Industrial steel structure system numerical simulation and optimization design system

Abstract

The invention relates to the technical field of steel structure optimization design, and discloses an industrial steel structure system numerical simulation and optimization design system which integrates numerical simulation and optimization design functions. The system comprises a numerical simulation unit used for constructing a steel structure numerical simulation model and simulating mechanical behaviors under different working conditions; the optimization design unit is used for constructing an optimization design model by adopting an intelligent optimization algorithm and automatically searching a design space to find an optimal scheme; the data input unit is responsible for collecting design parameters, load conditions and boundary conditions and transmitting the design parameters, the load conditions and the boundary conditions into the model; and the result analysis unit is used for analyzing a model output result and generating a steel structure performance evaluation report and an optimization design scheme. According to the method, the efficiency and accuracy of steel structure design are improved, the workload of engineers is relieved, optimization of the design scheme is achieved through an intelligent means, and the method is suitable for design of steel structure engineering such as large buildings, bridges and towers.

Description

Technical Field

[0001] The invention relates to the technical field of steel structure optimization design, and in particular to an industrialized steel structure system numerical simulation and optimization design system. Background Art

[0002] With the rapid development of the construction industry and the advancement of industrialization, steel structures have been widely used in large buildings, bridges, towers and other projects due to their high strength, light weight, fast construction speed and recyclability. However, the design process of steel structures is complex and involves multidisciplinary cross-cutting, which requires comprehensive consideration of the safety, economy, constructability and other aspects of the structure.

[0003] In the early stages of steel structure design, engineers usually need to manually calculate the stress conditions of the structure according to the design specifications, and make repeated adjustments and optimizations. This method is not only inefficient, but also difficult to fully consider the structural mechanical behavior under various working conditions, such as the impact of dynamic loads such as earthquakes and wind loads on the structure. With the advancement of computer technology and numerical analysis methods, numerical simulation technology has gradually become an important means in steel structure design. By constructing a numerical simulation model, the mechanical behavior of steel structures under different working conditions can be simulated more accurately, providing a reliable basis for design.

[0004] Numerical simulation technology alone is not enough to achieve the optimization of steel structure design. In the actual design process, engineers also need to adjust and optimize the design scheme based on the results of numerical simulation, which often requires a lot of trial calculations and empirical judgment. In order to further improve the efficiency and accuracy of steel structure design, it is necessary to introduce intelligent optimization algorithms into the design process. Intelligent optimization algorithms can automatically search the design space and find the optimal design scheme, thereby greatly reducing the workload of engineers and improving the design quality. Therefore, there is an urgent need for an industrialized steel structure system design system that integrates numerical simulation and optimization design. Summary of the invention

[0005] The purpose of the present invention is to provide an industrialized steel structure system numerical simulation and optimization design system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a numerical simulation and optimization design system for an industrialized steel structure system, the system comprising:

[0007] Numerical simulation unit, which constructs a numerical simulation model of steel structure to simulate the mechanical behavior of steel structure under different working conditions;

[0008] Optimization design unit, which builds a steel structure optimization design model based on intelligent optimization algorithm to find the optimal steel structure design solution;

[0009] Data input unit, which collects steel structure design parameters, load conditions and boundary conditions, and transfers the collected data to the numerical simulation model and the optimization design model to obtain the output results of the model;

[0010] The result analysis unit defines the result analysis algorithm, analyzes the model output results, and obtains the steel structure performance evaluation report and optimization design solution.

[0011] Preferably, the steel structure numerical simulation model is constructed using a finite element analysis method, including:

[0012] Input layer: receiving steel structure design parameters, load conditions and boundary conditions;

[0013] Meshing layer: meshing the steel structure and generating finite element mesh;

[0014] Solution layer: Calculate the stress, strain and displacement of the steel structure under load by solving the finite element equation;

[0015] Output layer: Output the simulation results of the mechanical behavior of the steel structure, including stress distribution, deformation and ultimate bearing capacity.

[0016] Preferably, the specific algorithm used to solve the finite element equation is:

[0017] [K T ]{Δu}={F ext}-{F int}

[0018] Among them, [K T ] is the tangent stiffness matrix, {Δu} is the displacement increment, {F ext} is the external load vector, {F int} is the internal force vector.

[0019] Preferably, the steel structure optimization design model is constructed using a genetic algorithm, comprising:

[0020] Input layer: receives the output results of the numerical simulation model as the performance evaluation of the initial design solution;

[0021] Initialization layer: Generate the initial population, each individual represents a possible design solution;

[0022] Selection layer: select excellent individuals for reproduction according to the fitness function;

[0023] Crossover layer: Perform crossover operations on selected individuals to generate new design solutions;

[0024] Mutation layer: Perform mutation operations on new design solutions to increase design diversity;

[0025] Output layer: Output the optimal design solution to meet the preset performance standards and constraints.

[0026] Preferably, the fitness function is defined as:

[0027]

[0028] Among them, C(x) is the construction cost, M(x) is the material consumption, D(x) is the structural deformation, and W1, W2 and W3 are weight coefficients.

[0029] Preferably, the steps of training the steel structure optimization design model include:

[0030] Use historical steel structure design solutions and corresponding performance evaluation data as training sets;

[0031] Initialize the parameters of the genetic algorithm, including population size, crossover probability, and mutation probability;

[0032] Use the training set to train the genetic algorithm and optimize the design solution by maximizing the fitness function;

[0033] Use the validation set to validate the genetic algorithm and evaluate the optimization effect and computational efficiency of the model;

[0034] When the model performance reaches the preset standard, the model parameters are saved and the trained steel structure optimization design model is obtained.

[0035] Preferably, the data input unit also includes a data verification module, which is responsible for consistency verification and error correction of the collected steel structure design parameters, load conditions and boundary conditions.

[0036] Preferably, in the result analysis unit, the implementation steps of the result analysis algorithm include:

[0037] Step 1: Define the stress distribution output by the numerical simulation model as σ(x, y, z), the deformation as u(x, y, z), and the ultimate bearing capacity as P ult ;

[0038] Step 2: Perform maximum value analysis on σ(x, y, z) to identify stress concentration areas;

[0039] Step 3: Perform displacement analysis on u(x,y,z) to evaluate the overall deformation of the structure;

[0040] Step 4: Combine P ult , conduct structural safety assessment to determine whether the structure meets the design requirements;

[0041] Step 5: Combine the stress concentration area, overall deformation and structural safety assessment results to form a steel structure performance assessment report;

[0042] Step 6: Combine the performance evaluation report with the optimization design plan to form the final steel structure design and optimization report.

[0043] Preferably, the structural safety assessment adopts the allowable stress method, and the specific formula is:

[0044] σ max ≤[σ]

[0045] Among them, σ max is the maximum stress value, and [σ] is the allowable stress value.

[0046] Preferably, the result analysis unit also includes a visualization module, which is responsible for displaying the steel structure performance evaluation report and the optimization design scheme in the form of graphics, tables and three-dimensional models.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] This industrialized steel structure system numerical simulation and optimization design system integrates two major functions: numerical simulation and optimization design, realizing the automation and intelligence of the design process. The numerical simulation unit quickly simulates the mechanical behavior of steel structures under different working conditions, avoiding the tedious and time-consuming traditional manual calculations, and significantly improving the design efficiency. At the same time, the optimization design unit uses intelligent optimization algorithms to automatically search the design space and find the optimal design solution, further shortening the design cycle. The system uses advanced numerical simulation technology to accurately simulate the mechanical response of steel structures under dynamic loads such as earthquakes and wind loads, providing a more reliable basis for design. Based on the intelligent optimization algorithm, the optimization design unit can comprehensively consider multiple aspects such as the safety, economy and constructability of the structure, ensure the comprehensiveness and optimality of the design solution, and greatly enhance the accuracy of the design.

[0049] In the traditional steel structure design process, engineers need to manually calculate, adjust and optimize, which is a huge workload. This system greatly reduces the workload of engineers through automated and intelligent design processes. Engineers only need to input design parameters, load conditions and boundary conditions, and the system can automatically complete numerical simulation and optimization design, which improves work efficiency and reduces labor intensity. The system integrates intelligent optimization algorithms, which can automatically explore various possibilities in the design space and provide engineers with more innovative design solutions. The innovativeness of this design method helps to promote technological progress in the field of steel structure design and promote the development and application of steel structure buildings. Through the numerical simulation and optimization design of the system, the performance of steel structures can be predicted and optimized more accurately. This helps to ensure the safety and reliability of steel structures in actual use, improve the overall structural performance, extend the service life, and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a working principle diagram of the numerical simulation and optimization design system for the industrialized steel structure system of the present invention;

[0051] Figure 2 Flowchart for constructing steel structure optimization design model using genetic algorithm;

[0052] Figure 3 This is a diagram of the implementation steps of the result analysis algorithm in the result analysis unit. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1-3 The present invention provides a technical solution: an industrialized steel structure system numerical simulation and optimization design system, the system comprising:

[0055] Numerical simulation unit: responsible for constructing a numerical simulation model of the steel structure. This model is based on the finite element method or similar numerical analysis methods, and can simulate the mechanical behavior of the steel structure under different working conditions (such as static load, dynamic load, earthquake, wind load, etc.). In specific implementation, a geometric model is established according to the design drawings and specifications of the steel structure, and the grid is divided to discretize the structure. According to the physical properties and mechanical parameters of the material (such as elastic modulus, yield strength, etc.), the corresponding material properties are assigned to the model. Load conditions and boundary conditions, such as fixed supports, concentrated forces, distributed forces, etc., are applied to simulate the actual working environment. Finally, the model is solved by a numerical solver to obtain the mechanical responses of the steel structure such as stress, strain, and displacement under different working conditions.

[0056] Optimization design unit: Construct a steel structure optimization design model based on intelligent optimization algorithm. Use intelligent optimization algorithm to optimize the design variables and obtain the optimal design solution that meets the objective function and constraint conditions.

[0057] Data input unit: responsible for collecting steel structure design parameters, load conditions and boundary conditions, and transferring the collected data to the numerical simulation model and the optimization design model. In specific implementation, design parameters such as the geometric dimensions of components, material types, etc. can be obtained through a graphical user interface (GUI) or file import. Load conditions and boundary conditions can be obtained through user input or preset load libraries and boundary condition libraries. After preprocessing, the collected data is passed to the numerical simulation unit and the optimization design unit for subsequent processing.

[0058] Result analysis unit: Define the result analysis algorithm and analyze the output results of the numerical simulation model and the optimization design model. In the specific implementation, the numerical simulation results are first processed, such as extracting stress cloud diagrams, displacement curves, etc., to evaluate the performance of the steel structure. Then, the optimization design results are analyzed, such as comparing the advantages and disadvantages of different design schemes and selecting the best one. Finally, a steel structure performance evaluation report and optimization design scheme are generated for engineers to refer to and make decisions.

[0059] The present invention will be further described below in conjunction with Examples 1 to 4:

[0060] Embodiment 1:

[0061] The numerical simulation unit is responsible for constructing a numerical simulation model of a steel structure, which includes the following levels:

[0062] Input layer: The input layer is used to receive the steel structure design parameters, load conditions and boundary conditions. The design parameters include the geometric dimensions of the steel structure, material properties (such as elastic modulus, Poisson's ratio, yield strength, etc.), and the cross-sectional shape of the components. The load conditions include various external loads on the structure, such as dead load, live load, wind load, seismic load, etc. The boundary conditions define the constraints of the structure, such as fixed supports, hinged supports, etc.

[0063] For a high-rise steel structure building, the input layer will receive design parameters such as the number of floors, the height of each floor, the cross-sectional dimensions of columns and beams, and the type of steel. At the same time, it will also receive load conditions such as wind loads and earthquake loads, as well as boundary conditions such as the way the building foundation is fixed.

[0064] Meshing layer: The meshing layer meshes the steel structure and generates finite element meshes. Meshing is one of the key steps in finite element analysis, which determines the accuracy and efficiency of the analysis. When meshing, it is necessary to select the appropriate unit type (such as bar unit, beam unit, shell unit, solid unit, etc.) and mesh density according to the geometric shape and force characteristics of the structure.

[0065] For the high-rise steel structure building mentioned above, the meshing layer may select beam elements to simulate the columns and beams of the building, and adjust the mesh density according to the stress characteristics of the structure to ensure that there is sufficient mesh density in key stress-bearing locations to capture stress changes.

[0066] Solution layer: The solution layer calculates the stress, strain and displacement of the steel structure under load by solving the finite element equation. The finite element equation is established based on the principle of virtual work or the principle of minimum potential energy, which describes the mechanical behavior of the structure under load. In this implementation, the specific algorithm used to solve the finite element equation is:

[0067] [KT ]{Δu}={F ext}-{F int}

[0068] Among them, [K T ] is the tangent stiffness matrix, which reflects the stiffness of the structure under the current displacement state; {Δu} is the displacement increment, that is, the displacement change of the structure under the load; {F ext} is the external load vector, which represents the external load on the structure; {F int} is the internal force vector, which represents the internal force generated by the structure due to deformation. In the solution process, the initial tangent stiffness matrix and external load vector of the structure are first calculated, and then an initial displacement increment is assumed, and the displacement increment and internal force vector are continuously updated through iterative calculation until the convergence condition is met.

[0069] For the high-rise steel structure building mentioned above, the solution layer will use this algorithm to solve the stress, strain and displacement of the building under wind load and earthquake load. Through iterative calculation, the mechanical behavior simulation results such as the displacement of each floor of the building, the stress distribution of columns and beams, etc. can be obtained.

[0070] Output layer: The output layer is used to output the simulation results of the mechanical behavior of steel structures, including stress distribution, deformation, and ultimate bearing capacity, etc. These results can be presented in the form of graphics, tables, or texts to facilitate engineers' analysis and decision-making.

[0071] For the above high-rise steel structure building, the output layer can output the stress cloud map, displacement curve and ultimate bearing capacity report of the building under wind load and earthquake load. Engineers can evaluate the performance of the structure based on these results and adjust and optimize the design plan.

[0072] Embodiment 2:

[0073] The steel structure optimization design model is constructed using a genetic algorithm, which includes the following levels:

[0074] Input layer: The input layer receives the output results of the numerical simulation model as the performance evaluation of the initial design scheme. These output results include mechanical behavior simulation results such as stress distribution, deformation, ultimate bearing capacity, and possible economic indicators such as cost and material usage. These performance evaluation data will serve as the basis for genetic algorithm optimization. For a steel structure bridge design to be optimized, the input layer will receive the stress and deformation of the bridge under various loads output by the numerical simulation model, as well as the initial cost and material usage of the bridge.

[0075] Initialization layer: The initialization layer generates the initial population, each individual represents a possible design scheme. These design schemes can be a combination of design parameters such as the cross-sectional size, material type, and connection method of the bridge. The initialization layer will randomly generate a certain number of individuals as the initial population based on the scope and possible values ​​of the design space. For the above steel structure bridge design scheme, the initialization layer may generate a series of bridge design schemes with different cross-sectional sizes, material types, and connection methods as the initial population.

[0076] Selection layer: The selection layer selects excellent individuals for reproduction according to the fitness function. The fitness function is a criterion for evaluating the quality of individuals, which comprehensively considers multiple factors such as cost, material usage, structural deformation, etc. In this embodiment, the fitness function is defined as:

[0077]

[0078] Among them, C(x) is the construction cost, M(x) is the material consumption, D(x) is the structural deformation, and W1, W2 and W3 are weight coefficients. The selection layer will select individuals with higher fitness for reproduction according to the value of the fitness function to generate new design solutions.

[0079] Crossover layer: The crossover layer performs crossover operations on selected individuals to generate new designs. The crossover operation is one of the core steps in the genetic algorithm. It generates individuals with new characteristics by exchanging some genes of two individuals. The crossover layer selects a certain proportion of individuals for crossover operations based on the set crossover probability. For the above steel structure bridge design, the crossover layer may select two bridge designs with different cross-sectional sizes and connection methods, and generate a new bridge design by exchanging some of their design parameters.

[0080] Mutation layer: The mutation layer performs mutation operations on new designs to increase design diversity. Mutation operation is another core step in genetic algorithms. It generates individuals with new characteristics by randomly changing certain genes of individuals. The mutation layer selects a certain proportion of individuals for mutation operations based on the set mutation probability. For the above steel structure bridge design, the mutation layer may randomly change the cross-sectional size or material type of a bridge design to generate a new bridge design with different characteristics.

[0081] Output layer: The output layer outputs the optimal design solution that meets the preset performance standards and constraints. After multiple iterations of genetic algorithm optimization, the output layer will output a design solution with the highest fitness and the best performance. This design solution will be used as the final steel structure design solution for actual engineering construction.

[0082] In order to obtain a trained steel structure optimization design model, the following steps need to be followed for training:

[0083] Step 1: Use historical steel structure design schemes and corresponding performance evaluation data as training sets. These historical data can come from actual engineering projects or numerical simulation results, which contain a large number of different design schemes and their performance evaluation information.

[0084] Step 2: Initialize the parameters of the genetic algorithm, including population size, crossover probability, and mutation probability, etc. These parameters will affect the optimization effect and computational efficiency of the genetic algorithm and need to be set and adjusted according to actual conditions.

[0085] Step 3: Use the training set to train the genetic algorithm. During the training process, the genetic algorithm will select excellent individuals for reproduction and mutation operations based on the fitness function, and continuously optimize the design solution. Through multiple iterative training, the genetic algorithm will gradually converge to an optimal design solution.

[0086] Step 4: Use the validation set to validate the genetic algorithm. The validation set is a data set independent of the training set, which is used to evaluate the optimization effect and computational efficiency of the genetic algorithm. By comparing the predicted results on the validation set with the actual results, it can be determined whether the performance of the genetic algorithm meets the preset standards.

[0087] Step 5: When the model performance reaches the preset standard, save the model parameters and obtain the trained steel structure optimization design model. This model can be used in actual steel structure design optimization work to improve design efficiency and quality.

[0088] Embodiment 3:

[0089] In the result analysis unit, the specific implementation steps of the result analysis algorithm include:

[0090] Step 1: Define the output volume

[0091] The stress distribution output by the numerical simulation model is defined as σ(x,y,z), where (x,y,z) represents any point in space and σ represents the stress value at that point; the deformation output by the numerical simulation model is defined as u(x,y,z), where (x,y,z) represents any point in space and u represents the displacement value at that point; the ultimate bearing capacity output by the numerical simulation model is defined as P_lim, which represents the maximum load that the structure can withstand before failure. For a steel structure bridge, the numerical simulation model will output the stress distribution σ(x,y,z), deformation u(x,y,z) and ultimate bearing capacity P_lim of the bridge under load.

[0092] Step 2: Stress Maximum Analysis

[0093] Perform maximum analysis on σ(x,y,z), that is, find the point or area with the maximum stress value in the entire structural space. Identify stress concentration areas, which are places in the structure with high stress values ​​and possible damage or failure. In the numerical simulation results of the bridge, by analyzing the stress distribution σ(x,y,z), stress concentration areas such as bridge supports and beam ends can be identified.

[0094] Step 3: Displacement Analysis

[0095] Perform displacement analysis on u(x,y,z) to evaluate the overall deformation of the structure. Calculate the maximum displacement, average displacement and other parameters of the structure to understand the degree of deformation of the structure under load. In the numerical simulation results of the bridge, by analyzing the deformation u(x,y,z), the maximum deflection, lateral displacement and other parameters of the bridge under load can be calculated.

[0096] Step 4: Structural safety assessment

[0097] Combined with the ultimate bearing capacity P_lim and the maximum stress analysis results, the structural safety assessment is carried out. The allowable stress method is used for assessment, and the specific formula is:

[0098] σ max ≤[σ]

[0099] Among them, σ max is the maximum stress value, and [σ] is the allowable stress value. If the formula is established, it means that the structure meets the design requirements and is safe; otherwise, it means that the structure does not meet the design requirements and needs to be optimized or reinforced. In the design of bridges, by comparing the maximum stress value and the allowable stress value, it can be judged whether the bridge meets the safety requirements. If the maximum stress value exceeds the allowable stress value, the bridge needs to be optimized or reinforced.

[0100] Step 5: Form a steel structure performance evaluation report

[0101] Combine the stress concentration area, overall deformation and structural safety assessment results to form a steel structure performance assessment report. The report should list in detail the location of the stress concentration area, the maximum stress value, the overall deformation, the ultimate bearing capacity and the structural safety assessment results. For the numerical simulation results of the bridge, a detailed performance assessment report can be formed, which includes the stress concentration area, maximum deflection, ultimate bearing capacity and safety assessment results of the bridge.

[0102] Step 6: Form the final steel structure design and optimization report

[0103] Combine the performance evaluation report with the optimized design plan to form the final steel structure design and optimization report. The report should include the original design plan, performance evaluation results, optimized design plan, and optimized performance prediction. In the design process of the bridge, the performance evaluation report can be combined with the optimized design plan to form a final design and optimization report. The report describes in detail the original design plan, performance evaluation results, optimized design plan, and optimized performance prediction of the bridge, providing a strong basis for the construction of the bridge.

[0104] Embodiment 4:

[0105] The data input unit is responsible for collecting input data such as steel structure design parameters, load conditions and boundary conditions. To ensure the accuracy and consistency of the input data, the data input unit also includes a data verification module. The implementation steps of the data verification module are as follows:

[0106] Consistency check: Perform consistency check on the collected steel structure design parameters to check whether the logical relationship between the parameters is reasonable. For example, the cross-sectional dimensions of the beam should match its material type, span and other parameters. Perform consistency check on the load conditions and boundary conditions to ensure that the position, direction and size of the load application are consistent with the boundary conditions and in line with the actual engineering situation.

[0107] Error correction: During the consistency check process, if data errors or inconsistencies are found, the data verification module will automatically correct the errors. For example, for unreasonable cross-sectional dimensions, the module will correct them according to relevant design specifications or empirical formulas. For errors in load conditions or boundary conditions, the module will prompt the user to make manual corrections, or automatically adjust according to preset rules.

[0108] For example, when inputting the design parameters of a steel structure bridge, the data verification module finds that the span of the bridge is not in a reasonable ratio to the cross-sectional height of the beam, which may lead to structural instability. The module will automatically adjust the cross-sectional height or prompt the user to make manual corrections to ensure the rationality of the design parameters.

[0109] The result analysis unit is responsible for analyzing and evaluating the numerical simulation results to form a steel structure performance evaluation report and an optimization design plan. To facilitate users to understand and apply these results, the result analysis unit also includes a visualization module. The implementation steps of the visualization module are as follows:

[0110] Graphical display: The key data in the steel structure performance evaluation report is displayed in the form of graphics, such as stress distribution diagram, deformation diagram, etc. These graphics can intuitively reflect the performance status of the structure and help users quickly identify problem areas.

[0111] Table display: Display the design parameters and performance indicators in the optimization design scheme in the form of a table. The table can clearly list various data, which is convenient for users to compare and analyze.

[0112] 3D model display: Using 3D modeling technology, the optimized steel structure is displayed in the form of a 3D model. Users can observe the details of the structure from multiple angles through operations such as rotation and zooming, and understand the effect of the optimization plan more intuitively.

[0113] For example, in the optimization design of a steel structure bridge, the visualization module will display the stress distribution diagram of the bridge, using different colors to represent different stress levels; at the same time, it will display a comparison table of bridge deformation before and after optimization, as well as the optimized three-dimensional model of the bridge. Through these visualization results, users can intuitively understand the performance status and optimization effect of the bridge, providing a basis for decision-making.

[0114] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Industrialized steel structure system numerical simulation and optimization design system, characterized by: The system comprises: Numerical simulation unit, which constructs a numerical simulation model of steel structure to simulate the mechanical behavior of steel structure under different working conditions; Optimization design unit, which builds a steel structure optimization design model based on intelligent optimization algorithm to find the optimal steel structure design solution; Data input unit, which collects steel structure design parameters, load conditions and boundary conditions, and transfers the collected data to the numerical simulation model and the optimization design model to obtain the output results of the model; The result analysis unit defines the result analysis algorithm, analyzes the model output results, and obtains the steel structure performance evaluation report and optimization design solution.

2. The industrialized steel structure system numerical simulation and optimization design system according to claim 1, characterized in that: The steel structure numerical simulation model is constructed using the finite element analysis method, including: Input layer: receiving steel structure design parameters, load conditions and boundary conditions; Meshing layer: meshing the steel structure and generating finite element mesh; Solution layer: Calculate the stress, strain and displacement of the steel structure under load by solving the finite element equation; Output layer: Output the simulation results of the mechanical behavior of the steel structure, including stress distribution, deformation and ultimate bearing capacity.

3. The industrialized steel structure system numerical simulation and optimization design system according to claim 2, characterized in that: The specific algorithm used to solve the finite element equation is: [K T ]{Δu}={F ext }-{F int } Among them, [K T ] is the tangent stiffness matrix, {Δu} is the displacement increment, {F ext } is the external load vector, {F int } is the internal force vector.

4. The industrialized steel structure system numerical simulation and optimization design system according to claim 1, characterized in that: The steel structure optimization design model is constructed using a genetic algorithm and includes: Input layer: receives the output results of the numerical simulation model as the performance evaluation of the initial design solution; Initialization layer: Generate the initial population, each individual represents a possible design solution; Selection layer: select excellent individuals for reproduction according to the fitness function; Crossover layer: Perform crossover operations on selected individuals to generate new design solutions; Mutation layer: Perform mutation operations on new design solutions to increase design diversity; Output layer: Output the optimal design solution to meet the preset performance standards and constraints.

5. The industrialized steel structure system numerical simulation and optimization design system according to claim 4, characterized in that: The fitness function is defined as: Among them, C(x) is the construction cost, M(x) is the material consumption, D(x) is the structural deformation, and W1, W2 and W3 are weight coefficients.

6. The industrialized steel structure system numerical simulation and optimization design system according to claim 5, characterized in that: The steps of training the steel structure optimization design model include: Use historical steel structure design solutions and corresponding performance evaluation data as training sets; Initialize the parameters of the genetic algorithm, including population size, crossover probability, and mutation probability; Use the training set to train the genetic algorithm and optimize the design solution by maximizing the fitness function; Use the validation set to validate the genetic algorithm and evaluate the optimization effect and computational efficiency of the model; When the model performance reaches the preset standard, the model parameters are saved and the trained steel structure optimization design model is obtained.

7. The industrialized steel structure system numerical simulation and optimization design system according to claim 1, characterized in that: The data input unit also includes a data verification module, which is responsible for consistency verification and error correction of the collected steel structure design parameters, load conditions and boundary conditions.

8. The industrialized steel structure system numerical simulation and optimization design system according to claim 1, characterized in that: In the result analysis unit, the implementation steps of the result analysis algorithm include: Step 1: Define the stress distribution output by the numerical simulation model as σ(x, y, z), the deformation as u(x, y, z), and the ultimate bearing capacity as P ult ; Step 2: Perform maximum value analysis on σ(x, y, z) to identify stress concentration areas; Step 3: Perform displacement analysis on u(x,y,z) to evaluate the overall deformation of the structure; Step 4: Combine P ult , conduct structural safety assessment to determine whether the structure meets the design requirements; Step 5: Combine the stress concentration area, overall deformation and structural safety assessment results to form a steel structure performance assessment report; Step 6: Combine the performance evaluation report with the optimization design plan to form the final steel structure design and optimization report.

9. The industrialized steel structure system numerical simulation and optimization design system according to claim 8, characterized in that: The structural safety assessment adopts the allowable stress method, and the specific formula is: s max ≤[σ] Among them, σ max is the maximum stress value, and [σ] is the allowable stress value.

10. The industrialized steel structure system numerical simulation and optimization design system according to claim 1, characterized in that: The result analysis unit also includes a visualization module, which is responsible for displaying the steel structure performance evaluation report and the optimization design scheme in the form of graphics, tables and three-dimensional models.

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