions, exception handling mechanisms, etc., and a smart contract programming language (usually Solidity) is used to write code according to the digital economic transaction rules defined above. The code covers all transaction logic, including asset transfer, authority management, transaction verification, etc. In the development environment (such as Ethereum Testnet), the smart contract is tested to ensure that the logic of the code is correct and has no loopholes, and performance testing is performed to evaluate its operating efficiency and stability. The logical data of the smart contract is collected from these tests and verifications. These data include but are not limited to contract execution results, transaction delays, resource usage, etc., which are used to optimize and adjust the smart contract. According to the needs, a suitable blockchain technology platform is selected, such as Ethereum, Hyperledger or other suitable blockchain frameworks, and the key parameters of the blockchain network are configured, such as node configuration, consensus mechanism selection (PoW, PoS, DPoS, etc.), block size, generation time, network topology, etc. All configuration parameters and network setting information are summarized to generate a detailed blockchain network configuration data report. These data are used to guide and record the actual deployment process of the network. Deploy thoroughly tested and optimized smart contracts to the configured blockchain network, ensure that the actual performance of the smart contracts is consistent with the test environment and has no security vulnerabilities, and develop the user interface (UI) and back-end system of the digital asset trading platform. This includes integrating blockchain technology and smart contracts to realize a complete trading platform that supports user registration, asset chain, transaction execution, monitoring and other functions, and conduct comprehensive testing of the entire platform, including security testing, stress testing, user acceptance testing, etc., to ensure that the platform meets the expected standards in terms of security, performance and user experience, and ultimately establish a digital asset trading platform.

[0105] Preferably, step S2 comprises the following steps:

[0106] Step S21: Obtaining the user's initial entity asset data;

[0107] Step S22: converting the user's initial entity asset data into effective entity asset data according to the ETL technology to generate user entity asset data;

[0108] Step S23: digitally encode the user entity asset data to generate user entity asset encoding data;

[0109] Step S24: encrypting the user entity asset code data to generate user encrypted entity asset code data;

[0110] Step S25: performing hash fingerprint generation processing according to the user encrypted entity asset encoding data to obtain the user entity asset hash fingerprint data;

[0111] Step S26: Through the user's physical asset hash fingerprint data, the user's encrypted physical asset encoding data is transmitted to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data.

[0112] The present invention obtains accurate initial physical asset data of the user to ensure that the basic data of subsequent data processing is true and complete. The initial physical asset data of the user is effectively converted by using ETL technology to generate user physical asset data, thereby improving the consistency and availability of the data. By digitally encoding the user physical asset data, standardized user physical asset encoding data is generated, which facilitates the subsequent transmission of data to the digital trading platform for digital asset mapping. The user physical asset encoding data is encrypted to generate user encrypted physical asset encoding data to ensure the security of data transmission and storage. A hash fingerprint is generated according to the user encrypted physical asset encoding data to obtain the user physical asset hash fingerprint data, providing data uniqueness verification and integrity verification. Through the user physical asset hash fingerprint data, the user encrypted physical asset encoding data is transmitted to the digital asset trading platform for mapping processing to generate digital physical asset data, thereby realizing the digital conversion and management of physical assets.

[0113] In the embodiment of the present invention, a data collection form and a data interface are designed to list the required asset information in detail, such as asset type, ownership proof, location information, value assessment, etc., determine the data source, including online form submission, API acquisition from other systems, or direct integration with the user's ERP system, implement data collection through the data interface, ensure that the asset information provided by the user or related party is complete and accurate, conduct a preliminary review of the collected data, exclude obvious errors or inconsistent information, and obtain the user's initial entity asset data. Use ETL tools (such as Talend, Informatica) to extract valid data from the collected user's initial entity asset data, clean and convert the data, including standardizing the format, correcting errors, filling missing values, etc., load the processed data into the intermediate storage system, prepare for further processing, and generate user entity asset data. Digital encoding processing is performed on the user's entity asset data through digital encoding technology, and the user's entity asset data is converted into a digital signal to generate user entity asset encoding data; the user's entity asset encoding data is encrypted through asymmetric encryption technology, and a high entropy key is set according to the user's account information (login mechanism data information, biometric verification information), and asymmetric encryption is performed through the high entropy key to generate user encrypted entity asset encoding data. Use a hash function (such as SHA-256) to generate a hash fingerprint for the user's encrypted physical asset encoding data, which is used to verify the data integrity and untampered status. Using the hash fingerprint as a verification mechanism, the user's encrypted physical asset encoding data is transmitted to the digital asset trading platform to ensure the security and integrity of the data during transmission. On the digital asset trading platform, the received asset encoding data is mapped to digital assets, which are digital signal information corresponding to the physical assets. Through smart contracts and other automated tools, the accurate conversion of asset data and correct performance on the platform are ensured.

[0114] Preferably, step S24 includes the following steps:

[0115] Step S241: extracting user coding features according to user entity asset coding data to generate user coding feature data;

[0116] Step S242: performing user coding feature entropy generation processing on the user coding feature data to obtain user coding feature entropy data;

[0117] Step S243: performing user high entropy random number analysis and processing according to the user coding feature entropy data to obtain the user high entropy random number;

[0118] Step S244: performing high entropy key generation processing based on the user high entropy random number to obtain user high entropy key data;

[0119] Step S245: Perform asymmetric encryption processing on the user's physical asset encoding data according to the user's high entropy key data to generate user encrypted physical asset encoding data.

[0120] The present invention extracts characteristic information from user entity asset coding data, generates user coding characteristic data, provides basic data for subsequent characteristic analysis and encryption processing, and ensures the accuracy and comprehensiveness of data processing. The characteristic entropy information generation processing is performed through the user coding characteristic data to obtain the user coding characteristic entropy data, measure the complexity and randomness of the user coding information, and provide a scientific basis for generating high entropy random numbers. The high entropy random number analysis processing is performed through the user coding characteristic entropy data to obtain the user high entropy random number, which has higher unpredictability and randomness, and increases the security and strength of data encryption. The high entropy key generation processing is performed according to the user high entropy random number to obtain the user high entropy key data, which has higher security and complexity, and provides strong security protection for the asymmetric encryption of the user entity asset coding data. The user entity asset coding data is asymmetric encrypted according to the user high entropy key data to generate the user encrypted entity asset coding data. Asymmetric encryption ensures the confidentiality and integrity of the data during transmission and storage, prevents unauthorized access and tampering, and improves the security of the overall system.

[0121] In the embodiment of the present invention, user coding features are extracted according to the user entity asset coding data, for example, the user's unique coding information is extracted through the user's account information (login mechanism data information, biometric verification information), such as the facial feature node data of the biometric verification information, to obtain the user coding feature data. The user coding feature entropy generation process is performed through the user coding feature data, and the entropy value of each feature in the user coding feature data is analyzed by using the information entropy calculation formula, and the entropy value of each feature is summarized to obtain the user coding feature entropy data. The user coding feature entropy data is used to generate a unique high entropy random number through the cryptographic random number generation algorithm to obtain the user high entropy random number. According to the security requirements, a suitable key generation technology is selected, such as asymmetric encryption technology such as RSA, and key generation parameters such as key length and key generation algorithm are designed. The user high entropy random number is used as input to generate a high entropy key to ensure that the generated key has sufficient strength and randomness to resist password attacks, and each corresponding user can log in through the traceable password information to obtain the user high entropy key data. Use the user's high entropy key data to asymmetrically encrypt the user's physical asset encoding data to ensure the security of the encryption process. The ciphertext data does not leak any information about the plaintext, and the user's encrypted physical asset encoding data is generated.

[0122] Preferably, step S3 comprises the following steps:

[0123] Step S31: performing classification processing on the digitized physical asset data according to the preset physical asset types to generate classified digitized physical asset data;

[0124] Step S32: performing a digital physical asset valuation characteristic index analysis based on the classified digital physical asset data to generate a digital physical asset valuation characteristic index;

[0125] Step S33: establishing a decision mapping node between digital physical asset data and digital physical asset valuation based on the digital physical asset valuation characteristic index, and integrating the decision mapping nodes through a random forest algorithm to generate an initial digital physical asset valuation model;

[0126] Step S34: obtaining a digital physical asset valuation training set;

[0127] Step S35: transmitting the digital physical asset valuation training set to the initial digital physical asset valuation model for model training to generate a digital physical asset valuation model;

[0128] Step S36: using the digital physical asset evaluation model to perform real-time prediction of the digital physical asset valuation on the digital physical asset data to generate real-time digital physical asset valuation data;

[0129] Step S37: The digital physical asset valuation model is trained and updated in real time using real-time digital physical asset valuation data to obtain an updated digital physical asset valuation model, and the updated digital physical asset valuation model is fed back to step S35 to perform iterative updates of the digital physical asset valuation model.

[0130] The present invention classifies and processes the digital physical asset data according to the preset physical asset type, generates classified digital physical asset data, ensures the structuring and standardization of different types of asset data, helps the subsequent feature analysis and valuation processing, and improves the efficiency of data management and analysis. According to the classified digital physical asset data, feature index analysis is performed to generate digital physical asset valuation feature indexes, which can reflect the value characteristics of different assets, provide a scientific basis for the subsequent valuation model, and ensure the accuracy and reliability of the valuation process. Based on the digital physical asset valuation feature index, a decision mapping node between the digital physical asset data and the valuation is established, and these nodes are integrated through the random forest algorithm to generate an initial digital physical asset valuation model, which can effectively capture the asset value characteristics and realize accurate valuation in a complex data environment. By obtaining a variety of valuation training set data, the extensiveness and representativeness of the training data are ensured, and a rich learning sample is provided for the evaluation model, which improves the generalization ability and prediction accuracy of the model. The training set data is transmitted to the initial digital physical asset valuation model for training to generate a digital physical asset valuation model. The training process extracts the key features and patterns of asset valuation through model learning, and improves the accuracy and practicality of the model. Through the trained digital physical asset evaluation model, the digital physical asset data is predicted in real time to generate real-time digital physical asset valuation data. The real-time prediction can timely reflect market changes and provide the latest value information for asset management and transaction decisions. The evaluation model is trained and updated in real time through real-time digital physical asset valuation data to obtain an updated digital physical asset evaluation model. The iterative update of the model can continuously optimize the evaluation accuracy and ensure that the valuation model always maintains sensitivity and adaptability to market dynamics.

[0131] In an embodiment of the present invention, the digital physical asset data is divided into digital physical asset types according to the preset physical asset types. The physical asset types are classification standards set in advance by classification basis, such as the purpose, physical characteristics, geographical location, etc. of the assets. An automated tool or script is used to identify and classify the digital physical asset data to obtain classified digital physical asset data. Correlation analysis and statistical analysis are used to identify the digital physical asset valuation feature indicators in the classified digital physical asset data, such as the area and orientation in the land entity, and analyze the indicators that are highly correlated with the asset valuation. Based on the random forest algorithm, corresponding digital physical asset valuation decision trees are established for each type of classified digital physical asset data, which are used to evaluate the corresponding digital physical asset valuation under each type of physical asset, and corresponding indicator evaluation decision nodes are established respectively through the digital physical asset valuation feature indicators, such as designing the structure of the decision tree, determining how to split at each node, so as to best predict the asset value, and finally integrating the decision trees for each type of digital physical asset valuation decision trees to establish an initial digital physical asset valuation model. Determine the data type and scope required, including all key features of the assets and historical transaction prices, collect corresponding digital physical asset valuation training set samples from the database, which should cover various asset types and market conditions, clean the data, remove incomplete or erroneous records, and unify the standardized data format to ensure the consistency and comparability of the data during the model training process. Transfer the digital physical asset valuation training set to the initial digital physical asset valuation model, conduct model training, adjust model parameters (e.g., the number and depth of trees) to optimize the model's predictive ability and prevent overfitting, use cross-validation and other techniques to evaluate the model's accuracy and stability, analyze the model's performance, and confirm that it meets the predetermined accuracy standards. Deploy the trained model to the production environment and integrate it into the real-time data processing process to ensure that the digital physical asset valuation model can receive real-time digital physical asset data and process it quickly, perform instant valuation, and generate real-time digital physical asset valuation data. Monitor the difference between real-time valuation data and actual market transaction prices, collect these feedback data as the basis for model updates, use the feedback data collected in real time to retrain the model, adjust model parameters to adapt to new market conditions, test the updated model to ensure that its performance meets expectations, and feed the updated model back to step S35 to continuously optimize the digital physical asset valuation model and ensure that the digital physical asset valuation model update process is automated and seamless to support ongoing business needs.

[0132] Preferably, step S5 comprises the following steps:

[0133] Step S51: Perform decentralized transaction monitoring processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data;

[0134] Step S52: Perform transaction behavior feature modeling based on the transaction monitoring data to generate a transaction behavior feature model;

[0135] Step S53: performing discriminant analysis of abnormal transactions and valid transactions on the transaction monitoring data according to the transaction behavior characteristic model, and obtaining abnormal transaction monitoring data and valid transaction monitoring data respectively;

[0136] Step S54: Transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations.

[0137] The present invention performs decentralized transaction monitoring and processing on the digital physical asset trading platform according to real-time digital asset data, generates transaction monitoring data, ensures the real-time and accuracy of transaction data, avoids single-point transaction monitoring omissions through decentralized monitoring, and improves the reliability and security of the system. Based on the transaction monitoring data, the transaction behavior feature modeling is performed to generate a transaction behavior feature model, which can effectively capture the laws and characteristics of transaction behavior, provide a comprehensive understanding and analysis basis for transaction behavior, and help identify abnormal behavior and assess transaction risks. According to the transaction behavior feature model, the transaction monitoring data is discriminated and analyzed, abnormal transactions are identified and marked, and abnormal transaction monitoring data is generated, which can timely discover potential transaction risks, prevent malicious transactions and fraudulent behaviors, and ensure the security of platform transactions. At the same time, through discriminant analysis, valid transactions are identified and valid transaction monitoring data is generated. Ensure the smooth progress of normal transactions, improve the credibility and efficiency of transactions, enhance users' trust in the platform, transmit abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations, and through timely feedback and processing of abnormal transactions, measures can be quickly taken to prevent the spread and influence of abnormal behaviors, protect user asset security, and improve the overall security protection capabilities of the platform.

[0138] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 FIG. 5 is a flow chart of detailed implementation steps of step S5, in this embodiment, step S5 includes:

[0139] Step S51: Perform decentralized transaction monitoring processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data;

[0140] In an embodiment of the present invention, a real-time monitoring engine is deployed on a digital physical asset trading platform to capture and record all transaction activities in real time, including the sale, transfer and other related activities of assets. Blockchain technology, such as a distributed ledger, is used to record the details of each transaction, including timestamps, participants, transaction amounts, etc., to ensure the immutability and transparency of the data. Transaction data, including transaction frequency, scale, behavior patterns of participants, etc., are extracted from the real-time monitoring system to generate transaction monitoring data.

[0141] Step S52: Perform transaction behavior feature modeling based on the transaction monitoring data to generate a transaction behavior feature model;

[0142] In the embodiment of the present invention, data mining technology is used to analyze transaction monitoring data to identify typical transaction behavior patterns and abnormal patterns, such as identifying different types of transaction groups through cluster analysis, and mining frequently occurring transaction patterns and behaviors through association rules. Machine learning methods, such as clustering algorithms, are used to perform data modeling based on transaction patterns and behaviors. The clustering algorithm performs cluster analysis on transaction patterns and behaviors to identify normal and abnormal transaction features and generate a transaction behavior feature model. The transaction behavior feature model is a parameter such as the frequency of normal transaction types and relevant thresholds of transaction volume.

[0143] Step S53: performing discriminant analysis of abnormal transactions and valid transactions on the transaction monitoring data according to the transaction behavior characteristic model, and obtaining abnormal transaction monitoring data and valid transaction monitoring data respectively;

[0144] In an embodiment of the present invention, the transaction monitoring data is analyzed for discrimination of abnormal transactions and valid transactions based on the transaction behavior characteristic model. When the transaction frequency and transaction data volume corresponding to the transaction monitoring data correspond to the transaction behavior characteristic model, the monitored transaction data is in normal transaction behavior, and corresponding valid transaction monitoring data is obtained; when the transaction frequency and transaction data volume corresponding to the transaction monitoring data do not correspond to the transaction behavior characteristic model, such as frequent transactions, malicious operations and other transaction behaviors, the monitored transaction data is in abnormal transaction behavior, and corresponding abnormal transaction monitoring data is obtained.

[0145] Step S54: Transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations.

[0146] In the embodiment of the present invention, abnormal transaction monitoring data is automatically sent to the security team terminal of the trading platform. After receiving the abnormal data, the security team will quickly take action, such as suspending transactions, notifying affected users, further investigating, etc. A blacklist and whitelist mechanism is also established.

[0147] Preferably, step S52 includes the following steps:

[0148] Step S521: extracting transaction behaviors from the transaction monitoring data to generate transaction behavior monitoring data, and performing cluster analysis on the transaction behavior monitoring data to generate clustered transaction behavior monitoring data;

[0149] Step S522: Analyze the transaction pattern based on the clustered transaction behavior monitoring data to generate transaction pattern data;

[0150] Step S523: Perform transaction behavior feature modeling processing according to the transaction pattern data to generate a transaction behavior feature model.

[0151] The present invention extracts transaction behaviors from transaction monitoring data to generate transaction behavior monitoring data, thereby ensuring the comprehensive capture of transaction behaviors, providing a rich data basis for subsequent analysis, and performing cluster analysis on transaction behavior monitoring data to generate clustered transaction behavior monitoring data. Cluster analysis can classify similar transaction behaviors, reveal the internal patterns and structures of transaction behaviors, help identify the regularity and abnormality of transactions, and improve the accuracy and depth of transaction behavior analysis. Transaction pattern analysis is performed based on clustered transaction behavior monitoring data to generate transaction pattern data. Through in-depth analysis of transaction patterns, different types of transaction patterns are identified, providing a clear pattern basis for transaction behavior feature modeling, helping to understand the diversity and complexity of transaction behaviors, and providing an effective reference for the identification of abnormal transactions. Transaction behavior feature modeling is performed based on transaction pattern data to generate a transaction behavior feature model, which can accurately characterize the characteristics of transaction behaviors, provide a comprehensive understanding and prediction capability of transaction behaviors, help identify abnormal transaction behaviors and assess transaction risks, and improve the security and stability of the platform.

[0152] In an embodiment of the present invention, key information such as transaction time, amount, frequency, participant information, etc. is extracted from the transaction monitoring data, which reflects the user's transaction habits and patterns. These key features are extracted from a large amount of transaction data using a data processing script such as Python or SQL query. A clustering algorithm (such as K-means or DBSCAN) is applied to analyze the extracted transaction behavior data, with the purpose of identifying similar transaction behavior groups and different types of transaction patterns. Statistical software or machine learning toolkits such as scikit-learn are used to perform clustering operations and generate clustering results. According to the clustering results, each transaction is marked as belonging to a specific behavior group, and this information is integrated into clustered transaction behavior monitoring data, which describes in detail the main types of transaction behaviors in the market. The clustered transaction behavior monitoring data is analyzed to identify the characteristics and common transaction patterns of each group. For example, a group may frequently conduct small and fast transactions, while another group may prefer large and long-term investments. Pattern recognition technology is used to determine the transaction patterns and behavioral characteristics of these groups. The identified transaction patterns are formalized, and the key features and behavioral indicators of each pattern are recorded. The generated transaction pattern data will describe the behavioral rules of each cluster group in detail for further analysis and monitoring. The transaction behavior feature modeling is performed based on the transaction pattern data. According to the characteristics of the transaction behavior, machine learning algorithms, such as logistic regression algorithms, are used to analyze the relevant transaction behavior parameters corresponding to the transaction category. According to the relevant transaction behavior parameters under each type of transaction, the transaction behavior feature model corresponding to the normal transaction data is set.

[0153] Preferably, step S6 comprises the following steps:

[0154] Step S61: Analyze and process the weighted correlation characteristics of transaction flows based on the effective transaction monitoring data to generate weighted transaction flow correlation data;

[0155] Step S62: Analyze transaction supply and demand characteristics based on the effective transaction monitoring data to generate transaction supply and demand characteristic data;

[0156] Step S63: performing potential transaction supply and demand relationship evaluation processing based on the weighted transaction flow association data and the transaction supply and demand characteristic data to generate potential transaction supply and demand relationship evaluation data;

[0157] Step S64: Performing priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data, generating priority transaction demand matching data, and executing intelligent transaction demand matching operations through the priority transaction demand matching data.

[0158] The present invention performs weighted correlation feature analysis and processing of transaction flow based on effective transaction monitoring data, generates weighted transaction flow correlation data, accurately measures transaction liquidity and correlation through weighted correlation feature analysis, and weights the correlation with transaction flow, reveals the correlation and influence between transactions, and provides important reference for supply and demand analysis. Transaction supply and demand feature analysis is performed based on effective transaction monitoring data to generate transaction supply and demand feature data, and through in-depth analysis of transaction supply and demand features, the supply and demand relationship and change trend of the market are identified, accurate supply and demand information is provided, and a data foundation is laid for the evaluation of potential transaction relationships. Potential transaction supply and demand relationship evaluation is performed based on weighted transaction flow correlation data and transaction supply and demand feature data to generate potential transaction supply and demand relationship evaluation data. The evaluation of potential transaction supply and demand relationships can predict potential transaction opportunities and demands in the market, help platforms to plan and optimize transaction strategies in advance, and improve market response speed and transaction success rate. Priority transaction demand matching information analysis is performed on potential transaction supply and demand relationship evaluation data to generate priority transaction demand matching data. Through priority analysis, the most potential transaction demand is determined, the efficiency and success rate of transaction matching are improved, and the allocation of transaction resources is optimized. By performing intelligent transaction demand matching operations based on priority transaction demand matching data, accurate transaction demand matching can be achieved, human intervention and errors can be reduced, and the automation and intelligence level of the platform can be improved.

[0159] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 FIG. 5 is a flow chart of detailed implementation steps of step S6 in FIG. 5 . In this embodiment, step S6 includes:

[0160] Step S61: Analyze and process the weighted correlation characteristics of transaction flows based on the effective transaction monitoring data to generate weighted transaction flow correlation data;

[0161] In the embodiment of the present invention, key information such as transaction frequency, amount, time and participants are extracted from the effective transaction monitoring data. Through time series analysis algorithms, such as long short-term memory network algorithm and Markov chain algorithm, the liquidity of assets, that is, the liquidity of asset transfer under each user, is evaluated to obtain transaction liquidity data corresponding to the effective transaction monitoring data, and the weight of transaction liquidity is set based on the correlation of transaction liquidity. For example, if the demand relationship between two users has a great similarity through transaction liquidity analysis, a higher weight is assigned to the transaction liquidity corresponding to the two users.

[0162] Step S62: Analyze transaction supply and demand characteristics based on the effective transaction monitoring data to generate transaction supply and demand characteristic data;

[0163] In the embodiment of the present invention, the trend characteristics of supply and demand are identified based on effective transaction monitoring data, and the corresponding transaction asset types are analyzed, and real-time recording is performed using automated scripts to obtain transaction supply and demand characteristic data.

[0164] Step S63: performing potential transaction supply and demand relationship evaluation processing based on the weighted transaction flow association data and the transaction supply and demand characteristic data to generate potential transaction supply and demand relationship evaluation data;

[0165] In an embodiment of the present invention, data analysis statistical techniques are applied to weighted transaction flow association data and transaction supply and demand characteristic data. Based on the transaction supply and demand correlation, the transaction flow association data and the corresponding weight information are used to perform potential transaction supply and demand relationship evaluation processing, with special attention paid to those nodes that show high correlation or centrality in the high transaction supply and demand and weighted transaction flow association data, and ultimately generate potential transaction supply and demand relationship evaluation data.

[0166] Step S64: Performing priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data, generating priority transaction demand matching data, and executing intelligent transaction demand matching operations through the priority transaction demand matching data.

[0167] In the embodiment of the present invention, the transaction supply and demand relationship is prioritized according to the potential transaction supply and demand relationship evaluation data, and the priority transaction demand matching relationship is set according to the output data after the priority sorting, so as to ensure that the transaction demand with a higher matching degree is processed first, and the priority transaction demand matching data is generated. The intelligent transaction demand matching operation is performed through the priority transaction demand matching data, including: the priority transaction demand matching data is transmitted to the transaction rule smart contract of step S1 to execute the transaction rule smart contract update, and the transaction rule smart contract automatically executes the matching transaction to ensure that the needs of both parties to the transaction are best met. The intelligent matching mechanism not only improves the transaction success rate, but also reduces manual intervention, and improves the efficiency and transparency of the transaction process.

[0168] Preferably, step S61 includes the following steps:

[0169] Step S611: Designing a decentralized transaction undirected graph through effective transaction monitoring data to establish a decentralized transaction undirected graph;

[0170] Step S612: Perform transaction liquidity analysis based on the effective transaction monitoring data to generate transaction liquidity data;

[0171] Step S613: performing undirected graph edge weight analysis on the decentralized transaction undirected graph according to the transaction liquidity data to generate undirected graph edge weight data;

[0172] Step S614: Analyze transaction flow association data according to the decentralized transaction undirected graph to generate transaction flow association data;

[0173] Step S615: weighting the associated features of the transaction flow associated data according to the undirected graph edge weight data to generate weighted transaction flow associated data.

[0174] The present invention designs a decentralized transaction undirected graph through effective transaction monitoring data to establish a decentralized transaction undirected graph, which can comprehensively describe the relationship between transaction participants and the transaction flow path, provide an intuitive network structure view, and facilitate the analysis and monitoring of transaction activities. According to the effective transaction monitoring data, transaction liquidity analysis can accurately reflect the liquidity level of the transaction, identify nodes and paths with frequent transactions, and provide a basis for optimizing transaction processes and resource allocation. According to the transaction liquidity data, the decentralized transaction undirected graph is subjected to undirected graph edge weight analysis and processing, and the edge weight data can quantify the strength of the transaction relationship, reveal the important nodes and key paths in the transaction network, and help focus on monitoring and management. According to the decentralized transaction undirected graph, transaction flow association data analysis is performed to generate transaction flow association data, and the association data can show the dynamic changes of transaction activities, identify the association mode and flow characteristics in the transaction network, and provide basic data for subsequent feature weighting and risk assessment. According to the undirected graph edge weight data, the transaction flow association data is weighted by the association characteristics of the transaction flow, and the weighting processing can accurately evaluate the importance and influence of the transaction relationship, provide an in-depth understanding of the transaction network structure, and enhance the scientific nature of transaction analysis and decision-making.

[0175] In the embodiment of the present invention, all transaction data, including the identity of the participants, transaction amount, transaction time, etc., are collected from the effective transaction monitoring system, and the data is cleaned to remove any incomplete or erroneous records. Using a graph database such as Neo4j or a graph processing library such as NetworkX, each transaction participant is set as a node in the graph. For each transaction, an edge is created between the participants. The edge does not indicate the transaction direction, but only indicates that there is a transaction relationship between the two. Using a graph visualization tool such as Gephi, an undirected graph of the transaction network is drawn to help intuitively understand the transaction mode, and the constructed undirected graph is stored in the graph database for further analysis and query. Set transaction liquidity metrics, such as transaction frequency, transaction volume, transaction time interval, etc. Use the Markov chain algorithm and the transaction liquidity metric to analyze the transaction liquidity of the effective monitoring data, calculate the probability of the effective monitoring data transferring from one transaction state to another, such as counting the frequency of transactions from one specific type (state) to another type within a given time window, and represent it with a transaction state transition matrix according to each state transition probability, and analyze the transaction trend through the transaction state transition matrix, and finally generate transaction liquidity data. According to the transaction liquidity data, a corresponding weight is assigned to each undirected edge of the decentralized transaction undirected graph and the undirected edge of the cross-border point, for example, a smaller weight is assigned to the undirected graph node with less transaction correlation, so as to generate undirected graph edge weight data. The analysis method of graph theory, such as community detection algorithm, is applied to identify close transaction groups or highly interactive node sets in the graph, and network centrality analysis, such as degree centrality, closeness centrality, betweenness centrality, etc., is performed to identify nodes with strategic positions in the transaction network. According to the analysis results, the identified key nodes and important transaction group information are organized into structured transaction flow correlation data, which include the centrality score of the node, the list of group members, the transaction density within the group, etc., and are stored in the database for further analysis. The importance and influence of each node or group of the transaction flow correlation data in the transaction network are evaluated, and the transaction flow correlation data is weighted according to the undirected graph edge weight data. The weighting of the transaction flow correlation data reflects its importance in the entire transaction network, so as to generate weighted transaction flow correlation data, which includes transaction nodes and corresponding associated transaction nodes, and corresponding transaction flow correlation weight information.

[0176] The present invention provides a digital economic conversion system for assets, which is used to execute the digital economic conversion method for assets as described above. The digital economic conversion system for assets includes:

[0177] The digital asset trading platform establishment module is used to design a trading mechanism smart contract based on preset digital economic trading rules to establish a trading mechanism smart contract; a digital asset trading platform is established based on blockchain technology and a trading mechanism smart contract.

[0178] The digital physical asset conversion module is used to obtain the user's initial physical asset data; perform user encrypted physical asset coding analysis based on the user's initial physical asset data to generate user encrypted physical asset coding data; transmit the user encrypted physical asset coding data to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data;

[0179] The digital physical asset real-time valuation module is used to analyze the digital physical asset valuation characteristic indicators based on the digital physical asset data and generate the digital physical asset valuation characteristic indicators; establish the decision mapping relationship between the digital physical asset data and the digital physical asset valuation based on the random forest algorithm and the digital physical asset valuation characteristic indicators to generate a digital physical asset valuation model; perform the digital physical asset valuation real-time analysis on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data;

[0180] A digital asset integration module is used to perform real-time digital asset integration processing based on digital physical asset data and real-time digital physical asset valuation data to generate real-time digital asset data;

[0181] The abnormal transaction feedback module is used to perform decentralized transaction monitoring and processing on the digital physical asset trading platform based on real-time digital asset data to generate transaction monitoring data; perform discrimination analysis on abnormal transactions and valid transactions on the transaction monitoring data to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations;

[0182] The transaction demand intelligent matching module is used to evaluate and process the potential transaction supply and demand relationship based on the effective transaction monitoring data, and generate potential transaction supply and demand relationship evaluation data; perform priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data, generate priority transaction demand matching data, and perform intelligent transaction demand matching operations through the priority transaction demand matching data.

[0183] The beneficial effect of the present application is that a method for digital economic conversion of assets of the present invention defines digital conversion standards by analyzing immediate asset benefits, improves the fairness of conversion of physical assets to digital assets, and improves the security of digital assets by performing asymmetric encryption and hash function digital asset protection methods with designed high entropy keys. Through decentralized trading modes, digital asset management and trading mode execution monitoring are performed to ensure the processing of abnormal digital transactions. Intelligent transaction demand matching operations are designed for liquidity and transaction supply and demand characteristics during the transaction process to better promote the execution of digital transactions. Thereby realizing digital mapping, real-time valuation and intelligent transaction management of physical assets, solving the problems of data conversion, security and transaction management faced in the current asset digitization process, and promoting the development and innovation of the digital economy.

[0184] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0185] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for digital economic conversion of assets, characterized in that: The following steps are involved: Step S1: Design a transaction mechanism smart contract based on the preset digital economic transaction rules to establish a transaction mechanism smart contract; establish a digital asset transaction platform based on blockchain technology and the transaction mechanism smart contract; Step S2: Acquire the user's initial physical asset data; perform user encrypted physical asset coding analysis based on the user's initial physical asset data to generate user encrypted physical asset coding data; transmit the user encrypted physical asset coding data to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data; Step S3: Analyze the characteristic index of digital physical asset valuation according to the digital physical asset data to generate the characteristic index of digital physical asset valuation; establish a decision mapping relationship between the digital physical asset data and the digital physical asset valuation based on the random forest algorithm and the characteristic index of digital physical asset valuation to generate a digital physical asset valuation model; perform real-time analysis of the digital physical asset valuation on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data; Wherein, step S3 comprises the following steps: Step S31: performing classification processing on the digitized physical asset data according to the preset physical asset types to generate classified digitized physical asset data; Step S32: performing a digital physical asset valuation characteristic index analysis based on the classified digital physical asset data to generate a digital physical asset valuation characteristic index; Step S33: establishing a decision mapping node between digital physical asset data and digital physical asset valuation based on the digital physical asset valuation characteristic index, and integrating the decision mapping nodes through a random forest algorithm to generate an initial digital physical asset valuation model; Step S34: obtaining a digital physical asset valuation training set; Step S35: transmitting the digital physical asset valuation training set to the initial digital physical asset valuation model for model training to generate a digital physical asset valuation model; Step S36: Performing real-time prediction of the valuation of digital physical assets on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data; Step S37: Performing real-time training and updating of the digital physical asset evaluation model through real-time digital physical asset valuation data to obtain an updated digital physical asset evaluation model, and feeding the updated digital physical asset evaluation model back to step S35 to perform iterative updating of the digital physical asset evaluation model; Step S4: Performing real-time digital asset integration processing according to the digital physical asset data and the real-time digital physical asset valuation data to generate real-time digital asset data; Step S5: Perform decentralized transaction monitoring and processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data; perform discrimination analysis on the transaction monitoring data for abnormal transactions and valid transactions to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations; Step S6: Evaluate and process the potential transaction supply and demand relationship based on the effective transaction monitoring data to generate potential transaction supply and demand relationship evaluation data; perform priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data to generate priority transaction demand matching data, and perform intelligent transaction demand matching operations through the priority transaction demand matching data.

2. The method for digital economic conversion of assets according to claim 1, characterized in that: Step S1 includes the following steps: Design the transaction mechanism smart contract logic according to the preset digital economic transaction rules, generate the transaction mechanism smart contract logic data, and establish the transaction mechanism smart contract based on the transaction mechanism smart contract logic data; Perform blockchain network configuration processing based on blockchain technology, generate blockchain network configuration data, and establish a digital asset trading platform based on the blockchain network configuration data and transaction mechanism smart contracts.

3. The method for digital economic conversion of assets according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Obtaining the user's initial entity asset data; Step S22: converting the user's initial entity asset data into effective entity asset data according to the ETL technology to generate user entity asset data; Step S23: digitally encode the user entity asset data to generate user entity asset encoding data; Step S24: encrypting the user entity asset code data to generate user encrypted entity asset code data; Step S25: performing hash fingerprint generation processing according to the user encrypted entity asset encoding data to obtain the user entity asset hash fingerprint data; Step S26: Through the user's physical asset hash fingerprint data, the user's encrypted physical asset encoding data is transmitted to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data.

4. The method for digital economic conversion of assets according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: extracting user coding features according to user entity asset coding data to generate user coding feature data; Step S242: performing user coding feature entropy generation processing on the user coding feature data to obtain user coding feature entropy data; Step S243: performing user high entropy random number analysis and processing according to the user coding feature entropy data to obtain the user high entropy random number; Step S244: performing high entropy key generation processing based on the user high entropy random number to obtain user high entropy key data; Step S245: Perform asymmetric encryption processing on the user's physical asset encoding data according to the user's high entropy key data to generate user encrypted physical asset encoding data.

5. The method for digital economic conversion of assets according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Perform decentralized transaction monitoring processing on the digital physical asset trading platform according to the real-time digital asset data to generate transaction monitoring data; Step S52: Perform transaction behavior feature modeling based on the transaction monitoring data to generate a transaction behavior feature model; Step S53: performing discriminant analysis of abnormal transactions and valid transactions on the transaction monitoring data according to the transaction behavior characteristic model, and obtaining abnormal transaction monitoring data and valid transaction monitoring data respectively; Step S54: Transmitting abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations.

6. The method for digital economic conversion of assets according to claim 5, characterized in that: Step S52 includes the following steps: Step S521: extracting transaction behaviors from the transaction monitoring data to generate transaction behavior monitoring data, and performing cluster analysis on the transaction behavior monitoring data to generate clustered transaction behavior monitoring data; Step S522: Analyze the transaction pattern based on the clustered transaction behavior monitoring data to generate transaction pattern data; Step S523: Perform transaction behavior feature modeling processing according to the transaction pattern data to generate a transaction behavior feature model.

7. The method for digital economic conversion of assets according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: Analyze and process the weighted correlation characteristics of transaction flows based on the effective transaction monitoring data to generate weighted transaction flow correlation data; Step S62: Analyze transaction supply and demand characteristics based on the effective transaction monitoring data to generate transaction supply and demand characteristic data; Step S63: performing potential transaction supply and demand relationship evaluation processing based on the weighted transaction flow association data and the transaction supply and demand characteristic data to generate potential transaction supply and demand relationship evaluation data; Step S64: Performing priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data, generating priority transaction demand matching data, and executing intelligent transaction demand matching operations through the priority transaction demand matching data.

8. The method for digital economic conversion of assets according to claim 7, characterized in that: Step S61 includes the following steps: Step S611: Designing a decentralized transaction undirected graph through effective transaction monitoring data to establish a decentralized transaction undirected graph; Step S612: Perform transaction liquidity analysis based on the effective transaction monitoring data to generate transaction liquidity data; Step S613: performing undirected graph edge weight analysis on the decentralized transaction undirected graph according to the transaction liquidity data to generate undirected graph edge weight data; Step S614: Analyze transaction flow association data according to the decentralized transaction undirected graph to generate transaction flow association data; Step S615: weighting the associated features of the transaction flow associated data according to the undirected graph edge weight data to generate weighted transaction flow associated data.

9. A digital economic conversion system for assets, characterized in that: For executing the digital economic conversion method of assets as claimed in claim 1, the digital economic conversion system of assets comprises: A digital asset trading platform establishment module is used to design a trading mechanism smart contract based on preset digital economic trading rules to establish a trading mechanism smart contract; a digital asset trading platform is established based on blockchain technology and a trading mechanism smart contract; The digital physical asset conversion module is used to obtain the user's initial physical asset data; perform user encrypted physical asset coding analysis based on the user's initial physical asset data to generate user encrypted physical asset coding data; transmit the user encrypted physical asset coding data to the digital asset trading platform for physical asset digital mapping processing to generate digital physical asset data; The digital physical asset real-time valuation module is used to analyze the digital physical asset valuation characteristic indicators based on the digital physical asset data and generate the digital physical asset valuation characteristic indicators; establish the decision mapping relationship between the digital physical asset data and the digital physical asset valuation based on the random forest algorithm and the digital physical asset valuation characteristic indicators to generate a digital physical asset valuation model; perform the digital physical asset valuation real-time analysis on the digital physical asset data through the digital physical asset valuation model to generate real-time digital physical asset valuation data; A digital asset integration module is used to perform real-time digital asset integration processing based on digital physical asset data and real-time digital physical asset valuation data to generate real-time digital asset data; The abnormal transaction feedback module is used to perform decentralized transaction monitoring and processing on the digital physical asset trading platform based on real-time digital asset data to generate transaction monitoring data; perform discrimination analysis on abnormal transactions and valid transactions on the transaction monitoring data to obtain abnormal transaction monitoring data and valid transaction monitoring data respectively; transmit the abnormal transaction monitoring data to the terminal to perform abnormal transaction feedback operations; The transaction demand intelligent matching module is used to evaluate and process the potential transaction supply and demand relationship based on the effective transaction monitoring data, and generate potential transaction supply and demand relationship evaluation data; perform priority transaction demand matching information analysis on the potential transaction supply and demand relationship evaluation data, generate priority transaction demand matching data, and perform intelligent transaction demand matching operations through the priority transaction demand matching data.

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