Economic resource management optimization method based on intelligent decision

Abstract

The invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision making. According to the method, multi-source heterogeneous data, including resource stock, demand fluctuation and the like, of an economic system are collected firstly; a dynamic resource pool is divided based on multi-dimensional feature analysis, and a nonlinear optimization model is constructed to predict resource supply and demand changes so as to generate an allocation scheme; and then optimizing a distribution path by using a multi-stage decision tree, updating model parameters through an adaptive learning mechanism according to market feedback and constraint condition changes, and correcting a deployment scheme in real time. In addition, key technical details such as an elastic quota adjustment formula and a fuzzy clustering algorithm membership function are given. According to the method, complex data can be effectively integrated, resources are scientifically scheduled, supply and demand are accurately predicted, a distribution path is optimized, environmental changes are adapted, the efficiency and benefits of economic resource management are remarkably improved, and scientific and reasonable resource management decision support is provided for economic subjects.

Description

Technical Field

[0001] The present invention relates to the technical field of economic resource management, and specifically provides an optimization method for economic resource management based on intelligent decision-making. Background Art

[0002] In today's complex and ever-changing economic environment, economic resource management faces many severe challenges. Traditional management methods can no longer meet the actual needs and urgently need to be innovated and optimized. From the data perspective, the data in the economic system exhibits multi-source and heterogeneous characteristics. The resource stock data not only involves multi-dimensional information such as the quantity and value of various different assets, but also varies in statistical caliber and storage format due to industry differences. The demand fluctuation data is highly uncertain and dynamic due to factors such as market trends, consumer preferences, and seasonal factors, making it difficult for traditional methods to accurately capture and effectively analyze it. The market environment parameters include economic situation, competition situation, technological innovation, etc. These parameters are intertwined and change rapidly. Traditional data processing means cannot integrate and analyze them in a timely manner, resulting in the inability to provide strong support for resource management decisions. The policy constraint conditions are updated frequently, and there may be intersections and conflicts between different policies. Under the traditional management mode, the acquisition and interpretation of policy information are often lagged, making it difficult to quickly adapt to policy changes in resource management.

[0003] Resource scheduling and allocation have always been the core problems in economic resource management. In the traditional method, the resource division lacks a scientific basis and often adopts a fixed classification mode without fully considering key factors such as resource complementarity, stock threshold, and scheduling cost. This makes it impossible to allocate resources reasonably, resulting in the coexistence of resource idleness and shortage. For example, in the manufacturing industry, if the deployment of raw materials and components does not consider their complementary relationship, it may cause the production line to stagnate due to the lack of some materials, while other materials are overstocked, occupying a large amount of funds and storage space. Moreover, the setting of decision-making nodes is not flexible enough, lacking a dynamic adjustment mechanism, and unable to optimize the scheduling strategy in a timely manner according to the real-time status of resources and changes in demand. In the face of sudden demand or supply interruption, the traditional scheduling method reacts slowly and cannot quickly reallocate resources, bringing huge losses to enterprises and economic operations.

[0004] Predicting changes in resource supply and demand is also a major shortcoming of traditional economic resource management. Most traditional prediction methods are based on simple statistical models or empirical judgments and cannot accurately describe the complex non-linear relationships in the economic system. In the face of economic globalization and intensified market competition, market supply and demand are comprehensively affected by various factors, showing a high degree of complexity and uncertainty. Taking the agricultural product market as an example, factors such as abnormal climate, changes in international trade policies, and the transformation of consumers' health concepts will have a significant impact on the supply and demand of agricultural products. Traditional prediction methods are difficult to comprehensively consider these factors, resulting in a large deviation between the prediction results and the actual situation, and further making the resource allocation plan lack scientificity and unable to achieve the optimal allocation of resources.

[0005] In addition, traditional economic resource management methods lack the ability of adaptive learning. Market feedback and constraint conditions are constantly changing, but it is difficult for management strategies under traditional models to adjust in a timely manner according to these changes. For example, when the market demand for a certain product suddenly increases, traditional management methods may not be able to quickly adjust the allocation of production resources, resulting in supply falling short of demand and enterprises missing market opportunities; when new restrictions on resource use are imposed by policies and regulations, traditional management methods may not be able to respond in a timely manner, facing the risks of violations and increased operating costs. Moreover, in terms of optimizing the resource allocation path, traditional methods usually rely on fixed processes and cannot be dynamically adjusted in combination with real-time risk indicators. When facing risks such as market fluctuations and natural disasters, the resource allocation efficiency is low and the stable operation of the economic system cannot be guaranteed. Summary of the Invention

[0006] The purpose of the present invention is to provide an optimization method for economic resource management based on intelligent decision-making to solve the problems raised in the above background technology.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: An optimization method for economic resource management based on intelligent decision-making, the method comprising:

[0008] Step S1: Collect multi-source heterogeneous data in the economic system, including resource stocks, demand fluctuations, market environment parameters and policy constraint conditions;

[0009] Step S2: Based on multi-dimensional feature analysis, divide resources into multiple dynamic resource pools according to types and priorities, and each resource pool is associated with a decision node responsible for resource scheduling and allocation strategies;

[0010] Step S3: Construct a non-linear optimization model, combine real-time data to predict resource supply and demand changes, and generate a global resource allocation plan;

[0011] Step S4: According to market feedback and changes in constraint conditions, dynamically update the parameters of the optimization model through an adaptive learning mechanism.

[0012] Preferably, the step S2 includes:

[0013] Step S21: Set the constraint conditions for resource pool division, including resource complementarity, minimum stock threshold and upper limit of scheduling cost;

[0014] Step S22: Construct an association network based on the resource attribute matrix, and use the analytic hierarchy process to determine the priority weights of each resource pool;

[0015] Step S23: Configure elastic quotas for each decision node, and the quotas are dynamically adjusted according to historical scheduling efficiency and current demand urgency;

[0016] Step S24: When the scheduling delay or cost exceeds the limit between resource pools is detected, trigger the resource reorganization mechanism, reallocate decision nodes, and optimize the association network.

[0017] Preferably, the construction of the non-linear optimization model in step S3 includes:

[0018] Step S31: Estimate the resource demand trend in the future period through the grey prediction algorithm, and generate a demand fluctuation curve;

[0019] Step S32: Identify the resource supply-demand contradiction points based on the fuzzy clustering algorithm, and divide the high-conflict area and the low-conflict area;

[0020] Step S33: Use the genetic algorithm to solve the multi-objective optimization problem and balance the resource utilization rate and the scheduling cost;

[0021] Step S34: If the model solution result deviates from the preset tolerance range, introduce slack variables to reconstruct the constraint conditions.

[0022] Preferably, the method further includes:

[0023] Step S5: Optimize the resource allocation path through a multi-stage decision tree, and adjust the policy branches in combination with real-time risk indicators; the specific steps of the multi-stage decision tree include:

[0024] Step S51: Generate an initial decision tree according to historical decision data, and define the resource allocation actions corresponding to each node;

[0025] Step S52: Evaluate the execution risks of different paths based on Monte Carlo simulation, and mark the high-risk branches;

[0026] Step S53: Use the dynamic programming algorithm to prune the inefficient branches and retain the optimal decision path;

[0027] Step S54: If an external environment mutation is detected, backtrack to the nearest stable node and regenerate the decision tree.

[0028] Preferably, the method further includes:

[0029] Step S6: Based on the resource status and market signals, revise the deployment plan in real time and dynamically adjust the allocation ratio of each resource pool; the specific steps include:

[0030] Step S61: Divide the resource sensitivity levels according to the market volatility index, and set the response thresholds for different levels;

[0031] Step S62: Match similar historical scenarios based on the collaborative filtering algorithm and load the corresponding deployment templates;

[0032] Step S63: When the policy constraint conditions are updated, recalculate the boundary conditions by the Lagrange multiplier method;

[0033] Step S64: If the inventory of a certain resource pool is lower than the safety line, initiate cross-pool emergency scheduling and freeze non-critical allocation tasks.

[0034] Preferably, the adjustment formula for the elastic quota in step S23 is:

[0035]

[0036] Where, is the quota value of the decision node , represents the historical scheduling efficiency score, represents the current demand urgency, represents the scheduling cost coefficient, , , are dynamic adjustment factors.

[0037] Preferably, the membership function of the fuzzy clustering algorithm in step S32 is defined as:

[0038]

[0039] Where, is the membership degree of the resource to the cluster , is the distance between the resource and the cluster center, is the smoothing parameter.

[0040] Preferably, the triggering conditions of the backtracking mechanism in step S54 include:

[0041] Step S541: Preset environmental mutation recognition rules, including the policy change range and the market volatility threshold;

[0042] Step S542: When multiple mutation conditions are met simultaneously, activate the emergency backtracking mode;

[0043] Step S543: After backtracking is completed, verify the compatibility of the new decision tree with the current data;

[0044] Step S544: If the verification fails, switch to the standby decision model and reset the learning parameters.

[0045] Preferably, the similarity calculation method of the collaborative filtering algorithm in step S62 is:

[0046] Step S621: Construct a resource-scenario matrix to record the historical deployment plan and scenario labels;

[0047] Step S622: Measure the correlation strength between the current scenario and the historical scenario using cosine similarity;

[0048] Step S623: Screen the set of scenarios with similarity higher than the preset threshold, and weighted-fuse their deployment templates;

[0049] Step S624: If there is a template conflict, recalculate the similarity after dimensionality reduction by principal component analysis.

[0050] Preferably, the optimization strategy of the dynamic adjustment factor includes:

[0051] Step S101: Initialize the , , benchmark values according to the number of resource pools and the conflict level;

[0052] Step S102: Monitor the scheduling efficiency volatility in real time. If the score of a certain node drops suddenly, increase the weight of the corresponding factor;

[0053] Step S103: Iteratively search for the optimal factor combination through the ant colony algorithm to optimize the global benefit of quota allocation.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] In the data processing link, this method collects multi-source heterogeneous data in the economic system, covering rich information such as resource stocks, demand fluctuations, market environment parameters, and policy constraints. By comprehensively integrating these complex and diverse data, it can provide accurate and comprehensive basis for subsequent decision-making. Under the traditional management method, due to incomplete data collection or in-depth analysis, decision-making mistakes frequently occurred. However, the present invention breaks the data barrier with advanced data collection technology, realizes in-depth data mining and analysis, enables managers to clearly grasp the operation trend of the economic system, and lays a solid foundation for accurate decision-making.

[0056] In terms of resource partitioning and scheduling, resources are divided into multiple dynamic resource pools based on multi-dimensional feature analysis, and decision nodes are associated with each resource pool. Constraint conditions for resource pool partitioning are set, including resource complementarity, minimum stock threshold, and upper limit of scheduling cost, to ensure the scientificity and rationality of resource partitioning. The analytic hierarchy process is used to determine the priority weights, enabling resource allocation to prioritize meeting critical needs. Elastic quotas are configured for decision nodes and dynamically adjusted according to historical scheduling efficiency and current demand urgency, improving the flexibility and response speed of resource scheduling. When scheduling delays or cost overruns are detected between resource pools, a resource reorganization mechanism is triggered to reallocate decision nodes and optimize the associated network, effectively avoiding resource waste and idleness and greatly improving resource utilization efficiency. In practical application scenarios, such as the warehousing resource management in the logistics industry, through this method, the warehousing area can be reasonably divided according to factors such as the inbound and outbound frequency of goods and storage condition requirements, the inventory allocation can be dynamically adjusted, the backlog of goods and warehousing costs can be reduced, and the logistics operation efficiency can be improved.

[0057] Constructing a non-linear optimization model is one of the core advantages of this invention. The grey prediction algorithm is used to estimate the resource demand trend and generate a demand fluctuation curve, which can accurately predict future resource demand changes and make preparations for resource allocation in advance. Based on the fuzzy clustering algorithm, the resource supply-demand conflict points are identified, and high-conflict areas and low-conflict areas are divided, providing a basis for targeted resolution of supply-demand contradictions. The genetic algorithm is used to solve multi-objective optimization problems, balancing resource utilization rate and scheduling cost, and reducing operating costs while ensuring efficient resource utilization. If the model solution result deviates from the preset tolerance range, slack variables are introduced to reconstruct the constraint conditions to ensure the accuracy and reliability of the model. Taking the energy industry as an example, through this model, the energy production and distribution plan can be optimized according to energy demand prediction and supply capacity, and the energy production cost and transmission loss can be reduced on the premise of meeting energy demand.

[0058] The introduction of a multi-stage decision tree further optimizes the resource allocation path. An initial decision tree is generated based on historical decision data, and the resource allocation actions corresponding to each node are defined, providing clear rules for resource allocation. Based on Monte Carlo simulation, the execution risks of different paths are evaluated, high-risk branches are marked, and the dynamic programming algorithm is used to prune inefficient branches, retaining the optimal decision path, effectively reducing resource allocation risks and improving the success rate of resource allocation. When external environmental mutations are detected, it backtracks to the nearest stable node to regenerate the decision tree, enabling resource allocation to quickly adapt to environmental changes and ensuring the stable operation of the economic system. In the investment field, the multi-stage decision tree can be used to dynamically adjust the investment portfolio according to market changes and risk assessment, reducing investment risks and increasing investment returns.

[0059] The dynamic correction mechanism of the deployment plan is also a major highlight of the present invention. Based on the resource status and market signals, the deployment plan is corrected in real time. By dividing the resource sensitivity levels according to the market volatility index and setting response thresholds for different levels, it can quickly respond to market changes. Based on the collaborative filtering algorithm, similar historical scenarios are matched, and the corresponding deployment templates are loaded, which improves the decision-making efficiency. When the policy constraint conditions are updated, the boundary conditions are recalculated by the Lagrange multiplier method to ensure that resource management meets the policy requirements. If the stock in a certain resource pool is lower than the safety line, cross-pool emergency scheduling is initiated and non-critical allocation tasks are frozen to ensure the stability of resource supply. In supply chain management, this dynamic correction mechanism can optimize production and distribution plans in a timely manner according to raw material supply conditions, market demand changes, and policy adjustments to ensure the smooth operation of the supply chain.

[0060] In terms of the elastic quota adjustment formula and the dynamic adjustment factor optimization strategy, the quota value of the decision node is calculated through a scientific and reasonable formula, and the adjustment factor is dynamically adjusted according to factors such as the number of resource pools, conflict levels, and scheduling efficiency volatility, making the quota allocation more scientific and reasonable, better adapting to different economic environments and resource management requirements, and further improving the overall efficiency of resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is the working principle diagram of the economic resource management optimization method based on intelligent decision-making according to the present invention;

[0062] Figure 2 is the flowchart for constructing the non-linear optimization model;

[0063] Figure 3 is the flowchart for optimizing the resource allocation path by the multi-stage decision tree;

[0064] Figure 4 is the flowchart for correcting the deployment plan in real time. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0066] Please refer to Figures 1 - 4 , the present invention provides a technical solution: an economic resource management optimization method based on intelligent decision-making, and the specific implementation solution is as follows:

[0067] Step S1: Collect multi-source heterogeneous data in the economic system: Use a variety of data collection techniques and tools to collect data from all levels of the economic system. Obtain resource stock data through the Enterprise Resource Planning (ERP) system, covering the quantity and status information of various resources such as raw materials, inventory goods, and funds. Collect demand fluctuation data with the help of data reports from market research agencies, industry databases, and web crawler technology, including the demand changes of various products or services in different regions and time periods. For market environment parameters, use financial data interfaces to obtain economic indicators such as interest rates, exchange rates, and inflation rates, and at the same time collect unstructured data such as industry competition trends and changes in consumer preferences. Obtain policy constraint conditions, such as relevant information on tax policy adjustments and changes in industry access standards, from government official websites, policy and regulation databases and other channels.

[0068] Step S2: Divide the dynamic resource pool based on multi-dimensional feature analysis: Comprehensively consider multi-dimensional features such as the physical attributes, economic value, and usage frequency of resources to classify resources. For manufacturing enterprises, resources can be divided into different types such as production equipment, raw materials, human resources, and funds. Determine the priority of resources based on their importance, scarcity, and support for the core business of the enterprise in economic activities. Key production equipment and core technical talents usually have a higher priority. Set a decision node for each resource pool, and the decision node is responsible for formulating resource scheduling and allocation strategies, and dynamically adjusting the flow direction and usage mode of resources according to real-time data.

[0069] Step S3: Construct a non-linear optimization model to generate a deployment plan: Use techniques such as time series analysis and machine learning algorithms to analyze the collected real-time data and predict the changing trends of resource supply and demand. Considering the uncertainty of market demand and the volatility of resource supply, adopt stochastic programming or robust optimization methods to construct a non-linear optimization model to achieve the optimal allocation of resources. Set objective functions in the model, such as maximizing resource utilization rate, minimizing scheduling costs, and improving customer satisfaction, and at the same time set various constraint conditions, such as total resource limits, production capacity constraints, and demand satisfaction rate requirements. By solving the non-linear optimization model, generate a global resource deployment plan to determine the quantity and method of resource allocation between different time periods and different business departments.

[0070] Step S4: Update the model parameters through an adaptive learning mechanism: Establish a market feedback mechanism to collect data on the market's response to the resource allocation plan, such as product sales, customer complaint rates, changes in corporate profits, etc. At the same time, monitor the changes in policy constraints in real time. Once it is found that the market feedback data or the constraints have changed, activate the adaptive learning mechanism. Use optimization algorithms such as the gradient descent algorithm and genetic algorithm in machine learning to adjust the parameters of the non-linear optimization model, so that the model can better adapt to the dynamic changes of the economic system and improve the accuracy and effectiveness of the resource allocation plan.

[0071] The following further details the specific implementation manners of the present invention through 5 embodiments:

[0072] Embodiment 1:

[0073] In step S2, setting the constraint conditions for resource pool division is a key link. Setting the resource complementarity constraint aims to ensure that the resources within the same resource pool can cooperate with each other and play a synergistic role.

[0074] In an electronic manufacturing enterprise, equipment such as lithography machines and etching machines required for chip production, as well as supporting technical personnel and raw materials, have strong complementarity due to their close cooperation in the chip production process and can be divided into the same resource pool. Setting the minimum stock threshold is to ensure the basic production and operation of the enterprise. For raw materials that are indispensable in production, such as silicon wafers in chip manufacturing, set a minimum inventory quantity. When the stock of silicon wafers approaches or is lower than this threshold, the decision-making node will promptly activate the replenishment mechanism to prevent production stagnation caused by raw material shortages. The upper limit constraint on scheduling costs is from the perspective of economic costs, controlling the costs generated when resources are allocated between different departments or projects. For example, when allocating a batch of equipment from one production workshop to another, it involves transportation costs, installation and commissioning costs, etc., and the sum of these costs cannot exceed the pre-set upper limit of scheduling costs.

[0075] When constructing an association network based on a resource attribute matrix, it is first necessary to determine the composition of the resource attribute matrix. Taking an automobile manufacturing enterprise as an example, the resource attribute matrix includes attribute information such as the model, quantity, quality, and cost of automobile parts (such as engines, tires, seats, etc.). According to these attributes, relevant algorithms in graph theory are used to construct an association network. The nodes in the network represent various resources, and the weights of the edges reflect the degree of association between resources. The analytic hierarchy process is used to determine the priority weights of each resource pool. The specific steps are as follows: construct a hierarchical structure model, set the target layer as the priority ranking of resource pools, the criterion layer includes factors such as the scarcity of resources, the key degree to the production process, and cost-benefit, and the scheme layer is each resource pool. Through expert scoring or data analysis, determine the relative importance of each factor in the criterion layer relative to the target layer, and construct a judgment matrix. Conduct a consistency test on the judgment matrix to ensure the rationality of the judgment. Calculate the relative weights of each resource pool relative to each factor in the criterion layer, and conduct a comprehensive calculation to obtain the priority weights of each resource pool. In this way, it is possible to clearly determine which resource pools have higher priorities in resource scheduling and allocation, providing a scientific basis for subsequent decision-making.

[0076] Example 2:

[0077] In step S2, configuring elastic quotas for each decision node is an important means to achieve flexible resource allocation.

[0078] The adjustment formula for elastic quotas is:

[0079]

[0080] Where, is the quota value of decision node , represents the historical scheduling efficiency score, which is a score obtained by evaluating the actual performance of the past decision node during the resource scheduling process. For example, scores are given in terms of the timeliness of completing scheduling tasks, the rationality of resource allocation, etc. The higher the score, the higher the historical scheduling efficiency. represents the current demand urgency, which is determined by analyzing factors such as the urgency of the current market demand and the order delivery deadline. The more urgent the demand, the larger the value of represents the scheduling cost coefficient, which is calculated by comprehensively considering various costs involved in the resource scheduling process, such as transportation costs and equipment loss costs. The higher the cost, the larger the value of , , are dynamic adjustment factors, and their role is to adjust the relative importance of the historical scheduling efficiency score, the current demand urgency, and the scheduling cost coefficient in the quota calculation according to different resource management scenarios and goals.

[0081] When the scheduling delay or cost overrun between resource pools is detected, a resource reorganization mechanism is triggered. Taking a logistics and distribution enterprise as an example, assume that during a certain peak distribution period, it is found that there is a delay in the allocation of goods between different warehouses (resource pools), resulting in the inability to deliver some orders on time, and the scheduling cost exceeds the budget. At this time, the decision-making system will initiate the resource reorganization mechanism. Reallocate the decision-making nodes, adjust the decision-making nodes originally responsible for the allocation of goods between warehouses, and possibly decentralize the decision-making power to the grass-roots management units closer to the actual operation to more flexibly respond to emergencies. At the same time, optimize the association network, re-evaluate the goods transportation routes, transportation methods, etc. between warehouses, reduce unnecessary transportation links, and lower the scheduling cost. By reallocating the decision-making nodes and optimizing the association network, the cooperation between resource pools becomes more efficient, and the performance of the entire logistics and distribution system is improved.

[0082] Example 3:

[0083] In step S3, the construction of the non-linear optimization model involves multiple key steps.

[0084] Estimate the resource demand trend in the future period through the grey prediction algorithm to generate a demand fluctuation curve. The grey prediction algorithm is a method for predicting systems with uncertain factors. Taking a clothing manufacturing enterprise as an example, collect data such as the sales volume of different styles of clothing and the usage amount of raw materials over a past period as the original data series. Perform cumulative generation processing on the original data to weaken the randomness of the data and enhance its regularity. Establish a grey prediction model based on the cumulative generated data, calculate the predicted values of the raw material demands of different styles of clothing in the future period through the model, and then generate a demand fluctuation curve. This curve can intuitively show the change trend of resource demand over time and provide an important basis for subsequent resource allocation.

[0085] Identify the resource supply-demand contradiction points based on the fuzzy clustering algorithm, and divide the high-conflict areas and low-conflict areas. The membership function of the fuzzy clustering algorithm is defined as:

[0086]

[0087] Where, is the membership degree of resource to clustering , which represents the extent to which resource belongs to clustering , and its value range is between and . The closer is to , the higher the similarity degree between resource and clustering ; ​is the resource The distance from the clustering center is determined by calculating the differences between the attribute values of the resource and the corresponding attribute values of the clustering center. The smaller the distance, the closer the resource is to the clustering center; is the smoothing parameter, which is used to adjust the shape of the membership function and control the tightness of clustering. In practical applications, taking an energy enterprise as an example, different types of energy resources (such as coal, oil, natural gas, etc.) are used as resource objects. According to the attributes such as supply quantity, demand quantity, and price fluctuation of the resources, fuzzy clustering algorithm is used for clustering analysis. By calculating the membership degrees of each resource to different clusters, the resources are divided into different categories, and the high-conflict areas with prominent resource supply-demand contradictions and the low-conflict areas with relatively balanced supply and demand are identified. For the resources in the high-conflict areas, key attention should be paid during resource allocation, and targeted measures should be taken to alleviate the supply-demand contradictions.

[0088] Embodiment 4:

[0089] In the resource management optimization method of the present invention, it also includes optimizing the resource allocation path through a multi-stage decision tree and adjusting the strategy branches in combination with real-time risk indicators. An initial decision tree is generated based on historical decision data, and the resource allocation actions corresponding to each node are defined.

[0090] Taking an investment company as an example, collect the investment decision data of the past many years, including information such as investment project type, investment amount, investment timing, and market environment. Organize and preprocess these data, and generate an initial decision tree according to certain decision rules. The root node of the decision tree can be the overall assessment of the current market environment, such as bull market, bear market, or oscillating market, etc. Starting from the root node, according to different market environment conditions, different branches are extended downward, and each branch corresponds to a resource allocation action, such as increasing the proportion of stock investment, reducing the proportion of bond investment, etc. Each intermediate node is a judgment on further subdivided conditions, such as industry development trend, enterprise financial status, etc., and the final leaf node corresponds to a specific resource allocation decision.

[0091] Evaluate the execution risks of different paths based on Monte Carlo simulation and mark high-risk branches. Monte Carlo simulation is a method that simulates uncertain events through random sampling. In the investment decision-making scenario, for each resource allocation path in the initial decision tree, it is assumed that there is a certain degree of randomness in factors such as market environment and industry development. By randomly generating the values of these uncertain factors multiple times, simulate the investment return situation of this path under different circumstances. For example, for an investment path, simulate the investment returns under 1000 different market environments, and calculate statistical quantities such as the mean and variance of the returns. According to the pre-set risk assessment criteria, such as the return variance being too large or the probability of incurring losses exceeding a certain threshold, mark this path as a high-risk branch. After marking the high-risk branches, use the dynamic programming algorithm to prune inefficient branches and retain the optimal decision path. The dynamic programming algorithm constructs the optimal solution to the entire problem step by step by recursively calculating the optimal solutions of each sub-problem. In the decision tree, starting from the leaf nodes, calculate the optimal values of each node upwards, that is, select the branch with the largest return or the smallest risk. For the branches marked as high-risk, discard them during the calculation process. The path from the root node to the leaf node finally obtained is the optimal decision path. In this way, through the combination of Monte Carlo simulation and the dynamic programming algorithm, it is possible to screen out resource allocation paths with lower risks and higher returns in complex resource allocation scenarios.

[0092] If an external environment mutation is detected, then backtrack to the nearest stable node and regenerate the decision tree. The triggering conditions for the backtracking mechanism include: preset environmental mutation recognition rules, including the amplitude of policy changes and the market volatility threshold. Taking the financial market as an example, when the country introduces major financial policy adjustments, such as a large increase or decrease in interest rates or a major change in the tax policy, and the amplitude of these policy changes exceeds the pre-set threshold, and at the same time the market volatility (such as the index fluctuation amplitude in the stock market, the exchange rate fluctuation amplitude in the foreign exchange market, etc.) also exceeds the corresponding threshold, it is determined that an external environment mutation has occurred. When multiple mutation conditions are met simultaneously, activate the emergency backtracking mode. Backtrack to the nearest stable node, which is a node in the decision tree that was in a relatively stable state before the external environment mutation occurred, and is usually determined based on conditions such as a relatively stable market environment and good decision-making effects. Regenerate the decision tree, and according to the new market environment and real-time data, repeat the above steps of generating the initial decision tree, evaluating risks, pruning, etc. to generate a resource allocation decision tree adapted to the new environment. After the backtracking is completed, verify the compatibility of the new decision tree with the current data, and check the rationality and feasibility of the new decision tree in the current market environment and resource situation. If the verification fails, then switch to the backup decision model and reset the learning parameters. The backup decision model is a set of pre-prepared decision-making schemes that are enabled when the main decision model cannot work properly. Resetting the learning parameters is to enable the backup decision model to re-learn and optimize according to the new data to ensure the accuracy and effectiveness of resource allocation decisions.

[0093] Example 5:

[0094] In the method of the present invention, based on the resource status and market signals, the deployment plan is corrected in real time, and the allocation ratio of each resource pool is dynamically adjusted.

[0095] The resource sensitivity levels are divided according to the market volatility index, and response thresholds for different levels are set. The market volatility index can be obtained by calculating the fluctuation ranges of various market indicators such as stock market indices and commodity price indices. Taking a retail enterprise as an example, the commodity resources are divided into three levels according to their sensitivity to market fluctuations: high-sensitivity level, such as fashion clothing products, whose market demand is greatly affected by factors such as fashion trends and seasonal changes; medium-sensitivity level, such as daily necessities, whose market demand is relatively stable but is still affected by factors such as inflation and promotional activities; low-sensitivity level, such as basic food products, whose demand is relatively rigid and the market fluctuations have little impact on it. Corresponding response thresholds are set for each level. When the market volatility index exceeds the response threshold of the high-sensitivity level, it indicates that the market has changed drastically, and the deployment plan for high-sensitivity level resources needs to be adjusted quickly.

[0096] Based on the collaborative filtering algorithm, similar historical scenarios are matched, and the corresponding deployment templates are loaded. The similarity calculation method of the collaborative filtering algorithm is as follows: A resource-scenario matrix is constructed to record the historical deployment plans and scenario labels. In a retail enterprise, the rows of the resource-scenario matrix represent different commodity resources, the columns represent different market scenarios, and the matrix elements record information such as the deployment quantity and selling price of the corresponding commodities in that scenario. The scenario labels can include factors such as seasons, holidays, and economic situations. The cosine similarity is used to measure the correlation strength between the current scenario and the historical scenarios. The calculation formula of the cosine similarity is:

[0097]

[0098] Where, represents the similarity between scenario and scenario , is the dot product of the two scenario vectors, and are the scenario vectors of scenario and scenario The norm of a vector. By calculating the cosine similarity between the current market scenario and historical scenarios, a set of scenarios with similarity higher than a preset threshold is screened. For the screened set of scenarios, their deployment templates are weighted and fused, and different weights are assigned to the deployment templates of each scenario according to the similarity level. The higher the similarity, the greater the weight. If there are template conflicts, the similarity is recalculated after dimensionality reduction by principal component analysis. Principal component analysis is a data dimensionality reduction technique that reduces the dimensionality of data by transforming multiple related variables into a few uncorrelated principal components while retaining the main information of the data. When there are template conflicts, principal component analysis is used to perform dimensionality reduction on the resource-scenario matrix, remove redundant information, and then recalculate the similarity to find a more suitable deployment template.

[0099] When the policy constraint conditions are updated, the boundary conditions are recalculated by the Lagrange multiplier method. Taking a chemical enterprise as an example, assume that the government issues a new environmental protection policy, which strictly restricts the enterprise's pollutant emission indicators, thus changing the resource allocation constraint conditions of the enterprise. In the resource allocation model, the environmental protection policy constraint is added as a constraint condition to the original model. Using the Lagrange multiplier method, a Lagrangian function is constructed:

[0100]

[0101] where, are the decision variables for resource allocation, such as the raw material purchase quantity, the operating time of production equipment, etc.; is the original objective function, such as maximizing the enterprise's profit; is the new constraint condition, such as the pollutant emission limit; are the Lagrange multipliers. By taking the partial derivatives of the Lagrangian function and setting the partial derivatives to zero, the optimal solution for resource allocation under the new constraint conditions is obtained, that is, the boundary conditions are recalculated to determine the reasonable allocation ratio of each resource pool under the new policy constraints.

[0102] If the stock in a certain resource pool is below the safety line, cross-pool emergency scheduling is initiated and non-critical allocation tasks are frozen. In a manufacturing enterprise, assume that the stock of a certain key component in the resource pool is below the pre-set safety line. At this time, the cross-pool emergency scheduling mechanism is initiated. The key component is allocated from other resource pools with surplus inventory to ensure that production activities can continue. At the same time, non-critical allocation tasks are frozen, such as the resource allocation for some new product R & D projects with little impact on the production schedule, and the resource requirements of key production links are preferentially guaranteed. During the cross-pool emergency scheduling process, factors such as scheduling cost and transportation time need to be comprehensively considered to select the optimal scheduling plan. When the stock in the resource pool returns above the safety line, the freeze on non-critical allocation tasks is lifted and the normal resource allocation order is restored.

[0103] It should be noted that, in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0104] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An economic resource management optimization method based on intelligent decision-making, characterized in that: The following steps are involved: Step S1: Collect multi-source heterogeneous data in the economic system, including resource stocks, demand fluctuations, market environment parameters and policy constraints; Step S2: Based on multi-dimensional feature analysis, resources are divided into multiple dynamic resource pools by type and priority. Each resource pool is associated with a decision node responsible for resource scheduling and allocation strategy; Step S3: construct a nonlinear optimization model, combine real-time data to predict resource supply and demand changes, and generate a global resource allocation plan; Step S4: Dynamically update and optimize model parameters through an adaptive learning mechanism based on market feedback and changes in constraints.

2. The economic resource management optimization method according to claim 1, characterized in that: The step S2 comprises: Step S21: setting constraints for resource pool division, including resource complementarity, minimum stock threshold, and scheduling cost upper limit; Step S22: constructing an association network based on the resource attribute matrix, and using the hierarchical analysis method to determine the priority weight of each resource pool; Step S23: configuring elastic quotas for each decision node, and dynamically adjusting quotas based on historical scheduling efficiency and current demand urgency; Step S24: When scheduling delays or cost overruns between resource pools are detected, a resource reorganization mechanism is triggered to reallocate decision nodes and optimize associated networks.

3. The economic resource management optimization method according to claim 1, characterized in that: The construction of the nonlinear optimization model in step S3 includes: Step S31: Predict the resource demand trend in the future period by using the grey prediction algorithm to generate a demand fluctuation curve; Step S32: Identify the contradiction points between resource supply and demand based on the fuzzy clustering algorithm, and divide the high-conflict area into low-conflict areas; Step S33: using a genetic algorithm to solve a multi-objective optimization problem and balance resource utilization and scheduling costs; Step S34: If the model solution deviates from the preset tolerance range, slack variables are introduced to reconstruct the constraint conditions.

4. The economic resource management optimization method according to claim 1, characterized in that: The method further comprises: Step S5: Optimize the resource allocation path through a multi-stage decision tree and adjust the strategy branch in combination with real-time risk indicators; the specific steps of the multi-stage decision tree include: Step S51: Generate an initial decision tree based on historical decision data and define the resource allocation action corresponding to each node; Step S52: Evaluate the execution risks of different paths based on Monte Carlo simulation and mark high-risk branches; Step S53: Prune inefficient branches using a dynamic programming algorithm to retain the optimal decision path; Step S54: If a sudden change in the external environment is detected, backtrack to the nearest stable node to regenerate the decision tree.

5. The economic resource management optimization method according to claim 1, characterized in that: The method further comprises: Step S6: Based on resource status and market signals, the allocation plan is modified in real time, and the allocation ratio of each resource pool is dynamically adjusted; the specific steps include: Step S61: Divide resource sensitivity levels according to market volatility indexes and set response thresholds for different levels; Step S62: matching similar historical scenarios based on a collaborative filtering algorithm and loading corresponding deployment templates; Step S63: when the policy constraints are updated, the boundary conditions are recalculated by the Lagrange multiplier method; Step S64: If the inventory of a resource pool is lower than the safety line, cross-pool emergency scheduling is initiated and non-critical allocation tasks are frozen.

6. The economic resource management optimization method according to claim 2, characterized in that: The adjustment formula of the elastic quota in step S23 is: in, Decision Node The quota value of represents the historical scheduling efficiency score, Indicates the urgency of current demand. represents the scheduling cost coefficient, , , is a dynamic adjustment factor.

7. The economic resource management optimization method according to claim 3, characterized in that: The membership function of the fuzzy clustering algorithm in step S32 is defined as: in, For resources Clustering The membership degree of For resources The distance from the cluster center, is the smoothing parameter.

8. The economic resource management optimization method according to claim 4, characterized in that: The triggering conditions of the backtracking mechanism in step S54 include: Step S541: presetting environmental mutation identification rules, including policy change range and market volatility threshold; Step S542: When multiple mutation conditions are met at the same time, the emergency backtracking mode is activated; Step S543: After the backtracking is completed, the compatibility of the new decision tree with the current data is verified; Step S544: If the verification fails, switch to the backup decision model and reset the learning parameters.

9. The economic resource management optimization method according to claim 5, characterized in that: The similarity calculation method of the collaborative filtering algorithm in step S62 is: Step S621: construct a resource-scenario matrix to record historical deployment plans and scenario labels; Step S622: using cosine similarity to measure the correlation strength between the current scene and the historical scene; Step S623: Filter the scene sets whose similarity is higher than a preset threshold, and weightedly fuse their deployment templates; Step S624: If the templates conflict, recalculate the similarity after dimensionality reduction through principal component analysis.

10. The economic resource management optimization method according to claim 6, characterized in that: The optimization strategy of the dynamic adjustment factor includes: Step S101: Initialize according to the number of resource pools and conflict level , , The baseline value of Step S102: monitor the scheduling efficiency fluctuation rate in real time. If the score of a certain node drops suddenly, increase the corresponding factor weight; Step S103: Iteratively search for the optimal factor combination through the ant colony algorithm to optimize the global benefit of quota allocation.

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