A digital lashing test method for container ships

Abstract

This invention discloses a digital lashing test method for container ships, relating to the field of container ship lashing test technology. The method includes the following steps: data acquisition to obtain point cloud data, which includes eye plates for the container feet and lashing bridge column areas; defining an eye plate template and then dividing the container positions, followed by extracting the container feet, including generating the container foot center based on the characteristics of the container feet in the divided container position data; eye plate matching, including obtaining the eye ring design position of the eye plate template, placing the corresponding eye plate model at the eye ring design position, and sampling to obtain a theoretical sampling point cloud; searching for the measured point cloud data of the eye ring at the eye ring design position, then sampling to obtain the actual sampling point cloud, and matching it with the theoretical sampling point cloud to obtain the eye plate center point, and obtaining the lashing residual value based on the center of each container foot and the center point of the eye plate; this method eliminates the need for manual climbing for measurement, achieving safe and efficient production.

Description

Technical Field

[0001] This invention relates to the field of container ship lashing test technology, and in particular to a digital lashing test method for container ships. Background Technology

[0002] Traditional physical lashing tests on actual ships require the fabrication of multi-layer, container-sized test rigs, and the use of cranes to lash and secure each hold and container individually, testing the interference and lashing rod allowance. The disadvantages are a long operation cycle (typically 2-3 months), significant crane usage time, and high labor costs.

[0003] Traditional lashing surveying typically relies on total stations to collect and locate eyeplate coordinates. This process is time-consuming, resulting in low overall efficiency and difficulty in ensuring real-time data updates and high accuracy. Furthermore, total stations require significant human intervention during operation, have limited automation, and cannot effectively meet the urgent needs of the modern shipbuilding industry for efficient and precise lashing management systems. Currently, 3D scanning is mainly used in industrial inspection, architectural surveying and cultural heritage protection. Common methods include comparing digital models and generating deviations, as well as reverse modeling. It is difficult to apply to container deck lashing measurement and analysis. Furthermore, existing 3D point cloud processing technologies rely heavily on manual selection of key points, which is inefficient and inconsistent, and cannot meet the shipbuilding industry's needs for high precision and rapid feedback. Summary of the Invention

[0004] The purpose of this invention is to provide a digital lashing test method for container ships, which eliminates the need for manual climbing for measurement, thus eliminating the safety hazards of the original measurement and testing methods and providing a reliable guarantee for safe and efficient production.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A digital lashing test method for container ships includes the following steps: Step S10: Perform data acquisition to obtain point cloud data, which includes the eyeboards of the box girder and the area of ​​the tied bridge column; Step S30: Define the eyeplate template, which contains the position of the center point of each eyeplate; Step S50: Box partitioning, which includes dividing the point cloud data into data for different boxes; Step S60: Extract the box feet, including generating the box foot center based on the box foot characteristics in the divided box position data; Step S70: Obtain the center point of the eyeplate through eyeplate matching; Step S80: Obtain the binding allowance based on the center points of each box foot and the eye plate.

[0006] Furthermore, it also includes step S20: deleting data outside the current cabin from the point cloud data.

[0007] Furthermore, it also includes step S40: reference adjustment, which includes taking the center point of the upper surface of the two long box legs on the port and starboard sides of the single-sided lashing bridge as the reference point, the center point of the upper surface of the port long box leg as the coordinate origin, the direction of the line connecting the center point of the upper surface of the starboard long box leg and the coordinate origin as the Y-axis direction, keeping the Z-axis of the origin point cloud data unchanged, and using a rotation transformation matrix to perform coordinate transformation to adjust the point cloud coordinate system to the ship's coordinate system.

[0008] Furthermore, step S70 includes first obtaining the eye ring design position of the eye plate template, placing the corresponding eye plate model at the eye ring design position, and sampling to obtain the theoretical sampling point cloud; then searching for the measured point cloud data of the eye ring at the eye ring design position, subsequently sampling to obtain the actual sampling point cloud, and matching it with the theoretical sampling point cloud.

[0009] Furthermore, in step S70, placing the corresponding eye plate model specifically includes rotating the original eye ring model around the Z-axis by 165°, 195°, 15°, and 345° respectively at four positions: port side-bow direction, port side-stern direction, starboard side-bow direction, and starboard side-stern direction, in any container position of any compartment. Then, the rotated model is translated and placed at the corresponding eye ring design position.

[0010] Furthermore, in step S70, the average density of the actual sampling point cloud and the theoretical sampling point cloud are set accordingly.

[0011] Furthermore, in step S70, during sampling, downsampling is used to ensure that the average density of the actual sampling point cloud and the theoretical sampling point cloud does not exceed one point per 8 cubic millimeters.

[0012] Furthermore, in step S70, before matching, a plane with the Y-axis is searched in the actual sampled point cloud of the eye ring using random sampling consistency to remove the point cloud of the pillar part and the lower long plate.

[0013] In summary, the present invention has the following beneficial effects: The software system has been put into practical use, and the cost has been reduced by more than 98% compared with the traditional ship lashing test method. In addition, it eliminates the need for manual climbing to measure, thus eliminating the safety hazards of the original measurement and testing methods and improving the reliability of safe and efficient production. It boasts high measurement accuracy and speed, with a maximum measuring range of 100 meters and an overall accuracy of 5mm. It requires no crane, saving crane time. Simultaneously, it captures on-site photos during scanning, saving them along with the scanned data for later review, achieving comprehensive, three-dimensional data acquisition. The system automatically generates reports, intuitively displaying the eyeplate position deviation. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the data acquisition steps in a digital lashing test method for container ships according to the present invention; Figure 2 This is a schematic diagram of the baseline adjustment steps in a digital lashing test method for container ships according to the present invention; Figure 3 This is a schematic diagram of the container position division steps in a digital lashing test method for container ships according to the present invention; Figure 4 This is a schematic diagram of the steps for extracting the container feet in a digital lashing test method for container ships according to the present invention; Figure 5 This is a schematic diagram of the eyeplate matching step in a digital lashing test method for container ships according to the present invention; Figure 6 This is a schematic diagram illustrating the principle of simulating container stacking and lashing in a digital lashing test method for container ships according to the present invention; Figure 7 This is a schematic diagram of the lashing residual value in a digital lashing test method for container ships according to the present invention; Figure 8 This is a schematic diagram of the eyeplate output report in a digital lashing test method for container ships according to the present invention. Detailed Implementation

[0015] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. These embodiments do not constitute a limitation of the present invention. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this application.

[0016] A digital lashing test method for container ships involves scanning the entire container deck with a 3D laser scanner, importing the scanned dense 3D coordinate data (point cloud) into the system software, processing the point cloud data, quickly extracting the coordinates of key points of the eyeplate structure on the container feet and columns, reconstructing a 3D coordinate system based on the container feet, calculating the deviation of the eyeplate on the columns, and finally outputting a data report. Specifically, it includes the following steps: Step S10: As Figure 1 As shown, data acquisition was performed by scanning the deck surface at 10-13 stations using a 3D laser scanner to obtain point cloud data. The point cloud data included the eyeboards in the box girder and lashing bridge column areas. To ensure the scanning coverage and obtain complete point cloud data of the eyeboards in the box girder and lashing bridge column areas, a 3D station setup was used to increase the point cloud density of the eyeboards, thereby improving the data extraction accuracy.

[0017] Step S20: Data processing, mainly including deleting data from other compartments in the point cloud data to avoid interference and reduce the pressure of point cloud file access, thereby improving the software processing speed; in this embodiment, small debris on the hatch cover of the compartment has little impact and does not require precise noise reduction.

[0018] Step S30: Define the eyeplate template, which contains the position of the center point of each eyeplate; An eyeplate template is generated based on the design theory model. The eyeplate template may also include the adjustment plate and the installation angle of the eyeplate. The purpose of creating the template is for the registration and extraction of the eyeplate later.

[0019] Step S40: Baseline adjustment, such as Figure 2 As shown, this includes securing two long box legs on the port and starboard sides of the bridge on one side. Figure 4 The center point of the upper surface of the third type of long box leg is used as the reference point, the center point of the upper surface of the port long box leg is used as the origin of the coordinate system, and the direction of the line connecting the center point of the upper surface of the starboard long box leg and the origin of the coordinate system is used as the Y-axis direction. The Z-axis of the origin point cloud data remains unchanged (vertically upward). The coordinate transformation is performed using a rotation transformation matrix to adjust the point cloud coordinate system to the ship's coordinate system, ensuring that the calculation of subsequent measurement deviations is consistent with the actual coordinates of the ship.

[0020] Step S50: As Figure 3 As shown, bin partitioning includes dividing point cloud data into data in different bins; After the reference coordinate system is determined, the point cloud in the entire coordinate system is divided into data for different container positions in the Y and Z directions by using information such as the gap between the columns, the layering of the floor height, the starting position, and the number of container positions. The data for each container position is then finely processed and extracted. The container position number corresponds one-to-one with the actual layout of the ship to ensure data traceability.

[0021] Step S60: As Figure 4 As shown, each box foot is extracted, including the box foot center generated based on the box foot characteristics in the data of different box positions. It can automatically generate box-foot centers based on box-foot features after segmenting the box-foot point cloud; for box-foot features that cannot be automatically extracted, they can also be manually added. Figure 4 The small blue ball at the center of the middle box foot is the extracted center point of the upper surface of the box foot.

[0022] Step S70: As Figure 5 As shown, eyeplate matching is performed to obtain the eyeplate center point. Matching of eyeplates or box feet can both use the same algorithm, such as a filtered translation registration algorithm from local point cloud to global point cloud. For matching the eyeplate, the square abdominal plate and pillar below the eye ring can be ignored; only the ring-shaped portion needs to be matched. Specifically, including, Step S71: First, obtain the template information. The template information tells us the position of all the eye rings in the design, that is, the position of the eye ring design in the eye plate template. Therefore, for each eye ring, we know where it should be placed. Step S72: Obtain the eyeplate model from the outside; Step S73: Place the corresponding eye plate model at the eye ring design position. In any container position of any compartment, rotate the original eye ring model around the Z-axis by 165°, 195°, 15°, and 345° respectively at the four positions of port-bow, port-stern, starboard-bow, and starboard-stern. Then, translate the rotated model and place it at the corresponding eye ring design position.

[0023] Step S74: Now consider a single eye ring model. Sample the model placed in the template position to obtain the theoretical point cloud of the model. This theoretical point cloud is downsampled to ensure that the average density does not exceed one point per 8 cubic millimeters, resulting in a theoretical sampled point cloud.

[0024] Step S75: Search for the measured point cloud data of the eye ring at the designed location (the point cloud has been transformed into the template coordinate system by the above process). The search range of the eye ring in the x, y, and z directions in the bow direction is (-30mm~100mm, -80mm~80mm, -95mm~95mm), and the search range of the eye ring in the x, y, and z directions in the stern direction is (-100mm~30mm, -80mm~80mm, -95mm~95mm). The searched point cloud is then downsampled to ensure that the average density does not exceed one point per 8 cubic millimeters. This is because it is necessary to ensure that the point clouds of the two points to be registered have corresponding densities, thus obtaining the actual sampled point cloud. A normal distribution is used to detect and exclude discrete points in the measured point cloud that are too far from the collective. Points outside five standard deviations are excluded.

[0025] Step S76: In the measured point cloud of the eye ring, a plane with the Y-axis is searched using random sampling consistency. All points within 10mm of this plane are removed to eliminate the point cloud of the column portion and the lower long plate. To ensure complete removal, in this embodiment, if the eye ring is the port side eye plate in the container position, only the y-values ​​within this range are taken. 最大 -110mm~y 最大 For the point cloud between points, if the eye ring is the eyeplate on the starboard side of the container, then only the y-values ​​within this range are taken. 最小 ~ y 最小 Point cloud between +110mm.

[0026] Step S77: Register the point cloud after model sampling (the theoretical sampled point cloud obtained in step S74) and the point cloud after segmentation (the point cloud processed in step S76), making the former the target and the latter the starting point. The matching result can then be obtained using ICP. During each transformation estimation, a custom translation transformation estimator is used for iterative positioning. Finally, under suitable parameters, the transformation matrix between the two can be obtained. Step S78: In the transformed cut point cloud, the point cloud of the plane with the specified direction is obtained again using the random sampling consensus algorithm, with the direction obtained after rotating the bow / stern direction according to the rotation rules in step S73 above as a reference. This is the second alignment step. After the second alignment, basically no error appears in this direction in the registration results of all eyeplates. Step S79: Registration is now complete, and the center points of each eye plate have been obtained. This registration method is not limited to the eye ring model; it can be used for single-box corner, double-box corner, and long-box corner models. The only differences are slight variations in the processing of some front-end cut point clouds, slight differences in parameters, and slight differences in the final registration parameters and the shape of the model sampled into point clouds. Given a certain quality original point cloud and a known model, this method achieves good translational registration results, and the secondary alignment ensures consistency in the reference direction. Furthermore, the computational efficiency of this method can guarantee the calculation of the entire cabin's eye ring within 2 minutes. Step S80: Using the box foot center data obtained in step S60 and the eyeplate center point data obtained in step S70, such as... Figure 6 and Figure 7 As shown, the simulation of stacking containers (stacked onto the container feet) and lashing is used to calculate the eye plate position deviation and lashing margin; as shown... Figure 8 As shown, in this embodiment, the output is in report form.

[0027] This embodiment has the following advantages: (1) It is compatible with automatic initial registration of eyeplates at different side and heading positions, which solves the problem that the traditional registration method requires manual setting of the initial pose; (2) To address the issue of insufficient scanner density, the density of both scanners is standardized to ensure density matching and thus guarantee registration accuracy. (3) The plane cutting filtering algorithm with orientation constraints can effectively remove unwanted point clouds and perform subsequent secondary alignment to optimize accuracy; (4) A unified registration framework for multiple types of models, which can be extended to registration detection between arbitrary shape models and point clouds; (5) It has a high degree of automation, guaranteed accuracy, strong adaptability, excellent efficiency and good robustness, and can overcome many errors in field measurement conditions and problems caused by poor quality point clouds; (6) The scanning point cloud method for the ship environment effectively avoids occlusion by setting the scanning position and angle, and ensures complete point cloud acquisition of key parts such as the eye plate and box corner; (7) Compatible with the processing of different data in the same scene under the coordinate system of the scanner and the overall coordinate system of the ship in traditional scanning detection; (8) Human-machine collaborative interaction allows for real-time viewing of detection status.

[0028] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art can make various modifications or equivalent substitutions to the present invention within the scope of its essence and protection. Such modifications or equivalent substitutions should also be considered to fall within the protection scope of the present invention.

Claims

1. A method of digitalizing a lashing test of a container ship, characterized by: Includes the following steps, Step S10: Perform data acquisition to obtain point cloud data, which includes the eyeboards of the box girder and the area of ​​the tied bridge column; Step S30: Define the eyeplate template, which contains the position of the center point of each eyeplate; Step S50: Box partitioning, which includes dividing the point cloud data into data for different boxes; Step S60: Extract the box feet, including generating the box foot center based on the box foot characteristics in the divided box position data; Step S70: Obtain the center point of the eyeplate through eyeplate matching; Step S80: Obtain the binding allowance based on the center points of each box foot and the eye plate.

2. A method of digital lashing test of a container ship according to claim 1, characterized in that: It also includes step S20: deleting data outside the current cabin from the point cloud data.

3. A digital lashing test method for container ships according to claim 1 or 2, characterized in that: It also includes step S40: reference adjustment, which includes taking the center point of the upper surface of the two long box legs on the port and starboard sides of the single-sided lashing bridge as the reference point, the center point of the upper surface of the port long box leg as the coordinate origin, the direction of the line connecting the center point of the upper surface of the starboard long box leg and the coordinate origin as the Y-axis direction, keeping the Z-axis of the origin point cloud data unchanged, and using a rotation transformation matrix to perform coordinate transformation to adjust the point cloud coordinate system to the ship's coordinate system.

4. The method of claim 1, wherein: Step S70 includes first obtaining the eye ring design position of the eye plate template, placing the corresponding eye plate model at the eye ring design position, and sampling to obtain the theoretical sampling point cloud; then searching for the measured point cloud data of the eye ring at the eye ring design position, subsequently sampling to obtain the actual sampling point cloud, and matching it with the theoretical sampling point cloud.

5. A method of digital lashing test of a container ship according to claim 4, characterized in that: The placement of the corresponding eye plate model specifically involves rotating the original eye ring model around the Z-axis by 165°, 195°, 15°, and 345° respectively in any container position of any compartment, at four positions: port-bow, port-stern, starboard-bow, and starboard-stern. Then, the rotated model is translated and placed at the corresponding eye ring design position.

6. The method of claim 4, wherein: In step S70, the average density of the actual sampling point cloud and the theoretical sampling point cloud are set accordingly.

7. The digital lashing test method for container ships according to claim 6, characterized in that: In step S70, during sampling, downsampling is used to ensure that the average density of the actual sampling point cloud and the theoretical sampling point cloud does not exceed one point per 8 cubic millimeters.

8. The digital lashing test method for container ships according to claim 4, characterized in that: In step S70, before matching, a plane with the Y-axis is searched in the actual sampled point cloud of the eye ring using random sampling consistency to remove the point cloud of the pillar part and the lower long plate.

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