High-precision historic building digital building block system and modeling method thereof

Abstract

The invention relates to a high-precision historic building digital building block system and a modeling method thereof, and relates to the technical field of digital cultural heritage protection and education, and the system comprises a three-dimensional modeling module which is used for obtaining millimeter-level precision geometric data of historic building components through employing a laser point cloud and multi-view image fusion technology, the module acquires data through a laser scanner and a digital single lens reflex and realizes spatial registration by using a feature point matching algorithm. Through the laser point cloud and multi-view image fusion technology, millimeter-level precision geometric data acquisition of the ancient building component is realized, the accuracy of a digital model and the application of a parametric modeling algorithm are ensured, the building block units can be generated and scaled in proportion to adapt to different application scenes, and meanwhile, the consistency of geometric characteristics is kept; the design of a standardized plug interface module is based on topological optimization, connection requirements of different ancient building elements are automatically adapted, and the characteristics of a tenon-and-mortise structure are reserved.

Description

Technical Field

[0001] The present application relates to the field of digital cultural heritage protection and educational technology, and in particular to a high-precision ancient building digital building block system and a modeling method thereof. Background Art

[0002] The application of digital technology is becoming increasingly widespread. The continuous development of 3D modeling and virtual reality technologies has provided important support for the accurate restoration of ancient buildings, making it possible to protect, display, and study these buildings through digital means. However, existing digital technologies still have certain limitations when processing the details of ancient buildings, especially in terms of material expression and structural refinement. It is difficult to fully and authentically present the unique charm and complex structures of ancient buildings, which to some extent restricts the in-depth development of digital cultural heritage protection and education.

[0003] Currently, digital building block systems mostly use simplified models and universal interfaces, a design that fails to truly reflect the complex structures and unique materials of ancient buildings. Traditional digital building block systems lack geometric accuracy, material authenticity, and modular compatibility, making them 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 accurately restore the structural details of ancient buildings, their limited material expressiveness prevents them from reproducing the texture and color of the original buildings, and the lack of standardized plug-in interfaces leads to poor compatibility, restricting the flexible combination of ancient architectural elements. These issues significantly reduce the effectiveness of existing technologies in scenarios such as ancient architectural cultural education, structural teaching and research, and the digital display of cultural heritage. To address this, we propose a high-precision digital building block system for ancient buildings 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 problem that the above-mentioned digital building block system mostly uses simplified models and universal interfaces, cannot truly reflect the complex structure and unique materials of ancient buildings, and is insufficient in geometric accuracy, material authenticity and modular compatibility, making it difficult to meet the needs of in-depth education and research.

[0005] In the first aspect, the high-precision ancient architecture digital building block system provided by this application adopts the following technical solutions: High-precision ancient building digital building block system, including: A 3D modeling module uses laser point cloud and multi-view image fusion technology to obtain millimeter-level precision geometric data of ancient architectural components. This module collects data through a laser scanner and a single-lens reflex camera and uses a feature point matching algorithm to achieve spatial registration. A building block unit generation module uses a parametric modeling algorithm to generate digital building block units consistent with the actual scale based on the geometric data. The algorithm defines the component size and shape 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 through finite element analysis algorithms, while preserving the concave-convex fit characteristics and stress transfer mechanism of the mortise and tenon structure. The material processing module uses high-fidelity material scanning technology to convert the original building wood and masonry textures into micron-level precision digital models, and then uses UV printing or nano-imprinting technology to replicate the textures on polymer substrates; The modular coding module is used to assign a unique identifier to each building block unit. The identifier records the historical background, structural characteristics and splicing logic, and enables information query and splicing guidance through a digital platform.

[0006] Preferably, the implementation method of the laser point cloud and multi-view image fusion technology in the 3D modeling module is: Scan the ancient building components with a laser scanner to obtain three-dimensional point cloud data. The sampling interval of the point cloud data shall not be less than the preset interval; Use a single-lens reflex camera to capture component images from several viewing angles, with the number of viewing angles being no less than 8; Import the point cloud data and image data into the 3D modeling software, identify common feature points using the SIFT feature point extraction algorithm, and use the least squares method to perform spatial coordinate transformation to spatially align the point cloud and image; After fusion, a high-precision three-dimensional model containing geometric coordinates and texture information is generated with a model accuracy of millimeter level. This model is used for subsequent building block unit generation.

[0007] Preferably, the implementation process of the parameterized modeling algorithm is: A parametric model framework is established based on the geometric data obtained from the 3D modeling module, and the size and shape geometric characteristics of the component are defined as adjustable parameters, including length, angle and curvature; The algorithm automatically generates digital building blocks that are consistent with the actual scale. 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 scale range according to the proportional factor to adapt to different application scenarios such as display and education. The scaling process maintains the consistency of geometric features.

[0008] Preferably, the specific steps of topology optimization in the standardized plug interface module are: Establish a mechanical model for the connection between brackets, beams and columns of ancient buildings. The input parameters of the mechanical model include interface dimensions, material properties and loads. The optimization goals are interface strength and ease of assembly and disassembly; The stress distribution of the interface is calculated by the finite element analysis algorithm, and the algorithm uses ANSYS software for iterative calculation; Automatically optimize the topology of interfaces. The optimization process adjusts material distribution to minimize weight while adapting the connection shapes of different elements. The concave and convex fitting features of the mortise and tenon structure are retained in the interface design.

[0009] Preferably, the implementation of high-fidelity material scanning technology in the material processing module includes: Use a high-precision texture scanner to clean and pre-treat the surface of the original building materials, including dust removal and drying; Scan to obtain micron-level precision texture patterns and color spectrum data, with a scanning resolution of no less than 1000dpi; The scanned data is imported into the material processing software for noise reduction and correction. The noise reduction uses the Gaussian filter algorithm, and the correction is based on the reflectance spectrum curve measured by the spectrometer; Generate digital models containing texture details and color information with micron-level accuracy; 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 the nanoimprint process, the texture is imprinted onto the surface of the polymer substrate through a mold, forming a surface with the desired texture under preset process conditions.

[0010] Preferably, the allocation and management of unique identifiers 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 logo records the historical background information of the unit, the location coordinates in the ancient building, the structural size parameters, and the splicing logic relationship data with other units. The splicing logic includes the connection order and direction; Users read identification information through the digital platform's scanning device. The platform is a web-based database system that queries and displays relevant data about the unit based on the identification; The platform provides assembly sequence guidance based on building structure logic, including 3D animation demonstrations and step-by-step instructions, to help users combine building blocks correctly and efficiently, with high matching accuracy.

[0011] In a second aspect, the high-precision ancient building digital building block modeling method provided by the present application adopts the following technical solution: The high-precision ancient building digital building block modeling method is used to implement the high-precision ancient building digital building block system, comprising the following steps: Obtain millimeter-level geometric data of ancient architectural components through laser point cloud and multi-view image fusion technology; Using a parametric modeling algorithm to generate digital building blocks that are consistent with the actual scale of the building blocks, the algorithm establishes a parametric model framework based on geometric data and defines size and shape as adjustable parameters; Based on topological optimization, standardized plug-in interfaces are designed. Finite element analysis algorithms are used to adapt to the connection requirements of different elements such as brackets, beams, and columns, while accurately preserving the concave-convex fit characteristics and stress transfer mechanism of the mortise and tenon structure. Micron-level texture and color information is acquired through high-fidelity material scanning, and the texture is reproduced using UV printing or nanoimprinting technology. Unique identifiers are assigned to building block units, and information management and splicing guidance are achieved through a digital platform. The identifiers include QR codes or RFID tags that record structural parameters and splicing logic.

[0012] Preferably, when obtaining the geometric data of the ancient building components, the components are scanned in all directions by a laser scanner 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 component from multiple different viewpoints, including orthogonal and oblique angles; Import point cloud data and photos into professional 3D modeling software; The SIFT feature point extraction algorithm is used to identify the common feature points between the point cloud and the photo, and the number of feature points is sufficient; The least square method is used for spatial coordinate transformation, and the transformation error is controlled within the preset accuracy; The point cloud and image are integrated to generate a three-dimensional model containing high-precision geometric information.

[0013] Preferably, when designing a standardized plug-in interface, a parametric model of the interface is established based on the connection requirements of different ancient architectural elements such as brackets, beams and columns. The model input parameters include the geometric dimensions, stress conditions and material types of the elements. Iterative calculations are performed using a topology optimization algorithm using the SIMP method. Each iteration adjusts the material distribution of the interface so that the interface bearing capacity meets the preset threshold. After the optimization is completed, a standardized interface model is generated, and the model automatically adapts to the connection shapes of different elements; The concave and convex fitting features of the mortise and tenon structure are retained in the model.

[0014] Preferably, when processing the material, the surface of the original building material is cleaned and pre-treated, and the pre-treatment includes removing stains and polishing; Use a spectrometer to measure the reflectance spectrum curve of the material, and the spectrum range covers the visible light band; Scan the surface texture with high resolution using a texture scanner in multiple angles; Integrate spectral data and texture data into a digital model of the material, and use image processing software 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 printing layer is thick and fine, and the color difference between the printed color and the original building material is extremely small; When using the nanoimprint process, the texture digital model is converted into a mold pattern. The mold material is nickel alloy. Under preset process conditions, the texture is imprinted onto the polymer substrate to form a surface with the desired texture.

[0015] In summary, this application includes at least one of the following beneficial technical effects: The present invention uses laser point cloud and multi-view image fusion technology to achieve millimeter-level precision geometric data acquisition of ancient architectural components, ensuring the accuracy of the digital model. The application of parametric modeling algorithm enables building block units to be generated and scaled in proportion to adapt to different application scenarios while maintaining the consistency of geometric features. The design of standardized plug-in interface modules, based on topological optimization, automatically adapts to the connection requirements of different ancient architectural elements, retains the characteristics of mortise and tenon structures, and improves the compatibility and stability of the building block system. The material processing module uses high-fidelity material scanning technology to replicate the texture and color of the original building materials, enhancing the realism of the digital building blocks. The modular coding module assigns a unique identifier to each building block unit, realizes information query and splicing guidance, and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a system framework diagram of the present invention; Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The following is combined with Figure 1 -Attached Figure 2 , further details of this application are given.

[0018] Example 1: High-precision ancient building digital building block system, refer to Figure 1 Shown, including: The core of the high-precision digital building block system for ancient buildings is to build a comprehensive technical solution that integrates high-precision three-dimensional data acquisition, intelligent parameterized modeling, mechanical optimization interface design, real material replication, and information management. The system first obtains millimeter-level precision geometric data of ancient building components through a three-dimensional modeling module. This process relies on laser point cloud and multi-view image fusion technology. In specific implementation, a high-precision laser scanner (such as a phase or pulse scanner) is used to scan the target ancient building component from all directions without any dead angles. During the scanning process, the sampling interval of the point cloud data is a critical parameter that needs to be strictly set according to the complexity of the component and the required precision, usually not less than 0.5 mm, to ensure that subtle carvings or structural features can be captured. The scanning operation needs to cover all surfaces of the component, including corners and grooves that are difficult to access. At the same time, a high-resolution single-lens reflex camera (usually requiring a full-frame sensor with a resolution of not less than 24 million pixels) is used to capture clear images of the component from at least 8 different angles (including orthogonal angles such as front view, side view, and top view, as well as multiple inclined angles to cover the details of the side and bottom surfaces). The lighting conditions need to be uniform and stable to avoid high light or deep shadows affecting texture recognition.

[0019] After obtaining the original point cloud data and high-resolution images, they need to be imported into professional three-dimensional modeling software (such as Agisoft Metashape, RealityCapture, or Autodesk ReCap). The core of the fusion is spatial registration. First, the scale-invariant feature transform algorithm is used to extract a large number of key feature points with rotation and scale invariance from the images. The SIFT algorithm detects local extreme points as candidate key points by constructing a Gaussian difference pyramid, then determines the exact position and scale by fitting a three-dimensional quadratic function, and generates a 128-dimensional feature descriptor based on the gradient direction histogram of the key point neighborhood. At the same time, the point cloud data itself also contains spatial coordinate information. The software algorithm finds the matching pairs between the SIFT feature points extracted from the image and the corresponding three-dimensional points in the point cloud. For each matching point pair, there is a projection relationship between its three-dimensional spatial coordinates and two-dimensional 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 of the projected two-dimensional coordinates of all matching point pairs and the image coordinates. This is solved by the least squares method: let there be a set of matching point pairs: the three-dimensional point set {P i =(X i ,Y i ,Z i )} in the point cloud and the two-dimensional point set {P i =(u i ,v i )} on the corresponding image. The goal is to solve the transformation matrix parameters to minimize the projection error E, where the point cloud and image registration error formula is: E=∑‖p i -Proj(R·P i +T)‖ 2 Where E is the total projection error, p i is the coordinate of the two-dimensional point on the image, Proj() is the projection function that projects the three-dimensional point onto the two-dimensional image plane (determined by the camera internal parameters), R is the three-dimensional rotation matrix (3×3), which describes the spatial rotation, P i are the three-dimensional point coordinates in the point cloud, and T is the three-dimensional translation vector (3×1), describing the spatial translation. The least squares method adjusts the parameters of R and T through iterative optimization (such as the Levenberg-Marquardt algorithm), continuously reducing E until it converges within a preset tolerance threshold (for example, the average reprojection error is less than 0.5 pixels). After registration, the precise geometric coordinates of the point cloud and the rich texture information of the image are combined to generate a high-precision three-dimensional model with complete geometric shapes and realistic surface textures. Its overall geometric accuracy reaches the millimeter level (usually the verification error is within ±1mm). This model forms the basis of all subsequent digital building blocks.

[0020] Based on the high-precision fused 3D model, the building block generation module utilizes a parametric modeling algorithm to convert it into digital building blocks that are strictly proportional to the physical object. The core concept of parametric modeling is to abstractly define the core geometric features of a component (length L, width W, height H, key angles θ, radius R, curvature κ, etc.) as independently adjustable parametric variables. The algorithm first analyzes the input 3D model geometry data to identify the component's primary structural features (such as the cross-sectional dimensions of beams, the height and diameter of columns, the stacking relationship of brackets, and the control points of specific curves). Based on this information, a parameter-driven model framework is constructed 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, sweeping, lofting, and Boolean operations), with input being a defined set of parameters.

[0021] For example, the parametric model frame of a rectangular beam is defined as: Length = L (length parameter) Width = W (width parameter) Height = H (height parameter) ChamferRadius=R (chamfer radius parameter) The modeling steps are: Draw a rectangle sketch on the XY plane and constrain its dimensions to Width × Height.

[0022] Apply chamfer features to the four corners of the rectangle, and set the chamfer radius constraint to ChamferRadius.

[0023] Stretch the sketch along the Z axis and constrain the stretch length to Length.

[0024] The algorithm automatically assigns the actual dimensional values ​​obtained from measuring the input 3D model to these parametric variables (such as 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 original component geometry. The power of parametric modeling lies in its associativity: when the model needs to be adjusted (for example, to reduce the model to adapt to different display scales, or to correct minor deviations in the original scan data), it is only necessary to modify the corresponding parameter value (such as setting `ScaleFactor` to 0.5, or fine-tuning `Length` to 5020mm). The software can automatically recalculate and update all relevant geometric features of the entire model based on the preset geometric constraints and modeling step logic, without the need for manual remodeling. The final generated digital building block unit structure is extremely accurate, and its key dimensional tolerances are strictly controlled within ±0.1 mm. This unit model has the ability to be scaled by a proportional factor within a preset scale range (such as 1:1 to 1:100). During the scaling process, by maintaining geometric constraints and feature generation logic, it ensures that all geometric features (such as chamfers, holes, and surface shapes) can be correctly changed in proportion, maintaining the consistency of geometric shape, and meeting the needs of diverse application scenarios from proportional research models to small-scale displays and educational models.

[0025] In order to achieve precise connection and mechanical transmission between building block units like real ancient building components, the system has designed a standardized plug-in interface module. Its core technology is interface design based on topological optimization. Topological optimization is a mathematical method that seeks the optimal material distribution under a given design space, load conditions and constraints. In view of the complex and changeable connection requirements in ancient buildings (such as the connection between brackets, brackets and beams, and beams and columns), it is first necessary to define an initial design space for each type of connection interface (usually a cube or cylinder containing potential connection areas). Then, a mechanical model of the interface is established. The key parameters of the model input include: InterfaceDimensions: The dimensions of the interface area.

[0026] MaterialProperties: Material properties used by the interface.

[0027] LoadCases: Loads borne by the interface in working condition. These loads are determined based on the structural analysis of ancient buildings or standard specifications, and need to consider combinations of static loads, live loads, wind loads, and earthquake effects.

[0028] ConnectionShapes: geometry description (point cloud or parametric surface) of the two brick units to be connected at the interface.

[0029] The optimization objective is usually set as a bi-objective: maximizing the load carrying strength (or minimizing the maximum stress / strain) of the interface, while maximizing the ease of disassembly (can be achieved by minimizing the insertion / withdrawal resistance or optimizing the guiding structure). The constraints include the volume fraction of the interface (upper limit of material usage), the maximum allowable stress (less than the yield strength of the material), and the regions of the mortise-tenon structure that must retain the key features of the male-female fit (these regions are defined as the non-design space, where material must be retained).

[0030] The topology optimization process employs a finite element analysis algorithm (FEA) for iterative computation, commonly using software such as ANSYS Mechanical or Abaqus. The algorithm is usually based on the solid isotropic material penalization (SIMP) model method. The SIMP model discretizes the design space into a finite element mesh (such as tetrahedral or hexahedral elements), and assigns each element a relative density variable ρ e (0≤v e ≤1), where ρ e = 1 represents solid material, and v e = 0 represents void. The effective elastic modulus E e of an element is a function of the density variable as: where E e is the effective elastic modulus of element e, ρ e is the relative density of element e, E min is a small positive number, p is the penalization factor (usually p = 3), penalizing intermediate densities, driving them towards 0 or 1, and E0 is the elastic modulus of the solid material In each iteration: 1. Calculate the effective material properties E e (ρ e ), v (Poisson's ratio is usually assumed to be constant) for each element based on the current density distribution ρ e of all elements.

[0031] 2. Apply the load and boundary conditions, and perform finite element static analysis to solve the displacement field and stress / strain field.

[0032] 3. Calculate the objective function (such as the overall compliance C = F T U, minimizing compliance is equivalent to maximizing stiffness; or the maximum stress) and the constraint function (such as the total volume where v e is the element volume).

[0033] 4. Use sensitivity analysis to calculate the objective function and constraint function for each unit density ρ e The derivative (sensitivity) of .

[0034] 5. Update the density variables ρ_e of all units based on an optimization algorithm (such as the moving asymptote method MMA or the optimization criterion method OC) and adjust them in the direction of satisfying the constraints and optimization goals.

[0035] 6. Check the convergence conditions (e.g., the objective function change is less than a threshold, or the maximum constraint violation is less than a threshold). If not, return to step 1.

[0036] After multiple iterations (usually dozens to hundreds of times), the algorithm converges and obtains a topological structure with the optimal distribution of materials in a given space. This structure automatically adapts to the geometric shapes of the two connected building block units input while meeting the strength requirements and volume constraints. The most critical thing is that during the optimization process, the pre-defined key areas used to realize the concave and convex fitting characteristics of the mortise and tenon structure (such as the raised part of the tenon and the recessed part of the mortise) are forced to be retained as solid materials, ensuring that the digital interface can accurately reproduce the traditional mortise and tenon bite mode and mechanical transmission path (pressure contact transfers loads rather than relying on the shear force of modern connectors). At the same time, the lightweight support structure generated by topological optimization improves the efficiency of the interface. After the optimization is completed, a standardized three-dimensional model of the interface is generated, which can be directly attached to the corresponding digital building block unit.

[0037] To imbue the digital building blocks with realistic visuals and tactile qualities, the material processing module utilizes high-fidelity material scanning technology. This process begins with rigorous cleaning and pretreatment of the original building's wood, masonry, tiles, and other materials (such as remnants from restored historic buildings or small, permitted samples). Pretreatment includes thorough dust removal with a soft brush and compressed air, gentle cleaning with a specialized wood or stone cleaner if necessary, and ensuring thorough drying (stable moisture content) in a constant temperature and humidity environment to prevent residue or moisture from affecting scanning accuracy. After cleaning, the material surface is scanned using a high-precision structured light or confocal texture scanner. Equipped with a high-resolution optical sensor (typically with a resolution of at least 1000 dpi, corresponding to approximately 25 microns per dot), the scanner is capable of capturing micron-level surface texture information (i.e., normal maps or height maps) such as the fine grain, vascular pores, and growth rings of wood, and the grain, weathering, and wear patterns of masonry. Simultaneously, a high-precision spectrometer (or spectrophotometer equipped with an integrating sphere) is used to measure the reflectance spectrum curve S(λ) of the material surface. The measurement needs to cover the visible light band (380nm-780nm) and be performed under a standard light source (such as D65) at multiple small angles (such as 10°) to obtain the reflectance data of the material at different wavelengths and accurately characterize its color properties.

[0038] The raw texture height data (which may contain noise) and spectral data acquired from the scan are imported into professional material processing software (such as Substance Suite, 3DCoat, or ZBrush). Noise reduction is first performed, typically using a Gaussian filter algorithm to smooth the height map through convolution. Selecting an appropriate σ value removes 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 conversion 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 or Adobe RGB) are as close as possible to the theoretical values ​​calculated based on S(λ), ensuring accurate color reproduction. Ultimately, the corrected texture height map (or normal map) and precise color information (usually a high dynamic range HDR map or a physically based rendering PBR map, containing channels such as albedo, roughness, and metallic) are integrated into a complete, micron-level accurate digital model of the material.

[0039] There are two main processes for replicating materials onto the surface of polymer building block components (such as ABS, PC, PMMA): UV printing and nano-imprinting. When using UV printing technology, the digital model of the material (especially the Albedo color map) is first color-separated and converted into CMYK or CMYK+W+varnish printing instructions suitable for specific UV flatbed printers. Use UV-curable ink with a wide color gamut (capable of covering most of the color range of natural materials). During the printing process, the inkjet print head accurately deposits tiny droplets of ink on the surface of the polymer substrate according to the color information of the digital model. By controlling the droplet size, the number of superimposed layers and halftone screen technology, complex color gradients and texture details can be reproduced. The key indicator is the color difference between the printed color and the original building material, using CIE76 Formula calculation: Where, is the total color difference value in the CIE Lab color space, ΔL * ,Δa * ,Δb * They are the brightness difference, red-green difference, and yellow-blue difference between the printed color sample and the original building material in the CIE Lab color space. Reaching a level where the human eye can hardly distinguish color difference.

[0040] After printing, the UV light source instantaneously cures the ink, forming a wear-resistant surface coating. If the nano-imprinting process is used, the high-precision texture height map (after magnification and edge processing) in the material digital model needs to be used to manufacture the metal mold (usually nickel alloy). Mold manufacturing usually uses precision electroforming or micro-milling technology. During imprinting, the mold with the texture pattern is pressed against the surface of the heated and softened polymer substrate (or the substrate coated with UV resin) under the preset process conditions (precisely controlled temperature T, pressure P, and holding time t). The micro-nano structure of the mold is transferred to the polymer surface, and after cooling (or UV curing), the mold is removed, forming a texture surface with real physical concave and convex fluctuations, which has a high similarity in touch feeling to the original material.

[0041] In order to realize the effective management of massive and complex digital building blocks and the user-friendly assembly experience, the modular coding module assigns a unique machine-readable identifier to each generated building block. The identifier is usually in the form of a two-dimensional code (QR Code) or a radio frequency identification (RFID) tag, which is physically attached or embedded on the building block. This unique identifier carries rich information, and the authenticity and tamper resistance are ensured through encryption or verification mechanisms. The core data recorded in the identifier includes: Historical Background: The name of the original ancient building component represented by the building block, the name of the building to which it belongs, the age, cultural background, historical value description, etc.

[0042] Position Data: The precise three-dimensional coordinate position (X, Y, Z) and orientation of the component in the original ancient building.

[0043] Structural Parameters: Key structural dimensions (length, width, height, diameter, angle, etc.), material type (such as gold thread nanmu, granite), weight estimation of the component.

[0044] Connection Logic: The connection logic relationship data of the unit with other units. This includes: Adjacent Units: Indicates which specific other units (by referencing their unique identifiers) it can be directly connected to.

[0045] Connection Type: The type of connection interface (such as dovetail, straight tenon, hoop tenon).

[0046] Connection Orientation: The direction and angle constraints of the connection (for example, the "east side" of unit A can only be connected to the "west side" of unit B at a specific rotation angle).

[0047] ConnectionSequence: Suggested connection sequence (dependency relationship) during the whole assembly process.

[0048] Metadata: Creation date, version number, data source, copyright information, etc.

[0049] The user reads the identification on the brick unit through the digital platform's supporting equipment (such as the camera of the smartphone APP scanning the QR code, or the RFID reader sensing the tag). This platform is a Web-based cloud database system, with a friendly user interface (webpage or APP) on the front end. The back-end database (such as MySQL, PostgreSQL, MongoDB) stores all the brick units and their associated detailed information. After receiving the identification information, the platform immediately initiates a query request to the database: SELECT * FROM BrickUnits WHERE UnitID='Scanned_ID' The database returns all the relevant data for this unit, and the platform presents it clearly and categorically to the user, including 3D model visualization, historical stories, and structural parameter details. The most core function of the platform is to provide intelligent assembly sequence guidance. Its algorithm is based on the inherent structural logic of ancient architecture (usually from bottom to top, from inside to outside, following the hierarchical order of column base -> column -> beam -> bracket -> roof truss) and the defined `ConnectionLogic` relationship between units (especially `ConnectionSequence` and dependency relationship). When the user starts assembling or selects a specific target (such as assembling a complete bracket), the platform calculates the optimal (or feasible) next assembly suggestion based on the current assembled state and the logical relationship between units. The guidance information is presented through various intuitive ways: 3D animation demonstration: In the virtual scene, highlight the brick unit that should be taken next and its unique identification, then demonstrate how to accurately move the unit to the target position with a smooth 3D animation, showing the correct connection direction (rotation angle) and insertion path, until it perfectly fits with the existing structure. The animation is based on a physical engine to simulate contact and constraints.

[0050] Step-by-step instructions: Clearly describe the next operation in words ("Please find the component with the identification QR_1024, take its north side with the tenon, align it with the mortise of the component with the identification QR_1005, and vertically insert it along the Z-axis. When you hear a 'click' sound, it means you are in place").

[0051] AR overlay guidance: If the user uses the camera of the mobile device to aim at the current assembly progress, the platform can superimpose virtual instruction arrows, highlight outlines, or animations in the real-time picture through augmented reality (AR) technology, intuitively indicating the position and operation method of the next unit.

[0052] This intelligent guidance significantly reduces the difficulty for users, especially non-professionals, to understand the complex structure of ancient buildings and perform accurate assembly, ensuring that the final digital ancient building model has high assembly precision and faithfully restores the mechanical rationality and aesthetic features of the original structure. The entire process not only has educational value but also has research and display value.

[0053] Embodiment 2: High-precision ancient building digitalization building block modeling method, refer to Figure 2 As shown in the figure, for realizing the high-precision ancient building digitalization building block system, comprising: Obtain millimeter-level geometric data of ancient building components through laser point cloud and multi-view image fusion technology; Generate digital building block units consistent with the scale of the real object using a parameterized modeling algorithm, which defines size and shape as adjustable parameters based on the geometric data to establish a parameterized model framework; Design standardized plug-in interfaces based on topological optimization, adapt to the connection requirements of different elements of the arch, beam, and column through finite element analysis algorithms, and accurately retain the concave-convex fitting features and stress transfer mechanism of the mortise and tenon structure; Obtain micron-level texture color information through high-fidelity material scanning, and use UV printing or nano-imprinting technology to reproduce the texture; Assign a unique identifier to the building block unit, which includes a two-dimensional code or RFID tag, to record structure parameters and assembly logic, and achieve information management and assembly guidance through a digital platform.

[0054] In summary, the high-precision ancient building digitalization building block system integrates 3D scanning, parameterized design, topological optimization, high-fidelity material reproduction, and information management technology to build a complete technical chain, achieving accurate digitalization, standardization, and interactivity of complex ancient building components, and providing a powerful digital tool for the preservation, research, education, and innovative heritage of ancient architectural culture.

[0055] The embodiments of the specific implementation are the preferred embodiments of the present application, and are not limited to the protection scope of the present application, wherein the same parts are represented by the same reference numerals. Therefore: any equivalent changes made according to the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. High-precision ancient building digital building block system, characterized by: include: A 3D modeling module, which uses laser point cloud and multi-view image fusion technology to obtain millimeter-level precision geometric data of ancient architectural components. This module collects data through a laser scanner and SLR camera, and uses a feature point matching algorithm to achieve spatial registration; a building block unit generation module, configured to generate digital building block units consistent with the physical proportions based on the geometric data using a parametric modeling algorithm, wherein 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 through finite element analysis algorithms, while preserving the concave-convex fit characteristics and stress transfer mechanism of the mortise and tenon structure. The material processing module uses high-fidelity material scanning technology to convert the original building wood and masonry textures into micron-level precision digital models, and then uses UV printing or nano-imprinting technology to replicate the textures on polymer substrates; The modular coding module is used to assign a unique identifier to each building block unit. The identifier records the historical background, structural characteristics and splicing logic, and enables information query and splicing guidance through a digital platform.

2. The high-precision ancient building digital building block system according to claim 1 is characterized in that: The implementation method of the laser point cloud and multi-view image fusion technology in the 3D modeling module is as follows: Scan the ancient building components with a laser scanner to obtain three-dimensional point cloud data. The sampling interval of the point cloud data shall not be less than the preset interval; Use a single-lens reflex camera to capture component images from several viewing angles, with the number of viewing angles being no less than 8; Import the point cloud data and image data into the 3D modeling software, identify common feature points using the SIFT feature point extraction algorithm, and use the least squares method to perform spatial coordinate transformation to spatially align the point cloud and image; After fusion, a high-precision three-dimensional model containing geometric coordinates and texture information is generated with a model accuracy of millimeter level. This model is used for subsequent building block unit generation.

3. The high-precision ancient building digital building block system according to claim 1 is 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, and the size and shape geometric characteristics of the component are defined as adjustable parameters, including length, angle and curvature; The algorithm automatically generates digital building blocks that are consistent with the actual scale. 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 scale range according to the proportional factor to adapt to different application scenarios such as display and education. The scaling process maintains the consistency of geometric features.

4. The high-precision ancient building digital building block system according to claim 1 is characterized in that: The specific steps of topology optimization in the standardized plug interface module are: Establish a mechanical model for the connection between brackets, beams and columns of ancient buildings. The input parameters of the mechanical model include interface dimensions, material properties and loads. The optimization goals are interface strength and ease of assembly and disassembly; The stress distribution of the interface is calculated by the finite element analysis algorithm, and the algorithm uses ANSYS software for iterative calculation; Automatically optimize the topology of interfaces. The optimization process adjusts material distribution to minimize weight while adapting the connection shapes of different elements. The concave and convex fitting features of the mortise and tenon structure are retained in the interface design.

5. The high-precision ancient building digital building block system according to claim 1 is characterized in that: The implementation of high-fidelity material scanning technology in the material processing module includes: Use a high-precision texture scanner to clean and pre-treat the surface of the original building materials, including dust removal and drying; Scan to obtain micron-level precision texture patterns and color spectrum data, with a scanning resolution of no less than 1000dpi; The scanned data is imported into the material processing software for noise reduction and correction. The noise reduction uses the Gaussian filter algorithm, and the correction is based on the reflectance spectrum curve measured by the spectrometer; Generate digital models containing texture details and color information with micron-level accuracy; 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 the nanoimprint process, the texture is imprinted onto the surface of the polymer substrate through a mold, forming a surface with the desired texture under preset process conditions.

6. The high-precision ancient building digital building block system according to claim 1 is characterized in that: The allocation and management of unique identifiers in the modular coding module are as follows: Generate a unique identifier for each building block unit in the form of a QR code or RFID tag; The logo records the historical background information of the unit, the location coordinates in the ancient building, the structural size parameters, and the splicing logic relationship data with other units. The splicing logic includes the connection order and direction; Users read identification information through the digital platform's scanning device. The platform is a web-based database system that queries and displays relevant data about the unit based on the identification; The platform provides assembly sequence guidance based on building structure logic, including 3D animation demonstrations and step-by-step instructions, to help users combine building blocks correctly and efficiently, with high matching accuracy.

7. A high-precision ancient building digital building block modeling method, characterized in that: The method for realizing the high-precision ancient building digitized building block system according to any one of claims 1 to 6 comprises the following steps: Obtain millimeter-level geometric data of ancient architectural components through laser point cloud and multi-view image fusion technology; Using a parametric modeling algorithm to generate digital building blocks that are consistent with the actual scale of the building blocks, the algorithm establishes a parametric model framework based on geometric data and defines size and shape as adjustable parameters; Based on topological optimization, standardized plug-in interfaces are designed. Finite element analysis algorithms are used to adapt to the connection requirements of different elements such as brackets, beams, and columns, while accurately preserving the concave-convex fit characteristics and stress transfer mechanism of the mortise and tenon structure. Obtain micron-level texture and color information through high-fidelity material scanning, and use UV printing or nano-imprinting technology to reproduce the texture; Assign unique identifiers to building block units, and implement information management and assembly guidance through a digital platform. The identifiers include QR codes or RFID tags that record structural parameters and assembly logic.

8. The high-precision ancient building digital building block modeling method according to claim 7 is characterized in that: When obtaining the geometric data of ancient building components, a laser scanner is used to perform a full-scale scan of the components at high-precision sampling intervals, covering all surfaces of the components to obtain point cloud data; Use a DSLR camera to take high-resolution photographs of the component from multiple different viewpoints, including orthogonal and oblique angles; Import point cloud data and photos into professional 3D modeling software; The SIFT feature point extraction algorithm is used to identify the common feature points between the point cloud and the photo, and the number of feature points is sufficient; The least square method is used for spatial coordinate transformation, and the transformation error is controlled within the preset accuracy; The point cloud and image are integrated to generate a three-dimensional model containing high-precision geometric information.

9. The high-precision ancient building digital building block modeling method according to claim 7, characterized in that: When designing standardized plug-in interfaces, a parametric model of the interface is established based on the connection requirements of different ancient architectural elements such as brackets, beams and columns. The model input parameters include the geometric dimensions, stress conditions and material types of the elements. Iterative calculations are performed using a topology optimization algorithm using the SIMP method. Each iteration adjusts the material distribution of the interface so that the interface bearing capacity meets the preset threshold. After the optimization is completed, a standardized interface model is generated, and the model automatically adapts to the connection shapes of different elements; The concave and convex fitting features of the mortise and tenon structure are retained in the model.

10. The high-precision ancient building digital building block modeling method according to claim 7, characterized in that: When processing materials, the surface of the original building materials should be cleaned and pre-treated, including removing stains and polishing; Use a spectrometer to measure the reflectance spectrum curve of the material, and the spectrum range covers the visible light band; Scan the surface texture with high resolution using a texture scanner in multiple angles; Integrate spectral data and texture data into a digital model of the material, and use image processing software 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 printing layer is thick and fine, and the color difference between the printed color and the original building material is extremely small; When using the nanoimprint process, the texture digital model is converted into a mold pattern. The mold material is nickel alloy. Under preset process conditions, the texture is imprinted onto the polymer substrate to form a surface with the desired texture.

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