Method for controlling installation details of metal structure of hydropower project based on absolute laser tracking

Abstract

The application discloses a water and electricity engineering metal structure installation detail control method based on absolute laser tracking, remotely reads original installation data, converts the original installation data into point cloud data and uploads the point cloud data to the cloud; carries out regional extraction on the point cloud data based on a clustering algorithm, carries out model segmentation according to the regions to obtain a point cloud model, and the point cloud model comprises a point cloud plane model and a point cloud spherical model; fits a water and electricity engineering electromechanical device to be installed based on the point cloud model, determines an installation detail guidance scheme, and realizes installation detail adjustment according to the installation detail guidance scheme. The application combines absolute laser tracking technology and proposes a point cloud data analysis method, which provides a new idea and a new direction for efficient and high-accuracy guidance scheme design of electromechanical devices.

Description

Technical Field

[0001] This invention belongs to the intersection of absolute laser tracking technology and metal structure installation detail control based on sampling point data, specifically involving a method for controlling the installation details of metal structures in hydropower projects based on absolute laser tracking technology. Background Technology

[0002] Currently, the installation of metal structures in hydropower projects involves setting up monitoring points and manually conducting continuous or periodic inspections using equipment such as total stations and dial gauges. The accuracy of the installation is then analyzed and feedback is used to control the coordinate deviations of these monitoring points. In this process, the accuracy of the sampling data and the timeliness of the feedback are crucial for controlling the metal structure installation process. However, traditional methods for controlling the installation of electromechanical metal structures can only perform random checks of key parameters during installation, making it difficult to achieve rapid, automated, and intelligent sensing and feedback control of the installation process data. Emerging absolute laser tracking technology can solve this problem. By employing absolute laser tracking, the position and angle of gates can be precisely adjusted to ensure perfect matching with upstream and downstream water conservancy and dam structures, improving the precise coordination with the hoisting and control systems, and achieving precise water flow control. For pressure steel pipes, laser tracking technology can ensure the continuity and stability of their installation and reduce the risk of leakage at joints. Therefore, this invention combines absolute laser tracking technology to study detailed control methods for the installation of metal structures in hydropower projects.

[0003] Given the complex installation site conditions, the quality of sampling point data is affected by various factors such as personnel movement, obstruction, and instrument parameters. Traditional manual detection methods are difficult to achieve satisfactory results in terms of efficiency and automation. As an emerging real-scene modeling technology, absolute laser tracking has matured in the field of positioning and mapping. Many scholars have conducted relevant research on the application of absolute laser trackers in engineering fields and the automatic extraction and analysis of point cloud data. Reference [1] verified that laser tracking technology can be fully applied to the welding requirements of complex components through the ten-point calibration method and the adjustment of multivariable parameters. Reference [2] proposed a method for rapid mapping of building facades in narrow blocks using laser vision tracking scanning technology. Compared with the traditional total station method and the traditional ground-based three-dimensional laser scanning method, laser vision tracking scanning technology has the characteristics of high data portability, good integrity, and high automation. Reference [3] used a laser rangefinder to obtain the relevant position information of the robot and the position information of obstacles. The extended Kalman filter algorithm was used to track the robot's movement path, which can obtain high-precision robot movement path tracking results and avoid obstacles on the movement path in real time. Reference [4] uses three-dimensional laser scanning technology to monitor large buildings such as tunnels and bridges, and uses the random sampling consensus algorithm to fit straight lines, calculate the central axis based on the geometric position, and realize the extraction of cross-section of tunnel scanning point cloud. Reference [5] proposes a boundary extraction algorithm for rectangular windows based on ground three-dimensional laser scanning data, which can quickly obtain rich details of building facades and has great application advantages in the three-dimensional fine modeling of digital cities.

[0004] In summary, the use of an absolute laser tracker during the installation of hydropower metal structures enables high-speed detection, scanning, and real-time control, reliably completing tasks such as online guided assembly and processing, and point cloud data acquisition. Furthermore, by analyzing the point cloud data, a guidance plan for on-site installation can be quickly formulated.

[0005] [1] Research on the application of laser tracking technology in welding of engineering machinery [J]. Laser & Infrared, 2023, 53(05):699-705. [2] A method for surveying the facades of buildings in a street based on laser visual tracking scanning technology [J]. Heilongjiang Engineering, 2023, 37(02):22-26+33. DOI:10.19352 / j.cnki.issn1671-4679.2023.02.004. [3] Robot movement path tracking and real-time obstacle avoidance based on laser ranging technology [J]. Automation and Instrumentation, 2023, 38(03):30-34. DOI:10.19557 / j.cnki.1001-9944.2023.03.008. [4] Cross-section extraction of tunnel point cloud based on RANSAC algorithm [J]. Bulletin of Surveying and Mapping, 2021(09):120-123. [5] A method for extracting window boundaries based on three-dimensional laser scanning data [J]. Exploration Science and Technology (2):5. Summary of the Invention

[0006] In response to the lack of automation and efficiency in the feedback process in current research on the detailed control of metal structure installation, this invention proposes a method for detailed control of metal structure installation in hydropower engineering based on absolute laser tracking. This method combines raw installation data collected from self-marking points with point cloud data for model segmentation. Based on the model, the electromechanical components of the hydropower project to be installed are fitted to determine the installation detail guidance scheme, and the installation details are adjusted according to the installation detail guidance scheme.

[0007] This invention is achieved using the following technical solution: This invention proposes a method for detailed control of the installation of metal structures in hydropower engineering based on absolute laser tracking. The method includes the following steps: Laser tracking is used to collect three-dimensional position data of marker points from the component to be installed, thus obtaining the original installation data; The original installation data is remotely read, converted into point cloud data, and uploaded to the cloud. The point cloud data is used to extract regions based on a clustering algorithm, and the regions are then segmented to obtain a point cloud model, which includes a point cloud planar model and a point cloud spherical model. Based on the point cloud model, fit the electromechanical components of the hydropower project to be installed, determine the installation detail guidance scheme, and implement the installation detail adjustments according to the installation detail guidance scheme.

[0008] Furthermore, the detailed installation guidance scheme further includes simulating the shape and position of the electromechanical installation based on the target installation data to obtain a fitting result, comparing the fitting result with the original installation data to obtain the installation error offset.

[0009] Furthermore, the model segmentation process of the point cloud planar model further includes the following steps: The iteration begins by finding the seed point with the lowest curvature as the initial seed point. The neighboring points around the seed point are compared with the seed point. Points within the normal threshold are classified but not used as seed points. Points with curvature differences less than the threshold are used as new seed points. This process continues until clustering is complete, resulting in a point cloud planar model.

[0010] Furthermore, the model segmentation process of the point cloud spherical model further includes the following steps: The iteration begins by selecting elements from the remaining point cloud data that can form a sphere model. Specifically, this includes: finding the point to be processed in the remaining point cloud space, finding the nearest neighbor point to the point to be processed using the K-nearest neighbor algorithm, determining the distance from the nearest neighbor point to the point to be processed, saving the nearest neighbor points with a distance less than a threshold as a class, finding the remaining points in the class except for the point to be processed, until the target sphere clustering is completed and the point cloud sphere model is obtained.

[0011] Furthermore, the step of solving for the target installation data based on the point cloud model and fitting the target installation data further includes: solving for the target installation data using a point cloud plane model; if the data is within a certain range, it is classified as an interior point; and the point cloud plane model containing the most interior points is selected as the best fitting result.

[0012] Furthermore, the step of solving for the target installation data based on the point cloud model and fitting the target installation data further includes: solving for the target installation data using the point cloud spherical model equation and selecting the point cloud spherical model containing the most interior points as the best fitting result.

[0013] Compared with existing technologies, the present invention can achieve the following beneficial technical effects: 1) This invention realizes the establishment of a point cloud data model of the metal structure installation site by combining point cloud data analysis with absolute laser tracking technology. The point cloud model is segmented by Euclidean clustering algorithm, and the electromechanical metal structure is simulated according to the center coordinates of the target sphere. The offset is obtained by comparing with the specifications and design drawings. In this way, the guidance scheme for installation details can be quickly and accurately determined in the actual engineering site installation, improving the automation and intelligence level of the installation process. At the same time, it provides a new idea and method for the quality control of metal structure installation details.

[0014] 2) To achieve speed and accuracy in determining the installation guidance scheme, and further improve the installation quality of the metal structure details of hydropower projects; 3) The Euclidean clustering algorithm can better discover hidden patterns and regularities in data, providing strong support for decision-making. Attached Figure Description

[0015] Figure 1 This is a detailed flowchart of the method for controlling the installation details of metal structures in hydropower projects based on absolute laser tracking, as described in this invention.

[0016] Figure 2 This is a flowchart of the model segmentation process based on clustering algorithm in this invention. Detailed Implementation

[0017] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0018] like Figure 1 As shown, the present invention proposes a method for detailed control of the installation of metal structures in hydropower engineering based on absolute laser tracking, which specifically includes the following steps: Step 1: Use laser tracking to collect marker points from the component to be installed, and obtain the three-dimensional position data of the marker points as the original installation data; that is, use an absolute laser tracker to collect the designated target points and three-dimensional position data of the hydropower project area to obtain the original installation data of the hydropower project metal structure; specifically, set up the measurement station in advance and designate the target point at the measurement station. Step 2: Use a computer to remotely read the original installation data of the metal structure of the hydropower project, upload it to the cloud, store it in the database, and use development tools in the background to interpret the original installation data into point cloud data; Step 3: Extract regions from the point cloud data based on the Euclidean clustering algorithm, and perform model segmentation based on the regions to obtain point cloud models, including point cloud planar models and point cloud spherical models. Step 4: Fit the metal structure to be installed based on the model. This further includes solving the target installation data based on the point cloud model, obtaining the installation error offset of the original installation data based on the target installation data, determining the installation detail guidance scheme, and adjusting the installation details according to the installation detail guidance scheme. This step simulates the shape and position of the electromechanical components, thereby enabling efficient comparison of coordinate deviation values, determining the final installation detail guidance scheme, and feeding it back to the mobile receiving platform of the on-site installation personnel.

[0019] The specific implementation process of this invention is described as follows: Using an absolute laser tracker as the data acquisition platform, it's necessary to consider the potential obstruction caused by moving construction machinery and personnel during the absolute laser tracking process. Therefore, a pre-planned field operation strategy is required to complete the point cloud data acquisition of the target area. Specifically: survey the site environment; design the placement of the laser tracker and the 3D position data of the target points for the metal structural components requiring data acquisition, ensuring the absolute laser tracker completes the point cloud data acquisition task at the designated location; and rationally set the absolute laser tracker parameters, including scanning angle, scanning resolution, and scanning quality, to ensure both scanning quality and scanning time, achieving rapid and high-precision point cloud data acquisition.

[0020] The raw point cloud data is uploaded to the cloud and stored in the database as a file stream. Based on the scan sequence number, the SDK tool reads the three-dimensional spatial information of the collected point cloud data and outputs it as a common point cloud format such as .txt or .xyz file, which facilitates subsequent point cloud analysis.

[0021] like Figure 2The diagram shows a detailed flowchart of the installation detail control model segmentation based on the random sampling consensus algorithm in this invention. The point cloud data is divided into facets and target sphere regions using a point cloud segmentation technique based on Euclidean clustering, resulting in a point cloud planar model and a point cloud spherical model, respectively.

[0022] Specifically: i. Point cloud planar model segmentation process: Selecting elements that can form a patch model from the point cloud data. This step includes calculating the normals and curvature of all point clouds, sorting them in ascending order of curvature, finding the seed point with the lowest curvature as the initial seed point for patch extraction; determining if there are new seed points. This step includes comparing the seed point with its neighboring points. Points with a normal angle less than a threshold are classified (i.e., classified as patches) but not as seed points. Points with a curvature difference less than a threshold are used as new seed points. That is, if the normal angle is sufficiently smooth and the surface is at the same degree of curvature, then the point is used as a seed point. The judgment of normal angle and curvature value is repeated until no new points are classified; the detected seed points are clustered to form patches to obtain the point cloud planar model; the point cloud model is saved separately and removed from the point cloud data. ii. Point cloud spherical model segmentation process: Detect the remaining point cloud data and select elements that can form a spherical model from the remaining point cloud data. Specifically, find a point p10 in the remaining point cloud space, use kdTree to find the n nearest neighboring points to p10, determine the distance of these neighboring points to p10, and put points with a distance less than the threshold into class Q (i.e., save as a class). Find another point other than p10 in class Q and repeat the above steps. When no new point can be added to class Q, the clustering of the target sphere is completed, and the point cloud spherical model is obtained. Output the spherical model parameters as a .txt file.

[0023] Based on the point cloud model, the target installation data is solved, and fitting is performed on the target installation data, including: 1) Solving the target installation data using a point cloud planar model; if the data is within the range, it is classified as an interior point, and the point cloud planar model containing the most interior points is selected as the best fitting result. 2) Solving the target installation data using the point cloud spherical model equation; the point cloud spherical model containing the most interior points is selected as the best fitting result. Furthermore, the electromechanical metal structure is simulated based on the coordinates of the target sphere's center, and compared with the specifications and design drawings to obtain the offset. This allows for the rapid and accurate determination of guidance schemes for installation details during actual on-site installation, improving the automation and intelligence level of the installation process.

[0024] Establish a set of analysis rules that conform to the site conditions, and simulate the on-site metal structure to obtain the error offset from the design drawings and specifications. Specifically, a detailed model of the target sphere is created and combined with the 3D model of the metal structure at the sampling point position. The installation details are controlled based on the center coordinates of the sphere fitted by the extracted target sphere point cloud data. The error offset (i.e., deviation) is obtained by comparing with the design drawings and specifications. The error offset results are summarized into an adjustment plan for the existing installation details, and the adjustment plan is sent to the mobile client of the on-site installation personnel.

[0025] In summary, the technical details of the present invention are disclosed as follows: Absolute laser tracking was used to acquire detailed information about the installation of the metal structure, ensuring that the acquired point cloud data contained sufficient information for further analysis. First, the main scanning area was determined, then the approximate location of the scanning station was determined within that area, the absolute laser tracker was placed, the instrument parameters were set, and data acquisition began.

[0026] Data from the absolute laser tracker is remotely read by a computer and packaged layer by layer in the form of data packets on the source computer. These packets are then forwarded through switches and routers via IP and MAC addresses before finally being sent to the target computer for point cloud analysis and to a cloud backup. The raw point cloud data of the collected metal structure is converted into a common point cloud format file using software development tools.

[0027] Point cloud data is analyzed and processed, and the Euclidean clustering algorithm is used to calculate the parameters of a mathematical model from a series of data containing outliers. The essence of Euclidean clustering in fitting point cloud data is to group point clouds into a single class. However, since the objects processed in this invention are parametric geometric figures or models, such as planes or spheres, it can also be called a model segmentation method. Compared to least squares fitting algorithms, Euclidean clustering can better discover hidden patterns and regularities in the data, providing strong support for decision-making. The collected spatial point cloud information is rich, and the point cloud model is segmented using the PCL point cloud library.

[0028] This paper presents a correlation method for determining the installation detail control scheme based on absolute laser tracking and point cloud analysis sampling point results. First, a detailed model of the metal structure and the target sphere is created according to specifications, design drawings, and target sphere dimensions. A combined model based on the actual location of the sampling points serves as a reference model for comparison. Second, after segmenting the model using the measured point cloud information acquired through absolute laser tracking, the target sphere's center coordinates are used to replace the measured 3D spatial sampling point positioning information. The positional deviation between the two is compared with the precise model's 3D spatial information of the target sphere. Then, based on the magnitude of the offset, a guidance scheme for on-site installation details is determined, and this scheme is abstracted into an adjustment strategy relative to the world coordinate system. Finally, output feedback is provided to the on-site installation work, displaying and reminding information on adjustment distance and relative orientation. The real-time feedback results guide the installation workers in the next step, such as the direction and distance of adjustment, all of which are crucial for ensuring the quality of the metal structure's installation details.

[0029] In summary, the use of absolute laser trackers during the installation of hydropower metal structures enables high-speed detection, scanning, and real-time control, reliably completing tasks such as online guided assembly and processing, and point cloud data acquisition. Furthermore, by analyzing the point cloud data, a guidance plan for on-site installation can be quickly formulated. The point cloud data analysis method provides new ideas and directions for the efficient and accurate design of guidance plans for metal structures. First, a large number of sampling points are arranged around the metal structure, and three-dimensional point cloud data is recorded using absolute laser tracking technology. Next, a point cloud segmentation and automatic extraction model is established. Then, based on the offset of coordinate displacement relative to design drawings and construction specifications, the corresponding adjustment plan is determined, and the results are fed back to the mobile terminal at the metal structure installation site, thereby improving the installation quality of details in hydropower metal structures.

[0030] It should be understood that the embodiments and examples discussed herein are merely illustrative and may be modified or altered by those skilled in the art, and all such modifications and alterations should fall within the scope of protection of the appended claims.

Claims

1. A method for detailed control of the installation of metal structures in hydropower projects based on absolute laser tracking, characterized in that, The method includes the following steps: Laser tracking is used to collect three-dimensional position data of marker points from the component to be installed, thus obtaining the original installation data; The original installation data is remotely read, converted into point cloud data, and uploaded to the cloud. The point cloud data is used to extract regions based on a clustering algorithm, and the regions are then segmented to obtain a point cloud model, which includes a point cloud planar model and a point cloud spherical model. Based on the point cloud model, fit the electromechanical components of the hydropower project to be installed, determine the installation detail guidance scheme, and implement the installation detail adjustments according to the installation detail guidance scheme.

2. The method for detailed control of installation of metal structures in hydropower projects based on absolute laser tracking according to claim 1, characterized in that, The detailed installation guidance plan further includes simulating the shape and location of the electromechanical installation based on the target installation data to obtain a fitting result, comparing the fitting result with the original installation data to obtain the installation error offset.

3. The method for detailed control of installation of metal structures in hydropower projects based on absolute laser tracking according to claim 1, characterized in that, The process of segmenting a point cloud planar model further includes the following steps: The iteration begins by finding the seed point with the lowest curvature as the initial seed point. The neighboring points around the seed point are compared with the seed point. Points within the normal threshold are classified but not used as seed points. Points with curvature differences less than the threshold are used as new seed points. This process continues until clustering is complete, resulting in a point cloud planar model.

4. The method for detailed control of installation of metal structures in hydropower projects based on absolute laser tracking according to claim 1, characterized in that, The model segmentation process of the point cloud spherical model further includes the following steps: The iteration begins by selecting elements from the remaining point cloud data that can form a sphere model. Specifically, this includes: finding the point to be processed in the remaining point cloud space, finding the nearest neighbor point to the point to be processed using the K-nearest neighbor algorithm, determining the distance from the nearest neighbor point to the point to be processed, saving the nearest neighbor points with a distance less than a threshold as a class, finding the remaining points in the class except for the point to be processed, until the target sphere clustering is completed and the point cloud sphere model is obtained.

5. The method for detailed control of installation of metal structures in hydropower projects based on absolute laser tracking according to claim 1, characterized in that, The step of solving the target installation data based on the point cloud model and fitting the target installation data further includes: solving the target installation data using a point cloud plane model; if the data is within the range, it is classified as an interior point; and the point cloud plane model containing the most interior points is selected as the best fitting result.

6. The method for detailed control of installation of metal structures in hydropower projects based on absolute laser tracking according to claim 1, characterized in that, in, The step of solving for the target installation data based on the point cloud model and fitting the target installation data further includes: solving for the target installation data using the point cloud spherical model equation and selecting the point cloud spherical model containing the most interior points as the best fitting result.

Images