Asset screening and management method and system based on automated rules

Abstract

The present invention discloses an asset screening and management method and system based on automated rules, which uses the Monte Carlo simulation algorithm to perform value assessment and resource management on asset management projects. The method first obtains asset management list data and historical data, uses Monte Carlo simulation to perform value assessment on the historical data, and obtains resource consumption information. The management difficulty is assessed according to the resource load. If the load is too high, a value impact probability distribution model is constructed, and the weight of the impact of historical data characteristics on asset value is determined. By constructing a value path simulation decision tree and determining the number of simulations based on tolerance, each asset management project is simulated and analyzed, and finally an asset screening and management strategy is generated. The present invention can improve the efficiency and accuracy of asset management decisions, optimize resource allocation, and has high practical value and promotion prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of asset management, and in particular to an asset screening and management method and system based on automated rules. Background Art

[0002] In modern asset management, the accuracy and efficiency of asset screening and management decisions are crucial for improving asset allocation and risk control. To this end, the Monte Carlo simulation algorithm, a powerful numerical computing tool, has been widely used in areas such as asset valuation, risk assessment, and decision optimization. The Monte Carlo algorithm generates a probability distribution of asset values ​​by performing multiple random simulations on historical data of asset management projects, and uses this probability distribution to optimize asset management strategies.

[0003] However, Monte Carlo simulation algorithms face the problem of excessive resource consumption when performing path simulation. Because Monte Carlo simulations require numerous computational iterations to generate simulation paths and calculate valuations, they significantly consume computing resources (such as CPU, memory, and network bandwidth). In traditional asset management systems, especially those with large asset sizes and complex historical data, Monte Carlo simulations can require tens of thousands of simulation paths and times, leading to degraded system performance, low computational efficiency, and even computational interruptions or delays when resources are limited.

[0004] While some research has attempted to reduce resource consumption by reducing the number of simulation paths or simplifying the simulation model, these approaches often sacrifice simulation precision and accuracy. Optimizing resource consumption while ensuring simulation accuracy remains a pressing technical challenge, particularly in the complex environment of asset management.

[0005] Therefore, effectively optimizing resource consumption during Monte Carlo simulations, particularly in determining the simulation path and number of simulations, has become a key issue in improving the performance of asset management systems. By rationally optimizing these paths and times, computing resource requirements can be significantly reduced, improving the efficiency of asset screening and management while maintaining high-precision asset valuation results. This is also the core issue addressed by this invention. Summary of the Invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes an asset screening and management method and system based on automated rules.

[0007] A first aspect of the present invention provides an asset screening and management method based on automated rules, comprising:

[0008] Obtain asset management list data, obtain historical data of each asset management project based on the asset management list data, introduce a Monte Carlo simulation algorithm to perform value assessment on the historical data of each asset management project, and obtain resource consumption data during the value assessment process;

[0009] Evaluate the resource load of the asset management list based on the resource consumption data; if the resource load is greater than a preset value, obtain data features of historical data and construct a value impact probability distribution model;

[0010] Determining the weight of the value impact of historical data features on the asset management project based on the value impact probability distribution model, constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm, analyzing the decision tree based on the value impact weight, and determining several simulation paths for valuing the asset management project using a Monte Carlo simulation algorithm;

[0011] Obtaining the tolerance of the target asset management system for the accuracy of asset value assessment, and determining the number of simulations of the simulation path by the Monte Carlo simulation algorithm based on the tolerance;

[0012] The simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project. The asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy.

[0013] In this solution, the asset management list data is obtained, and the historical data of each asset management project is obtained based on the asset management list data. The Monte Carlo simulation algorithm is introduced to perform value assessment on the historical data of each asset management project, and the resource consumption data during the value assessment process is obtained. Specifically,

[0014] Obtaining asset management list data of a target asset management system, and obtaining historical data of each asset management project based on the asset management list data, wherein the historical data includes asset value data, loan data, borrower credit data, early repayment data, default and recovery rate data;

[0015] Introducing a Monte Carlo simulation algorithm, importing the Monte Carlo simulation algorithm into the target asset management system, and continuously updating the historical data, performing calculations based on the historical data continuously updated by the Monte Carlo simulation algorithm, and continuously performing real-time value evaluation on each asset management item in the asset management item list data;

[0016] Obtaining data on historical data called by the Monte Carlo simulation algorithm in the target asset management system during the process of performing value assessment on asset management projects, obtaining a call timestamp of each data item in the historical data by the Monte Carlo simulation algorithm based on the call data, and constructing a data access path based on the call timestamp;

[0017] Performing statistics on the data access path, determining the initial price simulation path and the initial number of simulations in the process of the Monte Carlo simulation algorithm performing value assessment on the asset management project, and obtaining the single resource consumption of a single simulation of a single initial price simulation path of the Monte Carlo simulation algorithm;

[0018] The resource consumption data during the value assessment of each asset management project by the Monte Carlo simulation algorithm is determined based on the single resource consumption situation. The resource consumption data includes CPU usage, memory occupancy, database interaction frequency, and network bandwidth occupancy.

[0019] In this solution, the resource load of the asset management list is evaluated based on the resource consumption data. If the resource load is greater than a preset value, the data features of the historical data are obtained and a value impact probability distribution model is constructed. Specifically,

[0020] Obtaining operational load weight information of each resource consumption data item on the target asset management system based on the resource consumption data, performing resource load evaluation on the resource consumption data based on the operational load weight information, and obtaining resource load status of the target asset management system for managing the asset management list;

[0021] Comparing the resource load with a preset value, if the resource load is greater than the preset value, constructing a data matrix from the historical data, normalizing the data matrix, calculating a covariance matrix of the normalized data matrix, and performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0022] Sort the eigenvalues ​​from large to small, select the eigenvectors corresponding to the first k eigenvalues ​​to construct a projection matrix, and multiply the standardized data matrix by the projection matrix to obtain the data features of the historical data;

[0023] Calculating the mean and variance of each data feature based on the historical data, and constructing a probability density function of the data feature based on the mean and variance;

[0024] A probability distribution model of the impact of each data feature on the asset value is constructed based on the probability density function.

[0025] In this solution, the weight of the value impact of historical data features on the asset management project is determined based on the value impact probability distribution model, and a value simulation path decision tree for valuing the asset management project using the Monte Carlo simulation algorithm is constructed. The decision tree is analyzed based on the value impact weight to determine several simulation paths for valuing the asset management project using the Monte Carlo simulation algorithm, specifically:

[0026] Integrating the probability density function of each data feature according to the value impact probability distribution model to calculate the probability of each data feature taking values ​​at different values;

[0027] Calculate the conditional entropy of each data feature based on the Bayesian formula, calculate the value probability value and the conditional entropy based on the information entropy weight method to obtain the information entropy of each data feature, determine the information gain of each data feature based on the information entropy, and determine the value impact weight of each historical data feature on the asset management project based on the information gain;

[0028] Constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm based on the initial price simulation path, limiting the impact weight of each node in the decision tree based on the value impact weight, and calculating the comprehensive weight information of each branch path of the decision tree after the impact weight is limited;

[0029] Calculating the Euclidean distance between each branch path in the decision tree based on the K-means clustering algorithm, evaluating the similarity between the branch paths according to the Euclidean distance, and performing a clustering operation on each branch path according to the similarity to obtain a clustering result;

[0030] According to the comprehensive weight information, the branch path with the highest comprehensive weight of each clustering result is taken as the preferred value simulation path, and several simulation paths for valuing the asset management project using the Monte Carlo simulation algorithm are determined based on the preferred value simulation path.

[0031] In this solution, the target asset management system's tolerance for the accuracy of asset value assessment is obtained, and the number of simulations of the simulation path by the Monte Carlo simulation algorithm is determined based on the tolerance, specifically:

[0032] Determining the degree of influence of each historical data feature on the price fluctuation of the asset management project based on the weight of the historical data feature's influence on the value of the asset management project, and obtaining risk impact data on asset management from the degree of influence of price fluctuations;

[0033] Determine the target asset management system's tolerance for the accuracy of asset value assessment based on the risk impact data;

[0034] Obtaining relationship data between the number of simulations of a simulation path and the accuracy of value assessment of a Monte Carlo simulation algorithm, and constructing a simulation number-simulation accuracy relationship model based on the relationship data;

[0035] The tolerance is introduced into the simulation number-simulation accuracy relationship model to determine the number of simulations of the simulation path by the Monte Carlo simulation algorithm under the tolerance of the current target asset management system for the accuracy of asset value assessment.

[0036] In this solution, the simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project. The asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy, which is specifically:

[0037] Using the simulation path and number of simulations as automated optimization rules of the Monte Carlo simulation algorithm to perform a value path simulation operation on each asset management project, and outputting the asset value probability distribution of each asset management project;

[0038] Obtaining expected returns and risk level control data for asset management projects, and using the expected returns and risk level control data as screening conditions;

[0039] Extracting an asset value simulation sequence based on the asset value probability distribution, calculating the mean value and standard deviation of the asset value simulation sequence, and determining the value change trend and risk change trend of the asset management project based on the mean value and standard deviation;

[0040] Comparing the value change trend and risk change trend with the screening conditions, outputting the asset management projects that meet the screening conditions within a preset time period and marking them as preferred asset management projects;

[0041] Asset management is performed according to the preferred asset management project, wherein the asset management includes buying, selling, and adjusting asset ratio configuration to obtain an asset management strategy.

[0042] A second aspect of the present invention further provides an automated rule-based asset screening and management system, comprising: a memory and a processor, wherein the memory includes an automated rule-based asset screening and management method program, and when the automated rule-based asset screening and management method program is executed by the processor, the following steps are implemented:

[0043] Obtain asset management list data, obtain historical data of each asset management project based on the asset management list data, introduce a Monte Carlo simulation algorithm to perform value assessment on the historical data of each asset management project, and obtain resource consumption data during the value assessment process;

[0044] Evaluate the resource load of the asset management list based on the resource consumption data; if the resource load is greater than a preset value, obtain data features of historical data and construct a value impact probability distribution model;

[0045] Determining the weight of the value impact of historical data features on the asset management project based on the value impact probability distribution model, constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm, analyzing the decision tree based on the value impact weight, and determining several simulation paths for valuing the asset management project using a Monte Carlo simulation algorithm;

[0046] Obtaining the tolerance of the target asset management system for the accuracy of asset value assessment, and determining the number of simulations of the simulation path by the Monte Carlo simulation algorithm based on the tolerance;

[0047] The simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project. The asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy.

[0048] The present invention discloses an asset screening and management method and system based on automated rules, which uses the Monte Carlo simulation algorithm to perform value assessment and resource management on asset management projects. The method first obtains asset management list data and historical data, uses Monte Carlo simulation to perform value assessment on the historical data, and obtains resource consumption information. The management difficulty is assessed according to the resource load. If the load is too high, a value impact probability distribution model is constructed, and the weight of the impact of historical data characteristics on asset value is determined. By constructing a value path simulation decision tree and determining the number of simulations based on tolerance, each asset management project is simulated and analyzed, and finally an asset screening and management strategy is generated. The present invention can improve the efficiency and accuracy of asset management decisions, optimize resource allocation, and has high practical value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of an asset screening and management method based on automated rules according to the present invention is shown;

[0050] Figure 2 A flow chart showing the construction of a value impact probability distribution model according to the present invention is shown;

[0051] Figure 3 A flow chart showing the method of determining the number of simulations for a simulation path according to the present invention is shown;

[0052] Figure 4 A block diagram of an automated rule-based asset screening and management system according to the present invention is shown. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0055] Figure 1 A flow chart of an asset screening and management method based on automated rules of the present invention is shown.

[0056] like Figure 1 As shown, the first aspect of the present invention provides an asset screening and management method based on automated rules, comprising:

[0057] S102, obtaining asset management list data, obtaining historical data of each asset management project based on the asset management list data, introducing a Monte Carlo simulation algorithm to perform value assessment on the historical data of each asset management project, and obtaining resource consumption data during the value assessment process;

[0058] S104, evaluating the resource load of the asset management list based on the resource consumption data, and if the resource load is greater than a preset value, obtaining data features of historical data and constructing a value impact probability distribution model;

[0059] S106, determining the weight of the value impact of historical data features on the asset management project based on the value impact probability distribution model, constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm, analyzing the decision tree based on the value impact weight, and determining several simulation paths for valuing the asset management project using a Monte Carlo simulation algorithm;

[0060] S108, obtaining the target asset management system's tolerance for the accuracy of asset value assessment, and determining the number of simulations of the simulation path by the Monte Carlo simulation algorithm based on the tolerance;

[0061] S110, using the simulation path and the number of simulations as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project, screening and managing the asset management projects according to the value path simulation, and obtaining an asset management strategy.

[0062] It should be noted that the target asset management system requires continuous valuation and monitoring of each asset management project based on its accumulated historical data, and the Monte Carlo simulation algorithm significantly consumes system resources during valuation. Therefore, a Monte Carlo simulation algorithm is used to evaluate the historical data of the target asset management system's asset management projects, obtaining real-time resource consumption data and monitoring and evaluating the resource load during the asset management process. When resource load is excessively high, the historical data features are extracted and a value impact probability distribution model is constructed to analyze the impact of different data features on asset valuation. A value simulation path decision tree is constructed and analyzed based on the value impact weights to select several simulation paths for the Monte Carlo simulation algorithm to evaluate the asset management project. Monte Carlo simulation typically requires a significant amount of computing resources, especially when the number of simulation paths and the number of simulations are large, which can significantly increase the system burden. By selecting appropriate simulation paths, computational complexity can be effectively controlled, unnecessary computation can be reduced, and thus system resource consumption can be optimized. This can avoid repeated calculations of unimportant or redundant paths. During the asset valuation process, a greater number of simulations leads to higher accuracy of the calculation results, but this also results in increased resource consumption. By dynamically adjusting the number of simulations based on the system's tolerance (i.e., the requirement for evaluation accuracy), an optimal balance can be achieved between accuracy and efficiency. For example, when the evaluation tolerance is high, the number of simulations can be appropriately reduced to save computing resources; when the accuracy requirements are high, the number of simulations can be increased to improve the accuracy of the simulation results. This flexible adjustment mechanism helps ensure that the asset management system can operate efficiently and stably in different environments; ultimately, the Monte Carlo simulation algorithm values ​​and manages asset management projects based on the optimized value simulation path and number of simulations, which can effectively reduce the load on the asset management system and avoid performance bottlenecks or crashes due to excessive consumption of computing resources. When conducting large-scale asset management, the system can dynamically adjust simulation parameters based on the load conditions to ensure that the system always operates within a reasonable load range, thereby improving the stability and responsiveness of the system.

[0063] According to an embodiment of the present invention, the asset management list data is obtained, and historical data of each asset management project is obtained based on the asset management list data. A Monte Carlo simulation algorithm is introduced to perform value assessment on the historical data of each asset management project, and resource consumption data during the value assessment process is obtained. Specifically,

[0064] Obtaining asset management list data of a target asset management system, and obtaining historical data of each asset management project based on the asset management list data, wherein the historical data includes asset value data, loan data, borrower credit data, early repayment data, default and recovery rate data;

[0065] Introducing a Monte Carlo simulation algorithm, importing the Monte Carlo simulation algorithm into the target asset management system, and continuously updating the historical data, performing calculations based on the historical data continuously updated by the Monte Carlo simulation algorithm, and continuously performing real-time value evaluation on each asset management item in the asset management item list data;

[0066] Obtaining data on historical data called by the Monte Carlo simulation algorithm in the target asset management system during the process of performing value assessment on asset management projects, obtaining a call timestamp of each data item in the historical data by the Monte Carlo simulation algorithm based on the call data, and constructing a data access path based on the call timestamp;

[0067] Performing statistics on the data access path, determining the initial price simulation path and the initial number of simulations in the process of the Monte Carlo simulation algorithm performing value assessment on the asset management project, and obtaining the single resource consumption of a single simulation of a single initial price simulation path of the Monte Carlo simulation algorithm;

[0068] The resource consumption data during the value assessment of each asset management project by the Monte Carlo simulation algorithm is determined based on the single resource consumption situation. The resource consumption data includes CPU usage, memory occupancy, database interaction frequency, and network bandwidth occupancy.

[0069] It should be noted that the asset management project data refers to an ABS asset pool, including residential mortgages, auto loans, credit card receivables, and lease rentals. These underlying assets generate a series of cash flows. For example, in residential mortgage-backed securities (RMBS), the asset pool is composed of numerous individual mortgages. The principal and interest payments made monthly by borrowers constitute the cash flows of this asset pool. The loan data includes the loan amount, loan term, repayment method, interest rate type, and interest rate level; the borrower's credit data includes credit score, debt-to-income ratio, and credit history, including the number and duration of overdue payments; the prepayment data includes the prepayment rate; and the default and recovery rate data includes historical data on default probability, default rate, and recovery rate. The simulation path utilizes a Monte Carlo simulation algorithm to perform a large number of random sampling simulations. In each simulation, the estimated value of the asset management project under the corresponding scenario is calculated based on randomly generated variable values ​​and a pre-set asset valuation model (such as a discounted cash flow model). After multiple simulations (usually thousands or even tens of thousands of times), a series of value estimates are obtained, thereby constructing the probability distribution of the asset management project's value, and then determining its value assessment results (such as mean, median, confidence interval, etc.). The initial price simulation path is determined by the data access path. When performing the asset management project value assessment, the Monte Carlo simulation algorithm will repeatedly call different data items in the historical data (such as asset value data, loan data, borrower credit data, etc.) and perform multiple simulation calculations based on the changes in these data items. By tracking and recording the access to historical data, the call timestamps and access order of the data items during each simulation can be obtained, and the data access path can be constructed.

[0070] Figure 2 A flow chart of constructing a value impact probability distribution model according to the present invention is shown.

[0071] According to an embodiment of the present invention, the resource load of the asset management list is evaluated based on the resource consumption data. If the resource load is greater than a preset value, data features of historical data are obtained and a value impact probability distribution model is constructed, specifically:

[0072] S202, obtaining, based on the resource consumption data, operational load weight information of each item of resource consumption data on the target asset management system, performing a resource load assessment on the resource consumption data based on the operational load weight information, and obtaining a resource load status of the target asset management system for managing the asset management list;

[0073] S204: Compare the resource load with a preset value. If the resource load is greater than the preset value, construct a data matrix using the historical data, normalize the data matrix, calculate a covariance matrix of the normalized data matrix, and perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors.

[0074] S206, sorting the eigenvalues ​​from large to small, selecting the eigenvectors corresponding to the first k eigenvalues ​​to construct a projection matrix, and multiplying the standardized data matrix by the projection matrix to obtain data features of the historical data;

[0075] S208, calculating the mean and variance of each data feature based on the historical data, and constructing a probability density function of the data feature based on the mean and variance;

[0076] S210: Construct a probability distribution model of the impact of each data feature on the asset value based on the probability density function.

[0077] It's important to note that in asset management systems, historical data often contains a large number of variables and dimensions. Directly calculating these data can lead to excessive computational complexity and resource consumption. Feature extraction, particularly methods like eigenvalue decomposition and principal component analysis (PCA), can reduce the dimensionality of high-dimensional data, extract the core features that most impact asset value, and simplify the data structure. By calculating the mean and variance of historical data features, a probability density function (PDF) can be constructed for each feature, enabling quantitative analysis of asset value fluctuations. The mean indicates the central tendency of the data, while the variance measures the range of fluctuation. Based on these two statistics, the distribution of data features can be inferred, typically assuming that these features follow a normal distribution. The probability density function of a normal distribution, determined by the mean and variance, can be used to calculate the probability of occurrence of a particular feature value. In asset management, these PDFs can help quantify the range of asset value fluctuations and, in turn, assess the risk and return potential of assets. Through this method, each data feature (such as asset value, loan default rate, etc.) can be converted into a probability model. In a multi-feature scenario, the mean and variance of each feature can be calculated separately, and multiple independent probability density functions can be constructed. These functions can be combined using the Bayesian formula to form a value-impact probability distribution model for asset value. This model can not only accurately describe the impact of each data feature on asset value, thereby improving the efficiency and accuracy of asset management, but also reducing uncertainty and risk in decision-making. The data matrix is ​​in the shape of m*n, where m is the number of samples and n is the number of features; the standardization process ensures that each feature has the same scale, avoiding the impact of different feature dimensions on subsequent analysis.

[0078] According to an embodiment of the present invention, the weight of the value impact of historical data features on the asset management project is determined based on the value impact probability distribution model, and a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm is constructed. The decision tree is analyzed based on the value impact weight to determine several simulation paths for valuing the asset management project using a Monte Carlo simulation algorithm, specifically:

[0079] Integrating the probability density function of each data feature according to the value impact probability distribution model to calculate the probability of each data feature taking values ​​at different values;

[0080] Calculate the conditional entropy of each data feature based on the Bayesian formula, calculate the value probability value and the conditional entropy based on the information entropy weight method to obtain the information entropy of each data feature, determine the information gain of each data feature based on the information entropy, and determine the value impact weight of each historical data feature on the asset management project based on the information gain;

[0081] Constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm based on the initial price simulation path, limiting the impact weight of each node in the decision tree based on the value impact weight, and calculating the comprehensive weight information of each branch path of the decision tree after the impact weight is limited;

[0082] Calculating the Euclidean distance between each branch path in the decision tree based on the K-means clustering algorithm, evaluating the similarity between the branch paths according to the Euclidean distance, and performing a clustering operation on each branch path according to the similarity to obtain a clustering result;

[0083] According to the comprehensive weight information, the branch path with the highest comprehensive weight of each clustering result is taken as the preferred value simulation path, and several simulation paths for valuing the asset management project using the Monte Carlo simulation algorithm are determined based on the preferred value simulation path.

[0084] It should be noted that by selecting the branch path with the highest comprehensive weight for each clustering result as the preferred value simulation path based on comprehensive weight information, the efficiency and accuracy of the Monte Carlo simulation algorithm in the asset management project valuation process can be significantly improved. First, the K-means clustering algorithm is used to cluster the branch paths in the decision tree, evaluate the similarity between the paths, and group the paths with higher similarity into one category. This clustering process can effectively reduce unnecessary computational effort by aggregating multiple similar paths into a single group, thereby reducing redundant computations during the simulation process. Next, based on the comprehensive weight of each clustering result, the path with the highest weight is selected as the preferred simulation path. In this way, the system can focus on the most valuable simulation paths and avoid calculating those with less impact or redundant paths, thereby improving computational efficiency and ensuring more accurate simulation results. Ultimately, these preferred simulation paths serve as the valuation simulation paths for the asset management project, significantly reducing the system resource consumption of the target asset management system to calculate a large number of simulation paths.

[0085] Figure 3 A flow chart of determining the number of simulation times of a simulation path according to the present invention is shown.

[0086] According to an embodiment of the present invention, the tolerance of the target asset management system to the accuracy of asset value assessment is obtained, and the number of simulations of the simulation path by the Monte Carlo simulation algorithm is determined according to the tolerance, specifically:

[0087] S302: Determine the degree of influence of each historical data feature on the price fluctuation of the asset management project based on the weight of the historical data feature's influence on the value of the asset management project, and obtain risk impact data on asset management from the degree of influence of the price fluctuation;

[0088] S304, determining the target asset management system's tolerance for the accuracy of asset value assessment based on the risk impact data;

[0089] S306, obtaining relationship data between the number of simulations of the simulation path and the accuracy of the value assessment of the Monte Carlo simulation algorithm, and constructing a simulation number-simulation accuracy relationship model based on the relationship data;

[0090] S308, importing the tolerance into the simulation number-simulation accuracy relationship model to determine the number of simulations of the simulation path by the Monte Carlo simulation algorithm under the tolerance of the current target asset management system for the accuracy of asset value assessment.

[0091] It should be noted that by determining the number of Monte Carlo simulations based on the target asset management system's tolerance for asset valuation accuracy, a balance can be effectively achieved between computing resource consumption and valuation accuracy. First, based on the weight of historical data features' impact on asset value, the degree of influence of each data feature on price fluctuations is determined, and further data on the impact of these fluctuations on asset management risk is obtained. This data helps assess the risk level of asset management projects and, consequently, determines the accuracy tolerance the system can accept in asset valuation. Next, a relationship model between the number of simulations and simulation accuracy is constructed to quantify the impact of the number of simulations on the accuracy of valuation results. By inputting this tolerance information into this relationship model, the system automatically adjusts the number of simulations to ensure optimal use of computing resources within a defined accuracy range. This rationally determined number of simulations ensures accurate asset valuation while avoiding resource waste caused by excessive computation, improving the system's computing efficiency and resource utilization, while also ensuring the accuracy of asset management decisions. The higher the impact of price fluctuations on asset management risk, the lower the target asset management system's tolerance for asset valuation accuracy. Conversely, the lower the risk impact, the higher the target asset management system's tolerance for asset valuation accuracy.

[0092] According to an embodiment of the present invention, the simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project, and the asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy, which is specifically:

[0093] Using the simulation path and number of simulations as automated optimization rules of the Monte Carlo simulation algorithm to perform a value path simulation operation on each asset management project, and outputting the asset value probability distribution of each asset management project;

[0094] Obtaining expected returns and risk level control data for asset management projects, and using the expected returns and risk level control data as screening conditions;

[0095] Extracting an asset value simulation sequence based on the asset value probability distribution, calculating the mean value and standard deviation of the asset value simulation sequence, and determining the value change trend and risk change trend of the asset management project based on the mean value and standard deviation;

[0096] Comparing the value change trend and risk change trend with the screening conditions, outputting the asset management projects that meet the screening conditions within a preset time period and marking them as preferred asset management projects;

[0097] Asset management is performed according to the preferred asset management project, wherein the asset management includes buying, selling, and adjusting asset ratio configuration to obtain an asset management strategy.

[0098] It should be noted that using simulation paths and number of simulations as automated optimization rules for the Monte Carlo simulation algorithm can significantly improve the accuracy and efficiency of asset management decisions. For each asset management project, the system outputs the probability distribution of asset values ​​through value path simulation, providing a quantitative basis for subsequent decision-making. Combined with the project's expected returns and risk control data, the system extracts a simulated asset value sequence based on probability distribution analysis and calculates its mean and standard deviation. These statistical indicators help determine asset value and risk trends, providing scientific risk assessment and return forecasting for asset management. By comparing these trends with pre-set screening criteria, the system selects qualified asset management projects within a predetermined time period, ensuring that the selected projects achieve the optimal balance between return and risk. Finally, the system executes specific actions based on the selected asset management projects, such as buying, selling, or adjusting asset allocation, to form an optimized asset management strategy. This process improves the accuracy of asset management, reduces uncertainty in manual decision-making, and improves the overall efficiency and economic benefits of asset management through automated optimization and resource allocation. The simulated asset value sequence is a series of forecasts of future asset values ​​generated by the Monte Carlo simulation algorithm. It reflects the possible paths of asset value change under different scenarios given initial conditions. These paths are generated through random sampling and simulation based on historical data, market conditions, asset characteristics, and other factors. Each simulated path represents a possible future situation, and the collection of all simulated paths constitutes the overall picture of asset value.

[0099] According to an embodiment of the present invention, the further embodiment includes:

[0100] Constructing a resource consumption monitoring data interface and a data interaction interface for the target asset management system, and obtaining real-time resource consumption data and incremental information of historical data of the target system in real time according to the resource consumption monitoring data interface and the data interaction interface;

[0101] Determine the data transmission packet loss rate and data transmission delay of the target resource management system based on the real-time resource consumption data, and determine the data damage of the target asset management system receiving historical data based on the data transmission packet loss rate, data transmission delay and the incremental information;

[0102] Constructing a data buffer of the target resource management system. If the damage to the historical data received by the target asset management system is greater than a preset damage level, the historical data received in real time is imported into the data buffer for temporary storage to construct temporary data.

[0103] The resource consumption monitoring data interface and the data interaction interface are used to monitor in real time the damage to the historical data received by the target asset management system. When the damage to the data is less than a preset damage level, the priority of the temporary data transmission is determined according to the acquisition time of the temporary data.

[0104] The temporary data is transmitted and analyzed according to the priority.

[0105] It should be noted that with the increasing frequency of financial market transactions and the explosive growth in data volumes, target asset management systems face numerous challenges in processing large amounts of historical data and interacting with real-time data. For example, network fluctuations can cause packet loss and delays during data transmission, which not only compromises the integrity of historical data but also potentially biases valuation processes such as Monte Carlo simulations based on this data, leading to misleading asset screening and management decisions. By establishing a resource consumption monitoring data interface and data interaction interface for the target asset management system, real-time resource consumption data and incremental information on historical data can be obtained in real time. This allows for accurate assessment of packet loss and delay rates, and ultimately determines the extent of historical data reception impairment. When the impairment exceeds a preset threshold, the real-time historical data is transferred into a data buffer to create temporary data. This prevents data from being discarded or misused due to corruption, ensuring data integrity and backup. Furthermore, through continuous monitoring, when data impairment improves, the system prioritizes the transmission of temporary data based on its acquisition time, prioritizing data with high timeliness and a greater impact on current asset valuations. This ensures the timeliness and accuracy of historical data used in Monte Carlo simulations, effectively improving the reliability of asset screening and management strategies.

[0106] Figure 4 A block diagram of an automated rule-based asset screening and management system according to the present invention is shown.

[0107] A second aspect of the present invention further provides an automated rule-based asset screening and management system 4, comprising: a memory 41 and a processor 42. The memory includes an automated rule-based asset screening and management method program. When the automated rule-based asset screening and management method program is executed by the processor, the following steps are implemented:

[0108] Obtain asset management list data, obtain historical data of each asset management project based on the asset management list data, introduce a Monte Carlo simulation algorithm to perform value assessment on the historical data of each asset management project, and obtain resource consumption data during the value assessment process;

[0109] Evaluate the resource load of the asset management list based on the resource consumption data; if the resource load is greater than a preset value, obtain data features of historical data and construct a value impact probability distribution model;

[0110] Determining the weight of the value impact of historical data features on the asset management project based on the value impact probability distribution model, constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm, analyzing the decision tree based on the value impact weight, and determining several simulation paths for valuing the asset management project using a Monte Carlo simulation algorithm;

[0111] Obtaining the tolerance of the target asset management system for the accuracy of asset value assessment, and determining the number of simulations of the simulation path by the Monte Carlo simulation algorithm based on the tolerance;

[0112] The simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project. The asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy.

[0113] The present invention discloses an asset screening and management method and system based on automated rules, which uses the Monte Carlo simulation algorithm to perform value assessment and resource management on asset management projects. The method first obtains asset management list data and historical data, uses Monte Carlo simulation to perform value assessment on the historical data, and obtains resource consumption information. The management difficulty is assessed according to the resource load. If the load is too high, a value impact probability distribution model is constructed, and the weight of the impact of historical data characteristics on asset value is determined. By constructing a value path simulation decision tree and determining the number of simulations based on tolerance, each asset management project is simulated and analyzed, and finally an asset screening and management strategy is generated. The present invention can improve the efficiency and accuracy of asset management decisions, optimize resource allocation, and has high practical value and promotion prospects.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0115] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0116] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0117] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0118] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0119] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An asset screening and management method based on automated rules, characterized in that: The following steps are involved: Obtain asset management list data, obtain historical data of each asset management project based on the asset management list data, introduce a Monte Carlo simulation algorithm to perform value assessment on the historical data of each asset management project, and obtain resource consumption data during the value assessment process; The resource load of the asset management list is evaluated based on the resource consumption data. If the resource load is greater than a preset value, the data features of the historical data are obtained and a value impact probability distribution model is constructed, specifically: Obtaining operational load weight information of each resource consumption data item on the target asset management system based on the resource consumption data, performing resource load evaluation on the resource consumption data based on the operational load weight information, and obtaining resource load status of the target asset management system for managing the asset management list; Comparing the resource load with a preset value, if the resource load is greater than the preset value, constructing a data matrix from the historical data, normalizing the data matrix, calculating a covariance matrix of the normalized data matrix, and performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors; Sort the eigenvalues ​​from large to small, select the eigenvectors corresponding to the first k eigenvalues ​​to construct a projection matrix, and multiply the standardized data matrix by the projection matrix to obtain the data features of the historical data; Calculating the mean and variance of each data feature based on the historical data, and constructing a probability density function of the data feature based on the mean and variance; Constructing a probability distribution model of the impact of each data feature on the asset value based on the probability density function; The weight of the impact of the data characteristics of historical data on the value of the asset management project is determined based on the value impact probability distribution model, and a value simulation path decision tree for valuing the asset management project using the Monte Carlo simulation algorithm is constructed. The decision tree is analyzed based on the value impact weight to determine several simulation paths for valuing the asset management project using the Monte Carlo simulation algorithm, specifically: Integrating the probability density function of each data feature according to the value impact probability distribution model to calculate the probability of each data feature taking values ​​at different values; Calculate the conditional entropy of each data feature based on the Bayesian formula, calculate the value probability value and the conditional entropy based on the information entropy weight method to obtain the information entropy of each data feature, determine the information gain of each data feature based on the information entropy, and determine the value impact weight of each historical data feature on the asset management project based on the information gain; Constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm based on the initial price simulation path, limiting the impact weight of each node in the decision tree based on the value impact weight, and calculating the comprehensive weight information of each branch path of the decision tree after the impact weight is limited; Calculating the Euclidean distance between each branch path in the decision tree based on the K-means clustering algorithm, evaluating the similarity between the branch paths according to the Euclidean distance, and performing a clustering operation on each branch path according to the similarity to obtain a clustering result; According to the comprehensive weight information, the branch path with the highest comprehensive weight of each clustering result is selected as the preferred value simulation path, and according to the preferred value simulation path, several simulation paths for valuing the asset management project using the Monte Carlo simulation algorithm are determined; Obtaining the tolerance of the target asset management system for the accuracy of asset value assessment, and determining the number of simulations of the simulation path by the Monte Carlo simulation algorithm based on the tolerance; The simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project. The asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy.

2. The asset screening and management method based on automated rules according to claim 1, characterized in that: The asset management list data is obtained, and historical data of each asset management project is obtained based on the asset management list data. A Monte Carlo simulation algorithm is introduced to perform value assessment on the historical data of each asset management project, and resource consumption data is obtained during the value assessment process, specifically: Obtaining asset management list data of a target asset management system, and obtaining historical data of each asset management project based on the asset management list data, wherein the historical data includes asset value data, loan data, borrower credit data, early repayment data, default and recovery rate data; Introducing a Monte Carlo simulation algorithm, importing the Monte Carlo simulation algorithm into the target asset management system, and continuously updating the historical data, performing calculations based on the historical data continuously updated by the Monte Carlo simulation algorithm, and continuously performing real-time value evaluation on each asset management item in the asset management item list data; Obtaining data on historical data called by the Monte Carlo simulation algorithm in the target asset management system during the process of performing value assessment on asset management projects, obtaining a call timestamp of each data item in the historical data by the Monte Carlo simulation algorithm based on the call data, and constructing a data access path based on the call timestamp; Performing statistics on the data access path, determining the initial price simulation path and the initial number of simulations in the process of the Monte Carlo simulation algorithm performing value assessment on the asset management project, and obtaining the single resource consumption of a single simulation of a single initial price simulation path of the Monte Carlo simulation algorithm; The resource consumption data during the value assessment of each asset management project by the Monte Carlo simulation algorithm is determined based on the single resource consumption situation. The resource consumption data includes CPU usage, memory occupancy, database interaction frequency, and network bandwidth occupancy.

3. The asset screening and management method based on automated rules according to claim 1, characterized in that: The target asset management system's tolerance for asset value assessment accuracy is obtained, and the number of simulations of the simulation path by the Monte Carlo simulation algorithm is determined based on the tolerance, specifically: Determining the degree of influence of each historical data feature on the price fluctuation of the asset management project based on the weight of the historical data feature's influence on the value of the asset management project, and obtaining risk impact data on asset management from the degree of influence of price fluctuations; Determine the target asset management system's tolerance for the accuracy of asset value assessment based on the risk impact data; Obtaining relationship data between the number of simulations of a simulation path and the accuracy of value assessment of a Monte Carlo simulation algorithm, and constructing a simulation number-simulation accuracy relationship model based on the relationship data; The tolerance is introduced into the simulation number-simulation accuracy relationship model to determine the number of simulations of the simulation path by the Monte Carlo simulation algorithm under the tolerance of the current target asset management system for the accuracy of asset value assessment.

4. The asset screening and management method based on automated rules according to claim 1, characterized in that: The simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project, and the asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy, specifically: Using the simulation path and number of simulations as automated optimization rules of the Monte Carlo simulation algorithm to perform a value path simulation operation on each asset management project, and outputting the asset value probability distribution of each asset management project; Obtaining expected returns and risk level control data for asset management projects, and using the expected returns and risk level control data as screening conditions; Extracting an asset value simulation sequence based on the asset value probability distribution, calculating the mean value and standard deviation of the asset value simulation sequence, and determining the value change trend and risk change trend of the asset management project based on the mean value and standard deviation; Comparing the value change trend and risk change trend with the screening conditions, outputting the asset management projects that meet the screening conditions within a preset time period and marking them as preferred asset management projects; Asset management is performed according to the preferred asset management project, wherein the asset management includes buying, selling, and adjusting asset ratio configuration to obtain an asset management strategy.

5. An asset screening and management system based on automated rules, characterized in that: The automated rule-based asset screening and management system includes a storage device and a processor. The storage device includes an automated rule-based asset screening and management method program. When the automated rule-based asset screening and management method program is executed by the processor, the following steps are implemented: Obtain asset management list data, obtain historical data of each asset management project based on the asset management list data, introduce a Monte Carlo simulation algorithm to perform value assessment on the historical data of each asset management project, and obtain resource consumption data during the value assessment process; The resource load of the asset management list is evaluated based on the resource consumption data. If the resource load is greater than a preset value, the data features of the historical data are obtained and a value impact probability distribution model is constructed, specifically: Obtaining operational load weight information of each resource consumption data item on the target asset management system based on the resource consumption data, performing resource load evaluation on the resource consumption data based on the operational load weight information, and obtaining resource load status of the target asset management system for managing the asset management list; Comparing the resource load with a preset value, if the resource load is greater than the preset value, constructing a data matrix from the historical data, normalizing the data matrix, calculating a covariance matrix of the normalized data matrix, and performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors; Sort the eigenvalues ​​from large to small, select the eigenvectors corresponding to the first k eigenvalues ​​to construct a projection matrix, and multiply the standardized data matrix by the projection matrix to obtain the data features of the historical data; Calculating the mean and variance of each data feature based on the historical data, and constructing a probability density function of the data feature based on the mean and variance; Constructing a probability distribution model of the impact of each data feature on the asset value based on the probability density function; The weight of the impact of the data characteristics of historical data on the value of the asset management project is determined based on the value impact probability distribution model, and a value simulation path decision tree for valuing the asset management project using the Monte Carlo simulation algorithm is constructed. The decision tree is analyzed based on the value impact weight to determine several simulation paths for valuing the asset management project using the Monte Carlo simulation algorithm, specifically: Integrating the probability density function of each data feature according to the value impact probability distribution model to calculate the probability of each data feature taking values ​​at different values; Calculate the conditional entropy of each data feature based on the Bayesian formula, calculate the value probability value and the conditional entropy based on the information entropy weight method to obtain the information entropy of each data feature, determine the information gain of each data feature based on the information entropy, and determine the value impact weight of each historical data feature on the asset management project based on the information gain; Constructing a value simulation path decision tree for valuing the asset management project using a Monte Carlo simulation algorithm based on the initial price simulation path, limiting the impact weight of each node in the decision tree based on the value impact weight, and calculating the comprehensive weight information of each branch path of the decision tree after the impact weight is limited; Calculating the Euclidean distance between each branch path in the decision tree based on the K-means clustering algorithm, evaluating the similarity between the branch paths according to the Euclidean distance, and performing a clustering operation on each branch path according to the similarity to obtain a clustering result; According to the comprehensive weight information, the branch path with the highest comprehensive weight of each clustering result is selected as the preferred value simulation path, and according to the preferred value simulation path, several simulation paths for valuing the asset management project using the Monte Carlo simulation algorithm are determined; Obtaining the tolerance of the target asset management system for the accuracy of asset value assessment, and determining the number of simulations of the simulation path by the Monte Carlo simulation algorithm based on the tolerance; The simulation path and the number of simulations are used as simulation parameters of the Monte Carlo simulation algorithm to perform value path simulation on each asset management project. The asset management projects are screened and managed according to the value path simulation to obtain an asset management strategy.

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