New energy store decoration resource intelligent optimization configuration system and use method

Abstract

The invention relates to the technical field of intelligent engineering management, in particular to a new energy store decoration resource intelligent optimal configuration system and a use method, and the method comprises the following steps: obtaining initial parameters of a target store, including an available decoration area, a function partition, a target opening date, a decoration budget upper limit and an available construction man hour; candidate decoration modules matched with the function partition are selected from a pre-constructed standard decoration module library; each module is associated with a material list, module cost, occupied area, required construction person time and experience weight; based on the candidate decoration modules, constructing a resource combination optimization model; according to the invention, unified modeling and intelligent optimization are carried out on the new energy store decoration module, materials and construction resources, and on the premise of satisfying area, budget, man-hour and purchase rule constraints, an enforceable decoration scheme is automatically output, so that cost and resource waste are effectively reduced, construction conflicts are reduced, and decoration efficiency and operation certainty are improved.

Description

Technical Field

[0001] This invention relates to an intelligent optimization and allocation system for the decoration resources of new energy stores and its usage method, belonging to the field of intelligent engineering management technology. Background Technology

[0002] With the network expansion of new energy stores (such as charging station service points, new energy vehicle experience stores, etc.), store decoration has become an important project for chain expansion and brand consistency. However, there are several pain points in the existing decoration management and implementation: On the one hand, there are large differences in store size, functional zoning and local supply conditions, and decoration plans often rely on manual experience or a single template, resulting in duplicate material procurement, cost waste and inconsistent experience; On the other hand, material procurement is often affected by the minimum order quantity (MOQ), supply capacity and delivery time fluctuations, and manual scheduling is difficult to balance the procurement scale, inventory and delivery risks in a timely manner. Furthermore, construction scheduling management is usually based on overall work hour estimation, lacking time-based concurrent verification, which easily leads to concurrent conflicts of key trades or equipment, affecting opening dates. Moreover, BIM / floor plans, supplier performance, and actual execution data are not effectively integrated for closed-loop improvement, resulting in insufficient model-based decision-making capabilities, poor reusability, and difficulties in continuous optimization. Some existing automation methods have not yet tightly integrated modular design, procurement constraints (such as MOQ), multi-constraint optimization, and time-series simulation, and lack systematic processing of the feasibility of large-scale solutions and project delivery assurance (such as solution timeout rollback strategies). Therefore, there is an urgent need to improve a smart optimization and allocation system for new energy store decoration resources and its usage method to solve the above-mentioned problems. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent optimization and allocation system for the decoration resources of new energy stores and a method for using it, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A smart optimization and allocation system for the decoration resources of a new energy store and its usage method include the following steps: Step 1: Obtain the initial parameters of the target store, including available renovation area, functional zoning, target opening date, renovation budget limit, and available construction man-hours; Step 2: Select multiple candidate decoration modules that match the functional area from the pre-built standard decoration module library; wherein, each candidate decoration module is associated with a predefined set of parameters, which includes a material list, module cost, occupied area, required construction man-hours, and experience weight; Step 3: Based on the candidate decoration modules and their parameter sets, construct a resource combination optimization model; wherein, the selection status of the modules, the purchase quantity of each material and the allocation of construction workers' hours are used as decision variables, and the available decoration area of ​​the store, the upper limit of the decoration budget, the available construction workers' hours and the material supply capacity are used as model constraints. Step 4: Solve the resource combination optimization model using an integer programming algorithm to obtain the module selection scheme and material procurement quantity; if no feasible solution is obtained within a preset time limit, automatically switch to a heuristic algorithm or metaheuristic algorithm for approximate solution; Step 5: Perform construction sequence simulation on the module selection scheme obtained from the solution to detect whether there are any construction resource concurrency conflicts; if there are conflicts, adjust the construction schedule of the modules or replace some modules, and re-verify until the conflicts are eliminated. Step 6: Based on the final solution after conflict resolution, generate a material procurement list and construction task package, and transmit them to the enterprise resource planning system or supply chain management system via an interface; Step 7: Collect progress feedback and supplier delivery data during the actual implementation of the solution, and use the data to update the parameters in the standard decoration module library for subsequent store configuration optimization.

[0005] Furthermore, between steps 2 and 3, a feasibility pre-screening is performed on the candidate decoration modules; the pre-screening is based on the functional zoning, the available decoration area, and preset quantifiable decoration rules, eliminating modules that do not meet the matching conditions.

[0006] Furthermore, in step 3, the constructed resource combination optimization model is a mixed integer programming model; The module selection status is represented by binary decision variables, and the material procurement quantity is represented by integer decision variables. The objective function of the model is to minimize the total cost, or to introduce an experience level adjustment term based on the experience weights based on minimizing the total cost; The constraints of the model include: area constraints, budget constraints, construction man-hour constraints, and material supply capacity constraints.

[0007] Furthermore, in step 3, when modeling material procurement, the supplier's minimum order quantity constraint is considered; When a material is purchased due to the demand of any selected module, the purchase quantity must not be lower than the minimum order quantity corresponding to that material.

[0008] Furthermore, step 4 specifically includes: setting a maximum computation time limit for the integer programming solver; The integer programming solver is started to solve the model; If a feasible solution is obtained within the upper limit of the calculation time, the feasible solution is output as the module selection scheme and material procurement quantity; If no feasible solution is obtained within the upper limit of the computation time, a preset heuristic algorithm or metaheuristic algorithm is automatically invoked to approximate the solution of the model, and the obtained approximate feasible solution is used as the module selection scheme and material procurement quantity; wherein, the heuristic or metaheuristic algorithm includes at least one of large neighborhood search, genetic algorithm or simulated annealing.

[0009] Furthermore, in step 5, the method for construction sequence simulation is as follows: Discretize the overall project duration into multiple continuous time units; Within each time unit, apply a maximum constraint on the concurrent number of construction workers and critical equipment; Based on the required number of construction workers for each module in the module selection scheme, the construction process is simulated to detect whether there are conflict periods that violate the upper limit constraint on the number of concurrent users. If a conflict exists, a new solution is generated by adjusting the construction schedule of the conflicting module or replacing some modules, and then the simulation is repeated until the conflict is eliminated.

[0010] Furthermore, steps 1-7 are executed automatically by a computer program; During operation, the computer program automatically completes parameter acquisition, model building, optimization solution, time series simulation and solution output, and exchanges data with the enterprise resource planning system or supply chain management system through a predefined application programming interface to trigger procurement and construction scheduling. Meanwhile, the computer program records implementation feedback data and uses it to update the standard decoration module library.

[0011] Furthermore, it includes a memory for storing computer-executable instructions and a predefined library of standard building modules; A processor, coupled to the memory, is configured to execute the computer-executable instructions to implement the intelligent optimization configuration method for new energy store decoration resources as described in any one of claims 1-7.

[0012] Furthermore, when executing the instructions, the processor is specifically configured as follows: A resource combination optimization model is constructed, and the minimum order quantity rule for supplier materials is used as one of the constraints of the model.

[0013] Furthermore, when executing the instructions, the processor is specifically configured as follows: The computation time limit of the integer programming solver is preset, and the integer programming solver is called first to solve the problem; The solution process is monitored. If no feasible solution is obtained within the upper limit of the computation time, the integrated heuristic algorithm module is automatically invoked to perform a backtracking solution. The heuristic algorithm module integrates at least one of the following algorithms: large neighborhood search, genetic algorithm, or simulated annealing.

[0014] This invention has at least the following beneficial effects: This invention constructs a resource combination optimization model based on standard decoration modules, integrating module selection, material procurement, and construction manpower allocation into a unified model for modeling and optimization. Under the premise of meeting constraints such as store area, budget, construction capacity, and minimum order quantity of materials, it automatically generates feasible decoration configuration schemes. At the same time, by combining integer programming and heuristic algorithm backoff solution mechanisms with construction sequence simulation verification, it effectively reduces decoration costs and resource waste, avoids construction concurrency conflicts, improves the certainty of opening plans, and iteratively updates module parameters through implementation feedback, realizing intelligent, standardized, and continuous optimization of new energy store decoration resource allocation. Attached Figure Description

[0015] Figure 1 This is a system block diagram of an intelligent optimization and allocation system for the decoration resources of a new energy store, as described in this invention. Figure 2 This is a flowchart illustrating the usage method of the intelligent optimization and allocation system for the decoration resources of a new energy store, as described in this invention. Detailed Implementation

[0016] The following will describe in detail the implementation of this application with reference to the accompanying drawings and embodiments, so that the implementation process of how this application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0017] like Figure 1-2 As shown in the figure, this embodiment provides a smart optimization and allocation system and method for new energy store decoration resources. The system is implemented by a smart optimization and allocation system for new energy store decoration resources, which includes at least the following modules and components: Data layer (storage): Stores module library, material and supplier information, historical execution data, BIM / floor plans and store parameters; supports data standardization and cleansing; Pre-screening and modeling module: realizes candidate module matching, parameter injection and automatic generation of MILP model (generating the target and constraints of step 3); Solver module: integrates commercial / open-source MILP solvers and implements a rollback strategy (time limit monitoring and heuristic switching); supports hierarchical and parallel solving capabilities; Time-series simulation module: Enables time-based construction simulation and concurrent conflict detection, and can interact with the solver module to trigger re-optimization; Output and Interface Module: Generates BOM, procurement plan, and construction task package, and interfaces with ERP / SCM / BIM via REST / SDK / API; Closed-loop learning module: Records implementation feedback and updates module parameters and supplier performance libraries, supporting parameter calibration and risk assessment based on historical data; Controller / Processor: One or more servers / cloud instances execute the control logic and solution tasks of the above modules; the front end provides a decision panel for business personnel to view alternative solutions and sensitivity analysis.

[0018] The specific process is as follows: Step 1: Obtain store parameters: Obtain the initial parameter information of the target new energy vehicle store, including: Available renovation area Functional partition collection Target opening date Renovation budget limit and the total number of available construction man-hours The above parameters serve as the basic input conditions for the resource combination optimization model; Step 2: Selection and Pre-screening of Candidate Decoration Modules Select from the pre-built standard decoration module library the functional partition set. Matching candidate decoration modules

[0019] Each decoration module Associate the following parameters: Module cost Area occupied Required construction personnel Experience weight and the corresponding list of materials; After selecting the module, the decoration rules and available decoration area are used as a basis. Candidate modules are pre-screened for feasibility, and those that meet the requirements are eliminated. > ; Or decoration modules that do not conform to functional zoning constraints; Step 3: Construction of Resource Combination Optimization Model Based on the pre-screened set of candidate decoration modules Construct a resource combination optimization model and define the following decision variables: ; in, Indicates the selection of decoration modules , Indicates that this option is not selected; Construct a mixed-integer programming model whose objective function is to minimize the total renovation cost or a combined goal of cost and experience: ; in, To experience the weight adjustment coefficient; And apply the following constraints: Area constraints: ; Budget constraints: ; Construction time constraints: ; Material supply constraints: ; in, For module For materials The demand, For materials Maximum available supply; Step 4: Model Solving and Algorithm Backoff Within the preset solution time limit Within this framework, integer programming algorithms are preferentially used to solve the resource combination optimization model to obtain the solution vector for the decision variables:

[0020] When the integer programming algorithm is within the solution time limit If no feasible or satisfactory solution is found, the algorithm automatically switches to and invokes at least one heuristic or metaheuristic algorithm to generate an approximate feasible solution that satisfies the above constraints by performing an approximate search of the solution space. ; Step 5: Construction sequence simulation and conflict adjustment: Discretize the construction period into a set of time periods: ; The solution results are then mapped to different construction periods to establish a construction worker time allocation function: ; Apply concurrency constraints to each construction period: ; When a violation of concurrency constraints is detected at any time period, the module construction sequence is adjusted or the corresponding decision variables of the module are replaced. The value of is used to modify the scheme; Step 6: Execution Plan Generation and System Integration Based on the final determined solution vector of decision variables, generate a material procurement list: ; And the corresponding construction task package, and transmit the material procurement list and construction task package to the enterprise resource planning system or supply chain management system through a preset interface for material procurement and construction scheduling. Step 7: Implement feedback collection and parameter updates When collecting data on actual construction workers during the renovation process Actual cost Including material delivery data, and correcting module parameters according to the following update rules:

[0021] in, and Historical weighting coefficients; The updated parameters are used for iterative maintenance of the standard decoration module library to support the continuous optimization and configuration of decoration resources for new energy stores.

[0022] First, basic store parameters are obtained, and candidate modules are matched and pre-screened from the standard module library. Based on the module's cost, area, construction man-hours, experience weight, and material requirements, a mixed integer linear programming model is constructed (with binary variables for module selection and integer variables for material procurement as decision variables). Under constraints such as area, budget, construction man-hours, material supply, and MOQ, the goal is to minimize the total cost (while also taking experience into account). The solution prioritizes commercial integer programming solvers with time limits set. If the time limit is exceeded, it automatically reverts to heuristic / meta-heuristic algorithms to ensure a feasible solution. The obtained solution is subjected to time-segmented construction simulation to detect concurrent conflicts and resolve conflicts through scheduling adjustments or module replacements. Finally, a material list and construction task package are output and integrated with ERP / SCM for execution. During implementation, delivery and cost data are continuously collected to iteratively update the module library and supplier parameters, achieving closed-loop optimization and successive refinement.

[0023] The foregoing description illustrates and describes several preferred embodiments of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for using a smart optimization and allocation system for the decoration resources of a new energy store, characterized in that, Includes the following steps: Step 1: Obtain the initial parameters of the target store, including available renovation area, functional zoning, target opening date, renovation budget limit, and available construction man hours; Step 2: Select candidate decoration modules that match the functional areas from the pre-built standard decoration module library; each module is associated with a material list, module cost, occupied area, required construction time, and experience weight; Step 3: Based on the candidate decoration modules, construct a resource combination optimization model; the model uses module selection, material procurement and construction man-hour allocation as decision variables, and area, budget, man-hours and supply capacity as constraints; Step 4: Solve the model using an integer programming algorithm to obtain a module selection and material procurement plan; If no feasible solution is found within the preset time limit, the algorithm will automatically switch to a heuristic or metaheuristic algorithm to solve the problem. Step 5: Perform construction sequence simulation on the obtained plan to detect and adjust concurrent conflicts of construction resources; Step 6: Based on the final plan, generate a material procurement list and construction task package, and transmit them to the enterprise resource planning or supply chain management system; Step 7: Collect implementation feedback data and update the parameters in the standard decoration module library for subsequent optimization.

2. The method of using the intelligent optimization and allocation system for new energy store decoration resources according to claim 1, characterized in that: After step 2, based on the functional partitions, available decoration area, and preset decoration rules, the feasibility of candidate decoration modules is pre-screened.

3. The method of using the intelligent optimization and allocation system for new energy store decoration resources according to claim 1, characterized in that: In step 3, the resource combination optimization model is a mixed integer programming model, whose objective function is to minimize the total cost or a comprehensive function of cost and experience, and the constraints include area, budget, man-hours and supply constraints.

4. The method of using the intelligent optimization and allocation system for new energy store decoration resources according to claim 3, characterized in that: When constructing the model, the minimum order quantity rule for materials is used as one of the constraints.

5. The method of using the intelligent optimization and allocation system for new energy store decoration resources according to claim 1, characterized in that: Step 4 includes: setting a time limit for solving the problem, prioritizing integer programming; if no feasible solution is found within the time limit, automatically calling at least one of the following algorithms for approximate solution: large neighborhood search, genetic algorithm, or simulated annealing.

6. The method of using the intelligent optimization and allocation system for new energy store decoration resources according to claim 1, characterized in that: Step 5 includes: discretizing the project period into time periods, applying concurrent upper limit constraints on personnel and equipment within each time period for simulation; if a conflict is detected, the conflict is eliminated by adjusting the module schedule or replacing the module.

7. The method of using the intelligent optimization and allocation system for new energy store decoration resources according to claim 1, characterized in that: Steps 1-7 are executed automatically by a computer program, which interfaces with an enterprise resource planning or supply chain management system and uses feedback data to iteratively update the module library.

8. A smart optimization and allocation system for new energy store decoration resources according to any one of claims 1-7, characterized in that: Includes a memory that stores computer-executable instructions and a standard building module library; A processor, coupled to the memory, is used to execute the instructions to implement the method as described in any one of claims 1-7.

9. The intelligent optimization and allocation system for new energy store decoration resources according to claim 8, characterized in that: When the processor executes instructions to build an optimization model, it incorporates the minimum order quantity rule for materials into the model constraints.

10. The intelligent optimization and allocation system for new energy store decoration resources according to claim 8, characterized in that: When executing instructions, the processor is configured to first call the integer programming solver and solve the problem within a preset time limit. If the solution fails, it will automatically fall back to the enabled heuristic algorithm module for solving the problem.

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