Method and device for controlling the precision of modular design and construction of a ship section module

Abstract

This application discloses a method and apparatus for controlling the precision of modular design and construction of ship sections, relating to the field of shipbuilding technology. The method includes: establishing dimensional chain relationships among parts in a pre-defined module based on standard data for modular construction of ship sections; establishing a three-dimensional assembly model of the pre-defined module based on the dimensional chain relationships, and establishing a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model; solving for the closed-loop deviation rate and the influence weights of the component loops based on the three-dimensional assembly deviation analysis model; determining whether the simulation results meet the pre-defined requirements based on the closed-loop deviation rate and the influence weights of the component loops; establishing a comprehensive evaluation model for the deviation rate based on the simulation results when the simulation results meet the pre-defined requirements; solving for the influence coefficients of each component loop on the overall deviation rate based on the comprehensive evaluation model for the deviation rate; determining a construction precision control scheme based on the influence coefficients; and performing precision control of modular construction of ship sections based on the construction precision control scheme.

Description

Technical Field

[0001] This application relates to the field of precision control technology for modular design and construction of ship sections, and in particular to methods and devices for precision control of modular design and construction of ship sections. Background Technology

[0002] Modular construction offers advantages such as shortening design and construction cycles, reducing production costs, and improving product quality, but it also places higher demands on shipbuilding capabilities. Modular shipbuilding is characterized by high precision in docking and installation, long equipment installation dimensional chains, and numerous installation interfaces. This makes it more prone to problems such as inability to dock due to large deviations between equipment, and abnormal vibration and noise caused by poor docking precision. Therefore, rationally predicting and controlling shipbuilding precision during the construction process is crucial for improving the success rate and safety of shipbuilding. Establishing a comprehensive method for predicting and controlling shipbuilding precision, combining manufacturing capabilities and methods, can more effectively improve production efficiency, reduce unnecessary waste, and provide a scientific basis for decision-making.

[0003] Predicting construction accuracy requires assembly deviation analysis. A commonly used deviation propagation model is the dimensional chain. Deviation analysis calculation methods mainly include the extreme value method, the root mean square method, and the Monte Carlo method. The extreme value method linearly superimposes the tolerances of the component links under the worst-case scenario. This method is absolutely reliable and avoids failure risks, but the deviation prediction results obtained are overly conservative, significantly increasing manufacturing costs. The root mean square method is based on the square root of the sum of squares of a statistical distribution (assuming a normal distribution). It is suitable for linear dimensional chains and can balance accuracy and cost, reflecting actual fluctuations. However, it is no longer applicable if the actual distribution deviates from a normal distribution or if there are complex coupling relationships in the dimensional chain. The Monte Carlo method simulates system behavior through a large number of random samples and is suitable for complex nonlinear systems and multivariate coupled scenarios. This method is highly flexible, adaptable to arbitrary distributions and complex models, but it consumes significant computational resources. Summary of the Invention

[0004] The main purpose of this application is to provide a method and device for controlling the precision of modular design and construction of ship sections, aiming to solve the technical problems of low efficiency and high cost in the current modular construction of ships.

[0005] To achieve the above objectives, this application proposes a method for controlling the precision of modular design and construction of ship sections, the method comprising:

[0006] Establish the dimensional chain relationship of each part in the preset module based on the standard data for modular construction of ship sections;

[0007] A three-dimensional assembly model of the preset module is established based on the dimensional chain relationship, and a three-dimensional assembly deviation analysis model is established based on the three-dimensional assembly model.

[0008] The deviation analysis model of the three-dimensional assembly is used to solve the deviation rate of the closed loop and the influence weight of the component loops.

[0009] The simulation results are determined to meet the preset requirements based on the closed loop error rate and the influence weight of the constituent loops.

[0010] When the simulation results meet the preset requirements, a comprehensive evaluation model for the deviation rate is established based on the simulation results.

[0011] The influence coefficients of each component ring on the overall error rate are calculated based on the comprehensive evaluation model of the error rate.

[0012] The construction accuracy control scheme is determined based on the influence coefficient, and the construction accuracy control of the modular construction of the ship section is carried out based on the construction accuracy control scheme.

[0013] Furthermore, to achieve the above objectives, this application also proposes a precision control device for modular design and construction of ship sections, the device comprising:

[0014] A module is established to create dimensional chain relationships among the parts in a pre-set module based on the standard data for modular construction of ship sections.

[0015] The establishment module is also used to establish a three-dimensional assembly model of the preset module based on the dimension chain relationship, and to establish a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model.

[0016] The solver module is used to solve the closed loop error rate and the influence weight of the component loops based on the three-dimensional assembly deviation analysis model.

[0017] The determination module is used to determine whether the simulation results meet the preset requirements based on the closed loop error rate and the influence weight of the constituent loops.

[0018] The establishment module is also used to establish a comprehensive evaluation model of the deviation rate based on the simulation results when the simulation results meet the preset requirements;

[0019] The solution module is also used to solve the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model;

[0020] The control module is used to determine the construction accuracy control scheme based on the influence coefficient, and to perform modular construction accuracy control of ship sections based on the construction accuracy control scheme.

[0021] In addition, to achieve the above objectives, this application also proposes a precision control device for modular design and construction of ship sections, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the precision control method for modular construction of ship sections as described above.

[0022] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the modular design and construction precision control method for ship sections as described above.

[0023] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the modular construction precision control method for ship sections as described above.

[0024] One or more technical solutions proposed in this application have at least the following technical effects:

[0025] 1) By establishing a pre-defined dimensional chain relationship between modules, the dimensions of each part can be precisely controlled, thereby ensuring the accuracy of the entire module assembly process, reducing quality problems caused by dimensional errors, and improving the construction precision of ship sections. Through a three-dimensional assembly deviation analysis model, the impact of deviations of various components on overall assembly precision can be comprehensively analyzed, identifying potential deviations. This precise analysis helps to identify problems early, thus avoiding errors and rework during production, saving time and costs. Based on simulation results, the deviation rate of closed loops and the influence weight of component loops can be analyzed to determine which component loops have a greater impact on overall precision. This analysis can guide production personnel to focus on key links, adopt more effective precision control measures, and further optimize the modular construction process of ship sections. By establishing a comprehensive evaluation model for deviation rates, the influence coefficient of each component loop on the overall deviation rate can be precisely quantified. This provides a scientific basis for precision control, making the shipbuilding process more refined and controllable, ensuring that the final product meets design requirements. Through simulation and deviation analysis, potential problems in the construction process can be assessed in advance, avoiding downtime or adjustments due to precision issues. This helps improve the stability and efficiency of the construction process, reducing production cycles and unnecessary waste. The established precision control scheme can be implemented at every stage of shipbuilding, from parts processing to module assembly, ensuring that every step is carried out according to predetermined standards, reducing quality fluctuations, and improving the stability and reliability of the final ship quality.

[0026] 2) By precisely defining the tolerance values, first and second basic dimensions, and their upper and lower limit deviations of parts, the dimensional range of each part can be effectively standardized. This ensures the dimensional accuracy of each part during the manufacturing process, avoids mismatches or assembly difficulties caused by dimensional deviations, and guarantees high-quality construction of ship module sections. Analyzing the section docking process and determining the docking assembly deviations of parts based on process data can effectively predict possible errors during assembly. This precision analysis not only reduces deviations but also enables the development of precise assembly methods and process requirements, thereby reducing problems caused by assembly errors during modular ship construction. By analyzing and processing the data of the component links, the dimensional relationships between parts can be understood, and the tolerance types of each link can be clarified. This helps to establish more accurate dimensional chain relationships, avoids overall deviations caused by the accumulation of dimensional errors in each link, and thus ensures the overall accuracy of modular construction. Through precise dimensional control throughout the entire modular construction process, high precision can be maintained at every stage from part manufacturing to assembly. This facilitates the implementation of full-process precision control, ensuring that each module maintains good dimensional fit throughout the entire construction process.

[0027] 3) By starting from the dimensional chain relationship and clarifying the basic dimensions, deviations, assembly sequence, and position of parts, errors in the assembly process can be effectively controlled, ensuring that each part is assembled in a precise position. This avoids misfitting problems caused by dimensional errors or improper assembly sequence, thereby improving assembly accuracy. Obtaining the assembly process of the power module and assembling parts according to the assembly sequence and position not only clarifies the assembly position and method of each part but also optimizes the assembly process, ensuring efficient execution of each step in the modular construction process and reducing time and labor costs. By establishing a 3D assembly model, the assembly process of parts and their relative positions can be clearly presented. By establishing a 3D assembly deviation analysis model, the assembly deviations of each part can be clearly identified and analyzed. Potential deviations during the assembly process can be predicted and quantified, allowing for proactive measures to reduce the impact of deviations on assembly quality. When assembling multiple modules, the 3D assembly model provides high flexibility, enabling adjustments to each module during design, manufacturing, and assembly based on actual conditions. This flexibility helps to better adapt to various design requirements, improving the adaptability and accuracy of the shipbuilding process. By analyzing 3D assembly deviations in advance, potential assembly problems can be identified early on, thus avoiding rework and corrections caused by assembly errors and improving production efficiency. Reducing rework means saving time and costs, while also improving the overall efficiency of the production line. For large and complex ship modules, 3D assembly models can better manage and control the details of the assembly process, ensuring that the accuracy requirements of all parts are met at each stage of assembly. Especially in cases of complex assembly positions and sequences, 3D modeling can provide more intuitive and accurate guidance. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating the first embodiment of the method for controlling the precision of modular design and construction of ship sections in this application;

[0031] Figure 2 A simplified three-dimensional structural diagram of the power module provided in an embodiment of the modular design and construction precision control method for ship sections in this application;

[0032] Figure 3 A two-dimensional schematic diagram of a certain type of power module structure provided as an embodiment of the modular design and construction precision control method for ship sections in this application;

[0033] Figure 4 A schematic diagram showing the change in spatial position before and after radial assembly deviation loading, provided as an embodiment of the method for controlling the precision of modular design and construction of ship sections in this application;

[0034] Figure 5 The Lasso regression coefficient solution result diagram is provided for an embodiment of the method for controlling the precision of modular design and construction of ship sections in this application;

[0035] Figure 6 The simulation analysis results of the parallelism between the sealing device and the power shaft are shown in the figure, which is an embodiment of the method for controlling the precision of modular design and construction of ship sections in this application.

[0036] Figure 7 The parallelism simulation analysis results of the sealing device and the power shaft after precision control according to the construction precision control scheme are provided in an embodiment of the modular design and construction precision control method for ship sections of this application.

[0037] Figure 8 A flowchart illustrating the second embodiment of the modular design and construction precision control method for ship sections in this application;

[0038] Figure 9 A simplified flowchart illustrating the method for controlling the precision of modular design and construction of ship sections provided in Embodiment 2 of this application.

[0039] Explanation of icon numbers:

[0040] 1. Stern shaft; 2. Sealing device; 3. Stern bearing; 4. Rear axle; 5. Thrust shaft; 6. Front axle; 7. Thrust bearing; 8. Clutch; 9. Power shaft; 10. Platform.

[0041] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.

[0043] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0044] The main solution of this application embodiment is as follows: Establish the dimensional chain relationship of each part in the preset module based on the standard data of modular construction of ship sections; establish a three-dimensional assembly model of the preset module based on the dimensional chain relationship, and establish a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model; solve the closed-loop deviation rate and the influence weight of the component loops based on the three-dimensional assembly deviation analysis model; determine whether the simulation results meet the preset requirements based on the closed-loop deviation rate and the influence weight of the component loops; when the simulation results meet the preset requirements, establish a comprehensive evaluation model of deviation rate based on the simulation results; solve the influence coefficient of each component loop on the overall deviation rate based on the comprehensive evaluation model of deviation rate; determine the construction accuracy control scheme based on the influence coefficient, and perform modular construction accuracy control of ship sections based on the construction accuracy control scheme.

[0045] Current technologies for accuracy prediction in modular ship construction primarily utilize dimensional chains, with deviation analysis calculations mainly employing extreme value methods and root mean square (RMS) methods. The extreme value method involves the linear superposition of tolerances under worst-case conditions for the constituent links. This method is absolutely reliable and avoids failure risks, but the resulting deviation predictions are overly conservative, significantly increasing manufacturing costs. The RMS method is based on the square root of the sum of squares of a statistical distribution (assuming a normal distribution), suitable for linear dimensional chains. This method balances accuracy and cost, reflecting actual fluctuations. However, it becomes unsuitable if the actual distribution deviates from a normal distribution or if the dimensional chain has complex coupling relationships.

[0046] This application provides a solution for predicting and controlling the construction accuracy of modular design for ship sections. Based on the Monte Carlo method, it calculates the deviation rate of closed loops in the dimensional chain to predict construction accuracy. For construction accuracy control, an improved linear regression analysis method is proposed. By establishing the relationship between the deviation rate of each component loop and the overall deviation rate, a comprehensive evaluation model of the component loops is constructed. Finally, control suggestions are given based on the influence of each component loop on the overall deviation rate. By controlling the errors of a few parts, the overall control accuracy is maximized, which will significantly reduce the ship's production cost and improve production efficiency.

[0047] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a ship section modular construction precision control device. The following description uses a ship section modular construction precision control device as an example to illustrate this embodiment and the subsequent embodiments.

[0048] Based on this, the embodiments of this application provide a method for controlling the precision of modular design and construction of ship sections, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for controlling the precision of modular design and construction of ship sections in this application.

[0049] In this embodiment, the method for controlling the precision of modular construction of ship sections includes steps S10 to S40:

[0050] Step S10: Establish the dimensional chain relationship of each part in the preset module based on the modular construction standard data of the ship block.

[0051] It should be noted that the modular construction standard data for ship sections is data on the standard requirements and experience of modular construction of ship sections obtained in advance. The preset module is a certain module in the ship section module. The parts may include stern shaft, sealing device, rear shaft, thrust shaft, front shaft, thrust bearing, clutch, power shaft and platform, etc., and may also include other parts. This embodiment does not limit this.

[0052] In practice, the dimensional chain relationship between the parts can include data such as assembly deviations and manufacturing deviations of each part.

[0053] It should be noted that when establishing the dimensional chain relationships between various parts, the size of the constituent loops can be set in the 3D tolerance software CATIA to establish the dimensional chain relationships between the parts, such as... Figure 2 As shown, Figure 2This is a simplified 3D structural diagram of the power module. The stern bearing has a negligible impact on the overall structure, so its component rings are ignored. The dimensions of the stern shaft, sealing device, rear axle, thrust shaft, front axle, thrust bearing, clutch, power shaft, and platform are set separately.

[0054] In one feasible implementation, step S10 may include steps A11 to A15:

[0055] Step A11: Determine the tolerance values ​​of each part, the first basic dimension of the manufacturing deviation of each part, the first upper limit deviation, and the first lower limit deviation based on the modular construction standard data of the ship section;

[0056] It should be noted that, firstly, the modular construction standard requirements and experience of ship sections can be obtained from the modular construction standard data of ship sections. Then, based on the modular construction standard requirements and experience of ship sections, the basic dimensions, upper limit deviations and lower limit deviations of manufacturing deviations can be determined, namely the first basic dimension, the first upper limit deviation and the first lower limit deviation.

[0057] Step A12: Analyze the process data between the components of the current module according to the segmented docking process, and determine the second basic dimension, the second upper limit deviation, and the second lower limit deviation of the docking assembly deviation of each part according to the process data;

[0058] Understandably, continuing as Figure 2 As shown, when the parts are segmented and connected, the process data between the current module components can be analyzed, including forming process, surface treatment process, connection process, etc., so as to determine the basic dimensions, upper limit deviation and lower limit deviation of the deviations such as docking assembly deviation and welding deformation deviation, that is, the second basic dimensions, second upper limit deviation and second lower limit deviation of the docking assembly deviation.

[0059] Step A13: Obtain the ring data based on the first basic dimension, the first upper limit deviation, the first lower limit deviation, the second basic dimension, the second upper limit deviation, and the second lower limit deviation;

[0060] In practical implementation, the component ring data can be obtained by connecting the assembly deviation and manufacturing deviation data, as shown in Table 1. Table 1 is a table of deviation value settings for various components of the power module.

[0061] Table 1

[0062]

[0063] Using Table 1 above, the manufacturing deviations and assembly deviations of each part can be set, and the basic dimensions and upper and lower deviation values ​​of the manufacturing deviations and assembly deviations of each part can be obtained.

[0064] Step A14: Determine the type of constituent rings based on the tolerance value;

[0065] It should be noted that the tolerance value is The type of constituent loop can be determined based on the magnitude of the tolerance value and 0. The constituent loop types include increasing loops and decreasing loops. The rings are increasing rings, when The rings are decreasing rings, and the subscript i is the number of rings.

[0066] Step A15: Establish the dimensional chain relationship of each part in the preset module based on the component ring type, the tolerance value, and the component ring data.

[0067] In practical implementation, the basic equations of the dimensional chain for each part in the preset module can be established based on the type of the component ring, the tolerance value, and the component ring data, as shown below:

[0068]

[0069] In the formula: The basic dimensions of the closed loop are the multi-directional offsets of the module in the axial, lateral, or vertical directions. To form a ring, i.e., the basic dimensions of each deviation source, when , To increase the ring, when , To reduce the ring, subscript To determine the number of loops, specifically, the upper and lower deviations of the closed loop are calculated using the following formula:

[0070]

[0071]

[0072] In the formula, This represents the limit deviation on the closed loop. This represents the lower limit deviation of the closed loop. The upper limit deviation of the ring is the limit. The lower limit deviation of the increasing loop, To reduce the lower limit deviation of the ring, To reduce the upper limit deviation of the ring, To increase the ring, To reduce the number of links, the dimensional chain relationship between each part can be established using the above formula.

[0073] Step S20: Establish a three-dimensional assembly model of the preset module based on the dimensional chain relationship, and establish a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model.

[0074] It should be noted that after establishing the dimensional chain relationship of the closed loop, the geometric dimensions of the parts can be established in 3D drawing software using engineering drawings. Specifically, the 3D tolerances can be established based on the basic dimensions, upper deviation, and lower deviation of the constituent loops after the dimensional chain is established, thereby creating a 3D assembly model.

[0075] The 3D assembly model is a 3D model of the ship's power module, a parametric simulation model that includes the dimensions and shapes of each part. For example... Figure 3 As shown, Figure 3 This is a two-dimensional schematic diagram of a certain type of power module structure. From left to right, it includes a stern shaft 1, a sealing device 2, a stern bearing 3, a rear shaft 4, a thrust shaft 5, a front shaft 6, a thrust bearing 7, a clutch 8, a power shaft 9, and a platform 10. The Y-axis coincides with the centerline of the ship's power shaft system, and the positive direction of the Z-axis is perpendicular to the plane containing the X-axis and Y-axis. The coordinates used in subsequent modeling and calculations are the same as this coordinate system.

[0076] In practice, the three-dimensional assembly model only creates the positional information, but does not determine the assembly connection method in the actual process. To address this, a three-dimensional assembly deviation analysis model can be established based on the three-dimensional assembly model, thereby making the analysis results more accurate.

[0077] In one feasible implementation, step S20 may include steps A21 to A26:

[0078] Step A21: Obtain the basic dimensions, upper deviation, and lower deviation of the constituent rings based on the aforementioned dimensional chain relationship;

[0079] It should be noted that the basic dimensions, upper deviation, and lower deviation of the constituent rings after the dimensional chain is established can be obtained according to the dimensional chain relationship, as shown in Table 2. Table 2 shows the basic dimensions of the constituent rings of the ship's power module.

[0080] Table 2

[0081]

[0082] Step A22: Obtain the assembly process of the power module;

[0083] Understandably, the assembly process of the power module can be set in advance. Therefore, when a three-dimensional assembly model needs to be built, the corresponding assembly process of the power module can be used.

[0084] Step A23: Determine the assembly sequence and assembly position in the actual assembly process according to the assembly process described above;

[0085] In practice, the assembly sequence and position in the actual assembly process can be determined according to the assembly process. For example, the hole-shaft fit requires three-point assembly, which is more in line with the actual assembly process than the 3-2-1 assembly.

[0086] Step A24: Assemble the parts sequentially according to the basic dimensions, the upper deviation, and the lower deviation, following the assembly sequence and the assembly position, to obtain a three-dimensional assembly model of the preset module;

[0087] In practice, three-dimensional tolerances can be established based on basic dimensions, upper deviations, and lower deviations. Then, according to the assembly sequence and position in the actual assembly process, the parts are assembled sequentially to complete the establishment of the three-dimensional assembly model.

[0088] The assembly process of the power module is as follows:

[0089] 1) Install sealing devices according to the stern base points of the slipway structure;

[0090] 2) Equipment installation on the platform;

[0091] 2-1) The thrust shaft is mounted on the platform;

[0092] 2-2) Install the rear axle and stern shaft in sequence according to the position of the thrust shaft;

[0093] 2-3) Install the stern bearing according to the position of the stern shaft;

[0094] 2-4) Install the front axle, thrust bearing, clutch, and drive shaft in sequence according to the position of the thrust shaft;

[0095] 3) Install the platform according to the location of the sealing device.

[0096] Step A25: Determine the target assembly method according to the assembly process, and establish the assembly relationship of each part according to the target assembly method;

[0097] It is understandable that different assembly processes require different assembly methods. Therefore, the appropriate assembly method can be selected based on the assembly process, and the assembly settings can be configured in the 3DCS.

[0098] For example, the end faces of two shafts can be constrained at once using a 6-face assembly, which reduces complexity. However, the assembly of the platform and shafts can use a 3-2-1 assembly to constrain the degrees of freedom step by step, as this requires multi-station step-by-step positioning. The specific steps are as follows: Install the thrust shaft on the platform using a 3-2-1 positioning method; install the rear shaft on the thrust shaft using a 6-face assembly; install the stern shaft on the rear shaft using a 6-face assembly; install the front shaft on the thrust shaft using a 6-face assembly; install the thrust bearing on the front shaft using a 6-face assembly; install the clutch on the thrust bearing using a 6-face assembly; install the power shaft on the clutch using a 6-face assembly; and install the platform on the sealing device using a hole-shaft fit. Therefore, the assembly relationship between each part can be obtained based on the determined target assembly method.

[0099] Step A26: Assemble the parts on the three-dimensional assembly model according to the assembly relationship, and set the tolerance distribution data of each part end face in different directions and the part measurement data to obtain the three-dimensional assembly deviation analysis model.

[0100] In practical implementation, parts can be assembled on a 3D assembly model according to the assembly relationship, and tolerance distribution and part measurement data can be set. A suitable tolerance probability distribution can be selected based on the statistical patterns of data from the actual assembly process. Specifically, tolerance distribution settings can be performed in the 3DCS. Since the probability of each part reaching the tolerance extreme value is a low-probability event, while the probability near the tolerance median is higher, the axial installation deviation distribution of each part's end face is set to a normal distribution, the radial installation deviation distribution of each part's end face is set to a right-leaning distribution, and due to the asymmetry of process deviations, the installation deviation distribution of each part's end face along the axial rotation is set to a normal distribution, as is the diameter manufacturing deviation distribution of each part's end face. Therefore, tolerance distribution data for each part's end face in different directions, including axial, radial, and diameter tolerance distribution data, are obtained. Figure 4 As shown, Figure 4 This diagram illustrates the spatial position changes before and after radial assembly deviation loading. By setting the radial assembly deviation, installation accuracy can be improved.

[0101] Finally, you can set the measurement data, including the upper and lower deviations of the closed loop. By creating measurement points, measurement lines, and measurement surfaces, you can measure the size of the closed loop and compare the measured size with the upper and lower deviations of the closed loop to determine whether it is out of tolerance.

[0102] Specifically, measurement settings can be configured in the 3DCS. Parallelism can be set using straight-line angle measurement, selecting "nominal straight line" as the measurement type and "true angle" as the direction. This primarily measures the parallelism between the thrust shaft and power shaft, thrust shaft and stern shaft, stern shaft and power shaft, and the sealing device and power shaft. Distance can also be set using point-to-point measurement, selecting "point-to-point" as the measurement type and "true distance" as the direction. This primarily measures the distances between the thrust shaft and power shaft, thrust shaft and stern shaft, stern shaft and power shaft, and the sealing device and power shaft. Parts are then assembled on the 3D assembly model based on assembly relationships, with tolerance probability distributions and part measurement data set simultaneously, thus establishing a 3D assembly deviation analysis model.

[0103] Step S30: Solve for the closed loop error rate and the influence weight of the component loops based on the three-dimensional assembly deviation analysis model.

[0104] Understandably, after establishing a three-dimensional assembly deviation analysis model, the out-of-tolerance rate of the closed loop and the influence weight of the component loops can be solved based on the three-dimensional assembly deviation analysis model.

[0105] The closed-loop error rate refers to the cumulative error among all parts throughout the assembly process. The calculation must take into account the interaction between the parts, especially when they form a closed loop, the impact of the error will be more significant.

[0106] The component influence weight is the weight given to the contribution of each part or component to the overall error during the assembly process. Some components may have a greater impact on the final out-of-tolerance rate than others.

[0107] Specifically, simulation analysis can be performed in 3DCS to obtain the out-of-tolerance rate of the closed loop and the influence weight of the constituent loops.

[0108] Assume there are multiple critical connection points within a ship assembly module, forming a closed loop. A significant error at any connection point could affect the assembly accuracy of the entire module. Therefore, a three-dimensional assembly deviation analysis model can be used to calculate the degree of error impact on each component loop and determine the weight of each component's influence on the final deviation rate.

[0109] Step S40: Determine whether the simulation results meet the preset requirements based on the closed loop error rate and the influence weight of the constituent loops.

[0110] It should be noted that the simulation results are based on the assembly accuracy calculated using the closed-loop error rate and the influence weights of the component loops. The simulation model simulates the error propagation and accumulation during the assembly process.

[0111] Preset requirements refer to the predetermined range of precision during the design and construction process. For example, assembly errors cannot exceed a certain limit, otherwise it will affect the safety and functionality of the ship's modules. If simulation results show that the errors in some aspects exceed the preset allowable error range (e.g., the total error exceeds 2 mm), the simulation results can be determined to be unacceptable. If the errors are within the allowable range, the simulation results meet the preset requirements.

[0112] If the simulation results do not meet the preset requirements, the size and distribution of the component loops can be modified, and the process can be repeated until the simulation results meet the preset requirements. Specifically, if the deviation rate of some closed loops is 100%, it indicates that the simulation results are unreasonable. The size of the component loops needs to be reset, prioritizing the optimization of component loops with high contribution rates to make the deviation rate of closed loops less than 100%, thus making the simulation results reasonable. Based on this, an evaluation model between the total deviation rate of closed loops and the component loops is constructed.

[0113] Step S50: When the simulation results meet the preset requirements, establish a comprehensive evaluation model for the deviation rate based on the simulation results.

[0114] In practice, if the simulation results meet the preset requirements, a comprehensive evaluation model for the deviation rate can be established based on the simulation results.

[0115] The comprehensive evaluation model for deviation rate is a mathematical model constructed based on simulation results. It comprehensively considers the errors in each stage and their impact on the overall assembly accuracy. This model helps designers to evaluate all possible deviation rates during the construction process as a whole and propose adjustment and optimization schemes.

[0116] In one feasible implementation, step S50 may include steps A31 to A34:

[0117] Step A31: When the simulation results meet the preset requirements, obtain the overall closed loop error rate, the influence weight of the component loop in the closed loop, and the error rate of the component loop in the closed loop based on the simulation results;

[0118] Step A32: Set the correction factor according to the importance of the closed loop;

[0119] It should be noted that 3DCS, as a 3D tolerance analysis software, relies heavily on the Monte Carlo method for its core function, "Variation Modeling." Therefore, 3DCS software is used to implement the numerical solution of the Monte Carlo method. If the simulation results meet the preset requirements, the 3DCS analysis results are exported as an .xlsx file for data processing, as shown in Table 3. Set to 1; specifically, the correction factor can be set according to the importance of the closed loop. Because the dataset is small, with only 8 groups and as many as 16 variables, traditional linear regression cannot be directly applied, as it can lead to overfitting or unsolvable problems. The regularized regression method Lasso regression is used to solve this problem, as it adds a penalty term to constrain the coefficients and prevent overfitting.

[0120] Table 3

[0121]

[0122] When the simulation results meet the preset accuracy requirements, several key indicators are extracted from the simulation results: Overall closed-loop deviation rate: refers to the final deviation resulting from the cumulative errors of all components in the entire assembly system. Influence weight of each component in the closed loop: the degree to which the error of each component contributes to the overall deviation rate. This influence weight can be obtained by analyzing the interrelationships and importance between the components. Deviation rate of each component in the closed loop: the error level of each link in the entire closed loop, indicating the deviation inherent in that link itself.

[0123] Step A33: Determine the model parameters and error terms based on the multiple linear regression model;

[0124] It should be noted that the general form of a multiple linear regression model is:

[0125]

[0126] In the formula: , , , ..., These are the parameters of the model; This is the error term. The goodness-of-fit method is used in the multiple linear regression equation. Perform a goodness-of-fit test. The larger the size, the better the model fits.

[0127] Therefore, the model parameters can be obtained from the multiple linear regression model. , , , ..., and error terms .

[0128] Step A34: Construct a comprehensive evaluation model for deviation rate based on the model parameters, the error term, the correction coefficient, the overall closed-loop deviation rate, the influence weight of the component loop in the closed loop, and the deviation rate of the component loop in the closed loop.

[0129] In practical implementation, a comprehensive evaluation model for deviation rate can be constructed by integrating model parameters, error terms, and correction coefficients. This model can comprehensively assess the accuracy of the entire assembly system, considering the influence of each component and its correction coefficient. The purpose of the comprehensive evaluation model is to comprehensively evaluate the overall assembly accuracy based on multiple factors, thereby providing a scientific basis for accuracy control.

[0130] Specifically, the comprehensive evaluation model for the deviation rate is expressed as follows:

[0131]

[0132] in:

[0133]

[0134] This is the sum of the out-of-tolerance rates for all closed loops. Let M be the contribution of the Mth constituent ring in the i-th closed ring. Let M be the out-of-tolerance rate of the Mth component loop in the i-th closed loop. The summation of the out-of-tolerance rates of the Mth component loop with respect to the overall closed loop. The correction factor is set according to the importance of the closed loop, with a default value of 1.

[0135] Step S60: Solve the influence coefficient of each component ring on the overall deviation rate according to the comprehensive evaluation model of deviation rate.

[0136] In practical implementation, the influence coefficient of each component on the overall deviation rate can be calculated using a comprehensive evaluation model for deviation rate. This coefficient represents the contribution of the error of each component to the overall assembly accuracy of the ship module. This coefficient can identify which links are critical control points and which links have a smaller impact on overall accuracy.

[0137] In one feasible implementation, it is also necessary to determine whether the established comprehensive evaluation model for deviation rate meets the requirements, i.e., whether it is overfitting. Therefore, before step S60, the following steps are also included:

[0138] The initial regularized regression coefficients are obtained by solving the comprehensive evaluation model of the deviation rate.

[0139] The goodness of fit of the comprehensive evaluation model of deviation rate is calculated based on the initial regularized regression coefficients.

[0140] Determine whether the comprehensive evaluation model for deviation rate is overfitted based on the goodness of fit.

[0141] When the comprehensive evaluation model for the deviation rate does not exhibit overfitting, the step of obtaining the influence coefficient of each component loop on the overall deviation rate based on the comprehensive evaluation model for the deviation rate is performed.

[0142] It should be noted that the comprehensive evaluation model for deviation rate can be solved using numerical calculations or optimization algorithms to obtain the initial regularized regression coefficients. These initial regularized regression coefficients are Lasso regression coefficients. Regularization is used to prevent the model from overfitting (i.e., performing too well on training data but poorly on new data). Regularization imposes restrictions on the regression coefficients (e.g., L1 or L2 regularization), enabling the model to have a certain degree of simplicity and generalization ability while fitting the data.

[0143] It should be understood that the model's fit can be measured by comparing the model's predictions with the actual data based on the initial regularized regression coefficients. Common goodness-of-fit metrics include R² (coefficient of determination) or mean squared error (MSE). A higher goodness-of-fit indicates that the model fits the data better. If the model has a high goodness-of-fit, it suggests that the overall deviation rate evaluation model can predict the overall deviation rate well.

[0144] The larger the R², the better the model fits the data. The formula for calculating R² is:

[0145]

[0146] in, The number of samples; These are sample observations; The mean of the sample observations; SSE represents the regression value calculated using the regression equation; SSE is the sum of squared residuals, also known as the sum of squared errors. Clearly, the smaller the SSE, the better the corresponding regression equation performs. This method improves upon this, still using the R² value to evaluate the effectiveness of the regression equation; the larger the R², the better the model's fit.

[0147] For example, one can determine whether the goodness of fit R² is equal to 1. If the goodness of fit is equal to 1, it means that there is no overfitting. Figure 5 As shown, Figure 5 The graph shows the results of the Lasso regression coefficients. The model evaluation shows R²=1, indicating a perfect fit. This solution is reasonable given the small sample size and excessive variables. To determine the percentage contribution of each component loop to the overall result, regression coefficients are first calculated. The regression coefficients were determined using Lasso regression in Python. .

[0148] It should be noted that when the comprehensive evaluation model for the deviation rate does not exhibit overfitting, the influence coefficient of each component loop on the overall deviation rate can be obtained based on the comprehensive evaluation model for the deviation rate.

[0149] In practical implementation, the influence coefficient of each component loop on the overall error rate is:

[0150]

[0151] is the influence coefficient of the Mth component ring on the overall error rate.

[0152] If the overall evaluation model for the deviation rate is overfitted, regularized regression can be used to correct the overfitting, and the steps for establishing the overall evaluation model for the deviation rate can be repeated until the overall evaluation model for the deviation rate is no longer overfitted.

[0153] Step S70: Determine the construction accuracy control scheme based on the influence coefficient, and carry out modular construction accuracy control of ship sections based on the construction accuracy control scheme.

[0154] In practice, after calculating the influence coefficient, a construction accuracy control scheme can be determined based on the influence coefficient, thereby controlling the modular construction accuracy of the ship section.

[0155] To ensure that the overall assembly tolerance rate can be applied to actual production, after determining the accuracy of the component rings, we communicate with the equipment manufacturing unit and the final assembly unit to check whether the accuracy settings of the component rings and the setting range of the closed rings are reasonable based on existing engineering experience. If they are not reasonable, we adjust the accuracy level of the component rings and the measurement range of the closed rings.

[0156] In one feasible implementation, step S70 may include steps A41 to A44:

[0157] Step A41: Normalize the influence coefficients to obtain normalized influence coefficients;

[0158] In practice, the influence coefficients can be normalized to obtain the normalized influence coefficient of each component ring on the whole. After normalization, all influence coefficients will be within a standard range, which facilitates subsequent comparison with the threshold.

[0159] Table 4

[0160]

[0161] As shown in Table 4, Table 4 contains the solution data for each component of the ring.

[0162] The normalization process is as follows:

[0163]

[0164] By normalizing the influence coefficient of each component loop, the magnitude of the overall deviation rate of each component loop can be intuitively observed.

[0165] Step A42: Compare the normalized influence coefficient with a preset threshold;

[0166] It should be noted that the preset threshold can be set to 0.1 or other values. This embodiment does not limit this. Therefore, the normalized influence coefficient can be compared with the preset threshold to determine whether the constituent loop has a significant impact on the overall error rate.

[0167] If the normalized influence coefficient is less than the preset threshold of 0.1, it means that the corresponding component loop does not require additional control.

[0168] Step A43: When the normalized influence coefficient is greater than or equal to the preset threshold, the component loop corresponding to the normalized influence coefficient is taken as the component loop to be controlled;

[0169] In practice, if the normalized influence coefficient is greater than or equal to 0.1, then the component loops with a normalized influence coefficient greater than 0.1 need to be identified as component loops to be controlled, which are identified as component loops that have a significant impact on the overall deviation rate. Controlling these component loops during the production process can significantly reduce the overall deviation rate.

[0170] Step A44: Determine the construction accuracy control scheme based on the controllable component loop, and perform modular construction accuracy control of ship sections according to the construction accuracy control scheme.

[0171] As shown in Table 4, optimizing the five deviation sources—axial deviation of the rear axle end face, manufacturing deviation of the sealing device diameter, axial deviation of the front axle end face, axial deviation of the thrust shaft end face, and axial deviation of the stern shaft end face—can greatly reduce the overall deviation rate.

[0172] By reducing the assembly deviation by half for five sources of deviation—axial deviation of the rear axle end face, manufacturing deviation of the sealing device diameter, axial deviation of the front axle end face, axial deviation of the thrust shaft end face, and axial deviation of the stern shaft end face—the assembly deviation analysis model was used to analyze the results. For example... Figure 6 As shown, Figure 6 The image shows the simulation results of the parallelism analysis between the sealing device and the power shaft. (Example:) Figure 7 As shown, Figure 7 The simulation analysis results of the parallelism between the sealing device and the power shaft after precision control are shown in Table 5. The analysis found that the overall deviation rate was reduced by more than half. The variation of the deviation rate of each closed loop is shown in Table 5. The deviation rates for parallelism between the sealing device and the drive shaft decreased from 50.02% to 9.71%; for parallelism between the thrust shaft and the drive shaft, from 3.04% to 2.84%; for distance between the thrust shaft and the stern shaft, from 78.1% to 25.32%; for parallelism between the stern shaft and the drive shaft, from 53.34% to 35.11%; for distance between the thrust shaft and the drive shaft, from 64.68% to 47.39%; for perpendicularity between the thrust shaft and the stern shaft, from 39.62% to 23.13%; for distance between the sealing device and the drive shaft, from 54.53% to 36.23%; and for distance between the stern shaft and the drive shaft, from 50.01% to 9.71%. This has significant guiding significance for actual production.

[0173] Table 5

[0174]

[0175] This embodiment provides a method for controlling the precision of modular design and construction of ship sections. By establishing a pre-defined dimensional chain relationship between modules, the dimensions of each part can be precisely controlled, thereby ensuring the accuracy of the entire module assembly process, reducing quality problems caused by dimensional errors, and improving the construction precision of ship sections. Through a three-dimensional assembly deviation analysis model, the impact of deviations of each component on the overall assembly precision can be comprehensively analyzed, identifying potential deviations. This precise analysis helps to identify problems early, thus avoiding errors and rework during production, saving time and costs. Based on simulation results, the deviation rate of closed loops and the influence weight of component loops can be analyzed to determine which component loops have a greater impact on overall precision. This analysis can guide production personnel to focus on key aspects, adopt more effective precision control measures, and further optimize the modular construction process of ship sections. By establishing a comprehensive evaluation model for deviation rates, the influence coefficient of each component loop on the overall deviation rate can be precisely quantified. This provides a scientific basis for precision control, making the ship construction process more refined and controllable, ensuring that the final product meets design requirements. Through simulation and deviation analysis, potential problems in the construction process can be assessed in advance, avoiding work stoppages or adjustments due to precision issues. This helps improve the stability and efficiency of the construction process, reducing production cycles and unnecessary waste. The established precision control scheme can be implemented at every stage of shipbuilding, from parts processing to module assembly, ensuring that every step is carried out according to predetermined standards, reducing quality fluctuations, and improving the stability and reliability of the final ship quality.

[0176] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 8 Step S30 includes steps S301 to S306:

[0177] Step S301: Based on the three-dimensional assembly deviation analysis model, perform several dimensional chain simulations using a repeated random sampling strategy to obtain several closed-loop dimensional subsamples.

[0178] It should be noted that the repeated random sampling strategy uses the Monte Carlo method. This allows for multiple dimensional chain simulations of the 3D assembly deviation analysis model, yielding N closed-loop dimensional samples. Simulation analysis is then performed in 3DCS, a software based on the Monte Carlo method for tolerance analysis and dimensional chain simulation. 3DCS can predict the behavior of complex systems through random sampling and repeated trials, quickly locate critical tolerances through sensitivity analysis, and reduce redundant optimization costs. Therefore, 3DCS software is used for simulation analysis to achieve deviation prediction using the Monte Carlo method.

[0179] Given the basic dimensions, upper and lower deviations, and size distribution patterns of the constituent rings, probability theory and mathematical statistics dictate that given a continuously distributed random variable, any random variable with an arbitrary distribution can be obtained through transformation and selection. Among one-dimensional continuously distributed random variables, those continuously distributed on (0, 1) are the simplest. Therefore, when randomly simulating the dimensions of each constituent ring, it is common practice to first generate random numbers uniformly distributed on (0, 1), and then convert them into random numbers with other distribution patterns using calculation formulas. Random numbers uniformly distributed on (0, 1) can be generated using the `random()` function provided by high-level programming languages; in MATLAB, it is the `rand()` function.

[0180] (1) The transformation relationship between random numbers with a normal distribution and random numbers with a uniform distribution:

[0181] normal distribution There is a certain transformation relationship between random numbers and uniformly distributed random numbers on (0, 1). If , Let be two independent random numbers uniformly distributed on (0, 1). Then, two independent standard normal distributions... random numbers , for:

[0182]

[0183]

[0184] Standard normal distribution random numbers Compared with normal distribution random numbers The following transformation relationship exists between them:

[0185]

[0186] Therefore, random numbers uniformly distributed on (0, 1) , Transform into a normal distribution random numbers , The transformation formula is as follows:

[0187]

[0188]

[0189] (2) The transformation relationship between non-normally distributed random numbers and uniformly distributed random numbers:

[0190] If random variable It has a continuous distribution function ,and It consists of continuously increasing random numbers that are uniformly distributed on (0, 1). Then we have:

[0191]

[0192] Right now It is an equation The solution.

[0193] Therefore, if random variable With probability density function ,and For it, a random number, Let be a random number uniformly distributed on (0, 1), then we have:

[0194]

[0195] Taking the solution of uniformly distributed random numbers as an example, the random variable exist When the distribution is uniform, its probability density function is:

[0196]

[0197]

[0198] Therefore, in Uniformly distributed random numbers The formula is generated as follows:

[0199]

[0200] The dimensions of each component ring were determined by After simulation and solving the size chain equation, we obtained A sample of closed-loop size ( ).

[0201] Step S302: Obtain the average, maximum, and minimum values ​​of several closed-loop size samples based on several closed-loop size samples.

[0202] In specific implementation, it is possible to... The dimensions of the closed loop samples were statistically analyzed. The average, maximum, and minimum values ​​of the closed-loop dimensions for each sample, and the closed-loop dimensions. average of samples As shown in the following formula:

[0203]

[0204] Closed loop size Maximum and minimum values ​​of each sample , As shown in the following formula:

[0205]

[0206]

[0207] Step S303: Obtain the average value, maximum value, and minimum value of the closed loop size based on the average, maximum, and minimum values ​​of several closed loop size samples.

[0208] In practical implementation, the average size of the closed loop is The average value of a sample, i.e. , The maximum value of a single sample is the maximum value of the closed-loop size. The minimum value of a sample is the minimum value of the closed loop size, that is:

[0209] , ,in This represents the maximum value of the closed loop dimension. This is the minimum value of the closed loop size. This represents the average size of the closed loop.

[0210] Step S304: Calculate the upper deviation and lower deviation of the closed loop size based on the average value, maximum value and minimum value of the closed loop size.

[0211] In practical implementation, the deviation in the closed-loop size can be calculated using the average, maximum, and minimum values ​​of the closed-loop size. and lower deviation :

[0212]

[0213]

[0214] Step S305: Set the probability density function of the closed loop and the preset closed loop size based on the upper deviation of the closed loop size, the lower deviation of the closed loop size, and the maximum and minimum values ​​of several closed loop size samples.

[0215] In practical implementation, the probability density function of the closed loop and the preset closed loop size can be set according to the upper deviation of the closed loop size, the lower deviation of the closed loop size, and the maximum and minimum values ​​of several closed loop size samples.

[0216] Number of samplings Determining the success rate of assembly and calculating the number of samples: The general method: Let the probability density function of the size distribution of the closed loop be... ,go through Obtained by random sampling The maximum and minimum values ​​in the sample are , The size of the closed loop is between and The probability between for:

[0217]

[0218] because , It is a random variable, so It is also a random variable. But regardless of the distribution of the closed-loop size, the random variable... The probability density functions of the distributions are all:

[0219]

[0220] In the formula: The number of random simulations.

[0221] If a given value Then the random variable The value is greater than The probability is:

[0222]

[0223] After integration, we get:

[0224]

[0225] If given ( Even if The confidence level for the value is Then we have:

[0226]

[0227] Simplifying, we get:

[0228]

[0229] Solving the above equation yields the result. The value can be understood as: the preset closed loop size. Included and The ratio between them is not less than The probability is The number of random samplings (i.e., the required sample size) for the required closed-loop size under given conditions. Given different... and Different random sampling numbers can be obtained. Currently, Monte Carlo simulations are easily achieved by inputting the number of simulations, using computer-generated Monte Carlo programs. When performing tolerance analysis, to ensure computational accuracy, the number of random samplings is typically set to over 10,000.

[0230] Step S306: Solve for the out-of-tolerance rate of the closed loop and the influence weight of the constituent loops based on the preset closed loop size and the probability density function.

[0231] Therefore, the out-of-tolerance rate of the closed loop and the influence weights of the constituent loops can be solved based on the probability density function and the preset closed loop size.

[0232] In one feasible implementation, step S306 may include steps B11 to B16:

[0233] Step B11: Set the number of random samples;

[0234] The above settings allow you to set the number of random samples, specifically, the number of random samples N can be set to more than 10,000.

[0235] Step B12: Perform size chain simulation using a repeated random sampling strategy based on the number of random samplings, and obtain simulation results;

[0236] It should be noted that the Monte Carlo method can be used to simulate the size chain based on the number of random samplings, and the simulation results can be obtained. The simulation results are ( ).

[0237] Step B13: Calculate the assembly success rate based on the simulation results, the preset closed loop size, and the probability density function;

[0238] In practical implementation, the simulation results can be used as a basis. ), preset closed loop size and probability density function Determine the assembly success rate.

[0239] Specifically, the assembly success rate is shown in the following formula:

[0240]

[0241] Data obtained from simulation ( ) find out, that is, find the location in ( (meeting the conditions) ( Given the closed loop size, Provide the designer with the lower deviation value of the closed loop. The number of upper deviation values ​​(provided to the designer for the closed loop) The assembly success rate was thus calculated.

[0242]

[0243] The assembly success rate can be obtained from the above formula. .

[0244] Step B14: Obtain the closed-loop deviation rate based on the assembly success rate;

[0245] In practical implementation, the closed-loop tolerance rate can be calculated based on the assembly success rate. Specifically, the closed-loop tolerance rate... This can also be expressed as:

[0246]

[0247] Step B15: Obtain the number of component rings and the component ring tolerance;

[0248] Step B16: Calculate the influence weight of the component rings based on the number of component rings and the component ring tolerance.

[0249] It should be noted that the influence weight of each component loop, or contribution rate, represents the degree of influence of each component loop on the closed loop. Their impact on assembly success or deviation is equal. For example, if the contribution rate is large, increasing the same deviation will significantly increase the deviation rate compared to a component loop with a smaller contribution rate, while simultaneously reducing the assembly success rate by the same amount. When modifying dimensions, this value can be used as a reference to determine the key dimensions affecting the closed loop. The formula for calculating the contribution rate of each component loop is as follows:

[0250]

[0251]

[0252] in, It is the first The contribution rate of each component ring was obtained using the extreme value method. It is the first The contribution rates of each component ring were obtained using statistical methods. For the first The tolerance of each component ring, The number of rings.

[0253] The influence weights of the constituent rings can be calculated using either the extreme value method or the statistical method described above.

[0254] This embodiment employs a repeated random sampling strategy to perform multiple dimensional chain simulations, generating multiple closed-loop dimensional samples. This process helps obtain more accurate dimensional variation data and reduces the random errors that may arise from a single simulation. Analyzing these samples (such as obtaining the average, maximum, and minimum values) provides a more comprehensive understanding of the closed-loop dimensional distribution, thereby accurately predicting potential dimensional deviations during assembly. By calculating the average, maximum, minimum, and deviation values ​​of the closed-loop dimensions, the range and degree of variation in the closed-loop dimensions can be intuitively understood. The upper and lower deviations of the closed-loop dimensions provide a basis for further control, helping to determine which dimensional changes affect overall assembly accuracy, thus achieving effective deviation control. By combining the maximum, minimum, upper, and lower deviations of the closed-loop dimensions and setting the probability density function of the closed-loop, the dimensional distribution of the closed loop can be further accurately modeled. This provides a basis for formulating more reasonable preset closed-loop dimensions, reducing out-of-tolerance problems caused by dimensional inconsistencies during assembly. By solving for the closed-loop out-of-tolerance rate and the influence weights of the component loops, the influence of each component loop on the overall assembly accuracy can be quantified. The deviation rate reflects the proportion of deviations that may occur during actual assembly, while the influence weights reveal which component links play a decisive role in the final accuracy. These analytical results provide data support for subsequent accuracy control, production process optimization, and quality management, ensuring the efficiency and accuracy of the production process. Based on the above analytical results, a theoretical basis can be provided for the accuracy control of closed loops. Knowing the influence weights of each component link on the overall accuracy allows for the development of more scientific control strategies, such as strengthening the accuracy control of high-impact links, optimizing the production process, reducing production costs, and improving the overall quality of the product. Through precise dimensional simulation and analysis, key dimensions that may affect assembly accuracy can be identified, and effective intervention measures can be taken in advance. Such precise control can reduce rework and defective products caused by errors during assembly, thereby improving production efficiency, reducing production costs, and improving the overall reliability of the product.

[0255] For example, to help understand the implementation process of the ship block modular design and construction precision control method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 9 , Figure 9A simplified flowchart of a method for controlling the construction accuracy of modular design of ship sections is provided. Specifically, it includes two main parts: accuracy prediction and control of modular construction of ship sections. Accuracy prediction includes: establishing the dimensional chain relationships of each part in a module; establishing a 3D assembly model of the module; establishing a 3D assembly deviation analysis model of the module; solving for the closed-loop deviation rate and the influence weight of the component loops. The solution process includes: simulating the component loops using the Monte Carlo method, solving for the closed loops using the dimensional chain, calculating the assembly success rate, and calculating the contribution rate, thereby obtaining the closed-loop deviation rate and the influence weight of the component loops; judging the rationality of the simulation results based on the closed-loop deviation rate and the influence weight of the component loops; if unreasonable, modifying the size and distribution of the component loops and returning to establish a 3D assembly model of the module; if reasonable, performing the accuracy control of modular construction of ship sections, specifically including: establishing a comprehensive evaluation model for the overall deviation rate; setting a correction coefficient C according to the importance of each closed loop. a The process involves solving a modified multiple linear regression model and determining if overfitting exists. If overfitting is present, regularized regression is used to correct it, and the process returns to establishing a comprehensive evaluation model for the overall deviation rate. If no overfitting is found, the influence coefficients of each component loop on the overall deviation rate are calculated and normalized. A control scheme is then determined based on the actual situation and the influence coefficients, enabling the prediction and control of the modular construction accuracy of ship sections. This embodiment applies the Monte Carlo method for assembly accuracy prediction. By establishing an assembly simulation model for modular ship construction, the assembly success rate is accurately predicted during the product design stage. Simultaneously, an evaluation model for the overall deviation rate is established based on an improved linear regression equation. This allows for adjustments to as few component loops as possible to significantly reduce the overall deviation rate, greatly reducing production costs and improving production efficiency.

[0256] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the modular design and construction precision control method for ship sections in this application. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0257] This application also provides a precision control device for modular design and construction of ship sections, the precision control device for modular design and construction of ship sections comprising:

[0258] A module is created to establish the dimensional chain relationships of each part in a preset module based on the modular construction standard data of ship sections.

[0259] The establishment module is also used to establish a three-dimensional assembly model of the preset module based on the dimension chain relationship, and to establish a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model.

[0260] The solution module is used to solve the closed loop error rate and the influence weight of the component loops based on the three-dimensional assembly deviation analysis model.

[0261] The determination module is used to determine whether the simulation results meet the preset requirements based on the out-of-tolerance rate of the closed loop and the influence weight of the constituent loops.

[0262] The establishment module is also used to establish a comprehensive evaluation model of the deviation rate based on the simulation results when the simulation results meet the preset requirements.

[0263] The solution module is also used to solve the influence coefficient of each component ring on the overall error rate based on the comprehensive evaluation model of the error rate.

[0264] The control module is used to determine the construction accuracy control scheme based on the influence coefficient, and to perform modular construction accuracy control of ship sections based on the construction accuracy control scheme.

[0265] The ship block modular design and construction precision control device provided in this application adopts the ship block modular design and construction precision control method in the above embodiments, which can solve the technical problems of low efficiency and high cost in current ship modular construction. Compared with the prior art, the beneficial effects of the ship block modular design and construction precision control device provided in this application are the same as the beneficial effects of the ship block modular design and construction precision control method provided in the above embodiments, and other technical features in the ship block modular construction precision control device are the same as the features disclosed in the above embodiments, and will not be repeated here.

[0266] This application provides a ship block modular design and construction precision control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the ship block modular design and construction precision control method in the above embodiment 1.

[0267] The ship block modular design and construction precision control equipment provided in this application adopts the ship block modular design and construction precision control method in the above embodiments, which can solve the technical problems of low efficiency and high cost in current ship modular design and construction. Compared with the prior art, the beneficial effects of the ship block modular construction precision control equipment provided in this application are the same as the beneficial effects of the ship block modular construction precision control method provided in the above embodiments, and other technical features in the ship block modular construction precision control equipment are the same as the features disclosed in the previous embodiment method, and will not be repeated here.

[0268] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the ship block modular construction precision control method in the above embodiments.

[0269] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described method for controlling the precision of modular ship construction, thereby solving the technical problems of low efficiency and high cost in current modular ship construction. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the method for controlling the precision of modular ship construction provided in the above embodiments, and will not be repeated here.

[0270] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for precision control of modular construction of ship sections.

[0271] The computer program product provided in this application can solve the technical problems of low efficiency and high cost in current modular ship construction. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the ship section modular construction accuracy control method provided in the above embodiments, and will not be repeated here.

[0272] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for controlling the precision of modular design and construction of ship sections, characterized in that, The method for controlling the precision of modular construction of ship sections includes: Establish the dimensional chain relationship of each part in the preset module based on the standard data for modular construction of ship sections; A three-dimensional assembly model of the preset module is established based on the dimensional chain relationship, and a three-dimensional assembly deviation analysis model is established based on the three-dimensional assembly model. The deviation analysis model of the three-dimensional assembly is used to solve the deviation rate of the closed loop and the influence weight of the component loops. The simulation results are determined to meet the preset requirements based on the closed loop error rate and the influence weight of the constituent loops. When the simulation results meet the preset requirements, a comprehensive evaluation model for the deviation rate is established based on the simulation results. The influence coefficients of each component ring on the overall error rate are calculated based on the comprehensive evaluation model of the error rate. The construction accuracy control scheme is determined based on the influence coefficient, and the construction accuracy control of the modular construction of the ship section is carried out based on the construction accuracy control scheme.

2. The method as described in claim 1, characterized in that, The step of establishing the dimensional chain relationship of each part in the preset module based on the modular construction standard data of ship sections includes: Based on the modular construction standard data of the ship block, determine the tolerance values ​​of each part, the first basic dimension of the manufacturing deviation of each part, the first upper limit deviation, and the first lower limit deviation; Based on the segmented docking process, analyze the process data between the current module components, and determine the second basic dimension, second upper limit deviation, and second lower limit deviation of the docking assembly deviation of each component based on the process data. The ring data is obtained based on the first basic dimension, the first upper limit deviation, the first lower limit deviation, the second basic dimension, the second upper limit deviation, and the second lower limit deviation. The type of constituent ring is determined based on the tolerance value; Establish the dimensional chain relationship of each part in the preset module based on the component ring type, the tolerance value, and the component ring data.

3. The method as described in claim 1, characterized in that, The steps of establishing a three-dimensional assembly model of a preset module based on the dimensional chain relationship, and establishing a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model, include: Based on the aforementioned dimensional chain relationship, the basic dimensions, upper deviation, and lower deviation of the constituent rings are obtained; Obtain the assembly process of the power module; The assembly sequence and assembly position in the actual assembly process are determined based on the assembly process described above. Based on the basic dimensions, the upper deviation, and the lower deviation, the parts are assembled sequentially according to the assembly order and the assembly position to obtain a three-dimensional assembly model of the preset module. The target assembly method is determined based on the assembly process, and the assembly relationship of each part is established based on the target assembly method. Based on the assembly relationship, the parts are assembled on the three-dimensional assembly model, and the tolerance distribution data of each part end face in different directions and the part measurement data are set to obtain the three-dimensional assembly deviation analysis model.

4. The method as described in claim 1, characterized in that, The steps for solving the closed-loop deviation rate and the influence weights of the constituent loops based on the three-dimensional assembly deviation analysis model include: Based on the three-dimensional assembly deviation analysis model, a repeated random sampling strategy is used to perform several dimensional chain simulations to obtain several closed-loop dimensional subsamples. The average, maximum, and minimum values ​​of several closed-loop size samples are obtained based on several closed-loop size samples. The average value, maximum value, and minimum value of the closed loop size can be obtained by taking the average value, maximum value, and minimum value of several closed loop size samples; Calculate the upper deviation and lower deviation of the closed loop size based on the average value, maximum value, and minimum value of the closed loop size; The probability density function and preset closed loop size are set based on the upper deviation of the closed loop size, the lower deviation of the closed loop size, and the maximum and minimum values ​​of several closed loop size samples; The out-of-tolerance rate of the closed loop and the influence weight of the constituent loops are calculated based on the preset closed loop size and the probability density function.

5. The method as described in claim 4, characterized in that, The steps of calculating the out-of-range rate of the closed loop and the influence weights of the constituent loops based on the preset closed loop size and the probability density function include: Set the number of random samples; Based on the number of random samplings, a repeated random sampling strategy is used to simulate the size chain, and simulation results are obtained. The assembly success rate is calculated based on the simulation results, the preset closed loop size, and the probability density function. The closed-loop deviation rate is obtained based on the assembly success rate. Obtain the number of component rings and the component ring tolerance; The influence weight of the constituent rings is calculated based on the number of constituent rings and the tolerance of the constituent rings.

6. The method as described in claim 1, characterized in that, The step of establishing a comprehensive evaluation model for the deviation rate based on the simulation results when the simulation results meet the preset requirements includes: When the simulation results meet the preset requirements, the overall closed loop error rate, the influence weight of the component loop in the closed loop, and the error rate of the component loop in the closed loop are obtained based on the simulation results. Set correction factors according to the importance of the closed loop; Determine the model parameters and error terms based on the multiple linear regression model; A comprehensive evaluation model for deviation rate is constructed based on the model parameters, the error term, the correction coefficient, the overall closed-loop deviation rate, the influence weight of the component loops in the closed loop, and the deviation rate of the component loops in the closed loop.

7. The method as described in claim 1, characterized in that, The steps of determining the construction accuracy control scheme based on the influence coefficient and performing modular construction accuracy control of ship sections based on the construction accuracy control scheme include: The influence coefficients are normalized to obtain normalized influence coefficients; The normalized influence coefficient is compared with a preset threshold. When the normalized influence coefficient is greater than or equal to the preset threshold, the component loop corresponding to the normalized influence coefficient is taken as the component loop to be controlled. Based on the controllable component loop, a construction accuracy control scheme is determined, and the modular construction accuracy control of the ship section is carried out according to the construction accuracy control scheme.

8. The method according to any one of claims 1 to 7, characterized in that, Before the step of calculating the influence coefficient of each component loop on the overall error rate based on the comprehensive evaluation model of the error rate, the method further includes: The initial regularized regression coefficients are obtained by solving the comprehensive evaluation model of the deviation rate. The goodness of fit of the comprehensive evaluation model of deviation rate is calculated based on the initial regularized regression coefficients. Determine whether the comprehensive evaluation model for deviation rate is overfitted based on the goodness of fit. When the comprehensive evaluation model for the deviation rate does not exhibit overfitting, the step of obtaining the influence coefficient of each component loop on the overall deviation rate based on the comprehensive evaluation model for the deviation rate is performed.

9. A precision control device for modular design and construction of ship sections, characterized in that, The device includes: A module is established to create dimensional chain relationships among the parts in a pre-set module based on the standard data for modular construction of ship sections. The establishment module is also used to establish a three-dimensional assembly model of the preset module based on the dimension chain relationship, and to establish a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model. The solver module is used to solve the closed loop error rate and the influence weight of the component loops based on the three-dimensional assembly deviation analysis model. The determination module is used to determine whether the simulation results meet the preset requirements based on the closed loop error rate and the influence weight of the constituent loops. The establishment module is also used to establish a comprehensive evaluation model of the deviation rate based on the simulation results when the simulation results meet the preset requirements; The solution module is also used to solve the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model; The control module is used to determine the construction accuracy control scheme based on the influence coefficient, and to perform modular construction accuracy control of ship sections based on the construction accuracy control scheme.

10. A precision control device for the modular design and construction of ship sections, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for precision control of modular construction of ship sections as described in any one of claims 1 to 8.

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