An optimization method for economic resource management based on intelligent decision-making

Abstract

The present invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision-making. The method first collects multi-source heterogeneous data of the economic system, including resource stocks, demand fluctuations, etc. Then, based on multi-dimensional feature analysis, the dynamic resource pool is divided, and a nonlinear optimization model is constructed to predict changes in resource supply and demand to generate an allocation plan. Then, a multi-stage decision tree is used to optimize the allocation path, and the model parameters are updated through an adaptive learning mechanism according to market feedback and changes in constraints, and the allocation plan is corrected in real time. In addition, key technical details such as the elastic quota adjustment formula and the fuzzy clustering algorithm membership function are also given. The present invention can effectively integrate complex data, scientifically dispatch resources, accurately predict supply and demand, optimize allocation paths, adapt to environmental changes, significantly improve the efficiency and benefits of economic resource management, and provide scientific and reasonable resource management decision support for economic entities.

Description

Technical Field

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

[0002] In today's complex and volatile economic environment, economic resource management faces numerous challenges. Traditional management methods are no longer able to meet real-world needs and urgently require innovation and optimization. From a data perspective, data in economic systems is multi-source and heterogeneous. Resource inventory data not only covers multiple dimensions, such as the quantity and value of various assets, but also varies across industries in terms of statistical caliber and storage format. Demand fluctuation data, influenced by market trends, consumer preferences, and seasonal factors, is highly uncertain and dynamic, making it difficult for traditional methods to accurately capture and effectively analyze it. Market environment parameters, including economic conditions, competitive landscape, and technological innovation, are intertwined and rapidly changing. Traditional data processing methods are unable to integrate and analyze these parameters in a timely manner, hindering their ability to effectively support resource management decisions. Policy constraints frequently update, and different policies may overlap and conflict with each other. Traditional management models often lag in the acquisition and interpretation of policy information, making it difficult to quickly adapt to policy changes in resource management.

[0003] Resource scheduling and allocation have always been a core challenge in economic resource management. Traditional approaches lack scientific rationality in resource allocation, often employing fixed classification models that fail to fully consider key factors such as resource complementarity, inventory thresholds, and scheduling costs. This prevents rational resource allocation, leading to both idle and scarce resources. For example, in the manufacturing industry, if the allocation of raw materials and parts fails to consider their complementarity, production lines may stall due to a lack of some materials, while other materials accumulate, consuming significant capital and storage space. Furthermore, decision-making nodes are inflexible and lack dynamic adjustment mechanisms, making it impossible to optimize scheduling strategies based on real-time resource status and demand fluctuations. In the face of sudden demand or supply disruptions, traditional scheduling methods are slow to respond and unable to quickly reallocate resources, resulting in significant losses for businesses and the economy.

[0004] Predicting changes in resource supply and demand is also a major shortcoming of traditional economic resource management. Traditional forecasting methods, mostly based on simple statistical models or empirical judgments, cannot accurately depict the complex nonlinear relationships within economic systems. In the face of economic globalization and intensified market competition, market supply and demand are influenced by a variety of factors, presenting a high degree of complexity and uncertainty. For example, factors such as abnormal climate, changes in international trade policies, and shifting consumer health attitudes all have significant impacts on the supply and demand of agricultural products. Traditional forecasting methods struggle to fully account for these factors, resulting in significant deviations between forecast results and actual conditions. This, in turn, makes resource allocation plans unscientific and ineffective, preventing optimal resource allocation.

[0005] Furthermore, traditional economic resource management methods lack adaptive learning capabilities. Market feedback and constraints are constantly changing, but traditional management strategies struggle to adapt to these changes. For example, when market demand for a particular product suddenly increases, traditional management methods may be unable to quickly adjust production resource allocation, resulting in a supply shortage and missed market opportunities. When policies and regulations impose new restrictions on resource use, traditional management methods may be unable to respond promptly, exposing them to the risk of noncompliance and increased operating costs. Furthermore, when optimizing resource allocation paths, traditional methods often rely on fixed processes and fail to dynamically adjust based on real-time risk indicators. This results in inefficient resource allocation in the face of risks such as market fluctuations and natural disasters, making it impossible to ensure the stable operation of the economic system. Summary of the Invention

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

[0007] To achieve the above objectives, the present invention provides the following technical solution: an economic resource management optimization method 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 constraints;

[0009] 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;

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

[0011] Step S4: Based on market feedback and changes in constraints, the model parameters are dynamically updated and optimized through an adaptive learning mechanism.

[0012] Preferably, the step S2 includes:

[0013] Step S21: setting resource pool division constraints, including resource complementarity, minimum inventory threshold, and scheduling cost upper limit;

[0014] Step S22: constructing an association network based on the resource attribute matrix and using the analytic hierarchy process to determine the priority weight of each resource pool;

[0015] Step S23: Configure an elastic quota for each decision node, and the quota is dynamically adjusted based on historical scheduling efficiency and current demand urgency;

[0016] Step S24: When scheduling delays or cost exceeding limits are detected between resource pools, a resource reorganization mechanism is triggered to reallocate decision nodes and optimize associated networks.

[0017] Preferably, the construction of the nonlinear optimization model in step S3 includes:

[0018] Step S31: using a grey prediction algorithm to estimate resource demand trends in future cycles and generate a demand fluctuation curve;

[0019] Step S32: Identify resource supply and demand contradictions based on a fuzzy clustering algorithm and divide them into high-conflict areas and low-conflict areas;

[0020] Step S33: using a genetic algorithm to solve the multi-objective optimization problem and balance resource utilization and scheduling costs;

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

[0022] Preferably, the method further comprises:

[0023] Step S5: Optimize resource allocation paths through a multi-stage decision tree and adjust strategy 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 based on historical decision data and define the resource allocation action corresponding to each node;

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

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

[0027] Step S54: If a sudden change in the external environment is detected, the decision tree is regenerated by tracing back to the nearest stable node.

[0028] Preferably, the method further comprises:

[0029] 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:

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

[0031] Step S62: matching similar historical scenarios based on a collaborative filtering algorithm and loading corresponding deployment templates;

[0032] Step S63: When the policy constraints are updated, the boundary conditions are recalculated using the Lagrange multiplier method;

[0033] 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.

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

[0035]

[0036] in, Decision nodes 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.

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

[0038]

[0039] in, For resources Clustering The membership degree of For resources The distance from 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 identification rules, including policy change range and market volatility thresholds;

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

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

[0044] Step S544: If the verification fails, switch to the backup 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 historical deployment plans and scenario labels;

[0047] Step S622: using cosine similarity to measure the correlation strength between the current scene and the historical scene;

[0048] Step S623: Filtering scene sets with similarities higher than a preset threshold, and weightedly fusing their deployment templates;

[0049] Step S624: If the templates conflict, the similarity is recalculated after dimensionality reduction through principal component analysis.

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

[0051] Step S101: Initialize according to the number of resource pools and conflict level 、 、 The baseline value;

[0052] Step S102: Monitor the scheduling efficiency fluctuation rate in real time. If the score of a node drops sharply, increase the corresponding factor weight.

[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 present invention has the following beneficial effects:

[0055] During data processing, this method collects multi-source, heterogeneous data from the economic system, encompassing a rich array of information, including resource inventories, demand fluctuations, market environment parameters, and policy constraints. By comprehensively integrating this complex and diverse data, it can provide an accurate and comprehensive basis for subsequent decision-making. Traditional management approaches frequently lead to erroneous decisions due to incomplete data collection or insufficient analysis. However, this invention leverages advanced data collection technologies to break down data barriers and achieve in-depth data mining and analysis, enabling managers to clearly grasp the operational dynamics of the economic system and lay 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. Constraints for resource pool partitioning, including resource complementarity, minimum inventory thresholds, and scheduling cost caps, are set to ensure the scientific and rational nature of resource partitioning. Priority weights are determined using the Analytic Hierarchy Process (AHP) to prioritize resource allocation, ensuring that critical needs are prioritized. Flexible quotas are configured for decision nodes and dynamically adjusted based on historical scheduling efficiency and current demand urgency, improving the flexibility and responsiveness of resource scheduling. When scheduling delays or cost violations 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 significantly improving resource utilization efficiency. In practical application scenarios, such as warehouse resource management in the logistics industry, this method can rationally divide storage areas based on factors such as the frequency of goods entering and leaving the warehouse and storage requirements, dynamically adjust inventory allocation, reduce cargo backlogs and storage costs, and improve logistics operational efficiency.

[0057] Constructing a nonlinear optimization model is one of the core advantages of the present invention. The gray prediction algorithm is used to estimate the resource demand trend and generate a demand fluctuation curve, which can accurately predict future changes in resource demand and make preparations for resource allocation in advance. Based on the fuzzy clustering algorithm, the contradiction points between resource supply and demand are identified, and high-conflict areas and low-conflict areas are divided to provide a basis for targeted solutions to supply and demand contradictions. A genetic algorithm is used to solve multi-objective optimization problems, balance resource utilization and scheduling costs, and reduce operating costs while ensuring efficient resource utilization. If the model solution deviates from the preset tolerance range, slack variables are introduced to reconstruct the constraints to ensure the accuracy and reliability of the model. Taking the energy industry as an example, this model can be used to optimize energy production and distribution plans based on energy demand forecasts and supply capabilities, and reduce energy production costs and transmission losses while meeting energy demand.

[0058] The introduction of multi-stage decision trees further optimizes resource allocation paths. An initial decision tree is generated based on historical decision data, defining the resource allocation actions corresponding to each node and providing clear rules for resource allocation. Monte Carlo simulations are used to assess the execution risks of different paths, flag high-risk branches, and utilize dynamic programming algorithms to prune inefficient branches, retaining the optimal decision path. This effectively reduces resource allocation risk and improves the success rate of resource allocation. When a sudden change in the external environment is detected, the decision tree is regenerated by tracing back to the most recently stable node, enabling resource allocation to quickly adapt to environmental changes and ensuring the stable operation of the economic system. In the investment sector, the use of multi-stage decision trees can dynamically adjust investment portfolios based on market changes and risk assessments, reducing investment risk and increasing investment returns.

[0059] The dynamic correction mechanism of the allocation plan is also a highlight of the present invention. The allocation plan is corrected in real time based on resource status and market signals, resource sensitivity levels are divided according to market volatility indexes, and response thresholds of different levels are set to quickly respond to market changes. Based on the collaborative filtering algorithm, similar historical scenarios are matched and corresponding allocation templates are loaded to improve decision-making efficiency. When policy constraints are updated, the boundary conditions are recalculated using the Lagrange multiplier method to ensure that resource management meets policy requirements. 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 to ensure the stability of resource supply. In supply chain management, this dynamic correction mechanism can timely optimize production and distribution plans based on raw material supply, 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 dynamic adjustment factor optimization strategy, the decision node quota value 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 level, and scheduling efficiency fluctuation rate, making quota allocation more scientific and reasonable, and able to better adapt to different economic environments and resource management needs, further improving the overall benefits of resource management. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0062] Figure 2 Flowchart constructed for nonlinear optimization model;

[0063] Figure 3 Flowchart for optimizing resource allocation paths for a multi-stage decision tree;

[0064] Figure 4 A flowchart for real-time modification of deployment plans. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

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

[0067] Step S1: Collect multi-source heterogeneous data from the economic system: Utilize a variety of data collection technologies and tools to collect data from all levels of the economic system. Obtain resource inventory data through the Enterprise Resource Planning (ERP) system, covering the quantity and status of various resources such as raw materials, inventory, and funds. Leverage data reports from market research institutions, industry databases, and web crawler technology to collect demand fluctuation data, including changes in market demand for various products or services across different regions and time periods. Targeting market environment parameters, utilize financial data interfaces to obtain economic indicators such as interest rates, exchange rates, and inflation rates. Also, collect unstructured data such as industry competition trends and changes in consumer preferences. Obtain information on policy constraints, such as tax policy adjustments and changes in industry access standards, from official government websites and policy and regulatory databases.

[0068] Step S2: Divide dynamic resource pools based on multi-dimensional feature analysis: Classify resources based on their physical properties, economic value, frequency of use, and other multi-dimensional characteristics. For manufacturing companies, resources can be divided into different types, such as production equipment, raw materials, human resources, and funds. Resource priorities are determined based on their importance and scarcity in economic activities, as well as their support for the company's core business. Key production equipment and core technical personnel are generally given higher priority. A decision node is set for each resource pool. This decision node is responsible for formulating resource scheduling and allocation strategies, dynamically adjusting resource flows and usage based on real-time data.

[0069] Step S3: Construct a nonlinear optimization model to generate an allocation plan: Utilizing techniques such as time series analysis and machine learning algorithms, the collected real-time data is analyzed to predict resource supply and demand trends. Taking into account the uncertainty of market demand and the volatility of resource supply, a nonlinear optimization model is constructed using stochastic programming or robust optimization methods to achieve optimal resource allocation. The model sets objective functions such as maximizing resource utilization, minimizing scheduling costs, and improving customer satisfaction. Various constraints are also set, such as total resource limits, production capacity constraints, and demand fulfillment requirements. By solving the nonlinear optimization model, a global resource allocation plan is generated to determine the quantity and method of resource allocation across different time periods and business departments.

[0070] Step S4: Update model parameters through an adaptive learning mechanism: Establish a market feedback mechanism to collect data on market responses to resource allocation plans, such as product sales, customer complaint rates, and changes in corporate profits. Simultaneously, monitor changes in policy constraints in real time. Once market feedback data or changes in constraints are detected, the adaptive learning mechanism is activated. Optimization algorithms such as gradient descent and genetic algorithms in machine learning are used to adjust the parameters of the nonlinear optimization model, enabling the model to better adapt to the dynamic changes in the economic system and improving the accuracy and effectiveness of the resource allocation plan.

[0071] The specific embodiments of the present invention are further described in detail below through 5 examples:

[0072] Example 1:

[0073] In step S2, setting resource pool division constraints is a key step. Setting resource complementarity constraints is intended to ensure that resources within the same resource pool can cooperate and work together.

[0074] In an electronics manufacturing company, the equipment required for chip production, such as lithography machines and etching machines, as well as the supporting technicians and raw materials, work closely together in the chip production process and are highly complementary, allowing them to be grouped into the same resource pool. Minimum inventory thresholds are set to ensure the company's basic production operations. For essential raw materials, such as silicon wafers used in chip manufacturing, a minimum inventory level is set. When the inventory of silicon wafers approaches or falls below this threshold, the decision node promptly activates a replenishment mechanism to prevent production stagnation due to raw material shortages. The scheduling cost cap constraint, based on economic cost, controls the costs incurred when resources are allocated between different departments or projects. For example, when transferring a batch of equipment from one production workshop to another, there are transportation costs, installation and commissioning fees, and other expenses involved. The total of these costs cannot exceed the pre-set scheduling cost cap.

[0075] When constructing an association network based on a resource attribute matrix, the first step is to determine its composition. For example, for an automobile manufacturer, the resource attribute matrix contains information on the model, quantity, quality, and cost of auto parts (such as engines, tires, and seats). Based on these attributes, a correlation network is constructed using graph theory algorithms. Nodes in the network represent various types of resources, and edge weights reflect the degree of association between resources. The Analytic Hierarchy Process (AHP) is used to determine the priority weights of each resource pool. The specific steps are as follows: A hierarchical model is constructed, with the target layer representing the priority ranking of resource pools. The criterion layer includes factors such as resource scarcity, criticality to the production process, and cost-effectiveness. The solution layer represents each resource pool. Expert scoring or data analysis is used to determine the relative importance of each criterion layer factor relative to the target layer, and a judgment matrix is ​​constructed. The judgment matrix is ​​then checked for consistency to ensure the rationality of the judgments. The relative weights of each resource pool relative to each criterion layer factor are calculated and combined to determine the priority weights of each resource pool. This allows for a clear determination of which resource pools have a higher priority in resource scheduling and allocation, providing a scientific basis for subsequent decision-making.

[0076] Example 2:

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

[0078] The adjustment formula for flexible quotas is:

[0079]

[0080] in, Decision nodes The quota value of Represents the historical scheduling efficiency score, which is based on past decision nodes The score obtained by evaluating the actual performance in the resource scheduling process, such as the timeliness of completing scheduling tasks and the rationality of resource allocation. The higher the score, the higher the historical scheduling efficiency. Indicates the urgency of current demand, which is determined by analyzing factors such as the urgency of current market demand and order delivery deadline. The more urgent the demand, The larger the value of . It represents the scheduling cost coefficient, which is calculated by comprehensively considering the transportation cost, equipment loss cost and other expenses involved in the resource scheduling process. The higher the cost, The larger the value of . 、 、 They are dynamic adjustment factors. Their function is to adjust the relative importance of historical scheduling efficiency scores, current demand urgency, and scheduling cost coefficients in quota calculation according to different resource management scenarios and goals.

[0081] When delays or cost violations are detected in scheduling between resource pools, a resource reorganization mechanism is triggered. For example, consider a logistics and distribution company. During a peak delivery period, delays in allocating goods between warehouses (resource pools) are observed, resulting in some orders being delayed and scheduling costs exceeding budget. In this case, the decision-making system initiates a resource reorganization mechanism. Decision nodes previously responsible for allocating goods between warehouses are reallocated, potentially delegating decision-making authority to lower-level management units closer to actual operations, enabling more flexible response to emergencies. Simultaneously, the interconnected network is optimized, reassessing inter-warehouse transportation routes and methods, reducing unnecessary transportation links and lowering scheduling costs. By reallocating decision nodes and optimizing the interconnected network, collaboration between resource pools becomes more efficient, improving the performance of the entire logistics and distribution system.

[0082] Example 3:

[0083] In step S3, the construction of the nonlinear optimization model includes several key steps.

[0084] A gray forecasting algorithm is used to estimate resource demand trends over future periods and generate a demand fluctuation curve. The gray forecasting algorithm is a forecasting method for systems with uncertainties. For example, a clothing manufacturing company collects data on sales volume and raw material usage for different clothing styles over a period of time as raw data columns. This raw data is then accumulated and processed to reduce randomness and enhance regularity. A gray forecasting model is established based on this accumulated data. The model calculates forecasted raw material demand for different clothing styles over a period of time, generating a demand fluctuation curve. This curve visually demonstrates the changing trend of resource demand over time, providing an important basis for subsequent resource allocation.

[0085] Based on the fuzzy clustering algorithm, the contradiction points of resource supply and demand are identified and divided into high-conflict areas and low-conflict areas. The membership function of the fuzzy clustering algorithm is defined as:

[0086]

[0087] in, For resources Clustering The membership degree of To what extent is it clustered , the value range is arrive between, The closer , description resources and clustering The higher the similarity; For resources The distance from the cluster center is calculated by The smaller the distance, the better the resource. The closer to the cluster center; is a smoothing parameter used to adjust the shape of the membership function and control the compactness of clusters. In practical applications, for example, an energy company uses a fuzzy clustering algorithm to perform cluster analysis on different types of energy resources (such as coal, oil, and natural gas) based on their supply, demand, and price fluctuations. By calculating the membership of each resource to different clusters, resources are divided into different categories, identifying high-conflict areas with prominent supply and demand imbalances and low-conflict areas with relatively balanced supply and demand. Resources in high-conflict areas require special attention during resource allocation, and targeted measures are implemented to alleviate these imbalances.

[0088] Example 4:

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

[0090] For example, an investment company collects investment decision data from past years, including information such as project type, investment amount, investment timing, and market environment. This data is organized and preprocessed, and an initial decision tree is generated according to specific decision rules. The root node of the decision tree can be a general assessment of the current market environment, such as a bull market, a bear market, or a volatile market. From this root node, different branches extend downward based on different market conditions. Each branch corresponds to a resource allocation action, such as increasing the proportion of stock investment or reducing the proportion of bond investment. Each intermediate node determines further sub-conditions, such as industry development trends and corporate financial status. Finally, the leaf node corresponds to a specific resource allocation decision.

[0091] Monte Carlo simulation is used to assess the execution risk of different paths and flag high-risk branches. Monte Carlo simulation is a method that simulates uncertain events through random sampling. In an investment decision scenario, for each resource allocation path in the initial decision tree, it is assumed that factors such as market conditions and industry development have a certain degree of randomness. By repeatedly generating values ​​for these uncertain factors, the investment returns of this path are simulated under different circumstances. For example, for a single investment path, the investment returns under different market conditions are simulated 1,000 times, and statistical measures such as the mean and variance of the returns are calculated. Based on pre-defined risk assessment criteria, such as excessive variance or a probability of loss exceeding a certain threshold, the path is marked as a high-risk branch. After marking high-risk branches, a dynamic programming algorithm is used to prune inefficient branches, retaining the optimal decision path. The dynamic programming algorithm recursively calculates the optimal solution to each subproblem and gradually constructs the optimal solution to the entire problem. Starting from the leaf node in the decision tree, the optimal value of each node is calculated upwards, selecting the branch with the highest return or the lowest risk. Branches marked as high-risk are discarded during the calculation process, and the resulting path from the root node to the leaf node is the optimal decision path. In this way, through the combination of Monte Carlo simulation and 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 a sudden change in the external environment is detected, the decision tree is rebuilt by backtracking to the most recent stable node. The triggering conditions for this backtracking mechanism include pre-set rules for identifying sudden changes in the external environment, including policy change magnitude and market volatility thresholds. For example, in the financial market, a sudden change in the external environment is identified when the government implements major financial policy adjustments, such as significant interest rate increases or tax policy changes, and the magnitude of these policy changes exceeds a pre-set threshold. Simultaneously, market volatility (such as stock index fluctuations or exchange rate fluctuations) also exceeds the corresponding threshold. When multiple sudden change conditions are met simultaneously, an emergency backtracking mode is activated. Backtracking is performed to the most recent stable node in the decision tree, which was relatively stable before the sudden change in the external environment. This node is typically determined based on conditions such as a relatively stable market environment and good decision-making results. The decision tree is rebuilt, and the aforementioned steps of generating the initial decision tree, assessing risk, and pruning are repeated based on the new market environment and real-time data to generate a resource allocation decision tree adapted to the new environment. After the backtracking is complete, the new decision tree is verified for compatibility with the current data and its rationality and feasibility within the current market environment and resource conditions. If the verification fails, the system switches to the backup decision model and resets its learning parameters. The backup decision model is a pre-prepared decision solution that is activated when the primary decision model fails to function properly. Resetting learning parameters allows the backup decision model to relearn and optimize based on new data, ensuring the accuracy and effectiveness of resource allocation decisions.

[0093] Example 5:

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

[0095] Resource sensitivity levels are categorized based on the market volatility index, and response thresholds are set for each level. The market volatility index can be calculated by calculating the fluctuations of various market indicators, such as the stock market index and commodity price index. For example, a retail company categorizes commodity resources into three levels based on their sensitivity to market fluctuations: High sensitivity, such as fashion apparel, where market demand is significantly influenced by factors like fashion trends and seasonal changes; Medium sensitivity, such as daily necessities, where market demand is relatively stable but still affected by factors like inflation and promotional activities; and Low sensitivity, such as basic food products, where demand is relatively rigid and less affected by market fluctuations. A corresponding response threshold is set for each level. When the market volatility index exceeds the response threshold for the High sensitivity level, it indicates drastic market fluctuations, requiring rapid adjustments to the allocation of high-sensitivity resources.

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

[0097]

[0098] in, Representation scene and scenes The similarity of is the dot product of the two scene vectors, and The scenes are and scenes The modulus of the vector. By calculating the cosine similarity between the current market scenario and the historical scenario, the scenario set with a similarity higher than the preset threshold is screened. For the screened scenario set, its allocation template is weighted and fused, and different weights are assigned to the allocation template of each scenario according to the degree of similarity. The higher the similarity, the greater the weight. If there is a template conflict, the similarity is recalculated after dimensionality reduction through principal component analysis. Principal component analysis is a data dimensionality reduction technology that reduces the dimension of the data by converting multiple related variables into a few unrelated principal components while retaining the main information of the data. When there is a template conflict, principal component analysis is used to reduce the dimensionality of the resource-scenario matrix, remove redundant information, and then recalculate the similarity to find a more suitable allocation template.

[0099] When policy constraints are updated, boundary conditions are recalculated using the Lagrange multiplier method. For example, let's take a chemical company. Suppose the government introduces new environmental protection policies that impose strict limits on the company's pollutant emissions. This changes the company's resource allocation constraints. In the resource allocation model, the environmental protection policy constraint is added as a constraint to the original model. Using the Lagrange multiplier method, the Lagrange function is constructed:

[0100]

[0101] in, It is the decision variable for resource allocation, such as the purchase amount of raw materials, the operating time of production equipment, etc. is the original objective function, such as maximizing corporate profits; are new constraints, such as pollutant emission limits; is the Lagrange multiplier. By taking the partial derivatives of the Lagrange function and setting them to zero, we can find the optimal solution for resource allocation that satisfies the new constraints. This means recalculating the boundary conditions and determining the appropriate allocation ratio for each resource pool under the new policy constraints.

[0102] If the inventory of a resource pool is lower than the safety line, cross-pool emergency scheduling will be initiated and non-critical allocation tasks will be frozen. In a manufacturing enterprise, suppose the inventory of a resource pool for a key component is lower than the pre-set safety line. At this time, the cross-pool emergency scheduling mechanism will be activated. The key component will be allocated from other resource pools with remaining inventory to ensure that production activities can continue. At the same time, non-critical allocation tasks, such as resource allocation for some new product research and development projects that have less impact on production progress, will be frozen to give priority to resource needs in key production links. In the cross-pool emergency scheduling process, it is necessary to comprehensively consider factors such as scheduling costs and transportation time to select the optimal scheduling plan. When the inventory of the resource pool returns to above the safety line, the freeze on non-critical allocation tasks will be lifted and the normal resource allocation order will be restored.

[0103] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0104] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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: Build a nonlinear optimization model, combine real-time data to predict resource supply and demand changes, and generate a global resource allocation plan; The construction of the nonlinear optimization model in step S3 includes: Step S31: using a grey prediction algorithm to estimate resource demand trends in future cycles and generate a demand fluctuation curve; Step S32: Identify resource supply and demand contradictions based on a fuzzy clustering algorithm and divide them into high-conflict areas and low-conflict areas; The membership function of the fuzzy clustering algorithm in step S32 is defined as: in, For resources Clustering The membership degree, For resources The distance from the cluster center, is a smoothing parameter; by calculating the membership of each resource to different clusters, resources are divided into different categories, and high-conflict areas with prominent contradictions between resource supply and demand and low-conflict areas with relatively balanced supply and demand are identified; Step S33: using a genetic algorithm to solve the 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; Step S4: Dynamically update and optimize model parameters through an adaptive learning mechanism based on market feedback and changes in constraints; Step S5: Optimize resource allocation paths through a multi-stage decision tree and adjust strategy branches 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: using a dynamic programming algorithm to prune inefficient branches and retain the optimal decision path; Step S54: if a sudden change in the external environment is detected, backtrack to the nearest stable node and regenerate the decision tree; 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 index 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 using 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.

2. The economic resource management optimization method according to claim 1, characterized in that: The step S2 comprises: Step S21: setting resource pool division constraints, including resource complementarity, minimum inventory threshold, and scheduling cost upper limit; Step S22: constructing an association network based on the resource attribute matrix and using the analytic hierarchy process to determine the priority weight of each resource pool; Step S23: Configure an elastic quota for each decision node, and the quota is dynamically adjusted based on historical scheduling efficiency and current demand urgency; Step S24: When scheduling delays or cost violations 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 2, characterized in that: The adjustment formula for the flexible quota in step S23 is: in, Decision nodes 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.

4. The economic resource management optimization method according to claim 1, characterized in that: The triggering conditions of the backtracking mechanism in step S54 include: Step S541: Preset environmental mutation identification rules, including policy change range and market volatility thresholds; Step S542: When multiple mutation conditions are met simultaneously, 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.

5. The economic resource management optimization method according to claim 1, 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: Filtering scene sets with similarities higher than a preset threshold, and weightedly fusing their deployment templates; Step S624: If the templates conflict, the similarity is recalculated after dimensionality reduction through principal component analysis.

6. The economic resource management optimization method according to claim 3, 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; Step S102: Monitor the scheduling efficiency fluctuation rate in real time. If the score of a node drops sharply, 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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