A Modular Analysis and Optimization Method and System for ALC Wall Prefabricated Components Based on BIM

Abstract

The present invention discloses a modulus analysis and optimization method and system for precast ALC wall panel components based on BIM, which relates to the field of construction engineering technology. It includes constructing a basic database of ALC wall panels under the BIM platform; introducing the ISO modulus algorithm to cooperate with the ALC wall panel database for data learning; using BIM technology to read the existing wall panel model data and designing the wall body in combination with the modulus algorithm; for the walls that do not meet the requirements, introducing a hybrid optimization method to optimize the design of ALC wall panels. The method of the present invention realizes seamless docking from design to production. Through the BIM platform, three-dimensional modeling of precast ALC wall panel components is carried out to accurately simulate their dimensions and shapes, ensuring a high degree of consistency of design data. An advanced parametric design method is introduced, enabling the modulus design to automatically adapt to design changes and avoiding the error accumulation caused by human factors in traditional designs.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering construction, and specifically to a method and system for optimizing the module analysis of precast ALC wall panels based on BIM. Background Art

[0002] Under the background of the rapid development of the current construction industry, as a representative of green building materials, the market demand for precast ALC wall panels shows a booming growth trend. However, it still faces some challenges in practical applications. For example, the limitations of traditional module design methods result in inaccurate component sizes, making it difficult to meet the complex and ever-changing building requirements; the limitations of the module design of precast ALC wall panels are mainly reflected in their difficulty in adapting to complex and ever-changing building design requirements. Traditional module design methods often rely on fixed sizes and proportions, resulting in limited matching and adaptability of components in practical applications.

[0003] In addition, traditional module analysis methods also have obvious deficiencies in terms of accuracy and efficiency. Traditional two-dimensional drawing design is difficult to comprehensively reflect the three-dimensional form and spatial relationship of components, resulting in errors and omissions easily occurring during module analysis. According to industry statistics, approximately 30% of precast ALC wall panels need to be reprocessed or adjusted at the construction site, which not only increases the construction difficulty and cost but also prolongs the construction period. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is: the problem of discovering and solving potential module mismatch problems at the initial stage of model construction in the existing technology.

[0006] To solve the above technical problem, the present invention provides the following technical solution: A method for optimizing the module analysis of precast ALC wall panels based on BIM, including

[0007] Constructing a basic database of ALC wall panels on the BIM platform;

[0008] Introducing the ISO module algorithm to cooperate with the ALC wall panel database for data learning;

[0009] Using BIM technology to read the existing wall panel model data and design the wall in combination with the module algorithm;

[0010] For walls that do not meet the requirements, introduce a hybrid optimization method to optimize the design of ALC wall panels.

[0011] As a preferred solution of the BIM-based modular analysis and optimization method for precast ALC wall panel components of the present invention, wherein: constructing the basic database of ALC wall panels under the BIM platform includes establishing a basic database for precast ALC wall panel components by integrating three-dimensional modeling, parametric design, and data sharing functions; directly constructing a three-dimensional model of the wall panel through the BIM platform and accurately setting the size parameters of the wall panel according to project requirements;

[0012] Defining various parameters of the wall panel using parametric modeling tools within the BIM platform and adjusting the parameter values at any time in the model; when creating parametric families and components in BIM, setting the size parameters as formula-related values;

[0013] By setting the automatic calculation rules in the BIM platform, automatically generating the sizes of ALC wall panels that meet the design standards according to project requirements.

[0014] As a preferred solution of the BIM-based modular analysis and optimization method for precast ALC wall panel components of the present invention, wherein: introducing the ISO modular algorithm includes setting the basic module M0 for subsequent output of design drawings;

[0015] Reading the design requirements, obtaining the total length, width, and height requirements of the wall panel from the design drawings; determining the modular design standard, determining the basic module M0 according to project requirements, and selecting the size multiples that meet the requirements through the ISO modular standard; setting the arrangement in the length direction of the wall Let the total length of the wall be , and determine whether the length of each wall panel is an integer multiple of ;

[0016] Preliminarily arrange and set the preliminary length of the wall panel as:

[0017] ;

[0018] Wherein, represents the number of wall panels, represents the index number of the wall panel;

[0019] Through the optimization algorithm, make the sum of the number and length of the wall panels closest to the total length of the wall, while ensuring that the length of each wall panel is a multiple of , and the objective function formula is expressed as:

[0020] ;

[0021] Wherein, Represents the total length of the wall panel after arrangement. The goal is to minimize the gap between the total length and the wall length through modular optimization calculation, calculate the size of each wall panel, and determine the wall panel arrangement plan that can maximize the use of the module.

[0022] As a preferred solution of the modular analysis and optimization method for ALC wall panel prefabricated components based on BIM according to the present invention, wherein: the data learning in cooperation with the ALC wall panel database includes, according to the application analysis of the current ALC wall panel on site, further adopting the enlarged module and the sub-module;

[0023] When the actual design size of the wall cannot fully match the enlarged module, make up the difference through the sub-module to reduce the problems of gaps or non-compliance during the construction process; The formula is expressed as:

[0024] ;

[0025] Wherein, Represents the difference between the design length and the modular combined length, Represents the total length of the designed wall, Represents the basic module;

[0026] When Adopt the sub-module to make up the difference; For length compensation:

[0027] ;

[0028] Wherein, Represents the final compensation size for the length direction;

[0029] n is a suitable fraction. For width or thickness compensation:

[0030] ;

[0031] Wherein, Represents the final compensation size for the width or thickness direction;

[0032] The sub-module is a fractional value of the basic module, mainly used for the compensation content after the overall size design of the wall. Analyze in contrast to the big data of the current on-site application, and determine 3 types of enlarged modules and 2 types of sub-modules;

[0033] Introduce the genetic algorithm and the simulated annealing algorithm for hybrid optimization to ensure the maximum degree of meeting the actual on-site requirements;

[0034] In the design, a genetic algorithm is used to arrange and combine wall panels to generate multiple possible wall panel configurations; a fitness function is used to evaluate the advantages and disadvantages of each combination and adjust according to the objective function; based on the preliminary solution output by the genetic algorithm, a simulated annealing algorithm is further used to adjust the size and arrangement to find the optimal solution; the temperature of the system is gradually reduced to find the optimal design solution.

[0035] As a preferred solution of the BIM-based modular analysis and optimization method for precast ALC wall panel components of the present invention, wherein: the use of BIM technology to read existing wall panel model data includes reading the data of the internal partition wall in the existing model through BIM, and combining the current ALC modular base and algorithm for design. When determining the wall thickness H, with the wall length being L and the width being B, the basic principle of the wall length L is to first design with MOL1. When the quantity reaches the maximum value, design with MOL2. When the quantity reaches the maximum value again, design with MOL3 until the maximum value. If there is a design gap, use the sub-module MOL1 for supplementary design, and repeat the iteration to complete the design in the length direction; the width direction follows the length principle to complete the wall deepening design.

[0036] As a preferred solution of the BIM-based modular analysis and optimization method for precast ALC wall panel components of the present invention, wherein: the design of the wall in combination with the modular algorithm includes optimizing the preliminary design, identifying the walls that cannot complete the overall design and require customizing the size of the ALC wall panels, and introducing a hybrid algorithm of genetic algorithm and simulated annealing algorithm to optimize them. During the wall design process, the quantity of ALC wall panels is ensured to be sufficient and is not used as a limiting condition. For a single wall, randomly generate .

[0037] X represents the permutation and combination of X ALC wall panels, , i(X) represents the Xth wall panel. Adding * to the first wall panel represents the wall margin, forming . If , and , wherein, represents the length of the wall panel at the position, represents the total length of the wall, j represents the sequential number of the wall panels in the permutation, and S represents the total length of the wall formed by the current permutation; then insert * between to form an initial solution

[0038] ; in the optimized design mode, no longer follow the process of arranging the parameters of the ALC wall panels in a progressive manner, judge whether the initial solution is a feasible solution, and make the design meet the on-site construction requirements to the greatest extent and improve the on-site construction efficiency.As a preferred solution of the BIM-based modular analysis and optimization method for precast ALC wall panel components of the present invention, wherein: the introduced hybrid optimization method includes that in the actual design process, the in-depth design of ALC wall panels cannot fully fit the designed wall size, and the part that cannot be deepened in modulus is taken as the objective function value, and the objective function formula is expressed as:

[0039] ;

[0040] Wherein, represents the th iteration in the genetic algorithm;

[0041] When generating a new permutation in each generation, compare the current objective function value with the value of the previous generation. If the objective function value of the new path is better, then take the new permutation method as the objective function value.

[0042] As a preferred solution of the BIM-based modular analysis and optimization system for precast ALC wall panel components of the present invention, wherein: the ALC wall panel basic database construction module constructs a three-dimensional model of ALC wall panels within the BIM platform, stores and manages all parameters of the wall panels in the database, and automatically calculates and generates ALC wall panel sizes that meet the design standards;

[0043] The modular analysis and optimization module sets the basic modulus and optimizes according to the modular algorithm, and outputs the optimal wall panel layout plan;

[0044] The hybrid optimization algorithm module uses the genetic algorithm to generate multiple possible configurations of the wall panels, and further optimizes the design through the simulated annealing algorithm to find the optimal solution;

[0045] The BIM design implementation module reads the existing wall panel model data from the BIM platform; combines the modular optimization algorithm for design, and through the sub-module difference compensation design, ensures that the final design meets the on-site requirements.

[0046] A computer device includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the BIM-based modular analysis and optimization method for precast ALC wall panel components.

[0047] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the BIM-based modular analysis and optimization method for precast ALC wall panel components are implemented.

[0048] Advantages of the present invention: The method for analyzing and optimizing the module of ALC wall prefabricated components based on BIM provided by the present invention discloses a method for the lack of a set of mature and perfect module analysis of ALC wall prefabricated components in the current engineering construction field. By making full use of the powerful functions of BIM (Building Information Modeling) technology, seamless connection from design to production is achieved. Through the BIM platform, we can perform three-dimensional modeling on ALC wall prefabricated components, accurately simulate their dimensions and shapes, and ensure a high degree of consistency of design data. In this process, advanced parametric design methods are introduced, enabling the module design to automatically adapt to design changes and avoiding the error accumulation caused by human factors in traditional designs. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 It is the overall flowchart of a method for analyzing and optimizing the module of ALC wall prefabricated components based on BIM provided in the first embodiment of the present invention.

[0051] Figure 2 It is the overall flowchart of a system for analyzing and optimizing the module of ALC wall prefabricated components based on BIM provided in the third embodiment of the present invention. Detailed Embodiments

[0052] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0053] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides a method for analyzing and optimizing the module of ALC wall prefabricated components based on BIM, including:

[0054] S1: Construct a basic database of ALC wall panels under the BIM platform.

[0055] Further, constructing the ALC wall panel basic database under the BIM platform includes establishing a basic database for ALC wall panel precast components by integrating three-dimensional modeling, parametric design, and data sharing functions; directly constructing a three-dimensional model of the wall panel through the BIM platform and accurately setting the size parameters of the wall panel according to project requirements.

[0056] Use parametric modeling tools in the BIM platform to define various parameters of the wall panel and adjust the parameter values at any time in the model; when creating parametric families and components in BIM, set the size parameters as formula-related values;

[0057] It should be noted that by setting the automatic calculation rules in the BIM platform, the ALC wall panel sizes that meet the design standards are automatically generated according to project requirements.

[0058] S2: Introduce the ISO modulus algorithm to cooperate with the ALC wall panel database for data learning.

[0059] Further, the introduction of the ISO modulus algorithm includes setting the basic modulus M0 for subsequent output of design drawings.

[0060] Read the design requirements to obtain the total length, width, and height requirements of the wall panel from the design drawings; determine the modular design standard, determine the basic modulus M0 according to project requirements, and select the size multiples that meet the requirements through the ISO modulus standard; set the arrangement in the length direction of the wall Let the total length of the wall be and determine whether the length of each wall panel is an integer multiple of.

[0061] The preliminary arrangement sets the preliminary length of the wall panel as:

[0062] ;

[0063] Among them, represents the number of wall panels, represents the index number of the

[0064] Through the optimization algorithm, make the sum of the number and length of the wall panels closest to the total length of the wall , while ensuring that the length of each wall panel is a multiple of, and the objective function formula is expressed as:

[0065] ;

[0066] Among them, It represents the total length of the wall panel after arrangement. The goal is to minimize the difference between the total length and the wall length through modular optimization calculation, calculate the size of each wall panel, and determine the wall panel arrangement plan that can maximize the use of the module.

[0067] Furthermore, the data learning in conjunction with the ALC wall panel database includes further adopting enlarged modules and sub-modules based on the analysis of the current application of ALC wall panels on site.

[0068] When the actual design size of the wall cannot fully match the enlarged module, make up the difference through the sub-module to reduce the problems of gaps or non-compliance during the construction process; the formula is expressed as:

[0069] ;

[0070] Among them, represents the difference between the design length and the combined length of the module, represents the total length of the designed wall, represents the basic module;

[0071] When , make up the difference using the sub-module; for the length compensation:

[0072] ;

[0073] Among them, represents the final compensation size for the length direction;

[0074] n is a suitable fraction. For the width or thickness compensation:

[0075] ;

[0076] Among them, represents the final compensation size for the width or thickness direction;

[0077] The sub-module is a fractional value of the basic module, mainly used for the compensation content after the overall size design of the wall. Analyze it in contrast to the big data of the current on-site application, and determine 3 types of enlarged modules and 2 types of sub-modules.

[0078] Introduce the genetic algorithm and the simulated annealing algorithm for hybrid optimization to ensure the maximum degree of meeting the actual on-site requirements.

[0079] It should be noted that in the design, use the genetic algorithm to arrange and combine the wall panels to generate multiple possible wall panel configurations; use the fitness function to evaluate the advantages and disadvantages of each combination, and adjust according to the objective function; on the basis of the preliminary solution output by the genetic algorithm, use the simulated annealing algorithm to further adjust the size and arrangement to find the optimal solution; gradually reduce the temperature of the system to find the optimal design solution.

[0080] S3: Read the data of the existing wall panel model using BIM technology and design the wall in combination with the modular algorithm.

[0081] Furthermore, the step of reading the data of the existing wall panel model using BIM technology includes reading the data of the interior partition wall in the existing model through BIM, and designing in combination with the current ALC modular base number and algorithm. When determining the wall thickness H, with the wall length being L and the width being B, the basic principle of the wall length L is to first design with MOL1. When the quantity reaches the maximum value, design with MOL2. When the quantity reaches the maximum value again, design with MOL3 until the maximum value. If there is a design gap, use the sub-module MOL1 for supplementary design, and repeat the iteration to complete the design in the length direction; follow the length principle in the width direction to complete the in-depth design of the wall.

[0082] Even further, the step of designing the wall in combination with the modular algorithm includes optimizing the preliminary design, identifying the walls that cannot complete the overall design and require size customization of ALC wall panels, introducing a hybrid algorithm of genetic algorithm and simulated annealing algorithm to optimize them. During the wall design process, the quantity of ALC wall panels is ensured to be sufficient and is not used as a limiting condition. For a single wall, randomly generate .

[0083] X represents the permutation and combination of X ALC wall panels, , i(X) represents the Xth wall panel. Add * to represent the wall margin on the first wall panel to form , if , and , where, represents the length of the wall panel at the represents the total length of the wall, j represents the sequential number of the wall panel in the permutation, and S represents the total length of the wall formed by the current permutation. Then insert * between to form an initial solution ; In this mode, no longer follow the process of arranging the parameters of ALC wall panels in a progressive order as above, judge whether it is a feasible solution, so that the design can meet the on-site construction requirements to the greatest extent and improve the on-site construction efficiency.

[0084] It should be noted that compare the wall panel sizes on-site with the basic modulus, enlarged modulus, and sub-module, find out the sizes that need to be adjusted, and determine which design gaps can be filled by the enlarged modulus or sub-module. Compare the length, width, and thickness of each wall panel with the existing modulus for matching. Judge whether the size of each wall panel meets the requirements of the basic modulus, enlarged modulus, and sub-module, and calculate the required compensation value. Determine 3 types of enlarged modulus and 2 types of sub-module. Among them, the enlarged modulus is often selected as 2MO, 3MO, 6MO, and the sub-module is often selected as MO / 10, MO / 5.

[0085] S4: For the walls that do not meet the requirements, introduce a hybrid optimization method to optimize the design of ALC wall panels.

[0086] Furthermore, the introduction of the hybrid optimization method includes that in the actual design process, the detailed design of ALC wall panels cannot fully fit the size of the designed wall. The part that cannot be deepened according to the module is taken as the objective function value, and the objective function formula is expressed as:

[0087] ;

[0088] Wherein, represents the th iteration in the genetic algorithm;

[0089] When generating a new permutation in each generation, compare the current objective function value with the value of the previous generation. If the objective function value of the new path is better, then take the new permutation method as the objective function value.

[0090] After completing the preliminary optimization, verify the design results through the BIM platform to ensure that the wall panels meet the design requirements in terms of size, weight, strength, construction gap, etc. If the design meets the conditions, export the final result as construction drawings; if there are non - compliant parts, return to the previous step to re - adjust the module or conduct further optimization.

[0091] It should be noted that collision detection and structural analysis tools are introduced to verify the feasibility of the design, ensuring that the size and arrangement of each wall panel can be smoothly applied in actual construction. Through the feedback loop with the construction team, further optimize the design to ensure that the final design scheme can reduce the construction difficulty and cost.

[0092] Embodiment 2 is an embodiment of the present invention, which provides a method for analyzing and optimizing the module of ALC wall prefabricated components based on BIM. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0093] First, in this embodiment, the method for analyzing and optimizing the module of ALC wall panels based on the BIM platform is adopted to optimize the wall design.

[0094] The test purpose is to verify the effect of this method in actual application, including optimizing the prefabricated component size of ALC wall panels, ensuring the matching degree with the requirements of construction projects, and improving construction efficiency. The test steps are as follows:

[0095] Construct a 3D model of ALC wall panels through the BIM platform. Combine the actual building requirements to create wall panel data of different sizes. The size settings of the model are based on the common wall panel specifications in construction projects, such as the values of length, width, and height. Through the parametric design function in BIM, set the size parameters and related functional attributes of each wall panel. These wall panel size parameters will be stored as basic data in the ALC wall panel database.

[0096] Next, set the basic module (MO) as 100mm through the ISO modular algorithm, and use this standard to determine the size of the wall. As shown in Table 1-4, for example, the sizes of the wall length and width are set to be multiples of 2MO, 3MO, etc. Read the specific size requirements of the wall in the design drawings through BIM technology, and then adjust the length, width, and thickness of the wall panels through the algorithm to ensure that these sizes are integer multiples of the basic module. To ensure the optimization of the design, use the optimization algorithm to make the sum of the number and length of the wall panels closest to the total length of the wall, while ensuring that the length of each wall panel is an appropriate multiple.

[0097] Table 1 ALC internal partition wall panel parameter table

[0098]

[0099] Table 2 ALC internal partition board thickness module

[0100]

[0101] Table 3 ALC internal partition board length module table

[0102]

[0103] Table 4 ALC internal partition board width module

[0104]

[0105] After the preliminary design is completed, further optimize the design of the wall through the BIM platform. According to the requirements of the ISO modular standard, check whether the length, width, and thickness of the wall panels can be fully adapted. If there are sizes that cannot meet the requirements (such as gaps in the design), introduce sub-modules for compensation design to ensure that the size of the wall fully matches the design requirements.

[0106] When some designs cannot fully fit the modular standard, genetic algorithms and simulated annealing algorithms are introduced for optimization. This hybrid optimization algorithm generates multiple combinations of wall panel sizes through multiple iterations and evaluates the advantages and disadvantages of these combinations. During each optimization iteration, the objective function minimizes the dimensional difference between the wall design and the actual requirements. After the optimization is completed, the designed wall panel solution is generated and output to the BIM platform for on-site construction verification. Through on-site construction feedback, the design solution is continuously adjusted until the ALC wall panel sizes that meet the construction requirements and maximize the use of the ISO modular standard are finally formed.

[0107] The optimized design solution and the design optimized by genetic algorithms have the following obvious advantages compared with the traditional design solutions (Design Solution 1, Design Solution 2, Design Solution 3, Design Solution 4):

[0108] Proximity of the total wall length to the actual requirements: The total wall lengths of the optimized design solution and the design optimized by genetic algorithms (2590 mm and 2580 mm) are closer to the ideal requirements (predetermined wall length) than the lengths of other design solutions (2400 mm, 2600 mm, 2500 mm, 2550 mm). This can effectively reduce the adjustment costs caused by dimensional differences during on-site construction.

[0109] The optimized wall panel length and width can better meet the actual building requirements compared with Design Solution 1 and Design Solution 2. For example, the wall panel length in Design Solution 1 is 2400 mm, which may cause gaps or shortages during actual construction, while the optimized design solution can better adapt to the dimensional requirements. By introducing the ISO modular algorithm (basic module and enlarged module), the dimensions of the optimized design solution fully meet the modular design requirements, improving the efficiency of modular utilization during the design process. In the optimized design, a sub-module compensation strategy is adopted to effectively avoid the gap problem between walls and improve the construction accuracy.

[0110] Example 3, referring to Figure 2 , which is an embodiment of the present invention, provides a modular analysis and optimization system for ALC wall panel prefabricated components based on BIM, including:

[0111] ALC Wall Panel Basic Database Construction Module 100, constructs a three-dimensional model of ALC wall panels within the BIM platform, stores and manages all parameters of the wall panels in the database, and automatically calculates and generates ALC wall panel sizes that meet the design standards.

[0112] Modular Analysis and Optimization Module 200, sets the basic module and optimizes according to the modular algorithm, and outputs the optimal wall panel layout plan.

[0113] The hybrid optimization algorithm module 300 uses the genetic algorithm to generate multiple possible configurations of the wall panels and further optimizes the design through the simulated annealing algorithm to find the optimal solution.

[0114] The BIM design implementation module 400 reads the existing wall panel model data from the BIM platform; combines the modular optimization algorithm for design, and through the sub-module difference compensation design, ensures that the final design meets the on-site requirements.

[0115] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0116] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0117] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0118] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for analyzing and optimizing the module of prefabricated ALC wall panels based on BIM, characterized in that, Including: Construct a basic database of ALC wall panels under the BIM platform; Introduce the ISO modular algorithm to cooperate with the ALC wall panel database for data learning; Use BIM technology to read the data of existing wall panel models and design the wall according to the modular algorithm; For walls that do not meet the requirements, introduce a hybrid optimization method to optimize the design of ALC wall panels; The data learning in cooperation with the ALC wall panel database includes further adopting enlarged modules and sub-modules according to the application analysis of the current ALC wall panels on site; When the actual design size of the wall cannot fully match the enlarged module, make up the difference through the sub-module to reduce the gaps or non-compliance problems during the construction process; Formula representation: ΔL = L design -n × MO Among them, ΔL represents the difference between the designed length and the modular combined length, L design represents the total length of the designed wall, and MO represents the basic module; When ΔL≠0, use the sub-module to make up the difference; For length compensation: ΔL final = MO / n Among them, ΔL final represents the supplementary dimension finally used in the length direction; n is a suitable fraction, for width or thickness compensation: ΔB final = MO / n where ΔB final represents the final compensation dimension in the width or thickness direction; The sub-module is a fractional value of the basic module, mainly used for making up the difference after the overall size design of the wall. Analyze it in contrast to the big data of the current on-site application to determine 3 types of enlarged modules and 2 types of sub-modules; Introduce genetic algorithm and simulated annealing algorithm for hybrid optimization to ensure the maximum compliance with the actual on-site requirements; In the design, use the genetic algorithm to arrange and combine the wall panels to generate multiple possible wall panel configurations; Use the fitness function to evaluate the advantages and disadvantages of each combination and adjust according to the objective function; On the basis of the preliminary solution output by the genetic algorithm, use the simulated annealing algorithm to further adjust the size and arrangement to find the optimal solution; Gradually reduce the temperature of the system to find the optimal design solution; The design of the wall in combination with the modular algorithm includes optimizing the preliminary design, identifying the walls that cannot complete the overall design and need to customize the size of ALC wall panels, and introducing a hybrid algorithm of genetic algorithm and simulated annealing algorithm to optimize them; During the wall design process, ensure an adequate number of ALC wall panels, which is not a limiting condition. Randomly generate 1, 2, 3, …, X for a single wall; X represents the permutation and combination of X ALC wall panels, i(1)i(2)…i(X-1)i(X), and i(X) represents the Xth wall panel; Add * to represent the wall margin on the first wall panel to form 0i(1)i(2)…i(X-1)i(X); If And Among them, g(j) represents the length of the wall panel at i(j), Q represents the total length of the wall, j represents the sequence number of the wall panel in the arrangement, and S represents the total length of the wall formed by the current arrangement; Then insert * between i(s-1) and i(s) to form an initial solution, denoted as *i(1)i(2)i(3)…i(L-1)i(L)*; Under the optimized design mode, no longer follow the process of progressive arrangement of the sizes of ALC wall panel parameters, judge whether the initial solution is a feasible solution, make the design meet the on-site construction requirements to the greatest extent, and improve the on-site construction efficiency.

2. The method for optimizing the module analysis of precast components of ALC wall panels based on BIM according to claim 1, characterized in that: The construction of the basic database of ALC wall panels under the BIM platform includes establishing a basic database for precast components of ALC wall panels by integrating three-dimensional modeling, parametric design, and data sharing functions; Directly constructing a three-dimensional model of the wall panel through the BIM platform and accurately setting the size parameters of the wall panel according to the project requirements; Define various parameters of the wall panel using parametric modeling tools within the BIM platform, and adjust the parameter values at any time in the model; when creating parametric families and components in BIM, set the dimension parameters as formula-related values; By setting the automatic calculation rules in the BIM platform, automatically generate the ALC wall panel dimensions that meet the design standards according to the project requirements.

3. The method for analyzing and optimizing the module of the prefabricated ALC wall panel based on BIM according to claim 2, wherein: The introduction of the ISO modular algorithm includes setting the basic module M0 for the output of the design drawings; Read the design requirements and obtain the total length, width, and height requirements of the wall panel from the design drawings; Determine the modular design standard, determine the basic module M0 according to the project requirements, and select the dimension multiples that meet the requirements through the ISO modular standard; Set the length direction L of the wall wall Arrange them. Let the total length of the wall be L wall , and determine the length L of each wall panel 板 (i) is an integer multiple of M0; The preliminary arrangement sets the preliminary length of the wall panel as: where N represents the number of wall panels and i represents the index number of the wall panel; By optimizing the algorithm, make the sum of the number and length of the wall panels closest to the total length L of the wall wall , while ensuring that the length of each wall panel is a multiple of M0, and the objective function formula is expressed as: Among them, represents the total length of the wall panels after arrangement. The goal is to minimize the gap between the total length and the wall length through modular optimization calculation, calculate the size of each wall panel, and determine the wall panel arrangement plan that can maximize the use of the module.

4. The method for modulus analysis and optimization of prefabricated ALC wall components based on BIM according to claim 3, characterized in that: The use of BIM technology to read the existing wall panel model data includes reading the data of the internal partition walls in the existing model through BIM and conducting design in combination with the current ALC modular base and algorithm; When determining the wall thickness H, let the wall length be L and the width be B. The basic principle of the wall length L is to first use MOL1 for design. When the number of MOL1 reaches the maximum value, use MOL2 for design; when the number of MOL2 reaches the maximum value again, use MOL3 for design until the maximum value; if there is a design gap, use the sub-module MOL1 for supplementary design, and repeat the iteration to complete the design in the length direction; the width direction follows the basic principle of the length to complete the in-depth design of the wall.

5. The method for optimizing the modular analysis of precast components of ALC wall panels based on BIM according to claim 1, 2 or 4, characterized in that: The introduction of the hybrid optimization method includes that in the actual design process, the in-depth design of the ALC wall panel cannot fully fit the design wall dimensions. The part that cannot be deepened by the module is used as the objective function value, and the objective function formula is expressed as: where k represents the kth iteration in the genetic algorithm; When generating a new arrangement in each generation, compare the current objective function value with the value of the previous generation. If the objective function value of the new path is better, use the new arrangement method as the objective function value.

6. A system adopting the modular analysis and optimization method for precast components of ALC wall panels based on BIM as described in any one of claims 1 to 5, characterized in that: The ALC wall panel basic database module (100) constructs a three-dimensional model of the ALC wall panel within the BIM platform, stores and manages all parameters of the wall panel in the database, and automatically calculates and generates the ALC wall panel dimensions that meet the design standards; The modular analysis and optimization module (200) sets the basic module and performs optimization according to the modular algorithm, and outputs the optimal wall panel arrangement plan; The hybrid optimization algorithm module (300) uses the genetic algorithm to generate multiple possible configurations of the wall panel, and further optimizes the design through the simulated annealing algorithm to find the optimal solution; The BIM design implementation module (400) reads the existing wall panel model data from the BIM platform; Combined with the modular optimization algorithm for design, through the sub-module compensation design, ensure that the final design meets the on-site requirements.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it realizes the steps of the modular analysis and optimization method for precast components of ALC wall panels based on BIM as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the BIM-based modular analysis and optimization method for precast ALC wall panels described in any one of claims 1 to 5.

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