A 3D simulation design method and system based on the structure of rock wool curtain wall panels

Abstract

The present invention relates to the technical field of wall panel simulation design, and particularly to a three-dimensional simulation design method and system based on the structure of a rock wool curtain wall panel. The method includes the following steps: obtaining the source data of the microscopic morphology of the wall panel; performing isotropic microscopic feature enhancement on the source data of the microscopic morphology of the wall panel to obtain the microscopic fiber morphology data of the wall panel; performing digital twin reconstruction on the rock wool curtain wall panel based on the microscopic fiber morphology data of the wall panel to obtain the first wall panel reconstruction structure model; performing thermodynamic field mapping on the first wall panel reconstruction structure model to obtain the microscopic scale heat conduction feature map; performing cross-scale physical field coupling and superposition on the microscopic scale heat conduction feature map to obtain the multi-field comprehensive response map of the wall panel; performing cross-scale performance dynamic simulation on the rock wool curtain wall panel based on the multi-field comprehensive response map of the wall panel to obtain the comprehensive performance evaluation result of the wall panel. The present invention can realize the customized design of the rock wool curtain wall panel and improve the flexibility and applicability of the design.

Description

Technical Field

[0001] The present invention relates to the technical field of wall panel simulation design, and particularly to a three-dimensional simulation design method and system based on the structure of rock wool curtain wall panels. Background Art

[0002] The design of rock wool curtain wall panels involves multiple complex factors, such as structural stability, thermal insulation performance, and fire safety. In existing three-dimensional simulation design methods, accurately simulating the performance of rock wool curtain wall panels under different environmental conditions is a major challenge. For example, under extreme climatic conditions, such as high temperature, low temperature, or high humidity environments, the thermal insulation performance and structural stability of rock wool curtain wall panels will be significantly affected. However, existing simulation software often has difficulty accurately simulating the long-term effects of these environmental factors on the performance of curtain wall panels, resulting in a large deviation between the design results and the actual performance. This deviation not only causes a decline in the performance of the curtain wall system during actual use but also increases the costs of later maintenance and replacement. In practical applications, the design of rock wool curtain wall panels also needs to consider various factors during the construction process, such as installation accuracy, material selection, and construction technology. These factors have an important impact on the final performance of the curtain wall system. However, existing three-dimensional simulation design methods often ignore these detailed problems during the construction process, resulting in a large difference between the design results and the actual construction situation. For example, during the installation process, if the connection between the curtain wall panel and the steel structure is not tight or there are deviations, it will directly affect the thermal insulation performance and structural stability of the curtain wall system. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a three-dimensional simulation design method and system based on the structure of rock wool curtain wall panels to solve at least one of the above technical problems.

[0004] To achieve the above object, a three-dimensional simulation design method based on the structure of rock wool curtain wall panels includes the following steps:

[0005] Step S1: Obtain the source data of the wall panel's microscopic morphology; enhance the isotropic microscopic features of the source data of the wall panel's microscopic morphology to obtain the data of the wall panel's microscopic fiber morphology; perform digital twin reconstruction on the rock wool curtain wall panel based on the data of the wall panel's microscopic fiber morphology to obtain the first reconstructed structure model of the wall panel;

[0006] Step S2: Perform thermodynamic field mapping on the first reconstructed structure model of the wall panel to obtain a microscopic-scale heat conduction feature map; perform cross-scale physical field coupling and superposition on the microscopic-scale heat conduction feature map to obtain a multi-field comprehensive response map of the wall panel; perform cross-scale performance dynamic simulation on the rock wool curtain wall panel based on the multi-field comprehensive response map of the wall panel to obtain the comprehensive performance evaluation result of the wall panel;

[0007] Step S3: Obtain the wall panel construction process parameter set; conduct a construction error assessment on the wall panel construction process parameter set to obtain the wall panel construction error index set;

[0008] Step S4: Reconstruct the performance boundary conditions of the rock wool curtain wall panel according to the comprehensive performance evaluation result of the wall panel to obtain the second wall panel reconstruction structural model; conduct extreme environment multi-scenario simulations on the second wall panel reconstruction structural model based on the wall panel construction error index set to obtain the wall panel multi-scenario performance response data; conduct a fatigue failure risk assessment on the rock wool curtain wall panel according to the wall panel multi-scenario performance response data to obtain the wall panel performance degradation prediction result;

[0009] Step S5: Extract the target optimization factors from the wall panel performance degradation prediction result to obtain the key design adjustment factors of the wall panel; optimize the structural parameters of the rock wool curtain wall panel according to the key design adjustment factors of the wall panel to obtain the wall panel performance enhancement design scheme; reconstruct the second wall panel reconstruction structural model according to the wall panel performance enhancement design scheme to obtain the optimized wall panel three-dimensional simulation model.

[0010] By obtaining the microscopic morphology source data of the rock wool curtain wall panel and enhancing the isotropic microscopic features, the present invention can more accurately simulate the microscopic structure of the rock wool curtain wall panel, thereby improving the accuracy of the simulation. This helps to reduce the deviation between the design result and the actual performance, and enhance the performance and reliability of the curtain wall system. By performing thermodynamic field mapping and cross-scale physical field coupling superposition on the first wall panel reconstruction structural model, the thermal conduction characteristics and multi-field comprehensive response of the wall panel under different environmental conditions can be comprehensively evaluated, thereby optimizing the structural design and enhancing the structural stability of the rock wool curtain wall panel. By performing cross-scale performance dynamic simulation on the rock wool curtain wall panel, the comprehensive performance evaluation result of the wall panel can be obtained, which helps to comprehensively understand the performance of the material in actual applications and provide a scientific basis for the design. By obtaining the wall panel construction process parameter set and conducting a construction error assessment, the errors in the construction process can be quantified, providing data support for construction quality control and reducing the impact of construction errors on the performance of the curtain wall system. Conducting extreme environment multi-scenario simulations on the second wall panel reconstruction structural model based on the wall panel construction error index set can predict the performance response of the wall panel under extreme environments, thereby enhancing the environmental adaptability and durability of the rock wool curtain wall panel. Conducting a fatigue failure risk assessment on the wall panel multi-scenario performance response data can predict the performance degradation of the wall panel. By extracting the target optimization factors from the wall panel performance degradation prediction result, the key design adjustment factors can be identified, and the structural parameters of the rock wool curtain wall panel can be optimized, thereby obtaining a performance enhancement design scheme and enhancing the overall performance of the curtain wall system. Reconstructing the second wall panel reconstruction structural model according to the wall panel performance enhancement design scheme, the present invention can achieve the customized design of the rock wool curtain wall panel, meet the requirements of specific projects, and improve the flexibility and applicability of the design.

[0011] Preferably, the present invention further provides a three-dimensional simulation design system based on the rock wool curtain wall board structure, which is used to execute the three-dimensional simulation design method based on the rock wool curtain wall board structure as described above. The three-dimensional simulation design system based on the rock wool curtain wall board structure includes:

[0012] A digital twin module, which is used to obtain the source data of the wall board micro-topography; perform isotropic micro-feature enhancement on the source data of the wall board micro-topography to obtain the wall board micro-fiber morphology data; perform digital twin reconstruction on the rock wool curtain wall board based on the wall board micro-fiber morphology data to obtain the first wall board reconstruction structure model;

[0013] A performance simulation module, which is used to perform thermodynamic field mapping on the first wall board reconstruction structure model to obtain a micro-scale heat conduction feature map; perform cross-scale physical field coupling and superposition on the micro-scale heat conduction feature map to obtain a wall board multi-field comprehensive response map; perform cross-scale performance dynamic simulation on the rock wool curtain wall board based on the wall board multi-field comprehensive response map to obtain a wall board comprehensive performance evaluation result;

[0014] An error evaluation module, which is used to obtain the wall board construction process parameter set; perform construction error evaluation on the wall board construction process parameter set to obtain the wall board construction error index set;

[0015] A risk evaluation module, which is used to perform performance boundary condition reconstruction on the rock wool curtain wall board according to the wall board comprehensive performance evaluation result to obtain the second wall board reconstruction structure model; perform extreme environment multi-scenario simulation on the second wall board reconstruction structure model based on the wall board construction error index set to obtain the wall board multi-scenario performance response data; perform fatigue failure risk evaluation on the rock wool curtain wall board according to the wall board multi-scenario performance response data to obtain the wall board performance degradation prediction result;

[0016] A simulation model reconstruction module, which is used to extract the target optimization factor from the wall board performance degradation prediction result to obtain the wall board key design adjustment factor; perform structural parameter optimization on the rock wool curtain wall board according to the wall board key design adjustment factor to obtain the wall board performance enhancement design scheme; reconstruct the second wall board reconstruction structure model according to the wall board performance enhancement design scheme to obtain the optimized wall board three-dimensional simulation model.

[0017] The present invention improves the design efficiency and accuracy through automated module design, reduces human errors and repetitive work, and enhances the accuracy of design results by accurately acquiring and processing micro-topography data. The system can conduct comprehensive performance simulations, including thermodynamic field mapping and the generation of multi-field comprehensive response diagrams, providing a comprehensive perspective for the performance analysis of rock wool curtain wall panels. The performance degradation under extreme environments is predicted through the risk assessment module, providing a scientific basis for risk management and maintenance strategies. The key design adjustment factors are extracted according to the performance degradation prediction results through the simulation model reconstruction module, optimizing the structural parameters and enhancing the overall performance. The system can also provide customized design solutions to meet specific engineering requirements, reduce maintenance costs, improve reliability and safety, and enhance environmental adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description with reference to the accompanying drawings:

[0019] Figure 1 The schematic diagram of the step flow of a three-dimensional simulation design method based on the structure of a rock wool curtain wall panel in an embodiment is shown.

[0020] Figure 2 The detailed step flow schematic diagram of step S17 in an embodiment is shown.

[0021] Figure 3 The detailed step flow schematic diagram of step S265 in an embodiment is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0023] In addition, the drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0024] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0025] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a three-dimensional simulation design method based on the structure of rock wool curtain wall panels, including the following steps:

[0026] Step S1: Obtain the source data of the wall panel micro-topography; perform isotropic micro-feature enhancement on the source data of the wall panel micro-topography to obtain the wall panel micro-fiber morphology data; perform digital twin reconstruction on the rock wool curtain wall panel based on the wall panel micro-fiber morphology data to obtain the first wall panel reconstruction structure model;

[0027] Step S2: Perform thermodynamic field mapping on the first wall panel reconstruction structure model to obtain a micro-scale heat conduction feature map; perform cross-scale physical field coupling and superposition on the micro-scale heat conduction feature map to obtain a wall panel multi-field comprehensive response map; perform cross-scale performance dynamic simulation on the rock wool curtain wall panel based on the wall panel multi-field comprehensive response map to obtain a wall panel comprehensive performance evaluation result;

[0028] Step S3: Obtain the wall panel construction process parameter set; perform construction error evaluation on the wall panel construction process parameter set to obtain the wall panel construction error index set;

[0029] Step S4: Reconstruct the performance boundary conditions of the rock wool curtain wall panel according to the wall panel comprehensive performance evaluation result to obtain the second wall panel reconstruction structure model; perform extreme environment multi-scenario simulation on the second wall panel reconstruction structure model based on the wall panel construction error index set to obtain the wall panel multi-scenario performance response data; perform fatigue failure risk assessment on the rock wool curtain wall panel according to the wall panel multi-scenario performance response data to obtain the wall panel performance degradation prediction result;

[0030] Step S5: Extract the target optimization factor from the wall panel performance degradation prediction result to obtain the wall panel key design adjustment factor; optimize the structural parameters of the rock wool curtain wall panel according to the wall panel key design adjustment factor to obtain the wall panel performance enhancement design scheme; reconstruct the second wall panel reconstruction structure model according to the wall panel performance enhancement design scheme to obtain the optimized wall panel three-dimensional simulation model.

[0031] In this embodiment, first, a scanning electron microscope (SEM) is used to image the microstructure of the rock wool curtain wall board to obtain high-resolution image data, and the image analysis software ImageJ is used for image preprocessing and analysis. Subsequently, filters and edge enhancement algorithms are applied in ImageJ to process the SEM image, highlighting the microscopic structural features of the fibers, and an isotropic filter is used to enhance the isotropic features of the fibers, thereby obtaining the microscopic fiber morphology data of the wall board. Next, in the 3D modeling software Rhinoceros 3D, a digital twin model of the rock wool curtain wall board is reconstructed according to the microscopic fiber morphology data, and parametric modeling technology is used to adjust the geometric parameters of the model to obtain the first wall board reconstruction structure model. In COMSOL Multiphysics, a thermodynamic field simulation is performed on the first wall board reconstruction structure model, heat conduction analysis is set, material properties and boundary conditions are defined, the simulation is run to obtain a microscopic-scale heat conduction characteristic map, and it is coupled and superimposed with mechanical and electrical field data to obtain a multi-field comprehensive response map of the wall board. Based on the multi-field comprehensive response map, cross-scale performance dynamic simulation is carried out to evaluate the comprehensive performance of the rock wool curtain wall board under different environmental conditions to obtain an evaluation result. At the same time, the key process parameters during the wall board construction are collected, and the statistical software SPSS is used for data analysis. Based on the construction process parameter set, the Monte Carlo simulation method is used for construction error evaluation to obtain the wall board construction error index set. According to the comprehensive performance evaluation result, the performance boundary conditions of the rock wool curtain wall board are reconstructed in Rhinoceros 3D to obtain the second wall board reconstruction structure model, and in COMSOL, extreme environment multi-scenario simulation is performed on the second wall board reconstruction structure model based on the construction error index set to obtain the wall board multi-scenario performance response data. Using the fatigue analysis software FESAFE, a fatigue failure risk assessment is performed on the rock wool curtain wall board according to the multi-scenario performance response data to obtain the wall board performance degradation prediction result. In MATLAB, machine learning algorithms are used to analyze the performance degradation prediction result, extract the key design adjustment factors of the wall board, and in Rhinoceros 3D, the structural parameters of the rock wool curtain wall board are optimized according to the key design adjustment factors to obtain the wall board performance enhancement design scheme. Finally, in Rhinoceros 3D, the second wall board reconstruction structure model is reconstructed according to the performance enhancement design scheme to obtain the optimized 3D simulation model of the wall board.

[0032] Preferably, step S1 includes the following steps:

[0033] Step S11: Collect the three-dimensional topography of the microstructure of the rock wool curtain wall board to obtain the original data of the microscopic topography of the wall board;

[0034] Specifically, a laser scanner can be used to scan the surface of the rock wool curtain wall board. The scanner can emit laser beams and calculate the spatial coordinates of each point on the surface of the rock wool board by measuring the time and angle of the reflected laser beams. Place the rock wool curtain wall board on a stable platform to ensure that its surface is flat and unobstructed, so that the laser scanner can cover the entire surface. Start the scanner software and set the scanning parameters, including scanning resolution, scanning speed, and scanning range. For example, set the resolution to 0.01 mm to ensure that the tiny details on the surface of the rock wool board can be captured. Start the scanning process, and the laser scanner will move along the preset path to scan the surface of the rock wool board point by point and collect data. After the scanning is completed, the software will generate a point cloud data set containing the three-dimensional coordinate information of the surface of the rock wool board. Finally, import the point cloud data into professional 3D modeling software such as Autodesk Maya or SolidWorks, and convert the point cloud data into a 3D model through the software's algorithm to obtain the source data of the microscopic morphology of the rock wool curtain wall board.

[0035] Step S12: Extract the fiber microscopic geometric features from the source data of the wall board microscopic morphology to obtain the wall board fiber geometric feature vector set;

[0036] Specifically, professional software such as CloudCompare can be used to convert the source data of the wall board microscopic morphology into a two-dimensional image. Then, process the two-dimensional image using the MATLAB or Python OpenCV library, apply filters to remove noise and improve the image quality, and then use threshold segmentation or edge detection algorithms such as the Sobel or Canny algorithm to identify the edges and contours of the fibers. In the feature extraction stage, analyze the cross-sectional image of the fiber, use the circle fitting or ellipse fitting algorithm to determine the diameter of the fiber, trace the endpoints along the center line of the fiber to determine the length of the fiber, calculate the curvature change of the fiber center line to evaluate the degree of bending, and analyze the angle between the fiber center line and the reference coordinate system to determine the fiber direction. Convert these geometric features into numerical vectors to form the feature vector of each fiber. Finally, merge the geometric feature vectors of all fibers to form the wall board fiber geometric feature vector set.

[0037] Step S13: Adjust the fiber structure topological network of the rock wool curtain wall board according to the wall board fiber geometric feature vector set to obtain the wall board fiber network connection topological graph;

[0038] Specifically, the geometric feature vector set of the wallboard fibers can be input into professional topology analysis software, such as the igraph package in MATLAB or R language. In the software, each fiber is represented as a node in the network, and the connections between the nodes represent the spatial relationships between the fibers. The length and direction features of the fibers are used to determine the weights of the connections between the nodes. The shortest path algorithm or minimum spanning tree algorithm in graph theory is applied to optimize the connections between the fibers to ensure the connectivity and structural stability of the network. For example, fibers with shorter lengths and smaller angles can be preferentially connected to simulate the natural aggregation trend of the fibers in the rock wool board. The connection strength of the nodes is adjusted according to the curvature and diameter features of the fibers to reflect the differences in mechanical properties of different fibers. The software will generate a topological graph of the wallboard fiber network connections, showing the connection relationships between the fibers and the overall structure of the network.

[0039] Step S14: Reconstruct the spatial configuration of the topological graph of the wallboard fiber network connections to obtain a wallboard fiber space model;

[0040] Specifically, the topological graph of the wallboard fiber network connections can be imported into 3D modeling software, such as SolidWorks or 3ds Max. In the software, according to the connection relationships in the topological graph, three-dimensional space coordinates are assigned to each fiber node. The length and direction features of the fibers are used to determine the specific positions of the nodes in space. Based on the spatial coordinates and connection relationships of the nodes, a three-dimensional geometric model of the fibers is constructed. Curves or cylinders are used to represent the fibers to visually display the spatial orientations and interrelationships of the fibers. The fiber model is optimized in detail according to actual requirements, such as adjusting the thickness and surface texture of the fibers. The constructed wallboard fiber space model is compared with the physical properties of the actual rock wool board, such as density and strength, and the model is adjusted if necessary to ensure the accuracy and practicality of the model. Finally, the wallboard fiber space model is output.

[0041] Step S15: Numerically simulate the interfacial characteristics between the fibers of the rock wool curtain wall board based on the wallboard fiber space model to obtain the stress-strain coupling data of the rock wool fiber interface, and evaluate the interfacial connection strength of the stress-strain coupling data of the rock wool fiber interface to obtain the evaluation data of the fiber interface connection strength;

[0042] Specifically, the wall panel fiber space model can be imported into finite element analysis (FEA) software such as ANSYS or ABAQUS, which are capable of simulating complex mechanical behaviors. In the FEA software, define the material properties of the rock wool fibers, including elastic modulus, Poisson's ratio, yield strength, etc. These parameters can be obtained based on experimental data or literature. Mesh the fiber space model to create a finite element mesh for numerical simulation. Select an appropriate mesh size to ensure the accuracy and computational efficiency of the simulation. Set the simulation boundary conditions, such as fixing one end of the fiber to simulate the tension or compression of the fiber. Apply corresponding loads according to the actual working conditions, such as tensile force or pressure. Run the FEA software for numerical simulation to calculate the stress and strain distributions of the fibers under the given loads. The software will provide stress-strain coupling data at the fiber interface. After the simulation is completed, analyze the obtained stress-strain data to identify stress concentration areas and potential failure points. Use the post-processing tools in the FEA software to analyze the stress-strain coupling data at the fiber interface and evaluate the interface connection strength. Failure criteria for interface elements, such as the maximum stress criterion or the energy release rate criterion, can be adopted to evaluate the interface connection strength. Finally, output the fiber interface connection strength evaluation data.

[0043] Step S16: Quantify the toughness of the fiber network for the fiber interface connection strength evaluation data to obtain a fiber network connection ability index, and enhance the isotropic characteristics of the microscopic fiber morphology of the rock wool curtain wall panel according to the fiber network connection ability index to obtain the wall panel microscopic fiber morphology data;

[0044] Specifically, the fiber interface connection strength assessment data can be imported using material science software, such as MATLAB or Python's NumPy library. Statistical analysis methods, such as analysis of variance (ANOVA), are used to analyze the interface connection strength data to identify key toughness parameters in the fiber network, such as the average connection strength and standard deviation. A suitable toughness quantification model, such as the Weibull distribution model, is selected to fit the interface connection strength data to obtain the toughness parameters of the fiber network, such as shape parameters and scale parameters. According to the parameters of the Weibull distribution model, the toughness quantification indicators of the fiber network, such as reliability and failure probability, are calculated. The wallboard fiber spatial model is imported using three-dimensional modeling software, such as Rhinoceros 3D. According to the toughness quantification indicators, the spatial distribution and connection mode of the fibers are adjusted. For example, if the toughness analysis shows that the connection strength of certain areas is low, the connection capacity of these areas can be enhanced by increasing the fiber density or changing the fiber direction. The fiber network is spatially reorganized using an isotropic enhancement algorithm, such as the K-means clustering algorithm, to ensure the uniform distribution of fibers in all directions, thereby improving the overall performance of the material. Set specific parameter values, for example, increase the fiber density by 10%, or adjust the distribution range of the fiber direction to ±15°, to achieve isotropic feature enhancement. Output the microscopic fiber morphology data of the wallboard after isotropic feature enhancement.

[0045] Step S17: Perform digital twin reconstruction of the rock wool curtain wall panel based on the micro-fiber morphology data of the wall panel to obtain a reconstructed structural model of the first wall panel.

[0046] Specifically, please refer to the sub-steps of step S17 for the detailed implementation process of this embodiment.

[0047] The present invention can obtain fine design data, enhance design details, and improve the accuracy and reliability of the design through the acquisition of microstructure three-dimensional morphology. By extracting the fiber geometric feature vector set, the specific geometric characteristics of the fiber are optimized, and the material use efficiency and structural stability are improved. By optimizing the fiber structure topological network, the connection mode between fibers is improved, and the overall performance of the material is enhanced, especially when subjected to complex stress. The spatial configuration reconstruction makes the obtained model more in line with the actual application requirements, providing an accurate basis for performance simulation and structural design. By numerically simulating the interface characteristics between fibers, stress-strain coupling data is obtained to deeply understand the micromechanical behavior of the material. By evaluating the interface connection strength, identifying weak links, strengthening the design, and improving the structural durability. By quantifying the toughness of the fiber network, the material's impact resistance and deformation ability are improved. The microscopic fiber morphology is enhanced by isotropic characteristics, so that the material performance is more balanced, and the uniformity and reliability are improved. Finally, based on the digital twin reconstruction of the microscopic fiber morphology data, the obtained structural model can truly reflect the material properties and provide an accurate digital model for performance testing and verification.

[0048] Preferably, step S17 includes the following steps:

[0049] Step S171: Based on the microscopic fiber morphology data of the wall panel, construct discrete units of the microscopic structure of the rock wool curtain wall panel to obtain a set of discrete units of the wall panel microscopic structure;

[0050] Specifically, the microscopic fiber morphology data of the wall panel can be imported into computer-aided design (CAD) software, such as AutoCAD or SolidWorks. These data should include the geometric characteristics of each fiber, such as diameter, length, and spatial coordinates. In the CAD software, discrete units are constructed according to the geometric characteristics of the microscopic fibers. Each fiber can be regarded as a discrete unit, and a cylinder or an elongated cube is used to represent the fiber. The specific operations include: selecting appropriate cylinder parameters, for example, setting the diameter to 0.5 mm and the length to 10 mm to simulate the size of actual rock wool fibers. Using the copy and array functions of the software, arrange these discrete units according to the spatial distribution in the microscopic fiber morphology data to ensure that the relative positions between the discrete units are consistent with the actual situation. After completing the construction of the discrete units, use the mesh generation tool in the software to mesh the entire microscopic structure. Select an appropriate mesh size, for example, a mesh size of 1 mm. Finally, export the constructed set of discrete units of the wall panel microscopic structure as a standard format file (such as STL or OBJ).

[0051] Step S172: Perform topological reorganization on the set of discrete units of the wall panel microscopic structure to obtain a prototype of the reconstructed microscopic structure of the wall panel;

[0052] Specifically, the set of discrete units of the wall panel microscopic structure can be imported into 3D modeling software, such as Rhinoceros3D or Blender. These software can handle complex geometric shapes and topological structures. In the software, use the topology optimization tool to reorganize the set of discrete units. The specific operations include: selecting a topology optimization algorithm, for example, the density-based topology optimization method, and setting the optimization goal to improve the strength and toughness of the structure. According to the mechanical properties of the material and the actual application requirements, set the optimization parameters, such as the maximum allowable material usage of 50%. Run the topology optimization program, and the software will automatically adjust the connection method and arrangement of the discrete units to generate a new microscopic structure prototype. After obtaining the reconstructed microscopic structure prototype, use the visualization function of the software to check the connectivity and stability of the structure. If necessary, manually adjust the positions or connection methods of some discrete units. Finally, export the reconstructed prototype of the wall panel microscopic structure as a standard format file (such as STEP or IGES).

[0053] Step S173: Perform parametric digital mapping on the rock wool curtain wall panel based on the prototype of the reconstructed microscopic structure of the wall panel to obtain a parametric mapping diagram of the wall panel microscopic structure;

[0054] Specifically, the microstructural prototype of the wall panel can be imported into parametric modeling software such as CATIA or Revit. These software support parametric-based modeling, allowing the definition and adjustment of the geometric parameters of the structure. In the software, define the key parameters that affect the performance of the rock wool curtain wall panel, such as fiber diameter, fiber spacing, fiber direction, and fiber density. Set a parameter list for each parameter. For example, the fiber diameter is set to 0.5mm, 1mm, 1.5mm, etc., and the fiber spacing is set to 5mm, 10mm, 15mm, etc. Utilize the parametric function of the software to create a parametric model of the wall panel microstructure according to the defined parameters. For example, use the "table" or "family" function to define the geometric parameters of the fibers and adjust the position and size of the fibers in a parameter-driven manner. Set the parameter range and step size of the parametric model. For example, the step size of the fiber diameter is 0.5mm, and the step size of the fiber spacing is 5mm. Then, run the parameter scan function of the software to generate a series of parametric wall panel microstructure models, each model corresponding to a specific set of parameter values. The software will generate a parametric map of the wall panel microstructure, which is a database or chart containing all the parametric models, showing the influence of different parameter values on the wall panel microstructure.

[0055] Step S174: Encode the microfiber structure characteristics of the rock wool curtain wall panel based on the parametric map of the wall panel microstructure to obtain a set of wall panel microstructure characteristic codes;

[0056] Specifically, the parametric map of the wall panel microstructure can be imported into data analysis software such as the Pandas library of Python or MATLAB. These software can process and analyze large-scale data sets. In the software, extract the characteristics of the microfiber structure for each parametric model in the parametric map, such as the volume fraction of the fibers, orientation distribution, porosity, etc. For example, use image processing techniques to extract the orientation distribution of the fibers from the two-dimensional or three-dimensional images of the parametric models. Convert the extracted characteristics into numerical codes to form a set of wall panel microstructure characteristic codes. For example, encode the volume fraction of the fibers as 0.45, the orientation distribution as [0.2, 0.3, 0.5] (representing the fiber ratios in three main directions respectively), and the porosity as 0.15. Define a set of encoding rules to map the numerical codes of each characteristic to specific bits or fields. For example, define a 16-bit characteristic code, where the first 8 bits represent the fiber volume fraction, the next 4 bits represent the orientation distribution, and the last 4 bits represent the porosity. According to the encoding rules, generate a unique characteristic code for each parametric model in the parametric map. These characteristic codes form a set of wall panel microstructure characteristic codes.

[0057] Step S175: Use the set of wall panel microstructure characteristic codes to perform digital twin mapping transformation on the microstructure of the rock wool curtain wall panel to obtain a first reconstructed wall panel structure model.

[0058] Specifically, the microstructural feature code set of the wall panel can be obtained from step S174. These feature code sets contain numerical encodings of parameters such as fiber volume fraction, orientation distribution, and porosity. Ensure that the feature code set is stored in CSV or JSON format and read using data analysis tools such as Python or MATLAB. Select a suitable digital twin platform, such as Digital Twin Platform (DTP), which is a software platform capable of processing complex data and performing 3D modeling. DTP supports direct mapping from data to models and allows users to dynamically adjust models based on input parameters. In DTP, set the parameter mapping rules to map each parameter encoding in the feature code set to the corresponding attribute in the digital model. For example, map the encoding of the fiber volume fraction to "volume fraction" in the model material property, the orientation distribution encoding to "fiber direction", and the porosity encoding to "porosity". Use the modeling tools in DTP to construct a 3D digital model of the rock wool curtain wall panel according to the parameter mapping rules. During the model construction process, dynamically adjust the model parameters to match the numerical values in the feature code set. For example, if the fiber volume fraction in the feature code set is 0.45, then set the volume fraction of the fiber material in the model to 45%. After the model construction is completed, optimize the details of the model to ensure that it accurately reflects the microstructure of the rock wool curtain wall panel. This includes adjusting the arrangement of the fibers, optimizing the pore structure, and simulating the micro-mechanical behavior of the material. Run simulations in DTP to verify the accuracy of the digital twin model. By comparing with experimental data or known performance indicators, ensure that the model can accurately predict the actual behavior of the rock wool curtain wall panel. Finally, export the reconstructed structural model of the first wall panel after digital twin mapping transformation to a standard format file, such as STEP or IGES.

[0059] Through the construction of microstructural discrete units, the present invention can meticulously simulate the microstructure of the rock wool curtain wall panel, capture the complex interactions inside the material, and improve the simulation accuracy. Through topological reorganization, it provides the flexibility to optimize the material structure while maintaining physical properties to meet diverse engineering requirements. Through parametric digital mapping, it ensures the quantification and simulation of the microstructure. Through systematic management of material properties by feature encoding, it improves the data processing efficiency. Through digital twin mapping transformation technology, it creates a digital model corresponding to the actual physical structure, revolutionarily predicting the behavior and performance of the material.

[0060] Preferably, step S2 includes the following steps:

[0061] Step S21: Perform a thermodynamic boundary mapping on the reconstructed structural model of the first wall panel to obtain the initial thermodynamic field data of the wall panel;

[0062] Specifically, the reconstructed structural model of the wall panel can be imported into thermodynamic analysis software such as ANSYS or COMSOL Multiphysics. These software can handle complex thermodynamic problems and provide boundary mapping functions. In the software, define the thermodynamic boundary conditions of the model. For example, set one side of the wall panel as the heated surface with a temperature of 60 °C, and the other side as the ambient surface with a temperature of 20 °C. At the same time, define the convective boundary conditions, such as the convective heat transfer coefficient of 10 W / (m 2 ·K). Input the thermodynamic properties of the wall panel material, including specific heat capacity, density, and thermal conductivity. For example, set the thermal conductivity of rock wool to 0.04 W / (m·K), the specific heat capacity to 800 J / (kg·K), and the density to 150 kg / m 3 . The software will calculate the initial thermodynamic field data of the wall panel according to the input boundary conditions and material properties, including temperature distribution and heat flux density. Output the calculated initial thermodynamic field data in the form of graphs and numerical values, including temperature contour maps, heat flow line distributions, etc.

[0063] Step S22: Construct a thermal conduction discrete grid for the first reconstructed structural model of the wall panel based on the initial thermodynamic field data of the wall panel to obtain a wall panel thermal conduction grid model;

[0064] Specifically, the initial thermodynamic field data of the wall panel can be imported into finite element analysis software such as ABAQUS or ANSYS. In the software, perform mesh division on the reconstructed structural model of the wall panel. Select appropriate mesh types and sizes. For example, use tetrahedral meshes with a mesh size set to 2 mm to ensure the accuracy of the simulation and calculation efficiency. According to the thermodynamic properties of the wall panel material, assign corresponding thermal conductivity, specific heat capacity, and density to each element in the mesh model. For example, set the thermal conductivity of the rock wool material to 0.04 W / (m·K). Apply the thermodynamic boundary conditions defined in step S21 to the corresponding boundaries of the mesh model. For example, set the temperature of the heated surface to 60 °C, the temperature of the ambient surface to 20 °C, and apply the convective boundary conditions. Check the integrity and accuracy of the mesh model to ensure that there are no mesh quality problems, such as over-stretched or distorted elements. Optimize and adjust the mesh if necessary. Finally, output the constructed wall panel thermal conduction grid model.

[0065] Step S23: Numerically simulate the internal heat conduction characteristics of the fibers in the rock wool curtain wall panel based on the wall panel thermal conduction grid model to obtain microscopic-scale heat conduction flux data;

[0066] Specifically, finite element analysis software such as ABAQUS can be launched, and the wall panel heat conduction mesh model can be imported. Set up the heat conduction analysis task in the software and select an appropriate heat conduction analysis type, such as steady-state heat conduction or transient heat conduction analysis. Input the detailed thermophysical properties of the rock wool material in the model, including thermal conductivity (e.g., 0.04 W / (m·K)), specific heat capacity (e.g., 800 J / (kg·K)), and density (e.g., 150 kg / m 3 ³). Refine the mesh for the fiber part in the model and set the mesh size to 0.5 mm to improve the simulation accuracy. Run the heat conduction analysis, and the software will calculate the heat conduction flux at each mesh node, including heat flux density and temperature gradient. After the simulation is completed, extract the heat conduction flux data at the microscale, which includes the heat flux density vector and temperature value at each mesh node. Output the microscale heat conduction flux data in the form of numerical tables and contour plots.

[0067] Step S24: Reconstruct the spatial characteristics of the microscale heat conduction flux data to obtain a microscale heat conduction characteristic map;

[0068] Specifically, the microscale heat conduction flux data can be imported into data processing and visualization software such as MATLAB or the Matplotlib library of Python. Use mathematical tools in the software, such as Fourier transform or wavelet transform, to extract the spatial characteristics of the heat conduction flux data, including the main heat conduction paths and heat resistance regions. Reconstruct the microscale heat conduction characteristic map based on the extracted spatial characteristics. This includes creating isothermal line diagrams, heat flow line diagrams, and heat flux vector field diagrams. During the visualization process, select appropriate color mappings and scales to clearly display the changes in temperature gradient and heat flux density. Generate microscale heat conduction characteristic maps, including two-dimensional and three-dimensional views, to intuitively display the heat conduction characteristics inside the rock wool curtain wall panel.

[0069] Step S25: Perform cross-scale physical field coupling and superposition on the microscale heat conduction characteristic map to obtain a multi-field comprehensive response map of the wall panel;

[0070] Specifically, the microscale heat conduction characteristic map can be obtained from step S24, including data such as temperature distribution and heat flux density. At the same time, data of other physical fields are collected, such as mechanical stress field, electric field, etc., and these data are from previous simulations or experimental measurements. Select advanced simulation software, such as COMSOL Multiphysics or ANSYS. These software provide functions of cross-scale simulation and multi-physical field coupling analysis. In the software, integrate the heat conduction characteristic map with data of other physical fields. For example, overlay the temperature distribution data with the mechanical stress field data to analyze the thermal stress coupling effect. Set the coupling analysis parameters, and select appropriate coupling algorithms and solvers. For example, use an iterative solver to handle the thermal-mechanical coupling problem and set the convergence criterion. To improve the accuracy of the coupling analysis, refine the mesh of key regions. For example, for regions where stress concentration or large temperature gradients are expected, set the mesh size to 0.1 mm. Run the coupling simulation, and the software will calculate the interaction and comprehensive response of the cross-scale physical fields. This includes stress distribution caused by thermal expansion, thermoelectric effect, etc. After the simulation is completed, extract the multi-field comprehensive response map of the wall panel, including the thermal-mechanical coupling stress map, thermoelectric potential distribution map, etc. Verify the accuracy of the simulation results by comparing with experimental data. Visualize and output the multi-field comprehensive response map in the form of color cloud maps and vector fields to clearly show the interaction and comprehensive effect between different physical fields.

[0071] Step S26: Based on the reconstructed structural model of the first wall panel and the multi-field comprehensive response map of the wall panel, perform cross-scale performance dynamic simulation on the rock wool curtain wall panel to obtain the comprehensive performance evaluation result of the wall panel.

[0072] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of step S26.

[0073] Through thermodynamic boundary mapping, the present invention provides accurate initial thermodynamic field data for the reconstructed structural model of the wall panel. By constructing a discrete heat conduction grid model, the simulation of heat conduction characteristics is made more refined, and the heat transfer mechanism inside the material is deeply revealed. By numerically simulating the heat conduction characteristics inside the fiber, microscale heat conduction flux data can be obtained to evaluate the thermal performance of the material. The heat conduction characteristic map formed by spatial feature reconstruction helps to visually analyze the heat conduction characteristics of the material. The cross-scale physical field coupling and superposition enhance the understanding of the behavior of the material under the action of multi-physical fields, and the cross-scale performance dynamic simulation based on the reconstructed structural model and the multi-field comprehensive response map provides the comprehensive performance evaluation result of the wall panel.

[0074] Preferably, step S26 includes the following steps:

[0075] Step S261: Extract features from the reconstructed structural model of the first wall panel to obtain a set of wall panel microstructural feature vectors;

[0076] Specifically, the data file of the reconstructed structural model of the first wall panel can be imported into a structural analysis software, such as ABAQUS or ANSYS. In the software, set the feature extraction parameters, including geometric features (such as fiber diameter, spacing) and topological features (such as connection nodes, paths). Select a specific analysis tool, such as the "eigenvector extraction" tool, to identify the key microstructural features of the model. Run the feature extraction algorithm, and the software will automatically scan the model and identify the key features, such as the distribution density, direction, and connection pattern of the fibers. For example, extract the diameter and spatial coordinates of each fiber in the fiber bundle, and convert this data into eigenvectors. Vectorize the extracted features to form a parametric representation. For example, if there are 100 fibers in the model and the eigenvector of each fiber contains three parameters: its diameter, length, and direction, then a vector set containing 300 parameters will be obtained.

[0077] Step S262: Analyze the features of the multi-field comprehensive response diagram of the wall panel to obtain the spectral feature space of the wall panel response diagram;

[0078] Specifically, the data file of the multi-field comprehensive response diagram of the wall panel can be imported into a data analysis software, such as MATLAB or Python with the NumPy and SciPy libraries. Preprocess the response diagram data, including denoising and normalization. For example, use wavelet transform to remove the high-frequency noise in the signal, and then standardize the data through the Z-score normalization method. Apply a spectral analysis tool, such as the fast Fourier transform (FFT), to perform spectral decomposition on the response diagram data and extract the frequency domain features. Set the parameters of the FFT, such as a sampling frequency of 1000 Hz, to adapt to the characteristics of the data. Extract the key spectral features from the spectral analysis results, such as the main frequency components, frequency distribution range, and amplitude features. For example, identify the main vibration frequencies and the corresponding amplitude values in the response diagram. Construct the spectral feature space of the wall panel response diagram from the extracted spectral features. This feature space can be represented by a three-dimensional or multi-dimensional graph, where each dimension corresponds to a spectral feature. Output the data of the spectral feature space of the wall panel response diagram as a graphical and numerical report.

[0079] Step S263: Perform cross-scale performance correlation mapping modeling based on the eigenvector set of the wall panel microstructural features and the spectral feature space of the wall panel response diagram to obtain the wall panel performance correlation response function;

[0080] Specifically, the wallboard microstructure feature vector set and the spectral feature space of the wallboard response map can be imported into data analysis and modeling software, such as the pandas and scikit-learn libraries in MATLAB or Python. Select key microstructure features from the feature vector set, such as fiber diameter, spacing, and direction, and select key response features from the spectral feature space, such as the main frequency components and amplitude features. Use machine learning algorithms, such as random forest or neural network, to establish an association mapping model between the microstructure features and the response features. For example, using a neural network model, the input layer contains microstructure features, the hidden layer is used to extract non-linear relationships, and the output layer predicts the response features. Train the model using historical data or simulation data. Set training parameters, such as a learning rate of 0.01, 1000 iterations, and a batch size of 32. Evaluate the performance of the model using a cross-validation method, such as k-fold cross-validation, and set the k value to 5 to ensure the generalization ability of the model. After training, obtain the wallboard performance association response function, which can predict the response features based on the microstructure features.

[0081] Step S264: Perform a thermo-mechanical-electrical multi-field coupling numerical simulation on the rock wool curtain wall board based on the wallboard performance association response function to obtain the cross-scale dynamic response simulation results;

[0082] Specifically, the wallboard performance association response function can be imported into multi-physics simulation software, such as COMSOL Multiphysics. Set up a thermo-mechanical-electrical multi-field coupling analysis in the software. Define the heat conduction equation for the thermal field, the mechanical equilibrium equation for the force field, and the electric potential equation for the electric field. Set boundary conditions for the model, such as fixed boundaries, heat sources, force loads, and potential differences. Input the thermal, mechanical, and electrical material properties of the rock wool curtain wall board, such as thermal conductivity, elastic modulus, and conductivity. Run the coupled numerical simulation. Set the solver parameters, such as a time step of 0.01 seconds and a total simulation time of 1 second. After the simulation is completed, extract the cross-scale dynamic response simulation results, including temperature distribution, stress distribution, and electric potential distribution. Verify the accuracy of the simulation results by comparing with experimental data. If necessary, adjust the model parameters or boundary conditions and run the simulation again. Visualize the cross-scale dynamic response simulation results, such as contour plots and vector field plots, to intuitively display the multi-field coupling effect.

[0083] Step S265: Conduct a collaborative performance evaluation on the cross-scale dynamic response simulation results to obtain the wallboard comprehensive performance evaluation results.

[0084] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of Step S265.

[0085] The present invention deeply understands the microscopic structural characteristics of the wallboard through feature extraction, enhancing the understanding of material properties. By analyzing the characteristics of the multi-field comprehensive response map of the wallboard, the behavior patterns of the material under the action of multiple physical fields are revealed, enhancing the understanding of material responses. By establishing a cross-scale performance correlation mapping model, the performance of the material at different scales is accurately simulated, improving the accuracy of the simulation results. Through thermo-mechanical-electrical multi-field coupling numerical simulation, the dynamic responses of the material in complex environments are comprehensively evaluated, providing a comprehensive evaluation for the application of the material. Through collaborative performance evaluation, the comprehensive performance of the wallboard is systematically evaluated, ensuring the reliability and stability of the material in practical applications.

[0086] Preferably, step S265 includes the following steps:

[0087] Step S2651: Evaluate the confidence level of the cross-scale dynamic response simulation results to obtain a reliability evaluation set for wallboard performance simulation;

[0088] Specifically, the cross-scale dynamic response simulation results can be imported into statistical analysis software, such as using the R language or the SciPy and Statsmodels libraries in Python. Calculate the confidence intervals for the key performance parameters obtained from the simulation, such as the maximum stress, maximum displacement, etc. Select a 95% confidence level and use the Bootstrap method for 1000 resamplings to estimate the distributions of these parameters. Based on the results of the confidence intervals, evaluate the reliability of the simulation results. For example, if the 95% confidence interval of the maximum stress is completely below the yield strength of the material, the simulation results are considered reliable. Identify the input parameters that have the greatest impact on the simulation results. For example, use the Sobol index to quantify the contribution of different parameters to the output uncertainty. Output the reliability evaluation set for wallboard performance simulation as a report, including the confidence intervals, reliability ratings, and sensitivity analysis results for each performance parameter.

[0089] Step S2652: Randomly perturb the cross-scale dynamic response simulation results to obtain a dataset of randomly perturbed wallboard performance;

[0090] Specifically, the cross-scale dynamic response simulation results can be imported into data processing software, such as using the NumPy library in MATLAB or Python. Select a suitable random perturbation model, such as Monte Carlo simulation, to simulate the uncertainty and variability of the wall panel performance parameters. Set the parameters of the random perturbation, such as choosing a normal distribution or a uniform distribution, and determine the mean and standard deviation of the distribution. For example, for the maximum stress parameter, assume its mean is 300 MPa and the standard deviation is 15 MPa. Implement the random perturbation in the software to generate a large amount of perturbed data. For example, generate 1000 random perturbation values of the maximum stress to simulate the performance variation under different conditions. Combine the generated random perturbation values with the original simulation results to construct a random perturbation dataset of the wall panel performance. This dataset contains the performance parameter values considering random variation. Verify the rationality of the random perturbation dataset by comparing it with experimental data or on-site monitoring data. If necessary, adjust the perturbation parameters and regenerate the dataset. Output the random perturbation dataset of the wall panel performance.

[0091] Step S2653: Perform extreme value distribution mapping on the random perturbation dataset of the wall panel performance to obtain the probability distribution map of the wall panel performance response;

[0092] Specifically, the random perturbation dataset of the wall panel performance can be obtained from Step S2652, and these datasets contain the performance parameter values simulated under different random conditions. Select statistical analysis software, such as the R language or the SciPy library in Python, and use the extreme value distribution fitting tool in the software to perform extreme value distribution mapping on the performance parameters in the random perturbation dataset. For example, select the Generalized Extreme Value (GEV) model to fit the maximum and minimum values of the performance parameters. Estimate the parameters of the GEV model, including the location parameter, scale parameter, and shape parameter. Use the Maximum Likelihood Estimation (MLE) method, set the number of iterations of the optimization algorithm to 1000 times, and the tolerance to 1e -6 . Based on the estimated parameters, generate the probability distribution map of the wall panel performance response. For example, plot the Cumulative Distribution Function (CDF) and Probability Density Function (PDF) graphs of the maximum stress and maximum displacement. Output the probability distribution map of the wall panel performance response as a graphical and numerical report.

[0093] Step S2654: Reconstruct the confidence interval for the probability distribution map of the wall panel performance response according to the reliability evaluation set of the wall panel performance simulation to obtain the confidence domain mapping data of the wall panel performance;

[0094] Specifically, the reliability evaluation set of wall panel performance and the probability distribution diagram of wall panel performance responses can be imported into data analysis software, such as MATLAB or the NumPy and SciPy libraries in Python. Based on the confidence evaluation results in the reliability evaluation set, the confidence interval is calculated for each performance parameter in the probability distribution diagram of performance responses. For example, to calculate the 95% confidence interval, the Bootstrap method is used for 1000 resamplings. On the probability distribution diagram of performance responses, the confidence intervals of each performance parameter are marked. For example, on the cumulative distribution function (CDF) diagram, confidence bands are drawn for the 95% confidence intervals of the maximum stress and the maximum displacement. For each performance parameter, based on the results of the confidence interval, the confidence domain mapping data of the wall panel performance responses is reconstructed. This includes determining the boundaries of the confidence domain and the statistical characteristics within the interval. The confidence domain mapping data of the wall panel performance is output as graphical and numerical reports, including the boundary values of the confidence domain and the relevant statistical information.

[0095] Step S2655: Extract the sensitivity characteristics of the confidence domain mapping data of the wall panel performance to obtain the set of key influencing factors for the curtain wall panel performance, and perform risk grading on the confidence domain mapping data of the wall panel performance according to the set of key influencing factors for the curtain wall panel performance to obtain the risk stratification mapping data of the wall panel performance;

[0096] Specifically, the confidence domain mapping data of the wall panel performance can be obtained from step S2654, and these data include the confidence intervals and probability distribution diagrams of different performance parameters. Select a sensitivity analysis tool, such as the Sobol index or the Morris method. In this example, the Sobol index is selected for sensitivity analysis. Using Monte Carlo simulation combined with the Sobol index method, sensitivity analysis is performed on the confidence domain mapping data of the wall panel performance. Set the number of simulations to 5000 times to ensure the statistical significance of the results. Through Sobol index analysis, the top five parameters that have the greatest impact on the wall panel performance are determined. For example, fiber diameter, fiber orientation, thermal conductivity, density, and elastic modulus. Select a suitable risk grading method, such as the Analytic Hierarchy Process (AHP) or fuzzy logic, to perform risk assessment on the wall panel performance. In this example, the Analytic Hierarchy Process (AHP) is selected. In AHP, a judgment matrix is constructed to make pairwise comparisons of the key influencing factors and set weights. For example, according to expert experience and historical data, relative importance scores are set for each factor. Run the AHP algorithm to calculate the weights of each factor, and perform risk grading on the confidence domain mapping data of the wall panel performance according to these weights. For example, the risk levels are divided into five levels: extremely low, low, medium, high, and extremely high. According to the risk grading results, the risk stratification mapping data of the wall panel performance is generated. These data include the risk levels of each performance parameter and the corresponding confidence intervals.

[0097] Step S2656: Conduct a comprehensive performance evaluation on the hierarchical mapping data of the wall panel performance risks to obtain the comprehensive performance evaluation result of the wall panel.

[0098] Specifically, the hierarchical mapping data of the wall panel performance risks can be obtained from Step S2655, and these data include the risk levels of different performance parameters and the corresponding confidence intervals. Select a comprehensive performance evaluation tool, such as the weighted scoring method or multi-criteria decision-making analysis (MCDM), which can help comprehensively consider the impacts of multiple performance parameters. In this example, the weighted scoring method is selected. Assign weights according to the importance of each performance parameter. For example, according to expert experience and historical data, the weights of fiber diameter, fiber orientation, thermal conductivity, density, and elastic modulus are assigned as 0.2, 0.15, 0.25, 0.2, and 0.2 respectively. Develop a scoring standard for each performance parameter. For example, divide the performance parameters into five levels according to the risk level, and each level corresponds to a score, ranging from 1 (extremely high risk) to 5 (extremely low risk). Use the weighted scoring method to calculate the score of each performance parameter. For example, if the risk level of the fiber diameter is "medium" and the corresponding score is 3, then its weighted score is 3×0.2 = 0.6. Add up the weighted scores of all performance parameters to obtain the comprehensive performance score of the wall panel. For example, if the sum of the weighted scores of all parameters is 2.5, then the comprehensive performance of the wall panel is 2.5 on a scoring scale of 1 to 5. Analyze the comprehensive performance score result to determine the overall performance level of the wall panel. For example, if the comprehensive performance score is lower than 3, the wall panel design needs to be optimized. Output the comprehensive performance evaluation result of the wall panel as a report, including the scores, weights, and comprehensive performance score of each performance parameter.

[0099] The present invention enhances the credibility of the simulation results through confidence evaluation, ensuring the reliability of the wall panel performance simulation. Enhances the robustness of the simulation results by introducing stochastic perturbation simulation, effectively evaluating the changes in the wall panel performance under uncertain conditions. Achieves an intuitive understanding and analysis of the statistical characteristics and potential risks of the wall panel performance through extreme value distribution mapping and the generation of the performance response probability distribution diagram. Improves the accuracy of performance prediction through the precise construction of the confidence interval. Deepens the sensitivity analysis through sensitivity feature extraction, identifies the key factors affecting the wall panel performance, and guides material optimization and design improvement. Through risk classification and management, by classifying the risks of the wall panel performance confidence domain mapping data, effectively manages and controls the risks in the design and construction processes. Provides a comprehensive performance overview through comprehensive performance evaluation, helping to grasp the overall performance level of the wall panel.

[0100] Preferably, Step S3 includes the following steps:

[0101] Step S31: Obtain the wall panel construction process parameter set and perform feature quantification on the wall panel construction process parameter set to obtain the wall panel construction process parameter feature set;

[0102] Specifically, the wall panel construction process parameters can be measured on-site and historical project data can be collected, including but not limited to temperature, humidity, material ratio, curing time, etc. Select a suitable feature quantification method, such as principal component analysis (PCA) or statistical feature extraction (such as mean, standard deviation, maximum, and minimum). In this example, the statistical feature extraction method is selected. Use data preprocessing tools, such as the Pandas library in Python, to clean the collected construction process parameter data, including removing outliers and filling missing values. Extract features from the preprocessed data. For example, for the temperature parameter, calculate its mean, standard deviation, and extreme values during the construction process; for the material ratio, calculate its mean and standard deviation. Construct the statistical features extracted into a wall panel construction process parameter feature set. For example, the temperature feature set includes "Average temperature: 20°C", "Temperature fluctuation: 5°C", "Highest / Lowest temperature: 30°C / 10°C"; the material ratio feature set includes "Average ratio: 1.5", "Ratio fluctuation: 0.1".

[0103] Step S32: Perform entropy coding conversion on the wall panel construction process parameter feature set to obtain a wall panel process parameter entropy coding set;

[0104] Specifically, the wall panel construction process parameter feature set can be imported into data analysis software, such as MATLAB or the SciPy library in Python. Select a suitable entropy coding method, such as Shannon entropy coding, which is an entropy-based coding method used to quantify the uncertainty and information content of data. Calculate the entropy value for each parameter in the feature set. For example, for the temperature fluctuation parameter, use the Shannon entropy formula to calculate its entropy value: where P(x i ) is the probability of the parameter value x i appearing, and n is the total number of temperature fluctuation parameters. According to the calculated entropy values, perform coding conversion on each parameter. For example, convert the entropy values to binary codes, and the coding length of each parameter is proportional to its entropy value. Construct the converted entropy coding into a wall panel process parameter entropy coding set. For example, the entropy coding of the temperature fluctuation parameter is "1010", and the entropy coding of the material ratio fluctuation parameter is "1100". Output the wall panel process parameter entropy coding set in a structured data format.

[0105] Step S33: Perform error probability mapping on the wall panel process parameter entropy coding set to obtain construction error probability distribution mapping data;

[0106] Specifically, the entropy encoding set of wall panel process parameters can be imported into data analysis software. For example, the Pandas library in Python can be used for data processing. Select a suitable error analysis model, such as a Bayesian network or a Hidden Markov Model (HMM), to analyze the relationship between process parameters and construction errors. In this example, the Bayesian network model is selected. Using historical construction data and expert knowledge, establish the association rules between process parameters and construction errors. For example, associate temperature fluctuations with the error of uneven curing. Estimate the probability distribution of the entropy encoding of each process parameter to determine the probability distribution of construction errors. For example, use the Maximum Likelihood Estimation (MLE) method to estimate the error probability distribution of temperature fluctuation parameters. Map the estimated probability distribution to each process parameter to obtain the construction error probability distribution mapping data. For example, if the entropy encoding of temperature fluctuation is "1010", the corresponding error probability distribution has a mean of 0.03 and a standard deviation of 0.01.

[0107] Step S34: Perform spatial mapping on the construction error probability distribution mapping data to obtain the spatial distribution map of wall panel construction errors;

[0108] Specifically, the construction error probability distribution mapping data can be imported into Geographic Information System (GIS) software, such as ArcGIS or QGIS. In the GIS software, set the spatial mapping parameters, including the coordinate system, scale, and map projection. For example, select the UTM coordinate system and a scale of 1:1000. Combine the construction error probability distribution data with the location information of the wall panel for spatial processing. For example, match the error probability distribution data of each construction section to the corresponding wall surface position. Use the mapping function of the GIS software to generate the spatial distribution map of wall panel construction errors. For example, use color gradients to represent the error probabilities of different construction sections, with red indicating high error probability areas and green indicating low error probability areas. Perform spatial analysis on the generated spatial distribution map to identify high-error areas and potential risk points. For example, use HotSpot Analysis to identify the aggregation areas of construction errors. Output the spatial distribution map of wall panel construction errors as an image file, such as PNG or JPEG format, and an editable GIS project file.

[0109] Step S35: Conduct an error correlation assessment on the spatial distribution map of wall panel construction errors to obtain the wall panel error correlation assessment matrix, and perform hierarchical quantization encoding on the wall panel error correlation assessment matrix to obtain the wall panel construction error index set.

[0110] Specifically, the wall panel construction error spatial distribution map can be imported into data analysis software. For example, the Geopandas library in Python can be used for geospatial data analysis. Select appropriate statistical analysis tools, such as correlation analysis or clustering analysis, to evaluate the correlation between different construction errors. In this example, the Pearson correlation coefficient is selected to measure the linear relationship between errors. Conduct a correlation analysis on the construction error data in the spatial distribution map. For example, analyze the correlation between temperature error and material ratio error, calculate the correlation coefficient, and set the significance level to 0.05. Based on the results of the correlation analysis, construct a wall panel error correlation evaluation matrix. For example, if the correlation coefficient between temperature error and material ratio error is 0.8, fill 0.8 in the corresponding cell in the matrix. Select an appropriate hierarchical quantization coding method, such as fuzzy C-means clustering (FCM) or k-means clustering, to perform hierarchical quantization coding on the error correlation evaluation matrix. In this example, the FCM clustering method is selected. In the FCM clustering method, set the number of clusters to 3, representing low, medium, and high error correlation levels, and set the fuzzy coefficient to 2 to enhance the fuzziness of the clustering. Run the FCM clustering algorithm to perform hierarchical quantization coding on the error correlation evaluation matrix and obtain the correlation level of each construction error. Organize the results of the hierarchical quantization coding into a wall panel construction error index set. For example, "temperature error - material ratio error: high correlation", and output it in a structured data format.

[0111] By quantitatively characterizing the construction process parameters, the present invention can accurately capture the key variables during construction. Through entropy coding transformation, the processability of the parameter set is improved, making the data more compact. Through error probability mapping, the types and probabilities of errors prone to occur during construction are clarified, which helps to identify and prevent problems in advance. Through the visualization of the wall panel construction error spatial distribution map, it helps to locate high-risk areas and achieve targeted quality control. Through error correlation evaluation, the mutual influence between construction errors is revealed, promoting a comprehensive understanding of the complex relationships in the construction process. By structuring the wall panel construction error index set through hierarchical quantization coding, the management efficiency is improved.

[0112] Preferably, step S4 includes the following steps:

[0113] Step S41: Based on the first wall panel reconstruction structural model, perform semantic mapping of the performance boundary conditions of the curtain wall panel structure according to the wall panel comprehensive performance evaluation results to obtain wall panel performance boundary condition mapping data;

[0114] Specifically, the reconstructed structural model of the first wall panel and the evaluation results of the comprehensive performance of the wall panel can be imported into structural analysis software, such as ABAQUS or ANSYS. Define the key parameters affecting the performance of the curtain wall panel in the software, such as maximum stress, maximum displacement, durability, etc., and set performance standards or thresholds for each parameter. Set the semantic mapping rules for the performance boundary conditions. For example, if the maximum stress exceeds 300 MPa, it is defined as the "high stress area" and requires strengthening treatment. Use the script or built-in tools of the software to automatically perform semantic mapping according to the comprehensive performance evaluation results. For example, write a Python script to automatically mark all areas exceeding the stress threshold in ABAQUS. Output the mapping results of the performance boundary conditions as data files, such as CSV or XML formats, recording the performance parameters of each area and the corresponding semantic labels. Verify the accuracy of the performance boundary condition mapping by comparing with experimental data. If necessary, adjust the mapping rules and re-perform the mapping. Through the above steps, the mapping data of the wall panel performance boundary conditions can be obtained.

[0115] Step S42: Perform multi-scale decoupling on the mapping data of the wall panel performance boundary conditions to obtain the decoupling factors of the wall panel performance boundary conditions;

[0116] Specifically, the mapping data of the wall panel performance boundary conditions can be imported into data analysis software, such as MATLAB or the NumPy and SciPy libraries of Python. Select appropriate multi-scale analysis methods, such as wavelet transform or multi-scale entropy analysis, to process the performance data at different scales. Set the decoupling parameters, such as selecting the basis function and decomposition level of the wavelet transform. In this example, select the Daubechies wavelet basis function and perform 3-layer wavelet decomposition. Run the multi-scale analysis algorithm to decouple the mapping data of the performance boundary conditions. For example, use the wavelet decomposition toolbox of MATLAB to perform multi-scale decomposition on the stress and displacement data. Extract the decoupling factors from the multi-scale analysis results, and these factors represent the performance characteristics at different scales. For example, extract the coefficients of each wavelet decomposition layer as the decoupling factors.

[0117] Step S43: Based on the reconstructed structural model of the first wall panel and the decoupling factors of the wall panel performance boundary conditions, perform parametric reconstruction on the curtain wall panel structure to obtain the preliminary structural parameter model of the wall panel;

[0118] Specifically, the first wall panel reconstruction structure model and the decoupling factor of the wall panel performance boundary conditions can be imported into parametric modeling software, such as CATIA or Revit. In the software, set the parametric modeling environment, define the parameters of the model, such as the thickness of the board, the strength of the connecting parts, etc., and associate them with the decoupling factor. For example, set the thickness of the board as the variable "t" and adjust its value according to the decoupling factor. Adjust the model parameters according to the decoupling factor. For example, if the decoupling factor indicates that a certain area needs to be strengthened, increase the thickness of the board in that area or change the design of the connecting parts. Utilize the parametric function of the software to reconstruct the curtain wall panel structure. For example, use the "parametric design table" function in CATIA to dynamically adjust the model parameters according to the decoupling factor. Output the preliminary structural parameter model of the wall panel as a standard format file.

[0119] Step S44: Conduct a structural stability assessment on the preliminary structural parameter model of the wall panel to obtain the wall panel structural stability index;

[0120] Specifically, the preliminary structural parameter model of the wall panel can be imported into structural analysis software, such as SAP2000 or ETABS. In the software, select the type of structural stability analysis, such as linear stability analysis or nonlinear stability analysis. For example, select nonlinear stability analysis to consider material and geometric nonlinearities. Set the boundary conditions of the model and the applied loads. For example, set fixed supports and live loads, such as wind loads and self-weight. Run the structural stability analysis. For example, use the "performance-based design" tool in SAP2000 and set the performance goals as "no buckling" and "minimum displacement". Extract the structural stability index from the analysis results, such as the buckling factor, maximum displacement, and stress ratio. For example, a buckling factor greater than 1 indicates structural stability. Output the wall panel structural stability index as a report, including key data such as the buckling factor and maximum displacement.

[0121] Step S45: Reconstruct the boundary conditions of the rock wool curtain wall panel structure according to the wall panel structural stability index to obtain the second wall panel reconstruction structure model;

[0122] Specifically, based on the wall panel structure stability index, areas that do not meet the design requirements can be identified. For example, the buckling factor in certain areas is lower than the safety threshold. Select a structural design software, such as Autodesk Revit or Rhinoceros 3D. In the software, adjust the boundary conditions of the rock wool curtain wall panel according to the stability index. For example, if the buckling factor is too low, increase the support points or adjust the layout of the connectors to enhance the structural stability. Use the parametric function of the software to modify the structural model. For example, adjust the dimensions of beams and columns in Revit, or modify the thickness of the wall in Rhinoceros 3D. Apply the modified parameters to reconstruct the structural model. For example, use the "Regenerate" function in Revit to update the entire structural model. Conduct a structural stability analysis again to verify whether the reconstructed model meets all design requirements. If necessary, further adjust the parameters until all stability criteria are met. Export the reconstructed structural model of the second wall panel that meets the stability requirements as a standard format file.

[0123] Step S46: Design an extreme environment multi-scenario simulation plan for the rock wool curtain wall panel to obtain an extreme environment multi-scenario simulation plan;

[0124] Specifically, it is possible to collaborate with the engineering team to identify the extreme environmental conditions that rock wool curtain wall panels are likely to encounter in actual applications, including extreme temperatures, strong winds, earthquakes, etc. Select COMSOL Multiphysics as the multi-physics simulation software because it can handle multi-physics problems such as structure, heat transfer, and fluid dynamics. Create a new model project in COMSOL and name it "Extreme Environment Simulation of Rock Wool Curtain Wall Panels". Prepare an accurate three-dimensional geometric model of the rock wool curtain wall panel, including panel dimensions, connector positions, and material properties, etc. For example, define the first scenario where one side of the rock wool curtain wall panel will be exposed to high temperature, set the temperature to 50 °C, and simulate the situation of continuous exposure to the sun. Define the second scenario to simulate the impact of an earthquake on the curtain wall panel, set the ground acceleration to 0.4g, select "sine wave" as the earthquake waveform, set the frequency to 1 Hz, and the duration to 60 seconds. Define the third scenario to simulate the impact of strong wind on the curtain wall panel. According to the wind tunnel test data, set the wind pressure values in different areas, and simulate the wind direction changing from 0 degrees to 90 degrees, with each direction lasting for 3600 seconds. Define detailed boundary conditions and load types for each scenario. For example, for the high-temperature scenario, set the back of the curtain wall panel to be adiabatic; for the earthquake scenario, set the bottom of the curtain wall panel to be fixed support; for the strong-wind scenario, set the wind pressure distribution and wind direction change. Perform mesh generation for each scenario and select appropriate mesh sizes and types. For example, for the high-temperature scenario, set the mesh size to 2 mm to capture the temperature gradient change. Set the solver parameters for each scenario, such as time step and number of iterations, to ensure the stability and accuracy of the simulation. Record the setting parameters, boundary conditions, load types, and solver parameters of each scenario in detail in the simulation plan document, including the specific values of the simulation parameters, screenshots of the simulation process, and charts of the expected results. Review the simulation plan with the design team to ensure that the plan covers all key extreme environmental conditions. Output the final simulation plan as a PDF report.

[0125] Step S47: Based on the wall panel construction error index set and the extreme environment multi-scenario simulation plan, perform extreme environment multi-scenario simulation on the reconstructed structural model of the second wall panel to obtain the wall panel multi-scenario performance response data;

[0126] Specifically, the reconstructed structural model of the second wall panel and the multi-scenario simulation scheme for extreme environments can be imported into a multi-physics simulation software, such as COMSOL Multiphysics. Integrate the wall panel construction error index set into the simulation model. For example, if the error index set includes "temperature error ±3°C" and "material strength fluctuation ±5%", then set the corresponding parameter change range in the model. Set the simulation parameters for multiple scenarios in the software, including extreme temperature, wind pressure, earthquake, etc. For example, set a scenario as "high temperature + strong wind", with the temperature set at 50°C and the wind pressure set at 1.2 kPa. Run the simulation software to simulate each scenario. For example, use the "thermal-structural interaction" module of COMSOL to simulate the effects of high temperature and wind pressure on the wall panel. Extract the performance response data from the simulation results of each scenario, such as the maximum stress, displacement, and vibration frequency. For example, record the maximum stress value of the wall panel in the "high temperature + strong wind" scenario as 150 MPa. Organize the performance response data of all scenarios into a wall panel multi-scenario performance response data set.

[0127] Step S48: Perform extreme value mapping and boundary identification on the wall panel multi-scenario performance response data to obtain the wall panel performance limit response data; perform risk quantification on the preset fatigue failure assessment index based on the wall panel performance limit response data to obtain the wall panel performance degradation prediction result.

[0128] Specifically, the wall panel multi-scenario performance response data can be imported into a data analysis software, such as the Pandas library in Python. Conduct statistical analysis on the performance response data to identify the extreme values of each response parameter. For example, use descriptive statistical methods to find the 99% quantiles of the maximum stress and maximum displacement as the extreme values. Based on the extreme values and the preset performance boundaries, identify the performance state of the wall panel. For example, if the extreme value of the maximum stress exceeds the yield strength of the material, it is considered that the performance of the wall panel exceeds the boundary in this scenario. Organize the extreme values and boundary identification results into a wall panel performance limit response data set, including the extreme stress, displacement, etc. in each scenario. Use a fatigue analysis tool, such as the fatigue analysis toolbox in MATLAB, to conduct fatigue failure assessment on the wall panel performance limit response data. For example, calculate the fatigue life of the wall panel according to the S-N curve and the rain flow counting method. Based on the fatigue failure assessment results, perform risk quantification on the preset fatigue failure assessment index. For example, calculate the fatigue failure probability of the wall panel in different scenarios and compare it with the acceptable risk level. According to the risk quantification results, predict the performance degradation trend of the wall panel. For example, predict the time and degree of performance degradation of the wall panel in extreme environments. Output the wall panel performance limit response data and the performance degradation prediction result as a report and a data file.

[0129] The present invention precisely defines the performance limits of the wall panel structure through semantic mapping of performance boundary conditions, providing clear guidance for structural design. The analysis ability of factors influencing the wall panel performance is enhanced through multi-scale decoupling, improving the understanding of the performance of complex structures. The design flexibility is increased by parametrically reconstructing the wall panel structure, allowing for quick adjustment to meet different performance requirements. The safety and reliability of the structure are ensured through quantitative evaluation of structural stability. The structural design is optimized by reconstructing the boundary conditions based on structural stability indicators, improving the performance and durability of the wall panel. The adaptability and toughness of the wall panel are enhanced through extreme environment multi-scenario simulation scheme design. The problems prone to occur in actual construction are comprehensively evaluated through multi-scenario simulation combined with the construction error index set. The ultimate performance of the wall panel is accurately predicted through limit value mapping and boundary identification, providing key safety guarantees for the design. Risk quantification and performance degradation prediction based on performance limit response data help to take measures in advance to avoid potential structural failures.

[0130] Preferably, step S5 includes the following steps:

[0131] Step S51: Extract features from the prediction results of the wall panel performance degradation to obtain a set of wall panel performance degradation feature vectors;

[0132] Specifically, the prediction results of the wall panel performance degradation can be imported into data analysis software. For example, the Pandas library of Python is used for data processing. Select appropriate feature extraction techniques, such as principal component analysis (PCA) or independent component analysis (ICA), to reduce the data dimension and extract key features. In this example, the PCA method is selected, and PCA is used to extract features from the prediction results of the wall panel performance degradation. For example, in Python, the PCA function of the Scikit-learn library is used to retain the energy of 95% variance to obtain the main feature vectors. The extracted feature vectors are constructed into a set of wall panel performance degradation feature vectors. For example, if PCA extracts 3 main feature vectors, these vectors represent the main trends of the wall panel performance degradation.

[0133] Step S52: Conduct a Bayesian probability significance test on the set of wall panel performance degradation feature vectors to obtain a significance ranking table of key degradation influencing factors;

[0134] Specifically, the set of wall panel performance degradation feature vectors can be imported into statistical analysis software. For example, Bayesian analysis can be performed using the R language or the PyMC3 library in Python. A Bayesian model is constructed to evaluate the significance of the feature vectors. For example, prior distributions such as normal distributions are defined in PyMC3 for model parameters. The Bayesian model is used to conduct a significance test for each feature vector. For example, the posterior probability of each feature is calculated and compared with the prior probability to evaluate the significance of its impact on performance degradation. Based on the Bayesian test results, a significance ranking table of key degradation impact factors is constructed. For example, the features are ranked according to their posterior probabilities of significance of impact on performance degradation.

[0135] Step S53: Conduct an entropy weight fuzzy comprehensive evaluation on the significance ranking table of key degradation impact factors to obtain the wall panel degradation impact weight matrix;

[0136] Specifically, the significance ranking table of key degradation impact factors can be imported into data analysis software. For example, the Pandas library in Python is used for data management. The entropy value of each factor is calculated to evaluate its information entropy, reflecting the uncertainty of its information. For example, the entropy calculation function in the SciPy library in Python is used to calculate the entropy value based on the significance probability distribution of each factor. The weight of each factor is calculated according to the entropy value. The weight is inversely proportional to the information entropy of the factor. The lower the information entropy, the greater the weight. For example, if a certain factor has the lowest entropy value, it indicates that it provides the most effective information, so the highest weight is assigned. The fuzzy comprehensive evaluation method is adopted to conduct a comprehensive evaluation of each factor by combining entropy weight and expert scoring or other objective data. For example, a fuzzy evaluation matrix is constructed, where the rows represent factors and the columns represent different evaluation levels (such as "high", "medium", "low" impact), and the comprehensive values based on entropy weight and expert scoring are filled. Based on the fuzzy comprehensive evaluation results, the wall panel degradation impact weight matrix is constructed. For example, each element in the matrix represents the weighted score of a factor at different evaluation levels.

[0137] Step S54: Perform a multi-criteria decision mapping on the significance ranking table of key degradation impact factors according to the wall panel degradation impact weight matrix to obtain the key wall panel design adjustment factors;

[0138] Specifically, the wall panel degradation impact weight matrix can be imported into multi-criteria decision analysis software, such as using MATLAB or the PyMCDM library in Python. Define the criteria for multi-criteria decision-making, such as performance improvement potential, cost-effectiveness, and implementation difficulty. For example, determine three criteria: "performance improvement potential" (weight 0.5), "cost-effectiveness" (weight 0.3), and "implementation difficulty" (weight 0.2). Use multi-criteria decision analysis methods, such as the weighted sum method or the Analytic Hierarchy Process (AHP), to combine the weights in the weight matrix with the decision criteria for decision mapping. For example, use the "weighted sum" method in the PyMCDM library to calculate the comprehensive score based on the scores and weights of each factor under different criteria. According to the comprehensive scores, extract the design adjustment factors that are most critical for improving the wall panel performance. For example, select the top 5 factors with the highest comprehensive scores as the key design adjustment factors.

[0139] Step S55: Based on the key design adjustment factors of the wall panel, adjust the structural parameters of the rock wool curtain wall panel to obtain a design solution for enhancing the wall panel performance;

[0140] Specifically, the key design adjustment factors can be imported into structural design software, such as using Autodesk Revit or Rhinoceros 3D. Based on the key design adjustment factors, formulate decisions on structural parameter adjustments. For example, if "fiber diameter" and "fiber orientation" are key factors, decide to increase the fiber diameter to improve strength and adjust the fiber orientation to optimize the load distribution. In the software, adjust the structural parameters of the rock wool curtain wall panel. For example, modify the wall material properties in Revit or adjust the geometric parameters of the fibers in Rhinoceros 3D. Use finite element analysis (FEA) software, such as ANSYS or ABAQUS, to simulate the performance of the adjusted rock wool curtain wall panel to verify the effect of the structural parameter adjustments. For example, simulate the stress distribution and deformation under the new fiber configuration. According to the simulation results, further optimize the design solution. For example, if the simulation shows that increasing the fiber diameter improves strength but increases weight, it is necessary to readjust the fiber density to balance performance and cost, and output the final design solution for enhancing the wall panel performance as a detailed report.

[0141] Step S56: Reconstruct the second wall panel's reconstructed structural model according to the design solution for enhancing the wall panel performance to obtain an optimized 3D simulation model of the wall panel.

[0142] Specifically, the wall panel performance enhancement design scheme can be imported into 3D modeling software such as SolidWorks or CATIA. According to the design scheme, the reconstructed structural model of the second wall panel is adjusted in detail. For example, the distribution of fibers and the geometry of the wall are modified in SolidWorks. The built-in analysis tools of the 3D modeling software, such as stress analysis or thermal analysis, are used to verify the performance of the reconstructed model. For example, check whether the maximum stress points under the new design meet the safety requirements. If necessary, multidisciplinary optimization, such as thermal-structural coupling analysis, is carried out to ensure that the new design performs excellently in multiple aspects. For example, the heat conduction and structural stability of the new design are simulated in COMSOL Multiphysics. The optimized 3D simulation model of the wall panel is refined to add details to improve the authenticity of the model. For example, material textures and actual construction details are added in CATIA. The optimized 3D simulation model of the wall panel is output as a 3D format file.

[0143] Through feature extraction, the present invention deeply understands the process and key features of the performance degradation of the wall panel. The Bayesian probability significance test provides scientific probability support, enhancing the reliability of the analysis results. The objectivity of the evaluation is increased through the entropy weight fuzzy comprehensive evaluation method. The wall panel degradation influence weight matrix provides a clear basis for multi-criteria decision-making, making the design adjustment more scientific and accurate. The key design adjustment factors of the wall panel obtained through multi-criteria decision-making mapping guide targeted structural parameter adjustment, improving the design efficiency and effect. The systematic wall panel performance enhancement scheme is provided through the structural parameter adjustment based on the key design adjustment factors, ensuring the comprehensiveness of the performance improvement. The optimized 3D simulation model of the wall panel is closer to the actual application, improving the practicality and simulation accuracy of the model.

[0144] Preferably, the present invention also provides a 3D simulation design system based on the rock wool curtain wall panel structure for performing the 3D simulation design method based on the rock wool curtain wall panel structure as described above. The 3D simulation design system based on the rock wool curtain wall panel structure includes:

[0145] A digital twin module for obtaining the source data of the wall panel microtopography; performing isotropic microfeature enhancement on the source data of the wall panel microtopography to obtain the wall panel microfiber morphology data; performing digital twin reconstruction on the rock wool curtain wall panel based on the wall panel microfiber morphology data to obtain the first wall panel reconstructed structural model;

[0146] A performance simulation module for performing thermodynamic field mapping on the first wall panel reconstructed structural model to obtain a microscale heat conduction feature map; performing cross-scale physical field coupling and superposition on the microscale heat conduction feature map to obtain a wall panel multi-field comprehensive response map; performing cross-scale performance dynamic simulation on the rock wool curtain wall panel based on the wall panel multi-field comprehensive response map to obtain the wall panel comprehensive performance evaluation result;

[0147] An error evaluation module, configured to obtain a set of wall panel construction process parameters; perform construction error evaluation on the set of wall panel construction process parameters to obtain a set of wall panel construction error indicators;

[0148] A risk evaluation module, configured to reconstruct the performance boundary conditions of the rock wool curtain wall panel according to the wall panel comprehensive performance evaluation result to obtain a second wall panel reconstruction structural model; perform extreme environment multi-scenario simulation on the second wall panel reconstruction structural model based on the set of wall panel construction error indicators to obtain wall panel multi-scenario performance response data; perform fatigue failure risk evaluation on the rock wool curtain wall panel according to the wall panel multi-scenario performance response data to obtain a wall panel performance degradation prediction result;

[0149] A simulation model reconstruction module, configured to extract target optimization factors from the wall panel performance degradation prediction result to obtain key wall panel design adjustment factors; optimize the structural parameters of the rock wool curtain wall panel according to the key wall panel design adjustment factors to obtain a wall panel performance enhancement design scheme; reconstruct the second wall panel reconstruction structural model according to the wall panel performance enhancement design scheme to obtain an optimized wall panel three-dimensional simulation model.

[0150] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0151] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional simulation design method based on rock wool curtain wall panel structure, characterized in that: The following steps are involved: Step S1: Obtain wallboard micro-morphology source data; The isotropic microscopic feature enhancement is performed on the wallboard microscopic morphology source data to obtain the wallboard microscopic fiber morphology data; The rock wool curtain wall panel is digitally twinned and reconstructed based on the micro-fiber morphology data of the wall panel to obtain the reconstructed structural model of the first wall panel; Step S2: performing thermodynamic field mapping on the reconstructed structural model of the first wall panel to obtain a micro-scale heat conduction characteristic map; The micro-scale heat conduction characteristic diagram is coupled and superimposed on the cross-scale physical field to obtain the multi-field comprehensive response diagram of the wall panel; based on the multi-field comprehensive response diagram of the wall panel, the rock wool curtain wall panel is dynamically simulated across scales to obtain the comprehensive performance evaluation results of the wall panel; Step S3: Obtain a wall panel construction process parameter set; Conduct construction error evaluation on the wall panel construction process parameter set to obtain the wall panel construction error index set; Step S4: reconstructing the performance boundary conditions of the rock wool curtain wall panel according to the comprehensive performance evaluation results of the wall panel to obtain a reconstructed structural model of the second wall panel; performing extreme environment multi-scenario simulation on the reconstructed structural model of the second wall panel based on the wall panel construction error index set to obtain multi-scenario performance response data of the wall panel; The fatigue failure risk of rock wool curtain wall panels was evaluated based on the multi-scenario performance response data of the wall panels, and the performance degradation prediction results of the wall panels were obtained; Step S5: extracting target optimization factors from the wall panel performance degradation prediction results to obtain key design adjustment factors of the wall panel; The structural parameters of the rock wool curtain wall panels were optimized according to the key design adjustment factors of the wall panels, and the design scheme for enhancing the performance of the wall panels was obtained; The reconstructed structural model of the second wall panel is reconstructed according to the wall panel performance enhancement design scheme to obtain an optimized wall panel three-dimensional simulation model.

2. The three-dimensional simulation design method based on rock wool curtain wall panel structure according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting the three-dimensional morphology of the microstructure of the rock wool curtain wall panel to obtain the wall panel micromorphology source data; Step S12: extracting fiber micro-geometric features from the wallboard micro-morphology source data to obtain a wallboard fiber geometric feature vector set; Step S13: adjusting the fiber structure topology network of the rock wool curtain wall panel according to the wall panel fiber geometric feature vector set to obtain a wall panel fiber network connection topology diagram; Step S14: reconstructing the spatial configuration of the wall panel fiber network connection topology diagram to obtain a wall panel fiber space model; Step S15: numerically simulate the fiber interface characteristics of the rock wool curtain wall panel based on the wall panel fiber space model to obtain rock wool fiber interface stress-strain coupling data, and evaluate the interface connection strength of the rock wool fiber interface stress-strain coupling data to obtain fiber interface connection strength evaluation data; Step S16: quantifying the fiber network toughness of the fiber interface connection strength evaluation data to obtain a fiber network connection capacity index, and isotropically enhancing the microscopic fiber morphology of the rock wool curtain wall panel according to the fiber network connection capacity index to obtain microscopic fiber morphology data of the wall panel; Step S17: Perform digital twin reconstruction of the rock wool curtain wall panel based on the micro-fiber morphology data of the wall panel to obtain a reconstructed structural model of the first wall panel.

3. The three-dimensional simulation design method based on rock wool curtain wall panel structure according to claim 2 is characterized in that: Step S17 includes the following steps: Step S171: constructing a microstructure discrete unit of the rock wool curtain wall panel based on the microfiber morphology data of the wall panel to obtain a microstructure discrete unit set of the wall panel; Step S172: topologically reorganize the discrete unit set of the wall panel microstructure to obtain a reconstructed wall panel microstructure prototype; Step S173: performing parameterized digital mapping on the rock wool curtain wall panel based on the reconstructed microstructure prototype of the wall panel to obtain a microstructure parameter mapping diagram of the wall panel; Step S174: encoding the microscopic fiber structure characteristics of the rock wool curtain wall panel based on the wall panel microstructure parameter mapping diagram to obtain a wall panel microstructure characteristic code set; Step S175: Use the wall panel microstructure feature code set to perform digital twin mapping conversion on the microstructure of the rock wool curtain wall panel to obtain a first wall panel reconstructed structural model.

4. The three-dimensional simulation design method based on rock wool curtain wall panel structure according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing thermodynamic boundary mapping on the reconstructed structural model of the first wall panel to obtain initial thermodynamic field data of the wall panel; Step S22: constructing a heat conduction discrete grid for the first wall panel reconstructed structure model according to the initial thermodynamic field data of the wall panel to obtain a wall panel heat conduction grid model; Step S23: numerically simulate the internal heat conduction characteristics of the rock wool curtain wall panel based on the wall panel heat conduction grid model to obtain micro-scale heat conduction flux data; Step S24: reconstructing the spatial characteristics of the micro-scale heat conduction flux data to obtain a micro-scale heat conduction characteristic map; Step S25: performing cross-scale physical field coupling superposition on the micro-scale heat conduction characteristic diagram to obtain a multi-field comprehensive response diagram of the wall panel; Step S26: Based on the reconstructed structural model of the first wall panel and the multi-field comprehensive response diagram of the wall panel, a cross-scale performance dynamic simulation is performed on the rock wool curtain wall panel to obtain a comprehensive performance evaluation result of the wall panel.

5. The three-dimensional simulation design method based on rock wool curtain wall structure according to claim 4 is characterized in that: Step S26 includes the following steps: Step S261: extracting features from the reconstructed structural model of the first wall panel to obtain a microstructure feature vector set of the wall panel; Step S262: performing feature analysis on the multi-field comprehensive response diagram of the wall panel to obtain a frequency spectrum feature space of the wall panel response diagram; Step S263: performing cross-scale performance correlation mapping modeling based on the wallboard microstructure feature vector set and the wallboard response graph spectrum feature space to obtain the wallboard performance correlation response function; Step S264: performing a thermal-mechanical-electrical multi-field coupling numerical simulation on the rock wool curtain wall panel based on the wall panel performance correlation response function to obtain a cross-scale dynamic response simulation result; Step S265: Perform collaborative performance evaluation on the cross-scale dynamic response simulation results to obtain comprehensive performance evaluation results of the wall panels.

6. The three-dimensional simulation design method based on rock wool curtain wall panel structure according to claim 5 is characterized in that: Step S265 includes the following steps: Step S2651: performing confidence evaluation on the cross-scale dynamic response simulation results to obtain a wall panel performance simulation reliability evaluation set; Step S2652: performing random perturbations on the cross-scale dynamic response simulation results to obtain a random perturbation data set of wall panel performance; Step S2653: performing extreme value distribution mapping on the random disturbance data set of wall panel performance to obtain a probability distribution diagram of wall panel performance response; Step S2654: reconstructing the confidence interval of the wall panel performance response probability distribution diagram according to the wall panel performance simulation reliability evaluation set to obtain wall panel performance confidence region mapping data; Step S2655: extract sensitivity features from the wall panel performance confidence region mapping data to obtain a curtain wall panel performance key influencing factor set, and perform risk grading on the wall panel performance confidence region mapping data according to the curtain wall panel performance key influencing factor set to obtain wall panel performance risk stratification mapping data; Step S2656: Perform a comprehensive performance evaluation on the wall panel performance risk stratification mapping data to obtain a comprehensive performance evaluation result of the wall panel.

7. The three-dimensional simulation design method based on rock wool curtain wall structure according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: obtaining a wall panel construction process parameter set, and quantifying the characteristics of the wall panel construction process parameter set to obtain a wall panel construction process parameter characteristic set; Step S32: performing entropy coding conversion on the wall panel construction process parameter feature set to obtain an entropy coding set of wall panel process parameters; Step S33: performing error probability mapping on the entropy coding set of wallboard process parameters to obtain construction error probability distribution mapping data; Step S34: spatially mapping the construction error probability distribution mapping data to obtain a wallboard construction error spatial distribution map; Step S35: performing error correlation evaluation on the wall panel construction error spatial distribution diagram to obtain a wall panel error correlation evaluation matrix, and performing hierarchical quantization encoding on the wall panel error correlation evaluation matrix to obtain a wall panel construction error index set.

8. The three-dimensional simulation design method based on rock wool curtain wall structure according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Based on the first wall panel reconstruction structure model and according to the comprehensive performance evaluation result of the wall panel, semantic mapping of performance boundary conditions is performed on the curtain wall panel structure to obtain wall panel performance boundary condition mapping data; Step S42: performing multi-scale decoupling on the wall panel performance boundary condition mapping data to obtain a wall panel performance boundary condition decoupling factor; Step S43: performing parameterized reconstruction of the curtain wall panel structure based on the first wall panel reconstruction structural model and the wall panel performance boundary condition decoupling factor to obtain a preliminary wall panel structural parameter model; Step S44: performing structural stability evaluation on the preliminary structural parameter model of the wall panel to obtain a wall panel structural stability index; Step S45: reconstructing the boundary conditions of the rock wool curtain wall panel structure according to the wall panel structure stability index to obtain a reconstructed structural model of the second wall panel; Step S46: Designing an extreme environment multi-scenario simulation scheme for the rock wool curtain wall panel to obtain an extreme environment multi-scenario simulation scheme; Step S47: Based on the wall panel construction error index set and the extreme environment multi-scenario simulation scheme, the second wall panel reconstruction structure model is subjected to an extreme environment multi-scenario simulation to obtain the wall panel multi-scenario performance response data; Step S48: Perform limit value mapping and boundary identification on the multi-scenario performance response data of the wall panel to obtain the wall panel performance limit response data; perform risk quantification on the preset fatigue failure assessment index based on the wall panel performance limit response data to obtain the wall panel performance degradation prediction result.

9. The three-dimensional simulation design method based on rock wool curtain wall structure according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: extracting features from the wall panel performance degradation prediction results to obtain a wall panel performance degradation feature vector set; Step S52: performing a Bayesian probability significance test on the wallboard performance degradation feature vector set to obtain a significance ranking table of key degradation influencing factors; Step S53: performing entropy weight fuzzy comprehensive evaluation on the significance ranking table of key degradation influencing factors to obtain a wallboard degradation influencing weight matrix; Step S54: performing multi-criteria decision mapping on the significance ranking table of key degradation influencing factors according to the wallboard degradation influence weight matrix to obtain the key design adjustment factors of the wallboard; Step S55: adjusting the structural parameters of the rock wool curtain wall panel based on the key design adjustment factors of the wall panel to obtain a design scheme for enhancing the performance of the wall panel; Step S56: reconstructing the second wall panel reconstructed structural model through the wall panel performance enhancement design scheme to obtain an optimized wall panel three-dimensional simulation model.

10. A three-dimensional simulation design system based on rock wool curtain wall panel structure, characterized in that: Used to execute the three-dimensional simulation design method based on the rock wool curtain wall panel structure according to claim 1, the three-dimensional simulation design system based on the rock wool curtain wall panel structure comprises: The digital twin module is used to obtain the wall panel micro-morphology source data; perform isotropic micro-feature enhancement on the wall panel micro-morphology source data to obtain the wall panel micro-fiber morphology data; perform digital twin reconstruction of the rock wool curtain wall panel based on the wall panel micro-fiber morphology data to obtain the first wall panel reconstructed structural model; The performance simulation module is used to perform thermodynamic field mapping on the reconstructed structural model of the first wall panel to obtain a micro-scale heat conduction characteristic map; perform cross-scale physical field coupling and superposition on the micro-scale heat conduction characteristic map to obtain a multi-field comprehensive response map of the wall panel; perform cross-scale performance dynamic simulation of the rock wool curtain wall panel based on the multi-field comprehensive response map of the wall panel to obtain a comprehensive performance evaluation result of the wall panel; The error evaluation module is used to obtain a wall panel construction process parameter set; perform construction error evaluation on the wall panel construction process parameter set to obtain a wall panel construction error index set; The risk assessment module is used to reconstruct the performance boundary conditions of the rock wool curtain wall panel according to the comprehensive performance evaluation results of the wall panel to obtain the reconstructed structural model of the second wall panel; perform extreme environment multi-scenario simulation on the reconstructed structural model of the second wall panel based on the wall panel construction error index set to obtain the multi-scenario performance response data of the wall panel; perform fatigue failure risk assessment on the rock wool curtain wall panel according to the multi-scenario performance response data of the wall panel to obtain the wall panel performance degradation prediction results; The simulation model reconstruction module is used to extract the target optimization factors of the wall panel performance degradation prediction results to obtain the key design adjustment factors of the wall panels; optimize the structural parameters of the rock wool curtain wall panels according to the key design adjustment factors of the wall panels to obtain the wall panel performance enhancement design scheme; reconstruct the second wall panel reconstruction structural model according to the wall panel performance enhancement design scheme to obtain the optimized wall panel three-dimensional simulation model.

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