Force analysis method and system for large-span net shell column deflection based on VR technology

Abstract

The present application relates to the technical field of wisdom engineering, in particular to a stress analysis method and system for large-span net shell column deflection based on VR technology, comprising the following steps: generating a large-span net shell VR model through a Unity 3D engine in a VR interaction system and visualizing through a VR head-mounted device; identifying a column deflection high-risk area in the large-span net shell VR model through a risk positioning model loaded in a processor in the VR interaction system; and predicting structural stress data of the column deflection high-risk area according to interaction behavior data received by a VR handle in the column deflection high-risk area through an interaction response prediction model loaded in the processor in the VR interaction system. The present application renders a large-span net shell VR model through a VR interaction system, determines a deflection high-risk area using a risk positioning model, and simulates a deflection adjustment process based on user interaction behavior data using an interaction response prediction model, thereby improving user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wisdom engineering, and particularly relates to a stress analysis method for column deflection of large-span net shell based on VR technology. BACKGROUND

[0002] The net shell structure has good stress performance, large rigidity, small self weight and small steel consumption, and is a better structure type suitable for medium and large-span building roofs. The net shell structure can cause the decrease of the stability and load bearing capacity of the overall structure due to the occurrence of column deflection. Therefore, the stress analysis of the column deflection of the net shell structure can master the stability of the net shell structure.

[0003] In the prior art, the finite element analysis is usually used for the stress analysis of the net shell structure, the analysis process cannot be visualized, and the adjustment process of the deflection cannot be simulated, so that whether the deflection adjustment operation of the user is effective cannot be intuitively perceived. SUMMARY

[0004] The present application aims to provide a stress analysis method for column deflection of large-span net shell based on VR technology, so as to solve the technical problem that the analysis process cannot be visualized in the prior art.

[0005] To solve the above technical problems, the present application specifically provides the following technical solutions:

[0006] A stress analysis method for column deflection of large-span net shell based on VR technology, comprising the following steps:

[0007] Obtaining the geometric data of the net shell structure by three-dimensional scanning of the large-span net shell structure by a laser scanner, and comparing the geometric data of the net shell structure with the initial BIM modeling of the large-span net shell structure to generate a large-span net shell correction model with initial defects;

[0008] Generating a large-span net shell VR model according to the large-span net shell correction model by a Unity 3D engine in a VR interaction system, and visualizing by a VR head-mounted device;

[0009] Identifying the column deflection high-risk area in the large-span net shell VR model by a risk positioning model loaded in a processor in the VR interaction system, and visualizing and marking on the large-span net shell VR model by the VR head-mounted device;

[0010] According to the interaction behavior data received by the VR handle in the column deflection high-risk area, predicting the structure stress data of the column deflection high-risk area by an interaction response prediction model loaded in the processor in the VR interaction system, and visualizing and marking on the large-span net shell VR model by the VR head-mounted device.

[0011] As a preferred solution of the present invention, the method for constructing the risk positioning model includes:

[0012] Collect historical construction data of multiple long-span lattice shell structures, extract structural materials, construction technology, environmental conditions, load conditions, foundation conditions from the historical construction data, and mark areas where the column deviation amplitude and direction on the long-span lattice shell structures exceed the risk threshold as high-risk areas for column deviation;

[0013] The risk location model is obtained by training using a random forest algorithm with structural materials, construction technology, environmental conditions, load conditions, and foundation conditions as inputs and high-risk areas of column misalignment as outputs;

[0014] The risk positioning model is:

[0015] P high =RF({x1,x2,x3,x4,x5});

[0016] Where, P high is the high-risk area for column misalignment, x1, x2, x3, x4, and x5 are structural materials, construction technology, environmental conditions, load conditions, and foundation conditions, respectively, and RF is the random forest algorithm.

[0017] As a preferred solution of the present invention, the method for constructing the interactive response prediction model includes:

[0018] The stress of the long-span lattice shell structure is calculated by using the finite element analysis algorithm to extract the column displacement amplitude and direction on the long-span lattice shell structure based on historical construction data;

[0019] The interactive response prediction model is obtained by using a CNN network with the column deflection amplitude and direction on the long-span lattice shell extracted from historical construction data as input and the stress of the long-span lattice shell structure as output for training;

[0020] The interactive response prediction model is:

[0021] ;

[0022] Where, is the structural stress data, and D is the column deflection amplitude and direction.

[0023] As a preferred embodiment of the present invention, the finite element analysis method includes:

[0024] Calculate column displacement on a large-span lattice shell structure The nonlinear equilibrium equation , where is the residual force vector, is the external load vector, The internal force vector related to the column displacement ;

[0025] The column displacement is solved by an incremental iterative solution method for the nonlinear equilibrium equation, and the iterative solution formula is: , , , wherein, is the tangent stiffness matrix of the nth iteration i , is the displacement increment of the nth iteration , i is the residual force vector of the nth iteration , i is the displacement estimation value of the nth iteration , i is the displacement estimation value of the (n+1)th iteration , i , , is the geometric stiffness matrix is the material stiffness matrix

[0026] The column displacement obtained by solving the nonlinear equilibrium equation according to the incremental iterative solution method , and the strain tensor of the large-span net shell structure is obtained by using the Einstein summation convention, including normal strain , , , shear strain , , , wherein, , , wherein, , , are three components of the normal strain , , are three components of the shear strain , , are three components of ;

[0027] According to the generalized Hooke's law, the strain tensor is converted into stress to obtain normal stress , , , shear stress , , , wherein, , , wherein, , , three components of normal stress respectively, , , three components of shear stress respectively, E is the elastic modulus, is the Poisson's ratio.

[0028] As a preferred scheme of the present application, the column offset amplitude and direction include a column offset direction n and an offset distance d;

[0029] The large-span reticulated shell correction model coordinates , wherein, is the initial BIM modeling coordinates of the large-span reticulated shell structure.

[0030] As a preferred scheme of the present application, the interactive behavior data includes load adjustment operations and temporary support addition operations performed by the user through the VR handle.

[0031] As a preferred scheme of the present application, the method for predicting the structural stress data of the column offset high-risk area according to the interactive behavior data received by the VR handle in the column offset high-risk area includes:

[0032] The load adjustment operations and temporary support addition operations performed through the VR handle are fed back to the column offset high-risk area of the large-span reticulated shell VR model, triggering real-time adjustment of the column offset of the column offset high-risk area of the large-span reticulated shell VR model, and the adjusted column offset amplitude and direction D;

[0033] The adjusted column offset amplitude and direction D are input into the interactive response prediction model to obtain the current structural stress data of the column offset high-risk area .

[0034] As a preferred scheme of the present application, the present application provides a large-span reticulated shell column offset stress analysis system based on VR technology, which is applied to a large-span reticulated shell column offset stress analysis method based on VR technology, and the system includes:

[0035] A laser scanner is used to perform three-dimensional scanning on the large-span reticulated shell structure to obtain geometric data of the reticulated shell structure.

[0036] A model correction unit is used to compare the geometric data of the reticulated shell structure with initial BIM modeling of the large-span reticulated shell structure to generate a large-span reticulated shell correction model with initial defects.

[0037] A VR interactive system includes a Unity 3D engine, a processor, a VR handle, and a VR headset.

[0038] The Unity 3D engine is used to render a large-span latticed shell VR model according to the large-span latticed shell correction model, and the large-span latticed shell VR model is visualized through a VR head-mounted device;

[0039] The processor is loaded with a risk positioning model for identifying a column deflection high-risk area in the large-span latticed shell VR model, and the column deflection high-risk area is visualized and marked on the large-span latticed shell VR model through the VR head-mounted device;

[0040] The processor is loaded with an interaction response prediction model for predicting structural stress data of the column deflection high-risk area according to interaction behavior data received by the VR handle in the column deflection high-risk area, and the structural stress data is visualized and marked on the large-span latticed shell VR model through the VR head-mounted device.

[0041] As a preferred scheme of the present application, the risk positioning model is:

[0042] P high =RF({x1,x2,x3,x4,x5});

[0043] In the formula, P high is a column deflection high-risk area, x1, x2, x3, x4, and x5 are structural materials, construction technology, environmental conditions, load conditions, and foundation conditions, respectively, and RF is a random forest algorithm.

[0044] As a preferred scheme of the present application, the interaction response prediction model is:

[0045] ;

[0046] In the formula, is structural stress data, and D is a column deflection amplitude and direction.

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

[0048] The present application renders a large-span latticed shell VR model through a VR interaction system, determines a column deflection high-risk area of a large-span latticed shell structure by using a risk positioning model, further adjusts a column deflection amplitude and direction based on interaction behavior data of a user by using an interaction response prediction model, and predicts structural stress data, so that a deflection adjustment process can be simulated, the user can intuitively perceive whether a deflection adjustment operation is effective or not, the visualized stress analysis is achieved, and the user experience is improved. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can also be obtained from the provided drawings without creative labor.

[0050] Figure 1 The force analysis method flowchart of the column deflection of the large-span latticed shell based on the VR technology provided for the embodiments of the present application;

[0051] Figure 2 The system block diagram of the force analysis system of the column deflection of the large-span latticed shell based on the VR technology provided for the embodiments of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0053] As shown in Figure 1 The present application provides a force analysis method of column deflection of large-span latticed shell based on VR technology, comprising the following steps:

[0054] Obtain the geometric data of the latticed shell structure by three-dimensional scanning of the latticed shell structure through a laser scanner, and compare the geometric data of the latticed shell structure with the initial BIM modeling of the large-span latticed shell structure to generate a large-span latticed shell correction model with initial defects;

[0055] Render a large-span latticed shell VR model from the large-span latticed shell correction model through a Unity 3D engine in a VR interactive system, and visualize it through a VR head-mounted device;

[0056] Identify the column deflection high-risk area in the large-span latticed shell VR model through a risk positioning model loaded by a processor in the VR interactive system, and visually mark it on the large-span latticed shell VR model through a VR head-mounted device (HTC Vive Pro, Meta Quest 3);

[0057] According to the interaction behavior data received by the VR handle in the column deflection high-risk area, predict the structural stress data of the column deflection high-risk area through an interaction response prediction model loaded by a processor in the VR interactive system, and visually mark it on the large-span latticed shell VR model through a VR head-mounted device.

[0058] The VR technology is combined into the stress analysis of the column deflection of the large-span net shell structure, the stress analysis of the column deflection of the large-span net shell structure can be visualized, complex stress data can be displayed in the form of three-dimensional visualization, and the stress analysis is more intuitive and easy to understand.

[0059] The machine learning model (risk positioning model) is pre-established, the mapping relationship between the structural material, the construction technology, the environmental condition (temperature and humidity), the load condition, the foundation condition (settlement condition) of the large-span net shell structure and the high-risk area of column deflection is represented, so that the high-risk position of deflection is quickly positioned in the column deflection analysis process, and the high-risk position of deflection is visualized and displayed to the user, the deflection risk can be prompted in a virtual reality environment, and the data visualization helps to improve the efficiency of analysis and design.

[0060] After the high-risk area of deflection is positioned, the interactive behavior data of the user in the high-risk area is received, for example, the user adjusts the load condition in the high-risk area, that is, the wind and rain load is applied, or the rain load is adjusted to the snow load, and the user reinforces the column deflection in the high-risk area, that is, temporary support is added, and the change of the load condition and the addition of the reinforcing support (such as a diagonal rod and a ring-shaped cable) will cause the change of the column deflection amplitude and direction, and the stress condition of the column deflection at this time can be obtained, so that the stress condition of the reinforcing support under various load conditions is known, so that the user can judge the stability and load-bearing benefit of the reinforcing support to the large-span net shell structure, that is, the column deflection correction effect is judged. Therefore, various stress conditions can be simulated through the VR technology, the user can intuitively see the change of the structure under different stress conditions, so as to more accurately evaluate the stability and safety of the column deflection structure, and provide an interactive immersive experience for the user.

[0061] In order to ensure the timeliness of the interactive experience, another machine learning model (interactive response prediction model) is pre-established, the mapping relationship between the structural stress data and the column deflection amplitude and direction is represented, so that the structural stress data after the interactive behavior is directly determined according to the column deflection amplitude and direction generated by the interactive behavior, the long iterative algorithm process (such as incremental iterative solution and automatic updating of the finite element model after adding temporary support) of the finite element analysis is avoided, the efficiency of the structural stress calculation is improved, the stress analysis is quickly completed after the interactive behavior, and the interactive experience is improved.

[0062] The data used in the pre-established interactive response prediction model in the application is accumulated through finite element analysis, so that the VR system adopts the interactive response prediction model to respond to the interactive behavior, can inherit the accurate performance of the finite element analysis, obtain accurate structural stress analysis results, and at the same time, can cause minimum time loss, compared with directly using the finite element analysis in the VR system to respond to the interactive behavior, the performance is improved.

[0063] This invention pre-establishes a machine learning model (risk location model) to quickly locate high-risk locations during column misalignment analysis and visualize them for the user. Misalignment risks can be indicated in a virtual reality environment. This data visualization helps improve analysis and design efficiency, as follows:

[0064] The methods for constructing a risk positioning model include:

[0065] Collect historical construction data of multiple long-span lattice shell structures, extract structural materials, construction technology, environmental conditions, load conditions, foundation conditions from the historical construction data, and mark areas where the column deviation amplitude and direction on the long-span lattice shell structures exceed the risk threshold as high-risk areas for column deviation;

[0066] The random forest algorithm is used to train a risk location model using structural materials, construction technology, environmental conditions, load conditions, and foundation conditions as inputs and high-risk areas of column misalignment as outputs.

[0067] The risk positioning model is:

[0068] P high =RF({x1,x2,x3,x4,x5});

[0069] Where, P high is the high-risk area for column misalignment, x1, x2, x3, x4, and x5 are structural materials, construction technology, environmental conditions, load conditions, and foundation conditions, respectively, and RF is the random forest algorithm.

[0070] The present invention establishes an interactive response prediction model. Based on the column deflection amplitude and direction generated by the interactive behavior, the structural stress data after the interactive behavior is directly determined. This avoids the lengthy iterative algorithm process of finite element analysis, improves the efficiency of structural stress calculation, and achieves rapid completion of force analysis after the interactive behavior occurs, thus enhancing the interactive experience. The details are as follows:

[0071] The construction method of the interactive response prediction model includes:

[0072] The stress of the long-span lattice shell structure is calculated by using the finite element analysis algorithm to extract the column displacement amplitude and direction on the long-span lattice shell structure based on historical construction data;

[0073] The CNN network is trained using the column displacement amplitude and direction of the long-span lattice shell extracted from historical construction data as input and the stress of the long-span lattice shell structure as output to obtain an interactive response prediction model.

[0074] The interactive response prediction model is:

[0075] ;

[0076] Where, is the structural stress data, and D is the column deflection amplitude and direction.

[0077] The data used in the pre-established interactive response prediction model of the present invention is accumulated through finite element analysis. The finite element analysis method is:

[0078] Calculate column displacement on a large-span lattice shell structure The nonlinear equilibrium equation , where is the residual force vector, is the external load vector, For the displacement the associated internal resistance vector;

[0079] Column displacement of nonlinear equilibrium equations by incremental iterative solution The iterative solution formula is: , , where For the i The tangent stiffness matrix of the iteration , For the i The displacement increment of the iteration, For the i The residual force vector of the iteration, For the i The displacement estimate for the iteration, For the i +1 iteration of displacement estimates, , is the geometric stiffness matrix, is the material stiffness matrix;

[0080] The column displacement obtained by solving the nonlinear equilibrium equation according to the incremental iterative solution method , the strain tensor of the long-span lattice shell structure is obtained by using the Einstein summation convention, including the normal strain ( , , ), shear strain ( , , ),in, , , where , , are the three components of normal strain, , , respectively three components of shear strain, , , respectively three components of shear stress, ;

[0081] According to the generalized Hooke's law, the strain tensor is converted into stress, and the normal stress ( , , ), shear stress ( , , ), wherein, , , wherein, , , respectively three components of normal stress, , , respectively three components of shear stress, E is the elastic modulus, is the Poisson's ratio.

[0082] The column deflection amplitude and direction include the column deflection direction n and the deflection distance d;

[0083] Large-span net shell correction model coordinates , wherein, is the initial BIM modeling coordinates of the large-span net shell structure.

[0084] The interactive behavior data includes load adjustment operations and temporary support addition operations performed by the user through the VR handle.

[0085] According to the interactive behavior data received by the VR handle in the column deflection high-risk area, a method for predicting the structural stress data of the column deflection high-risk area comprises:

[0086] The load adjustment operation and the temporary support addition operation performed by the VR handle are fed back to the column deflection high-risk area of the large-span net shell VR model, triggering real-time adjustment of the column deflection of the column deflection high-risk area of the large-span net shell VR model, and the column deflection amplitude and direction D after real-time adjustment;

[0087] The column deflection amplitude and direction D after real-time adjustment are input into the interactive response prediction model to obtain the structural stress data of the current column deflection high-risk area .

[0088] As shown in Figure 2 , the present application provides a large-span net shell column deflection stress analysis system based on VR technology, which is applied to a large-span net shell column deflection stress analysis method based on VR technology, and the system comprises:

[0089] A laser scanner is used to scan the large-span latticed shell structure in three dimensions to obtain the geometric data of the latticed shell structure.

[0090] A model correction unit (any computer system capable of processing BIM models) is used to compare the geometric data of the latticed shell structure with the initial BIM model of the large-span latticed shell structure to generate a large-span latticed shell correction model with initial defects.

[0091] A VR interaction system includes a Unity 3D engine, a processor, a VR handle, and a VR headset.

[0092] The Unity 3D engine is used to render a large-span latticed shell VR model based on the large-span latticed shell correction model and visualize it through the VR headset device.

[0093] The processor is loaded with a risk positioning model to identify the high-risk area of column deviation in the large-span latticed shell VR model and visually mark it on the large-span latticed shell VR model through the VR headset device.

[0094] The processor is loaded with an interaction response prediction model to predict the structural stress data of the high-risk area of column deviation based on the interaction behavior data received through the VR handle in the high-risk area of column deviation and visually mark it on the large-span latticed shell VR model through the VR headset device.

[0095] The risk positioning model is:

[0096] P high =RF({x1,x2,x3,x4,x5});

[0097] where P high is the high-risk area of column deviation, x1, x2, x3, x4, and x5 are the structural material, construction technology, environmental conditions, load conditions, and foundation conditions, respectively, and RF is the random forest algorithm.

[0098] The interaction response prediction model is:

[0099] ;

[0100] where is the structural stress data, and D is the amplitude and direction of column deviation.

[0101] The application renders a large-span net shell VR model through a VR interaction system, determines a high-risk area of the large-span net shell structure by using a risk positioning model, further predicts structure stress data based on column deflection amplitude and direction adjustment generated by user interaction behavior data by using an interactive response prediction model, can simulate the deflection adjustment process, and can intuitively perceive whether the user's deflection adjustment operation is effective, so as to achieve intuitive visualization of stress analysis and improve user experience.

[0102] The above examples are only exemplary embodiments of the present application and are not used to limit the present application, and the protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements to the present application within the spirit and protection scope of the present application, and such modifications or equivalent replacements are also regarded as falling within the protection scope of the present application.

Claims

1. A stress analysis method for large-span lattice shell column deviation based on VR technology, characterized in that: The following steps are involved: The large-span lattice shell structure is three-dimensionally scanned by a laser scanner to obtain geometric data of the lattice shell structure, and the geometric data of the lattice shell structure is compared with the initial BIM modeling of the large-span lattice shell structure to generate a corrected model of the large-span lattice shell with initial defects; A large-span lattice shell VR model is generated by rendering the large-span lattice shell modified model using the Unity 3D engine in the VR interactive system, and is visualized through a VR head display device; Through the risk location model loaded into the processor of the VR interactive system, high-risk areas of column deviation are identified in the VR model of the long-span lattice shell, and visually marked on the VR model of the long-span lattice shell through the VR head display device; The interactive response prediction model loaded on the processor of the VR interactive system predicts the structural stress data of the high-risk column deviation areas based on the interactive behavior data received through the VR controller in the high-risk column deviation areas, and visually marks them on the long-span lattice shell VR model through the VR head display device; The construction method of the interactive response prediction model includes: The stress of the long-span lattice shell structure is calculated by using the finite element analysis algorithm to extract the column displacement amplitude and direction on the long-span lattice shell structure based on historical construction data; The CNN network is trained using the column displacement amplitude and direction of the long-span lattice shell extracted from historical construction data as input and the stress of the long-span lattice shell structure as output to obtain an interactive response prediction model. The interactive response prediction model is: ； Where, is the structural stress data, D is the column deflection amplitude and direction; Finite element analysis methods include: Calculate column displacement on a large-span lattice shell structure The nonlinear equilibrium equation , where is the residual force vector, is the external load vector, The displacement of the column the associated internal resistance vector; Column displacement of nonlinear equilibrium equations by incremental iterative solution The iterative solution formula is: , , where For the i The tangent stiffness matrix of the iteration , For the i The displacement increment of the iteration, For the i The residual force vector of the iteration, For the i The displacement estimate for the iteration, For the i +1 iteration of displacement estimates, , is the geometric stiffness matrix, is the material stiffness matrix; The column displacement obtained by solving the nonlinear equilibrium equation according to the incremental iterative solution method , the strain tensor of the long-span lattice shell structure is obtained by using the Einstein summation convention, including the normal strain ( , , ), shear strain ( , , ),in, , , , , , , where , , are the three components of normal strain, , , are the three components of shear strain, , , They are The three components of According to the generalized Hooke's law, the strain tensor is converted into stress and the normal stress is obtained ( , , ), shear stress ( , , ),in, , , , , , , where , , are the three components of normal stress, , , are the three components of shear stress, E is the elastic modulus, is Poisson's ratio.

2. The stress analysis method for large-span lattice shell column deflection based on VR technology according to claim 1 is characterized by: The method for constructing the risk positioning model includes: Collect historical construction data of multiple long-span lattice shell structures, extract structural materials, construction technology, environmental conditions, load conditions, foundation conditions from the historical construction data, and mark areas where the column deviation amplitude and direction on the long-span lattice shell structures exceed the risk threshold as high-risk areas for column deviation; The risk location model is obtained by training using a random forest algorithm with structural materials, construction technology, environmental conditions, load conditions, and foundation conditions as inputs and high-risk areas of column misalignment as outputs; The risk positioning model is: P high =RF({x1,x2,x3,x4,x5})? Where, P high is the high-risk area for column misalignment, x1, x2, x3, x4, and x5 are structural materials, construction technology, environmental conditions, load conditions, and foundation conditions, respectively, and RF is the random forest algorithm.

3. The stress analysis method for large-span lattice shell column deviation based on VR technology according to claim 1 is characterized by: The column deflection amplitude and direction include the column deflection direction n and the deflection distance d; The large-span lattice shell correction model coordinates , where Initial BIM modeling coordinates for the long-span lattice shell structure.

4. The stress analysis method for large-span lattice shell column deflection based on VR technology according to claim 1 is characterized by: The interactive behavior data includes the user's load adjustment operations and temporary support addition operations through the VR handle.

5. The stress analysis method for large-span lattice shell column displacement based on VR technology according to claim 1 is characterized by: Based on the interactive behavior data received through the VR controller in the high-risk area of ​​column deviation, the method for predicting the structural stress data of the high-risk area of ​​column deviation includes: The load adjustment operation and temporary support addition operation will be fed back to the high-risk area of ​​column deviation in the large-span lattice shell VR model through the VR handle, triggering the real-time adjustment of the column deviation in the high-risk area of ​​column deviation in the large-span lattice shell VR model, and the real-time adjustment of the column deviation amplitude and direction D; The real-time adjusted column deviation amplitude and direction D are input into the interactive response prediction model to obtain the current structural stress data of the high-risk area of ​​column deviation. .

6. A stress analysis system for large-span lattice shell column deviation based on VR technology, characterized by: The stress analysis method for large-span lattice shell column deviation based on VR technology as described in any one of claims 1 to 5 comprises: Laser scanner, used to perform three-dimensional scanning of large-span lattice shell structures to obtain geometric data of the lattice shell structures; A model correction unit is used to compare the geometric data of the lattice shell structure with the initial BIM modeling of the long-span lattice shell structure to generate a corrected model of the long-span lattice shell with initial defects; VR interaction system, including Unity 3D engine, processor, VR controller, and VR headset; The Unity 3D engine is used to render and generate a large-span lattice shell VR model based on the large-span lattice shell modified model, and visualize it through a VR head display device; The processor is loaded with a risk location model for identifying high-risk areas of column deviation in the long-span lattice shell VR model and visually marking them on the long-span lattice shell VR model through a VR head display device; The processor is loaded with an interactive response prediction model, which is used to predict the structural stress data of the high-risk area of ​​column deviation based on the interactive behavior data received in the high-risk area of ​​column deviation through the VR handle, and visually mark it on the long-span lattice shell VR model through the VR head display device.

7. The VR-based stress analysis system for large-span lattice shell column displacement according to claim 6 is characterized by: The risk positioning model is: P high =RF({x1,x2,x3,x4,x5})? Where, P high is the high-risk area for column misalignment, x1, x2, x3, x4, and x5 are structural materials, construction technology, environmental conditions, load conditions, and foundation conditions, respectively, and RF is the random forest algorithm.

8. The VR-based stress analysis system for large-span lattice shell column displacement according to claim 6 is characterized by: The interactive response prediction model is: ； Where, is the structural stress data, and D is the column deflection amplitude and direction.

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