A method and system for intelligent preassembly alignment of precast concrete components

Abstract

The present application discloses a method and system for intelligent pre-assembly alignment of precast concrete components, which relates to the field of computer vision technology, including: extracting the first assembly section feature point of the precast concrete component design model and the second assembly section feature point of the precast concrete component completion model; the assembly section feature point includes the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face; based on the extended orthogonal Proctor analysis, the first deviation value between the first assembly section feature point and the second assembly section feature point is calculated; if the first deviation value is less than or equal to the preset deviation threshold, the second deviation value between the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face of the precast concrete component completion model is calculated by the extended orthogonal Proctor analysis; if the second deviation value is less than or equal to the target deviation threshold, the precast concrete component is assembled and aligned. The problem of assembly rework caused by errors in the alignment of precast concrete components is solved.

Description

Technical Field

[0001] The present invention relates to the field of computer vision technology, and in particular to a method and system for intelligent preassembly alignment of precast concrete components. Background Art

[0002] Compared with cast-in-place structures, prefabricated structures have the advantages of standardized design and production, short construction period, energy saving and environmental protection. Therefore, in recent years, prefabricated structures have been widely used and promoted in buildings and infrastructure. For prefabricated structures, some or all of the prefabricated components are prefabricated in factories. Then they are transported to the site and hoisted and assembled. Since the hoisting and assembly of prefabricated components is achieved by connecting joints on site, if the cumulative dimensional error at the joints is large, it will lead to failure of on-site installation and require rework of prefabricated components. Therefore, whether it is a steel structure or a precast concrete project, physical preassembly of the entire or part of the structure is usually required. This is used to verify the assemblability of precast components. Due to the high stiffness of precast concrete (PC) components, PC components have poor adaptability to other components at the joints during on-site installation, so physical preassembly is particularly important for precast concrete structures. However, physical preassembly requires high costs due to factors such as manpower, available preassembly space, and machinery. Therefore, in order to shorten the construction time, reduce costs and avoid occupying a large space, Virtual Trial Assembly (VTA) is widely used in the field of construction engineering as a method to simulate the assembly process of prefabricated structures in a virtual environment, gradually replacing traditional physical pre-assembly. VTA verifies the assemblability of components by studying the correspondence between the as-built model and the design model, and avoids the rework problem caused by dimensional deviation or assembly deviation of prefabricated components. Therefore, the general steps of the VTA method include geometric quality assessment of components and VTA. Among them, the geometric quality assessment of components is completed to ensure that the components are within the manufacturing error range allowed by the specification, which is the basis of VTA. Next, VTA is used to simulate the assembly of prefabricated components to evaluate the impact of the accumulation of manufacturing errors and assembly errors of prefabricated components on the assembly accuracy of components. However, VTA is still lacking in research and application in the field of construction engineering of prefabricated concrete structures. Taking the hoisting, assembly and alignment of precast concrete columns connected by sleeve grouting as an example, the physical assembly and alignment process of precast concrete columns on the construction site is divided into two steps: the first step is to evaluate the geometric quality of the assembly section according to the design specifications before assembly and alignment (mainly to check the position deviation of the reserved steel bars and the sleeve at the bottom of the precast column); the second step is to hoist and assemble the precast columns, and verify the assemblability of the precast columns through on-site assembly. From the first step of the assembly and alignment process of precast columns connected by sleeve grouting, it can be found that for the geometric quality assessment of the assembly section, the traditional method usually uses traditional manual inspection methods such as measuring rulers to check the center position of the reserved steel bars and sleeve sections according to the design specifications. This process is time-consuming and labor-intensive, and manual inspection is prone to errors. In the second step, since the manufacturing error and assembly error of the assembly section will accumulate and cause the connection of the precast components to fail, it is necessary to lift the components repeatedly and adjust the position of the steel bars multiple times to achieve the alignment of the components.If the error is large and the steel bar position is adjusted multiple times but alignment is still not achieved, the prefabricated component needs to be replaced, resulting in huge rework costs. It can be seen that the traditional method of evaluating the assemblability of components by hoisting entities for on-site assembly has the disadvantages of high labor costs and low efficiency. Therefore, how to improve the efficiency of assembly and alignment of precast concrete components and reduce costs is an urgent problem to be solved. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method and system for intelligent pre-assembly alignment of precast concrete components, which can solve the assembly rework problem caused by the cumulative error of manufacturing error and assembly deviation during the hoisting and alignment process of precast concrete components connected by sleeve grouting, improve the assembly and alignment efficiency, and reduce costs. The specific scheme is as follows:

[0004] In a first aspect, the present application discloses a method for intelligent preassembly alignment of precast concrete components, comprising:

[0005] Extracting the first assembly section feature points of the precast concrete component design model and the second assembly section feature points of the precast concrete component completion model; the assembly section feature points include the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar;

[0006] Calculating a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on an extended orthogonal Proctor analysis;

[0007] If the first deviation value is less than or equal to a preset deviation threshold, the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the completed model of the precast concrete component is calculated by the extended orthogonal Proctor analysis;

[0008] If the second deviation value is less than or equal to the target deviation threshold, the precast concrete components are assembled and aligned.

[0009] Optionally, the step of extracting the first assembly section feature point of the precast concrete component design model includes:

[0010] Converting the precast concrete component design model into a point cloud model in a point cloud data format;

[0011] The point cloud model is subjected to straight-through filtering to obtain edge point clouds of the precast concrete column bottom sleeve and the reserved steel bar end face, and the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face of the precast concrete component design model are extracted based on the edge point clouds.

[0012] Optionally, extracting the second assembly section feature points of the precast concrete component completion model includes:

[0013] A Meka-Minder structured light camera is used to vertically photograph the bottom sleeve of the precast concrete column and the end face of the reserved steel bar of the completed model of the precast concrete component to obtain the corresponding original three-dimensional point cloud;

[0014] Acquire target point clouds of the precast concrete column bottom sleeve and the reserved steel bar end surface based on the original three-dimensional point cloud and through-filtering in the structured light shooting direction;

[0015] Using a random sampling consistency algorithm, plane fitting is performed on the target point clouds of the bottom sleeve of the precast concrete column and the end face of the reserved steel bar to obtain a corresponding fitted plane;

[0016] Projecting each of the target point clouds onto the fitted plane of the precast concrete column bottom sleeve and the fitted plane of the reserved steel bar end surface to obtain a corresponding three-dimensional point cloud plane;

[0017] The three-dimensional point cloud plane is converted into a binary image, and the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face of the precast concrete component completion model in the binary image are extracted through the Hough transform circle detection algorithm.

[0018] Optionally, the calculating a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on extended orthogonal Proctor analysis includes:

[0019] Constructing a first matrix based on the first assembly cross-section feature points, and constructing a second matrix based on the second assembly cross-section feature points;

[0020] Performing coordinate transformation on the second matrix based on the first matrix to obtain a first corresponding relationship expression between the first matrix and the second matrix;

[0021] The first deviation value is determined based on the first corresponding relationship expression.

[0022] Optionally, after calculating the first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on the extended orthogonal Proctor analysis, the method further includes:

[0023] If the first deviation value is greater than the preset deviation threshold, the deviation between the first assembly section feature point and the second assembly section feature point is corrected or the precast concrete component is replaced, and the process jumps again to the step of extracting the second assembly section feature point of the precast concrete component completion model.

[0024] Optionally, the step of calculating the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the completed model of the precast concrete component by using the extended orthogonal Proctor analysis includes:

[0025] A third matrix is ​​constructed based on the center points of the reserved steel bar end faces, and a fourth matrix is ​​constructed based on the center points of the sleeves on the bottom faces of the precast concrete columns;

[0026] Performing coordinate transformation on the third matrix based on the fourth matrix to obtain a second corresponding relationship expression between the third matrix and the fourth matrix;

[0027] The second deviation value is determined based on the second corresponding relationship expression.

[0028] Optionally, after calculating the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the completed model of the precast concrete component by the extended orthogonal Proctor analysis, the method further includes:

[0029] If the second deviation value is greater than the target deviation threshold, the deviation between the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end surface is corrected, and the process jumps again to the step of extracting the second assembly section feature point of the precast concrete component completion model.

[0030] In a second aspect, the present application discloses a precast concrete component intelligent preassembly alignment system, comprising:

[0031] A feature point extraction module is used to extract the first assembly section feature points of the precast concrete component design model and the second assembly section feature points of the precast concrete component completion model; the assembly section feature points include the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar;

[0032] A first deviation value calculation module, used for calculating a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on an extended orthogonal Proctor analysis;

[0033] A second deviation value calculation module is used to calculate the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the as-built model of the precast concrete component by using the extended orthogonal Proctor analysis if the first deviation value is less than or equal to a preset deviation threshold;

[0034] An assembly alignment module is used to assemble and align the precast concrete components if the second deviation value is less than or equal to a target deviation threshold.

[0035] The present application first extracts a first assembly section feature point of a precast concrete component design model and a second assembly section feature point of a precast concrete component completion model; the assembly section feature point includes a center point of a precast concrete column bottom sleeve and a center point of a reserved steel bar end face; then, based on an extended orthogonal Proctor analysis, a first deviation value between the first assembly section feature point and the second assembly section feature point is calculated; if the first deviation value is less than or equal to a preset deviation threshold, a second deviation value between the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face corresponding to the precast concrete component completion model is calculated by the extended orthogonal Proctor analysis; if the second deviation value is less than or equal to a target deviation threshold, the precast concrete component is assembled and aligned. It can be seen that the present application automatically extracts the assembly section feature points of the model through computer vision, and then uses the extended orthogonal Proctor analysis method to align the completed model and the design model based on the assembly section feature points, and automatically calculates the horizontal deviation between the completed position and the design position of the assembly section feature points to intuitively evaluate the manufacturing error of the prefabricated components; finally, the horizontal deviation value between the assembly section feature points is automatically calculated to evaluate whether the cumulative error of the manufacturing error and the assembly error in the construction stage meets the final assembly error allowed by the specification. In this way, the assembly rework problem caused by the cumulative error of manufacturing error and assembly deviation during the hoisting and alignment process of the prefabricated concrete components connected by sleeve grouting can be solved, thereby reducing labor costs and improving efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0037] Figure 1 A flow chart of a method for intelligent preassembly and alignment of precast concrete components disclosed in this application;

[0038] Figure 2 A schematic diagram of a method for extracting center features of a prefabricated column bottom sleeve and a reserved steel bar end face disclosed in the present application;

[0039] Figure 3 A schematic diagram of alignment between a completed model and a designed model for reserving characteristic points of a steel bar assembly section disclosed in the present application;

[0040] Figure 4 A schematic diagram of alignment deviation between a completed model and a design model for a reserved steel bar assembly section feature point disclosed in the present application;

[0041] Figure 5 A schematic diagram of alignment between a completed model and a designed model of a sleeve end face of a prefabricated column bottom face disclosed in the present application;

[0042] Figure 6 A schematic diagram of alignment deviation between a completed model and a designed model of a sleeve end face of a prefabricated column bottom face disclosed in the present application;

[0043] Figure 7 This is a schematic diagram of pre-assembly alignment of a prefabricated column bottom sleeve and reserved steel bars disclosed in the present application;

[0044] Figure 8 A schematic diagram of the horizontal deviation between a sleeve and the center of a connected steel bar after pre-assembly alignment of a precast concrete column disclosed in the present application;

[0045] Fig. 9 A flowchart of a specific intelligent preassembly alignment method for precast concrete components disclosed in this application;

[0046] Fig.10 This is a schematic structural diagram of an intelligent preassembly alignment system for precast concrete components disclosed in this application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] At present, the existing methods only stay at evaluating the size and position detection of the assembly section of the PC component, without evaluating the impact of the manufacturing error of the component itself and the accumulation of assembly errors in the subsequent construction stage on the final assembly accuracy. In the actual hoisting and assembly process, the components are often unable to be connected due to the accumulation of these errors. It is then necessary to repeatedly lift the components and adjust the positions of the steel bars multiple times to achieve the alignment of the components. If the error is large and the alignment cannot be achieved after adjusting the positions of the steel bars multiple times, the prefabricated components need to be replaced, resulting in huge rework costs. It can be seen that the traditional method evaluates the assemblability of components by hoisting entities for on-site assembly, which has the disadvantages of high labor costs and low efficiency. In order to solve the above technical problems, the present application discloses a method and system for intelligent pre-assembly alignment of precast concrete components, which can solve the assembly rework problem caused by the cumulative errors of manufacturing errors and assembly deviations during the hoisting and alignment process of precast concrete components connected by sleeve grouting, improve assembly alignment efficiency, and reduce costs.

[0049] See also Figure 1As shown, an embodiment of the present invention discloses a method for intelligent preassembly alignment of precast concrete components, comprising:

[0050] Step S11, extracting the first assembly section feature points of the precast concrete component design model and the second assembly section feature points of the precast concrete component completion model; the assembly section feature points include the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end surface.

[0051] In this embodiment, the assembly section feature points must first be extracted, where the assembly section feature points of the precast concrete component include the assembly section feature points of the design model and the completed model (the center of the sleeve and the end face of the reserved steel bar). For the extraction of assembly section feature points of the design model: First, the 3D design model of the precast column and the reserved steel bar of the pedestal is converted into a point cloud model in PCD (PointCloud Data) format (excluding surface point clouds); then, the point cloud model is straight-through filtered to obtain the edge point clouds of the sleeve at the bottom of the precast column and the end face of the reserved steel bar, and the diameter and end face center coordinates of the sleeve and the reserved steel bar are extracted. In order to extract the assembly section feature points of the completed model, a Mecha-Minder structured light camera is used to vertically shoot the precast concrete column bottom sleeve and the reserved steel bar end face of the precast concrete component completed model to obtain the corresponding original three-dimensional point cloud; the target point cloud of the precast concrete column bottom sleeve and the reserved steel bar end face is obtained based on the original three-dimensional point cloud and the straight-through filtering in the structured light shooting direction; the target point cloud of the precast concrete column bottom sleeve and the reserved steel bar end face is plane-fitted using a random sampling consistency algorithm to obtain the corresponding fitted plane; each target point cloud is projected onto the fitted plane of the precast concrete column bottom sleeve and the fitted plane of the reserved steel bar end face to obtain the corresponding three-dimensional point cloud plane; the three-dimensional point cloud plane is converted into a binary image, and the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face of the precast concrete component completed model in the binary image is extracted using the Hough transform circle detection algorithm. Specifically, Figure 2As shown: First, the Meka-Mind structured light camera (PRO M) is used to vertically shoot the sleeve of the bottom surface of the precast concrete column or the end surface of the reserved steel bar to obtain its original three-dimensional point cloud, and the point cloud of the sleeve of the bottom surface of the precast column or the end surface of the reserved steel bar is obtained by straight-through filtering on the Z axis (structured light shooting direction). Then, the point cloud plane of the bottom surface of the precast column or the end surface of the reserved steel bar is fitted based on the random sample consensus algorithm (RANSAC). When fitting the plane, RANSAC will randomly select three points and then calculate the plane model determined by these three points. Then, RANSAC will calculate the distance from all other points to this plane and determine whether these points conform to this plane model based on a preset threshold. This process will be repeated many times, and finally the plane model with the most conforming points will be selected as the final result. Next, the point cloud of the bottom surface of the precast column or the end surface of the reserved steel bar needs to be projected to the respective fitted planes to obtain the three-dimensional point cloud plane of the bottom surface of the precast column or the end surface of the steel bar. Finally, the above three-dimensional point cloud planes are converted into their respective binary images, and the centers of the sleeve and the steel bar in the image are extracted through the Hough transform circle detection algorithm of image processing technology, and the two are used as assembly alignment feature points. Hough transform circle detection is based on image gradient implementation and is divided into two steps: the first step is to detect the transformation and find the possible center of the circle; the second step is to extract the best center coordinates, i.e., the assembly feature point coordinates, from the candidate center according to the optimal radius size based on the first step.

[0052] Step S12: calculating a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on extended orthogonal Proctor analysis.

[0053] In this embodiment, when aligning the completed model and the design model based on the assembly section feature points, the horizontal deviation between the completed position and the design position of the assembly section feature points is calculated to intuitively evaluate the manufacturing error of the assembly point position of the prefabricated component. In this process, a first matrix is ​​constructed based on the first assembly section feature points, and a second matrix is ​​constructed based on the second assembly section feature points; the coordinate transformation of the second matrix is ​​performed based on the first matrix to obtain a first correspondence expression between the first matrix and the second matrix; and the first deviation value is determined based on the first correspondence expression. Specifically, the present application uses Extended Orthogonal Procrustes Analysis (EOPA) to calculate the least squares deviation between the sleeve and the steel bar assembly point positions and their design positions respectively to evaluate the position accuracy of the assembly point. EOPA is an extension of the Procrustes Analysis (PA) method, which is a mathematical method commonly used in computer vision, pattern recognition and shape analysis for aligning and comparing two sets of point sets (such as three-dimensional point clouds or shapes). The method transforms the coordinate matrix by the least squares method to optimize the alignment between the transformed matrix and the target matrix. Specifically, given two point sets: the target point set and the point set to be aligned , in this application, represent the spatial coordinates of the assembly points of the prefabricated component design model and the as-built model, or the assembly points of the connected steel bar design model and the as-built model. =3 represents the three-dimensional coordinates, =12 means 12 assembly alignment feature points. In order to achieve matching, the matrix Transform the coordinates to best match the matrix , thus, the first corresponding relationship expression between the first matrix and the second matrix is ​​shown in the following formula:

[0054] ;

[0055] in, (i.e. ) is the first assembly section feature point; (i.e. ) is the second assembly section feature point; (representing R in the above formula) is the rotation matrix; (Indicates that ) is the auxiliary unit vector; (i.e., t in the above formula) is the translation vector; E is the error matrix; T is the transposed symbol. The goal is to determine the transformation parameters R and t that minimize the square of the 2-norm of E; that is:

[0056] ;

[0057] This equation can be solved using the Lagrange multiplier method. According to the properties of the orthogonal matrix, the singular value decomposition (SVD) method is used to solve it. The specific process of singular value decomposition is as follows:

[0058] ;

[0059] in , , From the matrix The matrix obtained by matrix decomposition derived from the SVD of express The identity matrix of is the number of assembly alignment feature points; the decomposed matrix. The coordinate transformation parameters can be obtained by the following two formulas:

[0060] ;

[0061] ;

[0062] Then, a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point is finally determined.

[0063] Step S13: If the first deviation value is less than or equal to a preset deviation threshold, a second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the completed model of the precast concrete component is calculated by the extended orthogonal Proctor analysis.

[0064] In this embodiment, the as-built model and the design model of the reserved steel bar or sleeve end face are aligned by the above EOPA algorithm. The visualization diagrams of the alignment deviation are as follows: Figure 3 and Figure 5 , the alignment deviation value diagrams are as follows Figure 4 and Figure 6 This provides a more intuitive guide for the position correction of the assembly points of PC components. This step is particularly suitable for the verification of reserved steel bars at the construction site. Because reserved steel bars are often skewed due to construction collisions during the construction process, traditional methods, including existing automatic measurement methods, are limited to measuring the actual size of the section and fail to align with the design model to automatically measure the horizontal deviation of the assembly feature points to evaluate the dimensional quality of the assembly section. For example, from Figure 3 (Alignment visualization of feature points of reserved reinforcement assembly sections) and Figure 4(Alignment deviation of the characteristic points of the reserved steel bar assembly section) It can be seen intuitively that if any of the evaluated sleeves and reserved steel bars is not within the qualified range (the specification requires: the deviation of the center lines of adjacent sleeves or steel bars is not greater than 2mm), the component should be corrected or replaced; if the deviations of both are within the qualified range, proceed to the next step, that is, to calculate the second deviation value between the center point of the sleeve at the bottom of the precast concrete column and the center point of the end face of the reserved steel bar corresponding to the completed model of the precast concrete component through the extended orthogonal Prokhorst analysis.

[0065] The extended orthogonal Prototype Analysis (EOPA) is used to align the as-built model of the PC component based on the characteristic points of the assembly section (the center of the sleeve and the end face of the steel bar). Specifically, in this application, the spatial coordinates of the characteristic points are three-dimensional coordinates, is 3; 12 alignment feature points, is 12. By rotating the matrix The orthogonal transformation minimizes the Euclidean distance error after the two point sets are aligned. In this embodiment, is the characteristic point set of the as-built model of the sleeve section, It is the characteristic point set of the reserved steel bar completion model. The third matrix is ​​constructed based on the center point of the reserved steel bar end surface, and the fourth matrix is ​​constructed based on the center point of the sleeve on the bottom surface of the precast concrete column; the coordinate transformation of the third matrix is ​​performed based on the fourth matrix to obtain the second corresponding relationship expression between the third matrix and the fourth matrix; the second deviation value is determined based on the second corresponding relationship expression. Specifically:

[0066] ;

[0067] in, (i.e. ) is the fourth assembly section feature point; X (that is, ) is the third assembly section feature point; (representing R in the above formula) is the rotation matrix; (Indicates that ) is the auxiliary unit vector; (i.e., t in the above formula) is the translation vector; E is the error matrix; T is the transposition symbol. The goal is to determine the transformation parameters R and t that minimize the square of the 2-norm of E; and then calculate the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the completed model of the precast concrete component. The specific calculation process is the same as the specific calculation process in step S12, and will not be repeated here.

[0068] Step S14: if the second deviation value is less than or equal to the target deviation threshold, assembling and aligning the precast concrete components.

[0069] In this embodiment, if the second deviation value between the center point of the sleeve at the bottom of the precast concrete column and the center point of the end face of the reserved steel bar corresponding to the as-built model of the precast concrete component is less than or equal to the target deviation threshold, the precast concrete component is assembled and aligned. In a specific embodiment, the as-built model of the reserved steel bar and the sleeve end face is aligned using the EOPA algorithm. The visualization diagram of the alignment deviation is as follows: Figure 7 As shown in the figure, the alignment deviation value is as follows Figure 8 As shown. By calculating the alignment deviation value of the two, it is evaluated whether the cumulative error of the manufacturing error and the assembly error in the construction stage meets the final assembly error allowed by the specification (the deviation between the sleeve and the center line of the steel bar is not more than 3mm). If the second deviation value between the center point of the sleeve at the bottom of the precast concrete column and the center point of the end face of the reserved steel bar corresponding to the completed model of the precast concrete component is greater than the target deviation threshold, the deviation between the center point of the sleeve at the bottom of the precast concrete column and the center point of the end face of the reserved steel bar is corrected, and the process jumps back to the step of extracting the second assembly section feature point of the completed model of the precast concrete component.

[0070] In summary, the present application first extracts the first assembly section feature point of the precast concrete component design model and the second assembly section feature point of the precast concrete component completion model; the assembly section feature point includes the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face; then, based on the extended orthogonal Proctor analysis, the first deviation value between the first assembly section feature point and the second assembly section feature point is calculated; if the first deviation value is less than or equal to the preset deviation threshold, the second deviation value between the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face corresponding to the precast concrete component completion model is calculated by the extended orthogonal Proctor analysis; if the second deviation value is less than or equal to the target deviation threshold, the precast concrete component is assembled and aligned. It can be seen that the present application automatically extracts the assembly section feature points of the model through computer vision, and then uses the extended orthogonal Proctor analysis method to align the completed model and the design model based on the assembly section feature points, and automatically calculates the horizontal deviation between the completed position and the design position of the assembly section feature points to intuitively evaluate the manufacturing error of the prefabricated components; finally, the horizontal deviation value between the assembly section feature points is automatically calculated to evaluate whether the cumulative error of the manufacturing error and the assembly error in the construction stage meets the final assembly error allowed by the specification. In this way, the assembly rework problem caused by the cumulative error of manufacturing error and assembly deviation during the hoisting and alignment process of the prefabricated concrete components connected by sleeve grouting can be solved, thereby reducing labor costs and improving efficiency.

[0071] Based on the above embodiment, the present application discloses a method for intelligent preassembly and alignment of precast concrete components, which can solve the problem of assembly rework caused by the cumulative error of manufacturing error and assembly deviation during the hoisting and alignment of precast concrete components connected by sleeve grouting. Next, a specific method for intelligent preassembly and alignment of precast concrete components will be described in detail.

[0072] See also Fig. 9 As shown, the method of the present application is mainly divided into two modules: (1) PC component assembly point position inspection and (2) PC component pre-assembly alignment quality assessment. Specifically, first, the bottom sleeve of the prefabricated component and the center of the reserved steel bar end face are selected as the assembly section feature points, and the Meka-Mind structured light camera is used to collect point clouds of the bottom sleeve section and the reserved steel bar end face of the prefabricated concrete component. Then, the collected three-dimensional point cloud is subjected to point cloud processing techniques such as through filtering, plane fitting and projection to obtain the three-dimensional point cloud plane of the sleeve end face and the reserved steel bar end face. Then, the point cloud plane data is further converted into a two-dimensional image, and the assembly alignment feature points of the sleeve and the steel bar are extracted through Hough transform circle detection.

[0073] Secondly, based on the extracted assembly alignment feature points, the extended orthogonal Procter analysis method is used to align the sleeve and steel bar sections with their design models respectively, and their deviation values ​​are calculated, and it is evaluated whether the error value meets the manufacturing error allowed by the specification (the deviation of the center line of adjacent sleeves or steel bars is not more than 2mm). That is, the extended orthogonal Procter analysis (EOPA) method is used to align the as-built model and the design model based on the assembly section feature points, and the horizontal deviation between the as-built position and the design position of the assembly section feature points is automatically calculated to intuitively evaluate the manufacturing error of the assembly point position of the prefabricated component. If either of them is not within the qualified range, the component should be corrected or replaced; and jump back to the step of using the Meka-Mind structured light camera to collect the point cloud of the sleeve section on the bottom of the precast concrete component and the end face of the reserved steel bar, and perform a new round of assembly alignment. If the deviations of both are within the qualified range, proceed to the next step.

[0074] Finally, the sleeve and the steel bar will be pre-assembled and aligned, that is, the extended orthogonal Prospect Analysis (EOPA) will be used to align the precast column completion model based on the assembly section feature points, and the horizontal deviation value between the assembly section feature points will be automatically calculated to evaluate whether the cumulative error of the manufacturing error and the assembly error in the construction phase meets the final assembly error allowed by the specification (the deviation between the sleeve and the center line of the steel bar is not more than 3mm). If it is not within the qualified range, the deviation should be corrected and the step of using the Mecha-Mind structured light camera to collect the point cloud of the sleeve section on the bottom of the precast concrete component and the end face of the reserved steel bar will be jumped again for a new round of assembly alignment. If the deviation is within the qualified range, the on-site physical assembly alignment will be carried out.

[0075] It can be seen that the present application adopts the extended orthogonal Prokts analysis (EOPA) method to align the completed model and the design model based on the characteristic points of the assembly section, and automatically calculates the horizontal deviation between the completed position and the designed position of the characteristic points of the assembly section, so as to intuitively evaluate the manufacturing error of the prefabricated components; the method of pre-assembly alignment of the completed model of the prefabricated column based on the characteristic points of the assembly section is adopted by the extended orthogonal Prokts analysis (EOPA). This method automatically calculates the horizontal deviation value between the characteristic points of the assembly section to evaluate whether the cumulative error of the manufacturing error and the assembly error in the construction stage meets the final assembly error allowed by the specification. This solves the problem of assembly rework caused by the cumulative error of manufacturing error and assembly deviation during the hoisting and alignment process of prefabricated concrete components connected by sleeve grouting.

[0076] See also Fig.10 As shown, the embodiment of the present invention discloses a precast concrete component intelligent preassembly alignment system, comprising:

[0077] The feature point extraction module 11 is used to extract the first assembly section feature points of the precast concrete component design model and the second assembly section feature points of the precast concrete component completion model; the assembly section feature points include the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face;

[0078] A first deviation value calculation module 12, used for calculating a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on an extended orthogonal Proctor analysis;

[0079] A second deviation value calculation module 13 is used to calculate the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the as-built model of the precast concrete component by using the extended orthogonal Proctor analysis if the first deviation value is less than or equal to a preset deviation threshold;

[0080] The assembly alignment module 14 is configured to assemble and align the precast concrete components if the second deviation value is less than or equal to a target deviation threshold value.

[0081] In summary, the present application first extracts the first assembly section feature point of the precast concrete component design model and the second assembly section feature point of the precast concrete component completion model; the assembly section feature point includes the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face; then, based on the extended orthogonal Proctor analysis, the first deviation value between the first assembly section feature point and the second assembly section feature point is calculated; if the first deviation value is less than or equal to the preset deviation threshold, the second deviation value between the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face corresponding to the precast concrete component completion model is calculated by the extended orthogonal Proctor analysis; if the second deviation value is less than or equal to the target deviation threshold, the precast concrete component is assembled and aligned. It can be seen that the present application automatically extracts the assembly section feature points of the model through computer vision, and then uses the extended orthogonal Proctor analysis method to align the completed model and the design model based on the assembly section feature points, and automatically calculates the horizontal deviation between the completed position and the design position of the assembly section feature points to intuitively evaluate the manufacturing error of the prefabricated components; finally, the horizontal deviation value between the assembly section feature points is automatically calculated to evaluate whether the cumulative error of the manufacturing error and the assembly error in the construction stage meets the final assembly error allowed by the specification. In this way, the assembly rework problem caused by the cumulative error of manufacturing error and assembly deviation during the hoisting and alignment process of the prefabricated concrete components connected by sleeve grouting can be solved, thereby reducing labor costs and improving efficiency.

[0082] In some specific embodiments, the feature point extraction module 11 may specifically include:

[0083] A model conversion unit, used to convert the precast concrete component design model into a point cloud model in a point cloud data format;

[0084] The first center point extraction unit is used to perform straight-through filtering on the point cloud model to obtain edge point clouds of the precast concrete column bottom sleeve and the reserved steel bar end face, and extract the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face of the precast concrete component design model based on the edge point cloud.

[0085] In some specific embodiments, the feature point extraction module 11 may specifically include:

[0086] The original three-dimensional point cloud acquisition unit is used to use a Mecha-Minder structured light camera to vertically shoot the precast concrete column bottom sleeve and the reserved steel bar end surface of the precast concrete component completion model to obtain the corresponding original three-dimensional point cloud;

[0087] A target point cloud acquisition unit, used for acquiring target point clouds of the precast concrete column bottom sleeve and the reserved steel bar end surface based on the original three-dimensional point cloud and the through filtering in the structured light shooting direction;

[0088] A plane fitting unit is used to perform plane fitting on the target point cloud of the bottom sleeve of the precast concrete column and the end face of the reserved steel bar by using a random sampling consistency algorithm to obtain a corresponding fitted plane;

[0089] A three-dimensional point cloud plane acquisition unit is used to project each of the target point clouds onto the fitted plane of the precast concrete column bottom sleeve and the fitted plane of the reserved steel bar end surface, so as to acquire a corresponding three-dimensional point cloud plane;

[0090] The second center point extraction unit is used to convert the three-dimensional point cloud plane into a binary image, and extract the center point of the precast concrete column bottom surface sleeve and the center point of the reserved steel bar end surface of the precast concrete component completion model in the binary image through a Hough transform circle detection algorithm.

[0091] In some specific embodiments, the first deviation value calculation module 12 may specifically include:

[0092] A first matrix construction unit, configured to construct a first matrix based on the first assembly cross-section feature points, and to construct a second matrix based on the second assembly cross-section feature points;

[0093] A first expression acquisition unit, used for performing coordinate transformation on the second matrix based on the first matrix to obtain a first corresponding relationship expression between the first matrix and the second matrix;

[0094] The first deviation value determining unit is used to determine the first deviation value based on the first corresponding relationship expression.

[0095] In some specific embodiments, the system may further include:

[0096] The first jump module is used for correcting the deviation between the first assembly section feature point and the second assembly section feature point or replacing the precast concrete component if the first deviation value is greater than the preset deviation threshold, and jumping again to the step of extracting the second assembly section feature point of the precast concrete component completion model.

[0097] In some specific embodiments, the second deviation value calculation module 13 may specifically include:

[0098] A second matrix construction unit is used to construct a third matrix based on the center point of the reserved steel bar end surface, and to construct a fourth matrix based on the center point of the precast concrete column bottom surface sleeve;

[0099] A first expression acquisition unit, configured to perform coordinate transformation on the third matrix based on the fourth matrix to obtain a second corresponding relationship expression between the third matrix and the fourth matrix;

[0100] The second deviation value determining unit is used to determine the second deviation value based on the second corresponding relationship expression.

[0101] In some specific embodiments, the system may further include:

[0102] The second jump module is used to correct the deviation between the center point of the sleeve at the bottom of the precast concrete column and the center point of the end face of the reserved steel bar if the second deviation value is greater than the target deviation threshold, and jump again to the step of extracting the second assembly section feature point of the precast concrete component completion model.

Claims

1. A method for intelligent preassembly alignment of precast concrete components, characterized in that: include: Extracting the first assembly section feature points of the precast concrete component design model and the second assembly section feature points of the precast concrete component completion model; The characteristic points of the assembly section include the center point of the sleeve at the bottom of the precast concrete column and the center point of the end face of the reserved steel bar; Calculating a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on an extended orthogonal Proctor analysis; If the first deviation value is less than or equal to a preset deviation threshold, the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the completed model of the precast concrete component is calculated by the extended orthogonal Proctor analysis; If the second deviation value is less than or equal to the target deviation threshold, the precast concrete components are assembled and aligned.

2. The method for intelligent preassembly and alignment of precast concrete components according to claim 1, characterized in that: The step of extracting the first assembly section feature point of the precast concrete component design model comprises: Converting the precast concrete component design model into a point cloud model in a point cloud data format; The point cloud model is subjected to straight-through filtering to obtain edge point clouds of the precast concrete column bottom sleeve and the reserved steel bar end face, and the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face of the precast concrete component design model are extracted based on the edge point clouds.

3. The intelligent preassembly alignment method for precast concrete components according to claim 1, characterized in that: Extract the second assembly section feature points of the precast concrete component as-built model, including: A Meka-Minder structured light camera is used to vertically photograph the bottom sleeve of the precast concrete column and the end face of the reserved steel bar of the completed model of the precast concrete component to obtain the corresponding original three-dimensional point cloud; Acquire target point clouds of the precast concrete column bottom sleeve and the reserved steel bar end surface based on the original three-dimensional point cloud and through-filtering in the structured light shooting direction; Using a random sampling consistency algorithm, plane fitting is performed on the target point clouds of the bottom sleeve of the precast concrete column and the end face of the reserved steel bar to obtain a corresponding fitted plane; Projecting each of the target point clouds onto the fitted plane of the precast concrete column bottom sleeve and the fitted plane of the reserved steel bar end surface to obtain a corresponding three-dimensional point cloud plane; The three-dimensional point cloud plane is converted into a binary image, and the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end face of the precast concrete component completion model in the binary image are extracted through the Hough transform circle detection algorithm.

4. The intelligent preassembly alignment method for precast concrete components according to claim 1, characterized in that: The calculating the first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on the extended orthogonal Proctor analysis includes: Constructing a first matrix based on the first assembly cross-section feature points, and constructing a second matrix based on the second assembly cross-section feature points; Performing coordinate transformation on the second matrix based on the first matrix to obtain a first corresponding relationship expression between the first matrix and the second matrix; The first deviation value is determined based on the first corresponding relationship expression.

5. The method for intelligent preassembly and alignment of precast concrete components according to claim 1, characterized in that: After calculating the first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on the extended orthogonal Proctor analysis, the method further includes: If the first deviation value is greater than the preset deviation threshold, the deviation between the first assembly section feature point and the second assembly section feature point is corrected or the precast concrete component is replaced, and the process jumps again to the step of extracting the second assembly section feature point of the precast concrete component completion model.

6. The intelligent preassembly alignment method for precast concrete components according to claim 1, characterized in that: The method of calculating the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the completed model of the precast concrete component by using the extended orthogonal Proctor analysis includes: A third matrix is ​​constructed based on the center points of the reserved steel bar end faces, and a fourth matrix is ​​constructed based on the center points of the sleeves on the bottom faces of the precast concrete columns; Performing coordinate transformation on the third matrix based on the fourth matrix to obtain a second corresponding relationship expression between the third matrix and the fourth matrix; The second deviation value is determined based on the second corresponding relationship expression.

7. The method for intelligent preassembly and alignment of precast concrete components according to any one of claims 1 to 6, characterized in that: After calculating the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the precast concrete component completion model by the extended orthogonal Proctor analysis, the method further includes: If the second deviation value is greater than the target deviation threshold, the deviation between the center point of the precast concrete column bottom sleeve and the center point of the reserved steel bar end surface is corrected, and the process jumps again to the step of extracting the second assembly section feature point of the precast concrete component completion model.

8. An intelligent pre-assembly alignment system for precast concrete components, characterized in that: include: A feature point extraction module, used to extract the first assembly section feature points of the precast concrete component design model and the second assembly section feature points of the precast concrete component completion model; The characteristic points of the assembly section include the center point of the sleeve at the bottom of the precast concrete column and the center point of the end face of the reserved steel bar; A first deviation value calculation module, used for calculating a first deviation value between the first assembly cross-section feature point and the second assembly cross-section feature point based on an extended orthogonal Proctor analysis; A second deviation value calculation module is used to calculate the second deviation value between the center point of the sleeve on the bottom surface of the precast concrete column and the center point of the end surface of the reserved steel bar corresponding to the as-built model of the precast concrete component by using the extended orthogonal Proctor analysis if the first deviation value is less than or equal to a preset deviation threshold; An assembly alignment module is used to assemble and align the precast concrete components if the second deviation value is less than or equal to a target deviation threshold.

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