Resource allocation operation planning optimization method, system and device, storage medium and product

Abstract

The invention discloses an operation planning optimization method, system and device for resource allocation, a storage medium and a product, and relates to the technical field of resource allocation and optimization decision, and the method comprises the steps: collecting resource related data; through a pre-trained resource allocation large model, based on the constraint condition and the target function, outputting an operation planning optimization formula matched with the current service scene; based on the workflow arrangement mode, performing service logic definition and workflow node visualization processing on the service demand information to obtain a workflow execution model; executing operation planning optimization calculation according to the resource related data and an operation planning optimization formula to obtain an optimal solution set, and fusing a workflow model to generate a decision variable; and calling a decision engine to carry out variable replacement, and generating a resource allocation strategy. According to the scheme, large model modeling and operation planning optimization capabilities are fused, the modeling threshold is reduced, and the intellectualization, data driving and interpretability of a resource allocation decision are improved; and meanwhile, in combination with a workflow dynamic management mechanism, intelligent optimization decision making is realized.

Description

Technical Field

[0001] This application relates to the field of resource allocation and optimization decision-making technology, and in particular to resource allocation operation optimization methods, systems, devices, storage media and products. Background Technology

[0002] In traditional resource allocation and decision-making processes, business decision variables typically rely on manual experience or simple rules, lacking systematic and data-driven support, making it difficult to adapt to complex and ever-changing business scenarios. At the model abstraction and implementation level, existing technologies usually require operations personnel to possess strong mathematical modeling and programming skills. They must manually abstract business logic into mathematical models, then code and solve them using programming languages, resulting in a cumbersome process, long development cycles, and high skill requirements. Furthermore, traditional modeling methods struggle with step-by-step debugging and process tracking when faced with complex business logic, limiting model interpretability and optimization potential, thus impacting the realization of refined operations. Simultaneously, existing solutions generally neglect the systematic utilization of historical data, failing to effectively uncover potential patterns in historical experience, resulting in a lack of data support in the decision-making process and hindering the achievement of intelligent, automated, and efficient resource scheduling and optimization management. Summary of the Invention

[0003] The main purpose of this application is to provide a resource allocation operation optimization method, system, device, storage medium and product, which aims to solve the technical problem of how to achieve low-threshold, data-driven and highly interpretable resource allocation modeling and intelligent optimization decision-making in complex business scenarios.

[0004] To achieve the above objectives, this application proposes an operations research optimization method for resource allocation, which includes:

[0005] Collect resource-related data;

[0006] By using a pre-trained resource allocation model, based on user-input constraints and objective functions, an operations optimization formula matching the current business scenario is output.

[0007] Based on the preset workflow orchestration mode, the received business requirement information is used to define business logic and visualize workflow nodes to obtain a standardized workflow execution model.

[0008] Based on the resource-related data and the operations research optimization formula, perform operations research optimization calculations to obtain the optimal solution set;

[0009] By combining the standardized workflow execution model and the optimal solution set, decision variables are generated;

[0010] The preset decision engine is invoked to perform variable substitution on the decision variables and generate an executable resource allocation strategy.

[0011] In one embodiment, the step of outputting an operations research optimization formula that matches the current business scenario based on user-input constraints and objective function using a pre-trained resource allocation model includes:

[0012] Identify and parse the user-input optimization problem to obtain the operations research optimization variables, objective function, and constraints;

[0013] The operation research optimization variables, the objective function, and the constraints are input into the pre-trained resource allocation model, and an operation research optimization formula matching the current business scenario is output. The operation research optimization formula includes one or more of the following: linear programming model, integer programming model, and dynamic programming model.

[0014] In one embodiment, before the step of inputting the operations research optimization variables, the objective function, and the constraints into the pre-trained resource allocation model and outputting the operations research optimization formula with the highest matching degree to the current business scenario, the following steps are included:

[0015] By training on historical resource allocation cases and operations research modeling data, a large-scale resource allocation model is constructed, resulting in a pre-trained large-scale resource allocation model.

[0016] In one embodiment, the step of defining business logic and visualizing workflow nodes based on a preset workflow orchestration mode to obtain a standardized workflow execution model includes:

[0017] Under the preset workflow orchestration mode, the business requirement information is modularized and processed step by step to generate modular execution results;

[0018] Visual modeling and editing are performed on the workflow nodes in the business requirement information to generate adjusted node verification results and running status information;

[0019] By combining the modular execution results, the adjusted node verification results, and the running status information, a standardized workflow execution model is generated.

[0020] In one embodiment, the step of performing operations research calculations based on the resource-related data and operations research formulas to obtain the optimal solution set includes:

[0021] The preset solution engine is invoked to perform operations research and optimization calculations based on the resource-related data and the operations research and optimization formulas, and the solution results are obtained.

[0022] The solution results are verified to obtain the optimal solution set.

[0023] In one embodiment, the step of performing visualization processing on the workflow nodes in the business requirement information to generate adjusted node verification results and running status information includes:

[0024] The workflow nodes corresponding to the operations optimization variables are monitored, and the distribution information of the workflow nodes, operations optimization variables, and output results are visualized.

[0025] When an adjustment instruction is detected, the configuration of the target workflow node is changed, and a rerun verification is performed to generate the adjusted node verification results and running status information.

[0026] In one embodiment, after the step of invoking a preset decision engine, performing variable substitution and policy deployment based on the decision variables, and generating an executable resource allocation strategy, the method includes:

[0027] The resource allocation strategy was quantitatively evaluated from multiple dimensions to obtain the evaluation results;

[0028] Based on the evaluation results, the resource allocation strategy may be adjusted or optimized locally.

[0029] Furthermore, to achieve the above objectives, this application also proposes a resource allocation operations optimization system, which includes:

[0030] The data acquisition module is used to collect resource-related data;

[0031] The operations research formula matching module is used to output an operations research optimization formula that matches the current business scenario based on the user-input constraints and objective function, using a pre-trained resource allocation model.

[0032] The business processing module is used to define business logic and visualize workflow nodes based on the preset workflow orchestration mode, and obtain a standardized workflow execution model.

[0033] The operations research and solution module is used to perform operations research and optimization calculations based on the resource-related data and operations research and optimization formulas to obtain the optimal solution set.

[0034] The variable generation module is used to generate decision variables by combining the standardized workflow execution model and the optimal solution set;

[0035] The strategy generation module is used to call a preset decision engine to perform variable replacement on the decision variables and generate an executable resource allocation strategy.

[0036] In addition, to achieve the above objectives, this application also proposes a resource allocation operations optimization device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the resource allocation operations optimization method as described above.

[0037] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the resource allocation operation optimization method described above.

[0038] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the resource allocation operations optimization method described above.

[0039] This application proposes a resource allocation operations research optimization method, system, device, storage medium, and program product. The method includes: collecting resource-related data; using a pre-trained resource allocation model, based on user-input constraints and objective functions, outputting an operations research optimization formula matching the current business scenario; defining business logic and visualizing workflow nodes based on a preset workflow orchestration mode, obtaining a standardized workflow execution model; performing operations research optimization calculations based on resource-related data and the operations research optimization formula to obtain an optimal solution set; combining the standardized workflow execution model and the optimal solution set to generate decision variables; and calling a preset decision engine to perform variable substitution on the decision variables, generating an executable resource allocation strategy. This solution, based on a pre-trained resource allocation model and operations research optimization modeling capabilities, achieves low-threshold, data-driven, and highly interpretable resource allocation modeling in complex business scenarios; simultaneously, combined with workflow orchestration and dynamic operation management mechanisms, it realizes intelligent optimization decision-making. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1A flowchart illustrating the first embodiment of the operations research optimization method for resource allocation in this application;

[0043] Figure 2 A flowchart illustrating the second embodiment of the operations research optimization method for resource allocation in this application;

[0044] Figure 3 A flowchart illustrating the third embodiment of the operations research optimization method for resource allocation in this application;

[0045] Figure 4 A schematic diagram of the system structure of the resource allocation operation research optimization method provided in Embodiment 1 of this application;

[0046] Figure 5 A schematic diagram illustrating the solution of the model data formulas provided for the operations research optimization method for resource allocation in this application;

[0047] Figure 6 A first example diagram illustrating the calculation of average daily load per person for the resource allocation method of this application;

[0048] Figure 7 A second example diagram illustrating the calculation of average daily load per person for the resource allocation method of this application;

[0049] Figure 8 Detailed functional flowcharts of the workflow nodes provided for the operations research optimization method for resource allocation in this application;

[0050] Figure 9 This is a schematic diagram of the module structure of the resource allocation operation optimization system in an embodiment of this application;

[0051] Figure 10 This is a schematic diagram of the hardware operating environment involved in the resource allocation operation optimization method in the embodiments of this application.

[0052] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0054] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of this application embodiment is as follows: Collect resource-related data; using a pre-trained resource allocation model, output an operations research optimization formula matching the current business scenario based on user-input constraints and objective functions; based on a preset workflow orchestration mode, define business logic and visualize workflow nodes for the received business requirement information to obtain a standardized workflow execution model; perform operations research optimization calculations based on resource-related data and the operations research optimization formula to obtain an optimal solution set; combine the standardized workflow execution model and the optimal solution set to generate decision variables; call a preset decision engine to perform variable substitution on the decision variables and generate an executable resource allocation strategy.

[0056] In this embodiment, for ease of description, the following description will focus on the resource allocation operation optimization system as the execution subject.

[0057] In traditional resource allocation and decision-making processes, business decision variables typically rely on simple rules or human experience, lacking systematic and data-driven support, making it difficult to adapt to complex and ever-changing business scenarios. At the model abstraction level, traditional models require operations personnel to possess strong mathematical modeling and coding skills. After mathematical modeling, programming languages ​​are used to code and solve the problem, adjustments are made to obtain the results, and further coding is performed to analyze the results. This process is cumbersome, has a long development cycle, and requires high levels of professional skills. Furthermore, when faced with complex business logic, step-by-step debugging is difficult, hindering refined operations. Simultaneously, traditional solutions neglect the systematic utilization of historical daily case volume and personnel status data, resulting in a lack of data support in the decision-making process and making it difficult to achieve intelligent, automated, and efficient resource scheduling and optimization management.

[0058] This application provides a solution that assists business personnel in mathematical modeling through a pre-trained large-scale resource allocation model. This effectively compensates for the shortcomings of non-mathematical background personnel in optimization modeling capabilities, significantly lowering the modeling threshold and improving decision-making efficiency and quality. The large-scale model adopts a COT (Chain-of-Thought) model, pre-configures multiple operations research optimization problem templates, and possesses high adaptability to multiple scenarios. It can intelligently identify the problem type based on the input business description and recommend the operations research optimization formula with the highest matching degree. The system also supports automatic analysis and rationality verification of the solution results, assisting operations personnel in evaluating the scientific validity of the objective function setting and, based on this, optimizing model parameters and constraints. Simultaneously, through an intelligent workflow orchestration mechanism, complex operations research calculations and business logic are modularly integrated, visually displaying the execution process and intermediate data, clearly generating the input variables and change suggestions required for decision-making, and achieving full-process interpretability, traceability, and adjustability of resource allocation strategies.

[0059] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses a personal computer as an example to illustrate this embodiment and the subsequent embodiments.

[0060] Based on this, embodiments of this application provide an operations research optimization method for resource allocation, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the resource allocation optimization method of this application.

[0061] In this embodiment, the resource allocation operation optimization method includes steps S10 to S60:

[0062] Step S10: Collect resource-related data;

[0063] In this embodiment, the first step is to collect resource-related data. This resource-related data includes, but is not limited to, real-time resource data, offline data, and supplementary business data. Real-time data is the acquired current resource status information, which undergoes preprocessing such as cleaning, transformation, and normalization to provide accurate and standardized data support for subsequent modeling and optimization processes. Offline data includes historical processed data and historical solution data, used to support the identification of business problem types, the setting of model parameters, and the comparative analysis of solution results. Supplementary business data describes additional data sources and business rules for complex business logic, serving as an important basis for setting constraints and objective functions during the optimization modeling process.

[0064] Step S20: Using a pre-trained resource allocation model, based on the user-input constraints and objective function, output an operations optimization formula that matches the current business scenario.

[0065] It should be noted that the resource allocation model is a deep learning model pre-trained based on a large number of operations research optimization cases. It has the ability to recognize the business description, objective function and constraints input by the user, and match the corresponding operations research optimization modeling formula.

[0066] The constraints refer to the mathematical expression of restrictive factors in the resource allocation process, including but not limited to total resource limits, production capacity limits, time window constraints, supply and demand matching rules, etc.

[0067] The objective function is a mathematical expression used to measure the merits of a resource allocation scheme. It is usually expressed as maximizing revenue, minimizing cost, minimizing time consumption, or maximizing resource utilization.

[0068] The operations research optimization formula refers to a standardized operations research modeling expression matched according to the characteristics of the business scenario, including but not limited to linear programming, integer programming, and dynamic programming.

[0069] Understandably, given the existence of various resource allocation modes in business scenarios and the limitations of non-professionals in data modeling capabilities, step S20 is executed to identify the user-input constraints and objective functions through a pre-trained resource allocation model. This allows for the intelligent matching of the operational optimization formula with the highest adaptability to the current business scenario, thereby reducing the modeling threshold and improving modeling efficiency and accuracy.

[0070] In one feasible embodiment, step S20 includes steps S21 to S22:

[0071] Step S21: Identify and parse the optimization problem input by the user to obtain the operations research optimization variables, objective function, and constraints;

[0072] Specifically, in this embodiment, the Large Language Model (LLM) performs semantic understanding and structured parsing on the optimization problem in the form of natural language input by the user, and extracts key modeling elements such as variables, objective functions, and constraints.

[0073] Step S22: Input the operations optimization variables, the objective function, and the constraints into the pre-trained resource allocation model, and output an operations optimization formula that matches the current business scenario. The operations optimization formula includes at least one or more of linear programming, integer programming, and dynamic programming.

[0074] Subsequently, these elements are input into a pre-trained resource allocation model. This model is trained on a large number of historical operations research optimization cases and has the ability to identify problem types and match modeling templates. It can output operations research optimization modeling formulas with the highest degree of matching with the current business scenario, thereby assisting non-professional users in modeling, reducing the threshold for operations research optimization modeling, and improving modeling efficiency and accuracy.

[0075] For example, when a user inputs "How to minimize warehouse shipping costs while meeting daily order demands and ensuring that the capacity of each delivery point does not exceed limits," the LLM first performs semantic parsing on the statement, identifying the objective function as "minimize shipping costs," with constraints including "meeting order demands" and "capacity limitations," and the operations research optimization variables including "shipping volume at each delivery point." Subsequently, the resource allocation model, based on the extracted modeling elements, identifies the problem as a typical linear programming problem and outputs a matching mathematical modeling formula for the subsequent solution engine to perform optimization calculations.

[0076] Through the above steps, the system can assist non-professional users in modeling, significantly reducing the technical threshold for operations research and optimization modeling, improving modeling efficiency and accuracy, and thus supporting intelligent decision-making at the business level.

[0077] Step S30: Based on the preset workflow orchestration mode, the received business requirement information is used to define business logic and visualize workflow nodes to obtain a standardized workflow execution model.

[0078] It should be noted that the standardized workflow execution model refers to a process model with standard interfaces and execution structures generated by modularly decomposing business requirements information, defining logic, and modeling nodes. This model can be recognized and executed by the system, supports data transfer, logical judgment, process jump, and other operations between nodes, and can be adapted to operational optimization tasks in different business scenarios.

[0079] Understandably, traditional methods of determining decision variables often rely on simple rules or human experience. When faced with complex business logic, these methods not only require a long development cycle but also make it difficult to achieve step-by-step debugging and process tracking, thus limiting the system's refined operational capabilities. Therefore, step S30 is executed to systematically integrate the complex business logic of operations optimization calculations and supplementation. Through a preset workflow orchestration mechanism, the detailed display of the process and the visualization analysis of intermediate data are realized, which can improve the interpretability and operability of the system. It can also clearly output the input parameters for business decisions and corresponding change suggestions, thereby achieving closed-loop optimization of the entire process from modeling and solving to business implementation.

[0080] In one feasible embodiment, step S30 may include steps S31 to S33:

[0081] Step S31: Under the preset workflow orchestration mode, the business requirement information is modularly defined and processed step by step to generate modular execution results.

[0082] In this embodiment, the system modularizes the business logic carried by the operations optimization variables according to functional units, breaking down the originally complex business rules into multiple configurable and reusable business logic modules. Each module corresponds to the processing logic of one or more operations optimization variables, such as resource matching rules, capacity limitation rules, priority ranking rules, etc.

[0083] Meanwhile, the system supports breakpoint tracing and step-by-step verification for each module, enabling fine-grained debugging and process tracking. Through this mechanism, the system can record the input, output, execution status, and exception information of each module, generating corresponding modular execution results to provide data support for subsequent process feedback and decision output.

[0084] Step S32: Perform visual modeling and editing processing on the workflow nodes in the business requirement information to generate adjusted node verification results and running status information;

[0085] Understandably, in complex business scenarios, business logic often needs to be dynamically adjusted based on actual operational feedback. Traditional static processes are difficult to meet the flexible and ever-changing business needs. Therefore, in step S32, through visual modeling, users can intuitively adjust node logic, modify input and output parameters, optimize process sequence, and immediately trigger node rerun and verification after changes to ensure the correctness and effectiveness of process adjustments.

[0086] In another feasible embodiment, step S32 may further include steps S321 to S322:

[0087] Step S321: Monitor the workflow nodes in the business requirement information and visualize the distribution information, operation optimization variables and output results of the workflow nodes.

[0088] In this step, the system monitors the workflow nodes in the business requirements information in real time, records key indicators such as the running status, execution time, and input / output data of each node, and then displays this information through a visual interface. This includes showing the position and execution order of each node in the process, the input values ​​of variables in each node, the processing logic and output results, and using colors to indicate the status of nodes such as successful execution, failure, and pending execution. The output results of different nodes are compared to help analyze and optimize the effect.

[0089] This visualization mechanism enhances the transparency and understandability of the system's operational optimization process, making it easier for business personnel to quickly identify problems and evaluate process effectiveness.

[0090] Step S322: When an adjustment instruction is detected, the configuration of the target workflow node is changed, and a rerun verification is performed to generate the adjusted node verification results and running status information.

[0091] In this step, the system supports dynamic configuration and process adjustment of workflow nodes. When business personnel find through the visual interface that the execution result of a certain node does not meet expectations, or that the business logic needs to be optimized and adjusted, they can initiate an adjustment command.

[0092] After receiving an adjustment command, the system modifies and publishes the configuration of the target node to achieve dynamic adjustment and continuous optimization of the process. For example, when business personnel find through the visual interface that the output of a certain node does not meet expectations, or that adjustments to the business logic are needed, they can initiate an adjustment command. The system responds to this command by modifying the configuration parameters of the target node, such as adjusting variable values, updating logical judgment conditions, or replacing business rule modules.

[0093] Subsequently, the system publishes the modified configuration and automatically triggers the rerun process for this node and its subsequent nodes, generating adjusted node verification results and runtime status information. The adjusted node verification results include whether the execution was successful, whether the output meets expectations, and whether the constraints are satisfied; the runtime status information records the node execution time, execution duration, input / output values, and any exceptions.

[0094] By following the steps above, workflow nodes can be flexibly adjusted and verified in real time, improving the system's maintainability, interpretability, and business adaptability.

[0095] Step S33: Combine the modular execution results, the adjusted node verification results, and the running status information to generate a standardized workflow execution model.

[0096] In this embodiment, the system integrates the modular execution results generated in step S321, the node adjustment verification results and running status information generated in step S323, and performs multi-dimensional data fusion and analysis to finally generate a standardized workflow execution model.

[0097] In summary, this embodiment achieves interpretability, debuggability, and traceability of the operations optimization process by modularizing and processing the business logic in the operations optimization variables, combined with a visual monitoring and dynamic adjustment mechanism for workflow nodes. By collecting modular execution results and process adjustment feedback data, the system can dynamically evaluate the optimization effect and generate decision variables that meet business objectives, thereby significantly improving the intelligence level of resource allocation and scheduling and the efficiency of business implementation.

[0098] Step S40: Perform operations research optimization calculations based on the resource-related data and operations research optimization formulas to obtain the optimal solution set;

[0099] In this implementation, the system calls the underlying solver engine to perform operations research on the resource-related data collected in step S10 and the operations research optimization formula output in step S20. This process combines the preprocessed resource-related data (including real-time data, historical data, and business supplementary data) from step S10 to perform mathematical modeling and constraint solving, generating a solution set that satisfies the optimal objective function.

[0100] Meanwhile, the system automatically verifies the solution results, including but not limited to: feasibility verification, i.e., determining whether the optimal solution satisfies all constraints; rationality verification, i.e., evaluating whether the objective function meets business expectations; and boundary condition verification, i.e. checking for extreme values ​​or unreasonable allocations.

[0101] The final output set of optimal solutions includes combinations of values ​​for multiple operations optimization variables, such as resource allocation, scheduling path, and task priority, providing technical data for subsequent business logic processing and decision-making.

[0102] Through the above steps, efficient modeling and intelligent solving of complex resource allocation problems can be achieved, improving the automation level and business adaptability of the operations research optimization process.

[0103] Step S50: Combine the standardized workflow execution model and the optimal solution set to generate decision variables;

[0104] It should be noted that the decision variables refer to the variables determined by the solution process in the optimization problem to be solved, used to identify key information such as the final resource allocation scheme, scheduling arrangement, or selection strategy. For example, these include, but are not limited to, the allocation quantity of each resource node, path selection scheme, task scheduling order, and inventory adjustment value, which are executable parameters that support the implementation of business decisions.

[0105] In this step, the system performs multi-dimensional fusion analysis on the optimal solution set output in step S20 and the standardized workflow execution model generated in step S30 to generate executable decision variables that ultimately meet business objectives.

[0106] Through the above steps, based on the optimal solution set and standardized workflow execution model, decision variables that meet business objectives are generated, significantly improving the intelligence level of resource allocation and scheduling and the efficiency of business implementation.

[0107] Step S60: Invoke the preset decision engine to perform variable substitution on the decision variables and generate an executable resource allocation strategy.

[0108] It should be noted that the decision engine is an intelligent decision module that integrates variable mapping, rule matching, strategy orchestration and deployment execution functions. It is responsible for converting the decision variables generated by operations optimization into resource configuration instructions that can be recognized and executed by the business system.

[0109] In this embodiment, the system supports real-time replacement of decision variables and dynamic strategy deployment during workflow execution to address real-time changes in business requirements. For example, during the execution of the operations optimization process, if a resource configuration variable changes due to external factors (such as resource unavailability, changes in business rules, etc.), the system can update the value of the variable and re-execute the optimization calculations and strategy generation of the relevant nodes to ensure the real-time nature and accuracy of the final decision result.

[0110] Meanwhile, the system has a complete log recording mechanism that records in detail the variable inputs, processing logic, output results, and strategy deployment process at each node of the workflow. Through this mechanism, the system can trace and analyze the impact of variable value changes on the overall decision-making path throughout the entire process, making it easier for business personnel to understand the strategy routing logic, troubleshoot execution anomalies, and evaluate the effectiveness of adjustments.

[0111] Through the above steps, the system invokes a pre-defined decision engine, performs variable replacement based on the decision variables generated by operations research optimization, and generates an executable resource allocation strategy. The system supports dynamic replacement of key variables and strategy updates during workflow execution, ensuring the real-time nature and adaptability of the optimization results. Simultaneously, the system possesses a complete log recording mechanism, meticulously recording variable input, processing logic, output results, and strategy deployment processes. This supports full-process tracing and analysis of the impact of variable value changes on the overall decision-making path, enhancing the system's auditability and decision transparency.

[0112] The above-described embodiments involve collecting and preprocessing resource-related data; using a pre-trained resource allocation model, and based on constraints and objective functions, outputting an operations research optimization formula; performing operations research optimization calculations based on workflow orchestration, combining resource-related data and the optimization formula, and systematically processing business logic and workflow nodes to obtain decision variables; and calling a pre-defined decision engine to perform variable replacement and strategy deployment based on the decision variables to generate a resource allocation scheme. This scheme, based on a pre-trained resource allocation model and operations research optimization modeling capabilities, achieves low-threshold, data-driven, and highly interpretable resource allocation modeling in complex business scenarios; simultaneously, combined with workflow orchestration and dynamic operation management mechanisms, it enables intelligent optimization decision-making.

[0113] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S22, the resource allocation operation optimization method further includes step A1:

[0114] Step A1: By training on historical resource allocation cases and operations research modeling data, a large-scale resource allocation model is constructed and optimized to obtain a pre-trained large-scale resource allocation model.

[0115] This embodiment also proposes a method for training a large-scale resource allocation model based on historical resource allocation cases and operations research modeling data. This method trains a large-scale deep learning model with problem identification and model recommendation capabilities by constructing a training corpus centered on operations research optimization problem descriptions, modeling elements, and solution results.

[0116] Specifically, the system first extracts key elements from historical resource allocation cases, including but not limited to variable definitions, objective functions, constraints, and solution results. Then, the system pre-trains a large-scale resource allocation model based on joint training using natural language descriptions and structured modeling data. Natural language descriptions are used to model the user-inputted business problem description, while structured data is used for mathematical expressions such as variables, objective functions, and constraints. Through this training method, the large-scale resource allocation model can understand the user-inputted problem description and intelligently match suitable operations research optimization modeling formulas.

[0117] Finally, through multiple rounds of iterative optimization and verification evaluation, a pre-trained resource allocation model with model recognition and formula recommendation capabilities was obtained, providing model support for the automated modeling of the optimization problem based on user input in the above embodiments.

[0118] In summary, by training on historical resource allocation cases and operations research optimization modeling data, a large-scale resource allocation model is constructed and optimized, outputting a pre-trained model. Through the above embodiments, automated modeling support for business problems is achieved, improving modeling efficiency and accuracy, and reducing reliance on professional modelers.

[0119] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 After step 40, the resource allocation operation optimization method further includes steps B1 to B2:

[0120] Step B1: Perform a multi-dimensional quantitative evaluation of the resource allocation scheme to obtain the evaluation results;

[0121] Step B2: Based on the evaluation results, make partial adjustments or optimizations to the resource allocation strategy.

[0122] In this embodiment, the system performs a multi-dimensional quantitative evaluation of the resource allocation strategy generated and published by the decision engine. These evaluation dimensions include, but are not limited to, cost, benefit, and sustainability. Through this multi-dimensional evaluation mechanism, the system generates comprehensive and quantifiable evaluation results, providing data support for subsequent solution optimization.

[0123] Meanwhile, the system utilizes visualization technology to showcase the execution effects of resource allocation strategies from multiple dimensions, intuitively presenting the optimization effects, performance differences, and key advantages of resource allocation across different dimensions. For example, it uses visual charts to display key indicators such as cost change trends, resource utilization distribution, and comparisons of efficiency improvements, helping business personnel quickly understand the characteristics and execution effects of the solution.

[0124] Furthermore, the system bins the decision space based on the evaluation results, identifying key areas with optimization potential in resource allocation. Based on these evaluation results, the system can make partial adjustments to resource allocation strategies or trigger re-optimization processes. For example, based on evaluation feedback, it can dynamically adjust the workflow node sequence, execution strategy, or variable handling logic to improve process execution efficiency and optimization effects; or it can feed the evaluation results back to the large resource allocation model, fine-tuning model parameters or adapting them to specific scenarios based on the feedback data, thereby improving the modeling accuracy and optimization recommendation capabilities in specific business scenarios.

[0125] Through the methods described above, the system achieves a closed-loop iterative capability from evaluation feedback to strategy optimization, continuously improving the rationality of resource allocation, the achievement of business goals, and the system's intelligence level.

[0126] For example, to help understand the implementation process of the resource allocation operation optimization method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 4 , Figure 4 A system architecture diagram is provided for an operations research optimization method for resource allocation. Specifically:

[0127] The resource allocation operations optimization system provided in this embodiment is divided into a data acquisition and preprocessing module 10, a large model construction mathematical model module 20, a workflow orchestration module 30, a decision engine 40, and a result evaluation module 50. The workflow orchestration module 30 includes three sub-modules: operations research solution, business logic definition, and workflow node visualization.

[0128] Specifically, the data acquisition and preprocessing module 10 is responsible for acquiring and preprocessing resource-related data, wherein the resource-related data includes at least real-time data, offline data, and business supplementary data.

[0129] The large-scale model building module 20 contains a built-in large-scale resource allocation model trained on a large amount of resource allocation scenario data. This model receives mathematical description information output from the constraint and objective function definition module. Through learning from massive amounts of historical resource allocation cases and operations research modeling data, this large-scale model has the ability to identify problem characteristics and match modeling templates.

[0130] In practical applications, the system intelligently recommends the most suitable operations research optimization modeling formula based on the variable definitions, constraints, and optimization objectives in the current business scenario. The supported operations research formula types cover various standard operations research modeling paradigms such as linear programming (LP), integer programming (IP), and dynamic programming (DP), and can be customized and adapted according to actual data characteristics and changes in business needs.

[0131] This system employs a workflow model to systematically process operations research and optimization variables. In the workflow orchestration module 30, the operations research solution module is responsible for receiving the operations research and optimization modeling results output by module 20, and combining them with the preprocessed resource-related data from module 10 to perform operations research and optimization calculations and solutions.

[0132] Meanwhile, the system breaks down complex business logic into functional units, defining each step as multiple configurable business logic definition modules. It supports breakpoint setting, step-by-step execution, and process verification, improving the debuggability and maintainability of the workflow. Furthermore, the system monitors the operation of each node in the workflow and displays detailed information on the distribution of workflow nodes, input data, variable status, and output results through a visual interface. The workflow supports dynamic configuration changes and policy deployment, allowing the process to be rerun and verified after adjustments to ensure that the changes meet expectations.

[0133] Ultimately, the system outputs replaceable variable information that meets business decision-making needs, such as resource configuration suggestions, scheduling paths, and task priorities, supporting intelligent decision-making and efficient execution of business systems.

[0134] For further information on solving operations research problems, please refer to... Figure 5 , Figure 5 This diagram illustrates the solution process for the model's data formulas. Specifically, this implementation utilizes a large model to aid in problem abstraction and mathematical modeling. When faced with complex business problems, it identifies variables, objectives, constraints, parameters, and other elements. After recognizing the platform's one-click solution command, it abstracts different solvers for input and output, adjusts the solution strategy for different types and scales of problems, and further analyzes and optimizes the solution results after solving.

[0135] Let's take the scenario of manpower allocation on day T (the current day) as an example. For example... Figure 6 As shown, Figure 6The first example diagram is provided for calculating the average workload per person on day T. First, the average workload per person on day T is calculated based on the predicted data. The solution process is as follows: A portion of the manpower is declared as a conversion factor, which is a given parameter for the business. The total caseload is the total amount of real-time data obtained through data preprocessing. The values ​​of the average workload per person on day T, Xqh / Xch / Xch / Xcm, are obtained through solving and used as constraint terms in subsequent historical constraint calculations. Figure 6 The calculation results, taking into account the historical load distribution, yield the target constraints and objective function, such as... Figure 7 As shown, Figure 7 The second example diagram is provided for calculating the average load per person on day T. In the objective constraints, 19 represents the historical number of days for case allocation, ±10% is the fluctuation error rate, and the remaining data represents known historical average load per person. The system then transforms the textual description containing the above information into verifiable operations research formulas through interaction with a pre-trained resource allocation model. Finally, the optimal solution set Xql / Xql / Xch / Xcm is calculated using an operations research solution platform.

[0136] Furthermore, for scenarios involving complex business logic processing, this embodiment designs a workflow orchestration module 30. Through a visual orchestration interface, workflow nodes can be customized. These workflow nodes mainly include business logic nodes and operational nodes. For example... Figure 8 As shown, Figure 8 This is a detailed functional flowchart of a workflow node.

[0137] Specifically, during the initialization phase, the system first loads the process definition, that is, reads the pre-configured automated process template from the database to ensure the accurate loading of the process structure and business rules. Subsequently, the system performs context initialization operations, pre-setting the basic variables required for execution, such as execution time and data input sources. This part of the data preparation process is completed automatically by the system without manual intervention.

[0138] During the automated node processing phase, the system executes business logic nodes and operations nodes sequentially according to the process definition. In the business logic nodes, conditional judgments and process routing are automatically performed based on data in the initialization context (such as database query results and API return values). For example, "If the recovered amount > 46000, proceed to module A; otherwise, proceed to module B." This type of judgment logic is completed automatically by the system without manual intervention. In the operations nodes, the underlying solution engine (such as a linear programming library or integer programming solver) is invoked to perform operations optimization calculations. Typical application scenarios include automatic inventory replenishment calculations and finding optimal solutions for intelligent scheduling. After the optimization calculations are completed, the system automatically merges the results into the main process and continues to advance without manual coordination or intervention.

[0139] Finally, in the cyclical advancement mechanism, after all workflow nodes have been executed, the system automatically advances to the next node, forming a closed-loop execution process of "judgment-execution-advancement" to ensure the continuity and automation of the workflow.

[0140] The resource allocation operations optimization system can collect and preprocess resource-related data; output operations optimization formulas based on constraints and objective functions using a pre-trained resource allocation model; perform operations optimization calculations based on workflow orchestration, combining resource-related data and operations optimization formulas, and systematically process business logic and workflow nodes to obtain decision variables; and call a preset decision engine to perform variable replacement and strategy deployment based on the decision variables to generate resource allocation schemes. Based on a pre-trained resource allocation model and operations optimization modeling capabilities, this system achieves low-threshold, data-driven, and highly interpretable resource allocation modeling in complex business scenarios; simultaneously, combined with workflow orchestration and dynamic operation management mechanisms, it realizes intelligent optimization decision-making.

[0141] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the resource allocation optimization method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0142] This application also provides an operations research optimization system for resource allocation, please refer to... Figure 9 The resource allocation operation optimization system includes:

[0143] Data acquisition module 110 is used to collect and preprocess resource-related data;

[0144] The operations research formula matching module 120 is used to output an operations research optimization formula that matches the current business scenario based on the user input constraints and objective function, using a pre-trained resource allocation model.

[0145] The business processing module 130 is used to define business logic and visualize workflow nodes based on the received business requirement information according to the preset workflow orchestration mode, so as to obtain a standardized workflow execution model.

[0146] The operations research module 140 is used to perform operations research optimization calculations based on the resource-related data and operations research optimization formulas to obtain the optimal solution set.

[0147] The variable generation module 150 is used to generate decision variables by combining the standardized workflow execution model and the optimal solution set;

[0148] The strategy generation module 160 is used to call a preset decision engine to perform variable replacement on the decision variables and generate an executable resource allocation strategy.

[0149] The resource allocation operations optimization system provided in this application, employing the resource allocation operations optimization method described in the above embodiments, can solve the technical problem of how to achieve low-threshold, data-driven, and highly interpretable resource allocation modeling and intelligent optimization decision-making in complex business scenarios. Compared with the prior art, the beneficial effects of the resource allocation operations optimization system provided in this application are the same as those of the resource allocation operations optimization method provided in the above embodiments, and other technical features of the resource allocation operations optimization system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0150] This application provides a resource allocation operation optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the resource allocation operation optimization method in the above embodiment 1.

[0151] The following is for reference. Figure 10 This document illustrates a schematic diagram of a resource allocation optimization device suitable for implementing embodiments of this application. The resource allocation optimization device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 10 The resource allocation optimization device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0152] like Figure 10As shown, the resource allocation optimization device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the resource allocation optimization device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the resource allocation operations optimization device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows resource allocation operations optimization devices with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0153] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0154] The resource allocation operation optimization device provided in this application, employing the resource allocation operation optimization method in the above embodiments, can solve the technical problem of how to achieve low-threshold, data-driven, and highly interpretable resource allocation modeling and intelligent optimization decision-making in complex business scenarios. Compared with the prior art, the beneficial effects of the resource allocation operation optimization device provided in this application are the same as those of the resource allocation operation optimization method provided in the above embodiments, and other technical features in this resource allocation operation optimization device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0155] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0156] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0157] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the resource allocation operation optimization method in the above embodiments.

[0158] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0159] The aforementioned computer-readable storage medium may be included in the resource allocation operation optimization device; or it may exist independently and not be assembled into the resource allocation operation optimization device.

[0160] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a resource allocation operations optimization device, the resource allocation operations optimization device: collects resource-related data; outputs an operations optimization formula matching the current business scenario based on user-input constraints and objective functions using a pre-trained resource allocation model; defines business logic and visualizes workflow nodes for the received business requirement information based on a preset workflow orchestration mode, obtaining a standardized workflow execution model; performs operations optimization calculations based on resource-related data and the operations optimization formula, obtaining an optimal solution set; combines the standardized workflow execution model and the optimal solution set to generate decision variables; and calls a preset decision engine to perform variable substitution on the decision variables, generating an executable resource allocation strategy.

[0161] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0163] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0164] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described resource allocation operations optimization method. This solves the technical problem of how to achieve low-threshold, data-driven, and highly interpretable resource allocation modeling and intelligent optimization decision-making in complex business scenarios. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the resource allocation operations optimization method provided in the above embodiments, and will not be repeated here.

[0165] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the resource allocation operations optimization method described above.

[0166] The computer program product provided in this application can solve the technical problem of how to achieve low-threshold, data-driven, and highly interpretable resource allocation modeling and intelligent optimization decision-making in complex business scenarios. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the resource allocation operations optimization method provided in the above embodiments, and will not be repeated here.

[0167] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A resource allocation operations research optimization method, characterized in that, The resource allocation operation optimization method includes: Collect resource-related data; By using a pre-trained resource allocation model, based on user-input constraints and objective functions, an operations optimization formula matching the current business scenario is output. Based on the preset workflow orchestration mode, the received business requirement information is used to define business logic and visualize workflow nodes to obtain a standardized workflow execution model. Based on the resource-related data and the operations research optimization formula, perform operations research optimization calculations to obtain the optimal solution set; By combining the standardized workflow execution model and the optimal solution set, decision variables are generated; The preset decision engine is invoked to perform variable substitution on the decision variables and generate an executable resource allocation strategy.

2. The resource allocation optimization method as described in claim 1, characterized in that, The steps of using a pre-trained resource allocation model to output an operations research optimization formula that matches the current business scenario based on user-input constraints and objective functions include: Identify and parse the user-input optimization problem to obtain the operations research optimization variables, objective function, and constraints; The operation research optimization variables, the objective function, and the constraints are input into the pre-trained resource allocation model, and the operation research optimization formula with the highest matching degree with the current business scenario is output. The operation research optimization formula includes one or more of linear programming, integer programming, and dynamic programming.

3. The resource allocation optimization method as described in claim 2, characterized in that, Before the step of inputting the operations research optimization variables, the objective function, and the constraints into the pre-trained resource allocation model and outputting an operations research optimization formula that matches the current business scenario, the following steps are included: By training on historical resource allocation cases and operations research modeling data, a large-scale resource allocation model is constructed, resulting in a pre-trained large-scale resource allocation model.

4. The resource allocation optimization method as described in claim 1, characterized in that, The steps of defining business logic and visualizing workflow nodes based on a preset workflow orchestration mode to obtain a standardized workflow execution model include: Under the preset workflow orchestration mode, the business requirement information is modularized and processed step by step to generate modular execution results; Visual modeling and editing are performed on the workflow nodes in the business requirement information to generate adjusted node verification results and running status information; By combining the modular execution results, the adjusted node verification results, and the running status information, a standardized workflow execution model is generated.

5. The resource allocation optimization method as described in claim 1, characterized in that, The step of performing operations research optimization calculations based on the resource-related data and operations research optimization formulas to obtain the optimal solution set includes: The preset solution engine is invoked to perform operations research and optimization calculations based on the resource-related data and the operations research and optimization formulas, and the solution results are obtained. The solution results are verified to obtain the optimal solution set.

6. The resource allocation optimization method as described in claim 4, characterized in that, The step of performing visualization processing on the workflow nodes in the business requirement information to generate adjusted node verification results and running status information includes: The workflow nodes in the business requirement information are monitored, and the distribution information, operation and optimization variables and output results of the workflow nodes are visualized. When an adjustment instruction is detected, the configuration of the target workflow node is changed, and a rerun verification is performed to generate the adjusted node verification results and running status information.

7. The resource allocation optimization method as described in claim 1, characterized in that, After the step of invoking a preset decision engine, performing variable substitution and policy deployment based on the decision variables, and generating an executable resource allocation strategy, the following steps are included: The resource allocation strategy was quantitatively evaluated from multiple dimensions to obtain the evaluation results; Based on the evaluation results, the resource allocation strategy may be adjusted or optimized locally.

8. A resource allocation operations optimization system, characterized in that, The resource allocation operation optimization system includes: The data acquisition module is used to collect resource-related data; The operations research formula matching module is used to output an operations research optimization formula that matches the current business scenario based on the user-input constraints and objective function, using a pre-trained resource allocation model. The business processing module is used to define business logic and visualize workflow nodes based on the preset workflow orchestration mode, and obtain a standardized workflow execution model. The operations research and solution module is used to perform operations research and optimization calculations based on the resource-related data and operations research and optimization formulas to obtain the optimal solution set. The variable generation module is used to generate decision variables by combining the standardized workflow execution model and the optimal solution set; The strategy generation module is used to call a preset decision engine to perform variable replacement on the decision variables and generate an executable resource allocation strategy.

9. A resource allocation operation optimization device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the operations research optimization method for resource allocation as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the resource allocation operation optimization method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the resource allocation operations optimization method as described in any one of claims 1 to 7.

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