Hull structure positioning estimation method

Abstract

The invention relates to a ship body structure positioning estimation method, which comprises the following steps of: disassembling a ship body structure into a plurality of blocks to be carried, carrying the blocks in sequence according to a preset sequence, and before carrying the ith block to be carried, carrying out registration and multi-target iterative optimization according to the actually measured coordinate data of a plurality of feature points of the carried block and the actually measured coordinate data of a loop line to obtain the ith block to be carried. And finally, carrying out coordinate reverse conversion on the obtained target pose parameters, so that coordinate data of a plurality of feature points and loop coordinate data of the ith to-be-carried block can be obtained, and the coordinate data and the loop coordinate data are further used for guiding construction of the ith to-be-carried block. In the whole carrying process, the carried block is continuously utilized to carry out real-time prediction and accurate adjustment, so that the positioning error in the carrying process is reduced.

Description

Technical Field

[0001] This invention relates to the technical field of ship structure mounting, and in particular to a method for predicting the positioning of ship hull structures. Background Technology

[0002] As a core component of the global clean energy supply chain, the construction quality of liquefied gas (LNG) tankers directly determines the safety, operational efficiency, and lifespan of the vessel. LNG tanks require extremely high standards in terms of tank design, material properties, and construction precision. Currently, the mainstream LNG tank system utilizes the Mark III membrane containment system, which consists of multiple layers of stainless steel membrane, insulation boxes, and support systems laid on the hull structure. Strict control over the hull structure assembly precision and tank shape is essential during construction to ensure the tank's airtightness, thermal insulation performance, and structural strength.

[0003] Currently, the traditional method for evaluating liquid tanks involves measuring key data points after the overall structure is installed to assess the tank's shape and determine if it meets construction requirements. However, this method has the following drawbacks:

[0004] Large positioning error: During the liquid tank installation process, it mainly relies on manual measurement and experience adjustment, which is easily affected by human operation error and environmental factors, resulting in deviations in the positioning of the liquid tank structure;

[0005] Inefficient assessment: Real-time assessment cannot be conducted during construction; assessment can only be carried out after all components are assembled. If the cabin type is found to deviate significantly from expectations, rework or even reconstruction is often required, which not only prolongs the construction cycle but also increases construction costs.

[0006] The cabin type assessment algorithms currently used by foreign companies such as GTT have not been made public, which restricts the development of shipbuilding technology. Summary of the Invention

[0007] To at least partially solve the aforementioned problems in the prior art, the present invention provides a method for predicting the positioning of ship hull structures.

[0008] A method for predicting the positioning of a ship's hull structure, the method comprising:

[0009] The hull structure is disassembled into multiple sections to be assembled, and the multiple sections to be assembled are assembled sequentially in a preset order;

[0010] Before mounting the i-th segment to be mounted, the measured coordinate data of multiple feature points of the already mounted segments and the measured coordinate data of the loop are determined, wherein the i-th segment to be mounted is any segment to be mounted except the first segment, and i is an integer greater than 1;

[0011] The measured coordinate data of the multiple feature points and the measured coordinate data of the loop are registered with the corresponding coordinate data of the ship structure theoretical model to obtain the registered measured coordinate data.

[0012] Based on the registered measured coordinate data, a multi-objective optimization model is constructed and iterative optimization calculations are performed to obtain the target pose parameters of the i-th segment to be mounted.

[0013] The target pose parameters are inversely transformed to obtain the coordinate data of multiple feature points and the coordinate data of the loop line of the i-th segment to be mounted.

[0014] The mounting construction of the i-th segment to be mounted is completed based on the coordinate data of multiple feature points and the coordinate data of the loop line.

[0015] Repeat the above process until the hull structure is installed.

[0016] Optionally, the prediction method further includes:

[0017] Obtain the coordinates of multiple key points and the lengths of multiple baselines on the theoretical model, as well as the measured coordinates of the corresponding key points and the measured lengths of the baselines on the completed ship structure.

[0018] The coordinates of multiple key points and the lengths of multiple baselines on the theoretical model are compared and analyzed one by one with the actual measured coordinates of key points and the actual measured lengths of baselines on the completed ship structure to determine the final acceptance result.

[0019] Optionally, the registration of the measured coordinate data of the plurality of feature points and the measured coordinate data of the loop with the corresponding coordinate data of the ship structure theoretical model is based on the ICP algorithm and the least squares method.

[0020] Optionally, the registration based on the ICP algorithm and least squares method includes:

[0021] Initial registration is performed using the ICP algorithm. The initial registration formula is as follows:

[0022]

[0023] in, These are measured coordinates. These are the corresponding coordinates of the theoretical model, N is the number of points, R is the rotation matrix, and t is the translation vector;

[0024] Set the initial transformation, that is, select an initial rotation matrix. Translation vector ;

[0025] For fine registration, based on the initial registration, the LM algorithm is used to optimize the transform parameters and the target formula is optimized.

[0026]

[0027] in, These are the weighting coefficients;

[0028] Convergence criterion: The convergence criterion formula is used to determine the termination condition of the iteration. The convergence criterion formula is as follows:

[0029]

[0030] in, This is a preset small error threshold.

[0031] Optionally, the process of constructing a multi-objective optimization model based on the registered measured coordinate data and performing iterative optimization calculations to obtain the target pose parameters of the i-th segment to be mounted includes:

[0032] Calculate the average coordinates of multiple registered feature points in the X, Y, and Z directions. :

[0033]

[0034] Where N is the number of feature points. , , These are the coordinates of each feature point in the X, Y, and Z directions;

[0035] The measured coordinate data of the registered loop are compared with the coordinate data of the reference loop corresponding to the theoretical model to establish a deviation constraint model. The reference loops in the three directions corresponding to the theoretical model are X=0, Y=0, and Z=H / 2, where H is the height of the mounted segment. The deviation constraint model is as follows:

[0036] , , ;

[0037] A multi-objective optimization model is constructed, taking the minimization of the changes in deviation in the X and Y directions of the registered loop as the optimization objective, and establishing a multi-objective optimization model: , ;

[0038] After completing the construction of the multi-objective optimization model, the target pose parameters of the i-th segment to be mounted are obtained by iterative calculation.

[0039] Optionally, the step of obtaining the target pose parameters of the i-th segment to be mounted through iterative calculation includes:

[0040] Set initial pose parameters ,in, The angle represents the segmented rotation angle around each axis. Set the iteration step size. Convergence threshold Maximum number of iterations ;

[0041] Deviation calculation and objective function construction: In the k-th iteration, based on the current pose... Calculate the change in loop shape after registration, and minimize the change in loop shape as the objective:

[0042]

[0043] The optimization solution and pose update are performed using the gradient descent method, and the pose is updated based on the optimization results.

[0044]

[0045] in, Let be the pose parameters for the k-th iteration; This is the iteration step size; For the objective function in Gradient vector at:

[0046]

[0047] Convergence criteria, i.e., satisfying: or If the iteration stops, the target pose parameters are obtained. .

[0048] Optionally, the inverse coordinate transformation of the target pose parameters includes:

[0049] Based on the rotation matrix R and the translation vector t, the inverse transformation is obtained... :

[0050]

[0051] Optionally, the measured coordinate data of multiple feature points of the mounted section and the measured coordinate data of the loop are obtained by using a total station to perform a three-dimensional scan of the mounted section.

[0052] Optionally, the theoretical model is automatically constructed based on the design parameters of the hull structure.

[0053] In a method for predicting the positioning of a ship's hull structure according to the present invention, the hull structure is disassembled into multiple sections to be installed, and these sections are installed sequentially in a preset order. Before installing the i-th section to be installed, target pose parameters are obtained through registration and multi-objective iterative optimization based on the measured coordinate data of multiple feature points and the measured coordinate data of the loop of the already installed sections. Finally, the obtained target pose parameters are subjected to inverse coordinate transformation, thereby obtaining the coordinate data of multiple feature points and the loop of the i-th section to be installed, which can then be used to guide the construction of the i-th section to be installed. Throughout the installation process, real-time prediction and precise adjustment are continuously performed using the already installed sections, thereby reducing positioning errors during the installation process. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the disassembled structure of a ship hull according to an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of the positional structure of multiple feature points according to an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of multiple key points and multiple baseline lengths of a theoretical model according to an embodiment of the present invention. Detailed Implementation

[0057] The following reference Figures 1-3 This invention describes a method for predicting the positioning of a ship's hull structure.

[0058] refer to Figures 1-3 This invention provides a method for predicting the positioning of a ship's hull structure. The prediction method includes:

[0059] Step S1 involves disassembling the hull structure into multiple sections to be assembled. These sections are then assembled sequentially in a preset order to form a complete hull structure. This preset order can be set based on industry experience.

[0060] In one specific embodiment, reference is made to Figure 1 The ship's hull structure consists of liquid cargo tanks, which are disassembled into six sections: the 3-section group, the 4-section group, the 5-section group, the 60-section group, the 62-section group, and the 7-section group. The six sections are installed in the following order: 3-section group → 4-section group → 7-section group → 5-section group → 62-section group → 60-section group.

[0061] Step S2: Before mounting the i-th segment to be mounted, determine the measured coordinate data of multiple feature points of the already mounted segments and the measured coordinate data of the loop. The i-th segment to be mounted is any segment other than the first segment, and i is an integer greater than 1.

[0062] In one specific embodiment, reference is made to Figure 2 Taking the completion of the 3-character group segment and the start of the installation of the 4-character group segment as an example, a total station can be used to perform a three-dimensional scan of the installed 3-character group segment to determine the measured coordinate data of the four feature points of the 3-character group segment (CDK, CDG, CBK, CBG) and the measured coordinate data of the loop line of the docking end face between the 3-character group segment and the 4-character group segment.

[0063] Step S3 involves registering the measured coordinate data of multiple feature points and the loop with the corresponding coordinate data of the ship's structural theoretical model to obtain the registered measured coordinate data. The theoretical model can be automatically constructed based on the design parameters of the liquid tanks.

[0064] In some embodiments of the present invention, step S3 is based on the ICP (Iterative Closest Point) algorithm and the least squares method for registration, thereby realizing the conversion between the measured coordinate data and the corresponding coordinate data of the theoretical model. This process minimizes the error by optimizing the rotation matrix and translation vector, ensuring the accuracy of coordinate registration.

[0065] Furthermore, registration is performed based on the ICP algorithm and the least squares method, including:

[0066] Initial registration is performed using the ICP algorithm, which establishes an initial correspondence between the measured coordinate data and the corresponding coordinate data of the theoretical model, and establishes the source point set. and target point set And perform coarse matching. The initial registration goal is to minimize the error between the two sets of points by using the rotation matrix 𝑅 and the translation vector 𝑡.

[0067] Initial registration formula:

[0068]

[0069] in, These are the source point coordinates (i.e., the measured coordinates). t is the coordinates of the target point (i.e., the corresponding coordinates of the theoretical model), N is the number of points, R is the rotation matrix, and t is the translation vector.

[0070] Set the initial transformation, that is, select an initial rotation matrix. Translation vector A coarse matching process is performed to initially align the measured coordinate data with the corresponding coordinate data from the theoretical model. Typically, the identity matrix and the zero vector are used as initial values.

[0071]

[0072]

[0073] Fine registration, based on the initial registration, employs the Levenberg-Marquardt (LM) algorithm to optimize the transformation parameters. The goal of fine registration is to minimize the objective function, achieving accurate registration by iteratively updating the rotation matrix 𝑅 and the translation vector 𝑡.

[0074] Optimization target formula:

[0075]

[0076] in, These are the weighting coefficients.

[0077] Convergence criterion: The convergence criterion formula is used to determine the termination condition of the iteration. The convergence criterion formula is as follows:

[0078]

[0079] in, This is a preset small error threshold, typically set to 0.1 mm. The iteration terminates when the registration error is less than this threshold, and registration is complete.

[0080] Step S4: Based on the registered measured coordinate data, construct a multi-objective optimization model and perform iterative optimization calculations to obtain the target pose parameters of the i-th segment to be mounted.

[0081] In some embodiments of the present invention, step S4 includes:

[0082] Calculate the average coordinates of multiple registered feature points in the X, Y, and Z directions. .

[0083]

[0084] Where N is the number of feature points. , , These are the coordinates of each feature point in the X, Y, and Z directions.

[0085] The measured coordinate data of the registered loop are compared with the coordinate data of the reference loop corresponding to the theoretical model to establish a deviation constraint model. The reference loop for the three directions corresponding to the theoretical model is X=0, Y=0, and Z=H / 2, where H is the height of the mounted segment. The deviation constraint model is as follows:

[0086] , , .

[0087] Multi-objective optimization model construction. The optimization objective is to minimize the changes in the X and Y axis deviations of the registered loop, thus establishing a multi-objective optimization model. , . Since the configuration will change depending on the segmentation, it will not be considered as an optimization target.

[0088] After completing the construction of the multi-objective optimization model, the target pose parameters of the i-th segment to be carried are obtained by iterative calculation.

[0089] Furthermore, the target pose parameters of the i-th segment to be mounted are obtained by iterative calculation, including:

[0090] Set initial pose parameters ,in, The angle represents the segmented rotation angle around each axis. Set the iteration step size. Convergence threshold Maximum number of iterations .

[0091] Deviation calculation and objective function construction: In the k-th iteration, based on the current pose... Calculate the change in loop shape after registration, and minimize the change in loop shape as the objective:

[0092]

[0093] The optimization solution and pose update are performed using the gradient descent method, and the pose is updated based on the optimization results.

[0094]

[0095] in, Let be the pose parameters for the k-th iteration; This is the iteration step size; For the objective function in Gradient vector at:

[0096]

[0097] Convergence criteria, i.e., satisfying: or If the iteration stops, the target pose parameters are obtained. .

[0098] Alternatively, a heuristic optimization algorithm can be used to solve the problem and update the pose based on the optimization results.

[0099] Step S5: Perform inverse coordinate transformation on the target pose parameters to obtain the coordinate data of multiple feature points and the coordinate data of the loop line of the i-th segment to be mounted.

[0100] Based on the rotation matrix R and translation vector t obtained in step S3, the following is obtained through inverse transformation: :

[0101]

[0102] In one specific embodiment, reference is made to Figure 2 Taking the completion of the 3-letter group segment and the start of the 4-letter group segment as an example, the 4-letter group segment has 8 feature points, namely CDK, KDJ, CDG, FDG, CBK, KBJ, CBG17, and FBG.

[0103] Step S6: Complete the installation of the i-th segment to be installed based on the coordinate data of multiple feature points and the coordinate data of the loop line.

[0104] Step S7: Repeat steps S2-S6 until the hull structure is installed.

[0105] In one specific embodiment, after the 4-digit group of the overall segment is loaded, the subsequent 7-digit group of the overall segment, 5-digit group of the overall segment, 62-digit group of the overall segment, and 60-digit group of the overall segment repeat the above steps S2-S5 to finally complete the loading of the liquid cargo hold.

[0106] Specifically, after the 4-character group segment is mounted, the measured coordinate data of the 8 feature points of the 4-character group segment and the measured coordinate data of the loop line of the docking end face of the 4-character group segment and the 7-character group segment are determined. In this way, the coordinate data of the 12 feature points and the loop line required for the mounting of the 7-character group segment are calculated, and the mounting of the 7-character group segment is completed.

[0107] After the 7-character group segment is mounted, the measured coordinate data of 12 feature points of the 7-character group segment and the measured coordinate data of the loop line of the docking end face of the 7-character group segment and the 5-character group segment are determined. In this way, the coordinate data of the 12 feature points and the loop line required for the mounting of the 5-character group segment are calculated, and the mounting of the 5-character group segment is completed.

[0108] After the 5-character group segment is mounted, the measured coordinate data of 12 feature points of the 5-character group segment and the measured coordinate data of the loop line of the docking end face of the 5-character group segment and the 62-character group segment are determined. In this way, the coordinate data of 16 feature points and loop line required for the mounting of the 62-character group segment are calculated, and the mounting of the 62-character group segment is completed.

[0109] After the 62-character group is mounted, the measured coordinate data of 16 feature points of the 62-character group and the measured coordinate data of the loop line of the docking end face of the 62-character group and the 60-character group are determined. In this way, the coordinate data of the 16 feature points and the loop line required for the mounting of the 60-character group are calculated, and the mounting of the 60-character group is completed.

[0110] In some embodiments of the present invention, the estimation method further includes:

[0111] Obtain the coordinates of multiple key points and the lengths of multiple baselines on the theoretical model, as well as the measured coordinates of the corresponding key points and the measured lengths of the baselines on the completed ship structure.

[0112] The coordinates of multiple key points and the lengths of multiple baselines on the theoretical model are compared and analyzed one by one with the corresponding measured coordinates of key points and measured baselines on the completed hull structure to determine whether the completed hull structure meets the design requirements, thereby determining the final acceptance conclusion.

[0113] In one specific embodiment, reference is made to Figure 3 The theoretical model has 16 key point coordinates and 64 baselines. The 16 key points are the eight vertices on each of the front and rear end faces of the decahedron, totaling 16 vertices. The 64 baselines consist of three types: the first type comprises 24 lines formed by connecting adjacent vertices pairwise; the second type consists of 16 lines formed by the diagonals of the eight rectangular sides of the decahedron's liquid tank; and the third type consists of 24 lines formed by connecting every other vertex and diagonal points on the front and rear end faces of the liquid tank. It should be noted that every other vertex connection means a line separated by only one point.

[0114] In summary, this prediction method disassembles the hull structure into multiple sections to be assembled, which are then assembled sequentially according to a preset order. Before assembling the i-th section, the target pose parameters are obtained through registration and multi-objective iterative optimization based on the measured coordinate data of multiple feature points and loop lines from the already assembled sections. Finally, the obtained target pose parameters are subjected to inverse coordinate transformation, thereby obtaining the coordinate data of multiple feature points and loop lines of the i-th section to be assembled, which can then be used to guide the construction of the i-th section. Throughout the assembly process, real-time prediction and precise adjustment are continuously performed using the already assembled sections, thereby reducing positioning errors during the assembly process.

[0115] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for predicting the positioning of a ship's hull structure, characterized in that, The estimation method includes: The hull structure is disassembled into multiple sections to be assembled, and the multiple sections to be assembled are assembled sequentially in a preset order; Before mounting the i-th segment to be mounted, the measured coordinate data of multiple feature points of the already mounted segments and the measured coordinate data of the loop are determined, wherein the i-th segment to be mounted is any segment to be mounted except the first segment, and i is an integer greater than 1; The measured coordinate data of the multiple feature points and the measured coordinate data of the loop are registered with the corresponding coordinate data of the ship structure theoretical model to obtain the registered measured coordinate data. Based on the registered measured coordinate data, a multi-objective optimization model is constructed and iterative optimization calculations are performed to obtain the target pose parameters of the i-th segment to be mounted. The target pose parameters are inversely transformed to obtain the coordinate data of multiple feature points and the coordinate data of the loop line of the i-th segment to be mounted. The mounting construction of the i-th segment to be mounted is completed based on the coordinate data of multiple feature points and the coordinate data of the loop line. Repeat the above process until the hull structure is installed.

2. The method for predicting the positioning of ship hull structures according to claim 1, characterized in that, The estimation method also includes: Obtain the coordinates of multiple key points and the lengths of multiple baselines on the theoretical model, as well as the measured coordinates of the corresponding key points and the measured lengths of the baselines on the completed ship structure. The coordinates of multiple key points and the lengths of multiple baselines on the theoretical model are compared and analyzed one by one with the actual measured coordinates of key points and the actual measured lengths of baselines on the completed ship structure to determine the final acceptance result.

3. The method for predicting the positioning of ship hull structures according to claim 1, characterized in that, The registration of the measured coordinate data of the multiple feature points and the measured coordinate data of the loop with the corresponding coordinate data of the ship structure theoretical model is based on the ICP algorithm and the least squares method.

4. The method for predicting the positioning of ship hull structures according to claim 3, characterized in that, The registration based on the ICP algorithm and least squares method includes: Initial registration is performed using the ICP algorithm. The initial registration formula is as follows: in, These are measured coordinates. These are the corresponding coordinates of the theoretical model, N is the number of points, R is the rotation matrix, and t is the translation vector; Set the initial transformation, that is, select an initial rotation matrix. Translation vector ; For fine registration, based on the initial registration, the LM algorithm is used to optimize the transform parameters and the target formula is optimized. in, These are the weighting coefficients; Convergence criterion: The convergence criterion formula is used to determine the termination condition of the iteration. The convergence criterion formula is as follows: in, This is a preset small error threshold.

5. The method for predicting the positioning of a ship's structure according to claim 1, characterized in that, The aforementioned method involves constructing a multi-objective optimization model based on the registered measured coordinate data and performing iterative optimization calculations to obtain the target pose parameters of the i-th segment to be mounted, including: Calculate the average coordinates of multiple registered feature points in the X, Y, and Z directions. : Where N is the number of feature points. , , These are the coordinates of each feature point in the X, Y, and Z directions; The measured coordinate data of the registered loop are compared with the coordinate data of the reference loop corresponding to the theoretical model to establish a deviation constraint model. The reference loops in the three directions corresponding to the theoretical model are X=0, Y=0, and Z=H / 2, where H is the height of the mounted segment. The deviation constraint model is as follows: ， ， ； A multi-objective optimization model is constructed, taking the minimization of the changes in deviation in the X and Y directions of the registered loop as the optimization objective, and establishing a multi-objective optimization model: , ; After completing the construction of the multi-objective optimization model, the target pose parameters of the i-th segment to be mounted are obtained by iterative calculation.

6. The method for predicting the positioning of a ship's structure according to claim 5, characterized in that, The method of obtaining the target pose parameters of the i-th segment to be mounted by iterative calculation includes: Set initial pose parameters ,in, The angle represents the segmented rotation angle around each axis. Set the iteration step size. Convergence threshold Maximum number of iterations ; Deviation calculation and objective function construction: In the k-th iteration, based on the current pose... Calculate the change in loop shape after registration, and minimize the change in loop shape as the objective: The optimization solution and pose update are performed using the gradient descent method, and the pose is updated based on the optimization results. in, Let be the pose parameters for the k-th iteration; This is the iteration step size; For the objective function in Gradient vector at: Convergence criteria, i.e., satisfying: or If the iteration stops, the target pose parameters are obtained. .

7. The method for predicting the positioning of ship hull structures according to claim 4, characterized in that, The inverse coordinate transformation of the target pose parameters includes: Based on the rotation matrix R and the translation vector t, the inverse transformation is obtained... : 。 8. The method for predicting the positioning of a ship's hull structure according to claim 1, characterized in that, The measured coordinate data of multiple feature points of the installed section and the measured coordinate data of the loop were obtained by using a total station to perform a three-dimensional scan of the installed section.

9. The method for predicting the positioning of a ship's hull structure according to claim 1, characterized in that, The theoretical model is automatically constructed based on the design parameters of the hull structure.

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