Variable-based control of aluminum formwork parameter optimization method

Abstract

The application relates to the technical field of aluminum formwork optimization, and discloses an aluminum formwork parameter optimization method based on variable control. The method comprises the following steps: obtaining an initial parameter set of an aluminum formwork component and an optimization target parameter; performing a multistage control optimization process, which comprises multiple control stages, each control stage only processes part of the parameters in the initial parameter set, simultaneously generates a control factor used for transmission to the next control stage and a feedback factor used for optimizing the current control stage; and based on the data interaction of the control factor and the feedback factor, adjusting the initial parameter set so that it meets the optimization target parameter. The method avoids mutual interference of multiple parameters through hierarchical processing of the parameters, realizes dynamic optimization with the help of the interaction of the control factor and the feedback factor, improves the pertinence, controllability and efficiency of parameter optimization, reduces material waste and cost increase, guarantees the performance of the aluminum formwork, and adapts to the demand of modern building engineering for the aluminum formwork.

Description

Technical Field

[0001] This invention relates to the field of aluminum formwork optimization technology, specifically to a method for optimizing aluminum formwork parameters based on variable control. Background Technology

[0002] In the construction industry, aluminum formwork is widely used in concrete structure construction due to its light weight, high reusability, and high construction efficiency. The parameter design of aluminum formwork directly affects construction quality, cost control, and construction safety, encompassing multiple dimensions such as formwork thickness, support spacing, connection strength, and panel material properties. Currently, aluminum formwork parameter design largely relies on engineer experience or traditional single-variable adjustment methods, making it difficult to achieve multi-parameter synergistic optimization when facing complex building structural requirements.

[0003] Traditional parametric design methods often involve adjusting multiple parameters simultaneously, leading to interference between them and making it difficult to accurately assess the impact of individual parameters on the final formwork performance. Consequently, it becomes challenging to find the optimal parameter combination. For example, when adjusting the spacing of aluminum formwork supports, changing the panel thickness at the same time can obscure the relationship between the stability of the support structure and the panel's resistance to deformation. This could result in material waste due to over-design or insufficient load-bearing capacity of the formwork due to improper parameter matching, increasing construction safety hazards.

[0004] The existing optimization process lacks an effective data interaction mechanism, and the results of previous parameter adjustments cannot be fed back to subsequent optimization stages in a timely manner, resulting in a unidirectional and fragmented optimization process. When an adjustment of a parameter triggers a chain reaction, engineers struggle to quickly pinpoint the root cause and make targeted corrections, often requiring repeated testing and verification. This not only prolongs the parameter design cycle but also increases R&D costs. Furthermore, due to the lack of a hierarchical control mechanism, the optimization process easily becomes chaotic when faced with a large number of parameters, failing to proceed in an orderly manner according to parameter importance and scope of impact. This further reduces the efficiency and accuracy of parameter optimization, making it difficult to meet the demands of modern construction engineering for high-performance, low-cost, and high-reliability aluminum formwork. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing aluminum template parameters based on variable control, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for optimizing aluminum template parameters based on variable control, the method comprising:

[0007] Obtain the initial parameter set and optimization target parameters for the aluminum formwork assembly;

[0008] A multi-level control optimization process is performed, which includes multiple control levels. Each control level processes a portion of the parameters in the initial parameter set and generates control factors for passing to the next control level, as well as feedback factors for optimizing the current control level.

[0009] Based on the data interaction between the control factor and the feedback factor, the initial parameter set is adjusted to meet the optimization target parameters.

[0010] Preferably, the multi-level control optimization process includes:

[0011] Identify multiple control levels in the multi-level control optimization process and encode each control level;

[0012] The multiple control levels include a design parameter control level, a simulation analysis control level, and a production implementation control level;

[0013] The control factors of the design parameter control level are passed as input to the simulation analysis control level.

[0014] Preferably, in the design parameter control level:

[0015] Obtain a subset of design variables from the initial parameter set;

[0016] The intersection parameters of the design variable subset are detected, and the intersection parameters represent a range of compatible parameters shared by multiple aluminum formwork components;

[0017] If the intersection parameter exists, check whether the current design parameter value is within the parameter range corresponding to the intersection parameter;

[0018] If the current design parameter value is outside the parameter range corresponding to the intersection parameter, adjust the current design parameter value to the parameter range corresponding to the intersection parameter;

[0019] The parameter range corresponding to the intersection parameters serves as the control factor for the design parameter control level.

[0020] Preferably, adjusting the current design parameter value to the parameter range corresponding to the intersection parameter includes:

[0021] Determine the center parameter value of the parameter range corresponding to the intersection parameter;

[0022] Adjust the current design parameter value with the central parameter value as the target value;

[0023] The adjusted current design parameter values ​​are used as control factors for the design parameter control level.

[0024] Preferably, in the simulation analysis control level:

[0025] Based on the control factors of the design parameter control level, obtain the analog input parameters;

[0026] The simulated input parameters are subjected to performance analysis and calculation to obtain the simulated output parameters;

[0027] Based on the simulation output parameters, determine the control factor and feedback factor of the simulation analysis control level;

[0028] The control factors of the simulation analysis control level are transmitted to the production implementation control level, and the feedback factors are fed back to the design parameter control level.

[0029] Preferably, the performance analysis calculation of the simulated input parameters includes:

[0030] Identify the key performance indicators in the simulated input parameters;

[0031] Based on the aforementioned key performance indicators, calculate the optimized parameter values;

[0032] The optimized parameter values ​​are used as part of the simulation output parameters.

[0033] Preferably, in the production implementation control level:

[0034] Based on the control factors of the simulation analysis control level, production input parameters are obtained;

[0035] The production input parameters are optimized to obtain the output parameters.

[0036] Based on the implementation output parameters, determine the control factors and feedback factors of the production implementation control level;

[0037] The feedback factors of the production implementation control level are fed back to the simulation analysis control level.

[0038] Preferably, if the number of control levels in the multi-level control optimization process is greater than 2, it further includes:

[0039] When multiple control levels share the same type of parameter, the target value of the shared parameter is determined based on the priority parameter of each control level.

[0040] The target value of the shared parameter is used as a control factor or feedback factor for data interaction.

[0041] Preferably, the target values ​​for the shared parameters are determined based on the priority parameters of each control level, including:

[0042] Obtain the weighting factor and parameter percentage factor for each control level;

[0043] Calculate the target value of the shared parameter based on the weighting factor and the parameter proportion factor;

[0044] The target value of the shared parameter is used to adjust the initial parameter set.

[0045] Preferably, adjusting the initial parameter set based on the data interaction between the control factor and the feedback factor includes:

[0046] The multi-level control optimization process is executed iteratively until the optimization objective parameters are satisfied.

[0047] In each iteration, the control factor and the feedback factor update the initial parameter set.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] By breaking down the initial parameter set of the aluminum formwork assembly into multiple control levels, the mutual interference problem caused by simultaneous adjustment of multiple parameters in traditional optimization methods is avoided. Each control level only optimizes a portion of the initial parameter set, enabling more precise analysis of the impact of individual or partially related parameters on the performance of the aluminum formwork. This clearly defines the correspondence between parameter adjustments and optimization objectives, effectively reducing design deviations caused by the coupling effect between parameters and ensuring that the parameter optimization process is more targeted and controllable.

[0050] In the multi-level control optimization process, the data interaction mechanism between control factors and feedback factors constructs a closed-loop optimization system for parameter adjustment. Control factors can effectively transmit the parameter optimization results of the current control level to the next control level, providing a reference basis for subsequent parameter adjustments that aligns with the overall optimization objective. This ensures that the optimization work at each control level always revolves around a unified goal, avoiding a disconnect between local optimization and the overall objective. Feedback factors, on the other hand, can promptly relay problems or optimization needs discovered by subsequent control levels to the current control level. This allows the current control level to correct the adjusted parameters based on the overall optimization progress, achieving dynamic adjustment of the parameter optimization process. This breaks the limitations of the unidirectional advancement of traditional optimization methods and enhances the flexibility and adaptability of parameter optimization.

[0051] This hierarchical control and data interaction approach allows for the systematic adjustment of the initial parameter set of aluminum formwork, ensuring that the optimized parameters better match the target requirements. In practical applications, this effectively reduces material waste caused by improper parameter design, avoids cost increases due to over-design, and prevents insufficient formwork performance caused by unreasonable parameter matching, thus guaranteeing the stability and reliability of the aluminum formwork during construction. Furthermore, this method eliminates the need for extensive and repeated experimental verification. Through orderly hierarchical optimization and timely data feedback, it shortens the parameter design cycle, reduces R&D costs, and improves parameter optimization efficiency. This better adapts to the diverse and high-performance needs of modern construction projects for aluminum formwork, while also providing a more scientific and systematic optimization path for aluminum formwork parameter design. It promotes the transformation of aluminum formwork parameter design from experience-driven to data-driven, enhancing the standardization and precision of aluminum formwork design. Attached Figure Description

[0052] Figure 1 This is a schematic diagram illustrating the working principle of the aluminum template parameter optimization method based on variable control described in this invention.

[0053] Figure 2 A flowchart of the multi-level control optimization process;

[0054] Figure 3 A flowchart for simulating and analyzing the control level;

[0055] Figure 4 Flowchart for implementing control levels in production;

[0056] Figure 5 A flowchart for determining the target values ​​of shared parameters across multiple control levels. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1 This invention provides a method for optimizing aluminum template parameters based on variable control, the method comprising:

[0059] Parameter adjustment is achieved through a multi-level control optimization mechanism to meet preset optimization target parameters. The overall implementation scheme is based on obtaining the initial parameter set and optimization target parameters of the aluminum template assembly, and executing a multi-level control optimization process. This process includes multiple control levels. Each control level processes a portion of the parameters in the initial parameter set and generates control factors for transmission to the next control level, as well as feedback factors for optimizing the current control level. Based on the data interaction between control factors and feedback factors, the initial parameter set is adjusted to meet the optimization target parameters. Specifically, the multi-level control optimization process ensures the iterative and collaborative nature of parameter adjustment through a hierarchical parameter processing and data interaction mechanism. Control factors are transmitted between control levels as parameter values ​​or range information to drive subsequent processing; feedback factors are used to optimize the parameter decisions of the current control level, forming a closed-loop feedback. Data interaction is achieved through a parameter update mechanism, where control factors and feedback factors work together to adjust the initial parameter set until the optimization target parameters are met. This process utilizes variable control logic to decompose parameters into subsets for hierarchical processing, improving optimization efficiency and accuracy.

[0060] Example 1: See Figure 2 The system consists of various components, including wall formwork, floor slab formwork, and beam formwork. These components require parameter optimization to achieve efficient coordination and reuse. The implementation process begins with obtaining the initial parameter set for the aluminum formwork wall system. This set includes various design variables, such as the wall formwork's dimensions, height, and thickness, as well as the hole spacing and diameter at connection points. Optimization target parameters include the system's overall stability, assembly efficiency, and material utilization. During 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 as SAC-01, and the production implementation control level as PIC-01. The control factors of the design parameter control level are passed as input to the simulation analysis control level.

[0061] At the design parameter control level, the operation focuses on a subset of design variables in the initial parameter set, including parameters such as the wall formwork panel width, height, and connection hole spacing. The identification process requires detecting the intersection parameters among these parameters, i.e., the compatible parameter range shared by multiple aluminum formwork components. For example, the connection between the wall formwork and the floor slab formwork 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 formwork, floor slab formwork, and beam formwork, the system determines that the intersection parameter range for hole spacing is 150 mm to 180 mm. Another intersection parameter is the panel height, whose compatible range is determined to be 2700 mm to 3000 mm, constrained by both floor height and construction specifications.

[0062] The system checks whether the current design parameter value is within the parameter range corresponding to the intersection parameters. Assume that in the initial parameter set, the current design parameter value for the hole spacing of the wall formwork is 145 mm, and the current design parameter value for the panel height is 3050 mm. The system detects that 145 mm is below the lower limit of the hole spacing intersection range (150 mm), while 3050 mm is above the upper limit of the panel height intersection range (3000 mm). Since both current values ​​are outside their respective intersection parameter ranges, an adjustment mechanism is triggered. The current design parameter value is adjusted to the parameter range corresponding to the intersection parameters. For the hole spacing parameter, the center parameter value of its intersection range (150 mm to 180 mm) is calculated. By averaging, the center parameter value is found to be 165 mm. Using 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 parameter value of its intersection range (2700 mm to 3000 mm) is 2850 mm, thus the current value of 3050 mm is adjusted to 2850 mm. The adjustment process is not a simple replacement, but a smooth transition based on parameter priority and correlation. For example, adjusting the hole spacing needs to consider the compatibility of surrounding connectors, while adjusting the height needs to take into account structural strength. After the adjustment is completed, the parameter ranges corresponding to these intersecting parameters (hole spacing range 150-180 mm, height range 2700-3000 mm) serve as control factors at the design parameter control level. These control factors contain parameter range information and adjusted parameter values, which will be encapsulated into data packets and prepared for transmission to the simulation analysis control level.

[0063] Simultaneously, within the design parameter control level, feedback factors are generated and used to optimize the processing logic of the current control level. For example, during the adjustment of hole spacing, the system discovers that the initial intersection range calculation did not fully consider the requirements of the new connectors. Therefore, the feedback factor includes a suggestion to update the intersection range, correcting the hole spacing range to 155 mm to 175 mm. This feedback factor is not passed to the next level but is used within the current level to iteratively optimize the detection accuracy of the intersection parameters. After the control factor is passed to the simulation analysis control level, it becomes part of its input. The simulation analysis control level will perform structural mechanics and assembly simulation analysis based on these parameter ranges and data. The design parameter control level continuously optimizes itself through feedback factors, improving the accuracy of intersection parameter identification in subsequent processing. The entire implementation process demonstrates the synergy of multi-level control: the design parameter control level solves the compatibility problem between components by processing intersection parameters, the generated control factors ensure the input basis for subsequent analysis, and the feedback factors enhance the adaptive capability of this level. This example demonstrates how parameter optimization in the aluminum formwork design stage can be achieved through specific parameter detection, adjustment, and transmission.

[0064] Example 2: See Figure 3In the design parameter control level, when the initial value of the wall formwork hole spacing of 145 mm is detected to exceed the intersection range (150-180 mm), the system executes the center parameter value adjustment process. First, the center parameter value of this intersection range is determined: the arithmetic mean of the lower limit of 150 mm and the upper limit of 180 mm is taken, resulting in a center value of 165 mm. This value is set as the target value, and the parameter adjustment program is initiated. The adjustment process does not directly replace the original value, but rather performs a gradual correction based on parameter correlation. For example, if the hole spacing involves the connection holes between the wall formwork and the floor slab formwork, the system will detect the relative positional constraints of adjacent holes (such as minimum hole edge distance requirements), and gradually offset the current value with a target of 165 mm: starting from 145 mm, iteratively increasing in 5 mm increments, verifying at each step whether the hole arrangement rules are met, until 165 mm is reached and all correlation checks are passed. Similarly, for an initial panel height of 3050 mm, after calculating the center value of 2850 mm within the intersection range (2700-3000 mm), the system, combined with vertical load distribution data, adjusts the parameters in 10 mm increments to avoid abrupt changes affecting structural stability. After adjustment, the wall formwork hole spacing value of 165 mm and the panel height value of 2850 mm are output as control factors at the design parameter control level. This control factor contains specific parameter values, not ranges, and is encapsulated as a structured data package, labeled DPC-OUT-01. After the control factor DPC-OUT-01 is passed to the simulation analysis control level, it triggers the construction of simulation input parameters. The system parses the parameter values ​​in the data package and supplements the associated attributes: combining the hole spacing of 165 mm with the wall formwork thickness of 12 mm and the aluminum alloy type 6061-T6 to form a connection node parameter set; combining the panel height of 2850 mm with the wall formwork width of 600 mm and the reinforcing rib spacing of 400 mm to form a structural parameter set. These two sets of parameters constitute the complete simulation input parameters and are loaded into the finite element analysis module.

[0065] The performance analysis calculations were performed in two phases. The first phase involved static simulation: applying construction loads (such as 30 kN / m² concrete lateral pressure) to the structural parameter set and calculating the stress distribution and deformation of the wall formwork. The results showed that the maximum stress occurred at the bottom stiffening rib connection of the wall formwork, with a value close to the material yield limit; simultaneously, deformation in the height direction of the slab was detected to exceed the allowable tolerance. The second phase involved assembly simulation: based on the connection node parameter set, the assembly process of the wall formwork and the floor slab formwork was simulated. The simulation revealed that when the hole spacing was 165 mm, the edge connectors of the floor slab formwork required additional hole enlargement to match, leading to an increase in assembly time. The above calculation results were summarized into simulation output parameters, including key indicators such as maximum stress value, deformation, and assembly time increment.

[0066] Based on the simulation output parameters, the system generates control factors and feedback factors at the simulation analysis control level. The control factors focus on optimizable parameters: for stress concentration issues, it proposes reducing the stiffening rib spacing from 400 mm to 350 mm; for assembly efficiency issues, it suggests adjusting the hole spacing from 165 mm to 170 mm to match standard connectors. These suggestions are encapsulated as control factor SAC-OUT-01 and passed to the subsequent production implementation control level. The feedback factors relate to design parameter decisions: exceeding deformation limits due to panel height is marked as a structural risk, and the design parameter control level is advised to reassess the height intersection range; simultaneously, assembly conflicts in hole spacing are fed back as compatibility defects, and the intersection parameter detection logic is recommended to be updated. Feedback factor SAC-FB-01 is sent back to the design parameter control level. After receiving feedback factor SAC-FB-01, the design parameter control level initiates internal optimization: For the page height parameter, the layer height constraint and transportation limitation are re-verified, and the intersection range is corrected from 2700-3000 mm to 2800-2950 mm; for the hole spacing parameter, a new connector specification library is added, and the intersection range is updated from 150-180 mm to 160-175 mm. These updates are incorporated into the detection logic of the next iteration.

[0067] Example 3: See Figure 4 In the simulation analysis control level, the system constructs simulation input parameters based on control factors (such as 170 mm spacing between wall formwork holes and 350 mm spacing between reinforcing ribs) passed from the design parameter control level. Performance analysis calculations first identify key performance indicators among the input parameters: by analyzing parameter correlations, connection node strength, overall formwork stiffness, and assembly compatibility are determined as the current core indicators. The identification process prioritizes indicators based on their impact on system performance, with connection node strength given the highest priority due to its involvement in structural safety. Based on these indicators, the system initiates optimization parameter value calculations. The calculation employs a multi-objective optimization algorithm, setting constraints for each indicator: connection node strength requires a minimum shear capacity of 45 kN, overall formwork stiffness requires a maximum deformation ≤ 2 mm / m, and assembly compatibility requires a standard component matching rate ≥ 95%. The optimization process iteratively adjusts the input parameters, with the objective function defined as minimizing the sum of performance deviations.

[0068] ;

[0069] in: This represents the overall optimization target value; The number of key performance indicators; It is the first Weighting factors for each indicator (such as connection strength) stiffness ,compatibility ); The current parameter value; It is the target threshold; The allowable deviation range is determined by solving the gradient descent method. Minimized parameter combinations. Calculated optimized parameter values ​​include: 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, along with performance predictions (such as shear capacity of 47 kN), form the core of the simulation output parameters.

[0070] Based on the simulated output parameters, the system generates control factors and feedback factors. The control factor includes optimized executable solutions: a hole spacing of 172 mm and a rib thickness of 3.5 mm, marked as SAC-OUT-02 and passed to the production implementation control level. The feedback factor addresses issues at the design parameter control level: identifying that the punch height of 2850 mm is too sensitive in stiffness optimization, it is recommended to include it in the next round of intersection parameter detection, forming feedback factor SAC-FB-02. After receiving control factor SAC-OUT-02, the production implementation control level parses its parameter values ​​and integrates production environment data to construct production input parameters. For example, the hole spacing of 172 mm is associated with the positioning accuracy of the CNC punch press (0.1 mm), and the rib thickness of 3.5 mm is associated with the thickness tolerance of the rolling process (±0.15 mm). The optimization process is implemented in two steps: first, process adaptation is performed, converting parameter values ​​into equipment instruction sets, such as generating punching path planning files; second, virtual trial production is conducted, simulating the actual production process through a digital twin platform. These process data are recorded as implementation output parameters, including indicators such as actual working hours and material loss rate.

[0071] Based on the implemented output parameters, the system generates control factors and feedback factors at the production implementation control level. Control factor PIC-OUT-01 includes process improvement suggestions: adopting a bidirectional symmetrical stamping strategy for the punch press toolpath to shorten the cycle time, and fine-tuning the mill temperature control parameters to compensate for the speed reduction effect. Feedback factor PIC-FB-01 points to deficiencies in the simulation analysis control level: indicating that the assembly compatibility index does not consider the physical limitations of the production equipment (such as the minimum punch pitch), and suggesting adding equipment capability constraints in the performance analysis. This feedback factor is sent to the simulation analysis control level.

[0072] Upon receiving feedback factor PIC-FB-01, the simulation analysis control level immediately updates the performance analysis logic: a new sub-item, "Equipment Adaptability," is added to the assembly compatibility index, and the constraint is expanded to "punch position deviation ≤ 0.2 mm." This update is applied to subsequent iterative calculations, forming a cross-level dynamic optimization closed loop. The entire process improves simulation accuracy through precise identification of key performance indicators and parameter optimization calculations, while feedback from the production implementation layer continuously corrects the analysis model, achieving a synergistic evolution of theory and practice.

[0073] Example 4: See Figure 5 In this example, the aluminum formwork system comprises three main control levels: the design parameter control level (coded DPC-02), the simulation analysis control level (coded SAC-03), and the production implementation control level (coded PIC-02). These levels collectively address a shared parameter: the aperture size of the wall formwork connection holes. This parameter affects structural compatibility at the design level, relates to connection strength calculations at the simulation level, and determines punch specification selection at the production level. Therefore, cross-level coordination is required to determine a unified target value.

[0074] During the multi-level control optimization process, the system detected that all three control levels involve aperture size parameters, and the value range of this parameter differs at each level: the design parameter control level requires an aperture of 18-22 mm to meet the compatibility of multi-component connections; the simulation analysis control level recommends 20-24 mm based on mechanical calculations to ensure connection strength; the production implementation control level can only handle apertures of 19-23 mm due to equipment limitations. Since there are more than two control levels, the system initiates a shared parameter coordination mechanism.

[0075] Priority parameters for each control level were obtained. For the design parameter control level, the priority parameter was component reuse rate, with a weighting factor of 0.4; for the simulation analysis control level, the priority parameter was the safety factor, with a weighting factor of 0.5; and for the production implementation control level, the priority parameter was capacity efficiency, with a weighting factor of 0.3. The weighting factors were determined through hierarchical importance analysis, with the simulation level receiving the highest weight due to its involvement in structural safety. Simultaneously, parameter proportion factors were calculated: in the design level, aperture size affects 85% of connection compatibility calculations, with a parameter proportion factor of 0.85; in the simulation level, aperture size participates in 70% of strength analysis, with a parameter proportion factor of 0.7; and in the production level, aperture size is associated with 90% of punching operations, with a parameter proportion factor of 0.9.

[0076] Based on weighting factors and parameter proportion factors, the system calculates the target values ​​for shared parameters. The calculation process employs a weighted decision-making method, first determining recommended values ​​for each control level: for the design level, the recommended value is the median of the compatibility range, 20 mm; for the simulation level, the recommended value is the optimal strength value, 22 mm; and for the production level, the recommended value is the equipment adaptation value, 21 mm. Then, the weights and proportion factors are integrated to calculate the comprehensive target value. The table below shows the key data from the calculation process:

[0077] Table 1: Calculation data table of target values ​​for shared parameters (aperture size).

[0078] ;

[0079] The weighted contribution value is obtained by multiplying the suggested value by the weighting factor and then by the parameter proportion factor. For example, the calculation for the design level is 20.0 × 0.4 × 0.85 = 6.80. The sum of the weighted contribution values ​​for all levels is 20.17. Dividing this by the combined coefficient of the weighting factor and the parameter proportion factor (0.4 × 0.85 + 0.5 × 0.7 + 0.3 × 0.9 = 0.34 + 0.35 + 0.27 = 0.96), the target value for the aperture size is obtained as 21.01 mm. The system rounds this value to 21 mm as the final shared parameter target value.

[0080] The target value is passed as a control factor to each control level: 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 recommended value for subsequent strength calculations; for the production implementation control level, the target value directly matches the equipment capacity and requires no adjustment. Simultaneously, the target value serves as a feedback factor to adjust the initial parameter set: in the global parameter library, the wall formwork aperture size is updated from the initial value of 20 mm to 21 mm and marked as a cross-level optimization result.

[0081] Data interaction monitors parameter requests at all levels through a central coordination module. When a conflict in shared parameters is detected, the priority calculation process is automatically invoked. Weighting factors and parameter percentage factors are dynamically loaded from the system configuration library, allowing adjustments based on project needs. For example, if the project emphasizes production efficiency, the production-level weighting factor can be increased to 0.4, and the target value recalculated. This mechanism ensures consistency of shared parameters across a multi-level control environment, avoiding system conflicts caused by isolated optimization at different levels. This example demonstrates how priority parameters and weighted integration can solve the coordination problem of multi-level parameter sharing in complex engineering projects.

[0082] Example 5: The initial parameter set of the aluminum formwork system includes key variables such as wall formwork panel height, hole spacing, and rib thickness. The optimization target parameters are set as structural deformation ≤ 1.8 mm / m and assembly time ≤ 25 minutes / unit. The system initiates an iterative loop of multi-level control optimization until the above targets are met. The first iteration begins with the initial parameter set: panel 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 of 165 mm exceeds the intersection range (160-175 mm), and is adjusted to the center value of 167.5 mm; the panel height of 2850 mm is within the corrected range (2800-2950 mm), and the original value is retained. The generated control factor 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 finds that the panel height causes deformation of up to 2.2 mm / m. The feedback factor suggests tightening the height range at the design level. Simultaneously, a control factor was generated suggesting an increase in rib thickness to 3.2 mm, which was then passed to the production implementation control level. Virtual trial production was conducted at the production level, and feedback indicated that the 3.2 mm rib caused the stamping cycle to exceed the limit. The first iteration ended, and the initial parameter set was updated to: hole spacing 167.5 mm, plate height 2850 mm (to be corrected), rib thickness 3.2 mm (to be corrected), and deformation of 2.2 mm / meter, which did not meet the standard.

[0083] The second iteration triggered an update to the design parameter control level: based on feedback factors, the height range was narrowed to 2820-2900 mm, and the plate height was adjusted to 2860 mm; the hole spacing remained at 167.5 mm. The new control factors were passed to the simulation level, and performance analysis showed that the deformation decreased to 2.0 mm / m after the height adjustment, but still exceeded the target. The control factors suggested increasing the hole spacing to 169 mm to improve strength, while maintaining the rib thickness at 3.2 mm. After receiving the parameters at the production implementation level, trial production revealed that a 169 mm hole spacing reduced the number of punch changes, but the 3.2 mm rib thickness still affected efficiency. The feedback factors suggested adding production cycle constraints to the simulation level. The updated parameter set after the iteration was: height 2860 mm, hole spacing 169 mm, rib thickness 3.2 mm, deformation 2.0 mm / m.

[0084] In the third iteration, the design parameters at the control level maintained their current values. After receiving production feedback, the simulation analysis control level added a cycle time constraint to the performance calculation: assembly operations must be ≤28 seconds per connection point. The re-optimization suggested fine-tuning the hole position to 169.5 mm and changing the rib thickness to 3.1 mm to balance strength and production efficiency. The production implementation level verified this scheme: the 169.5 mm hole position achieved a 98% matching rate for standard connectors, and the 3.1 mm rib thickness ensured stable rolling within equipment tolerances. The implementation output parameters showed a deformation of 1.85 mm / m and an assembly time of 23 minutes / unit. The deformation was detected to still slightly exceed the target, triggering the fourth iteration.

[0085] In the fourth iteration, the simulation analysis control level refined the model based on the new data and found localized stress concentration in the edge area when the plate height of 2860 mm was combined with the rib plate height of 3.1 mm. The control factor was suggested to be reduced to 2845 mm to balance the load distribution. The design parameter control level verified that 2845 mm was within the intersection range and updated the parameter values. The production implementation level confirmed that the stamping process did not need to be changed after the adjustment. The final output parameters showed a deformation of 1.78 mm / m and an assembly time of 22 minutes / unit, meeting all optimization objectives. The system terminated the iteration cycle.

[0086] The entire iterative process executes four cycles, each going through a three-level sequence of "design → simulation → production". Control factors continuously propagate parameter optimization suggestions downwards: for example, gradually increasing hole positions from 167.5 mm to 169.5 mm, and dynamically adjusting rib thickness between 3.0 and 3.2 mm. Feedback factors propagate inter-level issues in reverse: the design level tightens the height range three times based on simulation feedback, and the simulation level adds process constraints twice based on production feedback. Each iteration updates the parameter set through a parameter merging module: height from 2850 mm → 2860 mm → 2845 mm, and hole positions from 165 mm → 167.5 mm → 169 mm → 169.5 mm. Termination conditions are determined in real-time by a monitoring module: the optimization process automatically stops when deformation is ≤1.8 mm / m and processing time is ≤25 minutes. This implementation achieves incremental parameter optimization and target convergence through multiple closed-loop iterations.

[0087] It should be noted that, in this document, relational 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 these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0088] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which 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, specifically comprising: 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 input to the simulation analysis control level; in the design parameter control level: obtaining a subset of design variables in the initial parameter set; detecting an intersection parameter in the subset of design variables, the intersection parameter representing a compatible parameter range shared by a plurality of aluminum template assemblies; if the intersection parameter exists, detecting whether a current design parameter value is within a parameter range corresponding to the intersection parameter; if the current design parameter value is outside the parameter range corresponding to the intersection parameter, adjusting the current design parameter value to be within the parameter range corresponding to the intersection parameter; the parameter range corresponding to the intersection parameter as the control factor of the design parameter control level; adjusting 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 as the control factor of the design parameter control level; in the simulation analysis control level: obtaining simulation input parameters based on the control factor of the design parameter control level; performing performance analysis calculation on the simulation input parameters to obtain simulation output parameters; determining the control factor and the feedback factor of the simulation analysis control level based on the simulation output parameters; 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; in the production implementation control level: obtaining production input parameters based on the control factor of the simulation analysis control level; performing implementation optimization processing on the production input parameters to obtain implementation output parameters; determining the control factor and the feedback factor of the production implementation control level based on the implementation output parameters; the feedback factor of the production implementation control level is fed back to the simulation analysis control level; if the number of control levels of the multi-level control optimization process is greater than 2, further comprising: when a plurality of control levels share the same type of parameter, determining a target value of the shared parameter according to a priority parameter of each control level; the target value of the shared parameter is used as a control factor or a feedback factor for data interaction; based on the data interaction of the control factor and the feedback factor, adjusting the initial parameter set to meet the optimization target parameter.

2. The variable control based aluminum formwork parameter optimization method according to claim 1, wherein, the performance analysis calculation on the simulation input parameters comprises: identifying a key performance indicator in the simulation input parameters; calculating an optimization parameter value based on the key performance indicator; The optimized parameter value is part of the simulation output parameter.

3. The variable control based aluminum formwork parameter optimization method of claim 1, wherein, Determining the target value of the shared parameter includes: 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.

4. The variable control based aluminum formwork parameter optimization method of claim 1, wherein, Adjusting the initial parameter set based on the data interaction of the control factor and the feedback factor includes: 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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