Steel structure physical digital twinborn model construction method, device, equipment and medium

Abstract

The invention relates to the technical field of civil engineering digitization, and discloses a steel structure physical digital twin model construction method, device and equipment and a medium, the method comprises the following steps: carrying out multi-source point cloud data acquisition on a steel structure, and generating a steel structure complete point cloud model based on the multi-source point cloud data; extracting a complete geometric line model and a section size of the steel structure from the complete point cloud model of the steel structure, and obtaining an actually measured material constitutive of the steel structure through a material property test; establishing a large-span space steel structure finite element model by adopting finite element analysis software based on the complete geometric line model, the section size and the actually measured material constitutive structure of the steel structure; and correcting the finite element model based on the residual error between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and taking the corresponding finite element model when the residual error is less than a preset threshold value as the physical digital twinborn model of the large-span space steel structure. According to the method, millimeter-level geometric reduction, real material parameter association, lightweight efficient calculation and dynamic data correction are realized.

Description

Technical Field

[0001] The present invention relates to the field of civil engineering digital technology, and particularly to a method, device, equipment and medium for constructing a physical digital twin model of a steel structure. Background Art

[0002] Long-span spatial steel structures have the characteristics of large span, complex joints, high construction accuracy requirements, etc. Their digital modeling faces the following technical bottlenecks: 1. Geometric modeling distortion: Traditional methods rely on design drawings for manual modeling, resulting in a significant difference between the steel structure model and the actual structure.

[0003] 2. Inaccurate section parameters: The wall thickness of the closed section is difficult to directly measure through point clouds, and existing technologies mostly use design values, resulting in errors in the calculation of section stiffness.

[0004] 3. Static material parameters: Material constitutive parameters are mostly based on the default values of specifications, without considering the performance differences of steel batches, resulting in stress prediction deviations.

[0005] 4. Model update lag: Existing finite element models are mostly static models and cannot reflect the state changes of steel structures in real time.

[0006] In summary, in the prior art, it is impossible to accurately construct a digital model of a steel structure throughout the entire life cycle of the steel structure. Summary of the Invention

[0007] In view of this, the present invention provides a method, device, equipment and medium for constructing a physical digital twin model of a steel structure to solve the problem that in the prior art, it is impossible to accurately construct a digital model of a steel structure throughout the entire life cycle of the steel structure.

[0008] In a first aspect, the present invention provides a method for constructing a physical digital twin model of a steel structure, the method comprising: Collect multi-source point cloud data of the steel structure, and generate a complete point cloud model of the steel structure based on the multi-source point cloud data; Extract the complete geometric wire model and section dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive of the steel structure through material property tests; Establish a finite element model of a long-span spatial steel structure using finite element analysis software based on the complete geometric wire model, section dimensions and measured material constitutive of the steel structure; Correct the finite element model based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and use the finite element model corresponding to when the residual is less than a preset threshold as the physical digital twin model of the long-span spatial steel structure.

[0009] A method for constructing a physical digital twin model of a steel structure provided by the present invention collects multi-source point cloud data of the steel structure, generates a complete point cloud model of the steel structure based on the multi-source point cloud data, accurately captures the actual geometric shape during construction, avoids geometric deviations caused by drawing simplification in traditional modeling, and can obtain an accurate complete point cloud model of the steel structure. Extract the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive of the steel structure through material property tests. Establish a finite element model of a long-span space steel structure using finite element analysis software based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive of the steel structure; correct the finite element model based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model. When the residual is less than a preset threshold, the corresponding finite element model is used as the physical digital twin model of the long-span space steel structure, realizing millimeter-level geometric restoration, real material parameter correlation, lightweight and efficient calculation, and dynamic data correction. Finally, an accurate physical digital twin model of the long-span space steel structure is obtained, providing accurate digital support for construction safety control and operation and maintenance decision-making, and solving the problem in the prior art that it is impossible to accurately construct a digital model of a steel structure throughout its life cycle.

[0010] In an alternative embodiment, the multi-source point cloud data includes bottom point cloud data and top point cloud data; Collecting multi-source point cloud data of the steel structure and generating a complete point cloud model of the steel structure based on the multi-source point cloud data includes: Extract the first feature point set and the second feature point set from the bottom point cloud data and the top point cloud data respectively; Coarsely register the first feature point set and the second feature point set by rotation and translation to obtain the coarsely registered bottom point cloud data and top point cloud data; Use the improved ICP algorithm to perform fine registration of rotation and translation on the coarsely registered bottom point cloud data and top point cloud data, so that the coincidence degree of the first feature point set and the second feature point set is less than a preset error, and obtain a complete point cloud model of the steel structure.

[0011] The present invention provides a method for constructing a physical digital twin model of a steel structure, which collects point cloud data of the bottom and top of the steel structure respectively, can cover all parts of the steel structure components, and avoid data loss caused by a single acquisition perspective. First, the first and second feature point sets are roughly aligned by rotation and translation, and the position difference between the bottom and top point cloud data is quickly reduced, reducing the calculation amount of subsequent fine alignment. The improved ICP algorithm is combined with feature point constraints, and the bottom and top point cloud data are finely aligned on the basis of coarse alignment. The goal is to make the overlap of the first and second feature point sets less than the preset error, and the rotation and translation parameters of the point cloud data can be accurately adjusted. In this way, the point cloud data misalignment problem caused by factors such as acquisition equipment errors and measurement angle deviations is effectively eliminated. The generated complete point cloud model of the steel structure has higher accuracy and can more truly reflect the actual shape of the steel structure, providing a reliable basis for subsequent geometric line model extraction and cross-sectional size measurement.

[0012] In an optional implementation, extracting a complete geometric line model of the steel structure from the complete point cloud model of the steel structure includes: Based on the Euclidean clustering segmentation and region growing algorithm, the component point cloud clusters are separated from the complete point cloud model of the steel structure; The component centerline is extracted from the component point cloud cluster using the RANSAC algorithm and cubic B-spline interpolation algorithm; The complete geometric line model of the steel structure is constructed through topological connection based on the center lines of the components.

[0013] The present invention provides a method for constructing a physical digital twin model of a steel structure. The Euclidean clustering segmentation performs clustering based on the spatial distance relationship between points, and can quickly divide discrete point cloud data into different regions. The regional growing algorithm starts from the seed point and continuously expands the region according to the similarity criterion of the point cloud. The two cooperate with each other to accurately identify components of various shapes in complex steel structures, avoid misclassification and omission of component point cloud data, and lay a reliable foundation for centerline extraction. For component point cloud clusters of different shapes, the RANSAC algorithm and the cubic B-spline interpolation algorithm are used to extract the center lines respectively, reflecting extremely strong flexibility and high efficiency.

[0014] In an optional embodiment, the cross section includes an open cross section and a closed cross section; When the cross section is an open cross section, the cross-sectional dimensions of the steel structure are extracted from the complete point cloud model of the steel structure, including: Extract flange width, web height and thickness from the cross-sectional slice of the complete point cloud model of the steel structure as the cross-sectional dimensions of the steel structure; When the section is a closed section, the section dimensions of the steel structure are extracted from the complete point cloud model of the steel structure, including: Extract the outer contour dimensions of the steel structure from the complete point cloud model of the steel structure, and use a digital ultrasonic thickness gauge to measure the wall thickness at the ends and middle parts of the steel structure members multiple times. Take the average value of the wall thickness measured multiple times as the actual thickness, and use the outer contour dimensions and actual thickness of the steel structure as the cross-sectional dimensions of the steel structure.

[0015] A method for constructing a physical digital twin model of a steel structure provided by the present invention adopts a targeted extraction strategy according to the structural differences between open and closed cross-sections. For open cross-sections, directly extract the flange width, web height, and thickness from the cross-sectional slices, which conforms to the characteristics of their exposed structure and intuitive measurable dimensional parameters; for closed cross-sections, first extract the outer contour dimensions, and then combine with a digital ultrasonic thickness gauge to measure the wall thickness, fully considering the characteristic that the wall thickness of closed cross-sections cannot be directly obtained from the point cloud. This differential processing method enables the extraction method to accurately adapt to various cross-sections and ensures that the dimension extraction conforms to the actual structural characteristics.

[0016] In an alternative embodiment, obtain the measured material constitutive of the steel structure through material property tests, including: During the steel structure processing stage, randomly intercept a preset number of standard specimens for each batch of steel; Conduct tensile tests on the preset number of standard specimens to obtain test data, and the test data includes the yield strength, elastic modulus, and Poisson's ratio of the steel; Bind the test data to the steel structure processed from the same batch of steel to obtain the measured material constitutive of the steel structure.

[0017] A method for constructing a physical digital twin model of a steel structure provided by the present invention, during the steel structure processing stage, randomly intercept a preset number of standard specimens for each batch of steel for tensile tests, following the principle of random sampling to avoid human deviation in sample selection, so that the obtained test data can truly reflect the material properties of this batch of steel. Directly measure key parameters such as the yield strength, elastic modulus, and Poisson's ratio of the steel through tensile tests. Compared with referring to standard values or empirical data, this measured method can obtain the material constitutive more accurately, reduce data errors caused by the discreteness of material properties, and bind the test data to the steel structure processed from the same batch of steel to ensure that during the design, analysis, and use of the steel structure, the material constitutive adopted is consistent with the actual material properties used. This effectively avoids potential structural safety hazards caused by inconsistent material properties, makes the finite element model established based on this measured material constitutive and the mechanical calculations more in line with the engineering reality, and improves the safety and reliability of the steel structure project during the construction and use stages.

[0018] In an alternative embodiment, the finite element analysis software includes beam elements; Based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive of the steel structure, establish a finite element model of a long-span space steel structure using finite element analysis software, including: Based on the complete geometric line model of the steel structure, the cross-sectional dimensions, and the measured material constitutive relations, a finite element model of the long-span spatial steel structure is established using beam elements.

[0019] A method for constructing a physical digital twin model of a steel structure provided by the present invention uses beam elements to replace solid elements, reduces the degrees of freedom of the model, shortens the calculation time for a single working condition, improves the calculation speed, reduces the calculation cost, and meets the real-time simulation requirements during the construction process.

[0020] In an alternative embodiment, the finite element model is corrected based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model. The finite element model corresponding to when the residual is less than or equal to the preset threshold is used as the physical digital twin model of the long-span spatial steel structure, including: Real-time collect the strain data, displacement data, and acceleration data under the construction load of the steel structure, input the strain data, displacement data, and acceleration data into the finite element model, and output the simulated data of the steel structure; Taking the minimization of the residual between the simulated data of the steel structure and the measured data of the steel structure as the goal, correct the finite element model using the Bayesian inversion algorithm or the response surface method; When the residual between the simulated data of the steel structure and the measured data of the steel structure is less than or equal to the preset threshold, the finite element model corresponding to when the residual is less than the preset threshold is used as the physical digital twin model of the long-span spatial steel structure.

[0021] A method for constructing a physical digital twin model of a steel structure provided by the present invention uses the dynamic data under real working conditions to iteratively optimize the model, effectively eliminating the calculation errors caused by factors such as material property deviations and boundary condition simplifications. When the residual is less than the preset threshold, the model can highly restore the actual mechanical behavior of the steel structure, greatly improving the accuracy and reliability of the finite element model. Using the Bayesian inversion algorithm or the response surface method for model correction endows the finite element model with dynamic adaptability, making the physical digital twin model no longer a static virtual copy, but a "digital mirror" that can reflect the real operating state of the steel structure in real time, providing an effective tool for construction process monitoring and abnormal warning. Determining the finite element model with the residual meeting the requirements as the physical digital twin model breaks through the barrier between virtual simulation and actual engineering. This model can be used to predict the performance changes of the steel structure under different working conditions and assist in optimizing the construction plan; by continuously comparing with the measured data, potential safety hazards of the structure can be detected in a timely manner.

[0022] In a second aspect, the present invention provides a device for constructing a physical digital twin model of a steel structure, and the device includes: A point cloud data acquisition and point cloud model construction module, configured to perform multi-source point cloud data acquisition on the steel structure and generate a complete point cloud model of the steel structure based on the multi-source point cloud data; A model data extraction module, configured to extract the complete geometric wire model and cross-sectional dimensions of a steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive of the steel structure through material property tests; A finite element model construction module, configured to establish a finite element model of a long-span spatial steel structure by using finite element analysis software based on the complete geometric wire model, cross-sectional dimensions, and measured material constitutive of the steel structure; A physical digital twin model determination module, configured to correct the finite element model based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and use the finite element model corresponding to when the residual is less than a preset threshold as the physical digital twin model of the long-span spatial steel structure.

[0023] Thirdly, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method for constructing a physical digital twin model of a steel structure according to the first aspect or any corresponding embodiment thereof.

[0024] Fourthly, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the method for constructing a physical digital twin model of a steel structure according to the first aspect or any corresponding embodiment thereof.

[0025] Fifthly, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the method for constructing a physical digital twin model of a steel structure according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0026] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a flowchart of the method for constructing a physical digital twin model of a steel structure according to an embodiment of the present invention; Figure 2 It is a flowchart of another method for constructing a physical digital twin model of a steel structure according to an embodiment of the present invention; Figure 3 It is a flowchart of yet another method for constructing a physical digital twin model of a steel structure according to an embodiment of the present invention; Figure 4Schematic diagram of the process of constructing a structural geometric line model according to the center line of components according to an embodiment of the present invention; Figure 5 Schematic diagram of the process of another method for constructing a physical digital twin model of a steel structure according to an embodiment of the present invention; Figure 6 Block diagram of the structure of a device for constructing a physical digital twin model of a steel structure according to an embodiment of the present invention; Figure 7 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] In the prior art, manual modeling is relied on design drawings, ignoring geometric deviations (such as member bending, joint dislocation, etc.) during the construction process, resulting in a significant difference between the steel structure model and the actual structure. When determining the section parameters, the section dimensions are not obtained according to the type of section. In particular, the wall thickness of closed sections (such as square tubes, round tubes) is difficult to directly measure through point clouds, resulting in calculation errors in section stiffness. When obtaining the constitutive parameters of steel structure materials, the performance differences of steel batches are not considered, making the obtained material constitutive parameters static values, resulting in large stress prediction errors. When constructing a steel structure model, the finite element models involved are mostly static models, lacking a dynamic correction mechanism for construction period monitoring data, resulting in the inability to reflect the state changes of the steel structure in real time.

[0030] However, the embodiments of the present invention provide a method for constructing a physical digital twin model of a steel structure, which achieves the effects of millimeter-level geometric reduction, real material parameter association, lightweight and efficient calculation, and dynamic data correction through the construction of a physical digital twin model driven by full-life cycle data, providing accurate digital support for construction safety control and operation and maintenance decision-making.

[0031] According to an embodiment of the present invention, an embodiment of a method for constructing a physical digital twin model of a steel structure is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0032] In this embodiment, a method for constructing a physical digital twin model of a steel structure is provided, which can be used in the above computer device. Figure 1 It is a flowchart of the method for constructing a physical digital twin model of a steel structure according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps: Step S101, collect multi-source point cloud data of the steel structure, and generate a complete point cloud model of the steel structure based on the multi-source point cloud data.

[0033] Specifically, during the construction stage of the steel structure, a ground 3D laser scanner and an airborne lidar on a drone are synchronously used to scan the structure in stages. The bottom point cloud data of the steel structure members is collected by scanning with the ground 3D laser scanner, and the top point cloud data of the structure members is collected by scanning with the airborne lidar on a drone. For areas with severe occlusion, data is supplemented by combining with the BIM model (Building Information Modeling) or design drawings.

[0034] The improved ICP algorithm (Iterative Closest Point, a point cloud registration algorithm) is used to introduce feature point constraints to register the two types of point clouds, namely the bottom point cloud data and the top point cloud data. Voxel filtering and SOR filtering (Statistical Outlier Removal, SOR, an algorithm for removing outliers in point cloud data) are used to statistically remove outliers and reduce noise, generating a complete point cloud model of the steel structure. Among them, voxel filtering of point clouds is a dimensionality reduction technology used to reduce high-density point cloud data to a lower resolution while retaining key information.

[0035] Step S102, extract the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive of the steel structure through material property tests.

[0036] Specifically, the complete geometric line model of the steel structure refers to the node line model extracted from the complete point cloud model, that is, the axis model of the member. The cross-sectional dimensions of the steel structure refer to the dimensions of each part of the steel member extracted from the complete point cloud model, including the width, height, and thickness of profiles such as I-beams and channel steels. The measured material constitutive of the steel structure refers to the stress-strain relationship of the steel obtained through tensile tests. Through the measured material constitutive, the mechanical behavior of the steel under various stress states can be accurately described, thus ensuring the safety and reliability of the steel structure.

[0037] Step S103, establish a finite element model of a long-span spatial steel structure using finite element analysis software based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive of the steel structure.

[0038] Specifically, the finite element analysis software is a computer-aided design tool based on numerical simulation, which is used to solve complex physical problems in the fields of engineering and science. It discretizes the continuum into a finite number of interconnected elements (such as triangular or quadrilateral meshes), and approximately solves the response of each element using mathematical equations, and finally obtains the performance prediction of the overall structure. In this embodiment, a finite element model of a long-span space steel structure is established using the finite element analysis software based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive relations of the steel structure.

[0039] Step S104: Correct the finite element model based on the residuals between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and use the finite element model corresponding to the case where the residual is less than the preset threshold as the physical digital twin model of the long-span space steel structure.

[0040] Specifically, at the key parts of the steel structure construction, such as stress concentration points, long-span mid-spans, support connection areas, etc., in accordance with the requirements of the "Technical Standard for Building Structure Monitoring", high-precision strain gauges, displacement sensors, and acceleration sensors are densely arranged. The strain gauge uses a fiber Bragg grating strain sensor, and its resolution can reach , which can accurately capture the small strain changes of the structure; the displacement sensor selects a laser displacement sensor with a measurement accuracy of 0.01 mm; the acceleration sensor uses a MEMS accelerometer to meet the vibration monitoring requirements under a range of ±2g.

[0041] Using the Bayesian inversion algorithm or the response surface method, with the goal of minimizing the residuals between the measured data and the simulated data of the finite element model, correct the boundary conditions and connection stiffness. When the iteration reaches the preset threshold, the obtained finite element model can be used as the physical digital twin model.

[0042] The method for constructing a physical digital twin model of a steel structure provided in this embodiment collects multi-source point cloud data of the steel structure, generates a complete point cloud model of the steel structure based on the multi-source point cloud data, accurately captures the actual geometric shape of the construction, avoids geometric deviations caused by drawing simplification in traditional modeling, and can obtain an accurate complete point cloud model of the steel structure. Extract the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive relation of the steel structure through material property tests. Establish a finite element model of a long-span spatial steel structure using finite element analysis software based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive relation of the steel structure; correct the finite element model based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model. When the residual is less than the preset threshold, the corresponding finite element model is used as the physical digital twin model of the long-span spatial steel structure, realizing millimeter-level geometric restoration, real material parameter correlation, lightweight and efficient calculation, and dynamic data correction. Finally, an accurate physical digital twin model of the long-span spatial steel structure is obtained, providing accurate digital support for construction safety control and operation and maintenance decision-making, and solving the problem in the prior art that it is impossible to accurately construct a digital model of a steel structure throughout its life cycle.

[0043] In this embodiment, a method for constructing a physical digital twin model of a steel structure is provided, which can be used in the above computer device. Figure 2 It is a flowchart of the method for constructing a physical digital twin model of a steel structure according to an embodiment of the present invention, as Figure 2 shown, and this process includes the following steps: Step S201: Collect multi-source point cloud data of the steel structure and generate a complete point cloud model of the steel structure based on the multi-source point cloud data.

[0044] Specifically, the multi-source point cloud data includes bottom point cloud data and top point cloud data; the above step S201 includes: Step S2011: Extract the first feature point set and the second feature point set from the bottom point cloud data and the top point cloud data respectively.

[0045] Specifically, feature point set extraction is performed separately from the bottom point cloud data and the top point cloud data. For example, targets pasted on-site, structural edges, etc. are used as feature points to obtain the first feature point set and the second feature point set.

[0046] Step S2012: Coarsely register the first feature point set and the second feature point set by rotation and translation to obtain the coarsely registered bottom point cloud data and top point cloud data.

[0047] Specifically, the extracted first set of feature points and the second set of feature points are roughly registered using the method of Random Sample Consensus (RANSAC) combined with Singular Value Decomposition (SVD). First, randomly select 3 non-collinear points from the first set of feature points, and find the corresponding point pairs that match them in the second set of feature points. Based on these 3 sets of corresponding point pairs, use the least squares method to calculate the initial rotation matrix R0 and translation vector T0. Through multiple random samplings (such as 1000 times), select R0 and T0 that can make more point pairs satisfy the matching condition (set the Euclidean distance between corresponding point pairs to be less than 0.1m) as the final rough registration parameters. Apply these parameters to the bottom point cloud data to achieve the preliminary alignment with the top point cloud data, and obtain the roughly registered bottom point cloud data and top point cloud data.

[0048] In step S2013, use the improved ICP algorithm to perform fine registration of rotation and translation on the roughly registered bottom point cloud data and top point cloud data, so that the coincidence degree of the first set of feature points and the second set of feature points is less than the preset error, and obtain the complete point cloud model of the steel structure.

[0049] Specifically, taking the first set of feature points and the second set of feature points as the reference, in each iteration process, give priority to matching feature points to reduce the influence of mis-matching of non-feature points.

[0050] Corresponding point search: For each point pi in the roughly registered bottom point cloud data, find the point qj with the closest Euclidean distance in the top point cloud data as its corresponding point. At the same time, for feature points, use the nearest neighbor search method based on feature descriptors to ensure accurate matching of feature points.

[0051] Parameter calculation: According to the corresponding point pairs, construct an error function, and use Singular Value Decomposition (SVD) to solve the rotation matrix R and translation vector T that minimize the error function.

[0052] Iterative optimization: Apply the calculated R and T to the bottom point cloud data, and repeat the above corresponding point search and parameter calculation steps until the coincidence degree of the first set of feature points and the second set of feature points is less than the preset error (set to 0.01m), or reach the maximum number of iterations (such as 50 times), stop the iteration, and obtain the final complete point cloud model of the steel structure.

[0053] In step S202, extract the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive of the steel structure through material property tests.

[0054] Specifically, the cross-section includes open cross-sections and closed cross-sections, which are distinguished according to whether the cross-section is closed. "I" and "C" shapes are open cross-sections, and "□" and "O" shapes are closed cross-sections.

[0055] The above step S202 includes: Step S2021, separating component point cloud clusters from the complete point cloud model of the steel structure based on the Euclidean clustering segmentation and region growing algorithm; extracting the component centerlines from the component point cloud clusters by using the RANSAC algorithm and the cubic B-spline interpolation algorithm; constructing the complete geometric line model of the steel structure through topological connection based on the component centerlines.

[0056] Specifically, the complete point cloud model of the steel structure is a model composed of many points, and each point contains information such as coordinate values and colors. The point cloud model containing the entire steel structure is called the "complete point cloud model", and the point cloud model with only a single component is called the "point cloud cluster".

[0057] In an alternative embodiment, the above step S2021 includes: Step a1, separating component point cloud clusters from the complete point cloud model of the steel structure based on the Euclidean clustering segmentation and region growing algorithm.

[0058] Specifically, for the Euclidean clustering segmentation: for the complete point cloud model of the steel structure, first set a distance threshold d (generally set to 0.05 - 0.1 m according to the steel structure component size and point cloud density). Randomly select an unlabeled point p from the point cloud model as the seed point. Taking this point as the center, search for all neighboring points within a spherical neighborhood with a radius of d, and label these neighboring points as belonging to the same cluster as the seed point p. Then, select a new seed point from the labeled points in this cluster, and repeat the neighborhood search and labeling operations until the neighborhood search for all points in this cluster is completed. Continuously randomly select unlabeled points as new seed points and repeat the above process until all points in the point cloud model are labeled, thereby dividing the point cloud model into multiple clusters.

[0059] Based on the Euclidean clustering segmentation, use the centroid of each cluster as the initial seed point for region growing. Set the growth conditions, including the normal vector angle threshold θ of the point (usually set to 15° - 20°) and the curvature change threshold c (set to 0.01 - 0.03 according to the actual situation). For the neighborhood points of the seed point, if the angle between its normal vector and the normal vector of the seed point is less than θ, and the difference between its curvature value and the curvature value of the seed point is less than c, then include this neighborhood point in the current growth region. Continuously select new seed points from the points that have been included in the region and repeat the growth condition judgment and point inclusion operations until there are no eligible points to include, and complete the region growing. Through this process, further eliminate noise points and mis-clustered points, and separate complete and accurate component point cloud clusters.

[0060] Step a2, extracting the component centerlines from the component point cloud clusters by using the RANSAC algorithm and the cubic B-spline interpolation algorithm.

[0061] When extracting the center line of a component from a component point cloud cluster, for the point cloud cluster of a linear component, the RANSAC algorithm is used to fit the central axis; for the point cloud cluster of a curved component, a continuous center line is generated through cubic B-spline interpolation.

[0062] Furthermore, for the point cloud cluster of a linear component, randomly select 3 points from the point cloud cluster, and use these 3 points to fit a straight line equation. Calculate the distance from all points in the point cloud cluster to this straight line, set a distance threshold D (set to 0.005 - 0.01 m according to the component accuracy requirements), regard the points with a distance less than D as inliers, and count the number of inliers. Repeat the above operations of randomly selecting points, straight line fitting, and inlier statistics (1000 times), and select the fitting straight line with the largest number of inliers as the initial estimate of the central axis of this component. Based on all inliers, use the least squares method to optimize the initial central axis to obtain the final center line of the linear component.

[0063] For the point cloud cluster of a curved component, first determine the main direction of the point cloud cluster through principal component analysis (PCA), and project the point cloud cluster onto a plane perpendicular to the main direction. On the projection plane, use the K - means algorithm to cluster the points into k point sets (k is determined according to the complexity of the curve, generally taking 5 - 10), calculate the centroid of each point set to obtain a series of control points. Based on these control points, use the cubic B-spline interpolation algorithm to generate a smooth curve as the center line of the curved component on the projection plane. Map this center line back to the three-dimensional space along the main direction and make fine adjustments in combination with the spatial distribution information of the point cloud cluster to obtain the continuous center line of the curved component.

[0064] Step a3, construct a complete geometric wireframe of the steel structure through topological connection based on the component center line.

[0065] Specifically, the schematic diagram of the process of constructing the structural geometric wireframe according to the component center line is as Figure 4 shown. The center lines of different components are connected according to the topological relationship. The specific method is as follows: calculate the curve equations of the center lines of each component. For any two center lines 1 and center line 2, select sampling points on their curves at a fixed interval (such as 0.1 m). Calculate the Euclidean distance between the sampling points of the two center lines. If there is a distance between sampling points less than the set threshold (such as 2 cm), it is considered that there may be a connection relationship between these two center lines, that is, it is considered that these center lines may intersect at a point. For the suspected connected center lines, near the sampling points with a distance less than the threshold, that is Figure 4The sampling points A (X, Y, Z) and B (X, Y, Z) in the sample are further refined (the interval is reduced to 0.01m), and the coordinates of the intersection O (X, Y, Z) are determined by calculating the average coordinates of these refined sampling points. With the intersection O as the connection point, the center lines of different components are connected to gradually build a complete geometric line model of the steel structure. At the same time, the topological relationship of the connected geometric line model is checked and optimized to ensure that the connection relationship of each component is accurate and in line with the actual structure of the steel structure.

[0066] Step S2022, when the cross section is an open cross section, extracting the cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, including: extracting the flange width, web height, and thickness from the cross-sectional slice of the complete point cloud model of the steel structure as the cross-sectional dimensions of the steel structure.

[0067] Specifically, the open cross-section includes I-beams, channel steels, and the like.

[0068] In an optional implementation, the above step S2022 includes: Step b1, cross-sectional slicing of the complete point cloud model of the steel structure: Based on the constructed complete point cloud model of the steel structure, set the slicing parameters according to the length and accuracy requirements of the component. Usually, cross-sectional slices are generated along the axis of the component at intervals of 5-10cm, and the slicing tool of the point cloud processing software is used to convert the 3D point cloud data into 2D cross-sectional point cloud data. During the slicing process, ensure that the slicing plane is perpendicular to the axis of the component to avoid dimensional measurement errors caused by angle deviation. Step b2, flange width and web height extraction: For each cross-section slice point cloud, use the edge detection algorithm to identify the contour. By calculating the point cloud normal vector, screen out the edge points with significant normal vector changes, and use the least squares method to fit the edge points to obtain the contour lines of the flange and web. For I-beams, measure the vertical distance between the two flange contour lines to obtain the flange width; measure the vertical distance between the two end points of the web contour line to obtain the web height. When measuring, select multiple positions of the contour line for measurement and take the average value to reduce errors. Step b3, thickness measurement: select at least 3 measurement areas at different positions of the flange and web, and select 3-5 measurement points in each area. Use the 3D coordinates of the measurement points in the point cloud data to calculate the distance between adjacent parallel surfaces and obtain thickness data. To improve the measurement accuracy, perform statistical analysis on the thickness data of each measurement point, remove outliers and take the average value as the thickness of the area. Finally, combine the average thickness of each area of ​​the flange and web to obtain the complete section thickness parameters.

[0069] Step S2023, when the cross-section is a closed cross-section, extract the cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, including: extract the outer contour dimensions of the steel structure from the complete point cloud model of the steel structure, and use a digital ultrasonic thickness gauge to measure the wall thickness multiple times at the ends and middle of the steel structure members. Take the average value of the multiple wall thickness measurements as the actual thickness, and use the outer contour dimensions and the actual thickness of the steel structure as the cross-sectional dimensions of the steel structure.

[0070] Specifically, the closed cross-section includes circular, box-shaped, etc. In an optional implementation manner, the above step S2023 includes: Circular cross-section: For the circular cross-section point cloud, use a circle fitting algorithm based on random sample consensus (RANSAC). Randomly select 3 points in the point cloud to fit the circle equation, calculate the distances from other points to the fitted circle, set a distance threshold to screen the inliers, and repeat the fitting process to select the circle with the most inliers as the final fitting result. Calculate the diameter of the circular tube according to the radius of the fitted circle. To ensure accuracy, take the average value of the measurement results of multiple cross-sections. Box-shaped cross-section: Use a point cloud clustering algorithm to divide the box-shaped cross-section point cloud into point cloud sets of four sides, and use a line fitting algorithm for each side point cloud to obtain the contour line. By calculating the intersection coordinates of the four contour lines, determine the vertices of the box-shaped cross-section, and then calculate the side length of the square tube. During the calculation process, optimize the vertex coordinates to eliminate the errors caused by point cloud noise. Wall thickness measurement: Use a digital ultrasonic thickness gauge for wall thickness measurement. Select 3 different positions at the end and middle of the member respectively, and perform 3 measurements at each position. Before measurement, the surface of the measurement part needs to be treated, removing impurities such as oil stains and rust, and applying an appropriate amount of coupling agent (such as glycerin, paste) to ensure that the probe is closely attached to the surface of the member. Place the probe of the ultrasonic thickness gauge vertically on the measurement point, and record the data after the instrument shows a stable reading. Take the average value of the 3 measurement results at each measurement position, and then comprehensively average the measurement positions at the end and middle to obtain the actual wall thickness of the member. Finally, combine the outer contour dimensions with the actual wall thickness to form the complete dimension parameters of the closed cross-section.

[0071] Step S2024, during the steel structure processing stage, randomly intercept a preset number of standard specimens from each batch of steel; conduct tensile tests on the preset number of standard specimens to obtain test data, and the test data includes the yield strength, elastic modulus, and Poisson's ratio of the steel; bind the test data to the steel structure processed from the same batch of steel to obtain the measured material constitutive relationship of the steel structure.

[0072] Specifically, the preset number of groups is 3 - 5 groups. The description of the material property test is as follows: During the processing stage of steel structure components, 3 - 5 groups of standard specimens are randomly intercepted from each batch of steel. For example, representative parts are selected on the steel coil or plate for interception, avoiding sampling at positions such as the edge of the steel and the welding area where there may be performance differences. A high-precision cutting machine (such as a numerical control plasma cutting machine or a wire cutting machine) is used to strictly control the cutting accuracy to ensure that the dimensional error of the specimen is within the standard allowable range (such as a length error of ±0.5 mm and a cross-sectional dimension error of ±0.1 mm). After cutting, the surface of the specimen is polished to remove burrs, oxide layers, etc., to ensure a smooth and clean surface and avoid affecting the subsequent test results.

[0073] A universal testing machine is used for the tensile test. The prepared standard specimen is installed on the fixture of the universal testing machine, and the test rate is set. In the elastic stage, the test rate is controlled at 6 - 60 MPa / s; before entering the yield stage, the rate is adjusted to a strain rate not exceeding 0.00025 / s; after the yield stage, the rate is restored to 6 - 60 MPa / s. During the test process, data such as test force, displacement, and strain are collected in real time, and the collection frequency is not less than 100 Hz. After the specimen breaks, key data such as the maximum test force (for calculating the tensile strength), the yield point test force (for calculating the yield strength), and the gauge length after fracture (for calculating the elongation after fracture) are recorded. The above operations are repeated to complete the tensile test of all standard specimens.

[0074] According to the collected test data, the mechanical property parameters of the steel, that is, the test parameters, are calculated according to the standard formulas in the relevant technology. The specific calculation process will not be elaborated here. The test data includes the yield strength, elastic modulus, and Poisson's ratio of the steel.

[0075] During the steel structure processing, a unique code is assigned to each component, which can be in the form of a two-dimensional code or an RFID tag. The two-dimensional code contains the basic information of the component (such as model, specification, processing batch), production time, inspection status, etc.; in addition to storing the above information, the RFID tag can also achieve non-contact data reading and writing, facilitating the rapid acquisition of component information during the construction process. The test data of each batch of steel is associated with the code of the steel structure components processed in the corresponding batch. A database management system is established to input the test data (including yield strength, elastic modulus, Poisson's ratio, etc.), component code information, and the binding relationship between the two. At the same time, the data is encrypted to ensure data security and integrity. During the design, construction, and operation and maintenance stages of the steel structure, the measured material constitutive data of the corresponding component can be quickly retrieved by scanning the two-dimensional code or reading the RFID tag.

[0076] Step S203, based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive of the steel structure, a finite element model of the long-span spatial steel structure is established using finite element analysis software. For details, please refer toFigure 1 Step S103 of the illustrated embodiment will not be elaborated herein.

[0077] Step S204: Based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, correct the finite element model. When the residual is less than the preset threshold, the corresponding finite element model is used as the physical digital twin model of the long-span space steel structure. For details, please refer to Figure 1 Step S104 of the illustrated embodiment will not be elaborated herein.

[0078] The method for constructing the physical digital twin model of the steel structure provided in this embodiment accurately captures the actual construction geometry (including initial defects such as weld deformation and installation misalignment) through the ground combined with the airborne lidar fusion technology, avoiding geometric deviations caused by drawing simplification in traditional modeling. The authenticity of the material constitutive parameters is enhanced, and the stress-strain simulation accuracy is significantly improved by binding the material properties tests (such as yield strength and elastic modulus) of the same batch of steel with the components.

[0079] In this embodiment, a method for constructing a physical digital twin model of a steel structure is provided, which can be used in the above computer device. Figure 3 It is a flowchart of the method for constructing the physical digital twin model of the steel structure according to the embodiment of the present invention. As Figure 3 shown, the process includes the following steps: Step S301: Collect multi-source point cloud data of the steel structure and generate a complete point cloud model of the steel structure based on the multi-source point cloud data. For details, please refer to Figure 2 Step S201 of the illustrated embodiment will not be elaborated herein.

[0080] Step S302: Extract the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive of the steel structure through material property tests. For details, please refer to Figure 2 Step S202 of the illustrated embodiment will not be elaborated herein.

[0081] Step S303: Establish a finite element model of the long-span space steel structure using finite element analysis software based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive of the steel structure.

[0082] Specifically, the finite element analysis software includes beam elements. The above step S303 includes: Establish a finite element model of the long-span space steel structure using beam elements based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive of the steel structure.

[0083] Specifically, professional finite element analysis software is selected, such as ANSYS, ABAQUS, or Midas Civil, etc.

[0084] Import the complete geometric wire model of the steel structure into the finite element analysis software in a general format (such as IGES, STEP). At the same time, organize the dimensional parameters of open and closed sections (flange width, web height, wall thickness, outer contour dimensions, etc.) into a tabular form, and prepare to assign them to the corresponding components during the modeling process. The measured material constitutive data (yield strength, elastic modulus, Poisson's ratio, etc.) are entered into the material library of the software to ensure that the material properties are consistent with the actual steel.

[0085] According to the length of the steel structure components and the requirements of analysis accuracy, divide the complete geometric wire model into beam elements. For straight components, use equal-spacing division, and the element length is generally set to 1 / 10 - 1 / 20 of the component length; for curved components, densify the division at the positions with large curvature changes to ensure that the elements can accurately fit the curve shape. Assign the corresponding section properties to each beam element, select the I-beam, channel steel, circular or box-section templates that match the actual section dimensions in the section library of the software, and input the specific dimension parameters. For special sections, the section model can be constructed by using the precise dimensions extracted from the point cloud data through the custom section function. At the same time, specify the measured material constitutive for the beam elements.

[0086] Boundary conditions: Apply boundary constraints in the finite element model according to the actual support conditions of the steel structure. For hinged supports, restrict the translational degrees of freedom in three directions; for fixed supports, restrict both the translational and rotational degrees of freedom in three directions. When applying the constraints, ensure that the constraint positions are consistent with the actual structure to avoid calculation result deviations caused by improper boundary condition settings. Connection settings: Simulate the connection methods between steel structure components, such as welding, bolt connection, etc. For welded connections, use rigid connections to simulate to ensure the continuity of displacement and rotation at the connection; for bolt connections, according to the number and specifications of bolts, simulate the connection stiffness by setting spring elements or coupling constraints, so that the model can truly reflect the mechanical properties of the connection part, and obtain the finite element model of the long-span spatial steel structure.

[0087] Step S304, correct the finite element model based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and use the finite element model corresponding to the residual less than the preset threshold as the physical digital twin model of the long-span spatial steel structure.

[0088] Specifically, the above step S304 includes: Step S3041, collect the strain data, displacement data and acceleration data under the construction load of the steel structure in real time, input the strain data, displacement data and acceleration data into the finite element model, and output the simulated data of the steel structure.

[0089] Specifically, strain gauges, displacement sensors, acceleration sensors, etc. are arranged at key parts of the steel structure to collect data such as strain, displacement, and acceleration of the steel structure components under construction loads in real time.

[0090] Step S3042: Aiming at minimizing the residual between the simulated data of the steel structure and the measured data of the steel structure, the finite element model is corrected using the Bayesian inversion algorithm or the response surface method.

[0091] In an alternative embodiment, step S3042 includes: Step c1: Implementation of the Bayesian inversion algorithm, including: Parameter definition and prior distribution setting: Set the boundary condition parameters (such as support stiffness coefficients), material property parameters (elastic modulus correction coefficients), and connection stiffness parameters in the finite element model as random variables. According to engineering experience and material test data, set a reasonable prior probability distribution for each parameter, such as normal distribution or uniform distribution. Likelihood function construction and posterior calculation: Construct a likelihood function based on the sum of squares of the errors between the measured data and the simulated data, and calculate the posterior probability distribution of the parameters through Bayes' formula. Use the Markov chain Monte Carlo (MCMC) method for sampling, set 2 - 3 independent Markov chains, each chain iterates 10,000 times, discard the first 2,000 times as the warm-up period, and judge the convergence of the chain through the Gelman - Rubin diagnosis.

[0092] Model parameter update: Update the finite element model parameters according to the mean or mode of the posterior distribution, re - conduct the simulation calculation, compare the residual between the new simulated data and the measured data. If the residual does not decrease, adjust the prior distribution or sampling parameters, and repeat the iteration until the residual converges.

[0093] Step c2: Implementation of the response surface method, including: Experimental design and data collection: Use the Latin hypercube design (LHD) method to sample the key parameters in the finite element model, and design 50 - 100 groups of parameter combinations. Conduct simulation calculations for each group of parameter combinations, record the corresponding simulated data (strain, displacement, acceleration), and form a response surface model training data set. Response surface model construction: Construct a response surface model using a second - order polynomial function, fit the training data by the least - squares method, and obtain a response surface equation with the model parameters as independent variables and the residual between the simulated and measured data as the dependent variable. Use analysis of variance (ANOVA) to evaluate the goodness of fit of the model, ensuring that the coefficient of determination R 2 is greater than 0.9. Parameter optimization and model correction: Use the genetic algorithm or particle swarm optimization algorithm to search for the parameter combination with the minimum residual in the response surface model, substitute the optimized parameters into the finite element model for correction, and repeat the above process until the residual meets the requirements.

[0094] Step S3043: When the residual between the steel structure simulation data and the measured steel structure data is less than the preset threshold, the finite element model corresponding to the case where the residual is less than the preset threshold is used as the physical digital twin model of the long-span spatial steel structure.

[0095] Specifically, the residual threshold is set according to the steel structure design code and the engineering accuracy requirements. The preset threshold in this embodiment is 5%. When the residuals between the simulation data and the measured data are less than the preset threshold for three consecutive sampling periods, 20% of the data that has not participated in model correction is selected from the monitoring data as the validation set. The validation set data is input into the corrected finite element model. If the average residual of the validation set is still within the threshold range and the modal frequency error of the structural dynamic response is less than 5%, then this model is determined as the physical digital twin model of the long-span spatial steel structure.

[0096] The method for constructing the physical digital twin model of the steel structure provided in this embodiment uses beam elements to replace solid elements, reduces the degrees of freedom of the model, shortens the single-case calculation time, improves the calculation speed, reduces the calculation cost, meets the real-time simulation requirements during the construction process, introduces construction monitoring data to optimize the finite element model in real time, reduces the error of the simulated structure, obtains a high-precision physical digital twin model, and discovers potential problems during construction in advance through the high-precision physical digital twin model.

[0097] As one or more specific application embodiments of the embodiment of the present invention, in combination with Figure 5 The method for constructing the physical digital twin model of the steel structure provided by the present invention is further described in detail as follows: Step 1: During the construction stage of the steel structure, a ground three-dimensional laser scanner and an airborne lidar are synchronously used to scan the structure in stages. The bottom point cloud data of the structural members is collected by scanning with the ground three-dimensional laser scanner, and the top point cloud data of the structural members is collected by scanning with the airborne lidar. For areas with severe occlusion, data is supplemented in combination with the BIM model or design drawings.

[0098] Step 2: The improved ICP algorithm (Iterative Closest Point, a point cloud registration algorithm) is used to introduce feature point constraints to register the two types of point clouds, namely the bottom point cloud data and the top point cloud data. Voxel filtering and SOR filtering (Statistical Outlier Removal, SOR, an algorithm for removing outliers in point cloud data) are used to statistically remove outliers and reduce noise, generating a complete point cloud model of the steel structure.

[0099] Step 3: ① Separate the component point cloud clusters from the complete point cloud model of the steel structure based on the Euclidean clustering segmentation and region growing algorithm; ② Extract the component centerlines from the component point cloud clusters using the RANSAC algorithm and the cubic B-spline interpolation algorithm; ③ Construct the complete geometric line model of the steel structure through topological connection based on the component centerlines.

[0100] In step ②, when extracting the centerlines from the component point cloud clusters, for the point cloud clusters of linear components, the RANSAC algorithm is used to fit the central axis; for the point cloud clusters of curved components, a continuous centerline is generated through cubic B-spline interpolation.

[0101] In step ③, the centerlines of different components are connected according to the topological relationship. The specific method is as follows: calculate the distance between adjacent centerlines according to the curve equation of the component centerlines. If the distance is less than the set threshold (such as 2 cm), it is considered that these centerlines intersect at a point, and the arithmetic mean of the coordinates of the closest points on each centerline is taken as the intersection point coordinates. Connecting these intersection points can obtain the complete geometric line model of the structure.

[0102] Step 4: Precise determination of cross-sectional dimensions: For open cross-sections (such as I-beams, channels, etc.), parameters such as flange width, web height, and thickness are extracted from the cross-sectional slices of the point cloud model; for closed cross-sections (such as circular, box-shaped, etc.), the outer contour dimensions (side length of square tube, diameter of circular tube) are extracted from the point cloud model, and a digital ultrasonic thickness gauge is used to measure the wall thickness at the ends and middle of the members, and the average value of three measurements is taken as the actual thickness.

[0103] Step 5: Dynamic binding of material constitutive parameters: During the component processing stage, 3 groups of standard specimens are randomly intercepted from each batch of steel, and parameters such as yield strength, elastic modulus, and Poisson's ratio are obtained through tensile tests; the test data are bound to the unique component code (such as QR code / RFID tag), and the model automatically associates with the measured material constitutive parameters.

[0104] Step 6: Lightweight finite element modeling: Based on the geometric line model, cross-sectional dimensions, and measured material constitutive, a finite element model of the long-span spatial steel structure is established using beam elements in the finite element calculation and analysis software.

[0105] Step 7: Strain gauges, displacement, acceleration and other sensors are arranged at the key parts of the structure to collect data such as strain, displacement, and acceleration under construction loads in real time.

[0106] Step 8: Model dynamic correction driven by monitoring data: Using the Bayesian inversion algorithm or the response surface method, with the goal of minimizing the residuals between the measured data and the simulated data, the boundary conditions and connection stiffness of the finite element model are corrected, and the iteration is carried out until the error ≤ 5%. At this time, the obtained finite element model can be used as the physical digital twin model of the long-span spatial steel structure.

[0107] The method for constructing a physical digital twin model of a steel structure provided in this embodiment accurately captures the actual geometric shape of the construction (including initial defects such as weld deformation and installation misalignment) through the fusion technology of ground combined with airborne lidar of drones, avoiding geometric deviations caused by drawing simplification in traditional modeling. The authenticity of the material constitutive parameters is enhanced, and the material properties tests of the same batch of steel materials (such as yield strength and elastic modulus) are bound to the components, significantly improving the stress-strain simulation accuracy. The beam element is used to replace the solid element, reducing the degrees of freedom of the model, shortening the calculation time for a single working condition, improving the calculation speed, reducing the calculation cost, meeting the real-time simulation requirements of the construction process, introducing construction monitoring data to optimize the finite element model in real time, reducing the error of the simulated structure, obtaining a high-precision physical digital twin model, and discovering potential problems during construction in advance through the high-precision physical digital twin model.

[0108] In this embodiment, a device for constructing a physical digital twin model of a steel structure is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0109] This embodiment provides a device for constructing a physical digital twin model of a steel structure, as Figure 6 shown, including: A point cloud data acquisition and point cloud model construction module 601, which is used to collect multi-source point cloud data of the steel structure and generate a complete point cloud model of the steel structure based on the multi-source point cloud data.

[0110] A model data extraction module 602, which is used to extract the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive of the steel structure through material property tests.

[0111] A finite element model construction module 603, which is used to establish a finite element model of a long-span space steel structure by using finite element analysis software based on the complete geometric line model, cross-sectional dimensions and measured material constitutive of the steel structure.

[0112] A physical digital twin model determination module 604, which is used to correct the finite element model based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and use the finite element model corresponding to when the residual is less than a preset threshold as the physical digital twin model of the long-span space steel structure.

[0113] In some alternative implementation manners, the multi-source point cloud data includes bottom point cloud data and top point cloud data; the point cloud data acquisition and point cloud model construction module 601 includes: The feature point set extraction unit is used to extract the first feature point set and the second feature point set from the bottom point cloud data and the top point cloud data respectively.

[0114] The rough registration unit is used to roughly register the first feature point set and the second feature point set by rotation and translation to obtain the roughly registered bottom point cloud data and top point cloud data.

[0115] The fine registration unit is used to perform fine registration of rotation and translation on the roughly registered bottom point cloud data and top point cloud data by using an improved ICP algorithm, so that the coincidence degree of the first feature point set and the second feature point set is less than a preset error, and a complete point cloud model of the steel structure is obtained.

[0116] In some alternative embodiments, the cross-section includes an open cross-section and a closed cross-section; the model data extraction module 602 includes: The geometric line model extraction unit is used to separate the component point cloud clusters from the complete point cloud model of the steel structure based on the Euclidean clustering segmentation and region growing algorithm; extract the component centerlines from the component point cloud clusters by using the RANSAC algorithm and the cubic B-spline interpolation algorithm; construct the complete geometric line model of the steel structure through topological connection based on the component centerlines.

[0117] The open cross-section size extraction unit is used to extract the flange width, web height, and thickness from the cross-sectional slices of the complete point cloud model of the steel structure as the cross-sectional dimensions of the steel structure.

[0118] The closed cross-section size extraction unit is used to extract the outer contour dimensions of the steel structure from the complete point cloud model of the steel structure, and use a digital ultrasonic thickness gauge to measure the wall thickness multiple times at the ends and middle of the steel structure members, and take the average value of the multiple wall thickness measurements as the actual thickness, and use the outer contour dimensions and actual thickness of the steel structure as the cross-sectional dimensions of the steel structure.

[0119] The measured material constitutive acquisition unit is used to randomly intercept a preset number of standard specimens from each batch of steel during the steel structure processing stage; perform tensile tests on the preset number of standard specimens to obtain test data, and the test data includes the yield strength, elastic modulus, and Poisson's ratio of the steel; bind the test data to the steel structure processed from the same batch of steel to obtain the measured material constitutive of the steel structure.

[0120] In some alternative embodiments, the finite element analysis software includes beam elements; the finite element model construction module 603 includes: The finite element model construction unit is used to establish a finite element model of a long-span spatial steel structure by using beam elements based on the complete geometric line model, cross-sectional dimensions, and measured material constitutive of the steel structure.

[0121] In some alternative embodiments, the physical digital twin model determination module 604 includes: The steel structure simulation data output unit is used to collect strain data, displacement data, and acceleration data under the construction load of the steel structure in real time, input the strain data, displacement data, and acceleration data into the finite element model, and output the steel structure simulation data.

[0122] The finite element model correction unit is used to minimize the residual between the steel structure simulation data and the measured data of the steel structure, and correct the finite element model by using the Bayesian inversion algorithm or the response surface method.

[0123] The physical digital twin model determination unit is used to, when the residual between the steel structure simulation data and the measured data of the steel structure is less than or equal to the preset threshold, use the finite element model corresponding to the residual less than the preset threshold as the physical digital twin model of the long-span spatial steel structure.

[0124] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0125] The device for constructing the physical digital twin model of the steel structure in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0126] An embodiment of the present invention further provides a computer device having the above Figure 6 shown device for constructing the physical digital twin model of the steel structure.

[0127] Please refer to Figure 7 , Figure 7 is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As Figure 7 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 7 In

[0128] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field-programmable gate array, a generic array logic, or any combination thereof.

[0129] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0130] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0132] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 can be connected through a bus or other means. Figure 7 Taking connection through a bus as an example.

[0133] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (such as an LED), and a tactile feedback device (such as a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some alternative embodiments, the display device can be a touch screen.

[0134] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0135] A part of the present invention can be applied as a computer program product, such as computer program instructions. When executed by a computer, through the operation of the computer, the method and / or technical solution according to the present invention can be called or provided. Those skilled in the art should be able to understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0136] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing a physical digital twin model of a steel structure, characterized in that: The method comprises: Collecting multi-source point cloud data of the steel structure, and generating a complete point cloud model of the steel structure based on the multi-source point cloud data; Extracting the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtaining the measured material constitutive model of the steel structure through material property tests; Based on the complete geometric line form, cross-sectional dimensions and measured material constitutive structure of the steel structure, the finite element model of the large-span spatial steel structure is established using finite element analysis software; The finite element model is modified based on the residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and the finite element model corresponding to the residual when the residual is less than or equal to the preset threshold is used as the physical digital twin model of the large-span spatial steel structure.

2. The method according to claim 1, characterized in that The multi-source point cloud data includes bottom point cloud data and top point cloud data; Collecting multi-source point cloud data of the steel structure and generating a complete point cloud model of the steel structure based on the multi-source point cloud data, including: Extracting a first feature point set and a second feature point set from the bottom point cloud data and the top point cloud data respectively; Roughly aligning the first feature point set and the second feature point set by rotation and translation to obtain bottom point cloud data and top point cloud data after rough alignment; The improved ICP algorithm is used to perform precise registration of the bottom point cloud data and the top point cloud data after rough registration by rotation and translation, so that the overlap degree of the first feature point set and the second feature point set is less than the preset error, and a complete point cloud model of the steel structure is obtained.

3. The method according to claim 1, characterized in that Extracting the complete geometric line model of the steel structure from the complete point cloud model of the steel structure includes: Separating component point cloud clusters from the complete point cloud model of the steel structure based on Euclidean clustering segmentation and region growing algorithm; The component centerline is extracted from the component point cloud cluster using the RANSAC algorithm and the cubic B-spline interpolation algorithm; A complete geometric line model of the steel structure is constructed through topological connection based on the center lines of the components.

4. The method according to claim 1, characterized in that The cross section includes an open cross section and a closed cross section; When the cross section is an open cross section, the cross-sectional dimensions of the steel structure are extracted from the complete point cloud model of the steel structure, including: Extracting flange width, web height, and thickness from the cross-sectional slice of the complete point cloud model of the steel structure as the cross-sectional dimensions of the steel structure; When the cross section is a closed cross section, the cross-sectional dimensions of the steel structure are extracted from the complete point cloud model of the steel structure, including: The outer contour dimensions of the steel structure are extracted from the complete point cloud model of the steel structure, and the wall thickness is measured multiple times at the ends and the middle of the steel structure rods using a digital ultrasonic thickness gauge. The average of the multiple measured wall thicknesses is taken as the actual thickness, and the outer contour dimensions of the steel structure and the actual thickness are used as the cross-sectional dimensions of the steel structure.

5. The method according to claim 1, characterized in that Obtain the measured material constitutive property of steel structures through material property tests, including: During the steel structure processing stage, a preset number of standard test pieces are randomly selected from each batch of steel; Performing a tensile test on the preset number of standard test pieces to obtain test data, wherein the test data includes the yield strength, elastic modulus and Poisson's ratio of the steel; The test data is bound to the steel structure processed from the same batch of steel to obtain the measured material constitutive property of the steel structure.

6. The method according to claim 1, characterized in that The finite element analysis software includes beam elements; The finite element model of the large-span spatial steel structure is established using finite element analysis software based on the complete geometric line form, cross-sectional dimensions and measured material constitutive structure of the steel structure, including: The beam unit is used to establish a finite element model of a large-span spatial steel structure based on the complete geometric line model, cross-sectional dimensions and measured material constitutive structure of the steel structure.

7. The method according to claim 1, characterized in that The residual between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model is used to correct the finite element model, and the corresponding finite element model when the residual is less than or equal to a preset threshold is used as the physical digital twin model of the large-span spatial steel structure, including: Collecting strain data, displacement data and acceleration data under the steel structure construction load in real time, inputting the strain data, displacement data and acceleration data into the finite element model, and outputting steel structure simulation data; The finite element model is modified by using a Bayesian inversion algorithm or a response surface method with the goal of minimizing the residual between the steel structure simulation data and the steel structure measured data; When the residual between the steel structure simulation data and the steel structure measured data is less than or equal to a preset threshold, the finite element model corresponding to the residual being less than the preset threshold is used as the physical digital twin model of the large-span spatial steel structure.

8. A device for constructing a physical digital twin model of a steel structure, characterized in that: The device comprises: A point cloud data acquisition and point cloud model construction module, used to acquire multi-source point cloud data of the steel structure and generate a complete point cloud model of the steel structure based on the multi-source point cloud data; A model data extraction module is used to extract the complete geometric line model and cross-sectional dimensions of the steel structure from the complete point cloud model of the steel structure, and obtain the measured material constitutive structure of the steel structure through material property tests; Finite element model building module, used to establish a finite element model of a large-span spatial steel structure using finite element analysis software based on the complete geometric line model, cross-sectional dimensions and measured material constitutive structure of the steel structure; The physical digital twin model determination module is used to correct the finite element model based on the residual between the measured data of the steel structure and the steel structure simulation data output by the finite element model, and use the finite element model corresponding to the residual less than a preset threshold as the physical digital twin model of the large-span spatial steel structure.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for constructing a physical digital twin model of a steel structure according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for constructing a physical digital twin model of a steel structure according to any one of claims 1 to 7.

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