Ship block modular design and construction precision control method and device

Abstract

The invention discloses a ship block modular design construction precision control method and device, and relates to the technical field of ship construction, and the method comprises the steps: building a size chain relation of all parts in a preset module according to ship block modular construction standard data; establishing a three-dimensional assembly model of the preset module based on the dimension chain relationship, and establishing a three-dimensional assembly deviation analysis model according to the three-dimensional assembly model; solving a closed ring out-of-tolerance rate and a composition ring influence weight according to the three-dimensional assembly deviation analysis model; determining whether a simulation result meets a preset requirement or not according to the closed ring out-of-tolerance rate and the component ring influence weight; when the simulation result meets a preset requirement, establishing an out-of-tolerance rate comprehensive evaluation model according to the simulation result; the influence coefficient of each composition ring on the overall out-of-tolerance rate is solved according to the out-of-tolerance rate comprehensive evaluation model; and determining a construction precision control scheme according to the influence coefficient, and performing ship block modular construction precision control according to the construction precision control scheme.

Description

Technical Field

[0001] The present application relates to the technical field of precision control of modular design and construction of ship sections, and in particular to a method and device for precision control of modular design and construction of ship sections. Background Art

[0002] Modular construction helps shorten design and construction cycles, reduce production costs, and improve product quality, but it places high demands on shipbuilding capabilities. Modular shipbuilding is characterized by high docking and installation precision, long equipment installation dimension chains, and numerous installation interfaces. This can easily lead to problems such as docking failures due to large deviations between docking equipment, as well as abnormal vibration and noise during equipment operation caused by poor docking and installation precision. Therefore, rationally predicting and controlling shipbuilding precision during the shipbuilding process is key to improving the success rate and safety of shipbuilding. Combining manufacturing capabilities and methods to establish a comprehensive shipbuilding precision prediction and control method can more effectively improve production efficiency, reduce unnecessary waste, and provide a scientific basis for decision-makers.

[0003] Predicting construction accuracy requires assembly deviation analysis. The dimensional chain is a commonly used deviation propagation model. Key deviation analysis methods include the extreme value method, the root mean square method, and the Monte Carlo method. The extreme value method linearly superimposes the worst-case tolerances of the component loops. While this method is highly reliable and mitigates the risk of failure, it produces overly conservative deviation predictions, significantly increasing manufacturing costs. The root mean square method, based on the square root of a statistical distribution (assuming normal distribution), is suitable for linear dimensional chains. This method balances accuracy and cost while reflecting actual fluctuations. However, it is not applicable if the actual distribution deviates from a normal distribution or if the dimensional chain has complex coupling relationships. The Monte Carlo method simulates system behavior through a large number of random samples and is suitable for complex nonlinear systems and multivariable coupling scenarios. This method is highly flexible and 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 current technical problems of low efficiency and high cost in modular construction of ships.

[0005] To achieve the above objectives, the present application proposes a method for controlling the precision of modular design and construction of ship sections, the method comprising: Establish the dimension chain relationship of each part in the preset module according to the standard data of modular construction of ship sections; Establishing a three-dimensional assembly model of a preset module based on the dimension chain relationship, and establishing a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model; Solve the closed loop deviation rate and component loop influence weight according to the three-dimensional assembly deviation analysis model; Determining whether the simulation result meets the preset requirements according to the closed loop deviation rate and the component loop influence weight; When the simulation results meet the preset requirements, a comprehensive evaluation model for the deviation rate is established according to the simulation results; Solve the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model; A construction precision control scheme is determined according to the influence coefficient, and modular construction precision control of the ship section is performed according to the construction precision control scheme.

[0006] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for controlling the precision of modular design and construction of ship sections, the device comprising: Establish a module for establishing the dimension chain relationship of each part in the preset module according to the standard data of modular construction of ship sections; The establishment module is further 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; A solution module, configured to solve the closed loop deviation rate and the component loop influence weight according to the three-dimensional assembly deviation analysis model; A determination module, configured to determine whether a simulation result meets preset requirements based on the closed loop deviation rate and the component loop influence weights; The establishment module is further configured to establish a comprehensive evaluation model for the deviation rate according to the simulation results when the simulation results meet the preset requirements; The solving module is further used to solve the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model; A control module is used to determine a construction accuracy control scheme based on the influence coefficient, and to perform modular construction accuracy control of the ship section according to the construction accuracy control scheme.

[0007] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for controlling the precision of modular design and construction of ship sections, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the method for controlling the precision of modular construction of ship sections as described above.

[0008] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for controlling the modular design and construction accuracy of ship sections as described above are implemented.

[0009] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the modular construction accuracy control method of ship sections as described above.

[0010] One or more technical solutions proposed in this application have at least the following technical effects: 1) By establishing a dimensional chain relationship for pre-set modules, the dimensions of each component can be precisely controlled, ensuring accuracy throughout the entire module assembly process, reducing quality issues caused by dimensional errors, and improving the construction accuracy of ship sections. A 3D assembly deviation analysis model comprehensively analyzes the impact of component deviations on overall assembly accuracy and identifies potential deviations. This precise analysis helps identify problems early, avoiding errors and rework during production, saving time and costs. Based on simulation results, the closed-loop deviation rate and the influence weights of component loops can be analyzed to determine which components have the greatest impact on overall accuracy. This analysis guides production personnel to focus on key links, implement more effective precision control measures, and further optimize the modular construction process of ship sections. A comprehensive deviation rate evaluation model can be established to precisely quantify the influence coefficient of each component loop on the overall deviation rate. This provides a scientific basis for precision control, enabling a more refined and controllable shipbuilding process, ensuring that the final product meets design requirements. Simulation and deviation analysis can proactively assess potential problems during the construction process, avoiding downtime or adjustments due to precision issues. This helps improve the stability and efficiency of the construction process, reducing production cycle time and unnecessary waste. The established precision control scheme can be implemented in all stages of ship construction, from parts processing to module assembly, to ensure that each link can be carried out according to predetermined standards, reduce quality fluctuations, and improve the stability and reliability of the final ship quality.

[0011] 2) By precisely defining component tolerances, primary and secondary basic dimensions, and their upper and lower limit deviations, the dimensional range of each component can be effectively standardized. This ensures the dimensional accuracy of each component during the manufacturing process, avoids mismatches or assembly difficulties caused by dimensional deviations, and ensures high-quality construction of ship block modules. Analyzing the block docking process and determining component docking assembly deviations based on process data effectively predicts potential errors during assembly. This precision analysis not only reduces deviations but also enables the development of precise assembly methods and process requirements, thereby mitigating problems caused by assembly errors during modular ship construction. By analyzing and processing component link data, the dimensional relationships between components can be understood and the tolerance types for each link can be clarified. This facilitates the establishment of more accurate dimensional chains, preventing overall deviations caused by the accumulation of dimensional errors in individual links, thereby ensuring the overall accuracy of modular construction. Precise dimensional control throughout the modular construction process ensures high precision at every stage, from component manufacturing to assembly. This facilitates comprehensive process precision control, ensuring consistent dimensional coordination between modules throughout the entire construction process.

[0012] 3) By defining the basic dimensions, deviations, and assembly sequence and position of parts based on dimensional chain relationships, assembly errors can be effectively controlled, ensuring that each part is assembled in the precise position. This avoids poor fit caused by dimensional errors or improper assembly sequence, thereby improving assembly accuracy. Obtaining the power module assembly process and assembling the 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 and relative positions of the parts can be clearly visualized. By establishing a 3D assembly deviation analysis model, assembly deviations of each part can be clearly identified and analyzed, predicting and quantifying potential deviations during the assembly process, allowing proactive measures to mitigate their impact on assembly quality. When assembling multiple modules, the 3D assembly model provides a high degree of flexibility, enabling adjustments to each module during design, manufacturing, and assembly based on actual conditions. This flexibility helps better adapt to various design requirements and improves the adaptability and accuracy of the shipbuilding process. By analyzing 3D assembly deviations in advance, potential assembly issues can be identified early on, avoiding rework and corrections due to assembly errors and improving production efficiency. Reducing rework saves time and costs while 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 all parts meet the required accuracy at each assembly stage. 3D modeling can provide more intuitive and precise guidance, especially in complex situations involving assembly location and sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0015] Figure 1 A flow chart illustrating the first embodiment of the method for precision control of modular design and construction of ship sections of the present application; Figure 2 A simplified three-dimensional structural diagram of the power module provided in accordance with an embodiment of the method for controlling the precision of modular design and construction of ship sections of this application; Figure 3 A two-dimensional schematic diagram of a certain type of power module structure provided in accordance with an embodiment of the method for controlling precision in modular design and construction of ship sections of this application; Figure 4 A schematic diagram of the change in spatial position before and after radial assembly deviation loading provided in an embodiment of the method for controlling precision in modular design and construction of ship sections of the present application; Figure 5 A diagram showing the Lasso regression coefficient solution results provided in an embodiment of the method for controlling the precision of modular design and construction of ship sections of this application; Figure 6 A diagram showing the results of a simulation analysis of the parallelism between the sealing device and the power shaft provided in accordance with an embodiment of the method for controlling precision in modular design and construction of ship sections of the present application; Figure 7 A diagram showing the simulation analysis results of the parallelism between the sealing device and the power shaft after precision control according to the construction precision control scheme provided in an embodiment of the modular design and construction precision control method for ship sections of the present application; Figure 8 A flow chart illustrating the second embodiment of the method for precision control of modular design and construction of ship sections of the present application; Figure 9 A brief flow chart of the method for controlling the modular design and construction accuracy of ship sections provided in Example 2 of the present application.

[0016] Description of Figure Numbers: 1. Stern shaft; 2. Sealing device; 3. Stern bearing; 4. Rear shaft; 5. Thrust shaft; 6. Front shaft; 7. Thrust bearing; 8. Clutch; 9. Power shaft; 10. Platform.

[0017] 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 DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not intended to limit the present application.

[0019] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0020] The main solution of the embodiment of the present application is: establishing a dimension chain relationship of each part in a preset module according to the standard data of modular construction of a ship section; establishing a three-dimensional assembly model of the preset module based on the dimension chain relationship, and establishing a three-dimensional assembly deviation analysis model according to the three-dimensional assembly model; solving the closed loop deviation rate and the component ring influence weight according to the three-dimensional assembly deviation analysis model; determining whether the simulation result meets the preset requirements according to the closed loop deviation rate and the component ring influence weight; when the simulation result meets the preset requirements, establishing a comprehensive evaluation model for deviation rate according to the simulation result; solving the influence coefficient of each component ring on the overall deviation rate according to the comprehensive evaluation model for deviation rate; determining a construction accuracy control scheme according to the influence coefficient, and performing modular construction accuracy control of the ship section according to the construction accuracy control scheme.

[0021] Because existing technology primarily uses dimensional chains for accuracy prediction in modular ship construction, the main deviation analysis calculation methods include the extreme value method and the root mean square method. The extreme value method linearly superimposes the worst-case tolerances of the component rings. This method is absolutely reliable and can avoid the risk of failure, but the resulting deviation predictions are overly conservative, significantly increasing manufacturing costs. The root mean square method, based on the square root of a statistical distribution (assuming normal distribution), is suitable for linear dimensional chains. This method balances accuracy and cost and reflects actual fluctuations. However, it is not applicable if the actual distribution deviates from a normal distribution or if the dimensional chain has complex coupling relationships.

[0022] This application provides a solution, including a method for predicting and controlling the modular design and construction accuracy of ship sections. This method uses the Monte Carlo method to solve the deviation rate of closed loops in the dimensional chain, thereby predicting construction accuracy. For construction accuracy control, an improved linear regression analysis method is proposed. By establishing the relationship between the component loops and the overall deviation rate, a comprehensive evaluation model for the component loops is constructed. Finally, control recommendations are given based on the impact of each component loop on the overall deviation rate. By controlling the errors of a small number of parts, the overall control accuracy can be maximized, which can significantly reduce the production cost of ships and improve production efficiency.

[0023] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of implementing the above functions, such as a precision control device for modular construction of ship sections. This embodiment and the following embodiments will be described below using a precision control device for modular construction of ship sections as an example.

[0024] Based on this, the embodiment of the present application provides a method for controlling the precision of modular design and construction of ship sections, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for controlling the modular design and construction accuracy of ship sections of the present application.

[0025] In this embodiment, the method for controlling the accuracy of modular construction of ship sections includes steps S10 to S40: Step S10: establishing the dimension chain relationship of each part in the preset module according to the modular construction standard data of the ship section.

[0026] It should be noted that the standard data for modular construction of ship sections is data obtained in advance about the standard requirements and experience for modular construction of ship sections. The preset module is a module in the ship section module. The parts may include a stern shaft, sealing device, rear shaft, thrust shaft, front shaft, thrust bearing, clutch, power shaft and platform, etc., and may also include other parts, which are not limited in this embodiment.

[0027] In a specific implementation, the dimensional chain relationship of each part may include data such as assembly deviation and manufacturing deviation of each part.

[0028] It should be noted that when establishing the dimension chain relationship of each part, the size of the component ring can be set in the 3D tolerance software CATIA software to establish the dimension chain relationship of each part, such as Figure 2 As shown, Figure 2 This is a simplified diagram of the three-dimensional structure of the power module. The stern bearing has little impact on the overall structure, so its components are ignored. The size information of the stern shaft, sealing device, rear shaft, thrust shaft, front shaft, thrust bearing, clutch, power shaft and platform are set respectively.

[0029] In a feasible implementation, step S10 may include steps A11 to A15: Step A11: determining the tolerance value of each component, the first basic dimension of the manufacturing deviation of each component, the first upper limit deviation, and the first lower limit deviation according to the standard data of modular construction of the ship section; It should be noted that, first, the standard requirements and experience for modular construction of ship sections can be obtained based on the standard data for modular construction of ship sections, and then the basic size, upper limit deviation and lower limit deviation of the manufacturing deviation, that is, the first basic size, the first upper limit deviation and the first lower limit deviation, can be determined based on the standard requirements and experience for modular construction of ship sections.

[0030] Step A12: Analyzing process data between components of the current module according to the segmented docking process, and determining a second basic dimension, a second upper limit deviation, and a second lower limit deviation of the docking assembly deviation of each component based on the process data; Understandably, continuing Figure 2 As shown, when the parts are docked in sections, the process data between the current module parts can be analyzed, including the molding process, surface treatment process, connection process, etc., so as to determine the basic size, upper limit deviation and lower limit deviation of the docking assembly deviation, welding deformation deviation and other deviations, that is, the second basic size, second upper limit deviation and second lower limit deviation of the docking assembly deviation.

[0031] Step A13: Obtaining component ring data according to the first basic size, the first upper limit deviation, the first lower limit deviation, and the second basic size, the second upper limit deviation, and the second lower limit deviation; In a specific implementation, the component ring data can be obtained by docking the assembly deviation and manufacturing deviation data, as shown in Table 1, which is a table for setting various deviation values ​​of the power module.

[0032] Table 1

[0033] Through Table 1 above, the manufacturing deviation and assembly deviation of each part can be set, and the basic size and upper and lower deviation values ​​of the manufacturing deviation of each part and the basic size and upper and lower deviation values ​​of the assembly deviation of each part can be obtained.

[0034] Step A14: determining the type of the component ring according to the tolerance value; It should be noted that the tolerance value is The ring type can be determined based on the size of the tolerance value and 0. The ring type includes increasing or decreasing rings. , the ring is an increasing ring, when , the constituent ring is a reduced ring, and the subscript i is the number of constituent rings.

[0035] Step A15: establishing a dimension chain relationship of each part in a preset module according to the component ring type, the tolerance value and the component ring data.

[0036] In specific implementation, the basic equation of the dimensional chain of each part in the preset module can be established according to the component ring type, tolerance value and component ring data, which is expressed as follows:

[0037] Where: The basic size of the closed loop, i.e., the multi-directional offset of the module in the axial, lateral or vertical directions; is the basic size of the ring, that is, each deviation source, when , To increase the ring, when , To reduce the ring, subscript is the number of rings. Specifically, the upper and lower deviations of the closed loop are calculated as follows:

[0038]

[0039] Where, is the upper limit deviation of the closed loop, is the lower limit deviation of the closed loop, is the upper limit deviation of the increase ring, is the lower limit deviation of the increase ring, is the lower limit deviation of the reduction ring, is the upper limit deviation of the reduction ring, To increase the ring, The above formula can be used to establish the dimension chain relationship of each part.

[0040] Step S20: establishing a three-dimensional assembly model of a preset module based on the dimension chain relationship, and establishing a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model.

[0041] It should be noted that after the dimension chain relationship of the closed loop is established, the geometric dimensions of the parts can be established in the 3D drawing software through engineering drawings. Specifically, the 3D tolerance can be established based on the basic dimensions, upper deviation and lower deviation of the constituent rings after the dimension chain is established, thereby establishing a 3D assembly model.

[0042] The 3D assembly model is a 3D model of the ship power module, which is a parametric simulation model that includes the size and shape of each part. Figure 3 As shown, Figure 3 This is a two-dimensional schematic diagram of the structure of a certain power module. From left to right, it includes the stern shaft 1, sealing device 2, stern bearing 3, rear shaft 4, thrust shaft 5, front shaft 6, thrust bearing 7, clutch 8, power shaft 9, and platform 10. The Y-axis coincides with the centerline of the ship's power shafting system, and the positive direction of the Z-axis is perpendicular to the plane of the X- and Y-axes. Subsequent modeling coordinates and calculations will be consistent with this coordinate system.

[0043] In specific implementation, the 3D assembly model only creates the position information, but does not determine the assembly connection method in the actual process. Therefore, a 3D assembly deviation analysis model can be established based on the 3D assembly model to make the analysis results more accurate.

[0044] In a feasible implementation, step S20 may include steps A21 to A26: Step A21: obtaining the basic size, upper deviation, and lower deviation of the ring components according to the dimension chain relationship; It should be noted that the basic dimensions, upper deviation, and lower deviation of the constituent ring after the dimension chain is established can be obtained according to the dimension chain relationship, as shown in Table 2. Table 2 shows the basic dimensions of the constituent ring of the ship power module.

[0045] Table 2

[0046] Step A22: Obtaining the assembly process of the power module; It is understandable that the assembly process of the power module can be set in advance. Therefore, when it is necessary to establish a three-dimensional assembly model, the assembly process of the power module can be set accordingly.

[0047] Step A23: determining the assembly sequence and assembly position in the actual assembly process according to the assembly process; In specific implementation, the assembly sequence and assembly position in the actual assembly process can be determined according to the assembly process. For example, if the hole-shaft fits, three-point assembly is required, which is more in line with the actual assembly process rather than 3-2-1 assembly.

[0048] Step A24: assembling parts in sequence according to the basic size, the upper deviation, and the lower deviation in the assembly order and the assembly position to obtain a three-dimensional assembly model of the preset module; In specific implementation, three-dimensional tolerances can be established based on basic dimensions, upper deviations, and lower deviations, and parts can be assembled in sequence according to the assembly sequence and assembly position in the actual assembly process to complete the establishment of a three-dimensional assembly model.

[0049] The assembly process of the power module is as follows: 1) Install the sealing device according to the stern base point of the slipway structure; 2) Equipment installation on the platform; 2-1) The thrust shaft is installed on the platform; 2-2) Install the rear shaft and stern shaft in sequence according to the position of the thrust shaft; 2-3) Install the stern bearing according to the position of the stern shaft; 2-4) Install the front axle, thrust bearing, clutch, and power shaft in sequence according to the position of the thrust shaft; 3) Install the platform according to the location of the sealing device.

[0050] Step A25: determining a target assembly method according to the assembly process, and establishing an assembly relationship of each part according to the target assembly method; It is understandable that different assembly processes correspond to different assembly methods. Therefore, a suitable assembly method can be selected according to the assembly process, and assembly settings can be made in 3DCS.

[0051] For example, the end faces of the two shafts can be universally assembled with 6 faces to constrain 6 degrees of freedom at one time, which reduces the complexity. The assembly of the platform and the shaft can adopt 3-2-1 assembly to constrain the degrees of freedom step by step, because multi-station step-by-step positioning is required. The specific steps are as follows: install the thrust shaft on the platform through 3-2-1 positioning; install the rear shaft on the thrust shaft through 6-face assembly; install the stern shaft on the rear shaft through 6-face assembly; install the front shaft on the thrust shaft through 6-face assembly; install the thrust bearing on the front shaft through 6-face assembly; install the clutch on the thrust bearing through 6-face assembly; install the power shaft on the clutch through 6-face assembly; install the platform on the sealing device through hole-shaft matching. Therefore, the assembly relationship between the parts can be obtained according to the determined target assembly method.

[0052] Step A26: assembling parts on the three-dimensional assembly model according to the assembly relationship, and setting tolerance distribution data of end faces of each part in different directions and part measurement data to obtain a three-dimensional assembly deviation analysis model.

[0053] In specific implementation, parts can be assembled on a three-dimensional assembly model according to the assembly relationship, and tolerance distribution and part measurement data can be set. According to the statistical laws of data in the actual assembly process, a suitable tolerance probability distribution can be selected. Specifically, the tolerance distribution setting can be performed in 3DCS. Since the probability of each part taking the extreme tolerance value is a low-probability event, and the probability near the median tolerance value is relatively high, the axial installation deviation distribution of each part end face is set to a normal distribution, and the radial installation deviation distribution of each part end face is set to a right-biased distribution. Due to the asymmetry of the process deviation, the installation deviation distribution of each part end face along the axial rotation is set to a normal distribution, and the diameter manufacturing deviation distribution of each part end face is set to a normal distribution. Therefore, the tolerance distribution data of each part end face in different directions are obtained, including axial, radial and diameter tolerance distribution data. As shown in the figure, Figure 4 As shown, Figure 4 This is a schematic diagram of the change in spatial position before and after radial assembly deviation loading. By setting the radial assembly deviation, the installation accuracy can be improved.

[0054] The last step is to set the measurement data. You can set 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. By comparing whether the measured size is within the upper and lower deviation range of the closed loop, you can determine whether it is out of tolerance.

[0055] Specifically, measurement settings can be made in 3DCS. Parallelism can be set using linear angle measurement. Select Nominal Line for the measurement type and True Angle for the direction. This primarily measures parallelism between the thrust shaft and power shaft, the thrust shaft and stern shaft, the stern shaft and power shaft, and the seal and power shaft. Distance can be set using point distance measurement. Select Point to Point for the measurement type and True Distance for the direction. This primarily measures the distances between the thrust shaft and power shaft, the thrust shaft and stern shaft, the stern shaft and power shaft, and the seal and power shaft. Parts are assembled on the 3D assembly model using assembly relationships, and tolerance probability distributions and part measurement data are simultaneously set to complete the creation of a 3D assembly deviation analysis model.

[0056] Step S30: solving the closed loop deviation rate and the component loop influence weight according to the three-dimensional assembly deviation analysis model.

[0057] It is understandable that after the three-dimensional assembly deviation analysis model is established, the closed loop deviation rate and the component loop influence weights can be solved based on the three-dimensional assembly deviation analysis model.

[0058] The closed loop tolerance rate refers to the cumulative error between all parts during the entire assembly process. The calculation should take into account the interaction between the parts, especially when they form a closed loop, the impact of the error will be more significant.

[0059] The component influence weight is a weight given to the contribution of each part or component link to the error in the entire assembly process. Some components may have a greater impact on the final deviation rate than others.

[0060] Specifically, simulation analysis can be performed in 3DCS to obtain the closed loop deviation rate and the influence weight of the component loops.

[0061] Consider a ship section module with multiple critical connection points, forming a closed loop. A significant error at any one connection point could affect the assembly accuracy of the entire module. Therefore, a 3D assembly deviation analysis model can be used to calculate the impact of errors in each component loop and determine the weight of each link's impact on the final deviation rate.

[0062] Step S40: determining whether the simulation result meets the preset requirements according to the closed loop deviation rate and the component loop influence weight.

[0063] It should be noted that the simulation results are based on the assembly accuracy calculated based on the closed loop deviation rate and the influence weight of the component loops. The simulation model simulates the error transmission and accumulation in the assembly process.

[0064] Preset requirements refer to the accuracy ranges predetermined during the design and construction process. For example, assembly errors must not exceed a certain limit, otherwise the safety and functionality of the ship module will be affected. If simulation results show that errors in certain links exceed the preset tolerances (for example, the total error exceeds 2 mm), the simulation results are considered to be non-compliant. If the errors are within the tolerances, the simulation results meet the preset requirements.

[0065] If the simulation results do not meet the preset requirements, the size and distribution of the component loops can be modified and the process of building the 3D assembly model can be repeated until the simulation results meet the preset requirements. Specifically, if the deviation rate of some closed loops is 100%, the simulation results are unreasonable and the component loops need to be resized. The component loops with high contribution rates should be optimized first to reduce the deviation rate of the closed loops to less than 100% and make the simulation results reasonable. Based on this, an evaluation model is constructed to evaluate the relationship between the total deviation rate of the closed loops and the component loops.

[0066] Step S50: When the simulation result meets the preset requirements, a comprehensive evaluation model for the deviation rate is established according to the simulation result.

[0067] In specific implementation, if the simulation results meet the preset requirements, a comprehensive evaluation model for the deviation rate can be established based on the simulation results.

[0068] The comprehensive deviation evaluation model, constructed based on simulation results, comprehensively considers errors in each step and their impact on overall assembly accuracy. This model helps designers comprehensively assess all possible deviations in the construction process and propose adjustments and optimization solutions.

[0069] In a feasible implementation, step S50 may include steps A31 to A34: Step A31: When the simulation result meets the preset requirements, the overall closed loop deviation rate, the influence weight of the component ring in the closed loop, and the deviation rate of the component ring in the closed loop are obtained according to the simulation result; Step A32: setting a correction coefficient according to the importance of the closed loop; It should be noted that 3DCS, as a three-dimensional tolerance analysis software, relies heavily on the Monte Carlo method to implement its core function, "Variation Modeling". 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 to the .xlsx format file for data collation, as shown in Table 3. Set to 1. Specifically, the correction coefficient can be set according to the importance of the closed loop. Due to the small data size of only 8 groups and 16 variables, traditional linear regression cannot be directly applied, resulting in overfitting or unsolvable problems. Lasso regression, a regularized regression method, is used to solve the problem. It adds a penalty term to constrain the coefficient size to prevent overfitting.

[0070] Table 3

[0071] When the simulation results meet the preset accuracy requirements, several key metrics are first extracted from the simulation results: The overall closed-loop deviation rate refers to the final deviation resulting from the cumulative errors of all components in the entire assembly system. The influence weight of each component in the closed loop refers to the contribution of each component's (component's) error to the overall deviation rate. This influence weight can be determined by analyzing the interrelationships and importance of each component. The deviation rate of each component in the closed loop refers to the error level of each link in the entire closed loop, indicating the inherent deviation of that link.

[0072] Step A33: Determine model parameters and error terms based on the multiple linear regression model; It should be noted that the general form of the multiple linear regression model is:

[0073] Where: , , ,…, are the parameters of the model; is the error term. The multiple linear regression equation uses the goodness of fit Perform a test of the degree of fit, goodness of fit The larger the value, the better the model fit.

[0074] Therefore, the model parameters can be obtained according to the multiple linear regression model , , ,…, and the error term .

[0075] Step A34: constructing a comprehensive evaluation model for the deviation rate based on the model parameters, the error term, the correction coefficient, the overall closed loop deviation rate, the influence weights of the component rings in the closed loop, and the deviation rates of the component rings in the closed loop.

[0076] In practice, a comprehensive evaluation model for deviation rate can be constructed based on a combination of model parameters, error terms, and correction coefficients. This model comprehensively assesses the accuracy of the entire assembly system, taking into account the influence of each component and its correction coefficient. The purpose of this comprehensive evaluation model is to comprehensively evaluate overall assembly accuracy based on multiple factors, thereby providing a scientific basis for precision control.

[0077] Specifically, the comprehensive evaluation model of the deviation rate is expressed as follows:

[0078] in:

[0079] is the sum of all closed loop deviation rates, is the contribution of the Mth component ring in the i-th closed ring, is the deviation rate of the Mth component ring in the i-th closed ring, The cumulative sum of the deviation rates of the Mth component ring to the overall closed ring. It is the correction coefficient, and its size is set according to the importance of the closed loop. The default value is 1.

[0080] Step S60: solving the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model.

[0081] In practice, a comprehensive deviation rate evaluation model can be used to determine the influence coefficient of each component link on the overall deviation rate. This coefficient represents the contribution of each component link's error to the overall ship module assembly accuracy. This coefficient can be used to identify critical control points and those with minimal impact on overall accuracy.

[0082] In a feasible implementation, it is also necessary to determine whether the established deviation rate comprehensive evaluation model meets the requirements, that is, whether it is overfitting. Therefore, before step S60, the following is also included: Solving the comprehensive evaluation model of the deviation rate to obtain an initial regularized regression coefficient; Calculating the goodness of fit of the comprehensive evaluation model of the deviation rate based on the initial regularized regression coefficient; Determining whether the comprehensive evaluation model of the deviation rate is overfitting according to the goodness of fit; When the comprehensive evaluation model for the deviation rate does not have overfitting, a step of obtaining an influence coefficient of each component ring on the overall deviation rate according to the comprehensive evaluation model for the deviation rate is performed.

[0083] It should be noted that the comprehensive evaluation model for the deviation rate can be solved through numerical calculation or optimization algorithms to obtain the initial regularized regression coefficients. The initial regularized regression coefficients are Lasso regression coefficients. Regularized regression is used to prevent the model from overfitting (i.e., performing too well on the training data but poorly on new data). Regularization imposes restrictions on the regression coefficients (such as L1 or L2 regularization) to ensure that the model has a certain degree of simplicity and generalization ability while fitting the data.

[0084] 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 coefficient. 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. A high goodness-of-fit indicates that the comprehensive evaluation model for the excess rate is able to effectively predict the overall excess rate.

[0085] The larger the R², the better the model fit. The calculation formula for R² is:

[0086] in, is the sample size; is the sample observation value; is the mean of the sample observations; is the regression value calculated from the regression equation; SSE is the residual sum of squares, also known as the error sum of squares. Obviously, the smaller the SSE, the better the corresponding regression equation. This method improves on this, still using the R² value to evaluate the performance of the regression equation. The larger the R², the better the model fit.

[0087] For example, we 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, such as Figure 5 As shown, Figure 5 The result of the Lasso regression coefficient solution is shown in the figure. The evaluation model R²=1, which is a perfect fit. Because the sample size is too small and the variables are too many, the solution is reasonable. To solve the influence of each component ring on the overall contribution, first perform the regression coefficient. Determine the regression coefficient using Lasso regression in Python .

[0088] It should be noted that when the comprehensive evaluation model for the deviation rate does not suffer from overfitting, the influence coefficient of each component ring on the overall deviation rate can be obtained according to the comprehensive evaluation model for the deviation rate.

[0089] In specific implementation, the influence coefficient of each component ring on the overall deviation rate is:

[0090] is the influence coefficient of the Mth component ring on the overall deviation rate.

[0091] If the comprehensive evaluation model of the deviation rate is overfitted, regularized regression can be used to correct the overfitting, and the steps of establishing the overall comprehensive evaluation model of the deviation rate can be returned until the comprehensive evaluation model of the deviation rate is no longer overfitted.

[0092] Step S70: determining a construction accuracy control scheme according to the influence coefficient, and performing modular construction accuracy control of the ship section according to the construction accuracy control scheme.

[0093] In specific implementation, after the influence coefficient is calculated, the construction accuracy control plan can be determined according to the influence coefficient, so as to control the modular construction accuracy of the ship section.

[0094] To ensure that the overall assembly tolerance rate can be applied to actual production, after determining the accuracy of the component rings, communicate with the equipment production unit and the final assembly manufacturing unit, and check whether the component ring accuracy settings are reasonable based on existing engineering experience, and whether the setting range of the closed loop is reasonable. If not, adjust the component ring accuracy level and the closed loop measurement range.

[0095] In a feasible implementation, step S70 may include steps A41 to A44: Step A41: normalizing the influence coefficient to obtain a normalized influence coefficient; In a specific implementation, the influence coefficients may 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.

[0096] Table 4

[0097] As shown in Table 4, Table 4 shows the various solution data that make up the ring.

[0098] The normalization process is as follows:

[0099] By normalizing the influence coefficient of each component ring, the size of the overall deviation rate of each component ring can be intuitively found.

[0100] Step A42: comparing the normalized influence coefficient with a preset threshold; It should be noted that the preset threshold can be set to 0.1 or other values, and this embodiment does not limit this. Therefore, the normalized influence coefficient can be compared with the preset threshold to determine whether the component ring has a greater impact on the overall deviation rate.

[0101] If the normalized influence coefficient is less than the preset threshold value of 0.1, it means that the corresponding component ring does not need additional control.

[0102] Step A43: When the normalized influence coefficient is greater than or equal to the preset threshold, the component ring corresponding to the normalized influence coefficient is used as the component ring to be controlled; In specific implementation, if the normalized influence coefficient is greater than or equal to 0.1, the component rings with a coefficient greater than 0.1 need to be identified as component rings to be controlled and confirmed as component rings with a greater impact on the overall deviation rate. Controlling these component rings during the production process can significantly reduce the overall deviation rate.

[0103] Step A44: Determine a construction accuracy control scheme based on the component loop to be controlled, and perform modular construction accuracy control of the ship section according to the construction accuracy control scheme.

[0104] As shown in Table 4, by optimizing the five deviation sources, namely, the axial deviation of the rear shaft end face, the manufacturing deviation of the sealing device diameter, the axial deviation of the front shaft end face, the axial deviation of the thrust shaft end face, and the axial deviation of the stern shaft end face, the overall deviation rate can be greatly reduced.

[0105] The assembly deviation analysis model results were analyzed by reducing the assembly deviation size by half for the five deviation sources, namely, the rear shaft end face axial deviation, the sealing device diameter manufacturing deviation, the front shaft end face axial deviation, the thrust shaft end face axial deviation, and the stern shaft end face axial deviation. Figure 6 As shown, Figure 6 The figure below shows the results of the simulation analysis of the parallelism between the sealing device and the power shaft. Figure 7 As shown, Figure 7 The figure shows the simulation analysis results of the parallelism between the sealing device and the power shaft after precision control. The analysis found that the overall tolerance rate was reduced by more than half. Among them, the change of the tolerance rate of each closed loop is shown in Table 5. Table 5 shows the change of the tolerance rate of each closed loop. The parallelism tolerance rate between the sealing device and the power shaft decreased from 50.02% to 9.71%, the parallelism tolerance rate between the thrust shaft and the power shaft decreased from 3.04% to 2.84%, the distance tolerance rate between the thrust shaft and the stern shaft decreased from 78.1% to 25.32%, the parallelism tolerance rate between the stern shaft and the power shaft decreased from 53.34% to 35.11%, the distance tolerance rate between the thrust shaft and the power shaft decreased from 64.68% to 47.39%, the perpendicularity tolerance rate between the thrust shaft and the stern shaft decreased from 39.62% to 23.13%, the distance tolerance rate between the sealing device and the power shaft decreased from 54.53% to 36.23%, and the distance tolerance rate between the stern shaft and the power shaft decreased from 50.01% to 9.71%. This has important guiding significance for actual production.

[0106] Table 5

[0107] This embodiment provides a method for precision control in the modular design and construction of ship sections. By establishing a dimensional chain relationship for pre-set modules, the dimensions of each component can be precisely controlled, thereby ensuring accuracy throughout the entire module assembly process, reducing quality issues caused by dimensional errors, and improving the construction accuracy of ship sections. A three-dimensional assembly deviation analysis model can be used to comprehensively analyze the impact of component deviations on overall assembly accuracy and identify potential deviations. This precise analysis helps identify problems early, avoiding errors and rework during production, saving time and costs. Based on simulation results, the closed-loop deviation rate and the influence weights of component loops can be analyzed to determine which component loops have the greatest impact on overall accuracy. This analysis can guide production personnel to focus on key links, implement more effective precision control measures, and further optimize the modular construction process of ship sections. By establishing a comprehensive deviation rate evaluation model, the influence coefficient of each component loop on the overall deviation rate can be accurately quantified. This provides a scientific basis for precision control, enabling a more refined and controllable shipbuilding process, 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 solution can be implemented at all stages of ship construction, from parts processing to module assembly, to ensure that each link is carried out according to the predetermined standards, reduce quality fluctuations, and improve the stability and reliability of the final ship quality.

[0108] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 8 , step S30 includes steps S301 to S306: Step S301: performing a number of dimension chain simulations using a repeated random sampling strategy according to the three-dimensional assembly deviation analysis model to obtain a number of closed-loop dimension samples.

[0109] It should be noted that the repeated random sampling strategy is the Monte Carlo method. This method can be used to perform multiple dimensional chain simulations on a 3D assembly deviation analysis model, resulting in N closed-loop dimension sub-samples. Simulation analysis is performed within 3DCS, a software program that uses the Monte Carlo method as its core technology for tolerance analysis and dimensional chain simulation. 3DCS predicts the behavior of complex systems through random sampling and repeated testing, quickly identifies critical tolerances through sensitivity analysis, and reduces redundant optimization costs. 3DCS software is used for simulation analysis to implement Monte Carlo deviation prediction.

[0110] Given the basic dimensions, upper and lower deviations, and size distribution patterns of the component rings, the theories of probability and mathematical statistics show that, as long as a random variable with a continuous distribution is available, it is possible to obtain random variables with any distribution pattern through transformation, rejection, and other methods. Among one-dimensional continuous random variables, those with a continuous distribution on (0, 1) are the simplest. Therefore, when performing random simulations on the dimensions of the component rings, random numbers uniformly distributed on (0, 1) are typically generated first, and then converted to random numbers with other distribution patterns according to a calculation formula. Random numbers uniformly distributed on (0, 1) can be generated using the Random() function provided by high-level programming languages, such as the rand() function in MATLAB.

[0111] (1) The transformation relationship between random numbers with normal distribution and random numbers with uniform distribution: normal distribution There is a certain transformation relationship between the random numbers and the random numbers uniformly distributed on (0, 1). , are two independent random numbers uniformly distributed on (0, l), then two independent standard normal distributions Random number , for:

[0112]

[0113] Standard normal distribution Random number With normal distribution Random number There is the following transformation relationship between them:

[0114] Therefore, a random number uniformly distributed on (0, 1) , Transformed to normal distribution Random number , The transformation formula is as follows:

[0115]

[0116] (2) The transformation relationship between random numbers with non-normal distribution and random numbers with uniform distribution: If the random variable Has a continuous distribution function ,and It is continuously increasing and has random numbers uniformly distributed on (0, 1) , then:

[0117] Right now It is an equation The solution.

[0118] Therefore, if the random variable There is a probability density function ,and A random number for it, is a random number uniformly distributed on (0, 1), then:

[0119] Taking the solution of uniformly distributed random numbers as an example, the random variable exist When it is uniformly distributed on the surface, its probability density function is:

[0120]

[0121] So, in A random number uniformly distributed on The generation formula is as follows:

[0122] The size of each ring is After simulation and solution of the dimensional chain equation, we get A closed loop size sample ( ).

[0123] Step S302: obtaining the average value, maximum value, and minimum value of the plurality of closed loop size samples according to the plurality of closed loop size samples.

[0124] In specific implementation, The closed loop size samples are statistically obtained The average value, maximum value, minimum value of the closed loop size samples, the closed loop size The average value of the samples As shown in the following formula:

[0125] Closed ring size The maximum and minimum values ​​of the samples , As shown in the following formula:

[0126]

[0127] Step S303: Obtaining the average value of the closed loop size, the maximum value of the closed loop size, and the minimum value of the closed loop size according to the average value, the maximum value, and the minimum value of the closed loop size of the plurality of closed loop size sub-samples.

[0128] In a specific implementation, the average closed loop size is The average value of the subsamples, , The maximum value of the sub-sample is the maximum value of the closed loop size. The minimum value of the sub-sample is the minimum value of the closed loop size, that is: , ,in is the maximum value of the closed loop size, is the minimum value of the closed loop size, is the average closed ring size.

[0129] Step S304: calculating the upper deviation of the closed loop size and the lower deviation of the closed loop size according to the average value of the closed loop size, the maximum value of the closed loop size, and the minimum value of the closed loop size.

[0130] In a specific implementation, the closed loop size deviation can be calculated by the closed loop size average value, closed loop size maximum value and closed loop size minimum value. and lower deviation :

[0131]

[0132] Step S305: setting a probability density function of the closed loop and a preset closed loop size according to the closed loop size upper deviation, the closed loop size lower deviation, and the maximum and minimum values ​​of a plurality of closed loop size subsamples.

[0133] In a specific 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.

[0134] Sampling times Determination and calculation of assembly success rate: Determine the number of sampling times The general method is: Let the probability density function of the closed loop size distribution be ,go through The random sampling The maximum and minimum values ​​in the samples are 、 , then the closed loop size is between and The probability between for:

[0135] because 、 is a random variable, so is also a random variable. But no matter what distribution form the closed loop size has, the random variable The probability density function of the distribution is:

[0136] Where: is the number of random simulations.

[0137] If a given value , then the random variable The value is greater than The probability is:

[0138] After integration, we get:

[0139] If given ( ), even if The confidence level of the value of is , then:

[0140] Simplified:

[0141] Solve the above equation to get The value of can be understood as: preset closed loop size Included and The ratio between The probability is The number of random samplings of closed loop size required under the conditions (i.e. the required sample size). Given different and , you can get different random sampling times Currently, when using computers to compile Monte Carlo calculation programs, Monte Carlo simulation can be easily implemented by inputting the number of simulations. When performing tolerance analysis, in order to ensure calculation accuracy, the number of random sampling is usually set to more than 10,000 times.

[0142] Step S306: Calculating the closed loop deviation rate and the component loop influence weights according to the preset closed loop size and the probability density function.

[0143] Therefore, the closed loop deviation rate and the influence weights of the component loops can be solved based on the probability density function and the preset closed loop size.

[0144] In a feasible implementation, step S306 may include steps B11 to B16: Step B11: Set the number of random sampling times; The number of random sampling times can be set through the above content, and specifically the number of random sampling times N can be set to more than 10,000 times.

[0145] Step B12: performing dimension chain simulation using a repeated random sampling strategy according to the random sampling times to obtain simulation results; It should be noted that the Monte Carlo method can be used to simulate the dimension chain according to the number of random sampling to obtain the simulation results. The simulation results are ( ).

[0146] Step B13: calculating the assembly success rate according to the simulation results, the preset closed loop size, and the probability density function; In specific implementation, according to the simulation results ( ), preset closed loop size and the probability density function Calculate the assembly success rate.

[0147] Specifically, the assembly success rate is shown as follows:

[0148] The data obtained from the simulation ( ) is found, that is, find out in ( ) meets the conditions ( For a given closed loop size, is the lower deviation value of the closed loop given by the designer, The number of closed loops given by the designer) , and the assembly success rate is obtained:

[0149] The assembly success rate can be obtained by the above formula .

[0150] Step B14: obtaining a closed loop tolerance rate according to the assembly success rate; In a specific implementation, the closed loop tolerance rate can be calculated based on the assembly success rate. Specifically, the closed loop tolerance rate This means:

[0151] Step B15: Obtain the number of component rings and the component ring tolerance; Step B16: Calculate the influence weight of the component rings according to the number of component rings and the component ring tolerance.

[0152] It should be noted that the influence weight of the component ring is the contribution. The contribution indicates the degree of influence of each component ring on the closed loop. The degree of influence on assembly success or deviation is the same. If the contribution rate is large, increasing the deviation will increase the deviation rate much more than the component ring with a smaller contribution rate. At the same time, the assembly success rate will also decrease by the same value. When modifying the size, you can refer to this value to determine the key size that affects the closed loop. The contribution rate calculation formula of the component ring is as follows:

[0153]

[0154] in, It is The contribution rate of the constituent rings obtained by the extreme value method is: It is The contribution rate of each component ring obtained by statistical method is: For the The tolerance of the constituent rings, is the number of rings.

[0155] The influence weights of the constituent rings can be calculated using the extreme value method or the statistical method.

[0156] This embodiment uses a repeated random sampling strategy to perform multiple dimensional chain simulations, generating multiple closed-loop size samples. This process helps obtain more accurate dimensional variation data and reduces the potential for accidental errors from a single simulation. Analysis of these samples (e.g., determining the average, maximum, and minimum values) provides a more comprehensive understanding of the closed-loop dimensional distribution, enabling accurate prediction of dimensional deviations that may occur during assembly. By calculating the average, maximum, minimum, and deviation of the closed-loop dimensions, the range of variation and degree of deviation can be intuitively understood. The upper and lower deviations of the closed-loop dimensions provide a basis for further control, helping to identify dimensional variations that affect overall assembly accuracy, thereby achieving effective deviation control. By combining the maximum, minimum, upper, and lower deviations of the closed-loop dimensions to establish a probability density function for the closed-loop dimensions, the closed-loop dimensional distribution can be accurately modeled. This provides a basis for developing more reasonable preset closed-loop dimensions, reducing deviations caused by dimensional inconsistencies during assembly. By calculating the closed-loop deviation rate and the influence weights of the component rings, the impact of each component ring on overall assembly accuracy can be quantified. The deviation rate reflects the proportion of deviations that may occur during the actual assembly process, while the impact weights reveal which components are most critical to final accuracy. These analysis results provide data support for subsequent precision control, production process optimization, and quality management, ensuring efficient and accurate production. These analysis results provide a theoretical basis for closed-loop precision control. Understanding the impact weights of each component on overall precision enables the development of more scientific control strategies, such as strengthening precision control of high-impact links, optimizing production processes, reducing production costs, and improving overall product quality. Through precise dimensional simulation and analysis, critical dimensions that may affect assembly accuracy can be identified, allowing effective intervention measures to be taken in advance. This precise control reduces rework and rejects caused by errors during the assembly process, thereby improving production efficiency, reducing production costs, and enhancing overall product reliability.

[0157] For example, in order to help understand the implementation process of the ship block modular design and construction accuracy control method obtained by combining this embodiment with the above embodiment 1, please refer to Figure 9 , Figure 9A brief flow chart of a method for precision control of modular design and construction of ship sections is provided. Specifically, it includes two parts, namely, precision prediction of modular construction of sections and precision control of modular construction of sections. The precision prediction of construction includes: establishing the dimension chain relationship of each part in a module; establishing a three-dimensional assembly model of a module; establishing a three-dimensional assembly deviation analysis model of a module; solving the closed loop tolerance rate and the influence weight of the component loops. The solution process includes: simulating the component loops by Monte Carlo method, solving the closed loops by dimension chain, calculating the assembly success rate, calculating the contribution rate, thereby obtaining the closed loop tolerance rate and the influence weight of the component loops; judging whether the simulation results are reasonable according to the closed loop tolerance rate and the influence weight of the component loops; if not, modifying the size and distribution of the component loops and returning to establish the three-dimensional assembly model of a module; if reasonable, performing precision control of modular construction of sections, specifically including: establishing a comprehensive evaluation model of the overall tolerance rate; setting the correction coefficient C according to the importance of each closed loop. a , solve the modified multivariate linear regression model, and determine whether the multivariate linear regression model has an overfitting problem. If there is an overfitting problem, regularize the regression to correct the overfitting, and return to the step of establishing a comprehensive evaluation model for the overall deviation rate. If there is no overfitting problem, solve the influence coefficient of each component ring on the overall deviation rate, and normalize the influence coefficient. Combined with the actual generation and the influence coefficient, the control scheme is determined to predict and control the modular construction accuracy of the ship section. This embodiment applies the assembly accuracy prediction method of the Monte Carlo method. By establishing an assembly simulation model for modular ship construction, the assembly success rate is accurately predicted in the product design stage. At the same time, an evaluation model for the overall deviation rate is established based on the improved linear regression equation, so that as few component rings as possible are adjusted to achieve a significant reduction in the overall deviation rate, greatly reducing production costs and improving production efficiency.

[0158] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the modular design and construction precision control method of the ship section of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0159] The present application also provides a device for controlling the precision of modular design and construction of ship sections, the device comprising: Establish a module for establishing the dimension chain relationship of each part in the preset module according to the standard data of modular construction of ship sections.

[0160] The establishment module is further 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 according to the three-dimensional assembly model.

[0161] A solution module is used to solve the closed loop deviation rate and the component loop influence weight according to the three-dimensional assembly deviation analysis model.

[0162] The determination module is used to determine whether the simulation result meets the preset requirements based on the closed loop deviation rate and the component loop influence weight.

[0163] The establishment module is further configured to establish a comprehensive evaluation model for the deviation rate based on the simulation results when the simulation results meet preset requirements.

[0164] The solution module is further used to solve the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model.

[0165] A control module is used to determine a construction accuracy control scheme based on the influence coefficient, and to perform modular construction accuracy control of the ship section according to the construction accuracy control scheme.

[0166] The device for controlling the modular design and construction accuracy of ship sections provided in this application adopts the method for controlling the modular design and construction accuracy of ship sections in the above-mentioned embodiment, and can solve the technical problems of low efficiency and high cost in current modular ship construction. Compared with the prior art, the device for controlling the modular design and construction accuracy of ship sections provided in this application has the same beneficial effects as the method for controlling the modular design and construction accuracy of ship sections provided in the above-mentioned embodiment, and the other technical features of the device for controlling the modular design and construction accuracy of ship sections are the same as those disclosed in the above-mentioned embodiment, and are not further described here.

[0167] The present application provides a device for controlling the precision of modular design and construction of ship sections, 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 so that the at least one processor can execute the method for controlling the precision of modular design and construction of ship sections in the first embodiment.

[0168] 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-mentioned embodiment, which can solve the current technical problems of low efficiency and high cost of ship modular design and construction. Compared with the existing technology, the beneficial effects of the ship block modular construction precision control device provided in this application are the same as the beneficial effects of the ship block modular construction precision control method provided in the above-mentioned embodiment, and the other technical features of the ship block modular construction precision control device are the same as the features disclosed in the above-mentioned embodiment method, and are not further described here.

[0169] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the method for controlling the modular construction accuracy of ship sections in the above-mentioned embodiment.

[0170] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for controlling the precision of modular ship section construction. This method can address the current technical issues of low efficiency and high cost in modular ship construction. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the method for controlling the precision of modular ship section construction provided in the aforementioned embodiments, and are not further elaborated here.

[0171] The present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method for controlling the modular construction accuracy of ship sections as described above.

[0172] The computer program product provided in this application can address the current technical issues of low efficiency and high cost in modular ship construction. Compared to existing technologies, the computer program product provided in this application offers the same beneficial effects as the method for precision control of modular ship construction provided in the aforementioned embodiments, and will not be further elaborated here.

[0173] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present 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 comprises: Establish the dimension chain relationship of each part in the preset module according to the standard data of modular construction of ship sections; Establishing a three-dimensional assembly model of a preset module based on the dimension chain relationship, and establishing a three-dimensional assembly deviation analysis model based on the three-dimensional assembly model; Solve the closed loop deviation rate and component loop influence weight according to the three-dimensional assembly deviation analysis model; Determining whether the simulation result meets the preset requirements according to the closed loop deviation rate and the component loop influence weight; When the simulation results meet the preset requirements, a comprehensive evaluation model for the deviation rate is established according to the simulation results; Solve the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model; A construction precision control scheme is determined according to the influence coefficient, and modular construction precision control of the ship section is performed according to the construction precision control scheme.

2. The method according to claim 1, wherein The step of establishing the dimension chain relationship of each part in the preset module according to the modular construction standard data of the ship section includes: Determine the tolerance value of each component, the first basic dimension of the manufacturing deviation of each component, the first upper limit deviation, and the first lower limit deviation according to the standard data of modular construction of the ship section; Analyzing process data between components of the current module according to the segmented docking process, and determining a second basic dimension, a second upper limit deviation, and a second lower limit deviation of the docking assembly deviation of each component based on the process data; Obtaining component ring data according to the first basic size, the first upper limit deviation, and the first lower limit deviation, and the second basic size, the second upper limit deviation, and the second lower limit deviation; determining the type of the constituent ring according to the tolerance value; A dimension chain relationship of each part in a preset module is established according to the component ring type, the tolerance value and the component ring data.

3. The method according to claim 1, wherein The steps of establishing a three-dimensional assembly model of a preset module based on the dimension chain relationship and establishing a three-dimensional assembly deviation analysis model according to the three-dimensional assembly model include: According to the relationship of the dimension chain, the basic dimension, upper deviation and lower deviation of the constituent ring are obtained; Obtain the assembly process of the power module; Determining the assembly sequence and assembly position in the actual assembly process according to the assembly process; Assembling parts in sequence according to the basic size, the upper deviation, and the lower deviation in the assembly order and the assembly position to obtain a three-dimensional assembly model of a preset module; Determining a target assembly method according to the assembly process, and establishing an assembly relationship between the parts according to the target assembly method; Parts are assembled on the three-dimensional assembly model according to the assembly relationship, and tolerance distribution data of end faces of each part in different directions and part measurement data are set to obtain a three-dimensional assembly deviation analysis model.

4. The method according to claim 1, wherein The step of solving the closed loop deviation rate and the component loop influence weight according to the three-dimensional assembly deviation analysis model includes: According to the three-dimensional assembly deviation analysis model, a repeated random sampling strategy is used to perform several dimension chain simulations to obtain several closed-loop dimension samples; Obtain the average value, maximum value and minimum value of several closed loop size sub-samples according to several closed loop size sub-samples; The average value of the closed loop size, the maximum value of the closed loop size and the minimum value of the closed loop size are obtained according to the average value, maximum value and minimum value of the closed loop size of several closed loop size samples; Calculate the upper deviation of the closed loop size and the lower deviation of the closed loop size according to the average value of the closed loop size, the maximum value of the closed loop size and the minimum value of the closed loop size; Setting a probability density function of the closed loop and a preset closed loop size 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 a plurality of closed loop size subsamples; The closed loop deviation rate and the component loop influence weights are solved according to the preset closed loop size and the probability density function.

5. The method according to claim 4, wherein The step of solving the closed loop deviation rate and the component loop influence weight according to the preset closed loop size and the probability density function includes: Set the number of random sampling; Performing dimension chain simulation using a repeated random sampling strategy according to the random sampling times to obtain simulation results; Calculating the assembly success rate based on the simulation results, the preset closed loop size, and the probability density function; Obtaining a closed loop tolerance rate according to the assembly success rate; Get the number of component rings and the component ring tolerance; The influence weight of the component rings is calculated according to the number of component rings and the component ring tolerance.

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

7. The method according to claim 1, wherein The steps of determining a construction accuracy control scheme according to the influence coefficient and performing modular construction accuracy control of the ship section according to the construction accuracy control scheme include: Normalizing the influence coefficient to obtain a normalized influence coefficient; Comparing the normalized influence coefficient with a preset threshold; When the normalized influence coefficient is greater than or equal to the preset threshold, the component ring corresponding to the normalized influence coefficient is used as the component ring to be controlled; A construction precision control scheme is determined based on the component loop to be controlled, and modular construction precision control of the ship section is performed according to the construction precision control scheme.

8. The method according to any one of claims 1 to 7, characterized in that Before the step of solving the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model, the method further includes: Solving the comprehensive evaluation model of the deviation rate to obtain an initial regularized regression coefficient; Calculating the goodness of fit of the comprehensive evaluation model of the deviation rate based on the initial regularized regression coefficient; Determining whether the comprehensive evaluation model of the deviation rate is overfitting according to the goodness of fit; When the comprehensive evaluation model for the deviation rate does not have overfitting, a step of obtaining an influence coefficient of each component ring on the overall deviation rate according to 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 comprises: Establish a module for establishing the dimension chain relationship of each part in the preset module according to the standard data of modular construction of ship sections; The establishment module is further 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; A solution module, configured to solve the closed loop deviation rate and the component loop influence weight according to the three-dimensional assembly deviation analysis model; A determination module, configured to determine whether a simulation result meets preset requirements based on the closed loop deviation rate and the component loop influence weights; The establishment module is further configured to establish a comprehensive evaluation model for the deviation rate according to the simulation results when the simulation results meet the preset requirements; The solving module is further used to solve the influence coefficient of each component ring on the overall deviation rate according to the deviation rate comprehensive evaluation model; A control module is used to determine a construction accuracy control scheme based on the influence coefficient, and to perform modular construction accuracy control of the ship section according to the construction accuracy control scheme.

10. A precision control device for modular design and construction of ship sections, characterized by: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for controlling modular construction accuracy of a ship section according to any one of claims 1 to 8.

Images