Avionics engineering electromechanical installation detail control method based on three-dimensional laser scanning technology

Abstract

The application discloses a kind of based on three-dimensional laser scanning technology's aviation engineering electromechanical installation detail control method: target area is explored, target point is specified, station point position is placed three-dimensional laser scanner in station point, and the original point cloud data collection work of target area is completed;Original point cloud data collected by three-dimensional laser scanner is read remotely using computer, uploaded to cloud, stored to database, and original point cloud data is interpreted as point cloud general format;The original point cloud data after interpretation is segmented using point cloud segmentation technology based on random sampling consistency algorithm, and the surface sheet is extracted, and the target ball is fitted;According to the target ball data calculated, the structure and position of the electromechanical component to be installed are simulated, and the coordinate deviation value is compared with the specification requirements and design drawings, to determine the final installation detail guidance scheme and feedback to the field installation personnel.The application provides a new idea and new direction for the efficient and accurate guidance scheme design of electromechanical.

Description

Technical Field

[0001] This invention belongs to the intersection of three-dimensional laser scanning technology and electromechanical installation detail control based on sampling point data. More specifically, it relates to a method for electromechanical installation detail control in avionics engineering based on three-dimensional laser scanning technology. Background Technology

[0002] The electromechanical installation process in avionics engineering requires extremely strict control over installation accuracy. Failure to follow specifications during installation can pose significant risks to the subsequent operation of the equipment. Therefore, it is crucial to manage every detail during installation, strictly adhere to specifications and technical standards during acceptance testing, ensure smooth equipment operation, and minimize malfunctions caused by oversights during installation that could impact the equipment's future use.

[0003] In the entire installation process of hydroelectric generators for aerospace engineering, measurement and testing technologies are particularly important. First, it is crucial to ensure the flatness and depth of the foundation construction, accurately assemble the stator, and strictly control welding quality. Precise adjustments and positioning are made according to the center, elevation, and level of the turbine's main shaft, and the air gap between the rotor and stator is measured to ensure uniform air clearance. Finally, the runout of the unit's main shaft is measured and adjusted to guarantee its accuracy. Establishing a precise sensing system is of great significance to the entire aerospace electromechanical installation process.

[0004] Currently, the electromechanical installation in avionics engineering involves setting up monitoring points and manually conducting continuous or periodic inspections using equipment such as total stations and dial indicators. The accuracy of 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 the control of the electromechanical installation process, thus impacting the safety and stable operation of the avionics engineering. However, traditional methods for controlling the installation of electromechanical metal structures can only achieve random checks of key parameters during installation, making it difficult to achieve rapid, automated, intelligent sensing and feedback control of the electromechanical installation process data. Emerging 3D laser scanning technology can solve this problem; therefore, this invention combines 3D laser scanning technology to research detailed control methods for the electromechanical installation in avionics engineering.

[0005] Given the complexity of installation sites, the quality of sampling point data is affected by various factors such as personnel movement, obstruction, and instrument parameters. Traditional manual inspection methods struggle to achieve satisfactory efficiency and automation. 3D laser scanning, as an emerging real-scene modeling technology, has matured considerably in the field of location mapping. Many scholars have conducted research on the application of 3D laser scanners in engineering and the automatic extraction and analysis of point cloud data. (Mali et al.) [1]Three-dimensional laser scanning technology was applied to road engineering surveying, and a starting point cloud analysis process was established to perform planar virtual measurement, DEM modeling, contour line generation, and longitudinal and cross-sectional model generation. The accuracy was compared with that of total station data acquisition, demonstrating that the technology meets the accuracy requirements of road engineering surveying. (Ren Hongwen et al.) [2] A method for contour boundary and spatial positioning of unknown goaf areas in mining areas based on a 3D laser scanner is proposed, overcoming the shortcomings of traditional methods such as poor accuracy and high risk. (Zhu Longjun) [3] Researchers used a 3D laser scanner to inspect building quality. The entire area was scanned, and the point cloud data was used to extract building condition points, heights, and outlines. Compared to traditional measurement methods, this method determined whether the accuracy of point locations, elevations, and side lengths met specifications, effectively avoiding rework caused by missed inspections. (Ding Ge) [4] Researchers used 3D laser scanning technology to monitor large structures such as tunnels and bridges, and employed a random sampling consensus algorithm for straight line fitting. Based on geometric location, they calculated the central axis, thus achieving cross-sectional extraction of the tunnel scanning point cloud. (Wu Junhe) [5] Based on terrestrial 3D laser scanning data, researchers proposed a boundary extraction algorithm for rectangular windows, which can quickly obtain rich details of building facades and has significant application advantages in the 3D fine modeling of digital cities.

[0006] In summary, existing research has yielded some findings on the application of 3D laser scanners in engineering and the automatic extraction and analysis of point cloud data. However, as the core equipment of a hydroelectric power station, the turbine unit has a complex structure and numerous components, requiring extremely high installation accuracy and quality control. Traditional measurement and modeling methods often fall short of these requirements, struggling to consider the timeliness of feedback information during the electromechanical installation process and the integrity of component facades, and lacking advanced sensing and analysis methods to perceive the spatial location information of sampling points. Therefore, 3D laser scanning technology can be used to capture the true shape and appearance of hydroelectric equipment, quickly and accurately collecting a large amount of detailed information, which can then be converted into point clouds or polygon meshes (such as STL file format). Then, by analyzing the point cloud data, a high-quality, accurate model can be built, enabling the rapid development of guidance plans for on-site installation. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method for detailed control of electromechanical installation in avionics engineering based on three-dimensional laser scanning technology. The invention combines three-dimensional laser scanning technology with a point cloud data analysis method, providing new ideas and directions for high-efficiency and high-accuracy guidance in the design of electromechanical systems.

[0008] The objective of this invention can be achieved through the following technical solutions.

[0009] This invention relates to a method for controlling the electromechanical installation details of avionics engineering based on three-dimensional laser scanning technology, comprising the following steps: Step 1: Data Collection Using a 3D laser scanner as a data acquisition platform, the target area is explored, the locations of target points and measurement stations are specified, and the 3D laser scanner is placed at the measurement station to complete the original point cloud data acquisition of the target area. Step 2: Data Processing The raw point cloud data collected by the 3D laser scanner is read remotely by computer, uploaded to the cloud, stored in the database, and interpreted into a common point cloud format. Step 3: Model Segmentation The original point cloud data after interpretation is segmented using a point cloud segmentation technique based on random sampling consensus algorithm, and surface patches are extracted and fitted to a target sphere. Step 4: Analysis and Feedback Based on the calculated target sphere data, the structure and location of the electromechanical components to be installed are simulated, and the coordinate deviation values ​​are compared with the specifications and design drawings to determine the final installation details guidance plan and provide feedback to the on-site installation personnel.

[0010] Furthermore, in the first step, the parameters of the 3D laser scanner are set, including scanning angle, scanning resolution, and scanning quality.

[0011] Furthermore, in the second step, the raw point cloud data collected by the 3D laser scanner is uploaded to the cloud in the form of a file stream and stored in the database. Based on the scan sequence number, the 3D spatial information of the collected raw point cloud data is read using the development tool SDK and output as a .txt or .xyz file, a common point cloud format.

[0012] Furthermore, in the third step, using the PCL point cloud library, a point cloud segmentation technique based on the random sampling consensus algorithm is employed to divide the interpreted raw point cloud data into patch and target sphere regions. The specific process is as follows: S1 plane segmentation S1.1 Preprocessing: Denoising and range filtering are performed on the interpreted raw point cloud data; S1.2 Randomly select three points from the preprocessed point cloud data to form a plane; S1.3 Use the remaining point cloud data to test the plane model, calculate the distance from all other points to the plane, and if it is less than the set threshold, then the points in the same plane are considered as interior points of the plane and added to the set of interior points of the plane. At the same time, record the number of interior points of the plane. S1.4 Repeat steps S1.2 to S1.3 for iterative calculation until the number of iterations reaches the set iteration threshold, at which point the loop terminates. At this point, several planar models are obtained, and each planar model corresponds to several interior points. S1.5 Saves the planar model with the most interior points and removes the corresponding interior points from the preprocessed point cloud data; S1.6 Repeat steps S1.2 to S1.5 until there are no significant planes, that is, all planes have been segmented.

[0013] S2 Sphere Segmentation S2.1 Randomly select four points from the remaining point cloud data after removal to form a sphere, and calculate the parameters of the sphere model; S2.2 Use the remaining point cloud data to examine the sphere model, calculate the distance from all other points to the sphere, and if the distance exceeds the set threshold, it is classified as an outlier; if it is within the set threshold, the points are considered to be inside the sphere and added to the set of inside points of the sphere, and the number of inside points of the sphere is recorded. S2.3 Repeat steps S2.1 and S2.2 iteratively until the number of iterations reaches the set iteration threshold, at which point the loop terminates. S2.4 Select the sphere model containing the most internal points as the target sphere model, and save and output its center coordinates, radius, and sphere model parameters.

[0014] Furthermore, the specific process of preprocessing in step S1.1 is as follows: S1.1.1 Denoising: Use statistical filtering or radius filtering to eliminate noise points in the interpreted raw point cloud data; S1.1.2 Range filtering: The denoised point cloud data is cropped to the region of interest, and irrelevant parts are removed.

[0015] Furthermore, in the fourth step, a set of analysis rules conforming to the site conditions is established, and the structure of the electromechanical components to be installed on site is simulated to obtain the deviation from the design drawings and specifications. Based on the deviation, the final installation detail guidance plan is determined and fed back to the on-site installation personnel. Specifically: First, a detailed model of the electromechanical components to be installed is created according to the specifications and design drawings, serving as a reference model for comparison. Then, a detailed model is created based on the feedback target sphere parameters, and combined with the 3D model of the electromechanical components to be installed at the measured sampling point locations to obtain the measured 3D model of the electromechanical components to be installed. Next, the measured 3D model of the electromechanical components to be installed is compared with the reference model to obtain the positional deviation value between the two. Finally, based on the magnitude of the deviation, a guidance and adjustment plan for the on-site installation details is determined and sent to the mobile client of the on-site installation personnel, providing output feedback for the on-site installation work, displaying and reminding information on the adjustment distance and relative orientation.

[0016] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention combines 3D laser scanning technology to establish a point cloud data model of the electromechanical installation site. The point cloud model is segmented using a random sampling consensus algorithm, and the electromechanical components to be installed (such as turbine units and generator components) are simulated based on the center coordinates of the target sphere. The model is then compared with the specifications and design drawings to obtain the deviation. This allows for the rapid and accurate determination of guidance schemes for installation details in actual engineering installations, improving the automation and intelligence level of the installation process. At the same time, it provides a new idea and method for quality control of installation details. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the detailed control method for electromechanical installation in avionics engineering based on three-dimensional laser scanning technology, as described in this invention.

[0018] Figure 2 This is a flowchart of point cloud segmentation based on the random sampling consensus algorithm in this invention. Detailed Implementation

[0019] The present invention will now be further described with reference to the accompanying drawings.

[0020] To address the lack of automation and efficiency in current research on the detailed control of electromechanical installation, and to improve the speed and accuracy of the installation guidance scheme determination process, thereby further enhancing installation quality, this invention proposes a detailed control method for electromechanical installation in avionics engineering based on three-dimensional laser scanning technology. Figure 1 As shown, the specific steps include: Step 1: Data Collection 3D laser scanning is used to collect installation details of electromechanical components to ensure that the collected point cloud data contains sufficiently rich information for further analysis. A 3D laser scanner is used as the data acquisition platform. The target area is determined based on the installation location of the electromechanical components to be installed. The target area is explored, target points (within the target area) and measurement station locations are designated, and the 3D laser scanner is placed at the measurement station. The 3D laser scanner parameters are set to complete the acquisition of the raw point cloud data for the target area.

[0021] Using a 3D laser scanner as a data acquisition platform, it's necessary to consider the potential obstruction caused by moving construction machinery and personnel during the 3D laser scanning process. Therefore, a 3D laser scanning operation strategy needs to be planned in advance to complete the point cloud data acquisition of the target area. Specifically: survey the site environment, design measurement stations and target points for the electromechanical components to be installed, ensuring the 3D laser scanner completes the point cloud data acquisition task at the designated locations; and rationally set the 3D laser scanner parameters, including scanning angle, scanning resolution, and scanning quality, to ensure both scanning quality and scanning time, achieving fast and high-precision point cloud data acquisition.

[0022] Step 2: Data Processing The raw point cloud data collected by the 3D laser scanner is read remotely by computer, uploaded to the cloud, stored in the database, and then interpreted into a common point cloud format by the media development tool in the background.

[0023] Data from a 3D laser scanner 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 using IP and MAC addresses, and finally sent to the target computer for point cloud analysis and cloud backup. The raw electromechanical point cloud data is then converted into a common point cloud format file using software development tools.

[0024] Specifically, the raw point cloud data acquired by the 3D laser scanner can be uploaded to the cloud as a file stream and stored in a database. Based on the scan sequence number, the 3D spatial information of the acquired raw point cloud data can be read using SDK tools and output as a common point cloud format such as .txt or .xyz file, which facilitates subsequent point cloud analysis.

[0025] Step 3: Model Segmentation The original point cloud data after interpretation is segmented using a point cloud segmentation technique based on random sampling consensus algorithm, surface patches are extracted, and a target sphere is fitted.

[0026] Point cloud data is analyzed and processed, and the parameters of a mathematical model are calculated from a series of data containing outliers using the Random Sample Consensus (RSC) algorithm. The essence of the RSC algorithm for fitting point cloud data is to match the point cloud with different original geometric shapes, and it is commonly used for shape detection or extraction. However, since the object processed in this invention is a parametric geometric shape or model, such as a plane or sphere, it can also be called a model segmentation method. Compared to the least squares fitting algorithm, the RSC algorithm is less affected by noise and has higher fitting accuracy. The collected spatial point cloud information is rich, and point cloud segmentation is performed using the PCL point cloud library. The specific workflow is as follows: Figure 2 As shown.

[0027] S1: Plane segmentation S1.1 Preprocessing: Denoising and range filtering are performed on the interpreted raw point cloud data. S1.1.1 Denoising: Use statistical filtering or radius filtering to eliminate noise points in the interpreted raw point cloud data.

[0028] Statistical filtering is based on neighborhood statistical analysis to remove outliers that do not conform to the distribution. The specific process involves processing each point p in the interpreted raw point cloud data. i Calculate the distance from each point in its neighborhood to p. i average distance μ i If μ corresponds to a certain point i If the value is greater than μ + ασ, it is identified as a noise point and removed. Here, α is the sensitivity, σ is the standard deviation, and μ is the sum of all μ values. i The global mean.

[0029] Radius filtering removes locally sparse points by filtering based on neighborhood density. The specific process is as follows: ① Neighborhood density statistics: For each point p in the interpreted raw point cloud data... i Count the number of neighboring points N within radius r respectively. i ② Threshold rejection: If N i <N min Remove that point, N min The minimum number of points for the set area.

[0030] S1.1.2 Range Filtering: The denoised point cloud data is cropped to the region of interest, and irrelevant parts are removed. Specifically, the cropping direction is set according to the scene requirements, and a coordinate axis is selected, such as the Z-axis for ground segmentation. An interval threshold is set along the specified coordinate axis (such as X / Y / Z) to retain point cloud data points whose coordinates are within the interval.

[0031] S1.2 Three points are randomly selected from the preprocessed point cloud data to form a plane. The formula for this plane model is shown in formula (1): A • x + B • y + C • z = D (1) S1.3 Use the remaining point cloud data (i.e., the point cloud data remaining after removing the three points selected in step S1.2 from the preprocessed point cloud data) to test the plane model, calculate the distance from all other points to the plane, which is the error. If the error is less than the set threshold, the points in the same plane are considered to be classified as interior points of the plane and added to the interior point set of the plane. At the same time, record the number of interior points of the plane.

[0032] S1.4 Repeat steps S1.2 to S1.3 for iterative calculation until the number of iterations reaches the set iteration threshold, at which point the loop terminates. At this point, several planar models are obtained, and each planar model corresponds to several interior points.

[0033] S1.5 saves the planar model with the most interior points and removes the corresponding interior points from the preprocessed point cloud data.

[0034] S1.6 Repeat steps S1.2 to S1.5 until there are no significant planes, that is, all planes have been segmented.

[0035] S2: Spherical Division After removing the S2.1 patch, the random sampling consensus algorithm is used to fit the target sphere. Four points are randomly selected from the remaining point cloud data after removal to form a sphere. The formula for the sphere model is shown in formula (2). The parameters of the sphere model are calculated using the coordinates of the four points according to formula (2).

[0036] (2) S2.2 Use the remaining point cloud data to test the sphere model, calculate the distance (i.e. error) from all other points to the sphere. If the distance exceeds the set threshold, it is classified as an outlier; if it is within the set threshold, the points are considered to be inside the sphere and added to the set of inside points of the sphere. At the same time, the number of inside points of the sphere is recorded.

[0037] S2.3 Repeat steps S2.1 and S2.2 iteratively until the number of iterations reaches the set iteration threshold, at which point the loop terminates. At this point, several sphere models are obtained, and each sphere model corresponds to several internal points.

[0038] S2.4 Select the sphere model containing the most internal points as the target sphere model, and save its center coordinates, radius, and sphere model parameters as a .txt file.

[0039] Step 4: Analysis and Feedback Based on the calculated target sphere data, the structure and position of the electromechanical components to be installed are simulated, thereby enabling efficient comparison of coordinate deviation values, determination of the final installation detail guidance plan, and feedback to the mobile terminal receiving platform of the on-site installation personnel.

[0040] A correlation method for determining the installation detail control scheme based on the sampling point results of 3D laser scanning and point cloud analysis is proposed. A set of analysis rules conforming to the site conditions is established, and the structure of the electromechanical components to be installed on site is simulated to obtain the deviation from the design drawings and specifications. Specifically: First, a detailed model of the electromechanical components to be installed is created according to the specifications and design drawings, serving as a reference model for comparison; then, the center coordinates of the target sphere are used to replace the measured 3D spatial sampling point positioning information, and the 3D spatial information of the accurate target sphere model is referenced. Simulation of the electromechanical components to be installed is then performed based on the center coordinates of the target sphere, i.e., a detailed model is created based on the feedback target sphere parameters, and compared with the 3D model of the electromechanical components to be installed (the 3D model of the electromechanical components to be installed is based on the 3D model of the electromechanical components to be installed). The installation positions of electromechanical components are established by combining and deploying samples at the measured sampling points to obtain a three-dimensional model of the electromechanical components to be installed. Next, the installation details are controlled by fitting the center coordinates of the target sphere based on the extracted target sphere point cloud data. The three-dimensional model of the electromechanical components to be installed is compared with the reference model at the comparison end to obtain the positional deviation value between the two. Finally, based on the magnitude of the deviation, a guidance and adjustment scheme for on-site installation details is determined, and the scheme is abstracted into an adjustment strategy relative to the world coordinate system. The adjustment scheme is sent to the mobile client of the on-site installation personnel, providing output feedback for the on-site installation work, displaying and reminding information on the adjustment distance and relative orientation.

[0041] This feedback guidance is particularly crucial during the installation of key components such as the stator and rotor. Real-time feedback allows installers to precisely control the installation position and angle of these components, ensuring maximum compatibility and stability with the overall unit structure. This not only improves installation efficiency but, more importantly, guarantees the quality of the turbine unit's installation details, laying a solid foundation for stable operation and performance enhancement.

[0042] This invention proposes a point cloud data analysis method combining 3D laser scanning technology, providing new ideas and directions for efficient and accurate design of electromechanical systems. First, a large number of sampling points are arranged around the electromechanical components to be installed, and 3D laser scanning technology is used to record 3D point cloud data. Further, 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 scheme is determined, and the results are fed back to a mobile terminal at the electromechanical installation site, thereby improving the installation quality of electromechanical details in avionics engineering.

[0043] Although the functions and working processes of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific functions and working processes described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these are within the protection scope of the present invention.

[0044] References: [1] Ma Li, Xie Kongzhen, Bai Wenbin, et al. Application of terrestrial three-dimensional laser scanning technology in road engineering surveying [J]. Beijing Surveying and Mapping, 2011(02):48-51. [2] Ren Hongwen, Liu Zhaofu, Han Zhiyao, et al. Application of three-dimensional laser scanning technology in surveying and mapping goaf areas [J]. Gold Science and Technology, 2013, 21(3):5. [3] Zhu Longjun, Zhou Jun. Application of 3D laser scanner in building planning acceptance[J]. Urban Surveying, 2021(2):5. [4] Ding Ge, Yan Lishuang, Peng Jian, Zhu Chuangguang, Huang Xueting. Cross-section extraction of tunnel point cloud based on RANSAC algorithm [J]. Bulletin of Surveying and Mapping, 2021(09):120-123. [5] Wu Junhe, Peng Haiqi, Sun Guibin, et al. Window boundary extraction method based on three-dimensional laser scanning data [J]. Exploration Science and Technology (2):5.

Claims

1. A method for detailed control of electromechanical installation in avionics engineering based on three-dimensional laser scanning technology, characterized in that, Includes the following steps: Step 1: Data Collection Using a 3D laser scanner as a data acquisition platform, the target area is explored, the locations of target points and measurement stations are specified, and the 3D laser scanner is placed at the measurement station to complete the original point cloud data acquisition of the target area. Step 2: Data Processing The raw point cloud data collected by the 3D laser scanner is read remotely by computer, uploaded to the cloud, stored in the database, and interpreted into a common point cloud format. Step 3: Model Segmentation The original point cloud data after interpretation is segmented using a point cloud segmentation technique based on random sampling consensus algorithm, and surface patches are extracted and fitted to a target sphere. Step 4: Analysis and Feedback Based on the calculated target sphere data, the structure and location of the electromechanical components to be installed are simulated, and the coordinate deviation values ​​are compared with the specifications and design drawings to determine the final installation details guidance plan and provide feedback to the on-site installation personnel.

2. The method for detailed control of electromechanical installation in avionics engineering based on three-dimensional laser scanning technology according to claim 1, characterized in that, The first step involves setting the parameters of the 3D laser scanner, including scanning angle, scanning resolution, and scanning quality.

3. The method for detailed control of electromechanical installation in avionics engineering based on three-dimensional laser scanning technology according to claim 1, characterized in that, In the second step, the raw point cloud data acquired by the 3D laser scanner is uploaded to the cloud as a file stream and stored in the database. Based on the scan sequence number, the 3D spatial information of the acquired raw point cloud data is read using the development tool SDK and output as a .txt or .xyz file, a common point cloud format.

4. The method for detailed control of electromechanical installation in avionics engineering based on three-dimensional laser scanning technology according to claim 1, characterized in that, In the third step, using the PCL point cloud library, a point cloud segmentation technique based on the random sampling consensus algorithm is employed to divide the interpreted raw point cloud data into patches and target sphere regions. The specific process is as follows: S1 plane segmentation S1.1 Preprocessing: Denoising and range filtering are performed on the interpreted raw point cloud data; S1.2 Randomly select three points from the preprocessed point cloud data to form a plane; S1.3 Use the remaining point cloud data to test the plane model, calculate the distance from all other points to the plane, and if it is less than the set threshold, then the points in the same plane are considered as interior points of the plane and added to the set of interior points of the plane. At the same time, record the number of interior points of the plane. S1.4 Repeat steps S1.2 to S1.3 for iterative calculation until the number of iterations reaches the set iteration threshold, at which point the loop terminates. At this point, several planar models are obtained, and each planar model corresponds to several interior points. S1.5 Saves the planar model with the most interior points and removes the corresponding interior points from the preprocessed point cloud data; S1.6 Repeat steps S1.2 to S1.5 until there are no significant planes, that is, all planes have been segmented. S2 Sphere Segmentation S2.1 Randomly select four points from the remaining point cloud data after removal to form a sphere, and calculate the parameters of the sphere model; S2.2 Use the remaining point cloud data to examine the sphere model, calculate the distance from all other points to the sphere, and if the distance exceeds the set threshold, it is classified as an outlier; if it is within the set threshold, the points are considered to be inside the sphere and added to the set of inside points of the sphere, and the number of inside points of the sphere is recorded. S2.3 Repeat steps S2.1 and S2.2 iteratively until the number of iterations reaches the set iteration threshold, at which point the loop terminates. S2.4 Select the sphere model containing the most internal points as the target sphere model, and save and output its center coordinates, radius, and sphere model parameters.

5. The method for detailed control of electromechanical installation in avionics engineering based on three-dimensional laser scanning technology according to claim 4, characterized in that, The specific process of preprocessing in step S1.1: S1.1.1 Denoising: Use statistical filtering or radius filtering to eliminate noise points in the interpreted raw point cloud data; S1.1.2 Range filtering: The denoised point cloud data is cropped to the region of interest, and irrelevant parts are removed.

6. The method for detailed control of electromechanical installation in avionics engineering based on three-dimensional laser scanning technology according to claim 1, characterized in that, The fourth step involves establishing a set of analysis rules that conform to the site conditions and simulating the structure of the electromechanical components to be installed on-site. This process determines the deviation from the design drawings and specifications, and based on the deviation, a final installation detail guidance plan is determined and fed back to the on-site installation personnel. Specifically: First, a detailed model of the electromechanical components to be installed is created according to the specifications and design drawings, serving as a reference model for comparison. Then, a detailed model is created based on the feedback target sphere parameters, and this model is combined with the 3D model of the electromechanical components to be installed at the measured sampling points to obtain a measured 3D model of the electromechanical components to be installed. Next, the measured 3D model of the electromechanical components to be installed is compared with the reference model to obtain the positional deviation value between them. Finally, based on the magnitude of the deviation, a guidance and adjustment plan for the on-site installation details is determined and sent to the mobile client of the on-site installation personnel, providing output feedback for the on-site installation work, displaying and reminding them of the adjustment distance and relative orientation.

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