Metacosm asset management method based on digital twinning

Abstract

The invention relates to the technical field of asset management, and discloses a meta universe asset management method based on digital twinning, which comprises the following steps: step 1, modeling virtual assets by constructing a digital twinning system, establishing digital twinning bodies corresponding to the virtual assets in a real world and a meta universe environment, the digital twin comprises basic attributes, historical transaction records and market behavior data of assets, an initial value state of the assets is set in the system, and a transaction rule, an asset circulation mechanism and external economic influence factors in a market environment are modeled. Virtual assets are modeled by combining a digital twin technology and a quantum information theory, market behaviors of the assets are described by using a quantum state, market value changes of the assets are calculated in real time, assets are evaluated in a dynamic market, and a reliable and accurate virtual asset value prediction effect in a complex market environment is obtained.

Description

Technical Field

[0001] The present invention relates to the field of asset management technology, and specifically to a metaverse asset management method based on digital twins. Background Art

[0002] With the rapid development of the Metaverse and virtual assets, the transactions and applications of virtual assets are gradually increasing around the world. However, the particularity and complexity of the virtual asset market pose many challenges to its value assessment. Traditional asset valuation methods, such as regression analysis based on historical data and simple market trend forecasting, cannot effectively capture nonlinear changes, external disturbances, and complex multi-dimensional correlations in the market.

[0003] Existing asset management methods mainly rely on static models and simple linear assumptions, ignoring the potential dynamic changes and complex interactions in the market, resulting in poor accuracy and timeliness of asset valuations in the face of market fluctuations, policy changes, and technological innovation emergencies. In addition, many traditional methods do not fully utilize real-time data, and are unable to update and dynamically adjust the value of assets in real time, resulting in errors and uncertainties in asset valuation results in a rapidly changing market environment.

[0004] To solve the above problems, scholars have tried to apply digital twin technology to virtual asset management. Digital twin technology creates a digital model of virtual assets to keep physical assets consistent with their digital mirror images. Using this technology, the accuracy and flexibility of asset management can be improved by simulating and predicting the behavior of virtual assets in different market environments. However, digital twin technology still faces the problem of insufficient modeling of market complexity, especially when faced with multi-factor influences and nonlinear fluctuations, traditional digital twin models cannot fully reflect the real market performance of assets.

[0005] On the other hand, quantum information theory has been proposed as a new method to deal with the complexity and uncertainty in asset value changes. Through quantum state modeling, the state of assets can be accurately described and deduced under the quantum computing framework, providing advanced tools for the dynamic evaluation of virtual assets. However, combining quantum information with virtual asset management, especially how to quantify market behavior in practical applications, is still a technical problem that has not been fully solved.

[0006] In addition, the nonlinear volatility characteristics in the market have not been fully addressed. The volatility of the virtual asset market is much higher than that of the traditional market. Its changes are affected by the macroeconomic environment and are affected by the interweaving of multiple factors such as speculation, sentiment fluctuations, and policy changes. Existing models have limitations in dealing with complex market fluctuations and lack effective models to capture the nonlinear dynamics of asset prices.

[0007] Therefore, those skilled in the art provide a metaverse asset management method based on digital twins to solve the above-mentioned problems. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides a metaverse asset management method based on digital twins to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A metaverse asset management method based on digital twins, comprising:

[0010] Step 1: Model virtual assets by building a digital twin system, and establish digital twins corresponding to virtual assets in the real world and the metaverse environment. The digital twins contain the basic attributes of the assets, historical transaction records, and market behavior data, and set the initial value state of the assets in the system. Model the transaction rules, asset circulation mechanisms, and external economic influencing factors in the market environment, so that the dynamic behavior of assets in the market can be tracked and simulated;

[0011] Step 2: Use quantum information theory to characterize the asset state in the digital twin, map the value state of the virtual asset into a quantum state, which is used to represent the evolution trend of the asset in market fluctuations. Combined with the historical data in the digital twin, the value distribution of the asset is defined through the quantum state modeling method, and the initial parameters of the quantum state are set, so that the market performance of the asset can be dynamically described under the framework of quantum information theory;

[0012] Step 3: Based on the market behavior data of the digital twin and the changes in the market environment, the market value changes of virtual assets are simulated through the time evolution of the quantum state. The value change process of the asset is calculated based on the evolution characteristics of the quantum state, so that the future value trend of the asset can be deduced under the quantum information framework, and the asset status is updated in real time in the digital twin system;

[0013] Step 4: Simulate the price fluctuations of the virtual asset market by constructing a nonlinear dynamics model. The nonlinear dynamics model uses the evolution results of the quantum state as input variables to perform nonlinear modeling on the price changes of virtual assets. In the modeling process, the market dynamic equation of the asset is established and parameters are set to characterize the nonlinear characteristics in the market.

[0014] Step 5: Use the Kalman filter method to estimate the asset market state calculated based on the nonlinear dynamics model. The Kalman filter uses the market state output of the nonlinear dynamics model as the initial state, combines the market observation data for recursive update, and adjusts the asset value state estimation result. During the filtering calculation process, the confidence of the state estimation is adjusted according to the historical transaction data of the asset. At the same time, the asset state changes at each moment are stored and recorded in the digital twin system.

[0015] Step 6: Combine the Bayesian reasoning method to optimize the asset value assessment results. The Bayesian reasoning method uses the estimation results of the Kalman filter as prior information and combines the latest transaction data in the market environment to perform a posteriori updates on the asset value. The reasoning process comprehensively considers price fluctuations in the market, changes in user trading behavior, and the impact of external policy factors. Ultimately, a dynamic assessment of the asset value is formed, and the assessment results are stored in the digital twin system for asset management and decision analysis.

[0016] Preferably, the initial value state of the asset in step 1 is obtained by calculating the asset's historical transaction data and market behavior data through quantum probability distribution. The calculation formula for the initial value state of the asset is as follows:

[0017]

[0018] Among them, V 0 is the initial value state of the asset, N is the historical transaction number of the asset, P i is the market impact probability of the ith transaction, V i is the transaction value of the asset at the i-th transaction.

[0019] Preferably, the quantum state evolution in step 2 is time-evolved through the action of Hamiltonian, and the evolution equation of the asset state under the influence of market dynamics is as follows:

[0020]

[0021] Where i is a complex unit, h is the reduced Planck constant, |ψ s (t)> is the quantum state of the asset at time t, is the Hamiltonian operator that describes the impact of the market environment on the asset status. Represents the rate at which the asset value state changes from time t.

[0022] Preferably, the market environment impact in step 3 is corrected by quantum interference effect, and the evolution trend of asset value is calculated by the following expected value:

[0023]

[0024] in, is the expected value of the asset market value, is the market value operator, |<ψ s (t) is the quantum state of the asset, <ψ s (t)| is the conjugate transpose of the asset quantum state.

[0025] Preferably, the nonlinear dynamics model in step 4 is modeled using a high-dimensional nonlinear system, and the dynamic equation of the virtual asset market is as follows:

[0026]

[0027] Among them, X a is the asset price dynamic state variable, Y a is the market transaction behavior variable, Z a is the external disturbance of the market, α, β, γ, δ, ∈ and ζ are system parameters, d represents the differential operator, dt represents the infinitesimal increment of the time variable,

[0028] Indicates the change in asset price status per unit time,

[0029] It indicates the change in market trading behavior per unit time.

[0030] Represents the change in external market impact per unit time.

[0031] Preferably, the Kalman filter in step 5 adopts an extended state estimation method, and the Kalman filter state estimation equation is as follows:

[0032] X k =M k X k-1 +N k U k +W k ,

[0033] Z k =P k X k +V k ,

[0034] Among them, X k is the asset state vector, M k is the state transfer matrix, X k-1 is the state vector of the asset at time k-1, N k is the control input matrix, U k is the control input vector, W k is the process noise, Z k is the market observation value, P k is the observation matrix, Vk is the observation noise.

[0035] Preferably, the Kalman gain calculation in step 5 adopts a dynamic adjustment strategy, and the calculation formula is as follows:

[0036]

[0037] Among them, K k is the Kalman gain at time k, S k is the prediction error covariance matrix, P k is the observation matrix, R k is the observation noise covariance matrix, is the transpose of the observation matrix,

[0038] is the inverse matrix of the covariance correction term.

[0039] Preferably, the Bayesian reasoning optimization method in step 6 adopts dynamic posterior distribution update, and the posterior probability of the asset status is calculated as follows:

[0040]

[0041] Among them, P(X t |Z t ) is the given market observation value Z t The posterior probability of the asset state, P(Z t |X t ) is the likelihood function of the market observation model, P(X t ) is the prior distribution, P(Z t ) is the normalization factor of the observed data.

[0042] Preferably, the asset evaluation process in step 6 is combined with the long-term market trend forecast, and the long-term market trend function is calculated as follows:

[0043]

[0044] Among them, T m (t) is the market trend function at time t, M is the number of historical trend data points, ω j is the j-th trend weight, λ j is the trend attenuation coefficient, t is the time variable, and e represents the base of the natural logarithm.

[0045] Preferably, the Metaverse asset management method includes asset allocation optimization based on the evaluation results, and the asset allocation optimization model is as follows:

[0046]

[0047] Among them, w iis the weight of the ith asset, w j is the weight of the jth asset, V i is the assessed value of the ith asset, θ is the risk aversion parameter, C ij is the covariance matrix between asset i and asset j, and N is the total number of assets in the portfolio.

[0048] The present invention provides a metaverse asset management method based on digital twins, which has the following beneficial effects:

[0049] 1. The present invention combines digital twin technology with quantum information theory to model virtual assets, uses quantum states to describe the market behavior of assets, calculates the changes in the market value of assets in real time, realizes the evaluation of assets in a dynamic market, and obtains reliable and accurate virtual asset value prediction effects in a complex market environment.

[0050] 2. The present invention introduces a nonlinear dynamic model to simulate the nonlinear fluctuations in the virtual asset market, capture the sudden fluctuations and complex interactions of the market, and achieve modeling of the dynamic changes in the virtual asset market, thereby obtaining a market behavior simulation effect that is more adaptable and predictive than traditional linear models.

[0051] 3. The present invention combines Kalman filtering and Bayesian reasoning methods to estimate and dynamically update the market status of virtual assets in real time, thereby achieving stability and real-time updating of asset evaluation results in a volatile market environment, and obtaining an asset evaluation effect that can quickly respond to market changes and reduce noise interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0053] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0054] The present invention is described in detail below in conjunction with the accompanying drawings:

[0055] Example:

[0056] Please see attached Figure 1 , an embodiment of the present invention provides a metaverse asset management method based on digital twins, comprising:

[0057] Step 1: Model virtual assets by building a digital twin system, and establish digital twins corresponding to virtual assets in the real world and the metaverse environment. The digital twins contain the basic attributes of the assets, historical transaction records, and market behavior data. The initial value state of the assets is set in the system, and the transaction rules, asset circulation mechanisms, and external economic influencing factors in the market environment are modeled, so that the dynamic behavior of assets in the market can be tracked and simulated;

[0058] Step 2: Use quantum information theory to characterize the asset status in the digital twin, map the value status of the virtual asset into a quantum state, which is used to represent the evolution trend of the asset in market fluctuations. Combined with the historical data in the digital twin, the value distribution of the asset is defined through the quantum state modeling method, and the initial parameters of the quantum state are set, so that the market performance of the asset can be dynamically described under the framework of quantum information theory;

[0059] Step 3: Based on the market behavior data of the digital twin and the changes in the market environment, the market value changes of virtual assets are simulated through the time evolution of the quantum state. The value change process of the asset is calculated based on the evolution characteristics of the quantum state, so that the future value trend of the asset can be deduced under the quantum information framework, and the asset status is updated in real time in the digital twin system;

[0060] Step 4: Simulate the price fluctuations of the virtual asset market by constructing a nonlinear dynamics model. The nonlinear dynamics model uses the evolution results of the quantum state as input variables to perform nonlinear modeling on the price changes of virtual assets. In the modeling process, the market dynamic equation of the asset is established and parameters are set to characterize the nonlinear characteristics in the market.

[0061] Step 5: Use the Kalman filter method to estimate the asset market state calculated based on the nonlinear dynamics model. The Kalman filter uses the market state output of the nonlinear dynamics model as the initial state, combines the market observation data for recursive update, and adjusts the asset value state estimation result. During the filtering calculation process, the confidence of the state estimation is adjusted according to the historical transaction data of the asset. At the same time, the asset state changes at each moment are stored and recorded in the digital twin system.

[0062] Step 6: Combine the Bayesian reasoning method to optimize the asset value assessment results. The Bayesian reasoning method uses the estimation results of the Kalman filter as prior information and combines the latest transaction data in the market environment to perform a posteriori updates on the asset value. The reasoning process comprehensively considers price fluctuations in the market, changes in user trading behavior, and the impact of external policy factors. Ultimately, a dynamic assessment of the asset value is formed, and the assessment results are stored in the digital twin system for asset management and decision analysis.

[0063] Benefits of Step 1: By establishing digital twins corresponding to virtual assets in the real world and the metaverse environment, the basic attributes, historical transaction records, and market behavior data of assets can be fully captured. The establishment of digital twins ensures that the dynamic behavior of assets can be tracked and simulated in real time, providing complete basic data for subsequent asset management and decision-making. By setting the initial value state of assets and considering the transaction rules, asset circulation mechanisms, and external economic factors in the market environment, the model can be more closely aligned with the real market environment, improving the accuracy and reliability of asset management.

[0064] Benefits of Step 2: Quantum information theory uses quantum state representation of the value state of virtual assets to describe the market fluctuations of assets under the quantum framework. The introduction of quantum states enables the value of assets to be accurately represented and quantified according to market fluctuations, thereby capturing the complex changing trends of virtual assets in the dynamic market. The application of quantum state models can greatly improve the accuracy of virtual asset value assessment and provide more efficient and innovative methods for subsequent value prediction and decision analysis.

[0065] Benefits of step 3: The time evolution simulation of quantum states can predict the future value trend of virtual assets based on market behavior data. By updating the status of assets in real time in the digital twin, this step can efficiently and dynamically adjust the asset valuation to ensure that market changes are reflected in a timely manner. Make accurate predictions in a complex and changing market environment, ensure the foresight and effectiveness of asset management decisions, and reduce asset valuation errors caused by market fluctuations.

[0066] Benefits of step 4: The introduction of nonlinear dynamics models can simulate price fluctuations in the virtual asset market and capture nonlinear characteristics and complex interactions in the market. Compared with traditional linear models, nonlinear dynamics models are suitable for describing complex changes and sudden fluctuations in the virtual asset market. By establishing market dynamic equations, nonlinear models can accurately reflect various influencing factors in the market.

[0067] Benefits of step 5: Kalman filtering can effectively estimate and dynamically update the state of the asset market, combining historical transaction data and market observations to reduce the impact of uncertainty and noise. During the filtering calculation process, the Kalman filter can dynamically adjust according to the confidence of the state estimate to ensure that the asset value assessment results are accurate and stable. By updating the market status in real time, it can help asset managers adjust strategies in a timely manner and enhance the responsiveness of asset management.

[0068] Benefits of Step 6: Bayesian reasoning can optimize the asset valuation based on Kalman filtering. By combining the latest market data and historical data, Bayesian reasoning can perform posterior updates while taking into account uncertainty, thereby achieving accurate predictions of asset values.

[0069] The initial value state of the asset in step 1 is obtained by calculating the asset's historical transaction data and market behavior data through quantum probability distribution. The calculation formula for the initial value state of the asset is as follows:

[0070]

[0071] Among them, V 0 is the initial value state of the asset, N is the historical transaction number of the asset, P i is the market impact probability of the ith transaction, V i is the transaction value of the asset at the i-th transaction.

[0072] By using historical asset transaction data and market behavior data, combined with quantum probability distribution to calculate the initial value state of the asset, the changing patterns of the asset in the historical transaction process can be accurately captured. The calculation method based on quantum probability distribution can avoid overly simplified assumptions in traditional methods and better reflect the complexity of market fluctuations and asset changes.

[0073] When calculating the initial value of an asset, the market impact probability and transaction value of the transaction are combined. This means that the initial value of an asset depends on its historical price, and the market impact of each transaction is comprehensively considered, which can accurately grasp the contribution of each transaction link to the asset value.

[0074] The introduction of quantum probability distribution, using the advantages of probability distribution in quantum information theory, can more effectively process complex market data. Compared with traditional statistical methods, quantum probability distribution can model the initial value of assets through a higher-dimensional state space, thereby providing more accurate and stable evaluation results.

[0075] The initial value state of the asset is based on historical data and dynamically considers the market scenario at each transaction point. It can adapt to market changes and more flexibly capture the value fluctuations of assets in a complex market environment.

[0076] The quantum state evolution in step 2 evolves over time through the action of the Hamiltonian. The evolution equation of the asset state under the influence of market dynamics is as follows:

[0077]

[0078] Where i is a complex unit, h is the reduced Planck constant, |ψ s (t)> is the quantum state of the asset at time t, is the Hamiltonian operator that describes the impact of the market environment on the asset status. Represents the rate at which the asset value state changes from time t.

[0079] Through quantum state evolution, the value change of assets can be accurately modeled in the time dimension. Hamiltonian, as the core of quantum system, can accurately describe the impact of market environment on the value state of assets through its role. The modeling method can capture the nonlinear dynamics and complex changes in the market, and provide more accurate predictions for the future value expectations of assets.

[0080] Quantum state evolution mathematically expresses the impact of the market environment on the asset state through the Hamiltonian operator. Compared with traditional linear regression models or static analysis methods, quantum state evolution can flexibly adapt to market fluctuations and respond to changes in asset behavior and market environment in real time. Especially in the face of drastic market fluctuations, quantum information theory can effectively process high-dimensional complex data and improve the stability and accuracy of asset value assessment.

[0081] Quantum state evolution can accurately and dynamically describe the state of an asset at time t, and can simultaneously consider the effects and interactions of multiple market factors on asset prices. By using a combination of quantum states and Hamiltonian operators, the present invention can deeply analyze the performance of virtual assets in different market environments and provide comprehensive predictions, especially in high-volatility market environments.

[0082] The market environment impact in step 3 is corrected by the quantum interference effect, and the evolution trend of asset value is calculated by the following expected value:

[0083]

[0084] in, is the expected value of the asset market value, is the market value operator, |<ψ s (t) is the quantum state of the asset, <ψ s (t)| is the conjugate transpose of the asset quantum state.

[0085] By introducing the quantum interference effect, the present invention can effectively correct the impact of the market environment on assets. Variable factors in the market environment, such as policy fluctuations, changes in investor sentiment, and external economic disturbances, will affect the market value of virtual assets in a nonlinear and multi-dimensional manner. The quantum interference effect can effectively handle these complex market factors, making the evolution of asset value more consistent with the complex interactions and dynamic changes in the real market.

[0086] By calculating the expected value of the market value, combined with the evolution of quantum states and quantum interference effects, the market behavior of virtual assets at different points in time can be accurately predicted. This method can fully consider the superposition effect of various factors in the market and improve the accuracy of market trend prediction. In the face of market fluctuations and emergencies, this method can update the prediction results in real time, avoiding the prediction errors caused by traditional methods due to ignoring dynamic changes.

[0087] By using the interaction between quantum states and market value operators, we can more carefully characterize the changes in asset value under different market conditions. The introduction of quantum states enables the model to handle traditional linear changes and more complex multi-factor interference. Through the quantum interference effect, the evolution trend of asset value can more finely reflect the slight changes in the market, improving the flexibility and adaptability of the asset valuation model.

[0088] The nonlinear dynamics model in step 4 adopts high-dimensional nonlinear system modeling, and the dynamic equation of the virtual asset market is as follows:

[0089]

[0090]

[0091] Among them, X a is the asset price dynamic state variable, Y a is the market transaction behavior variable, Z a is the external disturbance of the market, α, β, γ, δ, ∈ and ζ are system parameters, d represents the differential operator, dt represents the infinitesimal increment of the time variable,

[0092] Indicates the change in asset price status per unit time,

[0093] It indicates the change in market trading behavior per unit time.

[0094] Represents the change in external market impact per unit time.

[0095] The nonlinear dynamics model can effectively simulate the complex fluctuations in the virtual asset market, especially when faced with the combined effects of multiple market factors. Compared with the traditional linear model, this model can more accurately reflect the nonlinear characteristics and dynamic interactions in the market, and provide asset evaluation results that are more in line with actual market conditions.

[0096] This nonlinear dynamics model uses high-dimensional nonlinear system modeling, which can simultaneously handle the interaction of asset prices, market trading behaviors and external market disturbance factors. Through comprehensive modeling, it can accurately model the multi-dimensional fluctuations of the asset market, avoid the simplified assumptions of a single factor in traditional models, and better capture the complex dynamics of the market.

[0097] The dynamic equations in the model take into account the asset price status, market trading behavior and external disturbances in the market. In this way, the relationship between price fluctuations in the market and external factors can be more realistically reflected, especially in the face of market emergencies and irrational behavior, so that asset valuations can be quickly responded and adjusted.

[0098] The Kalman filter in step 5 adopts the extended state estimation method, and the Kalman filter state estimation equation is as follows:

[0099] X k =M k X k-1 +N k U k +W k ,

[0100] Z k =P k X k +V k ,

[0101] Among them, X k is the asset state vector, M k is the state transfer matrix, X k-1 is the state vector of the asset at time k-1, N k is the control input matrix, U k is the control input vector, W k is the process noise, Z k is the market observation value, P k is the observation matrix, V k is the observation noise.

[0102] The Kalman gain calculation in step 5 adopts a dynamic adjustment strategy, and the calculation formula is as follows:

[0103]

[0104] Among them, K k is the Kalman gain at time k, S k is the prediction error covariance matrix, P k is the observation matrix, R k is the observation noise covariance matrix, is the transpose of the observation matrix,

[0105] is the inverse matrix of the covariance correction term.

[0106] The Kalman filter method in step 5 provides accurate estimation and real-time update of the virtual asset state by extending the state estimation and dynamic adjustment strategy. Through recursive update, the Kalman filter can adjust the asset state estimation in real time according to historical data and market observation results, reduce the impact of market noise on the evaluation results, and improve the accuracy and stability of asset management. At the same time, the dynamic adjustment mechanism of the Kalman gain enables the system to flexibly adapt to market fluctuations, automatically optimize the asset estimation process, and ensure the accuracy and real-time performance of asset evaluation in complex dynamic markets. Through this method, the present invention greatly improves the management effect of virtual assets and helps managers make more scientific and efficient decisions.

[0107] The Bayesian inference optimization method in step 6 uses dynamic posterior distribution update, and the posterior probability of the asset status is calculated as follows:

[0108]

[0109] Among them, P(X t |Z t ) is the given market observation value Z t The posterior probability of the asset state, P(Z t |X t ) is the likelihood function of the market observation model, P(X t ) is the prior distribution, P(Z t ) is the normalization factor of the observed data.

[0110] The asset evaluation process in step 6 is combined with the long-term market trend forecast. The long-term market trend function is calculated as follows:

[0111]

[0112] Among them, T m (t) is the market trend function at time t, M is the number of historical trend data points, ω j is the j-th trend weight, λ j is the trend attenuation coefficient, t is the time variable, and e represents the base of the natural logarithm.

[0113] Step 6 combines Bayesian reasoning with long-term market trend forecasting to achieve dynamic optimization and long-term forecasting of virtual asset value assessment. The Bayesian reasoning method can adjust the asset value assessment in real time through dynamic posterior distribution updates, improving the accuracy, stability and ability of asset management decisions to cope with market fluctuations. The long-term market trend forecast provides a long-term perspective for asset assessment, allowing managers to make more scientific and rational decisions in a complex market environment. This innovative method enables virtual asset management to respond efficiently to market changes in the short term, and provides strong support for long-term trend forecasting and strategy adjustments.

[0114] The Metaverse asset management method includes asset allocation optimization based on the evaluation results. The asset allocation optimization model is as follows:

[0115]

[0116] Among them, w i is the weight of the ith asset, w j is the weight of the jth asset, V i is the assessed value of the ith asset, θ is the risk aversion parameter, C ij is the covariance matrix between asset i and asset j, and N is the total number of assets in the portfolio.

[0117] This asset allocation optimization model can balance the risks and returns of various assets when optimizing the investment portfolio by combining the asset evaluation value and covariance matrix. By dynamically adjusting the weights of each asset, it ensures that the asset portfolio can maximize returns under given risk aversion parameters. This allows asset managers to flexibly adjust asset allocation according to market changes, thereby achieving the goals of risk control and return optimization.

[0118] The model can effectively consider the correlation between different assets by introducing the covariance matrix between assets. The covariance matrix reflects the relationship between asset price fluctuations, making asset allocation dependent on the performance of a single asset and comprehensively considering the mutual influence between assets. By optimizing asset allocation, the volatility of the asset portfolio can be effectively reduced and the stability of the overall investment portfolio can be improved.

[0119] By introducing the risk aversion parameter, the model can dynamically adjust asset allocation according to the investor's risk preference. If the investor's risk tolerance is low, the risk aversion parameter can be increased, and the optimization model will automatically adjust the investment portfolio to reduce the allocation of high-risk assets; otherwise, it will increase the allocation of high-risk assets to maximize potential returns.

[0120] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A metaverse asset management method based on digital twins, characterized in that: include: Step 1: Model virtual assets by building a digital twin system, and establish digital twins corresponding to virtual assets in the real world and the metaverse environment. The digital twins contain the basic attributes of the assets, historical transaction records, and market behavior data, and set the initial value state of the assets in the system. Model the transaction rules, asset circulation mechanisms, and external economic influencing factors in the market environment, so that the dynamic behavior of assets in the market can be tracked and simulated; Step 2: Use quantum information theory to characterize the asset state in the digital twin, map the value state of the virtual asset into a quantum state, which is used to represent the evolution trend of the asset in market fluctuations. Combined with the historical data in the digital twin, the value distribution of the asset is defined through the quantum state modeling method, and the initial parameters of the quantum state are set, so that the market performance of the asset can be dynamically described under the framework of quantum information theory; Step 3: Based on the market behavior data of the digital twin and the changes in the market environment, the market value changes of virtual assets are simulated through the time evolution of the quantum state. The value change process of the asset is calculated based on the evolution characteristics of the quantum state, so that the future value trend of the asset can be deduced under the quantum information framework, and the asset status is updated in real time in the digital twin system; Step 4: Simulate the price fluctuations of the virtual asset market by constructing a nonlinear dynamics model. The nonlinear dynamics model uses the evolution results of the quantum state as input variables to perform nonlinear modeling on the price changes of virtual assets. In the modeling process, the market dynamic equation of the asset is established and parameters are set to characterize the nonlinear characteristics in the market. Step 5: Use the Kalman filter method to estimate the asset market state calculated based on the nonlinear dynamics model. The Kalman filter uses the market state output of the nonlinear dynamics model as the initial state, combines the market observation data for recursive update, and adjusts the asset value state estimation result. During the filtering calculation process, the confidence of the state estimation is adjusted according to the historical transaction data of the asset. At the same time, the asset state changes at each moment are stored and recorded in the digital twin system. Step 6: Combine the Bayesian reasoning method to optimize the asset value assessment results. The Bayesian reasoning method uses the estimation results of the Kalman filter as prior information and combines the latest transaction data in the market environment to perform a posteriori updates on the asset value. The reasoning process comprehensively considers price fluctuations in the market, changes in user trading behavior, and the impact of external policy factors. Ultimately, a dynamic assessment of the asset value is formed, and the assessment results are stored in the digital twin system for asset management and decision analysis.

2. A digital twin-based metaverse asset management method according to claim 1, characterized in that: The initial value state of the asset in step 1 is obtained by calculating the asset's historical transaction data and market behavior data through quantum probability distribution. The calculation formula for the initial value state of the asset is as follows: Among them, V0 is the initial value state of the asset, N is the historical transaction number of the asset, and P i is the market impact probability of the ith transaction, V i is the transaction value of the asset at the i-th transaction.

3. The method for managing metaverse assets based on digital twins according to claim 1, characterized in that: The quantum state evolution in step 2 evolves over time through the action of the Hamiltonian. The evolution equation of the asset state under the influence of market dynamics is as follows: Where i is a complex unit, h is the reduced Planck constant, |ψ s (t)> is the quantum state of the asset at time t, is the Hamiltonian operator that describes the impact of the market environment on the asset status. Represents the rate at which the asset value state changes from time t.

4. The method for managing metaverse assets based on digital twins according to claim 1, characterized in that: The market environment impact in step 3 is corrected by the quantum interference effect, and the evolution trend of asset value is calculated by the following expected value: in, is the expected value of the asset market value, is the market value operator, |<ψ s (t) is the quantum state of the asset, <ψ s (t)| is the conjugate transpose of the asset quantum state.

5. The method for managing metaverse assets based on digital twins according to claim 1, characterized in that: The nonlinear dynamics model in step 4 adopts high-dimensional nonlinear system modeling, and the dynamic equation of the virtual asset market is as follows: Among them, X a is the asset price dynamic state variable, Y a is the market transaction behavior variable, Z a is the external disturbance of the market, α, β, γ, δ, ∈ and ζ are system parameters, d represents the differential operator, dt represents the infinitesimal increment of the time variable, Indicates the change in asset price status per unit time, It indicates the change in market trading behavior per unit time. Represents the change in external market impact per unit time.

6. The method for managing metaverse assets based on digital twins according to claim 1, characterized in that: The Kalman filter in step 5 adopts an extended state estimation method, and the Kalman filter state estimation equation is as follows: X k =M k X k-1 +N k U k +W k , Z k =P k X k +V k , Among them, X k is the asset state vector, M k is the state transfer matrix, X k-1 is the state vector of the asset at time k-1, N k is the control input matrix, U k is the control input vector, W k is the process noise, Z k is the market observation value, P k is the observation matrix, V k is the observation noise.

7. A digital twin-based metaverse asset management method according to claim 6, characterized in that: The Kalman gain calculation in step 5 adopts a dynamic adjustment strategy, and the calculation formula is as follows: Among them, K k is the Kalman gain at time k, S k is the prediction error covariance matrix, P k is the observation matrix, R k is the observation noise covariance matrix, is the transpose of the observation matrix, is the inverse matrix of the covariance correction term.

8. The method for managing metaverse assets based on digital twins according to claim 1, characterized in that: The Bayesian inference optimization method in step 6 adopts dynamic posterior distribution update, and the posterior probability of the asset status is calculated as follows: Among them, P(X t |Z t ) is the given market observation value Z t The posterior probability of the asset state, P(Z t |X t ) is the likelihood function of the market observation model, P(X t ) is the prior distribution, P(Z t ) is the normalization factor of the observed data.

9. A digital twin-based metaverse asset management method according to claim 8, characterized in that: The asset evaluation process in step 6 is combined with the long-term market trend forecast, and the long-term market trend function is calculated as follows: Among them, T m (t) is the market trend function at time t, M is the number of historical trend data points, ω j is the j-th trend weight, λ j is the trend attenuation coefficient, t is the time variable, and e represents the base of the natural logarithm.

10. The method for managing metaverse assets based on digital twins according to claim 1, characterized in that: The Metaverse asset management method includes asset allocation optimization based on the evaluation results. The asset allocation optimization model is as follows: Among them, w i is the weight of the ith asset, w j is the weight of the jth asset, V i is the assessed value of the ith asset, θ is the risk aversion parameter, C ij is the covariance matrix between asset i and asset j, and N is the total number of assets in the portfolio.

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