Iterative optimization and cost pre-control method for modular architectural design

Abstract

The application discloses a kind of modularization building decoration design iteration optimization and cost precontrol method, belong to building decoration engineering technical field, including five core steps: project basic parameter input and benchmark model construction;Hierarchical iterative optimization framework construction;Multi-objective linkage iterative optimization;Whole cycle cost dynamic precontrol and closed loop adjustment;Iteration achievement deposit and standardization reuse.The application deeply binds design iteration and cost precontrol, constructs double closed loop linkage system, realizes hierarchical iterative optimization and whole cycle cost precision control by modularization component multi-objective iterative optimization algorithm and whole cycle cost dynamic precontrol algorithm, solves the industry pain points that design and cost are disconnected in prior art, optimization relies on artificial experience, cost precontrol precision is low, component reuse rate is low, can greatly shorten design cycle, improve cost precontrol precision, reduce whole cycle cost, suitable for the design and cost control of various modularization building decoration engineering.

Description

Technical Field

[0001] This invention belongs to the field of architectural decoration engineering technology, specifically involving an iterative optimization and cost pre-control method for modular architectural decoration design. Background Technology

[0002] With the rapid development of industrialized construction, modular building decoration has become the mainstream development direction of the building decoration industry due to its advantages such as factory prefabrication, on-site assembly, short construction period, and green environmental protection. However, in actual engineering applications, there are still many unresolved industry pain points in the design and cost control of modular building decoration:

[0003] 1. Design iteration and cost pre-control are seriously disconnected: The existing design process is mostly a linear model of "design first, then calculate quantity, and then verify price". Designers cannot keep track of cost changes in real time during the solution iteration process. This often results in the solution meeting the target but the cost being seriously exceeded, requiring repeated rework and modification. This not only greatly extends the design cycle, but also makes the contradiction between design and cost control prominent.

[0004] 2. Iterative optimization relies on human experience and lacks quantitative support: The iterative optimization of existing modular decoration design relies heavily on the personal experience of designers and cost estimators. It cannot simultaneously balance multiple dimensions of objectives such as decoration effect, structural safety, fire prevention and environmental protection, construction efficiency, and cost control. It is easy to lose sight of one aspect while focusing on another, and the stability and replicability of the optimization results are extremely poor.

[0005] 3. Cost pre-control is static and lacks precision: Existing cost pre-control is mostly static estimation after the design is completed. It cannot be dynamically updated in real time with the parameter changes of the design iteration, and it cannot predict the linkage impact of design changes on the cost of the entire process of processing, transportation, installation and operation and maintenance. This results in low pre-control precision. According to industry statistics, the cost overrun rate of existing modular decoration projects during the construction phase is generally over 10%.

[0006] 4. Iterative optimization results cannot be effectively accumulated and the reuse rate is low: The high-quality components and solutions of existing design iterations cannot form a standardized reuse system. Each project needs to be redesigned and optimized, resulting in a standardization reuse rate of less than 50% for modular components. It is impossible to reduce production and installation costs through large-scale reuse, and the ability to optimize costs throughout the entire life cycle is insufficient.

[0007] Therefore, an iterative optimization and cost pre-control method for modular architectural decoration design is needed to solve the problems of design and cost disconnect, lack of quantitative support for optimization, low pre-control accuracy, and low reuse rate in existing technologies. Summary of the Invention

[0008] The purpose of this invention is to provide an iterative optimization and cost pre-control method for modular building decoration design to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: an iterative optimization and cost pre-control method for modular building decoration design, comprising the following steps:

[0010] Step S1. Enter the project's basic building information, decoration design requirements, total cost threshold, and compliance requirements. Based on the BIM platform, construct a modular decoration benchmark BIM model for the project, and at the same time, build a component-level cost database that is bidirectionally linked with the benchmark model.

[0011] Step S2. Decompose the modular decoration design into three progressive iterative levels from top to bottom: scheme level, system level, and component level. Set corresponding optimization objectives, cost item thresholds, constraints, and iteration convergence conditions for each level.

[0012] Step S3. Following the order from the scheme level to the system level and then to the component level, complete the iterative optimization of each level in sequence. After the optimization of each level is completed, trigger the cost pre-control verification synchronously. If the verification fails, return to the current level and re-optimize. If the verification passes, lock the results of the current level and move to the next level.

[0013] Step S4. Based on the changes in design parameters during the iterative optimization process, update the cost database in real time and generate a dynamic cost tracking curve. When the dynamic cost exceeds the sub-item threshold of the corresponding level, trigger a hierarchical warning and drive the design iteration adjustment in reverse.

[0014] Step S5. Incorporate the modular components and system solutions that have passed the full-process verification into the standardized component library, update the full-dimensional tags and cost data of the component library, and provide optimization support and reuse basis for subsequent projects.

[0015] It should be noted in the solution that in step S1, the cost database includes six dimensions that are mapped one-to-one with the modular decorative components: main material cost, auxiliary material cost, factory processing cost, transportation cost, on-site installation cost, and operation and maintenance cost. When any parameter of the component's material, specifications, process, or quantity changes in the BIM model, the cost database will synchronously update the full-cycle cost data of the corresponding component, without the need for manual secondary quantity calculation and pricing.

[0016] It is further worth noting that in step S3, the iterative optimizations at the scheme level, system level, and component level are all solved quantitatively using a modular component multi-objective iterative optimization algorithm. The specific formulas, constraints, and solution logic of the algorithm are as follows:

[0017] First, construct a multi-objective comprehensive optimization function:

[0018]

[0019] In the formula:

[0020] To optimize the variable matrix, , For the first The design parameters of each modular component include component specifications, material, structural form, connection method, and surface treatment process;

[0021] The objective function is cost. , For the first The full-cycle unit cost of each component For the first The number of components used;

[0022] The performance objective function is... , For the first The performance compliance rate of each component is within the range of [0,1]. The performance indicators include structural bearing capacity, fire resistance, environmental protection level, and acoustic performance.

[0023] Let the objective function be construction efficiency. , For the first Single component installation time;

[0024] The objective function is the effect matching degree. , The value range is [0,1], representing the similarity between the design scheme and the client's requirements.

[0025] , , , The weight coefficients for each objective function satisfy... The weighting coefficients are assigned values ​​based on the priority of project requirements;

[0026] The constraints include:

[0027] Cost constraints: , The threshold for cost items at the corresponding level;

[0028] Performance constraints: , This refers to the minimum performance requirements for the corresponding components;

[0029] Specification constraints: , , For the first The upper and lower limits of the design parameters of each component conform to the standards for modular production and installation.

[0030] Compliance constraints: All design parameters comply with current building decoration design and fire protection codes; Iterative convergence condition: The optimization function of three consecutive iterations... The variation range is ≤5‰, or the number of iterations reaches the preset maximum number of iterations;

[0031] The solution logic is as follows: an improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization function, generating a Pareto optimal solution set, and then the comprehensive optimal solution is selected according to the project weight priority as the iterative result of the current level.

[0032] Furthermore, it should be noted that in step S4, the dynamic pre-control of full-cycle cost adopts a dynamic fitting and deviation early warning algorithm for full-cycle cost. The specific formula, hierarchical early warning and adjustment logic of the algorithm are as follows:

[0033] First, construct the dynamic cost pre-control fitting formula:

[0034]

[0035] In the formula:

[0036] for The dynamic total cost of the entire project lifecycle at each iteration node;

[0037] The total cost threshold set at the beginning of the project;

[0038] for When iterating over nodes, the first Cost changes resulting from changes in design parameters, benchmark. For the changed number The cost value is based on the first item. The baseline cost value of the item;

[0039] For the first The cost pass-through coefficient for a design parameter change, over the entire lifecycle, where the total lifecycle cost change is the total variation in processing, transportation, installation, and operation and maintenance costs caused by the parameter change. Based on historical project big data fitting, when parameter changes do not have a full-cycle impact... ;

[0040] Secondly, a cost deviation early warning formula is constructed:

[0041]

[0042] In the formula:

[0043] for Cost deviation rate of iterative nodes;

[0044] Threshold is The cumulative cost threshold for the corresponding level of the iterative node; the hierarchical early warning and closed-loop adjustment logic is as follows:

[0045] when At that time, the cost met the pre-control requirements, and the iteration proceeded normally;

[0046] when When a yellow alert is triggered, a detailed cost overrun list is automatically generated, the overrun components and reasons for changes are identified, and suggestions are made to optimize non-core design parameters at the current level until... ;

[0047] when When a red alert is triggered, the current level of iteration is paused, compliant content is locked, the core reasons for cost overruns are analyzed in reverse, and the corresponding design modules are re-optimized, or the threshold is adjusted after confirmation from the client, until... Only then can we continue to move forward.

[0048] In one preferred implementation, in step S2, the optimization objectives of the scheme-level iterative level are overall decoration style, spatial layout, modular system division, and core material positioning. The cost item threshold is 95% of the total project cost. The iteration convergence condition is that the matching degree between the scheme effect and the client's requirements is ≥95% and the total cost does not exceed the corresponding threshold. The system-level iterative level corresponds to five major modular systems: ceiling, walls, floors, electromechanical integration, and fixed furniture. The optimization objectives are the module layout, interface design, material selection, and structural optimization of each system. The cost item threshold is the upper limit of the item budget for the corresponding system. The iteration convergence condition is that the system performance compliance rate is 100% and the system cost does not exceed the corresponding threshold. The optimization objectives of the component-level iterative level are the specification breakdown, node design, and process optimization of standard components. The cost item threshold is the upper limit of the unit cost of the corresponding component. The iteration convergence condition is that the component installation time is ≤ the preset upper limit and the component cost does not exceed the corresponding threshold.

[0049] In a preferred implementation, in step S3, each level of iterative optimization is equipped with pre-verification and post-verification. Pre-verification confirms that the optimization results and cost data of the previous level have been locked. Post-verification verifies the compliance, performance, effect matching degree, and cost of the current level's optimization results. Only when all verifications are passed can the next level of iteration be entered.

[0050] In a preferred implementation, in step S4, the dynamic cost tracking curve uses the iteration node as the horizontal axis and the cumulative dynamic cost and the corresponding cumulative cost threshold as the vertical axis to display the cost deviation of each iteration node in real time. At the same time, it automatically generates a detailed list of cost overruns, clearly identifying the overrun components, the reasons for parameter changes, and the extent of cost impact, thus achieving full traceability of cost changes throughout the entire process.

[0051] In one preferred implementation, in step S5, the standardized component library is categorized and tagged according to component type, application scenario, cost range, performance level, and installation time. Each time a new standardized component is added, the full-cycle cost data, optimization parameters, BIM model, production drawings, and installation process of that component are updated synchronously. When the component is called in subsequent projects, the corresponding cost data and technical requirements are automatically matched. At the same time, the parameter accuracy of the algorithm is optimized by the historical data of the component library, forming a positive cycle of "optimization-accumulation-reuse-re-optimization".

[0052] Compared with existing technologies, the iterative optimization and cost pre-control method for modular building decoration design provided by this invention has at least the following beneficial effects:

[0053] (1) This invention embeds cost pre-control verification into each node of each iteration level to achieve a dual closed-loop management of "real-time cost update of design changes and reverse-driven design optimization of cost overruns". This avoids repeated rework caused by cost overruns only after the solution is completed in the traditional model. It can shorten the design cycle and greatly improve design efficiency.

[0054] (2) The modular component multi-objective iterative optimization algorithm proposed in this invention can simultaneously balance the four core objectives of cost, performance, construction efficiency and decorative effect, replacing the traditional manual experience mode and greatly improving the stability of optimization results; the full-cycle cost dynamic pre-control algorithm can accurately predict the linkage effect of design changes on the full-cycle cost, improve the cost pre-control accuracy, and greatly reduce the risk of cost overrun during the construction stage.

[0055] (3) This invention breaks down the design iteration into three levels: scheme level, system level and component level, and optimizes from top to bottom. Each level sets an independent cost threshold and convergence condition, and controls the cost in a hierarchical manner from the source, avoiding the impact of later minor optimizations on the core decision of the overall scheme, and greatly improving the pertinence and efficiency of iterative optimization.

[0056] (4) This invention uses a standardized component library to collect and label the iterative optimization results of each project, which enables the rapid reuse of high-quality components and solutions. It can improve the standardization and reuse rate of modular components, significantly reduce production and installation costs through large-scale reuse, and form a positive optimization cycle to achieve continuous cost optimization. Furthermore, the method of this invention is entirely based on the BIM platform. The BIM model and cost database are linked in two directions. The design results can be directly exported as production and processing drawings and installation guidance documents, seamlessly connecting factory production and on-site construction. At the same time, it can connect with component management in the operation and maintenance phase, realizing digital control of the entire life cycle of modular building decoration. Attached Figure Description

[0057] Figure 1 This is a flowchart of an iterative optimization and cost pre-control method for modular architectural decoration design according to the present invention. Detailed Implementation

[0058] The present invention will be further described below with reference to embodiments.

[0059] Please see Figure 1 This invention provides an iterative optimization and cost pre-control method for modular building decoration design, comprising the following steps:

[0060] Step S1. Enter the project's basic building information, decoration design requirements, total cost threshold, and compliance requirements. Based on the BIM platform, construct a modular decoration benchmark BIM model for the project, and simultaneously build a component-level cost database that is bidirectionally linked with the benchmark model.

[0061] Specifically, the cost database includes six dimensions that are mapped one-to-one with the modular decorative components: main material cost, auxiliary material cost, factory processing cost, transportation cost, on-site installation cost, and operation and maintenance cost. When any parameter of the component's material, specifications, process, or quantity changes in the BIM model, the cost database will synchronously update the full-cycle cost data of the corresponding component, eliminating the need for manual secondary quantity calculation and pricing.

[0062] Step S2. Decompose the modular decoration design into three progressive iterative levels from top to bottom: scheme level, system level, and component level. Set corresponding optimization objectives, cost item thresholds, constraints, and iteration convergence conditions for each level.

[0063] Specifically, the optimization goals at the scheme-level iteration level are overall decoration style, spatial layout, modular system division, and core material positioning. The cost item threshold is 95% of the total project cost. The iteration convergence condition is that the matching degree between the scheme effect and the client's requirements is ≥95% and the total cost does not exceed the corresponding threshold. The system-level iteration level corresponds to five major modular systems: ceiling, walls, floors, electromechanical integration, and fixed furniture. The optimization goals are module layout, interface design, material selection, and structural optimization for each system. The cost item threshold is the upper limit of the corresponding system's item budget. The iteration convergence condition is that the system performance compliance rate is 100% and the system cost does not exceed the corresponding threshold. The optimization goals at the component-level iteration level are specification breakdown, node design, and process optimization for standard components. The cost item threshold is the upper limit of the unit cost of the corresponding component. The iteration convergence condition is that the component installation time is ≤ the preset upper limit and the component cost does not exceed the corresponding threshold.

[0064] Step S3. Following the order from scheme level to system level and then to component level, complete the iterative optimization of each level in sequence. After the optimization of each level is completed, trigger the cost pre-control verification synchronously. If the verification fails, return to the current level for re-optimization. If the verification passes, lock the results of the current level and move to the next level.

[0065] Specifically, the iterative optimization at the scheme level, system level, and component level all employ a modular component multi-objective iterative optimization algorithm for quantitative solution. The specific formulas, constraints, and solution logic of the algorithm are as follows:

[0066] First, construct a multi-objective comprehensive optimization function:

[0067]

[0068] In the formula:

[0069] To optimize the variable matrix, , For the first The design parameters of each modular component include component specifications, material, structural form, connection method, and surface treatment process;

[0070] The objective function is cost. , For the first The full-cycle unit cost of each component For the first The number of components used;

[0071] The performance objective function is... , For the first The performance compliance rate of each component is within the range of [0,1]. The performance indicators include structural bearing capacity, fire resistance, environmental protection level, and acoustic performance.

[0072] Let the objective function be construction efficiency. , For the first Single component installation time;

[0073] The objective function is the effect matching degree. , The value range is [0,1], representing the similarity between the design scheme and the client's requirements.

[0074] , , , The weight coefficients for each objective function satisfy... The weighting coefficients are assigned values ​​based on the priority of project requirements;

[0075] The constraints include:

[0076] Cost constraints: , The threshold for cost items at the corresponding level;

[0077] Performance constraints: , This refers to the minimum performance requirements for the corresponding components;

[0078] Specification constraints: , , For the first The upper and lower limits of the design parameters of each component conform to the standards for modular production and installation.

[0079] Compliance constraints: All design parameters comply with current building decoration design and fire protection codes; Iterative convergence condition: The optimization function of three consecutive iterations... The variation range is ≤5‰, or the number of iterations reaches the preset maximum number of iterations;

[0080] The solution logic is as follows: an improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization function, generating a Pareto optimal solution set, and then the comprehensive optimal solution is selected according to the project weight priority as the iterative result of the current level.

[0081] Furthermore, each level of iterative optimization is equipped with pre-validation and post-validation. Pre-validation confirms that the optimization results and cost data of the previous level have been locked. Post-validation verifies the compliance, performance, effect matching degree, and cost of the current level's optimization results. Only when all validations are passed can the next level of iteration be entered.

[0082] Step S4. Based on the changes in design parameters during the iterative optimization process, update the cost database in real time and generate a dynamic cost tracking curve. When the dynamic cost exceeds the sub-item threshold of the corresponding level, trigger a hierarchical warning and drive the design iterative adjustment in reverse.

[0083] Specifically, the full-cycle cost dynamic pre-control adopts a full-cycle cost dynamic fitting and deviation early warning algorithm. The specific formula, hierarchical early warning and adjustment logic of the algorithm are as follows:

[0084] First, construct the dynamic cost pre-control fitting formula:

[0085]

[0086] In the formula:

[0087] for The dynamic total cost of the entire project lifecycle at each iteration node;

[0088] The total cost threshold set at the beginning of the project;

[0089] for When iterating over nodes, the first Cost changes resulting from changes in design parameters, benchmark. For the changed number The cost value is based on the first item. The baseline cost value of the item;

[0090] For the first The cost pass-through coefficient for a design parameter change, over the entire lifecycle, where the total lifecycle cost change is the total variation in processing, transportation, installation, and operation and maintenance costs caused by the parameter change. Based on historical project big data fitting, when parameter changes do not have a full-cycle impact... ;

[0091] Secondly, a cost deviation early warning formula is constructed:

[0092]

[0093] In the formula:

[0094] for Cost deviation rate of iterative nodes;

[0095] Threshold is The cumulative cost threshold for the corresponding level of the iterative node; the hierarchical early warning and closed-loop adjustment logic is as follows:

[0096] when At that time, the cost met the pre-control requirements, and the iteration proceeded normally;

[0097] when When a yellow alert is triggered, a detailed cost overrun list is automatically generated, the overrun components and reasons for changes are identified, and suggestions are made to optimize non-core design parameters at the current level until... ;

[0098] when When a red alert is triggered, the current level of iteration is paused, compliant content is locked, the core reasons for cost overruns are analyzed in reverse, and the corresponding design modules are re-optimized, or the threshold is adjusted after confirmation from the client, until... Only then can we continue to move forward.

[0099] Furthermore, the dynamic cost tracking curve uses iteration nodes as the horizontal axis and cumulative dynamic cost and corresponding cumulative cost thresholds as the vertical axis to display the cost deviation of each iteration node in real time. At the same time, it automatically generates a detailed list of cost overruns, clarifying the reasons for overrun components and parameter changes and the extent of cost impact, thus achieving full traceability of cost changes throughout the entire process.

[0100] Step S5. Incorporate the modular components and system solutions that have passed the full-process verification into the standardized component library, update the full-dimensional tags and cost data of the component library, and provide optimization support and reuse basis for subsequent projects.

[0101] Furthermore, the standardized component library is categorized and tagged according to component type, application scenario, cost range, performance level, and installation time. Each time a new standardized component is added, its full-cycle cost data, optimization parameters, BIM model, production drawings, and installation process are updated simultaneously. When a subsequent project calls upon the component, the corresponding cost data and technical requirements are automatically matched. At the same time, the historical data of the component library is used to reverse-optimize the parameter accuracy of the algorithm, forming a positive cycle of "optimization - accumulation - reuse - re-optimization".

[0102] 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.

[0103] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An iterative optimization and cost pre-control method for modular architectural decoration design, characterized in that: Includes the following steps: Step S1. Enter the project's basic building information, decoration design requirements, total cost threshold, and compliance requirements. Based on the BIM platform, construct a modular decoration benchmark BIM model for the project, and at the same time, build a component-level cost database that is bidirectionally linked with the benchmark model. Step S2. Decompose the modular decoration design into three progressive iterative levels from top to bottom: scheme level, system level, and component level. Set corresponding optimization objectives, cost item thresholds, constraints, and iteration convergence conditions for each level. Step S3. Following the order from the scheme level to the system level and then to the component level, complete the iterative optimization of each level in sequence. After the optimization of each level is completed, trigger the cost pre-control verification synchronously. If the verification fails, return to the current level and re-optimize. If the verification passes, lock the results of the current level and move to the next level. Step S4. Based on the changes in design parameters during the iterative optimization process, update the cost database in real time and generate a dynamic cost tracking curve. When the dynamic cost exceeds the sub-item threshold of the corresponding level, trigger a hierarchical warning and drive the design iteration adjustment in reverse. Step S5. Incorporate the modular components and system solutions that have passed the full-process verification into the standardized component library, update the full-dimensional tags and cost data of the component library, and provide optimization support and reuse basis for subsequent projects.

2. The iterative optimization and cost pre-control method for modular building decoration design according to claim 1, characterized in that: In step S1, the cost database includes six dimensions that are mapped one-to-one with the modular decorative components: main material cost, auxiliary material cost, factory processing cost, transportation cost, on-site installation cost, and operation and maintenance cost. When any parameter of the component's material, specifications, process, or quantity changes in the BIM model, the cost database synchronously updates the full-cycle cost data of the corresponding component, eliminating the need for manual secondary quantity calculation and pricing.

3. The iterative optimization and cost pre-control method for modular building decoration design according to claim 1, characterized in that: In step S3, the iterative optimizations at the scheme level, system level, and component level are all solved quantitatively using a modular component multi-objective iterative optimization algorithm. The specific formulas, constraints, and solution logic of the algorithm are as follows: First, construct a multi-objective comprehensive optimization function: In the formula: To optimize the variable matrix, , For the first The design parameters of each modular component include component specifications, material, structural form, connection method, and surface treatment process; The objective function is cost. , For the first The full-cycle unit cost of each component For the first The number of components used; The performance objective function is... , For the first The performance compliance rate of each component is within the range of [0,1]. The performance indicators include structural bearing capacity, fire resistance, environmental protection level, and acoustic performance. Let the objective function be construction efficiency. , For the first Single component installation time; The objective function is the effect matching degree. , The value range is [0,1], representing the similarity between the design scheme and the client's requirements. , , , The weight coefficients for each objective function satisfy... The weighting coefficients are assigned values ​​based on the priority of project requirements; The constraints include: Cost constraints: , The threshold for cost items at the corresponding level; Performance constraints: , This refers to the minimum performance requirements for the corresponding components; Specification constraints: , , For the first The upper and lower limits of the design parameters of each component conform to the standards for modular production and installation. Compliance constraints: All design parameters comply with current building decoration design and fire protection codes; Iterative convergence condition: The optimization function of three consecutive iterations... The variation range is ≤5‰, or the number of iterations reaches the preset maximum number of iterations; The solution logic is as follows: an improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization function, generating a Pareto optimal solution set, and then the comprehensive optimal solution is selected according to the project weight priority as the iterative result of the current level.

4. The iterative optimization and cost pre-control method for modular building decoration design according to claim 1, characterized in that: In step S4, the dynamic pre-control of full-cycle cost adopts a dynamic fitting and deviation early warning algorithm for full-cycle cost. The specific formula, hierarchical early warning and adjustment logic of the algorithm are as follows: First, construct the dynamic cost pre-control fitting formula: In the formula: for The dynamic total cost of the entire project lifecycle at each iteration node; The total cost threshold set at the beginning of the project; for When iterating over nodes, the first Cost changes resulting from changes in design parameters, benchmark. For the changed number The cost value is based on the first item. The baseline cost value of the item; For the first The cost pass-through coefficient for a design parameter change, over the entire lifecycle, where the total lifecycle cost change is the total variation in processing, transportation, installation, and operation and maintenance costs caused by the parameter change. Based on historical project big data fitting, when parameter changes do not have a full-cycle impact... ; Secondly, a cost deviation early warning formula is constructed: In the formula: for Cost deviation rate of iterative nodes; Threshold is The cumulative cost threshold for the corresponding level of the iterative node; the hierarchical early warning and closed-loop adjustment logic is as follows: when At that time, the cost met the pre-control requirements, and the iteration proceeded normally; when When a yellow alert is triggered, a detailed cost overrun list is automatically generated, the overrun components and reasons for changes are identified, and suggestions are made to optimize non-core design parameters at the current level until... ; when When a red alert is triggered, the current level of iteration is paused, compliant content is locked, the core reasons for cost overruns are analyzed in reverse, and the corresponding design modules are re-optimized, or the threshold is adjusted after confirmation from the client, until... Only then can we continue to move forward.

5. The iterative optimization and cost pre-control method for modular building decoration design according to claim 1, characterized in that: In step S2, the optimization objectives of the scheme-level iteration are overall decoration style, spatial layout, modular system division, and core material positioning. The cost item threshold is 95% of the total project cost. The iteration convergence condition is that the matching degree between the scheme effect and the client's needs is ≥95% and the total cost does not exceed the corresponding threshold. The system-level iteration hierarchy corresponds to five modular systems: ceiling, walls, floor, electromechanical integration, and fixed furniture. The optimization objectives are the module layout, interface design, material selection, and structural optimization of each system. The cost item threshold is the upper limit of the item budget for the corresponding system. The iteration convergence condition is that the system performance meets the target rate of 100% and the system cost does not exceed the corresponding threshold. The optimization objectives of the component-level iterative hierarchy are specification breakdown, node design, and process optimization of standard components. The cost item threshold is the upper limit of the unit cost of the corresponding component. The iterative convergence condition is that the component installation time is less than or equal to the preset upper limit and the component cost does not exceed the corresponding threshold.

6. The iterative optimization and cost pre-control method for modular building decoration design according to claim 1, characterized in that: In step S3, each level of iterative optimization is equipped with pre-verification and post-verification. Pre-verification confirms that the optimization results and cost data of the previous level have been locked. Post-verification verifies the compliance, performance, effect matching degree, and cost of the current level's optimization results. Only when all verifications are passed can the next level of iteration be entered.

7. The iterative optimization and cost pre-control method for modular building decoration design according to claim 1, characterized in that: In step S4, the dynamic cost tracking curve uses the iteration node as the horizontal axis and the cumulative dynamic cost and the corresponding cumulative cost threshold as the vertical axis to display the cost deviation of each iteration node in real time. At the same time, it automatically generates a detailed list of cost overruns, clarifying the reasons for overrun components and parameter changes and the extent of cost impact, so as to achieve full traceability of cost changes.

8. The iterative optimization and cost pre-control method for modular building decoration design according to claim 1, characterized in that: In step S5, the standardized component library is classified and tagged according to component type, application scenario, cost range, performance level, and installation time. When a new standardized component is added, the full life cycle cost data, optimization parameters, BIM model, production drawings, and installation process of the component are updated synchronously. When the component is called in subsequent projects, the corresponding cost data and technical requirements are automatically matched. At the same time, the parameter accuracy of the algorithm is optimized by back-optimizing the historical data of the component library, forming a positive cycle of "optimization-accumulation-reuse-re-optimization".

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