High-precision digital building block system for ancient buildings and modeling method thereof

Abstract

The application relates to a high-precision ancient building digital building block system and a modeling method thereof, relates to the technical field of digital cultural heritage protection and education, and comprises a three-dimensional modeling module used for acquiring millimeter-level precision geometric data of ancient building components by adopting a laser point cloud and multi-view image fusion technology, the module collects data by means of a laser scanner and a single-lens reflex camera, and realizes spatial registration by using a feature point matching algorithm. By means of the laser point cloud and multi-view image fusion technology, the application realizes the acquisition of millimeter-level precision geometric data of ancient building components, ensures the accuracy of the digital model, the application of the parametric modeling algorithm enables the building block units to be generated and scaled in proportion, adapts to different application scenarios, meanwhile, the consistency of geometric characteristics is maintained, the design of the standardized plug-in interface module is based on topological optimization, the connection requirements of different ancient building elements are automatically adapted, and the characteristics of the mortise and tenon structure are reserved.

Description

Technical Field

[0001] This application relates to the field of digital cultural heritage protection and education technology, and in particular to a high-precision digital building block system for ancient buildings and its modeling method. Background Technology

[0002] The application of digital technology is becoming increasingly widespread. With the continuous development of 3D modeling and virtual reality technologies, crucial support has been provided for the accurate restoration of ancient architecture, making it possible to protect, display, and research ancient buildings through digital means. However, existing digital technologies still have certain limitations in handling the details of ancient buildings, especially in terms of material representation and structural precision. They struggle to fully and realistically present the unique charm and complex structure of ancient buildings, which to some extent restricts the in-depth development of digital cultural heritage protection and education.

[0003] Currently, most digital modular systems employ simplified models and universal interfaces, failing to accurately reflect the complex structures and unique materials of ancient architecture. Traditional digital modular systems suffer from deficiencies in geometric accuracy, material realism, and modular compatibility, making it difficult to meet the demands of in-depth education and research for high-precision, highly realistic digital models of ancient buildings. For example, they cannot achieve high-precision restoration of structural details, their limited material expressiveness fails to replicate the texture and color of the original building, and the lack of standardized interlocking interfaces leads to poor compatibility, restricting the flexible combination of ancient architectural elements. These problems significantly diminish the effectiveness of existing technologies in scenarios such as ancient architectural culture education, structural teaching and research, and the digital display of cultural heritage. In response, we propose a high-precision digital modular system for ancient architecture and its modeling method. Summary of the Invention

[0004] The purpose of this application is to provide a high-precision digital building block system for ancient buildings and its modeling method. This technical solution solves the problems that the above-mentioned digital building block systems mostly use simplified models and general interfaces, which cannot truly reflect the complex structure and unique materials of ancient buildings. They are also insufficient in geometric accuracy, material authenticity and modular compatibility, making it difficult to meet the needs of in-depth education and research.

[0005] Firstly, the high-precision digital modular system for ancient architecture provided in this application adopts the following technical solution:

[0006] A high-precision digital modular system for ancient architecture includes:

[0007] The 3D modeling module is used to acquire millimeter-precision geometric data of ancient building components using laser point cloud and multi-view image fusion technology. This module collects data through a laser scanner and a DSLR camera and uses a feature point matching algorithm to achieve spatial registration. The building block unit generation module is used to generate digital building block units with the same scale as the real object based on the geometric data using a parametric modeling algorithm. The algorithm defines the size and shape of the components as adjustable parameters.

[0008] Standardized plug-in interface module is used to design interfaces based on topology optimization. It automatically adapts to the connection requirements of different ancient architectural elements such as brackets, beams and columns through finite element analysis algorithms, and retains the concave-convex fit characteristics and stress transmission mechanism of mortise and tenon structure.

[0009] The material processing module is used to convert the original wood and brick textures of buildings into micron-level precision digital models using high-fidelity material scanning technology, and to replicate the textures on polymer substrates using UV printing or nanoimprinting processes.

[0010] The modular coding module is used to assign a unique identifier to each building block unit. The identifier records the historical background, structural features and splicing logic, and realizes information query and splicing guidance through a digital platform.

[0011] Preferably, the implementation method of the laser point cloud and multi-view image fusion technology in the 3D modeling module is as follows:

[0012] The ancient building components are scanned with a laser scanner to obtain three-dimensional point cloud data. The sampling interval of the point cloud data shall not be lower than the preset interval.

[0013] Use a DSLR camera to capture images of the components from several perspectives, with no fewer than eight perspectives.

[0014] Point cloud data and image data are imported into 3D modeling software. Common feature points are identified by the SIFT feature point extraction algorithm, and spatial coordinate transformation is performed by the least squares method to spatially register the point cloud and the image.

[0015] After fusion, a high-precision 3D model containing geometric coordinates and texture information is generated, with a precision at the millimeter level. This model is used for subsequent generation of building blocks.

[0016] Preferably, the implementation process of the parametric modeling algorithm is as follows:

[0017] A parametric model framework is established based on the geometric data obtained from the 3D modeling module. The size and shape geometric features of the components are defined as adjustable parameters, including length, angle and curvature.

[0018] The algorithm automatically generates digital building block units that match the scale of the real objects. The algorithm uses the parametric design module in CAD software. When the geometric data changes, the model is automatically updated by modifying the parameters.

[0019] The generated building block unit structure has an accuracy of no less than 0.1 mm, and the building block unit can be scaled within a preset ratio range according to the scaling factor to adapt to different application scenarios such as display and education, while maintaining the consistency of geometric features during the scaling process.

[0020] Preferably, the specific steps for topology optimization in the standardized plug-in interface module are as follows:

[0021] Establish a mechanical model for the connection of ancient architectural elements such as brackets, beams and columns. The input parameters of the mechanical model include interface dimensions, material properties and stress loads.

[0022] The optimization goals are interface strength and ease of assembly / disassembly;

[0023] The stress distribution at the interface is calculated using a finite element analysis algorithm, which employs ANSYS software for iterative calculations.

[0024] The topology of the interface is automatically optimized. The optimization process adjusts the material distribution to minimize weight, while adapting to the connection shape of different elements.

[0025] The interlocking features of the mortise and tenon structure are retained in the interface design.

[0026] Preferably, the implementation of high-fidelity material scanning technology in the material processing module includes:

[0027] A high-precision texture scanner is used to clean and pre-treat the surface of the original building materials. The pre-treatment includes dust removal and drying.

[0028] Scanning to acquire texture patterns and color spectrum data with micron-level precision, with a scanning resolution of no less than 1000 dpi;

[0029] The scanned data is imported into the material processing software for noise reduction and correction. Noise reduction uses a Gaussian filtering algorithm, and correction is based on the reflectance spectrum curve measured by the spectrometer.

[0030] Generate digital models containing texture details and color information, with a model precision at the micrometer level;

[0031] When using UV printing technology, the digital model is converted into printing instructions, and wide color gamut UV ink is used to replicate the texture and color on the surface of the building block components. The color difference ΔE between the printed color and the original building material is less than 1.5.

[0032] When using nanoimprinting technology, textures are imprinted onto the surface of a polymer substrate using a mold, forming a surface with the desired texture under preset process conditions.

[0033] Preferably, the allocation and management method of the unique identifier in the modular coding module is as follows:

[0034] Generate a unique identifier for each building block unit, in the form of a QR code or RFID tag;

[0035] The label records the historical background information of the unit, its location coordinates in the ancient building, structural dimension parameters, and data on the logical relationship between the unit and other units. The logical relationship includes the connection order and direction.

[0036] Users read identification information through scanning devices on a digital platform. The platform is a web-based database system that queries and displays relevant data about a unit based on its identification.

[0037] The platform provides assembly sequence guidance based on architectural structural logic, including 3D animation demonstrations and step-by-step instructions, to help users assemble the blocks correctly and efficiently with high precision.

[0038] Secondly, the high-precision digital modular modeling method for ancient buildings provided in this application adopts the following technical solution: The high-precision digital modular modeling method for ancient buildings, used to realize the high-precision digital modular system for ancient buildings, includes the following steps:

[0039] Millimeter-level geometric data of ancient building components were obtained through laser point cloud and multi-view image fusion technology.

[0040] A parametric modeling algorithm is used to generate digital building block units that are consistent with the proportions of the real objects. The algorithm establishes a parametric model framework based on geometric data and defines size and shape as adjustable parameters.

[0041] Based on topology optimization design, standardized plug-in interfaces are designed and adapted to the connection requirements of different elements of brackets, beams and columns through finite element analysis algorithms, while accurately preserving the concave-convex fit characteristics and stress transfer mechanism of mortise and tenon structure.

[0042] Micron-level texture and color information is obtained through high-fidelity material scanning, and the texture is replicated using UV printing or nanoimprinting processes; a unique identifier is assigned to each building block unit, and information management and splicing guidance are realized through a digital platform. The identifier includes a QR code or RFID tag, which records structural parameters and splicing logic.

[0043] Preferably, when acquiring geometric data of ancient building components, a laser scanner is used to perform a full-range scan of the components at a high-precision sampling interval, covering all surfaces of the components to obtain point cloud data;

[0044] Use a DSLR camera to take high-resolution photographs of the components from multiple different perspectives, including orthogonal and tilted angles;

[0045] Import point cloud data and photos into professional 3D modeling software;

[0046] The SIFT feature point extraction algorithm is used to identify common feature points between point cloud and photograph, and the number of feature points is sufficient.

[0047] The least squares method is used for spatial coordinate transformation, and the transformation error is controlled within the preset accuracy.

[0048] The point cloud and image are fused to generate a 3D model containing high-precision geometric information.

[0049] Preferably, when designing standardized plug-in interfaces, a parametric model of the interface is established to meet the connection requirements of different ancient architectural elements such as brackets, beams and columns. The input parameters of the model include the geometric dimensions, stress conditions and material types of the elements.

[0050] Iterative calculations are performed using a topology optimization algorithm. The algorithm employs the SIMP method, and in each iteration, the material distribution of the interface is adjusted to ensure that the interface load-bearing capacity meets a preset threshold.

[0051] After optimization, a standardized interface model is generated, which automatically adapts to the connection shape of different elements.

[0052] The model retains the interlocking features of the mortise and tenon structure.

[0053] Preferably, when processing the material, the surface of the original building material is pre-treated by cleaning, including removing stains and polishing;

[0054] The reflectance spectrum of the material is measured using a spectrometer, with the spectral range covering the visible light band.

[0055] The surface texture is scanned at high resolution using a texture scanner, with multiple scanning directions.

[0056] Spectral data and texture data are integrated into a digital material model, and image processing software is used for registration and fusion.

[0057] When using UV printing technology, wide color gamut UV ink is used. The ink has a wide color gamut and is printed according to the color parameters of the digital model. The printed layer is fine and the color difference between the printed color and the original building material is minimal.

[0058] When using nanoimprinting technology, the digital texture model is converted into a mold pattern. The mold material is a nickel alloy. Under preset process conditions, the texture is imprinted onto a polymer substrate to form a surface with the desired texture.

[0059] In summary, this application includes at least one of the following beneficial technical effects:

[0060] This invention achieves millimeter-level precision geometric data acquisition for ancient architectural components through laser point cloud and multi-view image fusion technology, ensuring the accuracy of the digital model. The application of parametric modeling algorithms enables the building blocks to be generated and scaled proportionally to adapt to different application scenarios while maintaining the consistency of geometric features. The standardized plug-in interface module design, based on topology optimization, automatically adapts to the connection requirements of different ancient architectural elements and retains the characteristics of mortise and tenon structures, improving the compatibility and stability of the building block system. The material processing module replicates the texture and color of the original building materials through high-fidelity material scanning technology, enhancing the realism of the digital building blocks. The modular coding module assigns a unique identifier to each building block, enabling information query and splicing guidance, thus improving the user experience. Attached Figure Description

[0061] Figure 1 This is a system framework diagram of the present invention;

[0062] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0063] The following is in conjunction with the appendix Figure 1 - Appendix Figure 2 This application will be described in further detail below.

[0064] Example 1: High-precision digital modular system for ancient architecture, refer to Figure 1 As shown, it includes:

[0065] The core of the high-precision digital modular system for ancient architecture lies in constructing a comprehensive technical solution that integrates high-precision 3D data acquisition, intelligent parametric modeling, mechanical optimization interface design, realistic material replication, and information management. The system first acquires millimeter-level precision geometric data of the ancient architectural components through a 3D modeling module. This process relies on laser point cloud and multi-view image fusion technology. In practice, a high-precision laser scanner (such as a phase-detection or pulse-detection scanner) is used to perform a comprehensive, all-around scan of the target ancient architectural component. During the scanning process, the sampling interval of the point cloud data is a critical parameter and must be strictly set according to the complexity of the component and the required precision, typically not less than 0.5 millimeters, to ensure that minute carvings or structural features are captured. The scanning operation must cover all surfaces of the component, including hard-to-reach corners and grooves. Simultaneously, a high-resolution SLR camera (typically requiring a full-frame sensor with a resolution of at least 24 megapixels) is used to acquire clear images of the component from no fewer than eight different perspectives (including orthogonal perspectives such as front view, side view, and top view, as well as multiple oblique perspectives to cover side and bottom details). Lighting conditions need to be uniform and stable to avoid highlights or deep shadows affecting texture recognition.

[0066] After acquiring the raw point cloud data and high-resolution imagery, it needs to be imported into professional 3D modeling software (such as Agisoft Metashape, RealityCapture, or Autodesk ReCap). The core of the fusion lies in spatial registration. First, a large number of key feature points with rotation and scale invariance are extracted from the imagery using a scale-invariant feature transform algorithm. The SIFT algorithm detects local extrema as candidate keypoints by constructing a Gaussian difference pyramid, then determines the precise location and scale by fitting a 3D quadratic function, and generates a 128-dimensional feature descriptor based on the gradient direction histogram of the keypoint neighborhood. Simultaneously, the point cloud data itself also contains spatial coordinate information. The software algorithm finds matching pairs between the SIFT feature points extracted from the imagery and the corresponding 3D points in the point cloud. For each pair of matching points, there is a projection relationship between its 3D spatial coordinates and 2D image coordinates. The goal of registration is to find an optimal spatial coordinate transformation matrix (usually containing a rotation matrix R and a translation vector T) that minimizes the sum of squared errors between the projected 2D coordinates and image coordinates of all matching point pairs. This is solved using the least squares method: Given a set of matching point pairs: a set of 3D points in the point cloud {P} i =(X i ,Y i Z i )} and the corresponding two-dimensional point set {P} on the image i =(u i ,v i The goal is to solve for the transformation matrix parameters that minimize the projection error E, where the formula for the point cloud-image registration error is:

[0067] E=∑‖p i -Proj(R·P i +T)‖ 2

[0068] In the formula, E is the total projection error, p i Here, P represents the coordinates of a 2D point on the image, Proj() is the projection function that projects the 3D point onto the 2D image plane (determined by camera intrinsics), R is the 3D rotation matrix (3×3) describing spatial rotation, and P... iLet E be the coordinates of a 3D point in the point cloud, and T be the 3D translation vector (3×1) describing spatial translation. The least squares method iteratively optimizes (e.g., the Levenberg-Marquardt algorithm) by adjusting the parameters of R and T, continuously reducing E until it converges within a preset tolerance threshold (e.g., the average reprojection error is less than 0.5 pixels). After registration, the precise geometric coordinates of the point cloud are fused with the rich texture information of the image, generating a high-precision 3D model containing complete geometry and realistic surface texture, with an overall geometric accuracy reaching the millimeter level (typically verified to have an error within ±1mm). This model forms the basis for all subsequent digital building blocks.

[0069] Based on the high-precision 3D model generated through fusion, the building block unit generation module uses a parametric modeling algorithm to transform it into digital building block units that are strictly proportional to the physical object. The core idea of ​​parametric modeling is to abstract the core geometric features of the components (length L, width W, height H, key angle θ, radius R, curvature κ, etc.) into independently adjustable parametric variables. The algorithm first analyzes the input 3D model geometric data, identifies the main structural features of the components (such as the cross-sectional dimensions of beams, the height and diameter of columns, the layering relationship of brackets, control points of specific curves, etc.), and constructs a parameter-driven model framework in computer-aided design software (such as Autodesk Inventor, SolidWorks, or Rhino with Grasshopper). This framework is essentially a collection of geometric constraints and feature generation steps (such as extrusion, rotation, sweep, lofting, Boolean operations, etc.), and its input is a predefined set of parameters.

[0070] For example, the parametric model frame of a rectangular cross-section beam is defined as:

[0071] Length = L (length parameter)

[0072] Width = W (width parameter)

[0073] Height = H (height parameter)

[0074] ChamferRadius = R (Chamfer radius parameter)

[0075] The modeling steps are as follows:

[0076] Draw a rectangular sketch in the XY plane and constrain its dimensions to Width × Height.

[0077] Apply a chamfer feature to the four corners of the rectangle, with the chamfer radius constrained to ChamferRadius.

[0078] Extrude the sketch along the Z-axis, with the extrusion length constrained to Length.

[0079] The algorithm automatically assigns the actual dimensional values ​​measured from the input 3D model to these parameter variables (e.g., L = 5000mm, W = 300mm, H = 400mm, R = 10mm), thereby driving the model framework to generate a digital building block unit solid model that is completely consistent with the geometry of the original components. The power of parametric modeling lies in its associative nature: when adjustments to the model are needed (e.g., scaling down the model to accommodate different display ratios, or correcting minor deviations in the original scanned data), simply modify the corresponding parameter values ​​(e.g., setting `ScaleFactor` to 0.5, or fine-tuning `Length` to 5020mm), and the software can automatically recalculate and update all relevant geometric features of the entire model based on preset geometric constraints and modeling step logic, without requiring manual remodeling. The final generated digital building block unit structure has extremely high precision, with its key dimensional tolerances strictly controlled within ±0.1 mm. This unit model has the ability to scale by a scaling factor within a preset scale range (e.g., 1:1 to 1:100). During the scaling process, by maintaining geometric constraints and feature generation logic, it ensures that all geometric features (e.g., chamfers, holes, and curved surfaces) can change correctly according to the scale, maintaining the consistency of geometric shape and meeting the diverse application scenarios from scaled research models to small-scale display and educational models.

[0080] To achieve precise connections and mechanical transfer between building blocks, much like in real ancient architectural components, a standardized plug-in interface module was designed. Its core technology is interface design based on topology optimization. Topology optimization is a mathematical method for finding the optimal material distribution given a design space, load conditions, and constraints. For the complex and varied connection requirements in ancient architecture (such as connections between brackets, between brackets and beams, and between beams and columns), an initial design space (usually a cube or cylinder containing potential connection areas) needs to be defined for each type of connection interface. Then, a mechanical model of the interface is established. Key parameters for model input include:

[0081] InterfaceDimensions: The dimensions of the interface area.

[0082] MaterialProperties: Material properties used by the interface.

[0083] LoadCases: The loads that the interface bears during operation. These loads are determined based on the structural analysis of ancient buildings or standard specifications, and must take into account combinations of static load, live load, wind load, seismic action, etc.

[0084] ConnectionShapes: A description of the geometry (point cloud or parametric surface) at the interface of the two building blocks to be connected.

[0085] Optimization objectives are typically set as dual objectives: maximizing the load-bearing strength of the interface (or minimizing the maximum stress / strain) while maximizing ease of assembly and disassembly (achievable by minimizing insertion / removal resistance or optimizing the guide structure). Constraints include the interface volume fraction (maximum material usage), the maximum allowable stress (less than the material yield strength), and the critical feature areas of the mortise and tenon joint that must be retained (these areas are defined as non-design spaces, and the material must be preserved).

[0086] Topology optimization employs iterative calculations using the Finite Element Analysis (FEA) algorithm, with commonly used software including ANSYS Mechanical or Abaqus. The algorithm is typically based on a penalty model method for solid isotropic materials. The SIMP model discretizes the design space into a finite element mesh (such as tetrahedral or hexahedral elements) and assigns a relative density variable ρ to each element. e (0≤v e ≤1), where ρ e =1 represents solid material, v e =0 indicates a hole. The effective elastic modulus E of the element. e The function of density variable is:

[0087]

[0088] In the formula, E e ρ is the effective elastic modulus of element e. e Let E be the relative density of element e. min The value is a very small positive number, p is a penalty factor (usually p = 3), which penalizes the intermediate density, driving it to approach 0 or 1, and E0 is the elastic modulus of the solid material.

[0089] In each iteration:

[0090] 1. Based on the current density distribution ρ of all units e Calculate the effective material property E for each element. e (ρ e ),ν (Poisson's ratio is usually assumed to be constant).

[0091] 2. Apply loads and boundary conditions to perform finite element static analysis and solve for the displacement field and stress / strain field.

[0092] 3. Calculate the objective function (e.g., overall compliance C = F). T U, minimizing flexibility is equivalent to maximizing stiffness; or maximum stress) and constraint functions (such as total volume) Where v e (This refers to the unit volume).

[0093] 4. Calculate the objective function and constraint function for each element density ρ using sensitivity analysis.e The derivative (sensitivity).

[0094] 5. Update the density variable ρ_e of all elements based on optimization algorithms (such as Moving Asymptote Method (MMA) or Optimization Criterion Method (OC), adjusting it in the direction that satisfies the constraints and optimization objectives.

[0095] 6. Check the convergence conditions (e.g., the change in the objective function is less than a threshold, or the maximum constraint violation is less than a threshold). If convergence is not achieved, return to step 1.

[0096] After multiple iterations (typically dozens to hundreds of times), the algorithm converges, yielding a topological structure with optimal material distribution within a given space. This structure automatically adapts to the geometry of the two input connecting building blocks while meeting strength requirements and volume constraints. Crucially, during optimization, predefined key areas for realizing the interlocking features of mortise and tenon joints (such as the tenon protrusion and mortise recess) are forcibly retained as solid material. This ensures that the digital interface accurately replicates the interlocking method and mechanical transmission path of traditional mortise and tenon joints (pressure contact for load transfer, rather than relying on the shear force of modern connectors). Simultaneously, the lightweight support structure generated by topology optimization improves the interface's efficiency. After optimization, a standardized 3D model of the interface is generated, which can be directly attached to the corresponding digital building block.

[0097] To give the digital building blocks realistic visual effects and tactile feel, the material processing module employs high-fidelity material scanning technology. This process begins with rigorous pre-treatment of the original building materials (such as old materials replaced during restoration of ancient buildings or small, permitted samples) – including wood, brick, and tiles – through a thorough cleaning process. This includes thorough dust removal using soft brushes and compressed air, gentle cleaning with professional wood or stone cleaners when necessary, and ensuring complete drying in a constant temperature and humidity environment (with stable moisture content) to prevent residues or moisture from affecting scanning accuracy. After cleaning, the material surface is scanned using a high-precision structured light or confocal texture scanner. The scanner is equipped with a high-resolution optical sensor (typically no less than 1000 dpi, corresponding to approximately 25 micrometers per point), capable of capturing micrometer-level precision surface undulation information (i.e., normal maps or height maps) such as the fine texture of wood, pores, and growth rings; and the graininess, weathering, and wear marks of brick and stone. Simultaneously, a high-precision spectrometer (or a spectrophotometer equipped with an integrating sphere) is used to measure the reflectance spectrum curve S(λ) of the material surface. Measurements need to cover the visible light band (380nm-780nm), be performed at multiple small angles (e.g., 10°) under a standard light source (e.g., D65), and obtain reflectivity data of the material at different wavelengths to accurately characterize its color properties.

[0098] The raw texture height data (which may contain noise) and spectral data acquired through scanning are imported into professional material processing software (such as Substance Suite, 3DCoat, ZBrush). First, noise reduction is performed, commonly using Gaussian filtering algorithms to smooth the height map through convolution. Choosing an appropriate σ value can remove high-frequency noise while preserving important texture details. Color correction is based on the reflectance spectrum curve S(λ) measured by a spectrometer. The software compares the RGB image acquired by the scanner with the measured S(λ). By solving a color transformation matrix or applying a lookup table (LUT), the RGB values ​​of the scanned image are corrected so that the calculated chromaticity coordinates (such as CIE Lab values) in the target color space (such as sRGB, Adobe RGB) are as close as possible to the theoretical values ​​calculated based on S(λ), ensuring accurate color reproduction. Finally, the corrected texture height map (or normal map) and accurate color information (usually a high dynamic range HDR map or a physically based rendering PBR map, including channels for albedo, roughness, and metallicity) are integrated into a complete digital material model with micron-level precision.

[0099] There are two main processes for replicating materials onto the surface of polymer building blocks (such as ABS, PC, PMMA): UV printing and nanoimprinting. When using UV printing, the material's digital model (especially the Albedo color map) is first color-separated and converted into CMYK or CMYK+W+varnish printing instructions suitable for a specific UV flatbed printer. UV-curable inks with a wide color gamut (covering most natural material colors) are used. During printing, the inkjet printhead precisely deposits tiny ink droplets on the polymer substrate surface based on the color information of the digital model. By controlling droplet size, the number of layers, and halftone screen technology, complex color gradations and texture details are reproduced. A key indicator is the color difference between the printed color and the original building material, using CIE76. Formula calculation:

[0100]

[0101] In the formula, ΔL represents the total color difference value in the CIE Lab color space. * ,Δa * ,Δb * These are the differences in lightness, red-green hue, and yellow-blue hue between the printed color sample and the original building material in the CIE Lab color space. The system strictly controls these differences. It reaches a level where the human eye can hardly distinguish color differences.

[0102] After printing, the UV light source instantly cures the ink, forming a wear-resistant surface coating. If nanoimprinting is used, a high-precision texture height map (enlarged and edge-processed) from the material's digital model is required to manufacture a metal mold (usually a nickel alloy). Mold manufacturing typically employs precision electroforming or micro-milling techniques. During imprinting, the textured mold is pressed against the heated and softened polymer substrate surface (or a substrate coated with UV resin) under pre-defined process conditions (precisely controlled temperature T, pressure P, and holding time t). The mold's micro / nano structure is transferred to the polymer surface. After cooling (or UV curing), the mold is demolded, forming a textured surface with realistic physical undulations, its tactile feel highly similar to the original material.

[0103] To achieve effective management of massive quantities of complex digital building blocks and a user-friendly assembly experience, the modular coding module assigns a unique machine-readable identifier to each generated building block. This identifier typically takes the form of a QR code or RFID tag, physically attached to or embedded in the building block. This unique identifier carries rich information, and its authenticity and tamper-proof nature are ensured through encryption or verification mechanisms. The core data recorded within the identifier includes:

[0104] Historical Background: The name of the original ancient building component represented by this block, the name of the building to which it belongs, its age, cultural background, and a description of its historical value.

[0105] PositionData: The precise three-dimensional coordinates (X, Y, Z) and orientation of the component within the original ancient building.

[0106] Structural Parameters: Key structural dimensions of the component (length, width, height, diameter, angle, etc.), material type (e.g., Phoebe zhennan, granite), and weight estimation.

[0107] ConnectionLogic: This is the logical relationship data between this unit and other units. This includes:

[0108] AdjacentUnits: Specifies which specific other units it can directly connect to (referenced by their unique identifiers).

[0109] ConnectionType: The type of connection interface (e.g., dovetail, straight tenon, mortise and tenon joint).

[0110] ConnectionOrientation: Connection direction and angle constraints (e.g., the "east" side of element A can only be connected to the "west" side of element B with a specific rotation angle).

[0111] ConnectionSequence: The recommended order of connections (dependencies) during the overall assembly process.

[0112] Metadata includes creation date, version number, data source, copyright information, etc.

[0113] Users scan the identifiers on the building blocks using the platform's accompanying devices (such as a smartphone app's camera scanning QR codes or an RFID reader sensing tags). The platform is a web-based cloud database system with a user-friendly front-end interface (webpage or app). The back-end database (such as MySQL, PostgreSQL, or MongoDB) stores detailed information about all building blocks and their associated components. Upon receiving the identifier information, the platform immediately initiates a query request to the database: SELECT * FROM BrickUnits WHERE UnitID = 'Scanned_ID'

[0114] The database returns all relevant data for the unit, which the platform clearly and systematically presents to the user, including 3D model visualization, historical background, and detailed structural parameters. The platform's core function is to provide intelligent assembly sequence guidance. Its algorithm is based on the inherent structural logic of ancient architecture (typically from bottom to top, inside to outside, following the hierarchical order of column base -> column -> beam -> bracket -> roof frame) and the defined `ConnectionLogic` relationships between units (especially `ConnectionSequence` and dependencies). When the user begins assembly or selects a specific goal (such as assembling a complete bracket), the platform calculates the optimal (or feasible) next assembly suggestion based on the current assembly status and the logical relationships between units. Guidance information is presented in various intuitive ways:

[0115] 3D Animation Demonstration: In the virtual scene, the next block to be picked up and its unique identifier are highlighted. A smooth 3D animation then demonstrates how the block moves precisely to the target position, showing the correct connection direction (rotation angle) and insertion path, until it perfectly meshes with the existing structure. The animation simulates contact and constraints based on a physics engine.

[0116] Step instructions: Describe the next step in clear text ("Locate the component marked QR_1024, align the north side with the tenon with the mortise of the component marked QR_1005, and insert it vertically downwards along the Z-axis until you hear a 'click' sound to indicate that it is in place").

[0117] AR Overlay Guidance: If the user uses the camera of their mobile device to point at the current assembly progress, the platform can use augmented reality (AR) technology to overlay virtual indicator arrows, highlighted outlines, or animations on the real-time screen to intuitively indicate the location and operation method of the next unit.

[0118] This intelligent guidance significantly reduces the difficulty for users (especially non-professionals) to understand complex ancient building structures and perform precise assembly, ensuring that the final assembled digital model of the ancient building has extremely high precision and faithfully reproduces the mechanical rationality and aesthetic features of the original structure. The entire process is not only educational but also has research and exhibition value.

[0119] Example 2: High-precision digital modular modeling method for ancient buildings, referring to... Figure 2 As shown, the high-precision digital modular system for ancient architecture includes:

[0120] Millimeter-level geometric data of ancient building components were obtained through laser point cloud and multi-view image fusion technology.

[0121] A parametric modeling algorithm is used to generate digital building block units that are consistent with the proportions of the real objects. The algorithm establishes a parametric model framework based on geometric data and defines size and shape as adjustable parameters.

[0122] Based on topology optimization design, standardized plug-in interfaces are designed and adapted to the connection requirements of different elements of brackets, beams and columns through finite element analysis algorithms, while accurately preserving the concave-convex fit characteristics and stress transfer mechanism of mortise and tenon structure.

[0123] Micron-level texture and color information is obtained through high-fidelity material scanning, and the texture is replicated using UV printing or nanoimprinting processes; a unique identifier is assigned to each building block unit, and information management and splicing guidance are realized through a digital platform. The identifier includes a QR code or RFID tag, which records structural parameters and splicing logic.

[0124] In summary, this high-precision digital modular system for ancient architecture, through the deep integration of 3D scanning, parametric design, topology optimization, high-fidelity material replication, and information management technologies, has constructed a complete technology chain. It has achieved precise digitization, standardization, and interactivity of complex ancient architectural components, providing a powerful digital tool for the preservation, research, education, and innovative inheritance of ancient architectural culture.

[0125] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A high-precision digital modular system for ancient architecture, characterized in that, include: The 3D modeling module is used to obtain millimeter-precision geometric data of ancient building components using laser point cloud and multi-view image fusion technology. This module collects data through a laser scanner and a DSLR camera, and uses a feature point matching algorithm to achieve spatial registration. A building block unit generation module is used to generate digital building block units that are consistent with the proportions of the actual objects based on the geometric data using a parametric modeling algorithm. The algorithm defines the size and shape of the components as adjustable parameters. A standardized plug-in interface module is used to design interfaces based on topology optimization. It automatically adapts to the connection requirements of different ancient architectural elements such as brackets, beams, and columns using finite element analysis algorithms, while preserving the convex-concave fit characteristics and stress transfer mechanism of mortise and tenon structures. The specific steps of topology optimization in the standardized plug-in interface module are as follows: Establish a mechanical model for the connection of ancient architectural elements such as brackets, beams and columns. The input parameters of the mechanical model include interface dimensions, material properties and stress loads. The optimization goals are interface strength and ease of assembly / disassembly; The stress distribution at the interface is calculated using a finite element analysis algorithm, which employs ANSYS software for iterative calculations. The topology of the interface is automatically optimized. The optimization process adjusts the material distribution to minimize weight, while adapting to the connection shape of different elements. The interlocking features of the mortise and tenon structure should be retained in the interface design. The material processing module is used to convert the original wood and brick textures of buildings into micron-level precision digital models using high-fidelity material scanning technology, and to replicate the textures on polymer substrates using UV printing or nanoimprinting processes. The modular coding module is used to assign a unique identifier to each building block unit. The identifier records the historical background, structural features and splicing logic, and realizes information query and splicing guidance through a digital platform.

2. The high-precision digital modular system for ancient architecture according to claim 1, characterized in that, The implementation method of laser point cloud and multi-view image fusion technology in the 3D modeling module is as follows: The ancient building components are scanned with a laser scanner to obtain three-dimensional point cloud data. The sampling interval of the point cloud data shall not be lower than the preset interval. Use a DSLR camera to capture images of the components from several perspectives, with no fewer than eight perspectives. Point cloud data and image data are imported into 3D modeling software. Common feature points are identified by the SIFT feature point extraction algorithm, and spatial coordinate transformation is performed by the least squares method to spatially register the point cloud and the image. After fusion, a high-precision 3D model containing geometric coordinates and texture information is generated, with a precision at the millimeter level. This model is used for subsequent generation of building blocks.

3. The high-precision digital modular system for ancient architecture according to claim 1, characterized in that, The implementation process of the parametric modeling algorithm is as follows: A parametric model framework is established based on the geometric data obtained from the 3D modeling module. The size and shape geometric features of the components are defined as adjustable parameters, including length, angle and curvature. The algorithm automatically generates digital building block units that match the scale of the real objects. The algorithm uses the parametric design module in CAD software. When the geometric data changes, the model is automatically updated by modifying the parameters. The generated building block unit structure has an accuracy of no less than 0.1 mm, and the building block unit can be scaled within a preset ratio range according to the scaling factor to adapt to different application scenarios such as display and education, while maintaining the consistency of geometric features during the scaling process.

4. The high-precision digital modular system for ancient architecture according to claim 1, characterized in that, The implementation of high-fidelity material scanning technology in the material processing module includes: A high-precision texture scanner is used to clean and pre-treat the surface of the original building materials. The pre-treatment includes dust removal and drying. Scanning to acquire texture patterns and color spectrum data with micron-level precision, with a scanning resolution of no less than 1000 dpi; The scanned data is imported into the material processing software for noise reduction and correction. Noise reduction uses a Gaussian filtering algorithm, and correction is based on the reflectance spectrum curve measured by the spectrometer. Generate digital models containing texture details and color information, with a model precision at the micrometer level; When using UV printing technology, the digital model is converted into printing instructions, and wide color gamut UV ink is used to replicate the texture and color on the surface of the building block components. The color difference ΔE between the printed color and the original building material is less than 1.

5. When using nanoimprinting technology, textures are imprinted onto the surface of a polymer substrate using a mold, forming a surface with the desired texture under preset process conditions.

5. The high-precision digital modular system for ancient architecture according to claim 1, characterized in that, The allocation and management method of the unique identifier in the modular coding module is as follows: Generate a unique identifier for each building block unit, in the form of a QR code or RFID tag; The label records the historical background information of the unit, its location coordinates in the ancient building, structural dimension parameters, and data on the logical relationship between the unit and other units. The logical relationship includes the connection order and direction. Users read identification information through scanning devices on a digital platform. The platform is a web-based database system that queries and displays relevant data about a unit based on its identification. The platform provides assembly sequence guidance based on architectural structural logic, including 3D animation demonstrations and step-by-step instructions, to help users assemble the blocks correctly and efficiently with high precision.

6. A high-precision digital modular modeling method for ancient buildings, characterized in that, To implement the high-precision digital modular system for ancient architecture as described in any one of claims 1-5, the following steps are included: Millimeter-level geometric data of ancient building components were obtained through laser point cloud and multi-view image fusion technology. A parametric modeling algorithm is used to generate digital building block units that are consistent with the proportions of the real objects. The algorithm establishes a parametric model framework based on geometric data and defines size and shape as adjustable parameters. Based on topology optimization design, standardized plug-in interfaces are designed and adapted to the connection requirements of different elements of brackets, beams and columns through finite element analysis algorithms, while accurately preserving the concave-convex fit characteristics and stress transfer mechanism of mortise and tenon structure. Micron-level texture and color information is obtained by high-fidelity material scanning, and the texture is replicated by UV printing or nanoimprinting. Each building block is assigned a unique identifier, and information management and assembly guidance are achieved through a digital platform. The identifier includes a QR code or RFID tag and records structural parameters and assembly logic.

7. The high-precision digital modular modeling method for ancient buildings according to claim 6, characterized in that, When acquiring geometric data of ancient building components, a laser scanner is used to perform a full-range scan of the components at a high-precision sampling interval, covering all surfaces of the components to obtain point cloud data. Use a DSLR camera to take high-resolution photographs of the components from multiple different perspectives, including orthogonal and tilted angles; Import point cloud data and photos into professional 3D modeling software; The SIFT feature point extraction algorithm is used to identify common feature points between point cloud and photograph, and the number of feature points is sufficient. The least squares method is used for spatial coordinate transformation, and the transformation error is controlled within the preset accuracy. The point cloud and image are fused to generate a 3D model containing high-precision geometric information.

8. The high-precision digital modular modeling method for ancient buildings according to claim 6, characterized in that, When designing standardized plug-in interfaces, a parametric model of the interface is established to meet the connection requirements of different ancient architectural elements such as brackets, beams and columns. The input parameters of the model include the geometric dimensions, stress conditions and material types of the elements. Iterative calculations are performed using a topology optimization algorithm. The algorithm employs the SIMP method, and in each iteration, the material distribution of the interface is adjusted to ensure that the interface load-bearing capacity meets a preset threshold. After optimization, a standardized interface model is generated, which automatically adapts to the connection shape of different elements. The model retains the interlocking features of the mortise and tenon structure.

9. The high-precision digital modular modeling method for ancient buildings according to claim 6, characterized in that, When processing materials, the surface of the original building materials is cleaned and pre-treated, including removing stains and polishing. The reflectance spectrum of the material is measured using a spectrometer, with the spectral range covering the visible light band. The surface texture is scanned at high resolution using a texture scanner, with multiple scanning directions. Spectral data and texture data are integrated into a digital material model, and image processing software is used for registration and fusion. When using UV printing technology, wide color gamut UV ink is used. The ink has a wide color gamut and is printed according to the color parameters of the digital model. The printed layer is fine and the color difference between the printed color and the original building material is minimal. When using nanoimprinting technology, the digital texture model is converted into a mold pattern. The mold material is a nickel alloy. Under preset process conditions, the texture is imprinted onto a polymer substrate to form a surface with the desired texture.

Images