A steel structure virtual pre-assembly method and system

Abstract

The embodiment of the application provides a steel structure virtual pre-assembly method and system, the method comprises the following steps: determining an assembly control point set of a steel structure; wherein the assembly control point set comprises a plurality of sub-assembly control point sets corresponding to different parts; performing assembly control point pairing on the plurality of sub-assembly control point sets corresponding to different parts to determine a candidate matching point set of the steel structure; screening the candidate matching point set to determine the assembly control point pairs corresponding to the steel structure; calculating the optimal attitude assembly deviation of the steel structure based on the assembly control point pairs and the pre-determined optimal attitude assembly deviation equation; based on the optimal attitude assembly deviation, performing overall splicing error analysis and detail splicing error analysis respectively to determine global error display and local error display; and determining the virtual pre-assembly condition of the steel structure according to the global error display and the local error display. The above scheme can improve the construction efficiency and installation quality of the steel structure.

Description

Technical Field

[0001] This application relates to the field of intelligent detection technology for construction equipment, specifically to a method and system for virtual pre-assembly of steel structures. Background Technology

[0002] Steel structures offer advantages such as high strength, light weight, short construction period, convenient installation, and good seismic performance. Large-span steel structures are widely used in stadiums, convention centers, airports, and train stations.

[0003] However, as steel structures develop towards larger spans, greater complexity, and more irregular shapes, factors such as processing errors, transportation deformation, measurement errors, and hoisting posture deviations may cause problems such as misalignment of interfaces, abnormal gaps, and collisions of components during the assembly process, which in turn affect construction efficiency and installation quality. Summary of the Invention

[0004] This application aims to provide a method and system for virtual pre-assembly of steel structures, which can improve the construction efficiency and installation quality of steel structures.

[0005] The technical solution of this application is implemented as follows: In a first aspect, embodiments of this application provide a method for virtual pre-assembly of steel structures, the method comprising: Determine the set of assembly control points for the steel structure; wherein, the set of assembly control points includes multiple sub-sets of assembly control points corresponding to different parts; For the sub-assembly control point sets corresponding to the multiple different parts, the assembly control points are paired to determine the candidate matching point set of the steel structure; and the candidate matching point set is filtered to determine the assembly control point pair corresponding to the steel structure; wherein, the candidate matching point set includes a first candidate matching point set and a second candidate matching point set; the assembly control point pair includes a steel column assembly control point pair and a steel beam assembly control point pair; Based on the assembly control point pair, the optimal attitude assembly deviation of the steel structure is calculated by using a pre-determined optimal attitude assembly deviation equation; Based on the optimal posture assembly deviation, overall assembly error analysis and detailed assembly error analysis are performed respectively to determine the global error display and the local error display; and based on the global error display and the local error display, the virtual pre-assembly status of the steel structure is determined. Specifically, the process of pairing assembly control points for the respective sub-assembly control point sets of the multiple different parts to determine a candidate matching point set for the steel structure, and then filtering the candidate matching point set to determine the corresponding assembly control point pairs for the steel structure, includes: Based on the multiple first sub-assembly control point sets corresponding to the first assembly control point set, the assembly control points are paired to determine the first candidate matching point set of the steel column. Based on the multiple second sub-assembly control point sets corresponding to the second assembly control point set, the assembly control points are paired to determine the second candidate matching point set of the steel beam; The first candidate matching point set and the second candidate matching point set are filtered respectively to determine the steel column assembly control point pair and the steel beam assembly control point pair.

[0006] In the above scheme, determining the set of assembly control points for the steel structure includes: Obtain the first sub-assembly control point set corresponding to each of multiple different parts of the steel column, and determine the first assembly control point set of the steel column based on the first sub-assembly control point set; Obtain the sets of second sub-assembly control points corresponding to multiple different parts of the steel beam, and determine the set of second assembly control points of the steel beam based on the sets of second sub-assembly control points; Based on the first set of assembly control points for the steel columns and the second set of assembly control points for the steel beams, the set of assembly control points for the steel structure is determined.

[0007] In the above scheme, obtaining the set of first sub-assembly control points corresponding to multiple different parts of the steel column includes: Based on the pre-acquired IFC file, the assembly control points are extracted to determine the set of design assembly control points at the top of the pre-assembled steel column bracket. Based on the pre-acquired image of the top bracket of the pre-assembled steel column, adaptive corner point detection is performed to determine the actual assembly control point set of the top bracket of the pre-assembled steel column; Using the end plane correction algorithm, the assembly control points of the steel column interface data in the acquired scan point cloud are extracted to determine the actual assembly control point set at the bottom of the pre-assembled steel column and the actual assembly control point set at the top of the assembled steel column. Based on the design assembly control point set at the top corbel end of the pre-assembled steel column, the actual assembly control point set at the top corbel end of the pre-assembled steel column, the actual assembly control point set at the bottom of the pre-assembled steel column, and the actual assembly control point set at the top of the assembled steel column, the first sub-assembly control point set corresponding to each of the multiple different parts of the steel column is determined.

[0008] In the above scheme, obtaining the set of second sub-assembly control points corresponding to multiple different parts of the steel beam includes: Based on the pre-acquired end images of the pre-assembled steel beam, the key point data of the pre-assembled steel beam are obtained by detecting the corner points using the adaptive parameter 3D Harris corner detection algorithm. The interface data of the steel beam ends in the acquired scanned point cloud is extracted to determine the interface data of the pre-assembled steel beam. Based on the key point data and the interface data, an intersection operation is performed to determine the actual assembly control point set at both ends of the pre-assembled steel beam and the actual assembly control point set at the end of the corbel on the assembled steel column connected to the pre-assembled steel beam. Based on the actual assembly control points at both ends of the pre-assembled steel beam and the actual assembly control points at the ends of the corbels on the assembled steel columns connected to the pre-assembled steel beam, the corresponding second sub-assembly control point sets for multiple different parts of the steel beam are determined.

[0009] In the above scheme, the step of pairing assembly control points based on multiple first sub-assembly control point sets corresponding to the first assembly control point set to determine the first candidate matching point set for the steel column includes: For any two sets of first sub-assembly control points in the plurality of sets of first sub-assembly control points, calculate the target distance between each point in one set of first sub-assembly control points and each point in the other set of first sub-assembly control points; By using a preset error range and the target distance, points in any two sets of first sub-assembly control points are filtered to determine the first candidate matching point set for the steel column.

[0010] In the above scheme, the step of pairing assembly control points based on multiple second sub-assembly control point sets corresponding to the second assembly control point set to determine the second candidate matching point set of the steel beam includes: For any two sets of second sub-assembly control points in the plurality of sets of second sub-assembly control points, calculate the target distance between each point in one set of second sub-assembly control points and each point in the other set of second sub-assembly control points; By using a preset error range and the target distance, points in any two sets of second sub-assembly control points are filtered to determine the second candidate matching point set for the steel column.

[0011] In the above scheme, the step of calculating the optimal attitude assembly deviation of the steel structure based on the assembly control point pair and using a pre-determined optimal attitude assembly deviation equation includes: Substitute the steel column assembly control point pair and the steel beam assembly control point pair into the optimal attitude assembly deviation equation for solving to obtain the optimal rotation matrix and translation matrix. The optimal rotation matrix and the optimal translation matrix are substituted into the optimal attitude assembly deviation equation for solution, and the optimal attitude assembly deviation of the steel structure is obtained.

[0012] Secondly, embodiments of this application provide a virtual pre-assembly system for steel structures, which includes: a determination module, a screening module, a calculation module, and an analysis module, wherein... The determining module is used to determine the set of assembly control points for the steel structure; wherein, the set of assembly control points includes multiple sub-sets of assembly control points for different parts. The filtering module is used to pair the assembly control points of the corresponding sub-assembly control point sets of the multiple different parts to determine the candidate matching point set of the steel structure; and to filter the candidate matching point set to determine the assembly control point pair corresponding to the steel structure; wherein, the candidate matching point set includes a first candidate matching point set and a second candidate matching point set; the assembly control point pair includes a steel column assembly control point pair and a steel beam assembly control point pair; The calculation module is used to calculate the optimal attitude assembly deviation of the steel structure based on the assembly control point pair and through a pre-determined optimal attitude assembly deviation equation. The analysis module is used to perform overall splicing error analysis and detailed splicing error analysis based on the optimal posture splicing deviation, and to determine the global error display and the local error display. The determining module is further configured to determine the virtual pre-assembly status of the steel structure based on the global error display and the local error display; The filtering module is further configured to: pair assembly control points based on multiple first sub-assembly control point sets corresponding to the first assembly control point set to determine a first candidate matching point set for the steel column; pair assembly control points based on multiple second sub-assembly control point sets corresponding to the second assembly control point set to determine a second candidate matching point set for the steel beam; and filter the first candidate matching point set and the second candidate matching point set respectively to determine the steel column assembly control point pair and the steel beam assembly control point pair.

[0013] Thirdly, embodiments of this application provide a virtual pre-assembly device for steel structures, comprising: a processor and a memory; wherein, The memory is used to store computer programs; The processor is configured to call and run the computer program from the memory to perform the method as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing executable instructions for causing a processor to perform the method described in the first aspect.

[0015] This application provides a method and system for virtual pre-assembly of steel structures. The method includes: determining a set of assembly control points for the steel structure; wherein the set of assembly control points includes multiple sets of sub-assembly control points corresponding to different parts; pairing the sub-assembly control points corresponding to the multiple sets of sub-assembly control points corresponding to different parts to determine a set of candidate matching points for the steel structure; and filtering the set of candidate matching points to determine a pair of assembly control points corresponding to the steel structure; wherein the set of candidate matching points includes a first set of candidate matching points and a second set of candidate matching points; the pair of assembly control points includes a pair of steel column assembly control points and a pair of steel beam assembly control points; calculating the optimal posture assembly deviation of the steel structure based on the pair of assembly control points using a pre-determined optimal posture assembly deviation equation; and performing overall splicing error analysis and detailed splicing error analysis based on the optimal posture assembly deviation to determine the optimal posture assembly deviation of the steel structure. The process involves defining global and local error displays, and determining the virtual pre-assembly status of the steel structure based on these displays. Specifically, it includes pairing assembly control points for the various sub-assembly control point sets corresponding to different parts to determine a candidate matching point set for the steel structure; and filtering the candidate matching point set to determine the corresponding assembly control point pair for the steel structure. This includes: pairing assembly control points based on multiple first sub-assembly control point sets corresponding to a first assembly control point set to determine a first candidate matching point set for the steel columns; pairing assembly control points based on multiple second sub-assembly control point sets corresponding to a second assembly control point set to determine a second candidate matching point set for the steel beams; and filtering the first and second candidate matching point sets respectively to determine the steel column assembly control point pair and the steel beam assembly control point pair. In the above scheme, a set of assembly control points for the steel structure is determined, comprising multiple sets of sub-assembly control points corresponding to different parts. These sets are then paired to determine a candidate matching point set for the steel structure, eliminating the influence of error points. The candidate matching point set is then filtered to determine the corresponding assembly control point pairs for the steel structure. Based on these assembly control point pairs, the optimal posture assembly deviation of the steel structure is calculated using a pre-determined optimal posture assembly deviation equation. This allows for prediction of the assembly state of the steel components before formal hoisting, identification of issues such as interface misalignment, abnormal gaps, and component conflicts, and determination of the optimal assembly posture of the steel components. Based on the optimal posture assembly deviation, overall splicing error analysis and detailed splicing error analysis are performed, resulting in global and local error displays. Finally, based on these global and local error displays, a virtual pre-assembly state of the steel structure is determined, and construction is carried out according to this virtual pre-assembly state, thereby improving the construction efficiency and installation quality of the steel structure. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the technical solutions of this application. Obviously, the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0018] Figure 1 A schematic flowchart of an optional method for virtual pre-assembly of steel structures provided in this application embodiment; Figure 2 This application provides a schematic diagram illustrating the extraction of assembly control points for a virtual pre-assembly method of steel structures, based on design information. Figure 3 A schematic diagram of the interface data extraction of the scanning point cloud of a steel structure virtual pre-assembly method provided in this application embodiment; Figure 4 This is a schematic diagram illustrating the automated extraction of assembly control points in a virtual pre-assembly method for steel structures, provided in an embodiment of this application. Figure 5 This application provides a schematic diagram of a steel structure assembly method for a virtual pre-assembly of steel structures. Figure 6 This is a schematic diagram of the local coordinate system for assembling steel beams in a virtual pre-assembly method for steel structures provided in an embodiment of this application. Figure 7 An example diagram of the assembly spacing for a virtual pre-assembly method for steel structures provided in this application embodiment; Figure 8 This application provides a schematic diagram of a steel structure virtual pre-assembly system. Figure 9 This is a structural schematic diagram of a steel structure virtual pre-assembly equipment provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of this application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] In the following description, references to "some embodiments," "this embodiment," "this application embodiment," and examples, etc., describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subset of all possible embodiments and may be combined with each other without conflict.

[0022] If the application documents contain similar descriptions such as "first / second", the following explanation shall be added: In the following description, the terms "first / second / third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0023] This application provides a method for virtual pre-assembly of steel structures. Figure 1 This is an optional flowchart illustrating a virtual pre-assembly method for steel structures provided in an embodiment of this application, which will be combined with... Figure 1 The steps shown are explained.

[0024] S101. Determine the set of assembly control points for the steel structure.

[0025] In some embodiments of this application, the assembly control point set includes a first assembly control point set for steel columns and a second assembly control point set for steel beams. The required assembly control point locations for steel columns and steel beams differ during pre-assembly. The first assembly control point set for steel columns is mainly obtained by extracting assembly control points from pre-acquired IFC files, images of the top corbels of pre-assembled steel columns, and acquired scanned point clouds; the second assembly control point set for steel beams is mainly obtained by extracting assembly control points from interface data of pre-acquired pre-assembled steel beam end images and the steel beam ends in the acquired scanned point clouds.

[0026] Industry Foundation Classes (IFC) is an open data exchange standard developed by the buildingSMART organization, widely used in the architecture, engineering, and construction fields. It features cross-platform compatibility, rich information carrying capacity, and complete semantic representation, aiming to promote data interoperability between different BIM software and platforms, and support information sharing and collaboration throughout the entire lifecycle of building engineering projects. The IFC standard uses the EXPRESS language to define the entities of building components and their relationships, and its schema structure exhibits typical hierarchical characteristics. For steel structure models, commonly used entities include IfcBeam, IfcColumn, and IfcMember, used to describe primary and secondary load-bearing or supporting components; in complex scenarios, IfcElementAssembly or other composite objects may also appear.

[0027] In some embodiments of this application, the virtual pre-assembly method for steel structures is applicable to virtual pre-assembly devices for steel structures.

[0028] In some embodiments of this application, S101 can be implemented by S1011-S1013, as follows: S1011. Obtain the set of first sub-assembly control points corresponding to each of the multiple different parts of the steel column, and determine the set of first assembly control points of the steel column based on the set of first sub-assembly control points.

[0029] In some embodiments of this application, based on the pre-acquired IFC file, assembly control points are extracted to determine the set of designed assembly control points for the top corbel of the pre-assembled steel column; based on the pre-acquired image of the top corbel of the pre-assembled steel column, adaptive corner detection is performed to determine the set of actual assembly control points for the top corbel of the pre-assembled steel column; through an end plane correction algorithm, assembly control points are extracted from the interface data of the steel column in the acquired scanned point cloud to determine the set of actual assembly control points for the bottom of the pre-assembled steel column and the set of actual assembly control points for the top of the assembled steel column; based on the set of designed assembly control points for the top corbel of the pre-assembled steel column, the set of actual assembly control points for the top corbel of the pre-assembled steel column, the set of actual assembly control points for the bottom of the pre-assembled steel column, and the set of actual assembly control points for the top of the assembled steel column, the first sub-assembly control point sets corresponding to each of multiple different parts of the steel column are determined.

[0030] S1012. Obtain the set of second sub-assembly control points corresponding to each of the multiple different parts of the steel beam, and determine the set of second assembly control points of the steel beam based on the set of second sub-assembly control points.

[0031] In some embodiments of this application, based on pre-acquired images of the pre-assembled steel beam ends, key point data of the pre-assembled steel beam are obtained by using an adaptive parameter 3D Harris corner detection algorithm; interface data of the steel beam ends in the acquired scanned point cloud is extracted to determine the interface data of the pre-assembled steel beam; based on the key point data and interface data, an intersection operation is performed to determine the actual assembly control point set at both ends of the pre-assembled steel beam and the actual assembly control point set at the end of the corbel on the assembled steel column connected to the pre-assembled steel beam; based on the actual assembly control points at both ends of the pre-assembled steel beam and the actual assembly control point set at the end of the corbel on the assembled steel column connected to the pre-assembled steel beam, the corresponding second sub-assembly control point sets for multiple different parts of the steel beam are determined.

[0032] S1013. Based on the first set of assembly control points for steel columns and the second set of assembly control points for steel beams, determine the set of assembly control points for the steel structure.

[0033] For example, 1. Extraction of control points for pre-assembly of steel columns.

[0034] The data required for the pre-assembly calculation of the steel column are: (1) the set of design assembly control points at the end of the corbel at the top of the pre-assembly steel column; (2) the set of actual assembly control points at the end of the corbel at the top of the pre-assembly steel column; (3) the set of actual assembly control points at the bottom of the pre-assembly steel column; and (4) the set of actual assembly control points at the top of the assembled steel column.

[0035] 2. Extraction of design control points for steel column assembly brackets.

[0036] For the extraction of the design assembly control points at the end of the corbel at the top of the pre-assembled steel column, extract them directly from the IFC file. Figure 2 The assembly control points are shown.

[0037] 3. Extraction of actual control points for steel column assembly brackets.

[0038] An adaptive corner detection method is used to extract the actual assembly control points at the ends of the corbels at the top of pre-assembled steel columns. Traditional 3D Harris corner detection algorithms rely on manually setting multiple parameters, such as the radius (R) for normal vector estimation and the neighborhood search radius (RS) for keypoint estimation. Parameters that are too large can lead to missed detection of true corners, while parameters that are too small may incorrectly identify non-corner points as corners. To address this issue, an adaptive parameter 3D Harris corner detection algorithm is proposed. This method first performs corner detection based on initial parameters and removes corners with overlapping positions to avoid duplicate detection. Then, by comparing the number of assembly control points in the extracted IFC, it is determined whether to adjust the parameters. When the number of corners is lower than the design number of control points, the parameter value is decreased; conversely, the parameter value is increased. The step size for parameter changes is set to 0.1 to improve computational efficiency while ensuring optimized performance.

[0039] 4. Extract the interface control points at the ends of the steel columns.

[0040] To extract the actual assembly control point sets at the bottom of pre-assembled steel columns and the actual assembly control point sets at the connection ends of assembled steel columns, an End-Plane Correction and Optimization (EPCO) algorithm is proposed to achieve accurate segmentation of the steel column joints. The algorithm implementation process is as follows: Figure 3 As shown.

[0041] First, the centroid and principal axis directions of the point cloud are calculated by principal component analysis (PCA), where the centroid of the point cloud is calculated according to equation (1) and the principal axis directions are calculated according to equation (2).

[0042] (1) Where c is the centroid of the point cloud; p i Let be the i-th point in the point cloud; N is the number of points in the point cloud.

[0043]

[0044] in, is the unit vector along the principal axis of the point cloud; Let i be the i-th point in the point cloud; The number of points in the point cloud; The point cloud centroid obtained by equation (1); superscript Indicates matrix transpose; Indicates that in the satisfied Given that =1, find the unit vector that maximizes the variance of the projection of the point cloud along that direction.

[0045] Based on the calculation results, a plane perpendicular to the principal axis is generated, such as... Figure 3 As shown in (a) of the diagram.

[0046] Next, the projection of the point cloud along the principal axis onto the plane is calculated, and the plane is divided into 8 equal regions centered on the centroid. For each region, the maximum value of the point cloud projection onto the plane is calculated, such as... Figure 3 As shown in (b) of the diagram.

[0047] Then, calculate the maximum projection difference between any two relative regions and find the pair of regions with the largest difference. Based on the maximum projection difference, define this pair of regions as "adjustment regions," and the point with the maximum projection distance within the adjustment region is called the adjustment point, such as... Figure 3 As shown in (c) in the figure.

[0048] After obtaining the adjustment point pair, connect the adjustment points to form line segment L1, and project line segment L1 onto the plane to obtain line segment L2. Calculate the angle between line segments L1 and L2, and rotate the plane based on this angle so that L2 is parallel to L1. This rotation operation can effectively optimize the orientation of the plane, such as... Figure 3 As shown in (d) in the figure.

[0049] Next, repeat the above steps until the maximum projection difference between the two relative regions is less than 1 mm. At this point, the plane reaches an optimized state, as shown below. Figure 3 As shown in (e) in the diagram. Through continuous adjustments, the plane will tend towards the optimal state, thereby improving the geometric fitting accuracy of the point cloud.

[0050] After planar optimization, the plane is further moved along the direction of the optimized plane's normal vector until it reaches the end of the point cloud. Specifically, the optimized plane will gradually move along the principal axis until no point cloud data is located on one side of the plane, indicating that the end of the point cloud has been reached. Figure 3 As shown in (f), this process ensures accurate positioning of the top part of the point cloud, providing precise geometric data for subsequent analysis and processing.

[0051] Through the above steps, precise optimization and end positioning of the point cloud data plane were achieved, thereby completing the extraction of interface data at the end of the steel column.

[0052] 5. Extraction of control points for pre-assembly of steel beams.

[0053] The data required for the pre-assembly calculation of the steel beam are: (1) the actual assembly control point set at both ends of the pre-assembly steel beam; (2) the actual assembly control point set at the end of the corbel on the assembled steel column connected to the pre-assembly steel beam.

[0054] For the extraction of control points at the ends of the steel beams, a strategy was adopted to first extract key point data and interface data, and then find their intersection. First, an adaptive parametric 3D Harris corner detection algorithm was applied to detect key points on the pre-assembled steel beams. Then, the EPCO algorithm was applied to extract the interface data at the ends of the steel beams. Next, the intersection of the key point data and the interface data was calculated, such as... Figure 4 As shown, the assembly control points at the interface of the steel beam end are obtained.

[0055] S102. For the sub-assembly control point sets corresponding to multiple different parts, perform assembly control point pairing to determine the candidate matching point set of the steel structure; and filter the candidate matching point set to determine the corresponding assembly control point pair of the steel structure.

[0056] In some embodiments of this application, the candidate matching point set includes a first candidate matching point set and a second candidate matching point set; the assembly control point pair includes a steel column assembly control point pair and a steel beam assembly control point pair.

[0057] In some embodiments of this application, S102 can be implemented by S1021-S1023, as follows: S1021. Based on the multiple first sub-assembly control point sets corresponding to the first assembly control point set, perform assembly control point pairing to determine the first candidate matching point set for the steel column.

[0058] In some embodiments of this application, for any two first sub-assembly control point sets in a plurality of first sub-assembly control point sets, the target distance between each point in one of the first sub-assembly control point sets and each point in the other first sub-assembly control point set is calculated; by using a preset error range and target distance, the points in any two first sub-assembly control point sets are filtered to determine the first candidate matching point set for the steel column.

[0059] S1022. Based on the multiple sets of second sub-assembly control points corresponding to the second assembly control point set, perform assembly control point pairing to determine the second candidate matching point set of the steel beam.

[0060] In some embodiments of this application, for any two sets of second sub-assembly control points from a plurality of second sub-assembly control point sets, the target distance between each point in one set of second sub-assembly control points and each point in the other set of second sub-assembly control points is calculated; by using a preset error range and the target distance between each point in one set of second sub-assembly control points and each point in the other set of second sub-assembly control points, the points in any two sets of second sub-assembly control points are filtered to determine the second candidate matching point set for the steel column.

[0061] S1023. Filter the first candidate matching point set and the second candidate matching point set respectively to determine the steel column assembly control point pair and the steel beam assembly control point pair.

[0062] For example, during the calculation of assembly control points, the resulting set of control points cannot be directly input into the governing equations for calculation. Therefore, a super four-point fast robust matching algorithm is used to calculate the assembly control point pairs. This algorithm efficiently matches assembly control point pairs by utilizing four reference points to determine geometric relationships and combining the principles of error tolerance and geometric invariance.

[0063] First, assume there are two sets of assembly control points M and N to be matched, where, and Let M and N represent the sets of assembly control points, respectively. M and N can be any two sets of first-sub-assembly control points from multiple sets of first-sub-assembly control points, or any two sets of second-sub-assembly control points from multiple sets of second-sub-assembly control points. These points represent target control points with known locations in space. For matching, four coplanar and non-collinear points need to be selected from set M as base points p1, p2, p3, and p4. These four points determine a series of geometric features, such as point spacing, scale factor, and included angle. These features will play a crucial role in subsequent calculations.

[0064] Next, for each point N in N j Calculate its distance to each point in M, and filter out those with distances to N. j Distance within the preset error range Points within M and N are selected and included in the candidate matching point set Pmatch. This process effectively filters candidate matching points from M and N, ensuring that the geometric relationships of the candidate points are sufficiently close, thus laying the foundation for subsequent geometric transformations.

[0065] After selecting candidate matching points, based on the principle of affine invariance, the coordinates C of the intersection point are determined using the selected set of points and the pairs of projected points. i and C j , , and represent the projected coordinates of the matching points in M ​​and N, respectively. Due to affine invariance, the intersection coordinates remain stable, ensuring the geometric consistency of the matching point pairs even with scale transformations or rotational differences.

[0066] After calculating the intersection coordinates, the candidate point set is further filtered. First, by calculating the coordinates of each point pair p... i and p j The angle between Filter out those angles that are the same as the given value. Approximately equal pairs of points. Next, check the scale consistency between the point sets, calculate the scale factor in the base points, and select those with a scale error less than a threshold. The point pairs. These filtering criteria ensure that the geometry and spatial relationships of the matching point set remain consistent with the base point.

[0067] Finally, based on the above screening criteria, a pair of assembly control points (i.e., steel column assembly control point pairs or steel beam assembly control point pairs) can be determined from the candidate matching point set Pmatch. Each pair of assembly control points (p i , p j This will correspond to the actual location in space.

[0068] S103. Based on the assembly control point pair, calculate the optimal attitude assembly deviation of the steel structure through the predetermined optimal attitude assembly deviation equation.

[0069] In some embodiments of this application, after the extraction and matching of assembly control points are completed, it is also necessary to establish the optimal posture assembly deviation equation for steel components by combining the actual construction sequence of the steel structure and the assembly constraint relationship of steel beams and steel columns.

[0070] For example, steel structure construction typically follows a cyclical installation sequence of "column-beam-column". First, based on design drawings and total station measurements, the steel columns are positioned in three dimensions and their orientation adjusted. After adjacent steel columns are installed, the span steel beams are assembled, and high-strength bolts and welding are used to form a frame unit with a certain rigidity. Only after the steel beams of this layer are installed can the upper-layer steel columns be hoisted. This construction sequence helps to gradually form a stable spatial structure and reduces the cumulative effect of installation deviations layer by layer. Therefore, virtual pre-assembly should also follow the principle of "from bottom to top, expanding layer by layer" to simulate the spatial connection relationship of steel components in actual construction.

[0071] For steel columns and beams commonly used in steel structure construction, an optimal posture assembly deviation equation is established based on their different connection methods and end constraint relationships to determine the optimal spatial posture of the components to be assembled. To ensure that the measured control points of the components to be assembled are aligned as closely as possible with the design control points or the target interface control points of already installed components, a rigid body transformation model is adopted. (3) Where A is the target matrix; B is the reference matrix; R is the rotation matrix; T is the translation matrix; and E is the assembly deviation matrix.

[0072] Generalized Procrustes Analysis (GPA) is a method for spatial alignment of multiple sets of point sets or shape data. Its basic principle is to minimize the cumulative deviation between corresponding points by solving for the optimal rotation and translation transformations. In the virtual pre-assembly of steel beams and columns, the reference matrix B represents the coordinates of the target control points that the component should achieve, including design control points extracted from the BIM model and measured control points at the interfaces of already installed components; the target matrix A represents the coordinates of the actual control points of the component to be assembled, obtained through on-site measurement or 3D scanning. Through GPA, the optimal rotation and translation parameters required to align the component to be assembled from its actual position to its target position can be determined.

[0073] For steel components, it is necessary to ensure that the total assembly error at both ends of the component is minimized. Therefore, the optimal posture assembly deviation equation of the steel component is obtained as shown in equation (4). The set of control points at both ends of the steel component required for calculation is shown in equations (5), (6), (7) and (8).

[0074] (4) (5) (6) (7) (8) in, , These are the sets of actual control points at the first and second ends of the component to be assembled, respectively. , These are the sets of target control points corresponding to the first and second ends, respectively. and Represent the first set of actual control points and the first set of target control points, respectively. Three-dimensional control points, =1,2,...,n; and Represent the first and second sets of actual control points and target control points, respectively. Three-dimensional control points, =1,2,...,m; n and m are the number of control points at the first and second ends, respectively; superscript This indicates the matrix transpose.

[0075] To calculate the assembly deviation of the steel component in the optimal assembly posture, it is necessary to calculate the rotation matrix R and translation matrix T when the assembly deviation is minimized, as shown in equation (9). After substituting the rotation matrix and translation matrix into the optimal assembly deviation equation of the steel component, the residual deviation of each assembly control point in the optimal posture can be obtained.

[0076] (9) in, This represents the parameter value that minimizes the objective function. Therefore, Represents all possible rotation matrices Translation matrix In this process, we seek the optimal rotation and translation matrices that minimize the overall deviation of the assembly control points at both ends of the steel component.

[0077] like Figure 5As shown in (a), the orange steel column needs to be aligned with the welded joints or base of the blue assembled steel column and the top design reference during hoisting. Therefore, the optimal posture of the steel column is defined as the minimum comprehensive deviation between the control points at the top and bottom ends of the steel column and the design reference value. In actual construction, if the top and bottom ends match the design value well, the overall tilt of the steel column can be effectively avoided, ensuring the bearing capacity and subsequent connection accuracy. The data required in the pre-assembly calculation of the steel column are: (1) the actual assembly control point set of the top bracket end of the pre-assembled steel column, i.e., A1; (2) the design assembly control point set of the top bracket end of the pre-assembled steel column, i.e., B1; (3) the actual assembly control point set of the bottom end of the pre-assembled steel column, i.e., A2; (4) the actual assembly control point set of the connection end of the assembled steel column, i.e., B2.

[0078] like Figure 5 As shown in (b), the orange pre-assembled steel beam needs to be hoisted to the two pre-assembled steel beams at the construction site and smoothly connected to the interface. The optimal posture can be understood as: when the steel beam undergoes rigid body transformation in three-dimensional space, the comprehensive deviation between its left and right ends and the interface position of the installed steel beam is minimized. Such "comprehensive deviation" is usually measured by the sum of the distances between the key control points at the end of the steel beam and the assembly control points of the corbel. If the end interface matching degree is high, the weld misalignment error can be reduced, and the problem of uneven local stress can be reduced. The data required in the pre-assembly calculation of the steel beam are: (1) the actual assembly control point set at both ends of the pre-assembled steel beam, namely A1 and A2; (2) the actual assembly control point set at the end of the corbel on the pre-assembled steel column connected to the pre-assembled steel beam, namely B1 and B2.

[0079] In some embodiments of this application, the steel column assembly control point pair and the steel beam assembly control point pair in the assembly control point pair are substituted into the optimal attitude assembly deviation equation for solution to obtain the optimal rotation matrix and translation matrix; the optimal rotation matrix and translation matrix are substituted into the optimal attitude assembly deviation equation for solution to obtain the optimal attitude assembly deviation of the steel structure.

[0080] For example, during assembly, the error first needs to be calculated by optimizing the assembly posture. By substituting the coordinates of the assembly control points into the optimal posture assembly deviation equation, the optimal assembly posture of the steel component and its residual assembly deviation can be obtained. The calculation of the optimal posture assembly deviation consists of two main steps: solving for the optimal rotation matrix R and translation matrix T, and substituting these matrices into the error equation for calculation.

[0081] First, solving for the translation matrix T is the starting step of the entire process. Based on equation (9), equation (10) can be constructed to represent the relationship between the translation matrix and the target point. By solving for the partial derivative of this expression and setting it equal to zero, the optimal solution for the translation matrix T can be obtained. The specific calculation process is shown in equation (11).

[0082] (10) (11) After solving for the translation matrix, we proceed to the stage of solving for the rotation matrix R. At this point, substituting the already obtained T into equation (9), we obtain equation (12): (12) Furthermore, a suitable matrix M is constructed, as shown in equation (13), with the aim of aligning the original control point set to the target position. By performing singular value decomposition (SVD) on the constructed matrix, that is, decomposing it into the form of equation (14), the optimal rotation matrix R can be further solved: (13) (14) (15) in, It is a left singular vector matrix; It is a right singular vector matrix.

[0083] After solving for the translation and rotation matrices, the next step is to calculate the deviation. Substituting the optimal rotation matrix R and the optimal translation matrix T into the error equations at both ends of the component (Equations (16) and (17)), the assembly deviation at each control point can be obtained. The specific calculation formulas are shown in Equations (18) and (19). The assembly deviation can effectively reflect the deviation of the steel component in the pre-assembly stage under the optimal posture: (16) (17) (18) (19) in, and These are the assembly deviation matrices for the first and second ends of the component to be assembled, respectively. Indicates the first end of the component to be assembled. Three-dimensional assembly deviation of each control point =1,2,...,n; Indicates the second end of the component to be assembled. Three-dimensional assembly deviation of each control point =1,2...,m.

[0084] For steel columns, the calculated assembly deviations can be used to evaluate the misalignment at the interface between the pre-assembled steel column and the installed steel column. Furthermore, for steel columns, the assembly deviations in the global coordinate system will be projected into the local coordinate system of each component (see...). Figure 6 By calculating these deviations, gaps or conflicts that may occur during the assembly process can be identified.

[0085] Finally, the projection of the assembly deviation can be realized by formulas (Equation (20) - Equation (23)) to further analyze and optimize the assembly process.

[0086] (20) (twenty one) (twenty two) (twenty three) in, and These are the assembly deviation matrices of the first and second ends of the component to be assembled in the local coordinate system, respectively. and These are the rotation and translation matrices for transforming the global coordinate system to the component's local coordinate system, respectively. Indicates the first end of the component to be assembled. The three-dimensional assembly deviation of each control point in the local coordinate system =1,2,...,n; Indicates the second end of the component to be assembled. The three-dimensional assembly deviation of each control point in the local coordinate system =1,2...,m.

[0087] Through the above steps, not only can the translation and rotation matrices be accurately solved, but potential problems in the assembly process can also be identified through deviation calculation and projection analysis.

[0088] S104. Based on the optimal posture assembly deviation, perform overall splicing error analysis and detailed splicing error analysis respectively to determine the global error display and local error display; and determine the virtual pre-assembly status of the steel structure based on the global error display and local error display.

[0089] For example, the splicing accuracy of steel structures directly affects the stability and safety of the overall structure. To intuitively identify deviations in steel components during virtual pre-assembly, this paper proposes a visualization method for splicing deviations that combines global and local scales. The global scale displays the overall assembly status of the structure, while the local scale quantifies and analyzes specific splicing deviations for different connection forms of steel beams and columns. To effectively identify and correct splicing deviations, a visualization method based on global and local scales is proposed. This method analyzes the overall splicing error of the steel structure at the global scale, while at the local scale, it meticulously distinguishes and analyzes the splicing deviations of steel beams and columns.

[0090] At a global scale, a 3D model displays the relative positions of each steel component in its optimal orientation, and color coding reflects their assembly status. Green indicates assembly deviations within acceptable limits and no component conflicts; red indicates deviations exceeding allowable limits or component conflicts. This method can quickly locate components with assembly problems within the overall structure and their locations, providing a clear basis for adjusting construction plans.

[0091] The analysis of local-scale assembly deviations is more detailed, especially for different types of steel components, for which different deviation calculation methods are adopted. For example... Figure 7 As shown, this section mainly includes two aspects: steel beam assembly deviation and steel column assembly deviation.

[0092] The assembly deviation of steel beams mainly reflects the spacing and conflict between the steel beam ends and the steel column brackets. First, the deviation between the control points at the steel beam ends and the bracket control points is projected onto the Z-axis direction of the component's local coordinate system, and the assembly spacing is calculated accordingly. For example... Figure 7 As shown in (a), when the assembly spacing is greater than 0, it indicates that there is no geometric conflict between the steel beam and the corbel, and the components can be assembled normally; when the assembly spacing is less than 0, it indicates that the two overlap geometrically, resulting in an assembly conflict, and adjustments need to be made to the steel beam's posture or connection points. This method can accurately identify abnormal gaps and misalignments between the steel beam end and the corbel.

[0093] The assembly deviation of steel columns focuses on the misalignment of the steel column joints. The misalignment of the steel column joints is obtained by calculating the maximum deviation between the control points of the pre-assembled steel column and the connecting steel column. According to the standard GB 50205-2020 "Standard for Acceptance of Construction Quality of Steel Structures", the allowable deviation for misalignment of steel component joints is 2 mm. Figure 7 As shown in (b), when the maximum misalignment does not exceed the allowable value, the assembly is considered normal; when it exceeds the allowable value, the assembly is considered abnormal, and the construction personnel are prompted to make adjustments. This method can identify misalignment problems at the joints before the steel column is officially installed, reducing the risk of rework on site.

[0094] The system will combine a global view with magnified local views to provide a layered display of the steel structure assembly status. The global view is used to quickly locate abnormal components, while the local view displays the specific location and value of deviations. A graded color coding system can be used based on the magnitude of the deviation: green indicates normal assembly, yellow indicates the deviation is close to the allowable limit and requires close monitoring, and red indicates the deviation exceeds the limit or there is a serious assembly conflict.

[0095] Understandably, determining the assembly control point set of a steel structure, which includes multiple sets of sub-assembly control points corresponding to different parts, and then pairing these sub-assembly control point sets to determine a candidate matching point set for the steel structure, can eliminate the influence of error points. Filtering the candidate matching point set determines the corresponding assembly control point pairs for the steel structure. Based on these assembly control point pairs, the optimal posture assembly deviation of the steel structure is calculated using a pre-determined optimal posture assembly deviation equation. This enables the prediction of the assembly state of steel components before formal hoisting, as well as the identification of problems such as interface misalignment, abnormal gaps, and component conflicts. The optimal assembly posture of the steel components is also determined. Based on the optimal posture assembly deviation, overall splicing error analysis and detailed splicing error analysis are performed, determining global and local error displays. Finally, based on the global and local error displays, the virtual pre-assembly state of the steel structure is determined, and construction is carried out according to this virtual pre-assembly state, thereby improving the construction efficiency and installation quality of the steel structure.

[0096] The beneficial effects of this application are: (1) Human resources.

[0097] The virtual pre-assembly of steel structures achieved by this method can significantly reduce the labor costs and technical difficulties of physical assembly.

[0098] (2) Data records.

[0099] Data-driven virtual pre-assembly of steel structures can calculate and record the pre-assembly status of each steel component, making it easier for engineers to adjust construction plans based on the pre-assembly data.

[0100] Based on the steel structure virtual pre-assembly method described in the above embodiments, this application also provides a steel structure virtual pre-assembly system, such as... Figure 8 As shown, Figure 8 This is a structural schematic diagram of a virtual pre-assembly system for steel structures provided in an embodiment of this application. The virtual pre-assembly system 8 includes: a determination module 801, a screening module 802, a calculation module 803, and an analysis module 804, wherein... The determining module 801 is used to determine the assembly control point set of the steel structure; wherein, the assembly control point set includes multiple sub-assembly control point sets corresponding to different parts; The filtering module 802 is used to pair the assembly control points of the corresponding sub-assembly control point sets of the multiple different parts to determine the candidate matching point set of the steel structure; and to filter the candidate matching point set to determine the assembly control point pair corresponding to the steel structure; wherein, the candidate matching point set includes a first candidate matching point set and a second candidate matching point set; the assembly control point pair includes a steel column assembly control point pair and a steel beam assembly control point pair; The calculation module 803 is used to calculate the optimal posture assembly deviation of the steel structure based on the assembly control point pair and through a pre-determined optimal posture assembly deviation equation. The analysis module 804 is used to perform overall splicing error analysis and detailed splicing error analysis based on the optimal posture splicing deviation, and to determine the global error display and the local error display. The determining module 801 is further configured to determine the virtual pre-assembly status of the steel structure based on the global error display and the local error display; The filtering module 802 is further configured to perform assembly control point pairing based on multiple first sub-assembly control point sets corresponding to the first assembly control point set, to determine the first candidate matching point set of the steel column; perform assembly control point pairing based on multiple second sub-assembly control point sets corresponding to the second assembly control point set, to determine the second candidate matching point set of the steel beam; and filter the first candidate matching point set and the second candidate matching point set respectively to determine the steel column assembly control point pair and the steel beam assembly control point pair.

[0101] Based on the steel structure virtual pre-assembly method described in the above embodiments, this application also provides a steel structure virtual pre-assembly equipment, such as... Figure 9 As shown, Figure 9 This is a schematic diagram of a virtual pre-assembly device for steel structures provided in an embodiment of this application. The virtual pre-assembly device 9 includes a processor 901 and a memory 902. The memory 902 is used to store computer programs; the processor 901 is used to call and run the computer programs from the memory to execute the virtual pre-assembly method for steel structures as described in the above embodiment.

[0102] In the embodiments of this application, the processor 901 described above can be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that for different devices, the electronic device used to implement the above processor function can also be other types, and the embodiments of this application do not specifically limit it.

[0103] This application provides a computer-readable storage medium storing a computer program for implementing the steel structure virtual pre-assembly method as described in any of the above embodiments when executed by a processor.

[0104] For example, the program instructions corresponding to a virtual pre-assembly method for steel structures in this embodiment can be stored on storage media such as optical discs, hard disks, and USB flash drives. When the program instructions corresponding to a virtual pre-assembly method for steel structures in the storage media are read or executed by an electronic device, the virtual pre-assembly method for steel structures as described in any of the above embodiments can be implemented.

[0105] Furthermore, in the embodiments of this application, the functional modules can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional module.

[0106] If the integrated unit is implemented as a software functional module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the method of this embodiment. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" mentioned throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments; their similarities or commonalities can be referred to mutually, and for the sake of brevity, they will not be repeated here.

[0108] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0109] In addition, each functional module in the various embodiments of this application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the integrated modules can be implemented in hardware or in the form of hardware plus software functional units.

[0110] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0111] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0112] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0113] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0114] The above description is merely an embodiment of this application, but the protection scope of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for virtual pre-assembly of steel structures, characterized in that, The method includes: Determine the set of assembly control points for the steel structure; wherein, the set of assembly control points includes multiple sub-sets of assembly control points corresponding to different parts; For the sub-assembly control point sets corresponding to the multiple different parts, the assembly control points are paired to determine the candidate matching point set of the steel structure; and the candidate matching point set is filtered to determine the assembly control point pair corresponding to the steel structure; wherein, the candidate matching point set includes a first candidate matching point set and a second candidate matching point set; the assembly control point pair includes a steel column assembly control point pair and a steel beam assembly control point pair; Based on the assembly control point pair, the optimal attitude assembly deviation of the steel structure is calculated by using a pre-determined optimal attitude assembly deviation equation; Based on the optimal posture assembly deviation, overall assembly error analysis and detailed assembly error analysis are performed respectively to determine the global error display and the local error display; and based on the global error display and the local error display, the virtual pre-assembly status of the steel structure is determined. Specifically, the process of pairing assembly control points for the respective sub-assembly control point sets of the multiple different parts to determine a candidate matching point set for the steel structure, and then filtering the candidate matching point set to determine the corresponding assembly control point pairs for the steel structure, includes: Based on the multiple first sub-assembly control point sets corresponding to the first assembly control point set, the assembly control points are paired to determine the first candidate matching point set of the steel column. Based on the multiple second sub-assembly control point sets corresponding to the second assembly control point set, the assembly control points are paired to determine the second candidate matching point set of the steel beam; The first candidate matching point set and the second candidate matching point set are filtered respectively to determine the steel column assembly control point pair and the steel beam assembly control point pair.

2. The method according to claim 1, characterized in that, The set of assembly control points for the steel structure includes: Obtain the first sub-assembly control point set corresponding to each of multiple different parts of the steel column, and determine the first assembly control point set of the steel column based on the first sub-assembly control point set; Obtain the sets of second sub-assembly control points corresponding to multiple different parts of the steel beam, and determine the set of second assembly control points of the steel beam based on the sets of second sub-assembly control points; Based on the first set of assembly control points for the steel columns and the second set of assembly control points for the steel beams, the set of assembly control points for the steel structure is determined.

3. The method according to claim 2, characterized in that, The process of obtaining the set of first sub-assembly control points corresponding to multiple different parts of the steel column includes: Based on the pre-acquired IFC file, the assembly control points are extracted to determine the set of design assembly control points at the top of the pre-assembled steel column bracket. Based on the pre-acquired image of the top bracket of the pre-assembled steel column, adaptive corner point detection is performed to determine the actual assembly control point set of the top bracket of the pre-assembled steel column; Using the end plane correction algorithm, the assembly control points of the steel column interface data in the acquired scan point cloud are extracted to determine the actual assembly control point set at the bottom of the pre-assembled steel column and the actual assembly control point set at the top of the assembled steel column. Based on the design assembly control point set at the top corbel end of the pre-assembled steel column, the actual assembly control point set at the top corbel end of the pre-assembled steel column, the actual assembly control point set at the bottom of the pre-assembled steel column, and the actual assembly control point set at the top of the assembled steel column, the first sub-assembly control point set corresponding to each of the multiple different parts of the steel column is determined.

4. The method according to claim 2, characterized in that, The acquisition of the set of second sub-assembly control points corresponding to multiple different parts of the steel beam includes: Based on the pre-acquired end images of the pre-assembled steel beam, the key point data of the pre-assembled steel beam are obtained by detecting the corner points using the adaptive parameter 3D Harris corner detection algorithm. The interface data of the steel beam ends in the acquired scanned point cloud is extracted to determine the interface data of the pre-assembled steel beam. Based on the key point data and the interface data, an intersection operation is performed to determine the actual assembly control point set at both ends of the pre-assembled steel beam and the actual assembly control point set at the end of the corbel on the assembled steel column connected to the pre-assembled steel beam. Based on the actual assembly control points at both ends of the pre-assembled steel beam and the actual assembly control points at the ends of the corbels on the assembled steel columns connected to the pre-assembled steel beam, the corresponding second sub-assembly control point sets for multiple different parts of the steel beam are determined.

5. The method according to claim 1, characterized in that, The step of pairing assembly control points based on multiple first sub-assembly control point sets corresponding to the first assembly control point set to determine the first candidate matching point set for the steel column includes: For any two sets of first sub-assembly control points in the plurality of sets of first sub-assembly control points, calculate the target distance between each point in one set of first sub-assembly control points and each point in the other set of first sub-assembly control points; By using a preset error range and the target distance, points in any two sets of first sub-assembly control points are filtered to determine the first candidate matching point set for the steel column.

6. The method according to claim 1, characterized in that, The process of pairing assembly control points based on multiple second sub-assembly control point sets corresponding to the second assembly control point set to determine the second candidate matching point set for the steel beam includes: For any two sets of second sub-assembly control points in the plurality of sets of second sub-assembly control points, calculate the target distance between each point in one set of second sub-assembly control points and each point in the other set of second sub-assembly control points; By using a preset error range and the target distance, points in any two sets of second sub-assembly control points are filtered to determine the second candidate matching point set for the steel column.

7. The method according to claim 1, characterized in that, The calculation of the optimal attitude assembly deviation of the steel structure based on the assembly control point pair and using a pre-determined optimal attitude assembly deviation equation includes: Substitute the steel column assembly control point pair and the steel beam assembly control point pair into the optimal attitude assembly deviation equation for solving to obtain the optimal rotation matrix and translation matrix. The optimal rotation matrix and the optimal translation matrix are substituted into the optimal attitude assembly deviation equation for solution, and the optimal attitude assembly deviation of the steel structure is obtained.

8. A virtual pre-assembly system for steel structures, characterized in that, The virtual pre-assembly system for steel structures includes: a determination module, a screening module, a calculation module, and an analysis module, wherein... The determining module is used to determine the set of assembly control points for the steel structure; wherein, the set of assembly control points includes multiple sub-sets of assembly control points for different parts. The filtering module is used to pair the assembly control points of the corresponding sub-assembly control point sets of the multiple different parts to determine the candidate matching point set of the steel structure; and to filter the candidate matching point set to determine the assembly control point pair corresponding to the steel structure; wherein, the candidate matching point set includes a first candidate matching point set and a second candidate matching point set; the assembly control point pair includes a steel column assembly control point pair and a steel beam assembly control point pair; The calculation module is used to calculate the optimal attitude assembly deviation of the steel structure based on the assembly control point pair and through a pre-determined optimal attitude assembly deviation equation. The analysis module is used to perform overall splicing error analysis and detailed splicing error analysis based on the optimal posture splicing deviation, and to determine the global error display and the local error display. The determining module is further configured to determine the virtual pre-assembly status of the steel structure based on the global error display and the local error display; The filtering module is further configured to: pair assembly control points based on multiple first sub-assembly control point sets corresponding to the first assembly control point set to determine a first candidate matching point set for the steel column; pair assembly control points based on multiple second sub-assembly control point sets corresponding to the second assembly control point set to determine a second candidate matching point set for the steel beam; and filter the first candidate matching point set and the second candidate matching point set respectively to determine the steel column assembly control point pair and the steel beam assembly control point pair.

9. A virtual pre-assembly equipment for steel structures, characterized in that, include: Processor and memory, of which, The memory is used to store computer programs; The processor is configured to call and run the computer program from the memory to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores executable instructions for causing a processor to execute, thereby implementing the method of any one of claims 1 to 7.

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