Modeling method, equipment and medium for concrete slab hanging on large-span steel grid structure

Abstract

The present invention discloses a modeling method, device and medium for a concrete slab suspended on a large-span steel grid collaborative structure, which relates to the field of parametric modeling technology, including: obtaining the geometric parameters of the steel grid, the geometric parameters of the concrete slab and the geometric parameters of the suspension system, generating a geometric domain based on the geometric parameters of the steel grid, the geometric parameters of the concrete slab and the geometric parameters of the suspension system; extracting the connection lines between the steel grid and the concrete slab based on the geometric threshold; extracting the connection nodes based on the connection lines and determining the load distribution of the connection nodes; constructing an initial three-dimensional model, generating a parametric building model based on the geometric parameters of the steel grid, the geometric parameters of the concrete slab, the geometric parameters of the suspension system, the connection nodes and the load distribution of the connection nodes; constructing an optimization function, optimizing the parametric building model with the goal of minimizing load potential energy and strain energy, and obtaining an optimal building model. The load between the concrete slab and the steel grid is reasonably distributed, the construction accuracy of the model is improved, and the integration and automatic processing of building parameters are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of parametric modeling, and in particular to a modeling method, equipment and medium for hanging concrete slabs on a large-span steel grid collaborative structure. Background Art

[0002] Large-span steel grid structures are widely used in large public buildings such as stadiums, exhibition halls, and airport terminals due to their advantages of large spans, light weight, and fast construction. Concrete slabs, a common floor or roofing material, are often suspended and combined with steel grids to form composite structures. However, traditional design methods rely on manual calculations and two-dimensional drawings, making it difficult to efficiently handle complex geometric relationships and load distributions. This results in long design cycles, low accuracy, and limited optimization options.

[0003] With the development of parametric design and BIM technology, structural design is gradually shifting towards digitalization and intelligence. However, the existing technology has the following problems:

[0004] Traditional modeling methods struggle to accurately reflect the complex interactions between concrete slabs and steel grids, particularly the local stress distribution and deformation characteristics of hanging nodes. This means they struggle to efficiently handle the complex geometric relationship between concrete slabs suspended from a collaborative structure with a large-span steel grid, leading to deviations between the model and actual structural behavior. Modeling methods often rely on manual adjustments and lack efficient parametric tools, making it difficult to quickly generate and modify models. This leads to low design efficiency and difficulty supporting multiple scheme comparisons and optimizations. The model cannot accurately predict the overall mechanical behavior of the structure, impacting its safety and reliability. Oversimplification of the load distribution at the hanging nodes and failure to fully consider the collaborative performance of the concrete slab and steel grid can lead to local stress concentrations and increase the risk of structural failure. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the geometric relationship of concrete slabs suspended on the collaborative structure of large-span steel grids is complex and the modeling accuracy is low. The purpose is to provide a modeling method, equipment and medium for concrete slabs suspended on the collaborative structure of large-span steel grids. Through geometric parameters, connection nodes and load distribution data, a three-dimensional model of a complex building structure can be accurately constructed to solve the problem that traditional modeling methods are difficult to accurately reflect the actual structure. By constructing an optimization function, the parametric building model can be optimized to improve the mechanical properties of the structure. Based on the load distribution data of the connection nodes, the load can be reasonably distributed to avoid local stress concentration or structural failure. Parametric modeling and automated optimization reduce manual intervention, improve design efficiency, shorten the design cycle, and generate multiple design schemes through the optimization function to support designers to quickly compare and improve modeling efficiency.

[0006] The present invention is achieved through the following technical solutions:

[0007] A first aspect of the present invention provides a modeling method for a concrete slab suspended on a large-span steel grid structure, comprising the following specific steps:

[0008] Acquire geometric parameters of the steel grid, the concrete slab, and the hanging system, and generate a geometric domain based on the geometric parameters of the steel grid, the concrete slab, and the hanging system;

[0009] Extract the connection lines between steel grid and concrete slab based on geometric threshold;

[0010] According to the connection lines, the connection nodes are extracted and the load distribution of the connection nodes is determined;

[0011] Construct an initial 3D model and generate a parametric building model based on the geometric parameters of the steel grid, the concrete slab, the hanging system, the connection nodes, and the load distribution of the connection nodes;

[0012] Obtain load potential energy and strain energy;

[0013] An optimization function is constructed to optimize the parametric building model with the goal of minimizing load potential energy and strain energy to obtain the optimal building model.

[0014] The present invention can accurately construct a three-dimensional model of a complex building structure through geometric parameters, connection nodes and load distribution data, solving the problem that traditional modeling methods are difficult to accurately reflect the actual structure. By constructing an optimization function, the parametric building model can be optimized to improve the mechanical properties of the structure. Based on the load distribution data of the connection nodes, the load can be reasonably distributed to avoid local stress concentration or structural failure. Parametric modeling and automated optimization reduce manual intervention, improve design efficiency, shorten the design cycle, and generate multiple design schemes through the optimization function, supporting designers to quickly compare and select, thereby improving modeling efficiency.

[0015] Furthermore, the geometric parameters of the steel grid include: the horizontal distance between the support points at both ends of the grid, the vertical distance from the top to the bottom of the grid, the length and width of the grid, and the cross-sectional dimensions of the rods;

[0016] The geometric parameters of the concrete slab include: concrete slab size;

[0017] The geometric parameters of the hanging system include: the position of the hanging point and the size of the hanging rod.

[0018] Furthermore, the extraction of the connection line between the steel grid and the concrete slab specifically includes:

[0019] Starting from the root node of the geometric domain inclusion tree, the connection lines between parent and child nodes are constructed;

[0020] Get the contour lines of the geometric domain containing the tree and sort the contour lines;

[0021] Use vector cross product to determine whether the sorted adjacent contour lines intersect, and extract the intersecting contour lines;

[0022] Get the angle between the extracted contour line and the connecting line. If the angle between the extracted contour line and the connecting line is within the set threshold, output the current connecting line.

[0023] Furthermore, the sorting of the contour lines specifically includes:

[0024] Get the contour dataset and initialize the contour dataset status;

[0025] In each control step, an initial solution set is generated;

[0026] Use the sorting algorithm to sort the solution set, obtain the optimal solution, update the contour dataset status, and output the sorting result.

[0027] Furthermore, the determining of the load distribution of the connection nodes specifically includes:

[0028] Get the load value of the connection node;

[0029] Based on the load value of each node, a load matrix is ​​constructed;

[0030] Obtain the relative position of the node section and the relative position of the node unit load to obtain the load distribution coefficient;

[0031] Introduce the load distribution coefficient into the load matrix to generate the distribution coefficient matrix;

[0032] The linear equations of the node load distribution coefficients are constructed to obtain the load distribution of the connection nodes.

[0033] Furthermore, the optimization function is constructed to minimize the load potential energy and strain energy, and the optimization of the three-dimensional model specifically includes:

[0034] The total potential energy of a structure under load is defined as the sum of strain energy and load potential energy:

[0035] E P =U+W ;

[0036] in, E P represents the total potential energy, U represents the strain energy, V represents the load potential energy;

[0037] According to the total potential energy under the load, an optimization function is constructed, wherein the optimization function includes:

[0038] min E P=min( U+W );

[0039] Solve the optimization function and obtain the optimal solution to optimize the three-dimensional model.

[0040] Furthermore, the step of obtaining the strain energy includes:

[0041] ;

[0042] in, U represents strain energy, δ( x ) means at point x The stress tensor at , e ( x ) means at point x The strain tensor at , V Represents the volume of the structure.

[0043] Furthermore, the step of obtaining the load potential energy includes:

[0044] Obtaining the vertical distributed load and axial distributed load of the structure, obtaining the beam end displacement array and the beam end load array, and calculating the load potential energy based on the vertical distributed load and axial distributed load of the structure, the beam end displacement array and the beam end load array. The calculation process includes:

[0045] ;

[0046] in, W represents the load potential energy, q v ( x ) means at point x The vertical distributed load at q u ( x ) means at point x Axial distributed load at l Indicates the length of the beam, d e represents the end beam displacement array, T represents transpose, f e represents the beam end load array, V Represents the volume of the structure.

[0047] The second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements a modeling method for a concrete slab suspended on a large-span steel grid collaborative structure.

[0048] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a modeling method for a concrete slab suspended on a large-span steel grid collaborative structure.

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] By obtaining the geometric parameters of the steel grid, concrete slab and hanging system, and generating connection lines and connection nodes based on the geometric domain, the geometric characteristics and mechanical behavior of the structure can be accurately reflected, the accuracy of the model can be improved, and the consistency between the design and the actual structure can be ensured; by accurately extracting the connection lines and connection nodes, the collaborative working performance of the concrete slab and the steel grid can be better simulated; by extracting the connection nodes and determining the load distribution, the load between the concrete slab and the steel grid can be reasonably distributed, local stress concentration can be avoided, and the safety and reliability of the structure can be improved; based on the geometric parameters and connection nodes, a parametric building model is generated, which supports rapid modification and adjustment, can improve design efficiency, and is convenient for multiple Scheme comparison and optimization, by constructing optimization functions, optimizing parametric building models with the goal of minimizing load potential energy and strain energy, can improve model construction accuracy and thus enhance the yield of digital models; based on the use of parametric modeling and optimization functions, it can reduce manual adjustments and repeated calculations, improve design efficiency, shorten the design cycle, and lay the foundation for the subsequent introduction of artificial intelligence and machine learning technologies; through parametric modeling, load distribution optimization and structural optimization, it can achieve the integration and automated processing of geometric parameters, load parameters and material parameters, improve modeling accuracy, design efficiency and structural performance, while enhancing construction feasibility and calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0052] Figure 1 This is a parametric modeling method in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0054] As a possible implementation, Figure 1As shown, this embodiment provides a modeling method for hanging a concrete slab on a large-span steel grid collaborative structure, including the following specific steps:

[0055] Acquire geometric parameters of the steel grid, the concrete slab, and the hanging system, and generate a geometric domain based on the geometric parameters of the steel grid, the concrete slab, and the hanging system;

[0056] Extract the connection lines between steel grid and concrete slab based on geometric threshold;

[0057] According to the connection lines, the connection nodes are extracted and the load distribution of the connection nodes is determined;

[0058] Construct an initial 3D model and generate a parametric building model based on the geometric parameters of the steel grid, the concrete slab, the hanging system, the connection nodes, and the load distribution of the connection nodes;

[0059] Obtain load potential energy and strain energy;

[0060] An optimization function is constructed to optimize the parametric building model with the goal of minimizing load potential energy and strain energy to obtain the optimal building model.

[0061] In this embodiment, by obtaining the geometric parameters of the steel grid, concrete slab and hanging system, and generating connection lines and connection nodes based on the geometric domain, the geometric characteristics and mechanical behavior of the structure can be accurately reflected, the accuracy of the model can be improved, and the consistency between the design and the actual structure can be ensured; by accurately extracting the connection lines and connection nodes, the collaborative working performance of the concrete slab and the steel grid can be better simulated; by extracting the connection nodes and determining the load distribution, the load between the concrete slab and the steel grid can be reasonably distributed, local stress concentration can be avoided, and the safety and reliability of the structure can be improved; based on the geometric parameters and connection nodes, a parametric building model is generated, which supports rapid modification and adjustment, and can improve design efficiency. It is convenient for multiple scheme comparison and optimization. By constructing optimization functions and optimizing the parametric building model with the goal of minimizing load potential energy and strain energy, the construction accuracy of the model can be improved, thereby enhancing the yield of the digital model. Based on the use of parametric modeling and optimization functions, manual adjustments and repeated calculations can be reduced, design efficiency can be improved, the design cycle can be shortened, and the foundation can be laid for the subsequent introduction of artificial intelligence and machine learning technologies. Through parametric modeling, load distribution optimization and structural optimization, the integration and automatic processing of geometric parameters, load parameters and material parameters can be realized, thereby improving modeling accuracy, design efficiency and structural performance, while enhancing construction feasibility and calculation efficiency.

[0062] In some possible implementations, the geometric parameters of the steel grid include: the horizontal distance between the support points at both ends of the grid, the vertical distance from the top to the bottom of the grid, the length and width of the grid, and the cross-sectional dimensions of the rods;

[0063] in,

[0064] The horizontal distance between the support points at both ends of the grid: describes the horizontal span of the steel grid and is a key parameter for determining the overall size of the grid;

[0065] The vertical distance from the top to the bottom of the grid: describes the height of the steel grid in the vertical direction, affecting the overall stiffness and stability of the structure;

[0066] Grid length and width: describes the size of the grid unit in the steel grid, affecting the distribution of the grid and the load transfer path;

[0067] Cross-sectional dimensions of members: describes the cross-sectional shape and dimensions of members in the steel grid, which affects the bearing capacity and stiffness of the members;

[0068] The geometric parameters of the concrete slab include: concrete slab size;

[0069] The geometric parameters of the hanging system include: hanging point location and hanger rod size. These parameters describe the distribution of the hanging points in the hanging system, affecting the connection between the concrete slab and the steel grid and the load transfer path. Hanger rod size describes the length, diameter, and other dimensions of the hanger rod, affecting its load-bearing capacity and stiffness.

[0070] The above parameters can improve modeling accuracy, ensure that the model is consistent with the actual structure, and support the accurate determination of load distribution and connection nodes.

[0071] In some possible implementations, a geometric domain is generated based on the geometric parameters of the steel grid, the geometric parameters of the concrete slab, and the geometric parameters of the hanging system, specifically including:

[0072] After obtaining the geometric parameters of the steel grid, the concrete slab, and the suspension system, these parameters are integrated to generate a geometric domain, which includes the geometric relationships of each component. Based on the geometric relationships, the connection method of each component can be obtained.

[0073] In some possible implementations, extracting the connection line between the steel grid and the concrete slab specifically includes:

[0074] Starting from the root node of the geometric domain inclusion tree, the connection lines between parent and child nodes are constructed;

[0075] Get the contour lines of the geometric domain containing the tree and sort the contour lines;

[0076] Use vector cross product to determine whether the sorted adjacent contour lines intersect, and extract the intersecting contour lines;

[0077] Get the angle between the extracted contour line and the connecting line. If the angle between the extracted contour line and the connecting line is within the set threshold, output the current connecting line.

[0078] In some possible implementations, a connection line between a parent and a child node is constructed:

[0079] Starting from the root node of the tree containing the geometric domain, traverse each node of the tree;

[0080] For each node, find its child nodes and draw connecting lines between the parent node and the child nodes;

[0081] Its purpose is to visualize the structure of the tree for easier subsequent processing.

[0082] In some possible implementations, the contour lines of the geometric domain containing the tree are obtained and the contour lines are sorted:

[0083] Contour lines are the boundaries of geometric domains;

[0084] Traverse each node of the tree and extract the contour line of each node;

[0085] Sort the extracted contours, possibly in some specific order, such as by their length, area, or position in space.

[0086] In some possible implementations, vector cross product is used to determine whether the sorted adjacent contour lines intersect, and the intersecting contour lines are extracted:

[0087] For the sorted contour lines, check whether each pair of adjacent contour lines intersects;

[0088] Use the vector cross product method to determine whether two line segments intersect. If the vector cross product of the two line segments is zero, they are collinear; if the result is positive, they intersect at a certain point; if the result is negative, they do not intersect;

[0089] Extract all intersecting contour lines.

[0090] In some possible implementations, the angle between the extracted contour line and the connecting line is obtained. If the angle between the extracted contour line and the connecting line is within a set threshold, the current connecting line is output:

[0091] For each extracted intersecting contour line, find the angle between it and the connecting line;

[0092] If the angle meets a certain condition (such as being greater than a certain threshold), the current connecting line is output, that is, the connecting lines that intersect with the contour line and whose angle meets the specific condition are filtered out.

[0093] In some possible implementations, sorting contours specifically includes: obtaining all contour data that needs to be sorted, initializing the contour dataset state, setting initial parameters, and defining the data structure; defining a control step size, and within each control step size, generating an initial solution set, which may contain multiple possible contour sorting schemes; using a sorting algorithm to sort the solution set to obtain the optimal solution, updating the contour dataset state, and outputting the sorting result. An appropriate sorting algorithm is selected to sort the solution set. During the sorting process, an evaluation function is defined to evaluate the quality of each solution. This evaluation function will be used to guide the sorting algorithm to find the optimal solution. Based on the results of the sorting algorithm, the optimal solution is determined. The optimal solution is a contour sorting scheme that meets specific conditions (such as minimizing a certain error or maximizing a certain benefit). The contour dataset state is updated, and the optimal solution is applied to the dataset.

[0094] In some possible implementations, determining the load distribution of the connection nodes specifically includes:

[0095] To obtain the load value of the connection node, first, you need to determine the load value acting on each connection node. These loads may include concentrated forces, distributed forces, bending moments, etc.

[0096] Based on the load values ​​of each node, a load matrix is ​​constructed. This matrix will contain the load information of all nodes. In this implementation, the data can be a column vector, where each row corresponds to a node and each column corresponds to a type of load (such as vertical force, horizontal force, bending moment, etc.);

[0097] Obtain the relative position of the node section and the relative position of the node unit load, obtain the load distribution coefficient, determine the relative position of each node on the section to which it belongs, and the distribution of the unit load on the section;

[0098] Introduce the load distribution coefficient into the load matrix to generate a distribution coefficient matrix. Introduce the calculated load distribution coefficient into the load matrix to generate a new distribution coefficient matrix. This matrix will be used to distribute the total load to each node;

[0099] Construct a linear system of equations for the nodal load distribution coefficients to obtain the load distribution at the connection nodes: Using the distribution coefficient matrix and the original load matrix, a linear system of equations is constructed. This system of equations will be used to solve the load distribution at each node. Solving the linear system of equations will obtain the load distribution at each connection node.

[0100] In some possible implementations, an initial three-dimensional model is constructed, and a parametric building model is generated based on the geometric parameters of the steel grid, the geometric parameters of the concrete slab, the geometric parameters of the suspension system, the connection nodes, and the load distribution of the connection nodes.

[0101] Select a parametric modeling tool, such as Revit, Rhino with Grasshopper, Dynamo for Autodesk products, or CATIA. Use defined geometric parameters to create the steel grid in the parametric modeling tool, ensuring that the grid's nodes and members are correctly connected to form a stable structure. Create a slab model based on the geometric parameters of the concrete slab, correctly place the concrete slab on the steel grid, and ensure that the connection between them meets design requirements. Create a hanger model based on the geometric parameters of the hanging system, and correctly connect the hangers to the steel grid and concrete slab. Define the location and type of connection nodes in the model. Based on load distribution principles, determine the load distribution at each connection node. Perform a load analysis on the model, including self-weight, live load, wind load, etc., to ensure that the model meets structural safety and stability requirements. Based on the results of the load analysis, optimize and adjust the model, including adjusting member sizes and changing node connection methods. After all adjustments are completed, generate the final parametric building model.

[0102] In some possible implementations, constructing an optimization function with the goal of minimizing load potential energy and strain energy, and optimizing the three-dimensional model specifically includes:

[0103] The total potential energy of a structure under load is defined as the sum of strain energy and load potential energy:

[0104] E P =U+W ;

[0105] in, E P represents the total potential energy, U represents the strain energy, V represents the load potential energy;

[0106] According to the total potential energy under the load, an optimization function is constructed, wherein the optimization function includes:

[0107] min E P =min( U+W );

[0108] Solve the optimization function and obtain the optimal solution to optimize the three-dimensional model.

[0109] In some possible implementations, the step of acquiring strain energy includes:

[0110] ;

[0111] in, U represents strain energy, δ( x ) means at point x The stress tensor at , e ( x ) means at point x The strain tensor at , V Represents the volume of the structure.

[0112] In some possible implementations, the step of acquiring the load potential energy includes:

[0113] Obtaining the vertical distributed load and axial distributed load of the structure, obtaining the beam end displacement array and the beam end load array, and calculating the load potential energy based on the vertical distributed load and axial distributed load of the structure, the beam end displacement array and the beam end load array. The calculation process includes:

[0114] ;

[0115] in, W represents the load potential energy, q v ( x ) means at point x The vertical distributed load at q u ( x ) means at point x Axial distributed load at l Indicates the length of the beam, d e represents the end beam displacement array, T represents transpose, f e represents the beam end load array, V Represents the volume of the structure.

[0116] As a possible implementation method, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements a modeling method for a concrete slab suspended on a large-span steel grid collaborative structure.

[0117] As a possible implementation method, this embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a modeling method for hanging a concrete slab on a large-span steel grid collaborative structure is implemented.

[0118] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A modeling method for a concrete slab suspended on a large-span steel grid structure, characterized in that: The specific steps include: Acquire geometric parameters of the steel grid, the concrete slab, and the hanging system, and generate a geometric domain based on the geometric parameters of the steel grid, the concrete slab, and the hanging system; Extract the connection lines between steel grid and concrete slab based on geometric domain; According to the connection lines, the connection nodes are extracted and the load distribution of the connection nodes is determined; Construct an initial 3D model and generate a parametric building model based on the geometric parameters of the steel grid, the concrete slab, the hanging system, the connection nodes, and the load distribution of the connection nodes; Obtain load potential energy and strain energy; Construct an optimization function to optimize the parametric building model with the goal of minimizing load potential energy and strain energy to obtain the optimal building model; The method of extracting the connection line between the steel grid and the concrete slab specifically includes: Starting from the root node of the geometric domain inclusion tree, the connection lines between parent and child nodes are constructed; Get the contour lines of the geometric domain containing the tree and sort the contour lines; Use vector cross product to determine whether the sorted adjacent contour lines intersect, and extract the intersecting contour lines; Get the angle between the extracted contour line and the connecting line. If the angle between the extracted contour line and the connecting line is within the set threshold, output the current connecting line. The sorting of the contour lines specifically includes: Get the contour dataset and initialize the contour dataset status; In each control step, an initial solution set is generated; Use the sorting algorithm to sort the solution set, obtain the optimal solution, update the contour dataset status, and output the sorting result.

2. The modeling method of a concrete slab suspended on a large-span steel grid collaborative structure according to claim 1 is characterized in that: The geometric parameters of the steel grid include: the horizontal distance between the support points at both ends of the grid, the vertical distance from the top to the bottom of the grid, the length and width of the grid, and the cross-sectional dimensions of the rods; The geometric parameters of the concrete slab include: concrete slab size; The geometric parameters of the hanging system include: the position of the hanging point and the size of the hanging rod.

3. The modeling method of a concrete slab suspended on a large-span steel grid collaborative structure according to claim 1 is characterized in that: Determining the load distribution of the connection nodes specifically includes: Get the load value of the connection node; Based on the load value of each node, a load matrix is ​​constructed; Obtain the relative position of the node section and the relative position of the node unit load to obtain the load distribution coefficient; Introduce the load distribution coefficient into the load matrix to generate the distribution coefficient matrix; The linear equations of the node load distribution coefficients are constructed to obtain the load distribution of the connection nodes.

4. The modeling method of a concrete slab suspended on a large-span steel grid collaborative structure according to claim 1 is characterized in that: The optimization function is constructed to minimize the load potential energy and strain energy, and the optimization of the three-dimensional model specifically includes: The total potential energy of a structure under load is defined as the sum of strain energy and load potential energy: E P =U+W ； in, E P represents the total potential energy, U represents the strain energy, W represents the load potential energy; According to the total potential energy under the load, an optimization function is constructed, wherein the optimization function includes: my E P =min( U+W ); Solve the optimization function and obtain the optimal solution to optimize the three-dimensional model.

5. The modeling method of a concrete slab suspended on a large-span steel grid structure according to claim 4 is characterized in that: The step of obtaining the strain energy comprises: ； in, U represents strain energy, δ( x ) means at point x The stress tensor at , ε ( x ) means at point x The strain tensor at , V Represents the volume of the structure.

6. The modeling method of a concrete slab suspended on a large-span steel grid structure according to claim 5, characterized in that: The step of obtaining the load potential energy includes: Obtaining the vertical distributed load and axial distributed load of the structure, obtaining the beam end displacement array and the beam end load array, and calculating the load potential energy based on the vertical distributed load and axial distributed load, the beam end displacement array and the beam end load array. The calculation process of the load potential energy includes: ； in, W represents the load potential energy, q v ( x ) means at point x The vertical distributed load at q u ( x ) means at point x Axial distributed load at l Indicates the length of the beam, δ e represents the end beam displacement array, T represents transpose, f e represents the beam end load array, V Represents the volume of the structure.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the modeling method of hanging a concrete slab on a large-span steel grid collaborative structure as described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the modeling method of hanging a concrete slab on a large-span steel grid collaborative structure as described in any one of claims 1 to 6 is implemented.

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