An intelligent analysis and processing system for earthquake damage evolution of ancient building wood structure

Abstract

The present application relates to the technical field of computer-aided design and cultural heritage protection, in particular to an ancient building wood structure earthquake damage evolution intelligent analysis processing system, comprising six functional modules interconnected through a data bus: a reverse modeling module: for receiving multi-source point cloud data, identifying component instances and inferring topological relationships using a semantic segmentation algorithm. The ancient building wood structure earthquake damage evolution intelligent analysis processing system, by building an intelligent analysis processing system with the six modules working together, realizes the full-process automation and intelligentization of the ancient building wood structure from data acquisition to reinforcement design, uses the graph convolution network in the reverse modeling module and the parameterized family library to automatically identify complex special-shaped components and generate high-precision BIM models, completely replacing the traditional manual drawing mode; secondly, the combination of the attribute mapping module and the space-time indexing technology solves the problem that traditional static parameters cannot reflect the real mechanical properties of ancient buildings.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of computer-aided design and cultural heritage protection, specifically to an intelligent analysis and processing system for the earthquake damage evolution of ancient wooden structures. Background Technology

[0002] As an important historical and cultural heritage, the seismic performance assessment and reinforcement design of ancient wooden structures typically rely on traditional engineering mechanics analysis and computer-aided design (CAD) methods. In existing technologies, engineers first acquire three-dimensional point cloud data of ancient buildings using methods such as total stations, lidar, or photogrammetry. Then, they use general modeling software such as AutoCAD and Revit to perform manual digital reconstruction, constructing two-dimensional drawings or basic three-dimensional models containing geometric information. On this basis, researchers use finite element analysis software such as ANSYS and ABAQUS to establish numerical simulation models. By manually defining the component section properties, material parameters, and contact relationships of mortise and tenon joints, they apply seismic wave loads to conduct time history analysis to obtain the displacement, internal force, and stress distribution results of the structure. Finally, based on the simulation results and expert experience, reinforcement schemes are manually formulated and construction drawings are drawn. The entire process is highly dependent on manual operation and professional judgment, realizing a preliminary digital process from data acquisition to simulation analysis.

[0003] However, existing technologies suffer from significant automation and data flow disruptions when dealing with complex ancient wooden structures. First, traditional reverse modeling heavily relies on manual identification and drawing, which is extremely inefficient and prone to human error when dealing with a large number of non-standardized irregular components (such as columns and curved roofs). It is also difficult to achieve automatic conversion from point clouds to parametric BIM models. Second, in the simulation preprocessing stage, the nonlinear mechanical behavior of mortise and tenon joints is extremely complex. Existing technologies often require engineers to manually set thousands of contact pairs and nonlinear spring elements, which is labor-intensive and prone to non-convergence or distortion due to improper parameter settings. There is a lack of intelligent equivalent proxy model generation mechanisms. In addition, the damage visualization and analysis processes are disconnected. Simulation results are mostly presented in the form of tables or cloud maps, lacking real-time interactive mapping with the 3D model, making it difficult to intuitively display the entire damage evolution process. At the same time, the design of reinforcement schemes mainly relies on manual trial and error, lacking the ability to automatically generate based on multi-objective optimization algorithms. It is impossible to quickly find the optimal solution while meeting the constraints of the authenticity of cultural relics, resulting in a fragmented overall workflow and making it difficult to form an efficient and intelligent closed loop for analysis and processing. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent analysis and processing system for the earthquake damage evolution of ancient wooden structures. This system has the advantages of achieving full-process automation and high-precision modeling, thus solving the problems of low modeling efficiency and limited accuracy.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent analysis and processing system for the earthquake damage evolution of ancient wooden structures, comprising six functional modules interconnected via a data bus: Reverse modeling module: It is used to receive multi-source point cloud data, use semantic segmentation algorithms to identify component instances and infer topological relationships, and automatically generate an editable building information model (BIM) based on a parametric family library. Attribute mapping module: used to establish spatial index association between geometric entities and material properties, predict material degradation state based on building age and environmental data, and provide dynamic constitutive parameters for simulation; Mesh Assembly Module: Used to convert BIM models into finite element meshes, automatically identify mortise and tenon joint interfaces and generate nonlinear spring-damped elements, and realize automated equivalent processing of complex nodes; The solution calculation module is used to call the parallel solver to perform seismic time history analysis, track the stress and strain field in real time, and calculate global and local damage indices based on plastic strain and stiffness degradation. The visualization rendering module is used to map damage indicators onto the surface of a 3D model, generate damage heat maps and time-series animations of the failure process through voxelization rendering technology, and supports interactive exploration. Scheme optimization module: With the goal of minimizing intervention and improving seismic resistance, it uses heuristic algorithms to search for the optimal reinforcement topology distribution, automatically verifies the compliance of the scheme according to cultural relic regulations, and outputs draft construction drawings.

[0006] Furthermore, the reverse modeling module specifically includes: The point cloud registration submodule is used to eliminate coordinate deviations in scanned data at different scales and generate a hybrid point cloud in a unified coordinate system. The semantic segmentation submodule deploys a graph convolutional network algorithm to divide the point cloud into independent semantic clusters such as columns, beams, and purlins. The topology reconstruction submodule constructs a hierarchical tree-like data structure between components based on geometric adjacency relationships; The parameterized generation submodule extracts the cross-sectional profile and eccentricity through the constraint solver and instantiates the corresponding BIM parameterized object.

[0007] Furthermore, the attribute mapping module specifically includes: The spatiotemporal mapping submodule establishes a spatial quadtree index of three-dimensional coordinates and material property IDs to enable fast retrieval; The degradation prediction submodule integrates a time-series regression neural network, inputs historical detection data to predict the current elastic modulus decay coefficient of the wood, and writes the dynamic parameters into the simulation configuration table.

[0008] Furthermore, the mesh assembly module specifically includes: An adaptive mesh generation submodule automatically refines tetrahedral mesh elements in regions with small radii of curvature or high stress. The node identification submodule traverses the assembly constraints to detect component connections with gaps and identifies them as non-rigid nodes. The equivalent proxy submodule inserts triaxial nonlinear spring elements between nodes. The stiffness of these elements is determined by the force-slip curve polynomial equation, eliminating the need for manual setting of contact conditions.

[0009] Furthermore, the solution calculation module specifically includes: GPU-accelerated interface, utilizing CUDA technology to process large-scale sparse matrix operations in parallel; The incremental step size control submodule automatically adjusts the time step size according to the residual norm to ensure numerical stability. The damage definition submodule calculates the comprehensive damage factor. And serialize and store the state data for each step.

[0010] Furthermore, the visualization rendering module specifically includes: The scalar field mapping submodule uses a ray stepping algorithm to map damage factors to RGB color transparency, thereby achieving volume visualization. A time-series playback engine that generates animations of the destruction process based on keyframe interpolation, supporting drag-and-drop navigation along the timeline; The interactive probe tool allows users to click on components to query their node IDs, internal force vectors, and hierarchical information.

[0011] Furthermore, the scheme optimization module specifically includes: Objective function builder, which defines a multi-objective optimization function that includes the added weight, cost, and seismic toughness index; The genetic algorithm optimizer iteratively searches for the optimal hardened topology solution on the Pareto front through selection, crossover, and mutation operations. The spatial collision detection submodule uses the separating axis theorem to detect geometric interference between the proposed new component and the original structure, eliminating illegal solutions.

[0012] Furthermore, the scheme optimization module further includes: The standard rules engine has a built-in expert system rule base to check whether the solution violates the authenticity restriction clauses in the protection guidelines; The visual coordination assessment unit trains a convolutional neural network model to evaluate the subjective satisfaction of before-and-after comparison images of reinforcement, assisting in the selection of aesthetic solutions.

[0013] Furthermore, it also includes a user interaction management module, used for: It provides a graphical user interface that integrates control buttons and result display windows for each module; The access control subsystem restricts operators' permissions to modify core algorithm parameters and output the final solution. The report generator automatically summarizes simulation results, heat maps, and CAD drawings, and exports a comprehensive analysis report.

[0014] This application further proposes an intelligent analysis and processing method for the seismic damage evolution of ancient wooden structures, including the aforementioned intelligent analysis and processing system for the seismic damage evolution of ancient wooden structures, comprising the following steps: S1: Through the reverse modeling module, multi-source point clouds are transformed into BIM models with topological relationships; S2: Update the dynamic constitutive parameters reflecting the aging state through the attribute mapping module; S3: Automatically completes mesh discretization and equivalent processing of mortise and tenon nodes through the mesh assembly module; S4: Perform earthquake time history analysis and accumulate damage indicators through the solution calculation module; S5: Display the damage evolution process through a visualization rendering module; S6: Through the scheme optimization module, search for and verify the optimal reinforcement scheme, and output construction drawings.

[0015] Compared with the prior art, the technical solution of this application has the following beneficial effects: 1. This intelligent analysis and processing system for the seismic damage evolution of ancient wooden structures achieves full automation and intelligence of the entire process from data acquisition to reinforcement design by constructing an intelligent analysis and processing system with six collaborative modules. Utilizing the graph convolutional network and parametric family library in the reverse modeling module, it can automatically identify complex irregular components and generate high-precision BIM models, completely replacing the traditional manual drawing mode and significantly improving modeling efficiency and geometric accuracy. Secondly, the combination of the attribute mapping module and spatiotemporal indexing technology enables the material aging state to be dynamically updated over time, solving the problem that traditional static parameters cannot reflect the true mechanical performance of ancient buildings and providing more accurate boundary conditions for simulation.

[0016] 2. This intelligent analysis and processing system for the seismic damage evolution of ancient wooden structures has achieved groundbreaking technological improvements in the simulation and analysis stages. The mesh assembly module automatically identifies mortise and tenon interfaces and generates nonlinear spring elements, eliminating the tedious manual contact setting process and significantly improving the efficiency and computational convergence of finite element preprocessing. The solution calculation module, combined with GPU parallel acceleration, can quickly complete large-scale nonlinear time history analysis. The visualization rendering module transforms abstract damage data into intuitive 3D heat maps and time-series animations, enabling real-time interactive exploration of damage evolution. The scheme optimization module introduces genetic algorithms and a standard rule engine, which can automatically search for the optimal reinforcement topology while meeting the constraints of the authenticity of cultural relics. This overcomes the shortcomings of manual design, such as strong subjectivity, low efficiency, and easy violation of protection criteria. Ultimately, it forms a closed-loop system for seismic analysis and auxiliary design of ancient buildings that is efficient, accurate, and compliant. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 A smart analysis and processing system for the earthquake damage evolution of ancient wooden structures, comprising six functional modules interconnected via a data bus: Reverse modeling module: It is used to receive multi-source point cloud data, use semantic segmentation algorithms to identify component instances and infer topological relationships, and automatically generate an editable building information model (BIM) based on a parametric family library. Attribute mapping module: used to establish spatial index association between geometric entities and material properties, predict material degradation state based on building age and environmental data, and provide dynamic constitutive parameters for simulation; Mesh Assembly Module: Used to convert BIM models into finite element meshes, automatically identify mortise and tenon joint interfaces and generate nonlinear spring-damped elements, and realize automated equivalent processing of complex nodes; The solution calculation module is used to call the parallel solver to perform seismic time history analysis, track the stress and strain field in real time, and calculate global and local damage indices based on plastic strain and stiffness degradation. The visualization rendering module is used to map damage indicators onto the surface of a 3D model, generate damage heat maps and time-series animations of the failure process through voxelization rendering technology, and supports interactive exploration. Scheme optimization module: With the goal of minimizing intervention and improving seismic resistance, it uses heuristic algorithms to search for the optimal reinforcement topology distribution, automatically verifies the compliance of the scheme according to cultural relic regulations, and outputs draft construction drawings.

[0020] It has achieved a fully automated closed loop from point cloud acquisition to reinforcement design, completely replacing the traditional fragmented manual operation mode, and significantly improving the overall efficiency and data flow consistency of seismic analysis of ancient buildings.

[0021] The reverse modeling module specifically includes: The point cloud registration submodule is used to eliminate coordinate deviations in scanned data at different scales and generate a hybrid point cloud in a unified coordinate system. The semantic segmentation submodule deploys a graph convolutional network algorithm to divide the point cloud into independent semantic clusters such as columns, beams, and purlins. The topology reconstruction submodule constructs a hierarchical tree-like data structure between components based on geometric adjacency relationships; The parameterized generation submodule extracts the cross-sectional profile and eccentricity through the constraint solver and instantiates the corresponding BIM parameterized object.

[0022] By combining graph convolutional networks with constraint solvers, the problems of time-consuming and low-accuracy manual modeling of irregular components are solved, and high-precision, editable parametric BIM models are generated quickly and automatically.

[0023] The attribute mapping module specifically includes: The spatiotemporal mapping submodule establishes a spatial quadtree index of three-dimensional coordinates and material property IDs to enable fast retrieval; The degradation prediction submodule integrates a time-series regression neural network, inputs historical detection data to predict the current elastic modulus decay coefficient of the wood, and writes the dynamic parameters into the simulation configuration table.

[0024] By using spatiotemporal indexing and neural networks to dynamically predict material degradation, the shortcomings of traditional static parameters in reflecting the true mechanical properties of ancient buildings are overcome, providing more accurate boundary conditions that evolve over time for simulation.

[0025] The mesh assembly module specifically includes: An adaptive mesh generation submodule automatically refines tetrahedral mesh elements in regions with small radii of curvature or high stress. The node identification submodule traverses the assembly constraints to detect component connections with gaps and identifies them as non-rigid nodes. The equivalent proxy submodule inserts triaxial nonlinear spring elements between nodes. The stiffness of these elements is determined by the force-slip curve polynomial equation, eliminating the need for manual setting of contact conditions.

[0026] The automatic identification of mortise and tenon interfaces and generation of nonlinear spring elements eliminates the tedious manual contact setup process, significantly improving the efficiency of finite element preprocessing and the convergence stability of large-scale nonlinear calculations.

[0027] The solution calculation module specifically includes: GPU-accelerated interface, utilizing CUDA technology to process large-scale sparse matrix operations in parallel; The incremental step size control submodule automatically adjusts the time step size according to the residual norm to ensure numerical stability. The damage definition submodule calculates the comprehensive damage factor. And serialize and store the state data for each step.

[0028] Based on GPU parallel acceleration and adaptive incremental step size control, the computation time for time history analysis of complex structures is significantly shortened, and the degree of structural damage is accurately assessed through a quantified comprehensive damage factor.

[0029] The visualization rendering module specifically includes: The scalar field mapping submodule uses a ray stepping algorithm to map damage factors to RGB color transparency, thereby achieving volume visualization. A time-series playback engine that generates animations of the destruction process based on keyframe interpolation, supporting drag-and-drop navigation along the timeline; The interactive probe tool allows users to click on components to query their node IDs, internal force vectors, and hierarchical information.

[0030] This technology transforms abstract simulation data into intuitive 3D heatmaps and time-series animations, supporting interactive exploration and enabling researchers to intuitively and in real-time understand the damage evolution path of structures under seismic loading.

[0031] The solution optimization module specifically includes: Objective function builder, which defines a multi-objective optimization function that includes the added weight, cost, and seismic toughness index; The genetic algorithm optimizer iteratively searches for the optimal hardened topology solution on the Pareto front through selection, crossover, and mutation operations. The spatial collision detection submodule uses the separating axis theorem to detect geometric interference between the proposed new component and the original structure, eliminating illegal solutions.

[0032] By introducing a multi-objective genetic algorithm to automatically search for the optimal reinforcement topology, the technical bottlenecks of manual design, which are characterized by strong subjectivity, low efficiency, and easy violation of protection guidelines, are overcome while meeting the constraints of the authenticity of cultural relics.

[0033] The solution optimization module includes: The standard rules engine has a built-in expert system rule base to check whether the solution violates the authenticity restriction clauses in the protection guidelines; The visual coordination assessment unit trains a convolutional neural network model to evaluate the subjective satisfaction of before-and-after comparison images of reinforcement, assisting in the selection of aesthetic solutions.

[0034] Built-in expert system rule verification and AI aesthetic evaluation model ensure that the reinforcement plan not only meets the mandatory standards for cultural relic protection, but also takes into account visual harmony, thereby improving the overall quality and social acceptance of the design plan.

[0035] It also includes a user interaction management module, used for: It provides a graphical user interface that integrates control buttons and result display windows for each module; The access control subsystem restricts operators' permissions to modify core algorithm parameters and output the final solution. The report generator automatically summarizes simulation results, heat maps, and CAD drawings, and exports a comprehensive analysis report.

[0036] Built-in expert system rule verification and AI aesthetic evaluation model ensure that the reinforcement plan not only meets the mandatory standards for cultural relic protection, but also takes into account visual harmony, thereby improving the overall quality and social acceptance of the design plan.

[0037] This application further proposes an intelligent analysis and processing method for the seismic damage evolution of ancient wooden structures, including an intelligent analysis and processing system for the seismic damage evolution of ancient wooden structures, comprising the following steps: S1: Through the reverse modeling module, multi-source point clouds are transformed into BIM models with topological relationships; S2: Update the dynamic constitutive parameters reflecting the aging state through the attribute mapping module; S3: Automatically completes mesh discretization and equivalent processing of mortise and tenon nodes through the mesh assembly module; S4: Perform earthquake time history analysis and accumulate damage indicators through the solution calculation module; S5: Display the damage evolution process through a visualization rendering module; S6: Through the scheme optimization module, search for and verify the optimal reinforcement scheme, and output construction drawings.

[0038] It provides fine-grained access control and standardized automatic report generation services, ensuring data security and operational process standardization, lowering the barrier to entry for professional software and enhancing the professionalism of deliverables.

[0039] This invention proposes an intelligent analysis and processing system for the seismic damage evolution of ancient wooden structures based on the combination of BIM and finite element analysis. Its core principle is to automatically convert point cloud data into a high-precision parametric BIM model using a graph convolutional network, and to dynamically map the aging properties of the wood using spatiotemporal indexing and neural networks. During the simulation phase, the system automatically identifies mortise and tenon joints and generates nonlinear spring elements for adaptive mesh generation. It then uses GPUs to accelerate nonlinear time-history analysis to quantify structural damage. Finally, a multi-objective genetic algorithm automatically generates the optimal reinforcement scheme while satisfying the constraints of cultural relic authenticity. Expert rules and AI aesthetic evaluation are combined to ensure the compliance and coordination of the scheme, ultimately achieving a fully automated closed-loop process from data acquisition and damage visualization to intelligent reinforcement design.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent analysis and processing system for the seismic damage evolution of ancient wooden structures, characterized in that, It includes six functional modules interconnected via a data bus: Reverse modeling module: It is used to receive multi-source point cloud data, use semantic segmentation algorithms to identify component instances and infer topological relationships, and automatically generate an editable building information model (BIM) based on a parametric family library. Attribute mapping module: used to establish spatial index association between geometric entities and material properties, predict the degradation state of materials based on building age and environmental data, and provide dynamic constitutive parameters for simulation; Mesh Assembly Module: Used to convert BIM models into finite element meshes, automatically identify mortise and tenon joint interfaces and generate nonlinear spring-damped elements, and realize automated equivalent processing of complex nodes; The solution calculation module is used to call the parallel solver to perform seismic time history analysis, track the stress and strain field in real time, and calculate global and local damage indices based on plastic strain and stiffness degradation. The visualization rendering module is used to map damage indicators onto the surface of a 3D model, generate damage heat maps and time-series animations of the failure process through voxelization rendering technology, and supports interactive exploration. Scheme optimization module: With the goal of minimizing intervention and improving seismic resistance, it uses heuristic algorithms to search for the optimal reinforcement topology distribution, automatically verifies the compliance of the scheme according to cultural relic regulations, and outputs draft construction drawings.

2. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: The reverse modeling module specifically includes: The point cloud registration submodule is used to eliminate coordinate deviations in scanned data at different scales and generate a hybrid point cloud in a unified coordinate system. The semantic segmentation submodule deploys a graph convolutional network algorithm to divide the point cloud into independent semantic clusters such as columns, beams, and purlins. The topology reconstruction submodule constructs a hierarchical tree-like data structure between components based on geometric adjacency relationships; The parameterized generation submodule extracts the cross-sectional profile and eccentricity through the constraint solver and instantiates the corresponding BIM parameterized object.

3. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: The attribute mapping module specifically includes: The spatiotemporal mapping submodule establishes a spatial quadtree index of three-dimensional coordinates and material property IDs to enable fast retrieval; The degradation prediction submodule integrates a time-series regression neural network, inputs historical detection data to predict the current elastic modulus decay coefficient of the wood, and writes the dynamic parameters into the simulation configuration table.

4. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: The mesh assembly module specifically includes: An adaptive mesh generation submodule automatically refines tetrahedral mesh elements in regions with small radii of curvature or high stress. The node identification submodule traverses the assembly constraints to detect component connections with gaps and identifies them as non-rigid nodes. The equivalent proxy submodule inserts triaxial nonlinear spring elements between nodes. The stiffness of these elements is determined by the force-slip curve polynomial equation, eliminating the need for manual setting of contact conditions.

5. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: The solution calculation module specifically includes: GPU-accelerated interface, utilizing CUDA technology to process large-scale sparse matrix operations in parallel; The incremental step size control submodule automatically adjusts the time step size according to the residual norm to ensure numerical stability. The damage definition submodule calculates the comprehensive damage factor. And serialize and store the state data for each step.

6. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: The visualization rendering module specifically includes: The scalar field mapping submodule uses a ray stepping algorithm to map damage factors to RGB color transparency, thereby achieving volume visualization. A time-series playback engine that generates animations of the destruction process based on keyframe interpolation, supporting drag-and-drop navigation along the timeline; The interactive probe tool allows users to click on components to query their node IDs, internal force vectors, and hierarchical information.

7. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: The scheme optimization module specifically includes: Objective function builder, which defines a multi-objective optimization function that includes the added weight, cost, and seismic toughness index; The genetic algorithm optimizer iteratively searches for the optimal hardened topology solution on the Pareto front through selection, crossover, and mutation operations. The spatial collision detection submodule uses the separating axis theorem to detect geometric interference between the proposed new component and the original structure, eliminating illegal solutions.

8. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: The scheme optimization module further includes: The standard rules engine has a built-in expert system rule base to check whether the solution violates the authenticity restriction clauses in the protection guidelines; The visual coordination assessment unit trains a convolutional neural network model to evaluate the subjective satisfaction of before-and-after comparison images of reinforcement, thus assisting in the selection of aesthetic solutions.

9. The intelligent analysis and processing system for earthquake damage evolution of ancient wooden structures according to claim 1, characterized in that: It also includes a user interaction management module, used for: It provides a graphical user interface that integrates control buttons and result display windows for each module; The access control subsystem restricts operators' permissions to modify core algorithm parameters and output the final solution. The report generator automatically summarizes simulation results, heat maps, and CAD drawings, and exports a comprehensive analysis report.

10. A method for intelligent analysis and processing of earthquake damage evolution in ancient wooden structures, comprising the intelligent analysis and processing system for earthquake damage evolution in ancient wooden structures as described in any one of claims 1-9, characterized in that: Includes the following steps: S1: Through the reverse modeling module, multi-source point clouds are transformed into BIM models with topological relationships; S2: Update the dynamic constitutive parameters reflecting the aging state through the attribute mapping module; S3: Automatically completes mesh discretization and equivalent processing of mortise and tenon nodes through the mesh assembly module; S4: Perform earthquake time history analysis and accumulate damage indicators through the solution calculation module; S5: Display the damage evolution process through a visualization rendering module; S6: Through the scheme optimization module, search for and verify the optimal reinforcement scheme, and output construction drawings.

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