Construction error quantification method for underwater structures avoiding mutual contamination

Abstract

The application discloses a construction error quantification method of underwater structures avoiding mutual pollution, comprising the following steps: step 1: collecting full-section three-dimensional point cloud data of a target underwater structure; and step 2: adopting hierarchical registration and segmented rigid body alignment mechanism to realize alignment of the collected three-dimensional point cloud data and a BIM model. The application realizes high-precision registration and deviation detection, effectively solves the problem of mutual pollution of overall registration error and construction error of underwater structures, can accurately reflect the true construction quality of a box culvert, has multi-dimensional deviation quantification with standard guidance, includes three types of core indexes of radial deviation, axial deviation and construction joint misalignment amount which are directly extracted for engineering acceptance attention, and is automatically compared with national and industrial standards, realizes seamless conversion from 'three-dimensional data' to 'engineering eligibility determination', and avoids errors of manual secondary processing.

Description

Technical Field

[0001] This invention relates to the field of underwater engineering construction quality inspection and acceptance technology, specifically a method for quantifying construction errors in underwater structures to avoid cross-contamination. Background Technology

[0002] Underwater structures (such as immersed tunnels, underwater water conveyance box culverts, and offshore wind power foundation box culverts) often experience mutual contamination between registration errors and construction errors after construction. Registration error refers to the algorithmic error generated during the alignment of the measured point cloud with the BIM design model in three-dimensional space. The main reason for this mutual contamination is that the feature points inside underwater structures are sparse. In pursuit of global optimization, existing methods incorrectly absorb actual construction deviations as model deformations, leading to a significant decrease in registration accuracy. In other words, construction errors affect registration errors, causing mutual contamination between registration and construction errors, thus failing to accurately reflect the actual construction quality.

[0003] In addition, existing technologies have the following shortcomings in the quantitative acceptance of construction errors in underwater structures: 1. Existing methods mostly use the distance from a point to a surface to calculate the overall deviation, which cannot distinguish core indicators of engineering acceptance such as radial deviation, axial deviation, and construction joint misalignment. Furthermore, the calculated overall deviation is difficult to map to the tolerance thresholds of national or industry standards, requiring secondary manual processing, which is cumbersome and prone to errors.

[0004] 2. If acceptance testing is still required, the existing acceptance process has a low degree of automation. Traditional acceptance testing relies on manual sampling and subjective judgment, which has problems such as low testing efficiency, limited coverage, and poor data traceability. Furthermore, it cannot generate intuitive three-dimensional visualization deviation reports, which is not conducive to the rapid location and rectification of engineering quality problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method for quantifying construction errors in underwater structures to avoid cross-contamination, which can solve the problems described in the background art.

[0006] The technical solution to achieve the objective of this invention is: a method for quantifying construction errors of underwater structures to avoid mutual contamination, comprising the following steps: Step 1: Collect 3D point cloud data of the entire cross-section of the target underwater structure; Step 2: Employ a hierarchical registration and segmented rigid body alignment mechanism to align the acquired 3D point cloud data with the BIM model.

[0007] Furthermore, prior to step 2, the acquired 3D point cloud data undergoes preprocessing, including denoising, redundancy removal, stitching, and coordinate transformation. Among them, denoising uses statistical filtering algorithms to remove outliers, redundancy removal uses voxel downsampling to reduce point cloud density, stitching is based on inertial measurement units and underwater positioning data to achieve automatic stitching of point cloud data from multiple measurement stations, and coordinate transformation processing includes uniformly transforming point cloud data to the engineering coordinate system.

[0008] Furthermore, the hierarchical registration and segmented rigid body alignment mechanism is used to align the acquired 3D point cloud data with the BIM model. The specific implementation includes the following steps: Step 21: Perform global coarse registration based on the axis and longitudinal slope of the underwater structure. Global coarse registration includes: extracting the virtual axis and virtual longitudinal slope of the underwater structure from the BIM model, and using a random sampling consensus algorithm to fit the actual axis and actual longitudinal slope of the actual underwater structure from the collected 3D point cloud data. By minimizing the positional and angular deviations between the actual axis and the virtual axis, the optimal global transformation matrix is ​​solved. ; Step 22: Divide the underwater structure into sections according to construction joints or pre-set fixed lengths to obtain each section; Step 23: Perform rigid body registration on the segments. Segment rigid body configuration includes independently executing the iterative nearest point algorithm on each segment to perform rigid body transformation registration. For the th segment... Each segment, objective function The corresponding value when the minimum value is obtained The optimal rigid body transformation matrix for this segment. objective function Calculate using the following formula:

[0009] In the formula, Indicates the first The total number of point clouds corresponding to each segment. Indicates the first The rigid body transformation matrix of each piecewise segment. , This indicates that the collected 3D point cloud data contains information about the first... A segmented point cloud, express The Middle A point cloud, , This represents a pair of points in a BIM model. express The Middle Point cloud.

[0010] Furthermore, the optimal global transformation matrix is ​​solved by minimizing the positional and angular deviations between the actual and virtual axes. Its specific implementation includes: Optimal global change matrix The objective function is as follows: When taking the minimum value :

[0011] In the formula, , These are the coordinates of the reference points on the actual axis and the virtual axis, respectively. , These represent the unit vectors of the directions of the actual axis and the virtual axis, respectively. This represents the angle deviation weighting coefficient. , express The rotation matrix.

[0012] Furthermore, after performing step 2, the following steps are also included: Step 31: Generate theoretical cross-sections of the underwater structure in the BIM model; Step 32: Extract and obtain the actual cross-sectional point set of the underwater structure from the collected 3D point cloud data according to the positions corresponding to the theoretical cross-section; Step 33: For each section in the section point set, calculate the radial deviation, axial deviation, and misalignment of adjacent construction joints for that section. Step 34: Automatically compare the calculated deviations with the tolerance thresholds in the target specification to determine whether the quality of each section and each construction joint is up to standard.

[0013] Furthermore, radial deviation characterizes the normal distance from a point on the cross-section to the theoretical profile. A positive value indicates over-excavation, while a negative value indicates under-excavation. This normal distance... The calculation formula is as follows:

[0014] In the formula, Represents the theoretical contour line. Representing the theoretical contour line The upper coordinate is The unit normal vector at that location points inwards towards the interior of the underwater structure. This represents the coordinates of the cross-section points on the cross-section.

[0015] Furthermore, axis deviation includes lateral deviation. and vertical deviation , , , This represents the Y-axis coordinate of the center of the circle fitted by the least squares method for the cross-sectional point set. This represents the Y-axis coordinate of the center point of the section extracted from the theoretical section in the BIM model. This represents the Z-axis coordinate of the center of the circle fitted by the least squares method for the cross-sectional point set. This represents the Z-axis coordinate of the center point of the section extracted from the theoretical section in the BIM model.

[0016] Furthermore, the misalignment of adjacent construction joints characterizes the difference in elevation and axial offset of the contour lines of two adjacent segments at the construction joint. The calculation of the elevation difference of the contour lines includes the following steps: On the cross-section where the construction joint is located, project the point clouds of the two segments onto the same plane. Then, along the normal direction of the theoretical contour line, calculate the elevation difference or radial distance difference of the corresponding positions point by point. Take the elevation difference and radial distance difference, and take the maximum value as the contour line elevation difference. The calculation of axis offset includes the following steps: The axis offset includes both horizontal and vertical offsets. The coordinates of the center points of the cross-sectional point clouds at the construction joint for each of the two segments are calculated. The coordinates of the two center points are as follows: and The horizontal offset of the axis is The vertical offset is The offset direction is determined based on the positive or negative value.

[0017] Furthermore, after completing step 34, it also includes, Step 4: Generate a 3D visualization interface for construction deviations and an automated acceptance report. The 3D visualization interface includes a 3D chromatographic deviation distribution map, and the automated acceptance report includes a defect statistics report.

[0018] Furthermore, the specific implementation process of generating the 3D visualization display interface for construction deviations and the automated acceptance report includes the following steps: Step 41: Generate a three-dimensional chromatographic deviation distribution map: render the radial deviations of all cross sections according to their magnitude using chromatographic rendering, and use different colors to represent the degree of radial deviation; Step 42: Defect Statistics and Report Generation: Statistically count the over-excavation area, under-excavation area, maximum over-excavation value, maximum under-excavation value, number of points exceeding the axis deviation limit, and number of points exceeding the construction joint misalignment limit for the entire line, thereby generating a standardized defect statistics report. This defect statistics report includes the station location, deviation type, deviation value, and corresponding specification tolerance threshold for each defect. Step 43: Link the 3D chromatographic deviation distribution map and statistical defects with the generated report to respond to when the user clicks on any out-of-limit area in the 3D chromatographic deviation distribution map, and pop up detailed information of the clicked location in the 3D visualization display interface. This detailed information includes the station number, deviation type, deviation value, corresponding cross-sectional view and conclusion on whether rework is required, and responds to when the user clicks on the defect statistical report, and automatically locates the location associated with the 3D chromatographic deviation distribution map. Step 44: Integrate the three-dimensional chromatographic deviation distribution map, defect statistics report, standard comparison table and acceptance conclusion to generate a complete acceptance report that meets the project requirements.

[0019] The beneficial effects of this invention are as follows: 1. High-precision registration and deviation detection: By adopting the hierarchical strategy of "coarse registration + segmented rigid body fine registration", the problem of mutual contamination between overall registration error and construction error of underwater structures (such as long-distance box culverts in the open sea) is effectively solved, and the true construction quality of the box culvert can be accurately reflected.

[0020] 2. Standard-oriented multi-dimensional deviation quantification: Directly extracts three core indicators that are of concern in project acceptance: radial deviation, axial deviation, and construction joint misalignment, and automatically compares them with national and industry standards. This achieves a seamless conversion from "three-dimensional data" to "project qualification judgment" and avoids errors caused by manual secondary processing.

[0021] 3. Fully automated acceptance process: It realizes full automation from point cloud data acquisition, preprocessing, registration, deviation calculation to acceptance report generation, shortening the traditional acceptance work that takes several weeks to several hours, greatly improving acceptance efficiency, and eliminating the influence of human subjective judgment.

[0022] 4. Intuitive 3D visualization: By linking the chromatographic deviation chart with the defect report, construction quality problems can be located intuitively and quickly, providing clear guidance for project rectification.

[0023] 5. Strong data traceability: All test data and acceptance results are stored in digital form, which facilitates the full life cycle management of project quality and sharing among multiple parties, and provides important basic data for subsequent project operation and maintenance.

[0024] 6. Strong versatility and scalability: The whole method does not depend on specific box culvert structure dimensions and construction environment, and can be adapted to the construction quality acceptance of different types of underwater structures without changing the core framework, and has broad engineering application prospects. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a preferred embodiment of the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: like Figure 1 As shown, a method for quantifying construction errors in underwater structures to avoid cross-contamination includes the following steps: Step 1: Collect three-dimensional point cloud data of the entire cross-section of the target underwater structure.

[0027] Understandably, three-dimensional point cloud data can be acquired by constructing a mobile measurement platform suitable for complex underwater environments (such as the open sea). The mobile measurement platform integrates a three-dimensional laser scanner, an inertial measurement unit (IMU), a Doppler current profiler (ADCP), and an underwater positioning system.

[0028] The full cross section refers to each cross section perpendicular to the axis, which means that the three-dimensional point cloud data of the full cross section is composed of the three-dimensional point cloud data of all cross sections.

[0029] For example, after acquiring 3D point cloud data, the process also includes preprocessing the 3D point cloud data to obtain preprocessed 3D point cloud data. Preprocessing includes denoising, redundancy removal, stitching, and coordinate transformation. These processes can employ statistical filtering algorithms to remove outliers, voxel downsampling methods to reduce point cloud density, and automatic stitching of point clouds from multiple measurement stations based on IMU and underwater positioning data. Finally, the point cloud data is uniformly transformed to the engineering coordinate system.

[0030] Step 2: Employ a hierarchical registration and segmented rigid body alignment mechanism to align the acquired 3D point cloud data with the BIM model.

[0031] Understandably, this step proposes a hierarchical registration and segmented rigid body alignment mechanism between the 3D point cloud data obtained from underwater field measurements and the BIM model, in order to solve the problem of mutual contamination between the overall registration error and construction error of underwater structures, thereby achieving high-precision alignment between the point cloud and the BIM model.

[0032] For example, the hierarchical registration and segmented rigid body alignment mechanism is used to align the acquired 3D point cloud data with the BIM model. The specific implementation includes the following steps: Step 21: Perform global coarse registration based on the axis and longitudinal slope of the underwater structure, i.e., perform the first registration. This global coarse registration includes: extracting the virtual axis and virtual longitudinal slope of the underwater structure from the BIM model, and fitting the actual axis and actual longitudinal slope of the actual underwater structure from the collected 3D point cloud data using the Random Sampling Consensus Algorithm (RANSAC). By minimizing the positional and angular deviations between the actual axis and the virtual axis, the optimal global variation matrix is ​​solved. .

[0033] For example, the optimal global transformation matrix is ​​solved by minimizing the positional and angular deviations between the actual and virtual axes. Optimal global change matrix The objective function of formula (1) When taking the minimum value That is to say, there is Formula (1) is as follows: (1) In the formula, , These are the coordinates of the reference points on the actual axis and the virtual axis, respectively. , These represent the unit vectors of the directions of the actual axis and the virtual axis, respectively. This represents the angle deviation weighting coefficient. , express The rotation matrix.

[0034] This indicates the alignment error of the reference point. This indicates the angular alignment error along the axis.

[0035] The objective function can be solved through numerical optimization: First, calculate the rotation matrix based on the unit vector of direction (to make the direction of the virtual axis consistent with the direction of the actual axis), then calculate the translation vector based on the coordinates of the reference point, and finally minimize the objective function. The obtained optimal transformation matrix .

[0036] Step 21 uses only the axis as a reference, thus avoiding interference from local construction deviations of the inner wall of the underwater structure on the overall registration.

[0037] Step 22: Divide the underwater structure into sections according to construction joints or preset fixed lengths to obtain each section.

[0038] Understandably, the underwater structure in the BIM model and the collected 3D point cloud data are divided into several independent segments based on the actual construction joint location of the underwater structure or according to a fixed length (e.g., 20m), resulting in multiple segments. The principle of segmentation is to ensure that the underwater structure within each segment is a rigid body, and that there is only a small relative displacement between the segments.

[0039] Step 23: Perform rigid body registration on the segments. Segment rigid body registration involves independently executing the Iterative Closest Point (ICP) algorithm on each segment for rigid body transformation registration. During this registration process, only translation and rotation operations are allowed; scaling and deformation operations are not permitted. For the first... Divide the data into segments and solve for the optimal rigid body transformation matrix of each segment using the formula. The goal is to minimize the sum of squared distances between the acquired 3D point cloud data and the corresponding point cloud in the BIM model, which is achieved by using the point-to-point distance minimization objective function in the Iterative Closest Point (ICP) algorithm. The objective function as follows:

[0040] In the formula, Indicates the first The total number of point clouds corresponding to each segment. Indicates the first The rigid body transformation matrix of each piecewise segment. , This indicates that the collected 3D point cloud data contains information about the first... A segmented point cloud, express The Middle A point cloud, , This represents a pair of points in a BIM model. express The Middle Point cloud.

[0041] Understandably, for each point in the i-th segment of the collected point cloud (i.e., the point cloud in 3D point cloud data), the corresponding set of points in the BIM model corresponding to the i-th segment is obtained through nearest neighbor search or projection matching. The points in the BIM model do not refer to a regeneration of the measured point cloud from the BIM model itself, nor do they refer to a deformation of the two point clouds as a whole. Rather, they refer to establishing point-to-point relationships between points on a segment of the measured point cloud and segments on the BIM model. Furthermore, segmented rigid body registration only allows rotation and translation, not scaling, stretching, or local deformation. Therefore, this step does not "absorb" actual construction errors as model deformation, and can retain actual construction deviations such as radial deviation, axial deviation, and construction joint misalignment.

[0042] It also includes applying smoothing constraints to the transformation parameters of two adjacent segments, where the transformation parameters are... All rotational and translational components are included to avoid obvious discontinuities. The solution obtained above... This effectively preserves the true construction deviations and prevents registration errors from being mistakenly absorbed as construction errors.

[0043] For example, in order to achieve more cross-sectional, multi-dimensional, and engineering-compliant quantification of construction deviations, step 3 is also included.

[0044] Step 3: Automatically extract quantitative steps to obtain more dimensions of construction deviation.

[0045] The process of implementing automatic quantization includes the following steps: Step 31: Generate theoretical cross-sections of the underwater structure in the BIM model.

[0046] It is understandable that generating a theoretical cross-section can be achieved through the following steps: Perpendicular to the axis of the underwater structure in the BIM model, theoretical sections are cut at design station intervals (e.g., 1m) to obtain the theoretical cross-sections. Each theoretical cross-section contains the complete outline of the underwater structure and the coordinates of the cross-section center point.

[0047] Step 32: Extract and obtain the actual cross-sectional point set of the underwater structure from the collected 3D point cloud data according to the positions corresponding to the theoretical cross-section.

[0048] Understandably, at the location corresponding to the theoretical cross-section, a point set within a certain thickness range is extracted from the collected 3D point cloud data as the measured cross-section point set. Furthermore, a projection method can be used to project the point set from the 3D point cloud data onto the plane of the theoretical cross-section, thereby forming a measured 2D cross-section point cloud, which is the actual cross-section point set.

[0049] Step 33: For each section in the cross-section point set, calculate the radial deviation, axial deviation, and misalignment of adjacent construction joints for that section.

[0050] For example, radial deviation represents the normal distance from a cross-sectional point on the cross-section to the theoretical profile. A positive value indicates over-excavation, while a negative value indicates under-excavation. This normal distance... The calculation formula is as follows:

[0051] In the formula, Represents the theoretical contour line. Representing the theoretical contour line The upper coordinate is The unit normal vector at that location points inwards towards the interior of the underwater structure. This represents the coordinates of the cross-section points on the cross-section.

[0052] For example, axis deviation includes lateral deviation. and vertical deviation , , , This represents the Y-axis coordinate of the center of the circle fitted by the least squares method for the cross-sectional point set. This represents the Y-axis coordinate of the center point of the section extracted from the theoretical section in the BIM model. This represents the Z-axis coordinate of the center of the circle fitted by the least squares method for the cross-sectional point set. This represents the Z-axis coordinate of the center point of the section extracted from the theoretical section in the BIM model.

[0053] Understandably, for each cross-section, the offset between the measured cross-section center point and the theoretical cross-section center point is calculated. This offset is the axis deviation, which is then decomposed into lateral and vertical deviations. In the extracted cross-section center point coordinates, the X-axis represents the mileage direction, the Y-axis represents the horizontal direction, and the Z-axis represents the vertical direction. A positive value indicates a shift to the right, while a negative value indicates a shift to the left. A positive value indicates that the elevation is higher than the design elevation, while a negative value indicates that the elevation is lower than the design elevation.

[0054] For example, the misalignment of adjacent construction joints represents the difference in elevation of the outline and the offset of the axis between two adjacent segments at the construction joint.

[0055] The calculation of the elevation difference of the contour lines includes the following steps: On the cross-section where the construction joint is located, project the point clouds of the two segments onto the same plane, and then calculate the elevation difference or radial distance difference of the corresponding positions point by point along the normal direction of the theoretical contour line. Take the elevation difference and radial distance difference, and take the maximum value as the contour line elevation difference.

[0056] The calculation of axis offset includes the following steps: The axis offset includes both horizontal and vertical offsets. The coordinates of the center points of the cross-sectional point clouds at the construction joint for each of the two segments are calculated. The coordinates of the two center points are as follows: and The horizontal offset of the axis is The vertical offset is The direction of offset can be determined based on the positive or negative value.

[0057] Step 34: Automatically compare the calculated deviations with the tolerance thresholds in the target specification to determine whether the quality of each section and each construction joint is up to standard.

[0058] It is understandable that the target standard is a national mandatory standard or an industry recommended standard, such as the "Water Transport Engineering Quality Inspection Standard", and the above-mentioned tolerance threshold is the tolerance threshold specified in the "Water Transport Engineering Quality Inspection Standard".

[0059] After completing step 3, step 4 is also included to achieve 3D visualization of construction deviations and automated generation of acceptance reports, which are as follows: Step 4: Generate a 3D visualization interface for construction deviations and an automated acceptance report. The 3D visualization interface includes a 3D chromatographic deviation distribution map, and the automated acceptance report includes a defect statistics report.

[0060] For example, the specific implementation process of generating the 3D visualization display interface for construction deviations and the automated acceptance report includes the following steps: Step 41: Generate a 3D chromatographic deviation distribution map. Render the radial deviations of all cross-sections according to their magnitude using chromatographic representation, and use different colors to indicate the degree of radial deviation. For example, use blue to indicate under-excavation, red to indicate over-excavation, and green to indicate acceptable levels, to generate an intuitive 3D visualization of the deviation distribution map. This allows users to view the construction deviation distribution of underwater structures from any angle through rotation, scaling, and sectioning operations.

[0061] Step 42: Defect Statistics and Report Generation. Key indicators such as the over-excavation area, under-excavation area, maximum over-excavation value, maximum under-excavation value, number of points exceeding the axis deviation limit, and number of construction joint misalignment points exceeding the limit are statistically analyzed to generate a standardized defect statistics report. This report includes the station location, deviation type, deviation value, and corresponding specification tolerance threshold for each defect.

[0062] Step 43: Link the 3D chromatographic deviation distribution map and statistical defects to the generated report. In response to a user clicking on any out-of-limit area in the 3D chromatographic deviation distribution map, a pop-up window in the 3D visualization interface displays detailed information about the clicked location, including the station number, deviation type, deviation value, corresponding cross-sectional view, and a conclusion on whether rework is required. Similarly, users can also click on the defect statistics report, and the system will quickly locate the corresponding position in the 3D chromatographic deviation distribution map in response to the user's click on the defect statistics report.

[0063] Step 44: Integrate the three-dimensional chromatographic deviation distribution map, defect statistics report, standard comparison table and acceptance conclusion to generate a complete acceptance report that meets the project requirements.

[0064] The embodiments disclosed in this specification are merely illustrative of one aspect of the invention, and the scope of protection of the invention is not limited to these embodiments. Any other functionally equivalent embodiments fall within the scope of protection of the invention. Those skilled in the art can make various other corresponding changes and modifications based on the technical solutions and concepts described above, and all such changes and modifications should fall within the scope of protection of the claims of this invention.

Claims

1. A method for quantifying construction errors in underwater structures to avoid cross-contamination, characterized in that, Includes the following steps: Step 1: Collect 3D point cloud data of the entire cross-section of the target underwater structure; Step 2: Use a hierarchical registration and segmented rigid body alignment mechanism to align the acquired 3D point cloud data with the BIM model.

2. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 1, characterized in that, Before step 2, the acquired 3D point cloud data undergoes preprocessing, including denoising, redundancy removal, stitching, and coordinate transformation. Among them, denoising uses statistical filtering algorithms to remove outliers, redundancy removal uses voxel downsampling to reduce point cloud density, stitching is based on inertial measurement units and underwater positioning data to achieve automatic stitching of point cloud data from multiple measurement stations, and coordinate transformation processing includes uniformly transforming point cloud data to the engineering coordinate system.

3. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 1, characterized in that, The hierarchical registration and segmented rigid body alignment mechanism is used to align the acquired 3D point cloud data with the BIM model. The specific implementation includes the following steps: Step 21: Perform global coarse registration based on the axis and longitudinal slope of the underwater structure. Global coarse registration includes: extracting the virtual axis and virtual longitudinal slope of the underwater structure from the BIM model, and using a random sampling consensus algorithm to fit the actual axis and actual longitudinal slope of the actual underwater structure from the collected 3D point cloud data. By minimizing the positional and angular deviations between the actual axis and the virtual axis, the optimal global transformation matrix is ​​solved. ; Step 22: Divide the underwater structure into sections according to construction joints or pre-set fixed lengths to obtain each section; Step 23: Perform rigid body registration on the segments. Segment rigid body configuration includes independently executing the iterative nearest point algorithm on each segment to perform rigid body transformation registration. For the th segment... Each segment, objective function The corresponding value when the minimum value is obtained The optimal rigid body transformation matrix for this segment. objective function Calculate using the following formula: In the formula, Indicates the first The total number of point clouds corresponding to each segment. Indicates the first The rigid body transformation matrix of each piecewise segment. , This indicates that the collected 3D point cloud data contains information about the first... The point cloud is divided into segments. express The Middle A point cloud, , This represents a pair of points in a BIM model. express The Middle Point cloud.

4. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 3, characterized in that, The optimal global transformation matrix is ​​obtained by minimizing the positional and angular deviations between the actual and virtual axes. Its specific implementation includes: Optimal global change matrix The objective function is as follows: When taking the minimum value : In the formula, , These are the coordinates of the reference points on the actual axis and the virtual axis, respectively. , These represent the unit vectors of the directions of the actual axis and the virtual axis, respectively. This represents the angle deviation weighting coefficient. , express The rotation matrix.

5. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 4, characterized in that, After completing step 2, the following also includes: Step 31: Generate theoretical cross-sections of the underwater structure in the BIM model; Step 32: Extract and obtain the actual cross-sectional point set of the underwater structure from the collected 3D point cloud data according to the positions corresponding to the theoretical cross-section; Step 33: For each section in the section point set, calculate the radial deviation, axial deviation, and misalignment of adjacent construction joints for that section. Step 34: Automatically compare the calculated deviations with the tolerance thresholds in the target specification to determine whether the quality of each section and each construction joint is up to standard.

6. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 5, characterized in that, Radial deviation represents the normal distance from a point on the cross-section to the theoretical profile. A positive value indicates over-excavation, while a negative value indicates under-excavation. This normal distance... The calculation formula is as follows: In the formula, Represents the theoretical contour line. Representing the theoretical contour line The upper coordinate is The unit normal vector at that location points inwards towards the interior of the underwater structure. This represents the coordinates of the cross-section points on the cross-section.

7. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 6, characterized in that, Axis deviation includes lateral deviation and vertical deviation , , , This represents the Y-axis coordinate of the center of the circle fitted by the least squares method for the cross-sectional point set. This represents the Y-axis coordinate of the center point of the section extracted from the theoretical section in the BIM model. This represents the Z-axis coordinate of the center of the circle fitted by the least squares method for the cross-sectional point set. This represents the Z-axis coordinate of the center point of the section extracted from the theoretical section in the BIM model.

8. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 7, characterized in that, The misalignment of adjacent construction joints represents the difference in elevation of the outline and the offset of the axis between two adjacent segments at the construction joint. The calculation of the elevation difference of the contour lines includes the following steps: On the cross-section where the construction joint is located, project the point clouds of the two segments onto the same plane. Then, along the normal direction of the theoretical contour line, calculate the elevation difference or radial distance difference of the corresponding positions point by point. Take the elevation difference and radial distance difference, and take the maximum value as the contour line elevation difference. The calculation of axis offset includes the following steps: The axis offset includes both horizontal and vertical offsets. The coordinates of the center points of the cross-sectional point clouds at the construction joint for each of the two segments are calculated. The coordinates of the two center points are as follows: and The horizontal offset of the axis is The vertical offset is The offset direction is determined based on the positive or negative value.

9. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 8, characterized in that, After step 34 is completed, the following also includes: Step 4: Generate a 3D visualization interface for construction deviations and an automated acceptance report. The 3D visualization interface includes a 3D chromatographic deviation distribution map, and the automated acceptance report includes a defect statistics report.

10. The method for quantifying construction errors of underwater structures to avoid cross-contamination according to claim 9, characterized in that, The specific implementation process of generating the 3D visualization interface for construction deviations and the automated acceptance report includes the following steps: Step 41: Generate a three-dimensional chromatographic deviation distribution map: render the radial deviations of all cross sections according to their magnitude using chromatographic rendering, and use different colors to represent the degree of radial deviation; Step 42: Defect Statistics and Report Generation: Statistically count the over-excavation area, under-excavation area, maximum over-excavation value, maximum under-excavation value, number of points exceeding the axis deviation limit, and number of points exceeding the construction joint misalignment limit for the entire line, thereby generating a standardized defect statistics report. This defect statistics report includes the station location, deviation type, deviation value, and corresponding specification tolerance threshold for each defect. Step 43: Link the 3D chromatographic deviation distribution map and statistical defects with the generated report to respond to when the user clicks on any out-of-limit area in the 3D chromatographic deviation distribution map, and pop up detailed information of the clicked location in the 3D visualization display interface. This detailed information includes the station number, deviation type, deviation value, corresponding cross-sectional view and conclusion on whether rework is required, and responds to when the user clicks on the defect statistical report, and automatically locates the location associated with the 3D chromatographic deviation distribution map. Step 44: Integrate the three-dimensional chromatographic deviation distribution map, defect statistics report, standard comparison table and acceptance conclusion to generate a complete acceptance report that meets the project requirements.

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