Aluminum template parameter optimization method based on variable control

Abstract

The invention relates to the technical field of aluminum template optimization, and discloses an aluminum template parameter optimization method based on variable control. The method comprises the following steps: acquiring an initial parameter set and an optimization target parameter of an aluminum template assembly; executing a multi-stage control optimization process, the process including a plurality of control stages, each control stage processing only a portion of the parameters of the initial set of parameters while generating control factors for passing to the next control stage and feedback factors for optimizing the current control stage; and adjusting the initial parameter set based on data interaction of the control factor and the feedback factor so as to enable the initial parameter set to meet the optimized target parameter. According to the method, the parameters are processed stage by stage, mutual interference of multiple parameters is avoided, dynamic optimization is achieved by means of interaction of a control factor and a feedback factor, pertinence, controllability and efficiency of parameter optimization are improved, material waste and cost increase are reduced, the performance of the aluminum formwork is guaranteed, and the requirements of modern building engineering for the aluminum formwork are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aluminum formwork optimization, in particular to an aluminum formwork parameter optimization method based on variable control. BACKGROUND

[0002] In the field of building construction, aluminum formwork is widely used in concrete structure construction due to its light weight, high turnover frequency, and high construction efficiency. The parameter design of aluminum formwork is directly related to construction quality, cost control, and construction safety, and its parameters cover multiple dimensions such as formwork thickness, support spacing, connection node strength, and panel material properties. Currently, aluminum formwork parameter design relies on engineer experience or traditional single variable adjustment method, and it is difficult to achieve multi-parameter collaborative optimization when facing complex building structure requirements.

[0003] In traditional parameter design methods, multiple parameters are often adjusted simultaneously, resulting in mutual interference between parameters, making it difficult to accurately determine the influence of a single parameter on the final formwork performance, and further making it difficult to find the optimal parameter combination. For example, when adjusting the support spacing of aluminum formwork, if the panel thickness is also changed, the relationship between support structure stability and panel deformation resistance will become ambiguous, which may result in material waste due to overdesign, or insufficient formwork bearing capacity due to improper parameter matching, increasing construction safety hazards. The existing optimization process lacks effective data interaction mechanism, and the results of previous parameter adjustment cannot be fed back to the subsequent optimization process in a timely manner, resulting in one-way and fragmented characteristics of the optimization process. When a parameter adjustment triggers a chain reaction, engineers have difficulty quickly locating the root cause of the problem and making targeted corrections, often requiring repeated testing and verification, which not only prolongs the parameter design cycle but also increases research and development costs. At the same time, due to the lack of hierarchical control mechanism, when facing a large number of parameters, the optimization process is easily disordered, and it is difficult to proceed in order according to the importance and influence range of parameters, further reducing the efficiency and accuracy of parameter optimization, and making it difficult to meet the demand for high performance, low cost, and high reliability of aluminum formwork in modern building engineering. SUMMARY

[0004] The purpose of the present application is to provide an aluminum formwork parameter optimization method based on variable control to solve the problems raised in the background.

[0005] To achieve the above purpose, the present application provides an aluminum formwork parameter optimization method based on variable control, which comprises: obtaining an initial parameter set of an aluminum formwork component and an optimization target parameter; performing a multi-level control optimization process, the multi-level control optimization process comprising multiple control levels, each control level processing a part of the initial parameter set, and generating a control factor for passing to the next control level, and a feedback factor for optimizing the current control level; Based on the data interaction of the control factor and the feedback factor, the initial parameter set is adjusted to meet the optimization target parameter.

[0006] Preferably, the multi-level control optimization process includes: Identifying a plurality of control levels in the multi-level control optimization process, and coding each control level; Wherein, the plurality of control levels includes a design parameter control level, a simulation analysis control level and a production implementation control level; The control factor of the design parameter control level is transmitted as input to the simulation analysis control level.

[0007] Preferably, in the design parameter control level: Obtain a subset of design variables in the initial parameter set; Detect an intersection parameter in the subset of design variables, the intersection parameter representing a compatible parameter range shared by multiple aluminum template components; If the intersection parameter exists, detect whether the current design parameter value is within the parameter range corresponding to the intersection parameter; If the current design parameter value is outside the parameter range corresponding to the intersection parameter, adjust the current design parameter value to be within the parameter range corresponding to the intersection parameter; The parameter range corresponding to the intersection parameter is the control factor of the design parameter control level.

[0008] Preferably, adjusting the current design parameter value to be within the parameter range corresponding to the intersection parameter includes: Determine a center parameter value of the parameter range corresponding to the intersection parameter; Adjust the current design parameter value with the center parameter value as the target value; The adjusted current design parameter value is the control factor of the design parameter control level.

[0009] Preferably, in the simulation analysis control level: Based on the control factor of the design parameter control level, obtain simulation input parameters; Perform performance analysis calculation on the simulation input parameters to obtain simulation output parameters; Based on the simulation output parameters, determine the control factor and the feedback factor of the simulation analysis control level; The control factor of the simulation analysis control level is transmitted to the production implementation control level, and the feedback factor is fed back to the design parameter control level.

[0010] Preferably, the performance analysis calculation on the simulation input parameters includes: Identify key performance indicators in the simulation input parameters; based on the key performance indicator, calculate an optimization parameter value; the optimization parameter value is part of the simulation output parameter.

[0011] Preferably, in the production implementation control stage: based on the control factor of the simulation analysis control stage, obtain a production input parameter; perform an implementation optimization process on the production input parameter to obtain an implementation output parameter; based on the implementation output parameter, determine the control factor and the feedback factor of the production implementation control stage; the feedback factor of the production implementation control stage is fed back to the simulation analysis control stage.

[0012] Preferably, if the number of control stages of the multi-stage control optimization process is greater than 2, it further comprises: when multiple control stages share the same type of parameter, determine the target value of the shared parameter according to the priority parameter of each control stage; the target value of the shared parameter is used for data interaction as a control factor or a feedback factor.

[0013] Preferably, determining the target value of the shared parameter according to the priority parameter of each control stage comprises: obtain a weight factor and a parameter proportion factor of each control stage; based on the weight factor and the parameter proportion factor, calculate the target value of the shared parameter; the target value of the shared parameter is used to adjust the initial parameter set.

[0014] Preferably, based on the data interaction of the control factor and the feedback factor, adjusting the initial parameter set comprises: iteratively execute the multi-stage control optimization process until the optimization target parameter is met; in each iteration, the control factor and the feedback factor update the initial parameter set.

[0015] Compared with the prior art, the beneficial effects of the present application are: By disassembling the initial parameter set of the aluminum template assembly to multiple control stages for processing, the mutual interference problem caused by simultaneous adjustment of multiple parameters in the traditional optimization method is avoided. Each control stage only optimizes part of the parameters in the initial parameter set, which can more accurately analyze the influence of single or part of the related parameters on the performance of the aluminum template, clearly define the corresponding relationship between parameter adjustment and optimization target, thereby effectively reducing the design deviation caused by the coupling effect between parameters, and ensuring that the parameter optimization process is more targeted and controllable. In the multi-level control optimization process, the data interaction mechanism of the control factor and the feedback factor constructs a closed-loop optimization system of parameter adjustment. The control factor can effectively transmit the parameter optimization results of the current control level to the next control level, providing a reference basis for subsequent parameter adjustment in line with the overall optimization goal, ensuring that the optimization work of each control level always advances around the unified goal, and avoiding the situation that local optimization is out of line with the overall goal. The feedback factor can feedback the problems or optimization requirements found by the subsequent control level to the current control level in a timely manner, so that the current control level can modify the adjusted parameters according to the overall optimization progress, realize dynamic adjustment of the parameter optimization process, break the limitation of one-way advancement of the traditional optimization method, and improve the flexibility and adaptability of parameter optimization. In this way, the initial parameter set of the aluminum formwork can be systematically adjusted, so that the optimized parameters are more in line with the requirements of the optimization target parameters. In actual application, material waste caused by improper parameter design can be effectively reduced, cost increase caused by overdesign can be avoided, and performance deficiency problems caused by unreasonable parameter matching can be prevented, thereby ensuring the stability and reliability of the aluminum formwork in the construction process. In addition, this method does not need to rely on a large number of repeated tests and verifications, and through orderly hierarchical optimization and timely data feedback, the parameter design cycle can be shortened, the research and development cost can be reduced, the parameter optimization efficiency can be improved, the diversified and high-performance requirements of modern building engineering for aluminum formwork can be better adapted, and a more scientific and systematic optimization path for aluminum formwork parameter design is provided, which promotes the transformation of aluminum formwork parameter design from experience-driven to data-driven, and improves the standardization and precision level of aluminum formwork design. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A working principle diagram of the aluminum formwork parameter optimization method based on variable control described in the present application; Figure 2 A flowchart of the multi-level control optimization process; Figure 3 A flowchart of the simulation analysis control level; Figure 4 A flowchart of the production implementation control level; Figure 5 A flowchart of the determination of the target value of the shared parameters of the multiple control levels. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] Referring to Figure 1 The present application provides an aluminum formwork parameter optimization method based on variable control, which comprises the following steps: The parameter adjustment is realized through a multi-level control optimization mechanism to meet the preset optimization target parameters. The overall implementation scheme is based on obtaining the initial parameter set of the aluminum formwork components and the optimization target parameters, and performing a multi-level control optimization process, which includes multiple control levels, each of which processes a part of the parameters of the initial parameter set and generates control factors for passing to the next control level and feedback factors for optimizing the current control level. Based on the data interaction of the control factors and the feedback factors, the initial parameter set is adjusted to meet the optimization target parameters. Specifically, the multi-level control optimization process ensures the iteration and collaboration of parameter adjustment through a hierarchical parameter processing and data interaction mechanism. The control factors, as parameter values or range information, are passed between control levels to drive subsequent processing; the feedback factors are used to optimize the parameter decision of the current control level, forming a closed-loop feedback. Data interaction is realized through a parameter update mechanism, in which the control factors and the feedback factors jointly act on the adjustment of the initial parameter set until the optimization target parameters are met. This process uses variable control logic to decompose parameters into subsets for hierarchical processing, improving optimization efficiency and accuracy.

[0019] Embodiment 1: Referring to Figure 2 The system is composed of various components such as wall form, floor form, and beam form, which need to be optimized through parameters to achieve efficient cooperation and reuse. The implementation process begins with obtaining the initial parameter set of the aluminum formwork wall system. This set contains various design variables, such as the panel size, height, and thickness of the wall form, as well as the hole spacing and hole size of the connecting parts. The optimization target parameters include the overall stability of the system, the assembly efficiency, and the material utilization rate. When performing the multi-level control optimization process, three main control levels are first identified and coded: the design parameter control level is coded as DPC-01, the simulation analysis control level is coded as SAC-01, and the production implementation control level is coded as PIC-01. The control factors of the design parameter control level will be passed as input to the simulation analysis control level.

[0020] In the design parameter control stage, the operation focuses on a subset of design variables in the initial parameter set, which includes parameters such as the panel width, height, and hole spacing of the wall form. The identification process needs to detect intersection parameters, which are compatible parameter ranges shared by multiple aluminum formwork components. For example, the joint between the wall form and the floor form shares a hole spacing parameter, which must be within a compatible range to ensure successful assembly. By comparing the design drawings and specifications of the wall form, floor form, and beam form, the system determines that the intersection parameter range for hole spacing is 150-180 mm. Another intersection parameter is the panel height, which is determined to be compatible within the range of 2700-3000 mm, constrained by the floor height and construction specifications.

[0021] The system detects whether the current design parameter values are within the parameter ranges corresponding to the intersection parameters. Suppose that in the initial parameter set, the hole spacing of the wall form has a current design parameter value of 145 mm, and the panel height has a current design parameter value of 3050 mm. The system detects that 145 mm is below the lower limit of the hole spacing intersection range of 150 mm, and 3050 mm is above the upper limit of the panel height intersection range of 3000 mm. Since both current values are outside their respective intersection parameter ranges, the adjustment mechanism is triggered. The current design parameter values are adjusted to be within the parameter ranges corresponding to the intersection parameters. For the hole spacing parameter, the center value of its intersection range of 150-180 mm is calculated. By averaging, the center value is 165 mm. With this center value as the target value, the current value of 145 mm is adjusted to 165 mm. Similarly, for the panel height parameter, the center value of its intersection range of 2700-3000 mm is 2850 mm, which is used to adjust the current value of 3050 mm to 2850 mm. The adjustment process is not simply a replacement, but a smooth transition based on parameter priorities and correlations, such as considering the compatibility of surrounding connectors for hole spacing adjustment and structural strength for height adjustment. After adjustment, the parameter ranges corresponding to these intersection parameters (hole spacing range 150-180 mm, height range 2700-3000 mm) are used as control factors in the design parameter control stage. The control factors include parameter range information and adjusted parameter values, which are packaged as data packets and prepared for transmission to the simulation analysis control stage.

[0022] Meanwhile, within the design parameter control stage, feedback factors are generated and used to optimize the processing logic of the current control stage. For example, in the adjustment of hole spacing, the system finds that the calculation of the initial intersection range does not fully consider the needs of the new connector, so the feedback factor contains a suggestion to update the intersection range, correcting the hole spacing range to 155-175 mm. This feedback factor is not passed to the next stage, but is used within the current stage to iteratively optimize the detection accuracy of the intersection parameters. The control factors are passed to the simulation analysis control stage and become part of its input. The simulation analysis control stage will perform structural mechanics and assembly simulation analysis based on these parameter ranges and data. The design parameter control stage is constantly self-optimized through feedback factors, improving the accuracy of the identification of intersection parameters in subsequent processing. The entire implementation process embodies the synergy of multi-stage control: the design parameter control stage solves the compatibility problem between components by processing intersection parameters, the control factors generated ensure the input basis for subsequent analysis, and the feedback factors enhance the adaptive ability of the stage. This example demonstrates how to achieve parameter optimization in the aluminum formwork design stage through specific parameter detection, adjustment, and transmission.

[0023] Example 2: see Figure 3In the design parameter control stage, when the initial value of the hole spacing of the wall formwork 145 mm is detected to be out of the intersection range (150-180 mm), the system executes the central parameter value adjustment process. First, determine the central parameter value of the intersection parameter range: take the arithmetic mean of the lower limit 150 mm and the upper limit 180 mm of the range, get the central value 165 mm. This value is set as the target value, and the parameter adjustment program is started. The adjustment process is not a direct replacement of the original value, but a gradual correction based on parameter correlation. For example, the hole spacing involves the connecting hole group of the wall formwork and the floor formwork, and the system will detect the relative position constraints of adjacent holes (such as the minimum hole spacing requirement), and gradually offset the current value from 145 mm to 165 mm with a step of 5 mm: each step verifies whether it meets the hole group arrangement rules until it reaches 165 mm and passes all related checks. Similarly, for the initial value of the panel height 3050 mm, calculate the central value 2850 mm of the intersection range (2700-3000 mm), and then the system combines the vertical load distribution data to adjust it by 10 mm stepwise to avoid sudden changes affecting the stability of the structure. After adjustment, the wall formwork hole spacing value 165 mm and the panel height value 2850 mm are output as the control factors of the design parameter control stage. The control factor DPC-OUT-01 contains specific parameter values rather than ranges, and is packaged as a structured data packet, labeled as DPC-OUT-01. The control factor DPC-OUT-01 is transmitted to the simulation analysis control stage, triggering the construction of simulation input parameters. The system analyzes the parameter values in the data packet and supplements the related attributes: combine the hole spacing 165 mm with the wall formwork thickness 12 mm and the aluminum alloy model 6061-T6 into the connecting node parameter set; combine the panel height 2850 mm with the wall formwork width 600 mm and the reinforcement rib spacing 400 mm into the structural body parameter set. These two groups of parameters constitute complete simulation input parameters, which are loaded into the finite element analysis module.

[0024] The performance analysis calculation is executed in two stages, the first stage performs static simulation: applies construction load (such as concrete lateral pressure 30 kN / m²) to the structural body parameter set, calculates the stress distribution and deformation of the wall formwork. The results show that the maximum stress appears at the connecting joint of the reinforcement rib at the bottom of the wall formwork, and the value is close to the material yield limit; at the same time, the deformation in the panel height direction is detected to be out of the allowed tolerance. The second stage performs assembly simulation: based on the connecting node parameter set, the assembly process of the wall formwork and the floor formwork is simulated. The simulation finds that when the hole spacing is 165 mm, the edge connecting piece of the floor formwork needs to be additionally reamed to match, resulting in an increase in assembly time. The above calculation results are summarized as simulation output parameters, including key indicators such as maximum stress value, deformation, and assembly time increment.

[0025] Based on the simulation output parameters, the system generates control factors and feedback factors for the simulation analysis control stage. The control factors focus on optimizing parameters: for the stress concentration problem, it is proposed to reduce the reinforcement rib spacing from 400 mm to 350 mm; for the assembly efficiency problem, it is suggested to adjust the hole spacing from 165 mm to 170 mm to match the standard connector. These suggestions are encapsulated as control factor SAC-OUT-01 and transmitted to the subsequent production implementation control stage. The feedback factors are related to design parameter decisions: the deformation exceeding the height of the page is marked as a structural risk, and it is suggested that the design parameter control stage re-evaluate the height intersection range; at the same time, the assembly conflict of the hole spacing is fed back as a compatibility defect, and it is suggested to update the intersection parameter detection logic. Feedback factor SAC-FB-01 is sent back to the design parameter control stage. After receiving feedback factor SAC-FB-01, the design parameter control stage starts internal optimization: for the page height parameter, recheck the layer height constraint and transportation limit, and modify the intersection range from 2700-3000 mm to 2800-2950 mm; for the hole spacing parameter, add a new type of connector specification library, and update the intersection range from 150-180 mm to 160-175 mm. These updates are included in the detection logic of the next iteration.

[0026] Example 3: refer to Figure 4 In the simulation analysis control stage, the system constructs simulation input parameters based on the control factors passed by the design parameter control stage (such as wall form hole spacing 170 mm, reinforcement rib spacing 350 mm). Performance analysis calculation first identifies key performance indicators in input parameters: by analyzing parameter correlation, it determines that connection node strength, template overall stiffness, and assembly compatibility are the current core indicators. The identification process is based on the influence weight of indicators on system performance, where connection node strength is given the highest priority due to its involvement in structural safety. Based on these indicators, the system starts optimization parameter value calculation. The calculation uses a multi-objective optimization algorithm, and sets constraint conditions for each indicator: connection node strength requires minimum shear capacity of 45 kN, template overall stiffness requires maximum deformation of ≤2 mm / m, and assembly compatibility requires standard part matching rate of ≥95%. The optimization process iteratively adjusts input parameters, and the objective function is defined as minimizing the total performance deviation: ; Where: represents the comprehensive optimization target value; is the number of key performance indicators; is the weight factor of the th indicator (such as connection strength , stiffness , compatibility ); is the current parameter value; is the target threshold value; is the allowable deviation range. Solve it by gradient descent method. Minimized parameter combinations. Calculations revealed the following optimized parameters: increasing the hole spacing from 170 mm to 172 mm to improve connection strength, and maintaining the rib spacing at 350 mm but increasing the rib thickness to 3.5 mm to improve stiffness. These values ​​served as the core of the simulation output parameters, along with predicted performance values ​​(such as a shear capacity of 47 kN).

[0027] Based on the simulation output parameters, the system generates control factors and feedback factors. The control factors, including the optimized executable solution (hole spacing of 172 mm and rib thickness of 3.5 mm), are labeled SAC-OUT-02 and transmitted to the production implementation control level. The feedback factors address issues identified at the design parameter control level: identifying the excessive sensitivity of the 2850 mm printing height in stiffness optimization and recommending its inclusion in the next round of intersection parameter testing, resulting in the feedback factor SAC-FB-02. After receiving the control factor SAC-OUT-02, the production implementation control level analyzes its parameter values ​​and integrates production environment data to construct production input parameters. For example, the hole spacing of 172 mm correlates with the CNC punch press's positioning accuracy of 0.1 mm, and the rib thickness of 3.5 mm correlates with the rolling process's thickness tolerance of ±0.15 mm. The optimization process is implemented in two steps: first, process adaptation is performed to convert the parameter values ​​into a set of equipment instructions, such as generating a punching path planning file; second, a virtual pilot run is conducted to simulate the actual production process using the digital twin platform. These process data are recorded as implementation output parameters, including actual working hours, material loss rate and other indicators.

[0028] Based on the implementation output parameters, the system generates control factors and feedback factors for the production implementation control level. Control factor PIC-OUT-01 includes process improvement suggestions: adopting a bidirectional symmetrical stamping strategy for the punch press tool path to shorten cycle time, and fine-tuning the rolling mill temperature control parameters to compensate for the impact of speed reduction. Feedback factor PIC-FB-01 points out shortcomings in the simulation analysis control level: it points out that the assembly compatibility index does not consider the physical limitations of the production equipment (such as the minimum punch step) and recommends adding equipment capacity constraints to the performance analysis. This feedback factor is sent to the simulation analysis control level.

[0029] After receiving the feedback factor PIC-FB-01, the simulation analysis control level immediately updated the performance analysis logic: a new sub-item for equipment adaptability was added to the assembly compatibility index, and the constraint was expanded to a punching position deviation of ≤0.2 mm. This update was applied to subsequent iterative calculations, forming a cross-level dynamic optimization closed loop. The entire process improved simulation accuracy through the precise identification of key performance indicators and parameter optimization calculations. Feedback from the production implementation level continuously revised the analysis model, achieving a coordinated evolution of theory and practice.

[0030] Example 4: See Figure 5In this example, the aluminum template system contains three main control levels: design parameter control level (coded DPC-02), simulation analysis control level (coded SAC-03) and production implementation control level (coded PIC-02), which jointly handle a shared parameter: the hole diameter size of the wall template connecting hole. This parameter affects the structural compatibility in the design level, is associated with the connection strength calculation in the simulation level, and determines the punch specification selection in the production level, so it needs to be coordinated across levels to determine a unified target value.

[0031] When the multi-level control optimization process runs, the system detects that the three control levels are all related to the hole diameter size parameter, and there are differences in the value range of this parameter at each level: the design parameter control level requires a hole diameter of 18-22 mm to meet the multi-component connection compatibility; the simulation analysis control level recommends 20-24 mm based on mechanical calculation to ensure connection strength; the production implementation control level can only handle a hole diameter of 19-23 mm due to equipment limitations. Since the number of control levels is greater than two, the system starts the shared parameter coordination mechanism.

[0032] The priority parameters of each control level are obtained, the priority parameter of the design parameter control level is the component reuse rate, and the weight factor is set to 0.4; the priority parameter of the simulation analysis control level is the safety factor, and the weight factor is 0.5; the priority parameter of the production implementation control level is the production capacity efficiency, and the weight factor is 0.3. The weight factor is determined through level importance analysis, in which the simulation level obtains the highest weight due to its involvement in structural safety. At the same time, the parameter proportion factor is calculated: the hole diameter size in the design level affects 85% of the connection compatibility calculation, and the parameter proportion factor is 0.85; the hole diameter size in the simulation level participates in 70% of the strength analysis, and the parameter proportion factor is 0.7; the hole diameter size in the production level is associated with 90% of the punching operation, and the parameter proportion factor is 0.9.

[0033] Based on the weight factor and the parameter proportion factor, the system calculates the target value of the shared parameter, and the calculation process uses a weighted decision method to first determine the recommended value of each control level: the design level recommends taking the compatibility range midpoint of 20 mm, the simulation level recommends taking the strength optimal value of 22 mm, and the production level recommends taking the equipment adaptation value of 21 mm. Then integrate the weight and proportion factor to calculate the comprehensive target value. The following table shows the key data in the calculation process: Table 1: Shared parameter (hole diameter size) target value calculation data table.

[0034] ; The weighted contribution value is obtained by multiplying the suggestion value by the weight factor and then by the parameter proportion factor, for example, the design level calculation is 20.0 x 0.4 x 0.85 = 6.80. The sum of the weighted contribution values of all levels is 20.17, divided by the comprehensive coefficient of the weight factor and the parameter proportion factor (0.4 x 0.85 + 0.5 x 0.7 + 0.3 x 0.9 = 0.34 + 0.35 + 0.27 = 0.96), and the target value of the aperture size is 21.01 mm. The system rounds to 21 mm as the final shared parameter target value.

[0035] The target value is passed to each control level as a control factor: for the design parameter control level, the target value of 21 mm updates its intersection parameter range, adjusting the original 18-22 mm to 19-21 mm to focus on compatibility; for the simulation analysis control level, the target value replaces the original suggestion value for subsequent strength calculation; for the production implementation control level, the target value directly matches the equipment capacity and does not need to be adjusted. At the same time, the target value is used as a feedback factor to adjust the initial parameter set: in the global parameter library, the wall mold aperture size is updated from the initial value of 20 mm to 21 mm and marked as a cross-level optimization result.

[0036] Data interaction is monitored by the central coordination module for parameter requests at each level, and when a shared parameter conflict is detected, the priority calculation process is automatically called. The weight factor and the parameter proportion factor are dynamically loaded from the system configuration library, allowing adjustment according to project needs. For example, if the project emphasizes production efficiency, the production level weight factor can be increased to 0.4 to recalculate the target value. This mechanism ensures consistency of shared parameters in a multi-level control environment and avoids system conflicts caused by isolated optimization at different levels. The entire example demonstrates how to solve the coordination problem of multi-level parameter sharing in complex engineering through priority parameters and weighted integration.

[0037] Example 5: The initial parameter set of the aluminum template system contains key variables such as wall template height, hole spacing, and rib thickness. The optimization target parameters are set as structural deformation ≤ 1.8 mm / m and assembly time ≤ 25 min / unit. The system starts the iterative cycle of multi-level control optimization process until the above targets are met. The first iteration starts with the initial parameter set: template height 2850 mm, hole spacing 165 mm, and rib thickness 3.0 mm. When executing the design parameter control level, it is detected that the hole spacing 165 mm exceeds the intersection range (160-175 mm), and it is adjusted to the center value 167.5 mm; the template height 2850 mm is within the corrected range (2800-2950 mm), and the original value is retained. The control factor generated contains the adjusted parameter values and is passed to the simulation analysis control level. The simulation analysis is calculated based on the new parameters, and it is found that the template height causes a deformation of 2.2 mm / m, and the feedback factor suggests that the design level should tighten the height range. At the same time, the control factor suggests that the rib should be increased to 3.2 mm, and it is passed to the production implementation control level. The production level implements virtual trial production, and the feedback factor points out that the 3.2 mm rib makes the stamping cycle exceed the standard. The first iteration ends, and the initial parameter set is updated to: hole spacing 167.5 mm, template height 2850 mm (to be corrected), and rib thickness 3.2 mm (to be corrected), with a deformation of 2.2 mm / m that does not meet the standard.

[0038] The second iteration triggers the design parameter control level update: according to the feedback factor, the height range is narrowed to 2820-2900 mm, and the template height is adjusted to 2860 mm; the hole spacing remains 167.5 mm. The new control factor is passed to the simulation level, and the performance analysis shows that the deformation is reduced to 2.0 mm / m after adjusting the height, but it still exceeds the target. The control factor suggests that the hole should be increased to 169 mm to improve strength, and the rib thickness remains 3.2 mm. After receiving the parameters, the production implementation level finds that the hole 169 mm can reduce the number of punch tool changes, but the rib 3.2 mm still affects efficiency. The feedback factor suggests that the simulation level should add production cycle constraints. After iteration, the parameter set is updated to: height 2860 mm, hole 169 mm, and rib 3.2 mm, with a deformation of 2.0 mm / m.

[0039] The third iteration maintains the current values in the design parameter control level, and the simulation analysis control level adds a new cycle constraint in the performance calculation after receiving production feedback: the assembly operation needs to be ≤ 28 seconds / connection point. The re-optimization suggests that the hole should be adjusted to 169.5 mm, and the rib thickness should be changed to 3.1 mm to balance strength and production efficiency. The production implementation level verifies this scheme: the hole 169.5 mm makes the standard connector matching rate reach 98%, and the rib 3.1 mm realizes stable rolling within the equipment tolerance. The implementation output parameters show that the deformation is 1.85 mm / m, and the assembly time is 23 minutes / unit. It is detected that the deformation still slightly exceeds the target, triggering the fourth iteration.

[0040] In the fourth iteration, the simulation analysis control stage refines the model based on the new data and finds that the local stress concentration exists in the edge area when the height of the layout is 2860 mm and the rib plate is 3.1 mm. The control factor suggests that the height should be reduced to 2845 mm to balance the load distribution. The design parameter control stage verifies that 2845 mm is within the intersection range and updates the parameter value. The production implementation stage confirms that the stamping process does not need to be changed after adjustment. The final output parameter shows that the deformation is 1.78 mm / m, and the assembly time is 22 minutes / unit, which meets all optimization goals. The system terminates the iteration cycle.

[0041] The entire iteration process performs four rounds of circulation, and each round experiences the "design → simulation → production" three-stage sequence. The control factor continuously passes down the parameter optimization suggestions: for example, the hole position gradually increases from 167.5 mm to 169.5 mm, and the rib plate thickness is dynamically adjusted between 3.0-3.2 mm. The feedback factor reversely transmits the inter-level problems: the design stage tightens the height range three times according to the simulation feedback, and the simulation stage increases the process constraints twice according to the production feedback. Each iteration updates the set through the parameter merging module: the height from 2850 mm → 2860 mm → 2845 mm, and the hole position from 165 mm → 167.5 mm → 169 mm → 169.5 mm. The termination condition is compared in real time by the monitoring module: when the deformation is ≤1.8 mm / m and the working hours are ≤25 minutes, the optimization process is automatically stopped. This implementation realizes gradual optimization of parameters and convergence of goals through multiple rounds of closed-loop iteration.

[0042] It should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Also, the terms "comprises", "comprising", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article, or apparatus that comprises a list of elements does not only include those elements, but also includes other elements not expressly listed, or inherent to such process, method, article, or apparatus.

[0043] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A variable control based aluminum formwork parameter optimization method, characterized in that, The method comprises: obtaining an initial parameter set and an optimization target parameter of an aluminum template assembly; performing a multi-level control optimization process, the multi-level control optimization process comprising a plurality of control levels, each control level processing a part of parameters of the initial parameter set and generating a control factor for passing to a next control level and a feedback factor for optimizing a current control level; adjusting the initial parameter set based on data interaction of the control factors and the feedback factors to meet the optimization target parameter.

2. The variable control based aluminum formwork parameter optimization method according to claim 1, wherein, The performing of the multi-level control optimization process comprises: identifying a plurality of control levels in the multi-level control optimization process and coding each control level; wherein the plurality of control levels comprises a design parameter control level, a simulation analysis control level and a production implementation control level; the control factor of the design parameter control level is passed as an input to the simulation analysis control level.

3. The variable control based aluminum formwork parameter optimization method of claim 2, wherein, In the design parameter control level: a subset of design variables in the initial parameter set is obtained; an intersection parameter in the subset of design variables is detected, the intersection parameter representing a compatible parameter range shared by a plurality of aluminum template assemblies; if the intersection parameter exists, it is detected whether a current design parameter value is located within a parameter range corresponding to the intersection parameter; if the current design parameter value is located outside the parameter range corresponding to the intersection parameter, the current design parameter value is adjusted to be within the parameter range corresponding to the intersection parameter; the parameter range corresponding to the intersection parameter is taken as the control factor of the design parameter control level.

4. The variable control based aluminum formwork parameter optimization method of claim 3, wherein, The adjusting of the current design parameter value to be within the parameter range corresponding to the intersection parameter comprises: determining a center parameter value of the parameter range corresponding to the intersection parameter; adjusting the current design parameter value with the center parameter value as a target value; the adjusted current design parameter value is taken as the control factor of the design parameter control level.

5. The variable control based aluminum formwork parameter optimization method of claim 2, wherein, In the simulation analysis control level: based on the control factor of the design parameter control level, simulation input parameters are obtained; performance analysis calculation is performed on the simulation input parameters to obtain simulation output parameters; based on the simulation output parameters, control factors and feedback factors of the simulation analysis control level are determined; the control factor of the simulation analysis control level is passed to the production implementation control level, and the feedback factor is fed back to the design parameter control level.

6. The variable control based aluminum formwork parameter optimization method according to claim 5, wherein, The performance analysis calculation on the simulation input parameters comprises: identifying key performance indicators in the simulation input parameters; based on the key performance indicators, optimization parameter values are calculated; the optimization parameter values are taken as a part of the simulation output parameters.

7. The variable control based aluminum formwork parameter optimization method of claim 2, wherein, In the production implementation control level: based on the control factor of the simulation analysis control level, production input parameters are obtained; implementation optimization processing is performed on the production input parameters to obtain implementation output parameters; based on the implementation output parameters, control factors and feedback factors of the production implementation control level are determined; the feedback factor of the production implementation control level is fed back to the simulation analysis control level.

8. The variable control based aluminum formwork parameter optimization method of claim 1, wherein, If the number of control levels of the multi-level control optimization process is greater than 2, the method further comprises: when a plurality of control levels share the same type of parameters, target values of the shared parameters are determined according to priority parameters of each control level; The target value of the shared parameter is used as a control factor or a feedback factor for data interaction.

9. The variable control based aluminum formwork parameter optimization method of claim 8, wherein, The target value of the shared parameter is determined according to a priority parameter of each control level, including: obtaining a weight factor and a parameter proportion factor of each control level; calculating the target value of the shared parameter based on the weight factor and the parameter proportion factor; The target value of the shared parameter is used to adjust the initial parameter set.

10. The variable control based aluminum formwork parameter optimization method of claim 1, wherein, Based on the data interaction of the control factor and the feedback factor, the initial parameter set is adjusted, including: iteratively performing the multi-level control optimization process until the optimization target parameter is satisfied; In each iteration, the control factor and the feedback factor update the initial parameter set.

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