A ship structure assembly accuracy detection component

Abstract

The present invention relates to the field of precision detection technology, specifically to a ship structure assembly precision detection component, which includes an acoustic emission monitoring module, a welding quality analysis module, a dynamic load evaluation module and an assembly precision detection module. In the present invention, by comparing the data difference at the difference time point in real time, it is possible to identify potential material fatigue points, ensure real-time monitoring and problem identification during the assembly process, perform welding quality analysis in combination with the spectral image data of the welding area, compare the temperature peaks and chromatographic distributions of different welding points, detect welding defects of the hull structure, use hull model data and real-time load data to evaluate the impact of load on assembly precision, ensure the accuracy of load evaluation, compare the difference between actual measurement data and preset model data, evaluate the actual assembly precision of the structure, improve the accuracy of assembly precision detection, prevent safety hazards caused by assembly errors, and ensure the integrity and functional performance of the ship structure.

Description

Technical Field

[0001] The invention relates to the technical field of precision detection, and in particular to a ship structure assembly precision detection component. Background Art

[0002] The field of precision detection technology involves the use of various instruments and methods to measure and verify whether parameters such as size, position and shape of structures, devices or systems meet predetermined specifications and standards. It is widely used in manufacturing, mechanical engineering, construction, aerospace and other industries to ensure product quality and performance. Modern technical means, including laser scanning, optical image analysis, etc., can provide non-contact and high-resolution measurement results, help to achieve precise control in the production and assembly process of complex results, and can perform rapid detection and provide real-time feedback, thereby improving production efficiency and reducing human errors.

[0003] Among them, the ship structure assembly accuracy detection component is a technical component designed specifically for measuring and verifying the accuracy of the ship structure during the assembly process. Its main purpose is to ensure that the alignment, clearance and overall dimensions of the ship's structural components during assembly accurately meet the design requirements to ensure the ship's structural integrity and functional performance. By using such precision detection components, safety hazards caused by assembly errors can be effectively prevented, while improving the manufacturing quality and work efficiency of the ship. It usually integrates high-precision sensors and data processing software, and can monitor and analyze deviations in the assembly process in real time, making it an indispensable tool in shipbuilding.

[0004] Although existing technologies provide non-contact and high-resolution measurement results when measuring and verifying parameters such as the size, position and shape of structures, devices or systems, it is difficult to fully cover all key parts during the production and assembly of complex structures, resulting in some detailed defects not being discovered in time. There is a lag in real-time feedback and processing speed, and deviations cannot be corrected immediately during the assembly process, which easily leads to the problem of assembly error accumulation. Existing technologies are not comprehensive enough in processing comprehensive analysis of multi-source data, and a single technical means is difficult to cover all possible types of assembly errors, resulting in insufficient comprehensiveness and accuracy of the test results. These shortcomings will make it difficult to discover and correct detailed defects during the assembly process, affecting the overall structural quality and performance of the ship, increasing safety hazards, and reducing manufacturing efficiency and product reliability. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a ship structure assembly accuracy detection component.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A ship structure assembly accuracy detection component comprises:

[0007] The acoustic wave emission monitoring module records and analyzes the acoustic wave frequency during the assembly process based on the acoustic wave frequency data and timestamps during the assembly process of the hull structure. It compares the data differences at different time points in real time based on the recorded analysis results, identifies potential material fatigue points, and generates acoustic wave difference data.

[0008] The welding quality analysis module analyzes the welding quality according to the acoustic wave difference data and the spectral image data of the welding area, compares the temperature peaks and chromatographic distributions of the difference welding points according to the analysis results, detects welding defects during the assembly of the hull structure, and outputs the welding defect detection results;

[0009] The dynamic load evaluation module calculates the structural stress distribution in the hull structure assembly through the hull model data and the real-time load data, adjusts the load model parameters according to the calculation results combined with the welding defect detection results, evaluates the impact of the load on the assembly accuracy, and generates the load response analysis results;

[0010] The assembly accuracy detection module uses ultrasonic technology to measure the structural deformation after ship assembly according to the load response analysis results, evaluates the actual assembly accuracy of the structure by comparing the difference between the actual measurement data and the preset model data, and generates the ship structure assembly accuracy detection result.

[0011] As a further solution of the present invention, the steps of recording and analyzing the sound wave frequency during the assembly process are:

[0012] The frequency data and corresponding timestamps of the sound waves during the assembly of the hull structure are collected regularly, using the formula:

[0013]

[0014] in, Representative time The adjusted sound wave frequency, is the timestamp, for The actual sound wave frequency measured at the moment, is a small positive number used to ensure computational stability and generate a preliminary acoustic wave data set;

[0015] The preliminary sound wave data set is smoothed to eliminate noise and short-term fluctuations using a weighted moving average method using the formula,

[0016]

[0017] in, is the smoothed sound wave frequency, is the weight coefficient, which affects the importance of the time point in the averaging process, is the number of time points, generating a smoothed acoustic wave dataset;

[0018] Analyze the smoothed acoustic wave data set to determine the significant frequency change points, use the difference method, and adopt the formula,

[0019]

[0020] in, Indicates the detection time The frequency change value, It represents the threshold, uses division and absolute value to emphasize the relative size of the change, identifies significant change points, and generates a significant change point dataset.

[0021] As a further solution of the present invention, the step of acquiring the sound wave difference data is:

[0022] Based on the significant change point data set, the frequency difference between consecutive time points is calculated using the formula,

[0023]

[0024] in, Indicates time point and The frequency difference between the two time points is analyzed by using a score form to provide a standardized difference measure, and a frequency difference data set is generated;

[0025] Perform threshold comparison on each difference value in the frequency difference data set, set fatigue recognition threshold, and use formula:

[0026]

[0027] in, Indicates time point The marking results, It is the fatigue identification threshold set to determine whether the frequency change exceeds the normal working conditions and generate the fatigue point marking data set;

[0028] According to the fatigue point marking data set, all marked fatigue point timestamps and their corresponding sound wave frequencies are summarized, and the formula is used:

[0029]

[0030] in, A collection of timestamps for all points marked as fatigue points is collated to form a sound wave difference dataset.

[0031] As a further solution of the present invention, the analysis steps of the welding quality are:

[0032] The acoustic wave difference data is accumulated and the changing trend in the data is emphasized. The formula is used.

[0033]

[0034] in, Indicates the cumulative sound wave difference value, represents the difference in sound waves at a single point in time, Indicates the time window size, is the amplification factor, used to enhance changes in the signal, is a small constant used to ensure numerical stability and avoid division by zero errors to generate the cumulative acoustic wave difference data;

[0035] Collect spectral image data of the welding area during the assembly of ship structure, and apply Gaussian filter to the spectral image data Smoothing is performed to reduce noise and enhance data quality. The formula used is:

[0036]

[0037] in, represents the cumulative value of the spectral data after Gaussian filtering. Indicates location and time The original spectral data, Represents the standard deviation of the Sigmoid filter, which controls the degree of smoothing. Indicates the data center offset, Indicates the boundary of the filtering range and generates smoothed spectral image data;

[0038] Combining the accumulated acoustic wave difference data and the smoothed spectral image data, the welding quality is evaluated by a weighted average method, using the formula,

[0039]

[0040] in, Represents the quantitative evaluation value of welding quality, and It is the weight coefficient that adjusts the contribution of the accumulated acoustic wave difference data and the smoothed spectral image data to generate the welding quality assessment result.

[0041] As a further solution of the present invention, the steps of obtaining the welding defect detection results are:

[0042] Based on the welding quality evaluation results, the temperature peak and chromatographic distribution of the key area are extracted, and the formula is used.

[0043]

[0044]

[0045] in, and represent the corresponding temperature and spectrum data extracted from the welding quality evaluation results, respectively. is the spectral data The average value of is the number of spectral data points that generate the critical solder joint temperature peak and chromatographic distribution standard deviation ;

[0046] The critical solder point temperature peak and chromatographic distribution standard deviation are compared with the standard value, and the difference analysis formula is used.

[0047]

[0048] in, represents the difference value, and are the standard values ​​for temperature and spectral data, and It is a weight coefficient used to balance the influence of temperature and chromatographic data, and is used to adjust the sensitivity of the comparison and generate the difference analysis results of welding defects;

[0049] According to the welding defect difference analysis results, the welding quality is classified and the formula is used.

[0050]

[0051] in, Indicates the quantitative evaluation value of the defect level in the welding area, and are the minimum and maximum values ​​of the difference value in the area, respectively, generating the welding defect detection results.

[0052] As a further solution of the present invention, the calculation steps of the structural stress distribution in the hull structure assembly are:

[0053] Integrate the ship model data, including geometric dimensions and material properties, and use the finite element method to simulate the stress distribution. The calculation formula is:

[0054]

[0055] in, Indicates location The stress value at It's location The bending moment at is the vertical distance from the neutral axis, is the moment of inertia of the area, is the local load, is the force-bearing length, is the cross-sectional area, is the adjustment factor, which is used to adjust the nonlinear characteristics of the material and output the stress distribution model of the ship structure;

[0056] Combined with the ship structure stress distribution model, real-time load data is applied to update the model stress state, recalculate the strain, and use the formula,

[0057]

[0058] in, Indicates at location and time The strain value, Indicates real-time load data, is the real-time stress, is the elastic modulus of the material, is the load sensitivity factor, which is used to adjust the impact of real-time changes on the ship structure strain and output the updated ship structure stress distribution model;

[0059] According to the updated ship structure stress distribution model, the maximum stress in the key area is calculated using the formula:

[0060]

[0061] in, represents the maximum stress value, is the risk adjustment factor, is the critical stress value, generating the stress distribution record.

[0062] As a further solution of the present invention, the steps of obtaining the load response analysis results are:

[0063] Analyze the welding defect detection results, determine the key defect areas, adjust the load model parameters to reflect the welding quality, and use the formula,

[0064]

[0065] in, are the original load model parameters, is the welding defect grade, is the basic adjustment factor, It is the secondary influence coefficient, which increases the complexity of the model, reflects the nonlinear influence of defects, and outputs the adjusted load parameters ;

[0066] Apply the adjusted load parameters , update the structural model, re-evaluate the structural stress and deformation, using the formula,

[0067]

[0068] in, is the adjusted structural deformation, is the elastic modulus of the material, is the cross-sectional area, is the force-bearing length, It is the deformation amplification factor, which is used to adjust the nonlinear deformation effect under large load and output the adjusted structural deformation data;

[0069] Comprehensively adjusting the load parameters , stress distribution records and adjusted structural deformation data, evaluate the ship structure assembly accuracy and its impact on safety, use the formula,

[0070]

[0071] in, Represents the comprehensive evaluation value, and is a weighting factor used to adjust the contribution of stress and deformation to the total response, according to design requirements and safety standards, is the maximum stress, is the allowable stress, is the maximum allowable deformation, is the base of the natural logarithm and generates the results of the load response analysis.

[0072] As a further solution of the present invention, the steps for obtaining the ship structure assembly accuracy detection result are:

[0073] According to the load response analysis results, ultrasonic equipment is applied to the key structural positions of the ship, and the equipment is adjusted to cover the entire detection area. Data collection is performed to calculate the propagation time and speed of the sound wave in the material. The formula is used.

[0074]

[0075] in, Indicates the deformation depth measured by ultrasonic wave, is the propagation angle, which is used to adjust the effect of the actual propagation path, is the medium damping coefficient, which is used to adjust the attenuation of ultrasonic waves in different materials. is the ultrasonic speed, is the ultrasonic round trip time, generating measured deformation data;

[0076] According to the measured deformation data combined with the preset model deformation data ,Compare and , using the formula,

[0077]

[0078] in, represents the deformation difference value, is the interaction effect coefficient, which is used to adjust the complex relationship between model prediction and actual data. represents the deformation value predicted by the model, Indicates the actual deformation value and outputs the deformation difference data;

[0079] According to the deformation difference data, the actual assembly accuracy is evaluated using the formula,

[0080]

[0081] in, Indicates the actual assembly accuracy value, is the sensitivity coefficient is the stability coefficient, which is used to adjust the stability of assembly accuracy and generate the ship structure assembly accuracy evaluation result;

[0082] According to the ship structure assembly accuracy evaluation results, the formula is used:

[0083]

[0084] Output ship structure assembly accuracy test results , where 1 represents qualified and 0 represents unqualified.

[0085] Compared with the prior art, the advantages and positive effects of the present invention are:

[0086] In the present invention, by comparing the data differences at different time points in real time, potential material fatigue points can be identified, ensuring real-time monitoring and problem identification during the assembly process, combining the spectral image data of the welding area to perform welding quality analysis, comparing the temperature peaks and chromatographic distributions of different welding points, detecting welding defects during hull structure assembly, and using hull model data and real-time load data to evaluate the impact of load on assembly accuracy, ensuring the accuracy of load evaluation, and by comparing the difference between actual measurement data and preset model data, evaluating the actual assembly accuracy of the structure, improving the accuracy of assembly accuracy detection, preventing safety hazards caused by assembly errors, and ensuring the integrity and functional performance of the ship structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 is a system flow chart of the present invention;

[0088] Figure 2 The flowchart of recording and analyzing the sound wave frequency in the assembly process of the present invention;

[0089] Figure 3This is a flow chart for obtaining sound wave difference data of the present invention;

[0090] Figure 4 It is a flow chart of analyzing welding quality of the present invention;

[0091] Figure 5 A flowchart for obtaining welding defect detection results of the present invention;

[0092] Figure 6 The figure is a flow chart for calculating the structural stress distribution in the hull structure assembly of the present invention;

[0093] Figure 7 A flow chart for obtaining load response analysis results of the present invention;

[0094] Figure 8 This is a flow chart for obtaining the ship structure assembly accuracy detection results of the present invention. DETAILED DESCRIPTION

[0095] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0096] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. Embodiment 1

[0097] See also Figure 1 , a ship structure assembly accuracy detection component comprises:

[0098] The acoustic wave emission monitoring module records and analyzes the acoustic wave frequency during the assembly process based on the acoustic wave frequency data and timestamps during the assembly process of the hull structure. It compares the data differences at different time points in real time based on the acoustic wave frequency, identifies potential material fatigue points, and generates acoustic wave difference data.

[0099] The welding quality analysis module analyzes the welding quality based on the acoustic wave difference data and the spectral image data of the welding area. It compares the temperature peaks and chromatographic distribution of the difference welding points based on the analysis results, detects welding defects during the assembly of the hull structure, and outputs the welding defect detection results.

[0100] The dynamic load assessment module calculates the structural stress distribution in the hull structure assembly through the hull model data and real-time load data, adjusts the load model parameters according to the calculation results combined with the welding defect detection results, evaluates the impact of the load on the assembly accuracy, and generates load response analysis results;

[0101] The assembly accuracy detection module uses ultrasonic technology to measure the structural deformation after ship assembly based on the load response analysis results. By comparing the difference between the actual measurement data and the preset model data, the actual assembly accuracy of the structure is evaluated and the ship structure assembly accuracy detection results are generated.

[0102] The acoustic wave difference data include frequency peak, time difference value and fatigue identification point; the welding defect detection results include defect type, defect location and defect size; the load response analysis results include stress distribution, load extreme value and response variable; the ship structure assembly accuracy detection results include assembly deviation analysis results, alignment accuracy and stability assessment results.

[0103] See also Figure 2 , the steps for recording and analyzing the acoustic wave frequency during the assembly process are:

[0104] The frequency data and corresponding timestamps of the sound waves during the assembly of the hull structure are collected regularly, using the formula:

[0105]

[0106] in, Representative time The adjusted sound wave frequency, is the timestamp, for The actual sound wave frequency measured at the moment, is a small positive number used to ensure computational stability and generate a preliminary acoustic wave data set;

[0107] The preliminary sound wave data set is smoothed to eliminate noise and short-term fluctuations using the weighted moving average method, using the formula,

[0108]

[0109] in, is the smoothed sound wave frequency, is the weight coefficient, which affects the importance of the time point in the averaging process, is the number of time points, generating a smoothed acoustic wave dataset;

[0110] Analyze the smoothed sound wave data set to determine the significant frequency change points, use the difference method, and adopt the formula,

[0111]

[0112] in, Indicates the detection time The frequency change value, It represents the threshold, uses division and absolute value to emphasize the relative size of the change, identifies significant change points, and generates a significant change point dataset.

[0113] Assume that at time The actual measured sound frequency for ,Pick ,but,

[0114]

[0115] Adjusted sound frequency Approx. , indicating that a very small adjustment is added to the actual frequency to avoid any numerical problems in the calculation. The value is almost the same as the original frequency, indicating that the adjustment has a negligible effect and is mainly intended to ensure numerical stability.

[0116] Assume that during the ship structure assembly process, three time points are sampled : , , ,

[0117] The frequencies at each time point are The corresponding weights are ,

[0118] Substituting into the formula, we get:

[0119]

[0120]

[0121]

[0122] but,

[0123]

[0124]

[0125] Calculation results This means that under a given weight, the smoothed sound wave frequency is , which means that after adjusting the three most recent data points and their importance, a more stable estimate representing the recent trend of sound wave frequency changes is obtained.

[0126] Assume the smoothing frequency at the previous time point for , the smoothing frequency at the current time point is , set the threshold ,but,

[0127]

[0128] here , indicating that at time There is a significant frequency change, which is 2.4 times the threshold. This indicates that there is a frequency change that needs attention at this time point, which may be related to some abnormality in the assembly process.

[0129] See also Figure 3 , the steps for obtaining the acoustic wave difference data are:

[0130] Based on the significant change point data set, the frequency difference between consecutive time points is calculated using the formula,

[0131]

[0132] in, Indicates time point and The frequency difference between the two time points is analyzed by using a score form to provide a standardized difference measure, and a frequency difference data set is generated;

[0133] Perform threshold comparison on each difference value in the frequency difference data set, set the fatigue recognition threshold, and use the formula:

[0134]

[0135] in, Indicates time point The marking results, It is the fatigue identification threshold set to determine whether the frequency change exceeds the normal working conditions and generate the fatigue point marking data set;

[0136] According to the fatigue point marking data set, all marked fatigue point timestamps and their corresponding sound wave frequencies are summarized, and the formula is used:

[0137]

[0138] in, A collection of timestamps for all points marked as fatigue points is collated to form a sound wave difference dataset.

[0139] Assumptions and , substituting into the formula, we get,

[0140]

[0141] result Indicates from time arrive The relative frequency change is This relative measure helps to standardize the size of the difference so that the results do not depend on the size of the absolute frequency value, but instead reflect the proportion of the change.

[0142] If you set the fatigue recognition threshold (2%), based on the above calculation ,but,

[0143]

[0144] The condition is met and marked as True.

[0145] True means that at time The detected frequency change exceeds the threshold ,therefore Flagged as a potential fatigue point. This indicates that the frequency change is outside the set normal variation range and may require further analysis or inspection to identify potential problems.

[0146] Based on the above As a result, if several time points are marked as True within an observation window, for example and ;

[0147]

[0148] gather It means that at time points 10, 15 and 20, the frequency change exceeds the fatigue threshold, and these time points are marked as fatigue points. This means that the ship structure at these time points has potential structural fatigue or quality problems during the assembly process, and detailed inspections or improvement measures are required.

[0149] See also Figure 4 , the analysis steps of welding quality are:

[0150] The acoustic wave difference data is accumulated and the changing trend in the data is emphasized. The formula is used.

[0151]

[0152] in, Indicates the cumulative sound wave difference value, represents the difference in sound waves at a single point in time, Indicates the time window size, is the amplification factor, used to enhance changes in the signal, is a small constant used to ensure numerical stability and avoid division by zero errors to generate the cumulative acoustic wave difference data;

[0153] Collect spectral image data of the welding area during the assembly of ship structure, and apply Gaussian filter to the spectral image data Smoothing is performed to reduce noise and enhance data quality. The formula used is:

[0154]

[0155] in, represents the cumulative value of the spectral data after Gaussian filtering. Indicates location and time The original spectral data, Represents the standard deviation of the Sigmoid filter, which controls the degree of smoothing. Indicates the data center offset, Indicates the boundary of the filtering range and generates smoothed spectral image data;

[0156] Combining the accumulated acoustic wave difference data and the smoothed spectral image data, the welding quality is evaluated by the weighted average method, using the formula,

[0157]

[0158] in, Represents the quantitative evaluation value of welding quality, and It is the weight coefficient that adjusts the contribution of the accumulated acoustic wave difference data and the smoothed spectral image data to generate the welding quality assessment result.

[0159] Assume that in the time window (Adjust the last three time points), sound wave difference data .

[0160] Set the magnification factor and stability constant .

[0161] Substituting into the formula, we get:

[0162]

[0163]

[0164] result It represents the cumulative acoustic wave difference value after weight adjustment and square. This value is large, indicating that there is significant acoustic wave activity in this time window, which may be related to some abnormalities in the welding process.

[0165] Assumptions 2. Adjustment exist The times are:

[0166]

[0167] Substitute into the formula to calculate:

[0168]

[0169]

[0170]

[0171] result Represents the cumulative value of the spectral data after Gaussian filtering. Higher values ​​indicate that the spectral intensity is greater at the center, which may be related to the high temperature in the welding area.

[0172] Assumptions , at a specific point in time , known and .

[0173] but,

[0174]

[0175]

[0176] result It is a quantitative assessment of welding quality. The value reflects the quality of welding. The value indicates a good weld quality or a stable welding process.

[0177] See also Figure 5 , the steps to obtain welding defect detection results are:

[0178] Based on the welding quality evaluation results, the temperature peak and chromatographic distribution of the key areas are extracted, and the formula is used.

[0179]

[0180]

[0181] in, and represent the corresponding temperature and spectrum data extracted from the welding quality evaluation results, respectively. is the spectral data The average value of is the number of spectral data points that generate the critical solder joint temperature peak and chromatographic distribution standard deviation ;

[0182] Compare the critical solder point temperature peak and color spectrum distribution standard deviation with the standard value, and use the difference analysis formula to

[0183]

[0184] in, represents the difference value, and are the standard values ​​for temperature and spectral data, and It is a weight coefficient used to balance the influence of temperature and chromatographic data, and is used to adjust the sensitivity of the comparison and generate the difference analysis results of welding defects;

[0185] According to the welding defect difference analysis results, the welding quality is classified and the formula is used.

[0186]

[0187] in, Indicates the quantitative evaluation value of the defect level in the welding area, and are the minimum and maximum values ​​of the difference value in the area, respectively, generating the welding defect detection results.

[0188] Assume that the temperature data extracted from the welding quality evaluation results (Unit: °C) In a specific area:

[0189]

[0190] Spectral data for:

[0191]

[0192] but,

[0193]

[0194]

[0195] Substitute into the formula to calculate:

[0196]

[0197]

[0198]

[0199] The results show that in the analyzed welding area, the temperature peak is 490 degrees, and the standard deviation of the chromatographic distribution is 0.102, which shows that the relative dispersion of the chromatographic data is low, indicating that the spectral changes are relatively consistent.

[0200] Assuming standard temperature ℃, standard spectral distribution , weight ,but,

[0201]

[0202]

[0203] result Indicates the difference between the welding area and the standard value. A larger value indicates that there may be significant welding defects.

[0204] Assume that in different regions The minimum value is 10 and the maximum value is 20. Substituting into the formula, we get:

[0205]

[0206]

[0207] It indicates that the defect level in the welding area is at a medium level, and according to this level, corresponding repair or improvement measures can be taken.

[0208] See also Figure 6 , the calculation steps of the structural stress distribution in the hull structure assembly are:

[0209] Integrate the ship model data, including geometric dimensions and material properties, and use the finite element method to simulate the stress distribution. The calculation formula is:

[0210]

[0211] in, Indicates location The stress value at It's location The bending moment at is the vertical distance from the neutral axis, is the moment of inertia of the area, is the local load, is the force-bearing length, is the cross-sectional area, is the adjustment factor, which is used to adjust the nonlinear characteristics of the material and output the stress distribution model of the ship structure;

[0212] Combined with the ship structure stress distribution model, apply real-time load data, update the model stress state, recalculate the strain, use the formula,

[0213]

[0214] in, Indicates at location and time The strain value, Indicates real-time load data, is the real-time stress, is the elastic modulus of the material, is the load sensitivity factor, which is used to adjust the impact of real-time changes on the ship structure strain and output the updated ship structure stress distribution model;

[0215] According to the updated ship structure stress distribution model, the maximum stress in the key area is calculated using the formula:

[0216]

[0217] in, represents the maximum stress value, is the risk adjustment factor, is the critical stress value, generating the stress distribution record.

[0218] Assumptions for , for , for , is 0.1, for , for , for , substitute into the formula and get:

[0219]

[0220]

[0221]

[0222]

[0223] result Indicates at location The stress level at the point indicates that the stress level is relatively low and may be within the safe range, which needs to be compared with the yield strength of the material.

[0224] If real-time stress The size adopts the above calculation result, that is, , for , is 0.05, real-time load ,exist Time is ,but,

[0225]

[0226]

[0227]

[0228] result Indicates at location and time The higher the strain value, the more significant the real-time load has been. The lower the strain value, the less significant the real-time load has been.

[0229] Assumptions is 0.05, for , substitute into the formula and calculate:

[0230]

[0231]

[0232]

[0233] result It shows that even in the highest risk scenario, the maximum stress of the structure is still far below the critical stress value, indicating that the ship structure is safe under the current loads and conditions.

[0234] See also Figure 7 , the steps to obtain the load response analysis results are:

[0235] Analyze the welding defect detection results, determine the key defect areas, adjust the load model parameters to reflect the welding quality, use the formula,

[0236]

[0237] in, are the original load model parameters, is the welding defect grade, is the basic adjustment factor, It is the secondary influence coefficient, which increases the complexity of the model, reflects the nonlinear influence of defects, and outputs the adjusted load parameters ;

[0238] Apply adjusted load parameters , update the structural model, re-evaluate the structural stress and deformation, using the formula,

[0239]

[0240] in, is the adjusted structural deformation, is the elastic modulus of the material, is the cross-sectional area, is the force-bearing length, It is the deformation amplification factor, which is used to adjust the nonlinear deformation effect under large load and output the adjusted structural deformation data;

[0241] Load parameters after comprehensive adjustment , stress distribution records and adjusted structural deformation data, evaluate the ship structure assembly accuracy and its impact on safety, use the formula,

[0242]

[0243] in, Represents the comprehensive evaluation value, and is a weighting factor used to adjust the contribution of stress and deformation to the total response, according to design requirements and safety standards, is the maximum stress, is the allowable stress, is the maximum allowable deformation, is the base of the natural logarithm and generates the results of the load response analysis.

[0244] Assuming the original load model parameters for , basic adjustment coefficient 0.1, welding defect level , obtained from the welding defect detection results, assuming it is 0.3, the secondary influence coefficient is 0.05.

[0245] Substituting into the formula, we get:

[0246]

[0247]

[0248]

[0249]

[0250] result Represents the adjusted load model parameters. Reduction means that due to the existence of welding defects, the original load parameters need to be adjusted downward to reduce the potential overload and stress concentration on the structure.

[0251] Assuming the force length is 2m, deformation magnification factor is 0.01, The cross-sectional area is , material elastic modulus for a, combined with the above calculation results, substitute into the formula to calculate,

[0252]

[0253]

[0254]

[0255]

[0256]

[0257] result Represents the adjusted structural deformation. Although this is a small amount, it shows the deformation that may occur when the load is actually applied, which helps to evaluate the actual performance and safety of the ship structure.

[0258] Assumptions and Both are 0.5, The value obtained from the above calculation is 12.90625, for , As 0.01, substitute it into the formula and calculate:

[0259]

[0260]

[0261]

[0262]

[0263]

[0264] result It represents the comprehensive evaluation result of load response analysis. The closer it is to 1, the higher the assembly quality and structural safety. The analysis helps to judge the performance and reliability of the ship structure under actual operating conditions.

[0265] See also Figure 8, the steps for obtaining the ship structure assembly accuracy test results are:

[0266] According to the load response analysis results, ultrasonic equipment is applied to the key structural positions of the ship, and the equipment is adjusted to cover the entire detection area. Data collection is performed to calculate the propagation time and speed of the sound wave in the material. The formula is used.

[0267]

[0268] in, Indicates the deformation depth measured by ultrasonic wave, is the propagation angle, which is used to adjust the effect of the actual propagation path, is the medium damping coefficient, which is used to adjust the attenuation of ultrasonic waves in different materials. is the ultrasonic speed, is the ultrasonic round trip time, generating measured deformation data;

[0269] According to the measured deformation data combined with the preset model deformation data ,Compare and , using the formula,

[0270]

[0271] in, represents the deformation difference value, is the interaction effect coefficient, which is used to adjust the complex relationship between model prediction and actual data. represents the deformation value predicted by the model, Indicates the actual deformation value and outputs the deformation difference data;

[0272] According to the deformation difference data, the actual assembly accuracy is evaluated using the formula,

[0273]

[0274] in, Indicates the actual assembly accuracy value, is the sensitivity coefficient is the stability coefficient, which is used to adjust the stability of assembly accuracy and generate the ship structure assembly accuracy evaluation result;

[0275] According to the evaluation results of ship structure assembly accuracy, the formula is used:

[0276]

[0277] Output ship structure assembly accuracy test results , where 1 represents qualified and 0 represents unqualified.

[0278] Assumptions for (typical speed of sound in steel), for , assuming that the sound wave propagates in a straight line, that is , is 0.02, substitute it into the formula and get:

[0279]

[0280] because =1, =0, then,

[0281]

[0282]

[0283] It means that the deformation depth measured by ultrasound is 3.54 meters.

[0284] like From the above calculation, we can get , Assume that , Set to 0.05, then,

[0285]

[0286]

[0287]

[0288]

[0289]

[0290] It shows that the difference between the predicted and measured data is 0.7881 meters, reflecting that there is a certain degree of assembly deviation in the ship structure assembly.

[0291] Assumed sensitivity coefficient , set to 50, stability coefficient , set to 0.1.

[0292] Substituting the above calculated ,but,

[0293]

[0294]

[0295]

[0296]

[0297]

[0298] result ,This accuracy value indicates that the actual assembly accuracy is low and the ship structure assembly process needs to be re-evaluated.

[0299] Combined with the above calculation results ,

[0300]

[0301]

[0302] The results showed that the ship structure assembly accuracy did not meet the expected standards and the test results were unqualified.

[0303] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A ship structure assembly accuracy detection component, characterized in that: The components include: The acoustic wave emission monitoring module records and analyzes the acoustic wave frequency during the assembly process based on the acoustic wave frequency data and timestamps during the assembly process of the hull structure. It compares the data differences at different time points in real time based on the recorded analysis results, identifies potential material fatigue points, and generates acoustic wave difference data. The welding quality analysis module analyzes the welding quality according to the acoustic wave difference data and the spectral image data of the welding area, compares the temperature peaks and chromatographic distributions of the difference welding points according to the analysis results, detects welding defects during the assembly of the hull structure, and outputs the welding defect detection results; The dynamic load evaluation module calculates the structural stress distribution in the hull structure assembly through the hull model data and the real-time load data, adjusts the load model parameters according to the calculation results combined with the welding defect detection results, evaluates the impact of the load on the assembly accuracy, and generates the load response analysis results; The assembly accuracy detection module uses ultrasonic technology to measure the structural deformation after the ship is assembled according to the load response analysis results, evaluates the actual assembly accuracy of the structure by comparing the difference between the actual measurement data and the preset model data, and generates the ship structure assembly accuracy detection result; The calculation steps of the structural stress distribution in the hull structure assembly are: Integrate the ship model data, including geometric dimensions and material properties, and use the finite element method to simulate the stress distribution. The calculation formula is: ； in, Indicates location The stress value at It's location The bending moment at is the vertical distance from the neutral axis, is the moment of inertia of the area, is the local load, is the force-bearing length, is the cross-sectional area, is the adjustment factor, which is used to adjust the nonlinear characteristics of the material and output the stress distribution model of the ship structure; Combined with the ship structure stress distribution model, real-time load data is applied to update the model stress state, recalculate the strain, and use the formula, ； in, Indicates at location and time The strain value, Indicates real-time load data, is the real-time stress, is the elastic modulus of the material, is the load sensitivity factor, which is used to adjust the impact of real-time changes on the ship structure strain and output the updated ship structure stress distribution model; According to the updated ship structure stress distribution model, the maximum stress in the key area is calculated using the formula: ； in, represents the maximum stress value, is the risk adjustment factor, is the critical stress value, generating stress distribution record; The steps for obtaining the load response analysis results are as follows: Analyze the welding defect detection results, determine the key defect areas, adjust the load model parameters to reflect the welding quality, and use the formula, ； in, are the original load model parameters, is the welding defect grade, is the basic adjustment factor, It is the secondary influence coefficient, which increases the complexity of the model, reflects the nonlinear influence of defects, and outputs the adjusted load parameters ; Apply the adjusted load parameters , update the structural model, re-evaluate the structural stress and deformation, using the formula, ； in, is the adjusted structural deformation, is the elastic modulus of the material, is the cross-sectional area, is the force-bearing length, It is the deformation amplification factor, which is used to adjust the nonlinear deformation effect under large load and output the adjusted structural deformation data; Comprehensively adjusting the load parameters , stress distribution records and adjusted structural deformation data, evaluate the ship structure assembly accuracy and its impact on safety, use the formula, ； in, Represents the comprehensive evaluation value, and is a weighting factor used to adjust the contribution of stress and deformation to the total response, according to design requirements and safety standards, is the maximum stress, is the allowable stress, is the maximum allowable deformation, is the base of the natural logarithm, generating the load response analysis results; The steps for obtaining the ship structure assembly accuracy test result are as follows: According to the load response analysis results, ultrasonic equipment is applied to the key structural positions of the ship, and the equipment is adjusted to cover the entire detection area. Data collection is performed to calculate the propagation time and speed of the sound wave in the material. The formula is used. ； in, Indicates the deformation depth measured by ultrasonic wave, is the propagation angle, which is used to adjust the effect of the actual propagation path, is the medium damping coefficient, which is used to adjust the attenuation of ultrasonic waves in different materials. is the ultrasonic speed, is the ultrasonic round trip time, generating measured deformation data; According to the measured deformation data combined with the preset model deformation data ,Compare and , using the formula, ； in, represents the deformation difference value, is the interaction effect coefficient, which is used to adjust the complex relationship between model prediction and actual data. represents the deformation value predicted by the model, Indicates the actual deformation value and outputs the deformation difference data; According to the deformation difference data, the actual assembly accuracy is evaluated using the formula, ； in, Indicates the actual assembly accuracy value, is the sensitivity coefficient is the stability coefficient, which is used to adjust the stability of assembly accuracy and generate the ship structure assembly accuracy evaluation result; According to the ship structure assembly accuracy evaluation results, the formula is used: ； Output ship structure assembly accuracy test results , where 1 represents qualified and 0 represents unqualified.

2. The ship structure assembly accuracy detection component according to claim 1 is characterized in that: The steps of recording and analyzing the sound wave frequency during the assembly process are as follows: The frequency data and corresponding timestamps of the sound waves during the assembly of the hull structure are collected regularly, using the formula: ； in, Representative time The adjusted sound wave frequency, is the timestamp, for The actual sound wave frequency measured at the moment, is a small positive number used to ensure computational stability and generate a preliminary acoustic wave data set; The preliminary sound wave data set is smoothed to eliminate noise and short-term fluctuations using a weighted moving average method, using the formula, ； in, is the smoothed sound wave frequency, is the weight coefficient, which affects the importance of the time point in the averaging process, is the number of time points, generating a smoothed acoustic wave dataset; Analyze the smoothed acoustic wave data set to determine the significant frequency change points, use the difference method, and adopt the formula, ； in, Indicates the detection time The frequency change value, It represents the threshold, uses division and absolute value to emphasize the relative size of the change, identifies significant change points, and generates a significant change point dataset.

3. The ship structure assembly accuracy detection component according to claim 2 is characterized in that: The steps for obtaining the sound wave difference data are as follows: Based on the significant change point data set, the frequency difference between consecutive time points is calculated using the formula, ； in, Indicates time point and The frequency difference between the two time points is analyzed by using a score form to provide a standardized difference measure, and a frequency difference data set is generated; Perform threshold comparison on each difference value in the frequency difference data set, set fatigue recognition threshold, and use formula: ； in, Indicates time point The marking results, It is the fatigue identification threshold set to determine whether the frequency change exceeds the normal working conditions and generate the fatigue point marking data set; According to the fatigue point marking data set, all marked fatigue point timestamps and their corresponding sound wave frequencies are summarized, and the formula is used: ； in, A collection of timestamps for all points marked as fatigue points is collated to form a sound wave difference dataset.

4. The ship structure assembly accuracy detection component according to claim 1, characterized in that: The analysis steps of welding quality are as follows: The acoustic wave difference data is accumulated and the changing trend in the data is emphasized. The formula is used. ； in, Indicates the cumulative sound wave difference value, represents the difference in sound waves at a single point in time, Indicates the time window size, is the amplification factor, used to enhance changes in the signal, is a small constant used to ensure numerical stability and avoid division by zero errors to generate the cumulative acoustic wave difference data; Collect spectral image data of the welding area during the assembly of ship structure, and apply Gaussian filter to the spectral image data Smoothing is performed to reduce noise and enhance data quality. The formula used is: ； in, represents the cumulative value of the spectral data after Gaussian filtering. Indicates location and time The original spectral data, Represents the standard deviation of the Sigmoid filter, which controls the degree of smoothing. Indicates the data center offset, Indicates the boundary of the filtering range and generates smoothed spectral image data; Combining the accumulated acoustic wave difference data and the smoothed spectral image data, the welding quality is evaluated by a weighted average method, using the formula, ； in, Represents the quantitative evaluation value of welding quality, and It is the weight coefficient that adjusts the contribution of the accumulated acoustic wave difference data and the smoothed spectral image data to generate the welding quality assessment result.

5. The ship structure assembly accuracy detection component according to claim 4 is characterized in that: The steps for obtaining the welding defect detection result are: Based on the welding quality evaluation results, the temperature peak and chromatographic distribution of the key area are extracted, and the formula is used. ； ； in, and represent the corresponding temperature and spectrum data extracted from the welding quality evaluation results, respectively. is the spectral data The average value of is the number of spectral data points that generate the critical solder joint temperature peak and chromatographic distribution standard deviation ; The critical solder point temperature peak and chromatographic distribution standard deviation are compared with the standard value, and the difference analysis formula is used. ； in, represents the difference value, and are the standard values ​​for temperature and spectral data, and It is a weight coefficient used to balance the influence of temperature and chromatographic data, and is used to adjust the sensitivity of the comparison and generate the difference analysis results of welding defects; According to the welding defect difference analysis results, the welding quality is classified and the formula is used. ； in, Indicates the quantitative evaluation value of the defect level in the welding area, and are the minimum and maximum values ​​of the difference value in the area, respectively, generating the welding defect detection results.

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