Fabricated substation facade parameterized design generation method based on BIM (Building Information Modeling)

Abstract

The invention relates to the technical field of building design, and discloses a BIM-based prefabricated substation facade parameterized design generation method, which comprises the following steps: step 1, constructing a design parameter space, firstly defining parameters of prefabricated substation facade design, and then constructing a design parameter space; the parameters comprise the sizes of the components, the distance between the components, the facade windowing proportion, the attributes of materials used by the components and the connection mode of the components; 2, setting a corresponding value range for each design variable in the design parameter space to meet geometric constraint conditions; and step 3, on the basis of the design parameter space determined in the above step, establishing an objective function, and realizing multi-objective optimization through the objective function. Aesthetics, structural stability and construction feasibility are used as objective functions, comprehensive optimization is performed by adopting a weighted optimization method, the balance between design flexibility and implementability is realized, and the structural stability and the construction feasibility are ensured when aesthetic requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of architectural design, and in particular to a method for generating parametric design of assembled substation facades based on BIM. Background Art

[0002] With the widespread application of building information modeling technology, BIM has become a tool for modern building design, construction, and operation and maintenance management. Especially in the field of prefabricated buildings, BIM technology plays an important role. As a modular and standardized building form, prefabricated substations can be quickly assembled through prefabricated components, significantly improving construction efficiency and quality. In the design process of the prefabricated substation facade, how to simultaneously consider the aesthetic requirements of the design, the stability of the structure, and the feasibility of construction is an important and challenging issue.

[0003] Traditional prefabricated substation facade design usually takes aesthetics, structural stability and construction feasibility as independent design goals, ignoring the interrelationship and coordination between them. In addition, the design process lacks a comprehensive, multi-objective optimization framework, and the design scheme will lead to over-optimization of certain goals while ignoring the importance of other goals, affecting the effectiveness of the final design and the smoothness of construction, leading to design changes and construction delays.

[0004] In addition, traditional design methods usually lack flexibility and cannot dynamically adjust the design scheme to adapt to different design requirements or construction conditions. The lack of adaptive optimization methods will lead to low design efficiency, and the design results cannot be reasonably adjusted according to actual conditions. Therefore, how to improve the efficiency and quality of prefabricated substation facade design through intelligent design optimization methods has become a technical problem that needs to be solved urgently in the current design field.

[0005] Therefore, those skilled in the art provide a BIM-based parametric design generation method for prefabricated substation facades to solve the above-mentioned problems. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a BIM-based parametric design and generation method for the facade of an assembled substation to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A parametric design generation method for assembled substation facade based on BIM, comprising:

[0008] Step 1: Construct a design parameter space, first define the parameters of the facade design of the prefabricated substation, the parameters include the size of the components, the spacing between the components, the proportion of the facade openings, the material properties of the components, and the connection method of the components;

[0009] Step 2: Set the corresponding value ranges for each design variable in the design parameter space to meet the geometric constraint conditions;

[0010] Step 3: Based on the design parameter space determined in the above steps, establish an objective function to achieve multi-objective optimization through the objective function. The objective function includes aesthetics, structural stability, and construction feasibility. The weight coefficients of the objective function are set according to the design requirements, and the objective function is weighted and optimized in combination with the weight coefficients;

[0011] Step 4: During the optimization process, according to the values of each design variable in the design parameter space, consider the corresponding constraint conditions. The constraint conditions include geometric constraints, structural stability constraints, and construction constraints. Through the set constraint conditions, ensure that the generated design scheme meets the actual requirements of operations such as the production, transportation, and hoisting of prefabricated components, and can maintain the structural stability under external forces;

[0012] Step 5: After the optimization step is completed, use finite element analysis to perform mechanical simulation on the design scheme, calculate the stress and deformation of each design scheme under actual loads, and evaluate whether it meets the requirements of structural stability;

[0013] Step 6: After the finite element analysis, use an optimization algorithm to search the design parameter space, generate new design schemes and evaluate the fitness, and find the optimal solution through continuous screening, crossover, and mutation;

[0014] Step 7: According to the multiple solutions generated by the optimization algorithm, select the best design scheme that meets aesthetics, structural stability, and construction feasibility. According to the Pareto optimal solution set, the designer can determine the final scheme according to the actual requirements;

[0015] Step 8: Display the determined design scheme through a data visualization tool to help the designer evaluate the scheme.

[0016] Preferably, the construction of the design parameter space includes preliminary pre-processing of each design variable before optimization. The preliminary optimization pre-processing includes regularizing the dimensions of the components, the spacing between components, the elevation window opening ratio, the material properties of the components, and the connection methods of the components. The process of the regularization includes normalizing the design variable x i to make the value range become the standard interval:

[0017]

[0018] i = 1, 2,..., n,

[0019] where x i is the original design variable, l i and u iFor x i The minimum and maximum values, x′ i is the normalized design variable, and n represents the number of design variables.

[0020] Preferably, the objective function is comprehensively optimized by the weighted average method, and the aesthetic part f 1 (x) is defined as:

[0021]

[0022] where, f 1 (x) is the aesthetic part of the objective function, α i is the weight coefficient of the influence of the design variable x i on the aesthetic objective, x i is the design variable, i represents the index of the i-th design variable being calculated currently, and n represents the number of design variables.

[0023] Preferably, the structural stability objective function f 2 (x) is based on finite element analysis, and by calculating the stress and deformation of the design scheme under external forces, the calculation formulas for the stress and deformation are:

[0024]

[0025] where, f 2 (x) is the structural stability objective function, σ i (x) is the stress of the i-th node, δ i (x) is the deformation of the i-th node, N is the total number of nodes, and i represents the index of the i-th design variable being calculated currently.

[0026] Preferably, the construction feasibility objective function f 3 (x) is defined in the following form:

[0027]

[0028] where, f3(x) is the construction feasibility objective function, β i is the construction difficulty coefficient of the i-th component, x i is the design variable, i represents the index of the i-th design variable being calculated currently, and n represents the number of design variables.

[0029] Preferably, the optimization algorithm uses the genetic algorithm to search in the high-dimensional design space, and the genetic algorithm is implemented through the following steps:

[0030] a. Initialize the population: Generate the set of design variables x 1 , x 2 , …, x p, where p represents the population size;

[0031] b. Fitness evaluation: Calculate the fitness F(x i ) of each design scheme x i ) and evaluate it through the objective function F(x);

[0032] c. Screening, crossover, and mutation: Perform screening, crossover, and mutation operations on individuals according to fitness;

[0033] d. Constraint handling: For solutions that do not satisfy the constraint conditions, use the penalty function method to punish the solutions that do not satisfy the constraints.

[0034] Preferably, during the optimization process of the genetic algorithm, the generated multi-solution set is screened through the Pareto optimal solution set, and the Pareto optimal solution set is calculated by the following formula:

[0035] F(x) = w 1 f 1 (x) + w 2 f 2 (x) + w 3 f 3 (x),

[0036] where f 1 (x) is the aesthetic part of the objective function, f 2 (x) is the structural stability objective function, f 3 (x) is the construction feasibility objective function, and w 1 , w 2 and w 3 are the weight coefficients of the objective function.

[0037] Preferably, the optimization process includes performing fitness evaluation on each design variable x i , and the fitness evaluation formula is: A(x i ) = α 1 f 1 (x i ) + α 2 f 2 (x i ) + α 3 f 3 (x i ),

[0038] where A(x i ) is the fitness of the design variable x i , f 1 (x) is the aesthetic part of the objective function, f 2 (x) is the structural stability objective function, f 3 (x) is the construction feasibility objective function, and α1 and α 2 and α 3 are weight coefficients.

[0039] Preferably, a dynamic adjustment mechanism is introduced in the optimization process, and the weight coefficients of the objective function are dynamically adjusted according to the performance of the intermediate solutions in the optimization process. The adjustment formula is:

[0040]

[0041] where and are the original weight coefficients of the objective function, and are the weight coefficients after dynamic adjustment, and Δw 1 and Δw 2 and Δw 3 are adjustment factors based on the evaluation results of the fitness of the design scheme in the optimization process.

[0042] Preferably, the optimized design scheme is simulated and displayed through virtual reality technology. The virtual reality simulation display steps include:

[0043] a. Convert the optimized design scheme into a three-dimensional model and visualize it using virtual reality technology;

[0044] b. Simulate the assembly operation in the virtual reality environment to help designers evaluate the feasibility and construction difficulty of the design scheme in actual construction;

[0045] c. Adjust the design scheme according to the virtual reality simulation results.

[0046] The present invention provides a method for generating a parametric design of the facade of an assembled substation based on BIM. It has the following beneficial effects:

[0047] 1. By taking aesthetics, structural stability, and construction feasibility as the objective function and adopting a weighted optimization method for comprehensive optimization, the present invention achieves a balance between design flexibility and implementability, and ensures the effects of structural stability and construction feasibility when meeting aesthetic requirements.

[0048] 2. By introducing a dynamic adjustment mechanism and dynamically adjusting the weight coefficients of the objective function according to the intermediate solutions in the optimization process, the present invention enhances the flexibility and adaptability of the design optimization process, and the final design scheme can accurately meet different design requirements.

[0049] 3. By combining three-dimensional modeling and virtual reality technology and conducting construction simulation display in a virtual environment, the present invention realizes the simulation and verification of the construction process of the assembled substation, so as to detect potential problems and optimize the design scheme before actual construction. Brief Description of the Drawings

[0050] Figure 1 This is a flowchart of the present invention. Detailed Embodiment

[0051] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, rather than all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0052] The present invention will be described in detail below with reference to the accompanying drawings:

[0053] Embodiment:

[0054] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for generating parametric design of the facade of an assembled substation based on BIM, including:

[0055] Step 1: Construct a design parameter space. First, define the parameters for the facade design of the assembled substation. The parameters include the dimensions of the components, the spacing between components, the facade window opening ratio, the material properties of the components, and the connection methods of the components;

[0056] Step 2: Set the corresponding value ranges for each design variable in the design parameter space to meet the geometric constraint conditions;

[0057] Step 3: On the basis of the design parameter space determined in the above steps, establish an objective function to achieve multi-objective optimization through the objective function. The objective function includes aesthetics, structural stability, and construction feasibility. The weight coefficients of the objective function are set according to the design requirements, and the objective function is weighted and optimized in combination with the weight coefficients;

[0058] Step 4: During the optimization process, according to the values of each design variable in the design parameter space, consider the corresponding constraint conditions. The constraint conditions include geometric constraints, structural stability constraints, and construction constraints. Through the set constraint conditions, the generated design scheme is made to meet the actual requirements of operations such as the production, transportation, and hoisting of assembled components, and can maintain the structural stability under external forces;

[0059] Step 5: After the optimization step is completed, use finite element analysis to perform mechanical simulation on the design scheme, calculate the stress and deformation of each design scheme under actual loads, and evaluate whether it meets the requirements of structural stability;

[0060] Step 6: After finite element analysis, an optimization algorithm is used to search the design parameter space, generate a new design solution and evaluate the fitness, and through continuous screening, crossover, and mutation, find the optimal solution;

[0061] Step 7: According to the multiple solutions generated by the optimization algorithm, screen the best design solutions that meet aesthetics, structural stability, and construction feasibility, and based on the Pareto optimal solution set, designers can determine the final solution according to actual needs;

[0062] Step 8: Display the determined design solution through a data visualization tool to help designers evaluate the solution.

[0063] Benefits of Step 1: By defining the parameters of the prefabricated substation facade design and establishing the design parameter space, it lays the foundation for subsequent design optimization. The construction of the design parameter space ensures the comprehensiveness of the design;

[0064] Benefits of Step 2: By setting appropriate value ranges for the design variables, it ensures that the design solution is optimized within the scope of physical and engineering feasibility. The setting of geometric constraint conditions helps to avoid design solutions that do not meet the actual production and construction requirements in the design, ensuring the feasibility of the design;

[0065] Benefits of Step 3: By incorporating aesthetics, structural stability, and construction feasibility into the objective function and weighted optimization, it can comprehensively consider the balance of multiple design objectives. The multi-objective optimization method can ensure the best balance among aesthetics, practicality, and construction feasibility in the design solution, avoiding the deviation caused by single-objective optimization in traditional design methods;

[0066] Benefits of Step 4: Considering various constraint conditions of geometry, structure, and construction, it effectively ensures that the design solution meets the design objectives and is operable during the actual production and construction process. By setting clear constraint conditions, it avoids situations where the design solution cannot be realized and is infeasible in actual applications;

[0067] Benefits of Step 5: Using finite element analysis to conduct mechanical simulation on the design solution can accurately evaluate the stress and deformation of the design under actual loads, ensuring structural stability. This step can timely detect potential problems in the design solution and avoid safety hazards during actual construction;

[0068] Benefits of Step 6: By using an optimization algorithm to search the design space, quickly find the optimal solution that meets the design requirements. The optimization algorithm can automatically adjust the design variables, and through fitness evaluation, select the most suitable design solution, improving design efficiency and accuracy. The screening, crossover, and mutation operations of the genetic algorithm can help the algorithm quickly find the global optimal solution;

[0069] Benefits of Step 7: Through the generation and screening of multiple solutions and in combination with the Pareto optimal solution set, the most suitable design solution can be flexibly selected according to different priorities of design objectives. Designers can choose the best solution according to actual needs, improving the pertinence and operability of the design;

[0070] Benefits of Step 8: By using a data visualization tool to display the final design solution, designers can intuitively evaluate the design effect and optimize the design based on the visual feedback. The visual display improves the efficiency of solution evaluation, helps designers understand the actual effect of the design, and ensures the feasibility and optimization effect of the solution.

[0071] The construction of the design parameter space includes preliminary preprocessing before optimization for each design variable. The preliminary preprocessing before optimization includes regularizing the dimensions of components, the spacing between components, the elevation window opening ratio, the material properties of components, and the connection methods of components. The process of regularization includes normalizing the design variable x i to make its value range become the standard interval:

[0072]

[0073] i = 1, 2, …, n,

[0074] where, x i is the original design variable, l i and u i are the minimum and maximum values of x i respectively, x′ i is the normalized design variable, and n represents the number of design variables.

[0075] By regularizing and normalizing the design variables, the value range of the design variables can be converted into a unified standard interval, ensuring that different design variables have the same scale during the optimization process, enabling the optimization algorithm to efficiently process the high-dimensional parameter space;

[0076] Regularization makes the values of the design variables fall within the same standard interval, reducing the difference in the algorithm convergence speed caused by different value ranges. The normalized design variables can improve the search efficiency of the optimization algorithm, reduce the algorithm complexity, and enhance the speed and accuracy of the optimization process;

[0077] Normalization makes the variation ranges of the design variables numerically tend to be consistent, facilitating effective comparison between design variables. The unified scale of different design parameters enables the system to fairly evaluate the influence of each variable on the final design during optimization.

[0078] The objective function is comprehensively optimized by the weighted average method. The aesthetic part f 1 (x) of the objective function is defined as:

[0079]

[0080] Among them, f 1 (x) is the aesthetic part of the objective function, and α i is the weight coefficient of the influence of the design variable x i on the aesthetic objective. x i is the design variable, i represents the index of the i-th design variable currently being calculated, and n represents the number of design variables.

[0081] The weighted average method is used to optimize the objective function, comprehensively considering multiple objectives of aesthetics, structural stability, and construction feasibility. By allocating the influence of the weight coefficient on each objective, designers can flexibly adjust the priority between objectives according to the needs of the project, ensuring that the final design can achieve the best balance among multiple objectives;

[0082] The aesthetic objective is incorporated into the weighted average objective function, and the influence of the design variable on the aesthetic objective is quantified, clearly defining the specific requirements of design aesthetics, standardizing the aesthetic evaluation, and providing a clear quantitative basis for design optimization;

[0083] The introduction of the weight coefficient makes the design optimization process more flexible. Designers can dynamically adjust the weight of the aesthetic objective according to different design stages or project requirements, effectively controlling the aesthetic priority in the design process;

[0084] Through the weight coefficient and the weighted average method, the optimization process can clarify different focuses of the aesthetic objective in a numerical and quantitative way, reduce subjective judgments in the design process, and ensure the balance and fairness among objectives in the optimization process.

[0085] The structural stability objective function f 2 (x) is based on finite element analysis. By calculating the stress and deformation of the design scheme under external forces, the calculation methods of stress and deformation are as follows:

[0086]

[0087] Among them, f 2 (x) is the structural stability objective function, σ i (x) is the stress of the i-th node, and δ i (x) is the deformation of the i-th node. N is the total number of nodes, and i represents the index of the i-th design variable currently being calculated.

[0088] By calculating stress and deformation based on finite element analysis, the structural stability of the design scheme under actual loads can be accurately evaluated. Finite element analysis can conduct detailed mechanical simulations of the structure, simulate the stress distribution and deformation under external forces, provide reliable data support, and ensure that the design scheme has sufficient structural safety during construction and long-term use;

[0089] By defining the structural stability objective function and incorporating stress and deformation into the calculation, the stability of the structure is quantified, making the performance of the structure more controllable. Designers can clearly see the potential weak links in the design, conduct targeted optimizations, and improve the overall performance of the structure;

[0090] Using the calculation results of stress and deformation as part of the structural stability objective function enables the optimization process to accurately reflect the performance of the design scheme under actual loads, ensuring that the structural optimization is limited to the aesthetics of the form and spatial layout, and fully considering the bearing capacity, stability, and safety of the structure;

[0091] By calculating the stress and deformation of each node, the safety of the design scheme is systematically checked during the design stage, rather than relying on traditional manual checks and empirical judgments. The stress and deformation values of each node can be accurately calculated and continuously adjusted during the design optimization, effectively avoiding potential safety hazards during the design process;

[0092] Combining the optimization with the finite element analysis calculation results enables the design to quickly screen out the most suitable scheme that meets the stability requirements in the multi-parameter space. By automatically calculating stress and deformation, the design optimization efficiency is improved, while reducing human intervention and errors in the design.

[0093] The construction feasibility objective function f 3 (x) is defined in the following form:

[0094]

[0095] where f 3 (x) is the construction feasibility objective function, β i is the construction difficulty coefficient of the i-th component, x i is the design variable, i represents the index of the i-th design variable currently being calculated, and n represents the number of design variables.

[0096] Taking the construction difficulty coefficient as part of the construction feasibility objective function helps to quantify the construction difficulty of each component in the design. By assigning construction difficulty coefficients to each component, the difficulties caused by the components during construction can be clearly identified, enabling the design team to make adjustments during the optimization stage to ensure the effective allocation of resources and time during the construction process;

[0097] The introduction of the construction feasibility objective function enables the consideration of aesthetics and structural stability during the design process, and fully takes into account the construction feasibility. By quantitatively analyzing the construction difficulty of each component, the actual feasibility of the design can be accurately evaluated, ensuring the smooth implementation of the design scheme in actual construction;

[0098] By calculating the construction difficulty coefficient, the construction resource requirements of each component in the design can be effectively evaluated, which helps to optimize the resource allocation, reasonably arrange the construction sequence, reduce the construction cost, and improve the overall construction efficiency;

[0099] The construction feasibility objective function helps to identify construction problems during the design stage, avoiding unexpected difficulties and delays in the subsequent construction process. By pre-evaluating the construction difficulty of each component, designers can make corresponding adjustments to reduce the risks of design changes and construction stoppages;

[0100] Incorporating construction feasibility into the objective function for optimization makes the design optimization depend on traditional structural and aesthetic goals, taking into account the actual requirements of the construction stage. Through comprehensive consideration of multiple aspects, the optimal design scheme that meets the actual construction conditions can be quickly screened out, enhancing the overall efficiency of the design process.

[0101] The optimization algorithm uses the genetic algorithm to search in the high-dimensional design space, and the genetic algorithm is implemented through the following steps:

[0102] a. Initialize the population: Generate a set of design variables x 1 , x 2 , …, x p , where p represents the population size;

[0103] b. Fitness evaluation: Calculate the fitness F(x i ) of each design scheme x i through the objective function F(x);

[0104] c. Selection, crossover, and mutation: Perform selection, crossover, and mutation operations on individuals according to the fitness;

[0105] d. Constraint handling: For solutions that do not meet the constraint conditions, use the penalty function method to punish the solutions that do not meet the constraints.

[0106] During the optimization process of the genetic algorithm, the generated multi-solution set is screened through the Pareto optimal solution set, and the Pareto optimal solution set is calculated through the following formula:

[0107] F(x) = w 1 f 1 (x) + w 2 f 2 (x) + w 3 f 3(x),

[0108] where f 1 (x) is the aesthetic part of the objective function, f 2 (x) is the structural stability objective function, f 3 (x) is the construction feasibility objective function, and w 1 , w 2 and w 3 are the weight coefficients of the objective function.

[0109] The genetic algorithm is a powerful global optimization method that can conduct a comprehensive search in the high-dimensional design space and avoid falling into local optimal solutions. Through the evolution of the population, the genetic algorithm can explore the design space and approach the optimal solution. It is suitable for complex design problems, especially in the context of the facade design of prefabricated substations involving multiple objectives and multiple constraints, and can ensure finding the global optimal solution;

[0110] The genetic algorithm realizes multi-objective optimization through the Pareto optimal solution set and can consider multiple objectives of aesthetics, structural stability, and construction feasibility simultaneously. The Pareto optimal solution set can provide solutions that achieve the best balance among multiple objectives rather than a single optimal solution, allowing designers to select the most suitable solution according to specific requirements;

[0111] The selection, crossover, and mutation operations of the genetic algorithm have strong flexibility and adaptability. According to different design objectives, the genetic algorithm can dynamically adjust the search path, optimize the design variables, and flexibly adapt to various changes and different constraint conditions in the design process. Especially in the scenario of multi-objective optimization, it can adjust the weights of the objective functions according to the optimization process and precisely meet the design requirements;

[0112] By introducing the Pareto optimal solution set and weighted objective functions, the genetic algorithm can be flexibly adjusted according to the weight coefficients of different objectives to achieve multi-objective optimization. Designers can adjust the weight coefficients to achieve the best balance among aesthetics, structural stability, and construction feasibility according to actual needs, ensuring the coordination and adaptability of the design scheme among multiple objectives.

[0113] The optimization process includes performing fitness evaluation on each design variable x i , and the fitness evaluation formula is: A(x i ) = α 1 f 1 (x i ) + α 2 f 2 (x i ) + α 3 f 3 (x i ),

[0114] where A(x i) is the design variable x i 's fitness, f 1 (x) is the aesthetic part of the objective function, f 2 (x) is the structural stability objective function, f 3 (x) is the construction feasibility objective function, α 1 、α 2 and α 3 are the weight coefficients.

[0115] During the optimization process, a dynamic adjustment mechanism is introduced to dynamically adjust the weight coefficients of the objective function according to the performance of the intermediate solutions in the optimization process. The adjustment formula is:

[0116]

[0117] Among them, and are the original weight coefficients of the objective function, and are the weight coefficients after dynamic adjustment, Δw 1 、Δw 2 and Δw 3 are the adjustment factors based on the fitness evaluation results of the design scheme in the optimization process.

[0118] By introducing the dynamic adjustment mechanism, the present invention can flexibly adjust the weight coefficients of the objective function according to the performance of the intermediate solutions in the optimization process, thereby improving the flexibility, efficiency, and accuracy of the optimization process. Dynamically adjusting the weight coefficients makes the design optimization conform to the actual requirements, achieves a reasonable balance among multiple objectives, and avoids the problem of a single objective dominating in traditional methods. Through the dynamic adjustment mechanism, the design scheme can more accurately meet various design requirements, improving the overall effect and feasibility of the design optimization.

[0119] The optimized design scheme is simulated and displayed through virtual reality technology. The steps of virtual reality simulation display include:

[0120] a. Convert the optimized design scheme into a three-dimensional model and visualize it using virtual reality technology;

[0121] b. Simulate the assembly operation in the virtual reality environment to help designers evaluate the feasibility and construction difficulty of the design scheme in actual construction;

[0122] c. Adjust the design scheme according to the virtual reality simulation results.

[0123] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A parametric design generation method for assembled substation facade based on BIM, characterized in that: include: Step 1: Construct a design parameter space, first define the parameters of the facade design of the prefabricated substation, the parameters include the size of the components, the spacing between the components, the proportion of the facade openings, the material properties of the components, and the connection method of the components; Step 2: setting a corresponding value range for each design variable in the design parameter space to meet geometric constraints; Step 3: Based on the design parameter space determined in the above steps, an objective function is established, and multi-objective optimization is achieved through the objective function. The objective function includes aesthetics, structural stability and construction feasibility. The weight coefficient of the objective function is set according to the design requirements, and the objective function is combined with the weight coefficient for weighted optimization; Step 4: During the optimization process, the corresponding constraints are considered according to the values ​​of the design variables in the design parameter space. The constraints include geometric constraints, structural stability constraints and construction constraints. By setting the constraints, the generated design scheme meets the actual requirements of the production, transportation, hoisting and other operations of the prefabricated components, and can maintain the stability of the structure under the action of external forces. Step 5: After the optimization step is completed, use finite element analysis to perform mechanical simulation on the design scheme, calculate the stress and deformation of each design scheme under actual load, and evaluate whether it meets the requirements of structural stability; Step 6: After the finite element analysis, an optimization algorithm is used to search the design parameter space, generate new design solutions and evaluate the fitness, and find the optimal solution through continuous screening, crossover and mutation; Step 7: Based on the multiple solutions generated by the optimization algorithm, the best design solution that meets aesthetics, structural stability and construction feasibility is selected, and based on the Pareto optimal solution set, the designer can determine the final solution based on actual needs; Step 8: Display the determined design plan through data visualization tools to help designers evaluate the plan.

2. According to a BIM-based parametric design generation method for assembled substation facades according to claim 1, it is characterized in that: The construction of the design parameter space includes preliminary optimization processing of each design variable. The preliminary optimization pre-processing includes regularization processing of the size of the component, the spacing between the components, the proportion of the facade window, the material properties of the component and the connection method of the component. The regularization process includes the design variable x i Normalize so that the value range becomes the standard range: i=1,2,…,n, Among them, x i is the original design variable, l i and u i For x i The minimum and maximum values ​​of x′ i is the normalized design variable, and n is the number of design variables.

3. According to a BIM-based parametric design generation method for assembled substation facades according to claim 1, it is characterized in that: The objective function is comprehensively optimized by weighted average method, and the aesthetic part f1(x) of the objective function is defined as: Among them, f1(x) is the aesthetic part of the objective function, α i is the design variable x i The weight coefficient of the impact on aesthetic goals, x i is the design variable, i represents the index of the i-th design variable currently being calculated, and n represents the number of design variables.

4. According to a BIM-based parametric design generation method for assembled substation facades according to claim 1, it is characterized in that: The structural stability objective function f2(x) is based on finite element analysis, by calculating the stress and deformation of the design under the action of external forces. The calculation formula of the stress and deformation is: Among them, f2(x) is the structural stability objective function, σ i (x) is the stress at the i-th node, δ i (x) is the deformation of the i-th node, N is the total number of nodes, and i represents the index of the i-th design variable currently being calculated.

5. According to the BIM-based parametric design generation method for assembled substation facades according to claim 1, it is characterized in that: The construction feasibility objective function f3(x) is defined as follows: Among them, f3(x) is the construction feasibility objective function, β i is the construction difficulty coefficient of the i-th component, x i is the design variable, i represents the index of the i-th design variable currently being calculated, and n represents the number of design variables.

6. The method for generating parametric design of assembled substation facade based on BIM according to claim 1 is characterized in that: The optimization algorithm uses a genetic algorithm to search a high-dimensional design space, and the genetic algorithm is implemented by the following steps: a. Initialize the population: Generate a set of design variables x1, x2, …, x p , where p represents the population size; b. Fitness evaluation: Calculate each design solution x i The fitness F(x i ), evaluated by the objective function F(x); c. Screening, crossover and mutation: Screen, crossover and mutation operations are performed on individuals according to their fitness; d. Constraint processing: For solutions that do not meet the constraints, the penalty function method is used to punish the solutions that do not meet the constraints.

7. According to the BIM-based parametric design generation method for assembled substation facades according to claim 6, it is characterized in that: During the optimization process of the genetic algorithm, the generated multiple solution sets are screened by the Pareto optimal solution set, and the Pareto optimal solution set is calculated by the following formula: F(x)=w1f1(x)+w2f2(x)+w3f3(x), Among them, f1(x) is the aesthetic part of the objective function, f2(x) is the structural stability objective function, f3(x) is the construction feasibility objective function, and w1, w2 and w3 are the weight coefficients of the objective function.

8. The method for generating parametric design of assembled substation facade based on BIM according to claim 7 is characterized in that: The optimization process includes the following steps: i Perform fitness evaluation, the fitness evaluation formula is: A(x i )=α1f1(x i )+α2f2(x i )+α3f3(x i ), Among them, A(x i ) is the design variable x i fitness, f1(x) is the aesthetic part of the objective function, f2(x) is the structural stability objective function, f3(x) is the construction feasibility objective function, and α1, α2 and α3 are weight coefficients.

9. The method for generating parametric design of assembled substation facade based on BIM according to claim 8 is characterized in that: The dynamic adjustment mechanism is introduced in the optimization process to dynamically adjust the weight coefficient of the objective function according to the performance of the intermediate solution in the optimization process. The adjustment formula is: in, and is the original weight coefficient of the objective function, and is the dynamically adjusted weight coefficient, and Δw1, Δw2 and Δw3 are adjustment factors based on the fitness evaluation results of the design scheme in the optimization process.

10. The method for generating parametric design of assembled substation facade based on BIM according to claim 9 is characterized in that: The optimized design scheme is simulated and displayed by virtual reality technology, and the virtual reality simulation display step includes: a. Convert the optimized design into a 3D model and visualize it using virtual reality technology; b. Simulate assembly operations in a virtual reality environment to help designers evaluate the feasibility and construction difficulty of design solutions in actual construction; c. Adjust the design plan based on the virtual reality simulation results.

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