Reconstruction method of HBIM in ancient building digital twinning technology

Abstract

The invention discloses an HBIM reconstruction method in a historic building digital twinning technology. Firstly, fine modeling is carried out, and a standard parameter model and a triangulated irregular network model are fused to construct a basic geometric model; digital-analog decoupling is carried out, geometric space and semantic attribute information are separated, and an IFC format geometric model is generated; then entering a geometric conversion link, dividing a high-risk region, a medium-risk region and a low-risk region according to the ratio of the number of vertexes per unit area to the area, subdividing the high-risk region, merging the low-risk regions, converting the high-risk region and the medium-risk region into an OBJ format by adopting a geometric element extraction and reconstruction algorithm and a Delaunay triangulation algorithm, and finally converting the OBJ format into a glTF format; and finally, carrying out attribute mapping, associating attribute data with the glyTF-format geometric model by utilizing a unique identifier of the component, setting an updating mechanism to keep synchronization, completing HBIM reconstruction, and building a foundation for digital protection and application of the ancient building.

Description

Technical Field

[0001] The present invention relates to the technical field of ancient building digital twins, and specifically to an HBIM reconstruction method, which aims to solve the technical problem of efficiently and accurately reconstructing complex building structures and information in the process of ancient building digital twins. Background Art

[0002] In the context of the growing demand for the protection and digital presentation of ancient buildings today, digital twin technology has become a key means. As one of the core technologies, HBIM helps the inheritance of ancient buildings by constructing information-rich and highly restored architectural models. However, the existing technology faces many difficulties when converting the original geometric model data collected from ancient buildings to adapt to the HBIM system. Complex components are prone to data loss during data conversion due to their unique irregular shapes, fine textures, and complex internal structures. On the one hand, this is due to the differences in data formats between different software platforms, and the format conversion process cannot accurately match the multi-dimensional data description of complex components; on the other hand, traditional conversion algorithms focus on general model processing, and it is difficult to accurately capture the fine parts of ancient buildings such as carvings, mortise and tenon structures, resulting in a significant reduction in the accuracy of the converted model, which seriously affects the integrity and reliability of subsequent digital twin models of ancient buildings and cannot meet the needs of high-precision protection and research. Therefore, a new HBIM reconstruction method is urgently needed to improve the current situation.

[0003] HBIM technology is the key support for realizing the digital presentation of ancient buildings. One of its core links is the conversion of geometric model formats, especially the conversion process from IFC geometric models to OBJ models.

[0004] In the field of digital protection and inheritance of ancient buildings, building high-precision digital twin models with advanced technologies has become a key requirement for the development of the industry. Among them, the conversion of IFC geometric models to OBJ models is one of the core links to achieve this goal. However, the existing technologies at this stage are facing a series of problems that need to be solved urgently.

[0005] In terms of accuracy, existing technologies have obvious shortcomings when performing conversion tasks. Especially when faced with complex components that are widely found in ancient buildings, such as those with fine carvings, unique mortise and tenon structures, and irregular surfaces, due to the data structure characteristics of the IFC model itself and the inherent limitations of the OBJ model in describing complex geometric forms, many fine geometric details are often difficult to be fully preserved during the conversion process. This directly leads to serious damage to the accuracy of the converted model, and it is impossible to accurately reproduce the unique charm and style of the ancient buildings, which in turn has an extremely adverse impact on the quality of subsequent ancient building research, restoration, and virtual display based on the model, making it difficult for related results to meet professional needs.

[0006] From the perspective of conversion efficiency, the problem is equally prominent. Ancient buildings are usually large in size and have a large number of components. Traditional conversion methods require a lot of computing resources when processing such large-scale models. This not only makes the conversion process time-consuming, but also easily causes serious problems such as system downtime and crashes, causing the entire digital workflow to stagnate. It also causes a huge waste of manpower, material resources and time costs, greatly hindering the advancement of the ancient building digitalization project.

[0007] In addition, it is worth noting that in order to cope with the above difficulties, some researchers have explored and tried to segment the IFC geometric model according to the components. However, it was found in the actual operation process that this method has caused new challenges. Since the ancient building components themselves have complex and changeable shapes and are closely related to each other, over-reliance on the method of segmentation by component will make it difficult to clearly define the segmentation boundary. This brings great difficulty to the subsequent splicing process. On the one hand, it is easy to have obvious geometric defects such as splicing dislocation and wide gaps. On the other hand, complex splicing algorithms may also frequently cause data compatibility problems, further damaging the integrity and accuracy of the model, which greatly reduces the quality of the model.

[0008] In summary, there is an urgent need to develop a new IFC geometric model conversion strategy that can ensure high efficiency and high precision, so as to promote the steady and healthy development of the digital twin technology of ancient buildings and meet the growing needs of digital protection of ancient buildings. Summary of the invention

[0009] In order to achieve these purposes and other advantages according to the present invention, a reconstruction method of HBIM in the ancient building digital twin technology is provided, comprising the following steps: Step 1: Fine modeling: integrating the standard parameter model and the irregular triangulated network model to provide a basic geometric model for the entire HBIM reconstruction; Step 2: Decoupling of digital and model: storing and loading the geometric space information and semantic attribute information in the geometric model separately to generate a geometric model in IFC format; Step 3: Geometry conversion: convert the geometric model after digital-analog decoupling into the glTF format suitable for the digital twin system; Step 4: Attribute mapping: Establish a bidirectional link between the attribute data and the geometric model in glTF format through the component unique identifier, and build an update mechanism to keep them synchronized, thus completing the reconstruction of HBIM; The step of geometric transformation includes: a) Segment the geometric model data in IFC format according to the area, calculate the number of vertices in the unit area, calculate the ratio of the number of vertices to the unit area, and regard the unit area whose ratio exceeds the threshold A as the high-risk area, the unit area whose ratio exceeds the threshold B but is less than the threshold A as the medium-risk area, and the unit area whose ratio is less than the threshold B as the low-risk area; b) Continue to segment high-risk areas and merge low-risk areas; c) converting the low-risk area into OBJ format data using a geometric element extraction and reconstruction algorithm, and converting the medium-risk area and the segmented high-risk area into OBJ format using a geometric element extraction and reconstruction algorithm and then a Delaunay triangulation algorithm; and d) Convert the geometric model in OBJ format to glTF format.

[0010] Furthermore, the reconstruction method of HBIM in the ancient building digital twin technology, in step c), further includes merging the geometric model areas converted into the OBJ format, and then further includes the following steps: e) Set the checking accuracy and tolerance value to detect the topological manifold of the geometric model in the merged OBJ format, including: self-intersection, non-manifold edges or non-manifold vertices, non-manifold geometry, overlapping faces, and / or open edges; f) determining the unit area where the topological error occurs, distinguishing whether the topological error occurs at the edge or non-edge of the unit area, and forming a topological error database at the edge of the unit area and a topological error database at the non-edge of the unit area respectively; and g) If the topological error data of the edge exceeds the threshold C, return to step a); if the topological error data of the non-edge exceeds the threshold D, return to step b).

[0011] Furthermore, the reconstruction method of HBIM in the ancient building digital twin technology includes a conversion process from a component coordinate system to a model coordinate system in the construction of a standard parameter model, which specifically includes the following steps: h) describes the curved body in the component, where x, y, z are three-dimensional coordinates, r represents the average radius of the bottom surface of the curved body, θ represents the angle parameter, and h(θ) is a function that describes the change of the curved body with angle in the height direction: Calculate the second-order derivative: In the range of θ from 0 to 2π, go to a point every π / 10, and get a total of 20 discrete points. At each discrete point, calculate the second-order derivative: The absolute values ​​of the second-order derivatives at these discrete points are added together and then divided by the total number of discrete points to obtain the average value of the curvature change, which is used as a value to measure the magnitude of the curvature change. i) Then calculate the standard deviation of the curvature change, where i represents the discrete point and n represents the number of discrete points. is the second-order derivative of the ith discrete point, is the average value of the second-order derivative, which is used as a measure of the frequency of curvature change: j) Compare the average value of the curvature change with the threshold value E. If it is greater than the threshold value E, the curved surface body is judged as a complex component; compare the standard deviation of the curvature change with the threshold value F. If it is greater than the threshold value F, the curved surface body is judged as a complex component; if the average value of the curvature change is not greater than the threshold value E, but the standard deviation of the curvature change is greater than the threshold value F by more than 20% to 30%, the curved surface body is judged as a complex component; if the standard deviation of the curvature change is not greater than the threshold value F, but the average value of the curvature change is greater than the threshold value E by more than 30% to 50%, the curved surface body is judged as a complex component; k) Perform coordinate transformation on complex components, calculate the distance measurement between the surface point clouds of complex components before and after the transformation, and determine whether the shape is consistent by calculating the root mean square error between the point clouds. If the root mean square error is greater than the threshold F, then l) Subdivide the complex-shaped component into smaller geometric units, including using a triangulation algorithm to divide its surface into multiple small triangles, determine the vertex coordinates of each triangle in the component coordinate system, then transform these vertices into the model coordinate system through coordinate transformation, and finally recombine these triangles in the model coordinate system.

[0012] Furthermore, in the reconstruction method of HBIM in the ancient building digital twin technology, the digital-analog decoupling step includes: Identify simple components in the geometric model, where the simple components are defined as components without holes or composite structures, including walls, columns and beams, and directly read the geometric information and related attribute data of the simple components from the IFC model using conventional query statements, and convert the read data into a preset format to achieve preliminary coupling and docking with the target digital model; The digital-model coupling step for the component type with holes is as follows: locate the component with holes in the building information model, the hole of the component with holes is represented by the "Opening" entity in the IFC model, and on the basis of reading the basic geometric information of the component by using a conventional query statement, embed the hole field, and further read the detailed geometric shape, position and size information of the hole through the association relationship between the hole field and the "Opening" entity, and process the component data containing the hole information into a format adapted to the target digital model, so as to complete the coupling with the target digital model; The digital-analog coupling steps for objects containing aggregate components are as follows: the components containing aggregate components are identified based on the Aggregates attribute in the IFC model. After obtaining the main geometric information of the component using conventional queries, the geometric features, relative positions and assembly relationships of each sub-component in the aggregate component are deeply read according to their associated attributes. The information of the component body and the aggregate component is integrated and converted into a format acceptable to the target digital model to achieve digital-analog coupling.

[0013] Furthermore, in the digital-analog coupling method of the building information model based on component classification, in the digital-analog coupling step for simple component types, the conventional query statement follows the query syntax for simple geometric entities in the IFC standard specification, and the relevant attribute data read includes at least the material, dimensional tolerance and surface texture attributes of the component.

[0014] Furthermore, in the reconstruction method of HBIM in the digital twin technology of ancient buildings, in the digital-analog coupling step for component types with holes, the operation of embedding the hole field is implemented by adding specific data items to the data structure, and the reading of the association relationship is carried out with the aid of the object reference mechanism built into the IFC model, and the detailed geometric shape information of the holes read includes the contour curve type and surface curvature information of the holes.

[0015] Furthermore, the reconstruction method of HBIM in the ancient building digital twin technology and the attribute mapping method include: Relational database selection and data table design steps: Select a relational database as a tool to perform attribute mapping operations. According to the application requirements of the ancient building digital twin and the attribute definition in the IFC semantics, comprehensively analyze the non-geometric information types, and then classify and design attribute data tables, including at least identification information table, geometry information table, material information table, and location information table. Each table is used to accurately store the corresponding type of attribute information to build a structured attribute storage system; Attribute information extraction and storage steps: parse the JSON file generated by the digital-analog decoupling, accurately extract the attribute information from it, and store the extracted attribute information in the relational database according to the designed attribute data table structure to achieve structured storage of attribute information based on component ID, ensuring that the attribute information of each component can be organized in an orderly manner and retrieved efficiently; Steps for establishing a two-way link and updating mechanism: Use the unique identifier of the component to establish a two-way link between the attribute data and the glTF geometric model, so that the attribute data can be associated and interacted with the geometric model. On this basis, an update mechanism is built to monitor the status of the database attribute information and the glTF model in real time. Once one of them changes, the update process is immediately started to ensure that the two are always synchronized, providing accurate and consistent data support for the digital twin model of the ancient building; Attribute information classification and analysis steps: During the HBIM model data processing process, attribute information is subdivided into direct attributes, derived attributes and inverse attributes according to its source and definition method. Direct attributes are scalars or direct information, which are directly used to describe the basic characteristics of the object entity; derived attributes are attributes expressed by other entities, which enrich the description of the object entity by associating other entities; inverse attributes are attributes linked with the help of associated entities. Through the detailed classification and analysis of attribute information, a deep understanding and precise management of the attributes of the ancient building model can be achieved.

[0016] Furthermore, in the reconstruction method of HBIM in the digital twin technology of ancient buildings, in the relational database selection and data table design steps, the identification information table is used to store the unique identifier, name, number and other information used to identify the identity of the component, and the primary key constraint is used to ensure the uniqueness of the data; the geometric information table stores the geometric shape description information of the component, including vertex coordinates, face information, etc., and realizes efficient use of geometric information through the interface with the geometric modeling algorithm; the material information table records in detail the material name, material properties, material source and other information of the component, and combines with the material database to provide a data basis for the analysis of ancient building materials; the position information table records the spatial position information of the component in the building model, including coordinate values, relative position relationships, etc., to facilitate spatial layout analysis.

[0017] Furthermore, in the reconstruction method of HBIM in the digital twin technology of ancient buildings, in the attribute information extraction and storage steps, when parsing the JSON file, a recursive algorithm is used to traverse the file structure to ensure that no attribute information is missed. For attribute information with complex nested structures, a temporary cache area is established for temporary storage and organization, and then stored according to the data table structure to improve storage efficiency and accuracy.

[0018] Furthermore, in the attribute mapping method of the digital twin model of ancient buildings, in the steps of establishing a bidirectional link and an update mechanism, the bidirectional link is implemented using a pointer or index-based technology, and link information pointing to each other is established in the database and the glTF model respectively. When an update requirement is detected, the transaction processing mechanism is used to ensure the atomicity of the update process.

[0019] The technical solution proposed in the present invention of using the area method to divide the IFC geometric model and selecting an adaptive data conversion method according to the complexity of the components has brought many significant beneficial effects.

[0020] First, in solving the problem of model splicing, the area method used in this invention is different from the traditional method of segmenting by components, which leads to blurred segmentation boundaries and difficulty in subsequent splicing. By accurately calculating the number of vertices in a unit area, the IFC geometric model is reasonably divided into different risk areas based on this. This area-based segmentation has clear logic and clear boundaries, laying a solid foundation for subsequent splicing operations, effectively avoiding geometric defects such as splicing dislocation and excessive gaps, ensuring the overall integrity and coherence of the model, greatly improving the success rate of model construction, and effectively ensuring the accurate restoration of the digital twin model of the ancient building from the local to the whole.

[0021] Secondly, from the perspective of accuracy assurance, for the different areas divided, "teaching students in accordance with their aptitude" is carried out according to the complexity of the components. For areas with complex components, such as those containing exquisite carvings, unique mortise and tenon structures and other fine parts, the geometric element extraction and reconstruction algorithm combined with the Delaunay triangulation algorithm is used to assist in the conversion, which can delicately capture every detail of the complex geometric shapes and completely retain the key features of the ancient buildings, so that the converted model is still accurate at the micro level; and for areas with simple components, a relatively simple and efficient conversion method is used, which not only ensures the accurate transmission of basic geometric information, but also avoids the waste of resources caused by excessive processing, and comprehensively ensures that the model can highly restore the original appearance of the ancient buildings at both macro and micro scales, meeting the stringent requirements of high-precision research, restoration and virtual display.

[0022] Furthermore, in terms of improving conversion efficiency, the area method segmentation combined with differentiated data conversion strategies has achieved the optimal configuration of computing resources. Instead of uniformly and inefficiently processing the entire large-scale ancient building model like the traditional method, it focuses on the fine conversion of complex component areas according to the complexity of the components, and quickly converts simple component areas, which greatly shortens the overall conversion time and reduces the system computing load. It effectively avoids system downtime and crash problems caused by processing large-scale models, making the construction process of the ancient building digital twin model more efficient and smooth, and providing strong support for the rapid advancement of large-scale ancient building digitalization projects.

[0023] The innovative approach adopted by the present invention of performing coordinate transformation separately on complex components and simple components and providing a scientific method to distinguish between the two further expands the significant beneficial effects.

[0024] First, the accuracy is improved. Differentiated coordinate transformation is implemented specifically for complex components and simple components to precisely focus on their respective characteristics. For complex components, due to their unique irregular shapes, fine textures and complex internal structures, the traditional unified conversion method is very likely to cause precision loss. The present invention tailors the conversion process for them, deeply considers the high-precision restoration requirements of details such as carvings and mortise and tenon structures of complex components, and uses adaptation algorithms to ensure that every subtle feature in the conversion process is accurately mapped, retaining the exquisite craftsmanship and unique style of ancient buildings to the greatest extent, and building a solid foundation for the high-precision construction of the overall model from the key part of complex components; for simple components, an efficient coordinate conversion method that matches them is used to ensure that the basic geometric shapes are accurate while avoiding precision deviations caused by overly complex processing, so that the model can achieve fidelity in both macro-structure and micro-details, and fully meet the precision requirements of the digitization of ancient buildings.

[0025] Second, the conversion efficiency is improved. Different from the indiscriminate overall coordinate conversion in the past, the present invention implements the diversion processing according to the complexity of the components, thus realizing the refined allocation of computing resources. The large amount of computing resources required for the conversion of complex components are concentrated and accurately invested to ensure the quality of the conversion of complex components. At the same time, a simple and fast conversion path is adopted for simple components, avoiding unnecessary complex calculations on simple components, reducing the time cost of the overall conversion, making the entire coordinate conversion process coordinated and smooth, and speeding up the construction of the digital twin model of the ancient building.

[0026] Third, the scientific method of distinguishing complex components from simple components injects stability and operability into the entire technical system. Based on mathematical models and analysis of the structural characteristics of ancient buildings, such as quantitative calculation of the amplitude and frequency of the curvature change of the component surface, combined with threshold judgment, the boundary between complex and simple components is defined. This discrimination standard is objective, accurate and universal. It reduces human judgment errors and uncertainties, and enhances the adaptability and reliability of the present invention in practical applications.

[0027] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Flowchart of the present invention DETAILED DESCRIPTION

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0030] It should be understood that the terms such as “having”, “including” and “comprising” used herein do not exclude the existence or addition of one or more other elements or combinations thereof.

[0031] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials can be obtained from commercial sources unless otherwise specified; in the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "set" should be understood in a broad sense, for example, they can be fixedly connected, set, or detachably connected, set, or connected and set in one piece. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood in specific circumstances. The orientation or position relationship indicated by the terms "lateral", "longitudinal", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0032] like Figure 1 As shown, the present invention provides a HBIM (Historical Building Information Modeling) reconstruction method in the digital twin technology of ancient buildings. The method realizes efficient and accurate digital expression of ancient building information through a series of fine steps. The following is a detailed description of the specific implementation method of the method: Step 1: Detailed modeling Data collection: First, collect relevant design drawings, historical documents, on-site survey data, etc. of ancient buildings to ensure the comprehensiveness and accuracy of the information.

[0033] Model fusion: Use professional modeling software (such as AutoCAD, Revit, etc.), combine standard parameter models (such as the size, material and other parameters of standard components such as walls, doors and windows) with irregular triangulated network models (TIN, used to represent complex terrain or curved surface structures) to finely model the ancient buildings. This step aims to provide a basic and accurate geometric model for HBIM reconstruction.

[0034] Step 2: Digital-Analog Decoupling Information separation: Geometric spatial information (such as position, shape, size, etc.) and semantic attribute information (such as material, age, function, etc.) in the geometric model are stored and loaded separately. This can improve data processing efficiency and model flexibility.

[0035] IFC format generation: Generate a geometric model that complies with the Industry Foundation Classes (IFC) standard based on the separated geometric space information. IFC is an open and extensible data standard that is widely used in the field of building information modeling.

[0036] Step 3: Geometric transformation Region segmentation and risk assessment (a): The geometric model data in IFC format is segmented according to the preset area size to ensure that each segmented area has similar complexity.

[0037] Count the number of vertices in each unit area and calculate the ratio of the number of vertices to the unit area. This ratio reflects the geometric complexity of the area.

[0038] According to the set thresholds A and B (A>B), areas where the ratio exceeds A are considered high-risk areas, areas where the ratio is between B and A are considered medium-risk areas, and areas where the ratio is less than B are considered low-risk areas.

[0039] Area optimization (b): The high-risk areas are further segmented to reduce their geometric complexity.

[0040] Low-risk areas are merged to reduce data redundancy and improve processing efficiency.

[0041] Format conversion (c): Using geometric element extraction and reconstruction algorithms, the geometric information of low-risk areas is converted into OBJ format data. OBJ format is a simple and widely used 3D model format.

[0042] For the medium-risk area and the segmented high-risk area, the geometric element extraction and reconstruction algorithm is first used for preliminary conversion, and then the Delaunay triangulation algorithm is used for optimization. The Delaunay triangulation algorithm can generate high-quality triangular meshes and is suitable for representing complex surfaces.

[0043] glTF format conversion (d): Convert OBJ geometric model data to glTF format. glTF is a 3D model format optimized for web and mobile applications, with efficient data compression and rendering performance.

[0044] Step 4: Attribute Mapping Unique identifier: Assign a unique identifier (such as UUID) to each artifact to facilitate accurate identification and location in subsequent steps.

[0045] Bidirectional link: Using the unique identifier of the component, a bidirectional link is established between the attribute data and the glTF format geometry model. In this way, the attribute information of the component can be easily queried and updated.

[0046] Update mechanism: Build an update mechanism to ensure that when attribute data changes, it can be automatically synchronized to the glTF format geometry model. This helps to maintain the real-time and accuracy of HBIM.

[0047] Through the implementation of the above steps, the present invention provides an efficient and accurate HBIM reconstruction method in the ancient building digital twin technology. This method not only improves the efficiency and accuracy of the digitization of ancient building information, but also provides strong technical support for the protection, restoration and reuse of ancient buildings.

[0048] The present invention also provides the following method: 1. Merging the geometric model regions in step c When executing step c, when merging the geometric model areas converted to OBJ format, professional 3D model processing software tools are required. For example, using the relevant plug-ins in the Rhinoceros software, through the precise alignment and fusion functions it provides, the scattered OBJ format geometric model areas are spliced ​​according to the pre-set spatial coordinate rules. First, mark and classify each model area to be merged, and sort out the geometric model areas corresponding to different components such as walls, roofs, beams and columns according to the structural logic of the ancient building. Then, in the software environment, turn on the capture function to accurately locate the connection points of each area to ensure that the merged model is seamlessly connected in geometry, forming a complete preliminary overall geometric model to prepare for subsequent topology checks.

[0049] Step 2: Check the topological manifold Choosing the right testing tool Use professional geometric model analysis software, such as MeshLab, to perform topological manifold detection tasks. In the MeshLab software interface, open the merged OBJ format geometric model file and enter its topological analysis module.

[0050] Set the checking precision and tolerance values According to the actual accuracy requirements of the ancient building model and the previous experience in processing similar models, the inspection accuracy parameters are set reasonably. Generally speaking, for ancient building models with high accuracy requirements and complex structures, the inspection accuracy can be set between 0.1mm-0.5mm to ensure that subtle topological problems can be accurately captured. The setting of the tolerance value should comprehensively consider the model construction method and the possible error range. For example, if different precision measurement data are used for modeling during the model construction process, the tolerance value can be appropriately relaxed to 0.5mm-1mm. In the corresponding parameter setting column of the MeshLab software, enter the set inspection accuracy and tolerance value values ​​respectively.

[0051] Perform detection operations Click the "Detect Topological Manifold" button in the software, and the software will perform a comprehensive scan for topological problems such as self-intersections, non-manifold edges or non-manifold vertices, non-manifold geometry, overlapping faces, and open edges in the model based on the set parameters. During the detection process, the software will mark the model parts with problems in different colors, for example, red marks self-intersection areas, yellow marks non-manifold edges, and green marks overlapping faces, etc., to facilitate subsequent viewing and analysis.

[0052] Step 3: Build a topology error database Unit area division The entire merged geometric model is divided into several unit area areas according to certain rules. The division method can be based on the functional division of the ancient building, such as dividing a house into a unit area area; or according to the structural module, such as taking the model range corresponding to a roof truss and its attached components as a unit area area. In the model processing software, precise division is achieved by creating a virtual grid or using the area segmentation function of the software.

[0053] Identify topology error locations According to the topological error marks detected in step e, check the errors in each unit area one by one. For areas with topological errors, further determine whether the error occurs at the edge or non-edge position. Through the spatial coordinate query and geometric analysis functions of the model software, if the adjacent geometric elements of the topological error point or surface involve the regional boundary line, it is determined to be an edge topological error; conversely, if it is completely inside the region, it is a non-edge topological error.

[0054] Database creation and entry Use database management software, such as MySQL or SQLite, to create two special database tables, one for storing topological error information at the edge of the unit area and the other for storing topological error information at the non-edge. The database table structure design should include fields such as unit area number, topological error type, error coordinate range, error severity rating, etc. The topological error information identified by the investigation should be entered into the corresponding database table in sequence according to the corresponding fields.

[0055] 4. Step g error backtracking Threshold setting and monitoring According to the quality standards of the ancient building model and the actual application requirements, the edge topology error threshold C and the non-edge topology error threshold D are set in advance. The determination of the threshold can refer to the historical project experience data and industry specifications. For example, for general ancient building digital twin projects, the edge topology error threshold C can be set to no more than 3 critical errors per unit area (such as severe self-intersections, large overlapping surfaces, etc.), and the non-edge topology error threshold D can be set to no more than 5 general errors per unit area (such as a small number of non-manifold vertices, short open edges, etc.). In the actual processing process, the constructed topology error database is monitored in real time by writing a script program, and the situation exceeding the threshold in each unit area is counted.

[0056] Retroactive Adjustment When the topological error data of the edge exceeds the threshold C, it means that there are serious problems in the key connection parts of the model, and it is necessary to return to step a) to re-collect data and build the initial model. Re-examine the measurement plan, the accuracy of the acquisition equipment and other factors to ensure that more accurate data is obtained to build the basic model. If the non-edge topological error data exceeds the threshold D, it means that there are many unreasonable aspects in the internal structure of the model. It should return to step b) to optimize the preprocessing link, such as adjusting the model simplification algorithm, rechecking the integrity of the data cleaning, etc., to correct the model problems until the topological manifold requirements are met and a high-quality HBIM geometric model of the ancient building is obtained.

[0057] The following is the implementation method of the reconstruction method of HBIM in the digital twin technology of ancient buildings when constructing a standard parameter model: 1. Step h describes the curved surface in the component and the relevant values ​​of the calculated curvature Surface description and discrete point selection When dealing with curved surfaces in ancient building components, you first need to import component models containing curved surfaces using professional 3D modeling software, such as Blender or 3dsMax. For each curved surface that needs to be analyzed, establish a local coordinate system (component coordinate system) with the center of its bottom surface as the coordinate origin. At this time, x, y, and z represent the three-dimensional coordinates in this coordinate system. According to the geometric characteristics of the curved surface, the average radius r of its bottom surface is determined, which can be obtained by measuring the distance from multiple points on the bottom surface to the center of the circle and taking the average value. For the angle parameter θ, its value range is set from 0 to 2π, and a total of 20 discrete points are selected according to the principle of equal intervals. In the software, script programming or built-in mathematical operation functions are used to accurately extract the geometric information of the curved surface at each discrete point.

[0058] Second Derivative Calculation For each selected discrete point, a numerical calculation method is used to solve the second-order derivative of the surface height function h(θ). For example, the central difference method is used to approximate the second-order derivative based on the difference in function values ​​between adjacent points before and after the discrete point. In the scripting environment of the software, a loop code is written to traverse each discrete point, calculate its second-order derivative value in turn, and store these values ​​in an array for subsequent processing.

[0059] Calculation of curvature change average Take out all the elements in the array storing the second-order derivatives, use the built-in summation function of the programming language to calculate the sum of their absolute values, and then divide it by the total number of discrete points 20 to get the average value of the curvature change. This value can intuitively reflect the overall magnitude of the curvature change of the surface body in the entire circumferential direction, and can be used as one of the key indicators to measure the complexity of the surface body. In the process of code implementation, ensure the accuracy of numerical calculations to avoid result deviations caused by floating-point operation errors.

[0060] Step 2. Calculate the standard deviation of curvature change Utilize statistical library functions (such as the numpy library in Python), combine the second-order derivative array calculated previously and the second-order derivative average value that has been calculated, and perform calculations according to the standard deviation calculation formula. Specifically, for each discrete point i (i=0,1,…,19), based on , calculate the square of the difference between and , sum all these square differences and divide by the number of discrete points n (i.e. 20), and finally take the square root of the result to get the standard deviation of the curvature change. This standard deviation value represents the degree of discreteness of the curvature change, that is, the frequency of curvature change, which can reflect the complex characteristics of the surface from another perspective.

[0061] Step 3: Determine complex components Threshold setting basis The setting of thresholds E and F requires comprehensive consideration of the type, historical age, structural characteristics of the ancient buildings, and the subsequent application scenarios of the digital twin model. For ancient buildings with exquisite carvings and unique shapes, such as royal garden building components from the Ming and Qing dynasties, the thresholds E and F can be appropriately relaxed because their curved surfaces are usually more complex; while for relatively simple and structurally regular residential ancient building components, the thresholds should be set strictly to avoid misjudgment. Generally, the threshold range is preliminarily determined by conducting preliminary analysis and testing on a large number of sample data of different types of ancient building components, combined with the experience of industry experts, and then fine-tuned in actual projects based on model quality feedback.

[0062] Complex component decision logic Based on the calculated mean and standard deviation of the curvature change, determine whether the surface body is a complex component according to the following rules: When the average value of the curvature change is greater than the threshold E, the curved surface is directly judged as a complex component, because this indicates that the overall curvature change is large and the shape is relatively complex.

[0063] If the standard deviation of the curvature change is greater than the threshold F, it is also judged as a complex component, which means that the curvature of the curved surface changes frequently in the circumferential direction and has strong irregularity.

[0064] If the average value of the curvature change is not greater than the threshold E, but the standard deviation of the curvature change is greater than the threshold F by more than 20-30%, it means that although the overall curvature change is acceptable, the local curvature changes too frequently and should also be judged as a complex component. This ratio range is set based on the consideration of the diversity of ancient building components and can be adjusted according to specific needs in actual projects.

[0065] Similarly, if the standard deviation of the curvature change is not greater than the threshold F, but the average value of the curvature change is greater than the threshold E by more than 30-50%, the curved body is also classified as a complex component because the overall curvature change exceeds a certain proportion.

[0066] Because the frequency of curvature change has a greater impact on accuracy when the numerical value is transformed, the requirement for the frequency of curvature change is higher.

[0067] Step 4: Complex component coordinate transformation and shape consistency judgment Coordinate transformation implementation After determining that a curved surface is a complex component, use a professional coordinate transformation algorithm library (such as the coordinate transformation module in the OpenCV library, if it is developed based on the C++ language; or the scikit-image library in Python, which is suitable for the Python environment) to transform between the component coordinate system and the model coordinate system. First, clarify the translation and rotation relationship between the two coordinate systems, which is usually determined based on the overall layout of the ancient building and the position of the component in the building structure. By calling the corresponding transformation function in the library function and inputting the known transformation parameters, the coordinates of the complex component in the component coordinate system are converted to the coordinates in the model coordinate system.

[0068] Shape consistency judgment After completing the coordinate transformation, point cloud processing technology is used to verify whether the shape of the complex component remains consistent before and after the transformation. The surface point cloud data in the component coordinate system before the transformation and the point cloud data in the model coordinate system after the transformation are extracted separately, and the root mean square error (RMSE) between the two point clouds is calculated using point cloud processing software (such as CloudCompare) or a self-written point cloud processing algorithm. The specific calculation process is to calculate the sum of the squares of the coordinate differences for each pair of corresponding points (determined by the spatial position matching algorithm), sum the sum of the squares of all corresponding points and divide it by the total number of points, and then take the square root to get the RMSE value. If the RMSE value is greater than the threshold F, it indicates that the shape consistency is poor and further processing is required.

[0069] Step 1: Subdivision and reassembly of complex components Triangulation algorithm application For complex components with poor shape consistency, triangulation algorithms are used for subdivision. For example, the Delaunay triangulation algorithm is used to divide the surface of a complex component into multiple small triangles in professional geometry processing software (such as the relevant tools provided by the CGAL library, which supports multiple programming language calls). The algorithm constructs a triangular mesh based on the discrete point cloud data on the component surface, ensuring that each triangle is as close to an equilateral triangle as possible to ensure the quality of the subdivided geometric unit.

[0070] Coordinate transformation and recombination In the component coordinate system, the geometric query function of the software is used to accurately determine the vertex coordinates of each triangle and record them. Then, the coordinate conversion method in the previous step k is used to convert these vertex coordinates from the component coordinate system to the model coordinate system. Finally, in the model coordinate system environment, the model building function of the modeling software is used to recombine the converted triangle vertices according to the original topological relationship to form a subdivided complex component model to meet the subsequent high-precision digital twin model construction needs of the ancient building.

[0071] Through the above detailed implementation steps, it is possible to accurately handle the conversion of component coordinate system to model coordinate system during the HBIM reconstruction process of the ancient building digital twin technology, especially the effective identification and proper handling of complex components, to ensure the construction of a high-quality, high-precision standard parameter model.

[0072] The present invention also provides a digital-analog decoupling step, comprising: Decoupling of digital and analog components Identification and query: Use professional building information modeling (BIM) software, such as Autodesk Revit or ArchiCAD, to open the IFC file containing the ancient building model. In the model browser or query tool of the software, filter by writing a general query statement that complies with the IFC standard specifications based on the definition of simple components (without holes or composite structures, such as walls, columns and beams). For example, in Revit, use the query code based on its API to search according to the component category attributes and locate all simple components.

[0073] Data reading and conversion: For the simple components identified, the software’s built-in IFC data reading function is used to read their geometric information (such as the length, height, thickness of the wall, the diameter and height of the column, the cross-sectional dimensions and length of the beam, etc.) and related attribute data (including material, which can be obtained from the material library for specific material names and parameters; dimensional tolerances, which are read according to the model accuracy settings; surface texture properties, which are obtained through texture mapping information). The read data is sorted according to the preset format, such as converted to XML or CSV format, so as to perform preliminary coupling and docking with the target digital model to ensure data compatibility.

[0074] Digital-analog decoupling of components with holes Component positioning and basic information reading: Also in the BIM software environment, search for components with holes in the IFC model based on the "Opening" entity identifier. Obtain the basic geometric information of the component, such as the main shape and size of the component, through conventional query statements. Taking an ancient building wall with a circular hole as an example, the query statement can retrieve the length, width, height of the wall and the geometric description information of the wall as the main body.

[0075] Embedding and reading hole information: At the data structure level, specific data items are added as hole fields through programming means (such as using Python's data processing library in the code that processes the IFC data structure). With the help of the built-in object reference mechanism of the IFC model, the association between the hole field and the "Opening" entity is established. Following this association relationship, the detailed geometric shape of the hole (such as the radius and center coordinates of a circular hole, if it is an irregular hole, read its contour curve type, described by mathematical expressions or discrete point coordinates; surface curvature information, obtained using geometric calculation algorithms), position (coordinate offset relative to the component body) and size information are further read. Finally, the component data containing hole information is processed into a format that adapts to the target digital model (such as the format required by some specific rendering engines or analysis software) to complete the coupling process.

[0076] Decoupling of components including aggregate components Aggregate component identification and main body information acquisition: Based on the Aggregates attribute in the IFC model, use the general query function in the BIM software to identify the components containing aggregate components. First, obtain the geometric information of the main body of the component. For example, for a bracket composed of multiple wooden components, query and obtain the overall dimensions, spatial posture and other information of the bracket.

[0077] Sub-component information reading and integration: According to their associated attributes, the geometric features (such as the shape and size details of the sub-components), relative positions (the positions of the sub-components relative to the main components are determined through coordinate transformation relationships) and assembly relationships (such as the geometric constraints corresponding to the mortise and tenon connection methods) of each sub-component in the aggregate component are deeply excavated. Using professional data integration algorithms, the information of the component body and the aggregate component is integrated and converted into a format that can be received by the target digital model, such as a grid model format suitable for finite element analysis software, to achieve digital-analog coupling and meet the subsequent needs of structural performance analysis of ancient buildings.

[0078] The present invention also provides an attribute mapping method: Relational database selection and data table design Database selection: Considering the performance requirements, data size, and scalability of the ancient building digital twin application, mature relational databases such as MySQL and PostgreSQL are selected. These databases have powerful transaction processing capabilities and efficient query performance, and can meet the management requirements of complex attribute data of ancient buildings.

[0079] Data table design: Based on the application requirements of the ancient building digital twin and the attribute definition in the IFC semantics, a detailed classification of non-geometric information is performed. The identification information table is designed, and the unique identifier (such as the global unique ID, GUID), name, number, etc. of the component are used as fields. The primary key constraint is used to ensure the uniqueness of the data, which is convenient for quickly and accurately locating the component. The geometric information table stores the geometric shape description information of the component, including vertex coordinates (stored in the form of a three-dimensional array), face information (described by the vertex index of the face), etc., and realizes efficient use of geometric information through the interface with the geometric modeling algorithm (such as the OpenGL graphics library interface, which is convenient for subsequent rendering and display). The material information table records in detail the material name of the component (selecting the standard name from the ancient building material classification system), material properties (such as the density and elastic modulus of wood, the compressive strength of masonry, etc., which are entered based on material test data), and the source of the material (tracing back to the place of origin, supplier, etc., for the verification of cultural relics restoration materials). The location information table records the spatial location information of components in the building model, including coordinate values ​​(using a unified building coordinate system, such as the X, Y, and Z coordinates in the world coordinate system) and relative position relationships (such as orientation descriptions and distance values ​​to adjacent components) to facilitate spatial layout analysis.

[0080] Attribute information extraction and storage JSON file parsing: For the JSON files generated by the decoupling of digital and analog models, the JSON parsing library of programming languages ​​(such as Python) is used in combination with a recursive algorithm to traverse the file structure. Starting from the top-level component object, it goes down to the sub-attributes and nested attributes layer by layer to ensure that no attribute information is missed. For example, for a component JSON description that contains multiple layers of nested material attributes (such as a material texture that contains multiple levels of detail parameters), the recursive algorithm can completely extract all relevant information.

[0081] Data storage: For attribute information with complex nested structures, temporary cache areas (such as Python's list or dictionary data structures) are established in memory for temporary storage and organization. According to the designed attribute data table structure, the extracted attribute information is stored in the relational database using the database connection library (such as Python's SQLAlchemy) to achieve structured storage of attribute information based on component ID. During the storage process, database table attributes such as data type and field length are reasonably set to ensure data accuracy, improve storage efficiency, and facilitate subsequent retrieval and query.

[0082] Two-way link building and updating mechanism established Bidirectional link implementation: Use the component unique identifier as the key link to establish a bidirectional link between the relational database and the glTF geometric model. On the database side, add a new field in the attribute data table to store pointers or index information pointing to the corresponding component of the glTF model; on the glTF model side, use the model's metadata extension mechanism to add link information pointing to the corresponding component attribute record in the database. For example, in the JSON metadata part of the glTF model, add a "database_link" field and fill in the primary key value of the component attribute record in the database. The bidirectional link is implemented using pointer- or index-based technology to ensure that the two can quickly locate each other.

[0083] Update mechanism: Build a real-time monitoring system, use the database trigger mechanism or regular polling scripts (such as in Python combined with a scheduled task framework) to monitor the status of the database attribute information and the glTF model in real time. Once a change is detected on one side, the update process is started immediately. During the update process, the transaction processing mechanism of the database is used to ensure the atomicity of the update process, that is, either all updates are successful or all are rolled back to avoid data inconsistency. For example, when the component material properties in the database are updated, the update process is triggered to synchronously update the material rendering parameters in the glTF model to ensure that the two are always synchronized, providing accurate and consistent data support for the digital twin model of ancient buildings.

[0084] Attribute information classification analysis Direct attribute processing: In the process of HBIM model data processing, attribute information is finely classified. Direct attributes are scalar or direct information, which are directly used to describe the basic characteristics of the object entity. For example, the length, width, height and other dimensional information of the component, the color value and other appearance attributes, these information are directly extracted from the data obtained by digital-analog decoupling, stored in the corresponding attribute data table fields, and used for the most basic model construction and display.

[0085] Derived attribute analysis: Derived attributes are attributes expressed by other entities, which enrich the description of object entities by associating with other entities. For example, the fire rating attribute of a component may be associated with a specific building specification entity. By querying the building specification database, a detailed description of the fire rating that the component should have according to the specification is obtained, and it is associated with the component's own attributes and stored, providing more in-depth information for the fire safety analysis of ancient buildings.

[0086] Application of inverse attributes: Inverse attributes are attributes that are linked with the help of associated entities. For example, the opening direction attribute of a door and window component of an ancient building may need to be associated with the functional zoning entity of the building or the surrounding pedestrian channel entity. Through this link, it is determined whether the opening direction of the door and window is reasonable and meets the functional requirements of personnel evacuation. Through the fine classification and analysis of attribute information, a deep understanding and precise management of the attributes of the ancient building model can be achieved, thereby enhancing the application value of the digital twin model of the ancient building.

[0087] Experimental data: 1. Purpose of the experiment The effectiveness of the above-mentioned HBIM reconstruction method in the conversion of ancient building models of different complexity was verified, its effect on improving model accuracy, conversion efficiency and data compatibility was evaluated, and compared with the traditional modeling conversion method.

[0088] 2. Experimental Samples Three representative ancient buildings were selected as experimental samples, namely: Small courtyard ancient buildings (relatively regular structure, fewer component types) Medium-sized ancient temple buildings (including complex bracket structures, curved roofs, and various components) Large-scale ancient palace buildings (large scale, complex spatial layout, and numerous decorative components) 3. Experimental Setup Hardware environment: A workstation equipped with a high-performance graphics processing unit (GPU), with an Intel Core i9-10980XE processor, 128 GB of memory, and an NVIDIA GeForce RTX 3090 graphics card.

[0089] Software environment: Use the independently developed HBIM modeling software, the digital twin platform adopts the Unity engine, and the auxiliary algorithm tools are developed based on Python.

[0090] IV. Experimental process and data recording 1. Detailed modeling stage For the three ancient buildings, we first collected on-site surveying data, historical drawings and other materials as basic input. When integrating the standard parameter model with the irregular triangulated network model: Small courtyard ancient building: A total of 500 standard parameter component models were created, the number of vertices of the irregular triangulated network model was about 2,000, and the total modeling time was about 4 hours.

[0091] Medium-sized ancient temple building: Create 1,200 standard parameter component models, the number of vertices of the irregular triangulated network model is about 8,000, and the total modeling time is about 10 hours.

[0092] Large-scale ancient palace buildings: 3,000 standard parameter component models were generated, the number of vertices of the irregular triangulated network model was about 20,000, and the total modeling time was about 30 hours.

[0093] 2. Digital-Analog Decoupling Stage Perform digital-analog decoupling operations on the constructed geometric model to generate an IFC format geometric model: Small courtyard ancient building: The decoupled geometric model file size is about 5MB, and the semantic attribute information file size is about 2MB.

[0094] Medium-sized ancient temple building: The geometry model file size is about 15MB, and the semantic attribute information file size is about 6MB.

[0095] Large palace ancient buildings: The geometric model file size is about 40MB, and the semantic attribute information file size is about 15MB.

[0096] 3. Geometric transformation stage Follow the geometry transformation steps as described: Threshold A is set to 100 and Threshold B is set to 50.

[0097] 1. Risk area division Small courtyard ancient buildings: high-risk areas account for 5% of the total area, mainly concentrated in the fine parts of carved doors and windows; medium-risk areas account for 15%, such as roof ridges and other places with a certain curvature; low-risk areas account for 80%, which are flat parts such as walls and floors.

[0098] Medium-sized ancient temple buildings: high-risk areas account for 10%, concentrated in the complex structure of brackets; medium-risk areas account for 30%, including some curved roofs; low-risk areas account for 60%, such as the ground and pillars inside the main hall.

[0099] Large palaces and ancient buildings: high-risk areas account for 15%, mostly seen in the palace's eaves and decorative carved belts; medium-risk areas account for 40%, such as the slopes of multiple hip roofs; low-risk areas account for 45%, such as the palace's base and the internal partition walls of the hall.

[0100] 2. Regional processing and format conversion Small courtyard ancient buildings: After the low-risk areas were merged, it took 1 hour to convert them into OBJ format using the geometric element extraction and reconstruction algorithm, and the file size was about 3MB; the medium-risk areas took 2 hours to convert, and the file size was about 5MB; the high-risk areas were further divided and converted, which took 3 hours, and the file size was about 4MB. The final conversion to glTF format took 2 hours, and the generated glTF model file size was about 8MB.

[0101] Medium-sized temple ancient buildings: The operation took 3 hours in low-risk areas, and the file size was about 8MB; the operation took 6 hours in medium-risk areas, and the file size was about 12MB; the operation took 9 hours in high-risk areas, and the file size was about 10MB. It took 5 hours to convert to glTF format, and the glTF model file size was about 20MB.

[0102] Large palace ancient buildings: The conversion time for low-risk areas is 8 hours, and the file size is about 15MB; the conversion time for medium-risk areas is 16 hours, and the file size is about 25MB; the conversion time for high-risk areas is 20 hours, and the file size is about 20MB. It takes 10 hours to convert to glTF format, and the generated glTF model file size is about 40MB.

[0103] (IV) Attribute Mapping Phase Establish links through unique artifact identifiers and build update mechanisms: Small courtyard ancient building: It took 1 hour to establish the link. In the subsequent simulation update test, the data synchronization delay was within 0.5 seconds on average.

[0104] Medium-sized ancient temple building: It takes 3 hours to establish the link, and the data synchronization delay is about 1 second during the simulation update.

[0105] Large palace ancient buildings: It takes 6 hours to establish a link, and the data synchronization delay is about 2 seconds.

[0106] V. Comparative Experiment The traditional method of directly converting from 3D CAD models to glTF format is selected for comparison: Model accuracy: For small courtyard ancient buildings, the model converted by the traditional method has obvious jagged edges and deformations in fine components (such as door and window carvings), with an accuracy loss of about 20%; while the model reconstructed by this method can be accurately restored with an accuracy loss of less than 5%.

[0107] For medium-sized ancient temple buildings, the traditional method has a 30% loss of accuracy in brackets and curved roofs, while this method has a loss of accuracy within 10%.

[0108] For large-scale ancient palace buildings, traditional conversion makes details such as eaves and decorative carvings blurred, with a precision loss of 40%. The precision loss of this reconstruction method is about 15%.

[0109] Conversion efficiency: For a small courtyard ancient building, the total conversion time of the traditional method is about 8 hours, while this method takes a total of 11 hours (including high-precision investment in fine modeling), but considering the improvement in model quality, it is more cost-effective.

[0110] For a medium-sized ancient temple building, the traditional conversion takes 20 hours, while this method takes 27 hours. In the long run, this method is more advantageous for the maintenance and management of complex ancient buildings.

[0111] For a large ancient palace building, the traditional method takes 45 hours, while this method takes 70 hours. Although the single time consumption is slightly longer, the data can be more fully utilized in subsequent digital twin applications.

[0112] Data compatibility: After the model converted by the traditional method is imported into the digital twin system, compatibility issues such as material loss and abnormal animation effect loading often occur, with an occurrence rate of about 30%; the model of this reconstruction method has good compatibility after import, and the problem occurrence rate is less than 5%.

[0113] 6. Experimental Conclusion In the application of digital twins of ancient buildings, this HBIM reconstruction method takes a little longer in the initial modeling and conversion process than the traditional modeling conversion method, but it has significant advantages in model accuracy, data compatibility and adaptability to complex ancient building structures. It can provide a more reliable model foundation for the subsequent digital protection, simulation analysis and intelligent operation and maintenance of ancient buildings.

[0114] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0115] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A reconstruction method of HBIM in ancient building digital twin technology, characterized in that: The following steps are involved: Step 1: Fine modeling: integrating the standard parameter model and the irregular triangulated network model to provide a basic geometric model for the entire HBIM reconstruction; Step 2: Decoupling of digital and model: storing and loading the geometric space information and semantic attribute information in the geometric model separately to generate a geometric model in IFC format; Step 3: Geometry conversion: convert the geometric model after digital-analog decoupling into the glTF format suitable for the digital twin system; Step 4: Attribute mapping: Establish a bidirectional link between the attribute data and the geometric model in glTF format through the component unique identifier, and build an update mechanism to keep them synchronized, thus completing the reconstruction of HBIM; The step of geometric transformation includes: a) Segment the geometric model data in IFC format according to the area, calculate the number of vertices in the unit area, calculate the ratio of the number of vertices to the unit area, and regard the unit area whose ratio exceeds the threshold A as the high-risk area, the unit area whose ratio exceeds the threshold B but is less than the threshold A as the medium-risk area, and the unit area whose ratio is less than the threshold B as the low-risk area; b) Continue to segment high-risk areas and merge low-risk areas; c) converting the low-risk area into OBJ format data using a geometric element extraction and reconstruction algorithm, and converting the medium-risk area and the segmented high-risk area into OBJ format using a geometric element extraction and reconstruction algorithm and then a Delaunay triangulation algorithm; and d) Convert the geometric model in OBJ format to glTF format.

2. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 1, characterized in that: In step c), the geometric model regions converted into the OBJ format are merged, and then the following steps are included: e) Set the checking accuracy and tolerance value to detect the topological manifold of the geometric model in the merged OBJ format, including: self-intersection, non-manifold edges or non-manifold vertices, non-manifold geometry, overlapping faces, and / or open edges; f) determining the unit area where the topological error occurs, distinguishing whether the topological error occurs at the edge or non-edge of the unit area, and forming a topological error database at the edge of the unit area and a topological error database at the non-edge of the unit area respectively; and g) If the topological error data of the edge exceeds the threshold C, return to step a); if the topological error data of the non-edge exceeds the threshold D, return to step b).

3. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 1 or 2, characterized in that: The construction of the standard parameter model includes the conversion process from the component coordinate system to the model coordinate system, which specifically includes the following steps: h) describes the curved body in the component, where x, y, z are three-dimensional coordinates, r represents the average radius of the bottom surface of the curved body, θ represents the angle parameter, and h(θ) is a function that describes the change of the curved body with angle in the height direction: Calculate the second-order derivative: In the range of θ from 0 to 2π, go to a point every π / 10, and get a total of 20 discrete points. At each discrete point, calculate the second-order derivative: The absolute values ​​of the second-order derivatives at these discrete points are added together and then divided by the total number of discrete points to obtain the average value of the curvature change, which is used as a value to measure the magnitude of the curvature change. i) Then calculate the standard deviation of the curvature change, where i represents the discrete point and n represents the number of discrete points. is the second-order derivative of the ith discrete point, is the average value of the second-order derivative, which is used as a measure of the frequency of curvature change: j) Compare the average value of the curvature change with the threshold value E. If it is greater than the threshold value E, the curved surface body is judged as a complex component; compare the standard deviation of the curvature change with the threshold value F. If it is greater than the threshold value F, the curved surface body is judged as a complex component; if the average value of the curvature change is not greater than the threshold value E, but the standard deviation of the curvature change is greater than the threshold value F by more than 20% to 30%, the curved surface body is judged as a complex component; if the standard deviation of the curvature change is not greater than the threshold value F, but the average value of the curvature change is greater than the threshold value E by more than 30% to 50%, the curved surface body is judged as a complex component; k) Perform coordinate transformation on complex components, calculate the distance measurement between the surface point clouds of complex components before and after the transformation, and determine whether the shape is consistent by calculating the root mean square error between the point clouds. If the root mean square error is greater than the threshold F, then l) Subdivide the complex-shaped component into smaller geometric units, including using a triangulation algorithm to divide its surface into multiple small triangles, determine the vertex coordinates of each triangle in the component coordinate system, then transform these vertices into the model coordinate system through coordinate transformation, and finally recombine these triangles in the model coordinate system.

4. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 1, characterized in that: The digital-analog decoupling step comprises: Identify simple components in the geometric model, where the simple components are defined as components without holes or composite structures, including walls, columns and beams, and directly read the geometric information and related attribute data of the simple components from the IFC model using conventional query statements, and convert the read data into a preset format to achieve preliminary coupling and docking with the target digital model; The digital-model coupling step for the component type with holes is as follows: locate the component with holes in the building information model, the holes of the component with holes are represented by "Opening" entities in the IFC model, and on the basis of reading the basic geometric information of the component by using a conventional query statement, embed the hole field, and further read the detailed geometric shape, position and size information of the hole through the association relationship between the hole field and the "Opening" entity, process the component data containing the hole information into a format adapted to the target digital model, and complete the coupling with the target digital model; The digital-analog coupling steps for objects containing aggregate components are as follows: the components containing aggregate components are identified based on the Aggregates attribute in the IFC model. After obtaining the main geometric information of the component using conventional queries, the geometric features, relative positions and assembly relationships of each sub-component in the aggregate component are deeply read according to their associated attributes. The information of the component body and the aggregate component is integrated and converted into a format acceptable to the target digital model to achieve digital-analog coupling.

5. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 4 is characterized in that: In the digital-analog coupling step for simple component types, the conventional query statement follows the query syntax for simple geometric entities in the IFC standard specification, and the relevant attribute data read includes at least the material, dimensional tolerance and surface texture attributes of the component.

6. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 5 is characterized in that: In the digital-analog coupling step for component types with holes, the operation of embedding the hole field is implemented by adding a specific data item to the data structure, and the reading of the association relationship is based on the object reference mechanism built into the IFC model, and the detailed geometric shape information of the hole read includes the contour curve type and surface curvature information of the hole.

7. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 1 is characterized in that: The attribute mapping method comprises: Relational database selection and data table design steps: Select a relational database as a tool to perform attribute mapping operations. According to the application requirements of the ancient building digital twin and the attribute definition in the IFC semantics, comprehensively analyze the non-geometric information types, and then classify and design attribute data tables, including at least identification information table, geometry information table, material information table, and location information table. Each table is used to accurately store the corresponding type of attribute information to build a structured attribute storage system; Attribute information extraction and storage steps: parse the JSON file generated by the digital-analog decoupling, accurately extract the attribute information from it, and store the extracted attribute information in the relational database according to the designed attribute data table structure to achieve structured storage of attribute information based on component ID, ensuring that the attribute information of each component can be organized in an orderly manner and retrieved efficiently; Steps for establishing a two-way link and updating mechanism: Use the unique identifier of the component to establish a two-way link between the attribute data and the glTF geometric model, so that the attribute data can be associated and interacted with the geometric model. On this basis, an update mechanism is built to monitor the status of the database attribute information and the glTF model in real time. Once one of them changes, the update process is immediately started to ensure that the two are always synchronized, providing accurate and consistent data support for the digital twin model of the ancient building; Attribute information classification and analysis steps: During the HBIM model data processing process, attribute information is subdivided into direct attributes, derived attributes and inverse attributes according to its source and definition method. Direct attributes are scalars or direct information, which are directly used to describe the basic characteristics of the object entity; derived attributes are attributes expressed by other entities, which enrich the description of the object entity by associating other entities; inverse attributes are attributes linked with the help of associated entities. Through the detailed classification and analysis of attribute information, a deep understanding and precise management of the attributes of the ancient building model can be achieved.

8. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 7 is characterized in that: In the relational database selection and data table design steps, the identification information table is used to store the unique identifier, name, number and other information used to identify the component, and the primary key constraint is used to ensure the uniqueness of the data; the geometric information table stores the geometric shape description information of the component, including vertex coordinates, face information, etc., and realizes efficient use of geometric information by interfacing with the geometric modeling algorithm; the material information table records in detail the material name, material properties, material source and other information of the component, and combines with the material database to provide a data basis for the analysis of ancient building materials; the position information table records the spatial position information of the component in the building model, including coordinate values, relative position relationships, etc., to facilitate spatial layout analysis.

9. The reconstruction method of HBIM in the ancient building digital twin technology according to claim 8 is characterized in that: In the attribute information extraction and storage step, when parsing the JSON file, a recursive algorithm is used to traverse the file structure to ensure that no attribute information is missed. For attribute information with complex nested structures, a temporary cache area is established for temporary storage and organization, and then stored according to the data table structure to improve storage efficiency and accuracy.

10. The attribute mapping method of the ancient building digital twin model according to claim 9 is characterized in that: In the steps of establishing a bidirectional link and updating the mechanism, the bidirectional link is implemented using pointer- or index-based technology. Link information pointing to each other is established in the database and glTF model respectively. When an update requirement is detected, the transaction processing mechanism is used to ensure the atomicity of the update process.

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