CN-121997409-A - Intelligent building structure construction method based on point cloud data and digital twinning

CN121997409ACN 121997409 ACN121997409 ACN 121997409ACN-121997409-A

Abstract

The invention discloses an intelligent building method of a building structure based on point cloud data and digital twinning, which comprises the steps of firstly obtaining building point cloud through three-dimensional laser scanning, carrying out high-precision registration and pretreatment, and then carrying out component feature extraction and parameterized reverse modeling by utilizing RANSAC (random sample association algorithm) and other algorithms to generate a high-fidelity BIM (building information modeling). And then, carrying out depth fusion on the model, the GIS, the finite element and the data of the Internet of things under a unified space-time reference, and constructing a dynamically updated digital twin scene. And finally, developing a virtual simulation platform based on the scene, and realizing real-time simulation, visualization and intelligent decision support of construction progress and mechanical response. The method solves the problems of insufficient precision and difficult multi-source data fusion of the traditional modeling method, realizes the accurate mapping and interaction from physical entities to digital virtual entities of the building structure, and remarkably improves the intelligentized level and decision scientificity of the building process.

Inventors

LI DENGGUO
YI GANG
GUO RUIQI
WANG ZIXIAO
NIU JIN
JIN YU
ZHENG GANG

Assignees

嘉兴大学
巨匠建设集团股份有限公司

Dates

Publication Date: 20260508
Application Date: 20251219

Claims (5)

1. The intelligent building structure construction method based on the point cloud data and the digital twinning is characterized by comprising the following steps of: s1, acquiring and preprocessing high-precision point cloud data of a building structure, namely utilizing a three-dimensional laser scanner to perform multi-station scanning on a target building structure to acquire complete site point cloud data, and then sequentially executing automatic registration, noise reduction filtering and light weight processing on the site point cloud data; S2, building structure digital twin body reverse modeling, namely automatically identifying and reconstructing a main body structural member based on the preprocessed point cloud data through a feature extraction and parameterization modeling algorithm to generate a high-fidelity building information model; S3, constructing a digital twin scene by fusion of multi-source heterogeneous data, namely carrying out space-time reference unification and semantic integration on a reversely generated building information model, geographic information system data, a finite element analysis model and sensing data of the Internet of things to construct a dynamically updated digital twin scene; And S4, building a virtual simulation and intelligent decision of the building structure, namely carrying out real-time simulation and visualization on construction progress and structural mechanical response based on the digital twin scene, and carrying out dynamic optimization and intelligent decision support on a construction scheme by comparing and analyzing simulation data and actual measurement data.
2. The intelligent building structure construction method based on point cloud data and digital twinning according to claim 1, wherein in the step S1, Adopting a rough registration algorithm based on least square fitting to perform initial positioning, adopting an iterative closest point algorithm to perform fine registration, taking the square sum of the distances between the source point cloud points and the closest points corresponding to the target point cloud after rotation and translation as a registration error, and controlling the integral registration error within +/-2 mm through iterative optimization of a rotation matrix and a translation vector; Firstly calculating the average distance from each point in the point cloud to the nearest neighbors of the preset number of the point cloud, and judging the point as a noise point and removing if the average distance is larger than the sum of the average value of the average distances of all the points, the threshold coefficient and the standard deviation product; And the light weight adopts a voxel gridding method to uniformly downsample the noise-reduced point cloud.
3. The intelligent building method based on point cloud data and digital twinning according to claim 1, wherein the step S2 specifically includes: 2.1 Based on the space geometric feature and Euclidean clustering algorithm, automatically dividing the point cloud into point sets corresponding to different structural members including columns, beams and plates; 2.2 Fitting the central axis and the section outline of the segmented component point cloud by adopting a RANSAC algorithm, and maximizing the number of data points meeting the condition that the distance from the point to the geometric model to be fitted is smaller than a preset threshold value by searching the optimal model parameter; 2.3 Optimizing the spatial relationship of the connecting nodes of the components based on a fitting algorithm of the neighboring points, and minimizing the square sum of the distances of the axial end points of the adjacent components after rigid transformation to obtain an optimization target; 2.4 After the geometric parameters of the length, the direction and the section size are extracted, a parameterized three-dimensional solid model is generated through a section sweep method.
4. The intelligent building method based on point cloud data and digital twinning according to claim 1, wherein the step S3 specifically includes: 3.1 A unified space reference conversion model based on a spherical coordinate system is constructed, a Cesium engine is used for integrating a digital elevation model and an orthophoto, so that the position alignment of data of different sources is realized, and a three-dimensional virtual geographic environment with a real geographic background is constructed; 3.2 The building information model is converted into glTF/3D Tiles format through IFC standard analysis and light weight treatment, and is optimized by combining a detail level method, so that efficient loading, rendering and visualization under the Web environment are realized; 3.3 A unified data standard and interface are established, and node-level semantic association and dynamic mapping of finite element analysis results and a geometric model are realized based on a Parquet-column-type stored structured data analysis engine.
5. The intelligent building method based on point cloud data and digital twinning according to claim 1, wherein the step S4 specifically includes: 1) Developing a virtual simulation platform of a B/S architecture based on Cesium engines, and integrating a construction progress simulation module and a stress-displacement simulation module; The stress-displacement simulation module integrates a light finite element analysis engine, and completes the mechanical performance simulation through a finite element control equation formed by a structural rigidity matrix, a node displacement vector and a node load vector; 2) The multi-dimensional data fusion rendering and immersive interaction of the hundred million-level point cloud, the large-scale BIM model and the mechanical cloud image are realized through the WebGL technology, and the mechanical simulation result is mapped into the digital twin model in real time; 3) Establishing a closed-loop control flow of data acquisition, model correction, simulation prediction and instruction feedback: and comparing the simulation predicted value with the actual measured value, diagnosing potential risks, and providing a data-driven decision suggestion for construction scheme adjustment.

Description

Intelligent building structure construction method based on point cloud data and digital twinning Technical Field The invention relates to the technical field of building information technology and intelligent construction, in particular to an intelligent construction method of a building structure by combining three-dimensional laser scanning, reverse modeling, digital twin and virtual simulation technologies. Background Currently, the construction industry is transforming and upgrading towards digitization, industrialization and intellectualization. Although the building information model technology is widely applied, the modeling process is seriously dependent on design drawings, and for complex structures with deviations in existing buildings, historical buildings or construction processes, it is difficult to quickly and accurately construct a model reflecting actual physical states. In addition, the traditional construction management relies on two-dimensional drawings and experience judgment, and lacks of accurate perception and prospective prediction on dynamic changes of the construction process. The three-dimensional laser scanning technology can quickly acquire high-precision and high-resolution space three-dimensional information (point cloud) of the building surface in a non-contact manner, and provides possibility for 'modeling according to reality'. However, automated conversion of massive amounts of point cloud data into a semantical BIM model for analysis and simulation remains a technical difficulty. Meanwhile, the multi-source heterogeneous data such as progress, quality, mechanical property and the like generated in the construction process are isolated from each other, so that an 'information island' is formed, and a collaborative decision cannot be effectively supported. The digital twin technology serves as a bridge for connecting the physical world and the information world, and provides a framework for solving the problems. However, the digital twin application in the existing building field focuses on the operation and maintenance stage, and the application in the building stage is still in the starting stage, so that the problems of low model fidelity, insufficient data fusion depth, poor dynamic update mechanism, weak simulation and decision making capability and the like are generally existed. Therefore, a systematic method integrating high-precision reverse modeling, depth data fusion and high-fidelity simulation is urgently needed to achieve precise mapping and bidirectional interaction from a physical entity to a digital virtual entity of a building structure, so that intelligent decision and management of a construction process are driven. Disclosure of Invention The invention aims to overcome the defects of the prior art and provides an intelligent building structure construction method based on point cloud data and digital twinning, which is realized by the following steps: a) The method comprises the steps of carrying out rapid and high-precision digital reverse reconstruction on a building structure (especially a complex space structure and an existing structure); b) The barriers among the multi-source data such as point cloud, BIM, mechanical analysis, monitoring of the Internet of things and the like are broken, and deep semantic fusion and dynamic association are realized; c) A virtual simulation platform capable of reflecting the construction state in real time and supporting process simulation and intelligent decision is constructed, and construction quality, safety and efficiency are improved. In order to achieve the above purpose, the present invention adopts the following technical scheme: an intelligent building method based on point cloud data and digital twinning comprises the following steps: s1, acquiring and preprocessing high-precision point cloud data of a building structure, namely utilizing a three-dimensional laser scanner to perform multi-station scanning on a target building structure to acquire complete site point cloud data, and then sequentially executing automatic registration, noise reduction filtering and light weight processing on the site point cloud data; S2, building structure digital twin body reverse modeling, namely automatically identifying and reconstructing a main body structural member based on the preprocessed point cloud data through a feature extraction and parameterization modeling algorithm to generate a high-fidelity building information model; S3, constructing a digital twin scene by fusion of multi-source heterogeneous data, namely carrying out space-time reference unification and semantic integration on a reversely generated building information model, geographic information system data, a finite element analysis model and sensing data of the Internet of things to construct a dynamically updated digital twin scene; And S4, building a virtual simulation and intelligent decision of the building structure, namely carrying out re