A digital asset equity management system

Abstract

The present application relates to the technical field of digital asset management, in particular to a digital asset right and interest management system, which comprises a right and interest identification module, a right and interest distribution optimization module, an operation instruction generation module, a right and interest circulation optimization module and an instruction distribution module.In the present application, through accurate collection and analysis of digital asset state information, efficient identification and dynamic distribution of digital asset right and interest are realized, the rights and responsibilities in the asset life cycle are ensured to be clear and transparent, the standardization and safety in the asset circulation process are improved, and through intelligent decision set optimization and abnormal processing, the response capability and efficiency of the system are enhanced.The optimized asset right and interest distribution and circulation decision, combined with real-time data and historical trends, reduces the efficiency bottleneck and information lag in the traditional management mode, improves the transparency, traceability and safety of asset management, and enables more accurate control and protection of digital assets in a decentralized environment.

Description

Technical Field

[0001] This invention relates to the field of digital asset management technology, and in particular to a digital asset rights management system. Background Technology

[0002] The field of digital asset management technology encompasses the management of the creation, storage, circulation, trading, and ownership of digital assets. Digital assets, existing in digital form, include digital copyrights, virtual goods, and digital certificates. Core issues include the lifecycle management of digital assets, confirmation of corresponding rights, access control, transaction security, and compliance. With the application of technologies such as blockchain, encryption, and smart contracts, the management of digital assets has become more complex, requiring more efficient and secure management systems to ensure asset security, traceability, and transparency. Traditional digital asset rights management systems identify, allocate, and control the rights of digital assets throughout their lifecycle. These systems manage the allocation and circulation of digital asset rights through centralized databases and access control mechanisms. Based on traditional identity authentication and authorization mechanisms, such as digital signatures and cryptographic methods, they guarantee the ownership and usage rights of digital assets. However, traditional methods suffer from security vulnerabilities and efficiency bottlenecks due to centralized management and struggle to meet the demands for decentralization and high transparency.

[0003] Traditional digital asset rights management relies on centralized databases and access control mechanisms, which face security vulnerabilities and efficiency bottlenecks. Due to the lack of dynamic, real-time adjustments to asset status management, timely rights adjustments cannot be made based on different stages or real-time changes in the asset's lifecycle. This leads to an inability to cope with unforeseen events in complex transaction scenarios, resulting in uneven rights distribution or information lag during asset transfer. Furthermore, centralized management models lack sufficient transparency; the records of rights corresponding to asset transfers are susceptible to human intervention, failing to ensure accurate management of digital assets in a decentralized environment. This increases compliance and operational risks, and fails to meet the requirements for efficient, secure, and transparent management. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a digital asset rights management system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a digital asset rights management system includes:

[0006] The rights identification module, based on the collection and analysis of digital asset status information, combined with the corresponding characteristics of rights and their time distribution patterns, identifies and marks the key nodes corresponding to rights, and generates a rights distribution and status mapping diagram.

[0007] The equity allocation optimization module extracts the time series and state characteristics corresponding to the equity based on the equity distribution and state mapping diagram, and selects the optimal allocation strategy combination based on the asset status and equity demand to establish an intelligent dynamic equity allocation optimization model.

[0008] The operation instruction generation module, based on the intelligent dynamic rights allocation optimization model, matches operation requirements and execution logic, generates a corresponding operation instruction set using the instruction compilation mechanism, and embeds normative requirements and security constraints to form a standardized intelligent operation instruction set.

[0009] The rights and interests transfer optimization module incrementally updates the basic operation instruction text library based on the standardized intelligent operation instruction set and the anomaly handling records uploaded during operation, extracts high-frequency anomalies and key operation links, and constructs an intelligent digital asset rights and interests management decision set.

[0010] As a further embodiment of the present invention, the rights distribution and state mapping diagram includes asset number, state interval, time identifier, asset state mapping value and rights demand label; the intelligent dynamic rights allocation optimization model includes allocation strategy number, time series chain, resource balancing parameter, strategy priority label and state fluctuation range; the standardized intelligent operation instruction set includes rights object operation requirements, standard index list, security constraint description, instruction number and execution step sequence; and the intelligent digital asset rights management decision set includes an anomaly handling scheme library, high-frequency anomaly label, key operation point index, knowledge base update cycle and operation specification standardization table.

[0011] As a further aspect of the present invention, the rights identification module includes:

[0012] The status information acquisition submodule is based on the real-time acquisition of digital asset status information, removes interference signals and abnormal fluctuation information, analyzes the rate of change of status per unit time, and generates equity benchmark value.

[0013] The status interval identification submodule identifies the status interval corresponding to the equity based on the equity benchmark value, combined with the asset status and equity demand, and marks the upper and lower limits of the interval to obtain the equity distribution characteristics.

[0014] The feature distribution construction submodule calls the equity distribution features to identify the time series and state characteristics corresponding to the equity, formulates the feature structure and sets the association weights based on the asset number and state interval, and establishes an equity distribution and state mapping diagram.

[0015] As a further aspect of the present invention, the rights allocation optimization module includes:

[0016] The time series extraction submodule calls the equity distribution and state mapping diagram to extract the time series chain and state characteristics corresponding to the equity, analyze the time dependence and state change rules, and obtain the time series relationship matrix;

[0017] The resource balancing calculation submodule calculates the resource balancing value of the beneficiaries of resource allocation based on the time series relationship matrix and the asset status and equity demand, and selects the optimal allocation strategy combination.

[0018] The dynamic strategy generation submodule, based on the optimal allocation strategy combination, identifies the allocation strategy number and time series chain, recognizes the strategy priority and state fluctuation range, and generates an intelligent dynamic rights allocation optimization model.

[0019] As a further aspect of the present invention, the operation instruction generation module includes:

[0020] The matching submodule is required to call the intelligent dynamic rights allocation optimization model, extract the operation requirements and execution logic, analyze the complexity of the requirements and the logical correlation, and generate an operation requirement matching table.

[0021] The specification embedding processing submodule, based on the operation requirement matching table, uses an instruction compilation mechanism to standardize the specification requirements, embeds the operation requirements in the matching table, and forms a specification index list.

[0022] The instruction generation submodule calls the standardized index list, decomposes the execution action based on the rights object parameters, assigns priority and execution conditions, limits the scope of requirements and establishes an exception handling path, marks the instruction number and version, performs permission verification, executes tabular output, writes back the status and records logs, forming a standardized intelligent operation instruction set.

[0023] As a further aspect of the present invention, the rights transfer optimization module includes:

[0024] The exception handling collection submodule calls the standardized intelligent operation instruction set, extracts the exception handling records uploaded during the operation, analyzes the applicable scenarios and resolution efficiency of the processing records, and classifies them into an exception handling solution library.

[0025] The high-frequency anomaly extraction submodule, based on the anomaly handling scheme library, counts high-frequency anomaly points and key operational steps to obtain high-frequency anomaly tags;

[0026] The knowledge base update submodule incrementally updates the basic operation instruction text library based on the high-frequency anomaly tags, combined with the key operation point index and the operation specification standardization table, records the knowledge base update cycle, and constructs an intelligent digital asset rights management decision set.

[0027] As a further aspect of the present invention, the applicable scenarios and resolution efficiency of the analysis and processing records refer to the determination of the applicable scenarios and resolution efficiency of the processing records by classifying the anomaly types and occurrence environments recorded in the anomaly processing records and quantitatively evaluating the success rate and average processing time of the anomaly processing solutions under the classification.

[0028] The record knowledge base update cycle refers to the current knowledge base update cycle when the total amount of updated content in the basic operation instruction text library exceeds a preset character threshold, or when the number of changes of high-frequency abnormal tags within a preset monitoring period exceeds a preset number.

[0029] As a further aspect of the present invention, the system also includes an instruction distribution module:

[0030] Based on the intelligent digital asset rights management decision set, the instruction distribution module automatically allocates the corresponding operation instruction set for the rights object according to the rights object number and rights type, marks the instruction version number and update time, and outputs the intelligent instruction distribution path object mapping table.

[0031] The intelligent instruction distribution path object mapping table includes the rights object number, operation instruction number, instruction version number, update timestamp, and distribution path identifier.

[0032] As a further aspect of the present invention, the instruction distribution module includes:

[0033] The instruction version management submodule calls the intelligent digital asset rights management decision set, identifies the version number and update time of the operation instruction set, compares the current version with the original version, selects the current version file, and generates an instruction version mapping table.

[0034] The distribution path generation submodule, based on the instruction version mapping table and combining the rights object number and rights type, identifies the distribution path of the corresponding rights object, records the path identifier and update timestamp, and generates an intelligent instruction distribution path object mapping table.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0036] This invention achieves efficient identification and dynamic allocation of digital asset rights through precise collection and analysis of digital asset status information. Based on time series and status characteristics, it optimizes the rights allocation strategy, ensuring clear and transparent rights and responsibilities throughout the asset's lifecycle. By generating standardized operating instructions, it enhances the standardization and security of digital asset circulation. Furthermore, by optimizing anomaly handling through intelligent decision sets, it further improves the system's responsiveness and operational efficiency. The optimized decision-making for asset rights allocation and circulation, combined with real-time data and historical trends, reduces efficiency bottlenecks and information lags in traditional management methods, improving asset management transparency, traceability, and security. Ultimately, this allows for more precise control and protection of digital assets in a decentralized environment. Attached Figure Description

[0037] Figure 1 This is a system flowchart of the present invention;

[0038] Figure 2 This is a flowchart of the rights identification module in this invention;

[0039] Figure 3 This is a flowchart of the rights allocation optimization module in this invention;

[0040] Figure 4 This is a flowchart of the operation instruction generation module in this invention;

[0041] Figure 5 This is a flowchart of the rights transfer optimization module in this invention;

[0042] Figure 6 This is a flowchart of the instruction distribution module in this invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0044] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0045] Please see Figure 1 A digital asset rights management system includes:

[0046] The rights identification module, based on the collection and analysis of digital asset status information, combined with the corresponding characteristics of rights and their time distribution patterns, identifies and marks the key nodes corresponding to rights, and generates a rights distribution and status mapping diagram.

[0047] The equity allocation optimization module extracts the time series and state characteristics corresponding to the equity based on the equity distribution and state mapping diagram, and selects the optimal allocation strategy combination based on the asset status and equity demand to establish an intelligent dynamic equity allocation optimization model.

[0048] The operation instruction generation module is based on an intelligent dynamic rights allocation optimization model. It matches operation requirements with execution logic, uses an instruction compilation mechanism to generate corresponding operation instruction sets, and embeds normative requirements and security constraints to form a standardized intelligent operation instruction set.

[0049] The rights and interests transfer optimization module incrementally updates the basic operation instruction text library based on the standardized intelligent operation instruction set and the abnormal handling records uploaded during operation, extracts high-frequency abnormal points and key operation links, and constructs an intelligent digital asset rights and interests management decision set.

[0050] The instruction distribution module is based on the intelligent digital asset rights management decision set. According to the rights object number and rights type, it automatically allocates the corresponding rights object operation instruction set, marks the instruction version number and update time, and outputs the intelligent instruction distribution path object mapping table.

[0051] The rights distribution and status mapping diagram includes asset number, status range, time stamp, asset status mapping value, and rights demand label. The intelligent dynamic rights allocation optimization model includes allocation strategy number, time series chain, resource balancing parameters, strategy priority label, and status fluctuation range. The standardized intelligent operation instruction set includes rights object operation requirements, standard index list, security constraint description, instruction number, and execution step sequence. The intelligent digital asset rights management decision set includes an anomaly handling scheme library, high-frequency anomaly label, key operation point index, knowledge base update cycle, and operation standardization table. The intelligent instruction distribution path object mapping table includes rights object number, operation instruction number, instruction version number, update timestamp, and distribution path identifier.

[0052] Please see Figure 2 The rights identification module includes:

[0053] The status information acquisition submodule is based on the real-time acquisition of digital asset status information, removes interference signals and abnormal fluctuation information, analyzes the rate of change of status per unit time, and generates equity benchmark value.

[0054] The system collects real-time on-chain transaction data for the digital supply chain finance bill DBF2023001, order fulfillment rates and credit ratings from the core enterprise's ERP system, and secondary market transaction quotes from third-party financial markets. Data is collected once per second. The collected data undergoes preliminary cleaning to identify and remove interference signals. For example, market price data deviating from the past 5-second moving average by more than 0.5% is considered interference and corrected using a median filtering algorithm. Simultaneously, it identifies abnormal fluctuations, such as an absolute change in the bill market price exceeding 2.0% within 60 consecutive seconds or a decline in the core enterprise's credit rating exceeding 1% within one hour. The system is divided into several levels. Once a detection is detected, an early warning is activated and a stable value is retained, awaiting manual or external verification. Then, the rate of change of each status parameter of DBF2023001 per minute is calculated. The market value change rate is 0.0503%, the order fulfillment rate change rate is -0.102%, and the individual change rates are weighted and summed with weights of 0.6 and 0.4 to obtain a comprehensive status change rate of -0.01062%. Finally, based on this comprehensive status change rate, the current effective market value is multiplied by (1 plus the comprehensive status change rate) and then multiplied by the benchmark value adjustment coefficient of 1.0005 to generate an equity benchmark value of 994,946.88 yuan.

[0055] The status interval identification submodule identifies the status interval corresponding to the equity based on the equity benchmark value, combined with the asset status and equity demand, and marks the upper and lower limits of the interval to obtain the equity distribution characteristics.

[0056] Based on the equity benchmark value of DBF2023001 of RMB 994,946.88, and categorized according to the real-time asset status of DBF2023001, its current status is "endorsed and awaiting discounting." The core enterprise's AAA credit rating corresponds to low risk, and less than 30 days until maturity corresponds to high liquidity. Furthermore, its attractiveness to liquidity-seeking investors is assessed based on equity demand standards. The liquidity score for the bill market is set at 0.95, and the risk score at 0.05. The liquidity score considers clearing costs, clearing timeframes, and expected daily trading volume. The risk score is quantified based on the core enterprise's historical default rate and industry risk coefficient. Based on the benchmark value and assessment results, the state intervals were identified using a decision tree model. The benchmark value of DBF2023001 equity is greater than RMB 990,000, the liquidity score is greater than 0.9, and the risk score is less than 0.1. Therefore, it belongs to the high-liquidity, low-risk, high-quality equity interval. At the same time, the upper limit of the interval is marked as RMB 1,000,000 face value, and the lower limit is 98% of the face value, i.e., RMB 980,000. The interval setting is based on the 95% confidence interval of similar AAA-rated notes in the secondary market. Finally, the structured equity distribution characteristics were obtained, including asset number, equity benchmark value, state interval, upper and lower limits of the interval, liquidity score, and risk score.

[0057] The feature distribution construction submodule calls the equity distribution features to identify the time series and state characteristics of the equity, formulates the feature structure and sets the association weights based on the asset number and state interval, and establishes an equity distribution and state mapping diagram.

[0058] We obtained the equity distribution characteristics of the digital supply chain finance bill DBF2023001. The time-series information includes key status change timestamps such as bill issuance, initial endorsement, and market quotation updates. Status characteristics include historical changes in the bill's credit rating, frequency of holder changes, and secondary market trading activity. Its credit rating has consistently been AAA, the historical average holding period is 20 days, and the average daily trading volume accounts for 3% of the face value. Subsequently, based on the asset number DBF2023001-EQ and the high-liquidity, low-risk, high-quality equity range within the status interval, a feature structure was developed. This structure is defined as a JSON object containing multi-dimensional attributes, including asset number, current status range, credit rating, remaining maturity category, market liquidity score and risk score, and holder change frequency. Based on this, weights are assigned to each attribute. These weights reflect the degree to which different characteristics affect the stability of equity value and allocation priority. The weights are set based on historical data analysis and expert experience, and are obtained through backtracking training of machine learning models. For example, when assessing the stability of equity value, credit rating has a weight of 0.4, market liquidity score has a weight of 0.3, and risk score has a weight of 0.2, ensuring the subsequent allocation priority of high-credit, high-liquidity, and low-risk assets. Finally, an equity distribution and state mapping graph in the form of a graph database is established, with DBF2023001 as a node and the above-mentioned multi-dimensional equity distribution features (such as asset number, current state interval, credit rating, liquidity score, and risk score) as node attributes. The relationship between nodes represents the state transition or asset association at a point in time.

[0059] Please see Figure 3 The rights and interests allocation optimization module includes:

[0060] The time series extraction submodule calls the stake distribution and state mapping graph to extract the time series chain and state characteristics corresponding to the stake, analyze the time dependence and state change patterns, and obtain the time series relationship matrix;

[0061] By invoking the equity distribution and state mapping graph, and specifically for DBF2023001, the time series chain and state characteristics corresponding to the equity are extracted from the graph database. The time series chain includes issuance, initial endorsement, market quotation update time, and key state timestamps and state values. For example, the order fulfillment rate increases after the core enterprise releases its Q3 financial report, forming a historical time series. State characteristics include a lifecycle credit rating of AAA, an average holder holding period of 20 days, an average daily trading volume in the secondary market accounting for 3% of the face value, and the duration of stay in the high-liquidity, low-risk, high-quality equity range. Subsequently, the time dependence and state change patterns are analyzed. Granger causality tests are performed on the time series chain to analyze the causal relationship between market price fluctuations and order fulfillment rate and credit rating time. A significant positive correlation exists between the continuous increase in single-company fulfillment rate and the rise in secondary market prices of bills, with a correlation coefficient of 0.75. The ARIMA model is used to predict the market value trend of bills over the next 30 days. A Hidden Markov Model is used to analyze the probability of bill state transitions and the average dwell time. For example, the average time for endorsed bills awaiting discount to become discounted bills is 72 hours. Finally, a time series relationship matrix is ​​obtained. This matrix is ​​a multi-dimensional array, where rows and columns represent different time series parameters, and elements represent the covariance, correlation coefficient, and causal relationship strength between parameters. It includes a lagged correlation coefficient of 0.75 between market price and order fulfillment rate, a negative correlation coefficient of -0.60 between market price and interbank lending rate, and a distribution of state transition probability, quantifying the time evolution pattern and interrelationship of digital bill states.

[0062] The resource balancing calculation submodule, based on the time series relationship matrix and considering asset status and equity demand, uses the following formula:

[0063] ;

[0064] Calculate the resource equilibrium value for the beneficiaries of resource allocation and select the optimal allocation strategy combination;

[0065] in, The resource equilibrium value representing the beneficiaries of resource allocation. This represents the initial state of the i-th asset. This represents the demand adjustment coefficient for the i-th asset. The efficiency factor representing the i-th asset. This represents the utilization efficiency of the i-th asset. Represents the demand weight of the i-th asset. The number of representative resource items;

[0066] Based on the time series relationship matrix, in this formula, The resource equilibrium value represents the beneficiaries of resource allocation. This value measures the overall matching degree and benefit maximization level of digital assets under a specific rights allocation strategy. The higher the value, the more balanced the resource allocation and the higher the efficiency.

[0067] Molecular part : Representing the first in the resource project The asset in question refers to a pool of digital assets to be allocated, which may include multiple digital supply chain finance notes, etc. This represents the number of resource items, i.e., the total number of digital assets to be allocated; Representing the The initial state of the asset, for the DBF2023001 note, is quantified as the assessed value of the note in the current secondary market, which is RMB 995,000. This value is a stable market quote obtained through real-time data collection and cleaning, reflecting the original value basis of the asset. Representing the The demand adjustment coefficient for an asset is adjusted based on the preference of a specific equity demander (e.g., a liquidity-oriented investor) for asset characteristics (such as remaining maturity and risk level). The coefficient ranges from 0.8 to 1.2. When an equity demander has higher requirements for the asset's liquidity or security, and the asset meets these requirements, the coefficient will be higher than 1; conversely, it will be lower than 1. For the DBF2023001 note, its high liquidity (0.95) and low risk (0.05) characteristics highly match the needs of liquidity-oriented investors; therefore, the demand adjustment coefficient is higher. The coefficient is set at 1.15; this coefficient is based on investor profile analysis. If an investor's historical trading data shows that they prefer highly liquid assets, and those assets meet the standard of a liquidity score higher than 0.9, then... The value should be 1.15; otherwise, adjust downwards based on the matching degree. Representing the The performance factor of an asset quantifies the comprehensive benefits an asset can generate under a anticipated allocation strategy, including potential returns, risk mitigation capabilities, and strategic value. It ranges from 0.9 to 1.1. For example, the annualized yield of the DBF2023001 note is 3.5%, and its potential risk exposure is assessed at 0.01%. Considering the note's strategic role in maintaining supply chain stability, its performance factor... The efficiency factor was set at 1.08. It was determined by a multi-dimensional assessment of the expected return on investment of assets, risk-weighted return on assets, and contribution to the business ecosystem, and then determined by a combination of expert scoring and historical data regression analysis. The expression represents the square root of the adjusted value of a single asset. Its purpose is to smooth the absolute value of the asset, reduce the extreme impact of high-value assets on the overall equilibrium value, and reflect the positive contribution of value. Indicates all The adjusted values ​​of each resource item are summed to obtain a total that reflects the comprehensive value potential of all assets.

[0068] denominator : Representing the The utilization efficiency of an asset assesses the degree to which it is effectively utilized in actual operation, ranging from 0.7 to 1.0. High utilization efficiency means short idle time and fast turnover. For the DBF2023001 note, as a highly liquid note, it is expected to be quickly discounted or transferred after allocation. Its historical average turnover period is 7 days, far lower than the average turnover period of 15 days for similar notes. Therefore, its utilization efficiency is high. The efficiency is set at 0.98, and this efficiency setting depends on the historical turnover data of the assets and the degree of optimization of the expected operating process; Representing the The demand weight of an asset reflects the urgency or strategic importance of a particular asset to different equity demanders, ranging from 0.5 to 1.5. For example, for a company urgently needing to replenish short-term working capital, the DBF2023001 note has extremely high strategic importance, and its demand weight is... The weight is set at 1.20. This weight is determined based on a comprehensive assessment of factors such as the business type of the party requesting the rights, the urgency of the request, and the relevance of the assets to their core business. Higher priority requests correspond to higher weights. The expression represents the product of the utilization efficiency of a single asset and the demand weight. Its purpose is to quantify the combined impact of the actual utility of the asset and the urgency of demand during the allocation process. Indicates all The product of the utilization efficiency of each resource project and the demand weight is summed to obtain a total that reflects the comprehensive utilization pressure and demand intensity of all assets.

[0069] The advantage of this formula lies in its ability to effectively balance the intrinsic value of assets with external demand and potential returns by multiplying and taking the square root of the product of the initial state of the asset, the demand adjustment coefficient, and the efficiency factor as the numerator. This avoids the excessive influence of a single dimension on the resource equilibrium value. Simultaneously, the denominator, through the accumulation of the product of asset utilization efficiency and demand weights, reflects the actual utility of the asset in the allocation process and the urgency of satisfying the demand. This design ensures that the resource equilibrium value... It can comprehensively assess the value contribution and allocation pressure of assets, thereby accurately selecting the optimal allocation strategy combination that maximizes overall benefits in complex scenarios with high dimensions;

[0070] It is now assumed that the digital asset project to be allocated includes two digital supply chain finance notes: DBF2023001 and DBF2023002;

[0071] assets (DBF2023001): Yuan (initial appraised value), (Demand adjustment coefficient, high liquidity preference) (Efficiency factor, high returns and low risk) (Efficient utilization, fast turnover) (Demand weight, high strategic importance);

[0072] assets (DBF2023002): Assume this is a bill with a face value of 800,000 yuan, an initial appraised value of 790,000 yuan, a credit rating of AA, a remaining term of 60 days, and is a medium-liquidity, medium-risk bill, allocated to a income-oriented investor. Yuan (initial appraised value), (The demand adjustment coefficient generally doesn't match the needs of income-oriented investors because liquidity isn't a primary consideration.) (Efficiency factor, expected return is moderate to high, risk is moderate) (Utilization efficiency and turnover speed are average). (Demand weight, medium strategic importance);

[0073] Now we will perform formula calculations, specifically the numerator:

[0074] Item 1: ;

[0075] Item 2: ;

[0076] Sum of numerators: ;

[0077] Denominator calculation:

[0078] Item 1: ;

[0079] Item 2: ;

[0080] Sum of denominators: ;

[0081] Resource equilibrium value : ;

[0082] Calculate the resource equilibrium value for the beneficiaries of resource allocation. This result indicates that, under the current asset portfolio and demand settings, the overall resource allocation of digital assets has reached a relatively high equilibrium level, and its value itself represents a relative measure of comprehensive benefits and costs. The value is compared with a preset "optimal resource balance benchmark value" (e.g., set to 1000, indicating a relatively optimal allocation level). This indicates that the allocation scheme is superior to the benchmark. Therefore, the optimal allocation strategy combination is obtained through screening. This combination is the one that makes... A series of asset-demand matching schemes that maximize value, for example, allocating DBF2023001 notes to liquidity-seeking investor A and DBF2023002 notes to income-seeking investor B. This combination is due to its... Value reached And is marked as the optimal strategy at the current time point.

[0083] The dynamic strategy generation submodule is based on the optimal allocation strategy combination, identifies the allocation strategy number and time series chain (the time series chain includes issuance, initial endorsement, market quotation update time and key state timestamps and state values), identifies the strategy priority and state fluctuation range, and generates an intelligent dynamic equity allocation optimization model.

[0084] Based on the optimal allocation strategy combination, a unique strategy number OPT-EQ-20231024-001 is generated for the optimal allocation strategy combination. This is linked to form a timestamp, and its underlying equity distribution characteristics and time series relationship matrix are traced. A strategy time series chain is constructed, recording key time points and state changes from generation to execution. Next, the strategy priority and state fluctuation range are identified. The strategy priority is assessed based on the expected overall return increment, the degree of risk reduction, and the contribution to stability. For example, an expected return increment of 1%, a risk exposure reduction of 0.5%, and a stability contribution of 0.2 are weighted averaged and normalized to 0-10. The 0-priority scoring system will categorize the assets and define the fluctuation range of key asset parameters such as the market value of bills and the credit rating status of core enterprises. The market value fluctuation range of DBF2023001 is ±1.5%, and slight fluctuations within the AAA credit rating are allowed. If the fluctuation range exceeds this range, the strategy needs to be re-evaluated or adjusted. The fluctuation range is set based on historical market volatility data and risk tolerance to ensure the robustness of the strategy. Finally, an intelligent dynamic equity allocation optimization model is generated, which embeds the allocation strategy number, time series chain, strategy priority, and parameter status fluctuation range, serving as an independent optimization decision unit to guide the generation of operation instructions.

[0085] Please see Figure 4 The operation instruction generation module includes:

[0086] The matching submodule is required to call the intelligent dynamic rights allocation optimization model, extract the operation requirements and execution logic, analyze the complexity of the requirements and the logical correlation, and generate an operation requirement matching table.

[0087] The intelligent dynamic equity allocation optimization model is invoked. For allocation strategy OPT-EQ-20231024-001, the required operational steps and conditions for executing the optimal allocation strategy are analyzed from the model. For example, the operational requirements for allocating DBF2023001 to investor A include verifying the investor's identity, checking the validity of the digital wallet address, initiating a blockchain transfer transaction request, and recording the transaction hash timestamp. The execution logic clearly defines the order of operations, dependencies, and conditional branches. Successful identity verification is a prerequisite for wallet address checking, and a successful transaction request triggers transaction hash recording. Furthermore, the complexity of the operational requirements is analyzed. Logical correlation and complexity assessment are based on the number of interactions, data processing volume, and number of decision points involved in the operation steps. Initiating a blockchain transfer transaction request is marked as a high-complexity operation with a complexity score of 80, while recording the transaction hash is a low-complexity operation with a complexity score of 20. Logical correlation analysis constructs an operation step dependency graph to identify critical paths and parallel opportunities. For example, verifying investor qualifications and checking wallet addresses can be done in parallel, and the transfer transaction must be completed after both are completed. Finally, an operation requirement matching table is generated, which stores the operation requirements and their execution logic, complexity score, preconditions, postconditions, and estimated execution time in key-value pairs.

[0088] The specification embedding processing submodule standardizes the specification requirements based on the operation requirement matching table and uses the instruction compilation mechanism to embed the operation requirements in the matching table to form a specification index list.

[0089] Based on the operation requirement matching table, the system receives various operation requirements from the table, such as verifying investor A's identity and qualifications. This involves conducting compliance checks according to industry standards and laws and regulations. The instruction compilation mechanism parses these requirements into a call to the KYC service interface, passing in the investor ID and receiving the returned qualification level. It then determines whether the qualification level meets the transaction requirements, avoiding ambiguous expressions and transforming them into standardized, programmatic instructions. Secondly, a predefined instruction compilation mechanism standardizes the compliance requirements. All identity verification-related operations are compiled into a unified CALL_IDENTITY_VERIFICATION(InvestorID) instruction format. This compilation mechanism includes lexical, syntactic, and semantic analysis to ensure that all operation requirements are converted into instructions conforming to internal specifications. The standardized format embeds the standardized requirements into the corresponding operation items in the operation requirement matching table. For example, the operation item of initiating a blockchain transfer transaction (DBF2023001) embeds the standardized blockchain transaction instruction CALL_SMART_CONTRACT_TRANSFER(AssetID, FromAddress, ToAddress, Amount), ensuring that each operation has an executable and precise instruction. Finally, a standardized index list is formed, which is an extension of the operation requirement matching table. Each standardized instruction is assigned a unique standardized index IDNCL-TX-001 and associated with the original operation requirement and the specific execution instruction after compilation, providing a detailed standardized execution blueprint for subsequent instruction generation.

[0090] The instruction generation submodule calls the standard index list, decomposes the execution action based on the rights object parameters, assigns priority and execution conditions, limits the scope of requirements and establishes an exception handling path, marks the instruction number and version, performs permission verification, executes tabular output, writes back the status and records logs, forming a standardized intelligent operation instruction set.

[0091] The standardized operation specific instructions are retrieved from the standardized index list, such as the transfer transaction instruction for index NCL-TX-001. Based on the DBF2023001 bill parameters, the standardized instruction is broken down into a series of atomic operations, such as constructing the transaction parameter package, estimating gas fees, signing the transaction, and broadcasting the transaction. Priorities and execution conditions are assigned to each atomic operation. Each atomic operation is assigned an execution priority, with the signing transaction having a priority of 90, higher than the broadcast transaction's 80. Strict execution conditions are set: the signing transaction condition is that the transaction parameter package is correctly constructed and the user has completed authentication. The requirements are limited, and an exception handling path is established. The gas fee estimation requirement ranges from 10 Gwei to 100 Gwei; exceeding this range triggers an exception. The exception handling path includes automatically retrying lower gas fee rates and sending... Alarm requests for manual confirmation, etc., are labeled with instruction numbers and versions. Each atomic operation is assigned a unique instruction number INST-TX-001-01 and version number v1.0 to ensure instruction traceability and version management. Permission verification is performed, and the current user or module permission is automatically verified before execution. The execution output is tabulated, and the results are tabulated after the operation is completed, such as transaction hash, gas consumption, execution time, status write-back, updating the status of DBF2023001 ticket, and log recording. The execution process, parameters and results are recorded in detail to form a standardized intelligent operation instruction set. This instruction set includes all atomic operations, priorities, execution conditions, exception handling paths, instruction numbers, versions, permission requirements, tabulated output formats and log recording specifications, which can be scheduled by the execution engine.

[0092] Please see Figure 5 The equity transfer optimization module includes:

[0093] The exception handling collection submodule calls the standardized intelligent operation instruction set, extracts the exception handling records uploaded during the operation, analyzes the applicable scenarios and resolution efficiency of the processing records, and classifies them into an exception handling solution library.

[0094] The analysis of the applicable scenarios and resolution efficiency of the exception handling records refers to classifying the exception types and occurrence environments recorded in the exception handling records, and quantitatively evaluating the success rate and average processing time of the exception handling solutions under the classification to determine the applicable scenarios and resolution efficiency of the handling records.

[0095] The system invokes standardized intelligent operation instruction sets, such as private key service response timeout failure records, including exception type, occurrence time, associated asset IDDBF2023001, operation stage, and number of automatic retries. Subsequently, it analyzes the applicable scenarios and resolution efficiency of these records, classifying exception types and occurrence environments. Private key service response timeouts are categorized as external service dependency exceptions, occurring during high-concurrency transaction periods. The system quantitatively evaluates the success rate and average processing time of exception handling solutions. An automatic retry mechanism with an 85% success rate and an average processing time of 15 seconds is assessed as high resolution efficiency, applicable to situations where services are temporarily unavailable but core services are not down. If the success rate is below 50% or the average processing time exceeds 60 seconds, it is assessed as low resolution efficiency. The applicable scenarios and resolution efficiency of the processing records are determined and structured for storage. Finally, an exception handling solution library is formed, including solution description, success rate, average processing time, and applicable conditions. For example, an external service dependency exception - private key service timeout includes automatic retries of 3 times and switching to a backup private key service solution, providing a decision-making basis for subsequent exception handling and high-frequency exception identification.

[0096] The high-frequency anomaly extraction submodule is based on the anomaly handling solution library, which counts high-frequency anomaly points and key operation steps to obtain high-frequency anomaly tags.

[0097] The knowledge base update submodule incrementally updates the basic operation instruction text library based on high-frequency anomaly tags, combined with the key operation point index and the operation specification standardization table, records the knowledge base update cycle, and constructs an intelligent digital asset rights management decision set.

[0098] Record the knowledge base update cycle, which means that when the total amount of updated content in the basic operation instruction text library exceeds the preset character threshold, or when the number of changes of high-frequency abnormal tags exceeds the preset number within the preset monitoring period, the current time in timestamp format is recorded as the current knowledge base update cycle.

[0099] Based on high-frequency anomaly tags, "private key service timeout in the signature transaction process" is identified as a P1-critical anomaly. Combining the key operation point index and the standardized operation specification table, the basic operation instruction text library is incrementally updated. The key operation point index points to the signature transaction operation, and the standardized operation specification table provides the standard execution flow and dependencies for that operation. In the basic operation instruction text library, the descriptions, execution logic, and anomaly handling branches of the signature transaction operation are modified and improved. For example, in the signature transaction anomaly handling logic, a command is added to automatically switch to a backup private key service provider when the private key service times out 5 times consecutively, or the current private key service call parameters are optimized. The knowledge base update cycle is recorded. During this period, when the total updated content of the basic operation instruction text library exceeds the preset character threshold of 500 characters, or when the number of changes of high-frequency anomaly tags exceeds the preset 5 times within the preset 24-hour monitoring period, the current timestamp format is recorded as the knowledge base update cycle. This update triggers the update recording mechanism because the newly added instruction content exceeds 500 characters, and an intelligent digital asset rights management decision set is constructed. This decision set contains the latest and optimized operation instructions and processing solutions, integrates the latest high-frequency anomaly tags and their associated solutions, and serves as the core decision engine to guide all future digital asset rights management operations, ensuring that the system adapts to changes in the external environment and internal anomalies, and optimizes operational efficiency and stability.

[0100] Please see Figure 6 The instruction distribution module includes:

[0101] The instruction version management submodule calls the intelligent digital asset rights management decision set, identifies the version number and update time of the operation instruction set, compares the current version with the original version, selects the current version file, and generates an instruction version mapping table.

[0102] The system invokes the intelligent digital asset rights management decision set to obtain its latest decision information, including updating and optimizing the basic operation instruction text library and the high-frequency anomaly tag association solution. It identifies the operation instruction set version number and update time, reads the currently effective instruction set version v2.3.1 and its most recent update time (10:00-24:04:30:00) from the decision set, compares the current version with the original version, and performs a difference comparison between the currently effective instruction set version v2.3.1 and the initial deployment original version v1.0.0. Through file hash value comparison and semantic difference analysis, it identifies the specific modifications to the instruction set. For example, it detects that the abnormal handling logic for signature transaction operations has been modified in v2.3.1, and adds a backup private key service switching logic. It selects the current version file and chooses the latest verified v2.3.1 version instruction file as the execution basis to ensure that subsequent operations are based on the latest optimized logic. It generates an instruction version mapping table, which records each operation instruction version number, modification time, associated decision set ID, and corresponding specific instruction file storage path in the form of hash mapping or database index, providing an efficient mechanism for accurate instruction lookup and version backtracking.

[0103] The distribution path generation submodule, based on the instruction version mapping table, combines the rights object number and rights type to identify the distribution path of the corresponding rights object, records the path identifier and update timestamp, and generates an intelligent instruction distribution path object mapping table.

[0104] Based on the instruction version mapping table, the latest version information v2.3.1 of instruction IDINST-TX-001 is obtained. For digital supply chain finance bill DBF2023001, its rights type is on-chain transferable digital bill. According to the rule engine, the operation instructions for this type of rights object need to be distributed to the blockchain service execution platform and financial accounting. For centralized registration type digital equity rights objects, the instructions need to be distributed to centralized registration and compliance audit. The path identifier and update timestamp are recorded. A unique path identifier PATH-DBF-BLOCKCHAIN-001 is generated for each identified distribution path, and the generation or update timestamp 10-2404:35:00 is recorded to ensure the transparency and auditability of the distribution path. A smart instruction distribution path object mapping table is generated. This mapping table is stored in structured data and includes rights object ID, rights type, instruction ID, version number, corresponding distribution path identifier and its detailed routing information, such as target service IP address, port, API endpoint, to ensure that the operation instructions are accurately transmitted to the target for execution and efficiently complete the transfer and management of digital asset rights.

[0105] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A digital asset rights management system, characterized in that, The system includes: The rights identification module includes: The status information acquisition submodule collects digital asset status information in real time, removes interference signals and abnormal fluctuation information, and generates equity benchmark value. The digital asset status information refers to real-time collected on-chain transaction data, order fulfillment rate and credit rating of core enterprise ERP system, and secondary market transaction quotations of third-party financial markets; The status interval identification submodule identifies the status interval corresponding to the equity based on the equity benchmark value, combined with the asset status and equity demand, and marks the upper and lower limits of the interval to obtain the equity distribution characteristics. The equity distribution characteristics include asset number, equity benchmark value, status range, upper and lower limits of the range, liquidity score and risk score; The feature distribution construction submodule calls the equity distribution features to identify the time series and state characteristics of the equity, formulates the feature structure and sets the association weights based on the asset number and state interval, and establishes an equity distribution and state mapping diagram. The time series covers key status change timestamps for digital asset issuance, initial endorsement, and market price updates. The status characteristics include historical changes in the credit rating of digital assets, frequency of changes in holders, and secondary market trading activity. The defined feature structure includes asset number, current status range, credit rating, remaining maturity category, market liquidity score, and risk score; The set association weight refers to the weight of each characteristic attribute in the defined feature structure. This weight reflects the degree of influence of different characteristic attributes on the stability of equity value and allocation priority. The equity distribution and state mapping diagram uses the asset number as a node and the state characteristics of the digital asset as a node attribute. The relationship between nodes represents the state changes of the asset at different points in time. The equity allocation optimization module extracts the time series and state characteristics corresponding to the equity based on the equity distribution and state mapping diagram, and selects the optimal allocation strategy combination based on the asset status and equity demand to establish an intelligent dynamic equity allocation optimization model. The intelligent dynamic rights allocation optimization model embeds allocation strategy number, time series chain, strategy priority and parameter state fluctuation range, which serve as an independent optimization decision unit to guide the generation of operation instructions; The allocation strategy number refers to a unique identifier in the optimal allocation strategy combination; The operation instruction generation module is based on the intelligent dynamic rights allocation optimization model. It parses the operation steps and conditions required to execute the optimal allocation strategy and extracts the execution logic from the model. It generates the corresponding operation instruction set using the instruction compilation mechanism and embeds the normative requirements and security constraints to form a standardized intelligent operation instruction set. The rights and interests transfer optimization module incrementally updates the basic operation instruction text library based on the standardized intelligent operation instruction set and the abnormal handling records uploaded during operation, extracts high-frequency abnormal points and key operation links, and constructs an intelligent digital asset rights and interests management decision set. The intelligent digital asset rights management decision set includes an anomaly handling scheme library, high-frequency anomaly tags, key operation point index, knowledge base update cycle, and standardized operation specification table.

2. The digital asset rights management system according to claim 1, characterized in that, The standardized intelligent operation instruction set includes operation requirements, a list of standard indexes, a description of safety constraints, instruction numbers, and a sequence of execution steps.

3. The digital asset rights management system according to claim 1, characterized in that, The rights allocation optimization module includes: The resource balancing calculation submodule uses the following formula: ； Calculate the resource equilibrium value for the beneficiaries of resource allocation and select the optimal allocation strategy combination; in, The resource equilibrium value representing the beneficiaries of resource allocation. This represents the initial state of the i-th asset. This represents the demand adjustment coefficient for the i-th asset, which is adjusted according to the preference of specific equity demanders for asset characteristics. The efficiency factor represents the i-th asset, which quantifies the overall benefits the asset can generate under the expected allocation strategy, including potential return, risk mitigation capability, and strategic value. This represents the utilization efficiency of the i-th asset, which assesses the degree to which the asset is effectively utilized in actual operation. Represents the demand weight of the i-th asset. The number of representative resource items; The dynamic strategy generation submodule, based on the optimal allocation strategy combination, identifies the allocation strategy number and time series chain, recognizes the strategy priority and state fluctuation range, and generates an intelligent dynamic rights allocation optimization model.

4. The digital asset rights management system according to claim 3, characterized in that, The operation instruction generation module includes: The matching submodule is required to call the intelligent dynamic rights allocation optimization model, extract the operation requirements and execution logic, analyze the complexity of the requirements and the logical correlation, and generate an operation requirement matching table. The specification embedding processing submodule, based on the operation requirement matching table, uses an instruction compilation mechanism to standardize the specification requirements, embeds the operation requirements in the matching table, and forms a specification index list. The instruction generation submodule calls the standardized index list, decomposes the execution action based on the rights object parameters, assigns priority and execution conditions, limits the scope of requirements and establishes an exception handling path, marks the instruction number and version, performs permission verification, executes tabular output, writes back the status and records logs, forming a standardized intelligent operation instruction set.

5. The digital asset rights management system according to claim 4, characterized in that, The rights and interests transfer optimization module includes: The exception handling collection submodule calls the standardized intelligent operation instruction set, extracts the exception handling records uploaded during the operation, analyzes the applicable scenarios and resolution efficiency of the processing records, and classifies them into an exception handling solution library. The high-frequency anomaly extraction submodule, based on the anomaly handling scheme library, counts high-frequency anomaly points and key operational steps to obtain high-frequency anomaly tags; The knowledge base update submodule incrementally updates the basic operation instruction text library based on the high-frequency anomaly tags, combined with the key operation point index and the operation specification standardization table, records the knowledge base update cycle, and constructs an intelligent digital asset rights management decision set.

6. The digital asset rights management system according to claim 5, characterized in that, The applicable scenarios and resolution efficiency of the analysis and processing records refer to the determination of the applicable scenarios and resolution efficiency of the processing records by classifying the anomaly types and occurrence environments recorded in the anomaly processing records, and quantitatively evaluating the success rate and average processing time of the anomaly processing solutions under the classification. The record knowledge base update cycle refers to the current knowledge base update cycle when the total amount of updated content in the basic operation instruction text library exceeds a preset character threshold, or when the number of changes of high-frequency abnormal tags within a preset monitoring period exceeds a preset number.

7. The digital asset rights management system according to claim 1, characterized in that, The system also includes an instruction distribution module: Based on the intelligent digital asset rights management decision set, the instruction distribution module automatically allocates the corresponding operation instruction set for the rights object according to the rights object number and rights type, marks the instruction version number and update time, and outputs the intelligent instruction distribution path object mapping table. The intelligent instruction distribution path object mapping table includes the rights object number, operation instruction number, instruction version number, update timestamp, and distribution path identifier.

8. The digital asset rights management system according to claim 7, characterized in that, The instruction distribution module includes: The instruction version management submodule calls the intelligent digital asset rights management decision set, identifies the version number and update time of the operation instruction set, compares the current version with the original version, selects the current version file, and generates an instruction version mapping table. The distribution path generation submodule, based on the instruction version mapping table and combining the rights object number and rights type, identifies the distribution path of the corresponding rights object, records the path identifier and update timestamp, and generates an intelligent instruction distribution path object mapping table.

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