Method and system for vibration suppression and quality optimization of copper-aluminum composite wire harness twisting equipment

Abstract

This application provides a method and system for vibration suppression and quality optimization of copper-aluminum composite wire harness twisting equipment, including: Step S2, based on the frequency distribution benchmark value, real-time vibration characteristic data of copper and aluminum materials are collected by sensors during the operation of the twisting equipment to determine the degree of matching between the current vibration frequency and the natural frequency; Step S3, if the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold, the spectral characteristics of the vibration characteristic data are analyzed by Fourier transform to determine whether there is a resonant frequency region; Step S6, after obtaining the adjusted vibration characteristic data, the diameter and twist angle of the wire harness are analyzed by a geometric detection algorithm to calculate the trend of geometric accuracy change and determine whether the quality consistency has improved; Step S8, if the fatigue damage risk value exceeds a preset damage risk threshold, a regression analysis of the equipment parameters and vibration characteristic data is performed by a machine learning algorithm to determine a further optimized parameter adjustment scheme.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to a method and system for vibration suppression and quality optimization of copper-aluminum composite wire harness twisting equipment. Background Technology

[0002] Research on the manufacturing process of copper-aluminum composite wire harnesses is an important direction in modern electrical engineering and materials science. Its core lies in achieving efficient connection and stable performance between dissimilar metals like copper and aluminum, which is crucial for improving power transmission efficiency and reducing manufacturing costs. With the rapid development of industries such as new energy vehicles and aerospace, the demand for lightweight and high-reliability wire harnesses is constantly increasing, making the manufacturing technology of copper-aluminum composite wire harnesses a significant breakthrough for technological innovation in the industry.

[0003] However, existing production processes often reveal significant limitations when facing complex operating conditions. Currently, many copper-aluminum composite wire harness production methods rely on traditional machining and simple parameter adjustments. While these methods can meet basic performance requirements to some extent, they lack precise control over dynamic factors during the twisting process. Particularly in the twisting stage, current technologies often fail to effectively address deviations caused by the interaction between equipment and materials, leading to inconsistent product quality and even potential failure risks. This limitation is particularly pronounced in high-precision applications, becoming a bottleneck restricting further technological development. Focusing on specific challenges, the differences in physical properties between copper and aluminum wires, such as density and elastic modulus, result in significantly different inherent vibration frequencies during twisting. When equipment operating parameters approach the resonance points of these frequencies, vibration deviations are amplified. This resonance deviation not only affects the geometric accuracy of the wire harness but may also induce fatigue damage during long-term use. Furthermore, existing twisting equipment lacks real-time monitoring and dynamic adjustment capabilities for resonance frequencies, meaning that process parameter optimization often remains at a static, empirical level, making it difficult to adapt to the personalized needs of different wire harness specifications. These two core technical factors—identification of resonant frequency and dynamic control of deviation—have become the main challenges in current process optimization.

[0004] Therefore, how to accurately identify and avoid resonance frequency regions by analyzing the vibration characteristics of copper and aluminum materials in real time during the twisting process, and at the same time realize the intelligent adjustment of equipment operating parameters to suppress the amplification of vibration deviation and improve product quality, has become a key problem that this research urgently needs to solve. Summary of the Invention

[0005] To address the technical problems mentioned in the background section, a first aspect of the present invention provides a method for vibration suppression and quality optimization of copper-aluminum composite wire harness twisting equipment, the method comprising:

[0006] S1. Acquire physical property data of copper and aluminum wires, including density and elastic modulus. Calculate the natural vibration frequency range of both during the twisting process using numerical analysis to obtain a frequency distribution baseline value. S2. Based on the frequency distribution baseline value, collect real-time vibration characteristic data of the copper and aluminum materials using sensors during the twisting equipment operation to determine the degree of matching between the current vibration frequency and the natural frequency. S3. If the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold, analyze the spectral characteristics of the vibration characteristic data using Fourier transform to determine if a resonance frequency region exists. S4. Based on the identified resonance frequency region, extract the current rotational speed and torque values ​​from the equipment operating parameters. Use an adaptive filtering algorithm to calculate the parameter adjustment amount to obtain an optimized set of operating parameters. S5. Based on the optimized set of operating parameters, adjust the rotational speed and torque of the twisting equipment in real-time using the control system. Obtain the adjusted vibration characteristic data to determine if vibration deviation amplification has been suppressed. S6. After obtaining the adjusted vibration characteristic data, analyze the diameter and twist angle of the wire bundle using a geometric detection algorithm to calculate the trend of geometric accuracy changes and determine if quality consistency has improved. S7. Based on the changing trend of geometric accuracy, extract the fatigue damage prediction model corresponding to the current parameters from historical operating data, and use finite element analysis to calculate the stress distribution of the wire harness during long-term use to obtain the fatigue damage risk value; S8. If the fatigue damage risk value exceeds the preset damage risk threshold, then use machine learning algorithms to perform regression analysis on the equipment parameters and vibration characteristic data to determine a further optimized parameter adjustment scheme; S9. Based on the further optimized parameter adjustment scheme, use a closed-loop control system to dynamically adjust the twisting equipment, obtain the final vibration characteristics and geometric accuracy data, and determine whether the product quality meets the target requirements.

[0007] Optionally, step S1 involves acquiring physical property data of the copper and aluminum wires, including density and elastic modulus, and calculating the natural vibration frequency range of both during the twisting process through numerical analysis to obtain a frequency distribution reference value, including:

[0008] Step S11: Obtain the density and elastic modulus data of copper wire and aluminum wire from the material database, import the data into MATLAB for numerical analysis and processing, and obtain the original values ​​of physical properties.

[0009] Step S12: Use ANSYS software to simulate the twisting process based on the finite element analysis algorithm and calculate the inherent vibration parameters of the copper wire and aluminum wire.

[0010] Step S13: Based on the inherent vibration parameters, use the FFT tool in MATLAB to perform a fast Fourier transform on the vibration signal during the twisting process to obtain the frequency distribution characteristic values.

[0011] Step S14: If the frequency distribution characteristic value exceeds the preset distribution characteristic threshold, adjust the rotational speed and tension parameters during the twisting process, recalculate the natural vibration parameters using ANSYS, and perform FFT analysis again to determine the stability of the frequency distribution characteristic value.

[0012] Step S15: Use statistical analysis tools in MATLAB to perform stability analysis on the frequency distribution characteristic values ​​and determine the reference values ​​for the frequency distribution of copper wire and aluminum wire.

[0013] Step S16: Based on the frequency distribution benchmark value, the difference in vibration characteristics between copper wire and aluminum wire is quantified using the statistical toolbox in MATLAB to obtain the final frequency distribution comparison value.

[0014] Optionally, step S2, based on the frequency distribution reference value, involves collecting real-time vibration characteristic data of the copper and aluminum materials using sensors during the operation of the twisting device to determine the degree of matching between the current vibration frequency and the natural frequency, including:

[0015] Step S21: Collect real-time vibration characteristic data of copper and aluminum materials during the operation of the twisting device using sensors to obtain the original vibration signal;

[0016] Step S22: Calculate the frequency distribution using Fast Fourier Transform based on the original vibration signal, and extract the frequency with the largest amplitude from the frequency distribution as the current vibration frequency value.

[0017] Step S23: Obtain the natural frequency range of the corresponding material from the pre-established natural frequency database of copper and aluminum materials;

[0018] Step S24: Compare the current vibration frequency value with the natural frequency range. If the current vibration frequency value is within the natural frequency range, the matching degree is determined to be high, and the matching state is obtained.

[0019] Step S25: Perform mean filtering on the vibration signal according to the matching state to obtain smoothed characteristic data;

[0020] Step S26: Calculate the standard deviation of the frequency distribution using the smoothed characteristic data. If the standard deviation of the frequency distribution is less than the standard deviation threshold of the frequency distribution, the frequency distribution is determined to be stable, and the final matching result is determined.

[0021] Step S27: Generate vibration characteristic analysis data including vibration frequency, matching status and stability index based on the final matching result, and output complete evaluation content.

[0022] Optionally, the standard deviation threshold of the frequency distribution is set to 5.

[0023] Optionally, in step S3, if the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold, the spectral characteristics of the vibration characteristic data are analyzed by Fourier transform to determine whether a resonant frequency region exists, including:

[0024] Step S31: If the difference between the vibration frequency and the natural frequency is less than the frequency difference threshold, then obtain the vibration characteristic data.

[0025] Step S32: Perform Fourier transform on the vibration characteristic data to obtain the spectral characteristics;

[0026] Step S33: Extract the transformation result from the spectral features and determine whether there are resonant frequency features in the transformation result;

[0027] Step S34: If the transformation result contains a resonant frequency feature, then the specific value of the resonant frequency is determined through data analysis.

[0028] Step S35: Based on the specific value of the resonant frequency, obtain the frequency difference between the resonant frequency and the natural frequency, and determine whether to adjust the vibration characteristics by comparing the frequency difference with the frequency difference threshold.

[0029] Step S36: Using the adjusted vibration characteristic data, perform Fourier transform to obtain new spectral characteristics;

[0030] Step S37: Extract the transformation result from the new spectral features and determine whether the resonant frequency region has disappeared;

[0031] Step S38: If the resonant frequency region still exists, adjust the natural frequency through data analysis to obtain a new region judgment result.

[0032] Optionally, step S33, extracting the transformation result from the spectral features and determining whether there are resonant frequency features in the transformation result, includes:

[0033] Step S331: Obtain the transformation result from the spectral features and use the transformation result as input data; Step S332: Perform a fast Fourier transform on the transformation result to generate frequency distribution data; Step S333: Extract the characteristic value of the resonant frequency from the frequency distribution data and calculate the amplitude of the characteristic value.

[0034] Step S334: Determine whether the amplitude of the feature value exceeds the feature value amplitude threshold;

[0035] Step S335: If the amplitude of the characteristic value exceeds the characteristic value amplitude threshold, the vibration characteristic data is smoothed and filtered to generate adjusted vibration characteristic data.

[0036] Step S336: Perform a fast Fourier transform on the adjusted vibration characteristic data to obtain new spectral features;

[0037] Step S337: Extract the trend of resonant frequency change from the new spectral features and calculate the rate of change of resonant frequency;

[0038] Step S338: Determine whether the frequency region is stable based on the rate of change of the resonant frequency;

[0039] Step S339: If the frequency region is stable, extract the characteristic value of the resonant frequency from the stable region as the final output result.

[0040] Optionally, step S7, based on the changing trend of geometric accuracy, extracts a fatigue damage prediction model corresponding to the current parameters from historical operating data, and uses finite element analysis to calculate the stress distribution of the harness during long-term use to obtain a fatigue damage risk value, including:

[0041] Step S71: Obtain the trend of geometric precision change through historical data, and use data extraction technology to determine the dataset corresponding to the current parameters;

[0042] Step S72: Extract features from the changing trend of geometric precision, use the random forest algorithm to build a prediction model, and obtain a preliminary assessment value of fatigue damage;

[0043] Step S73: Based on the preliminary evaluation values, use finite element analysis to calculate the stress distribution of the wire harness during use and obtain the stress concentration area;

[0044] Step S74: Determine the distribution characteristics of fatigue damage based on the stress concentration area, and determine the damage accumulation mode during long-term use.

[0045] Step S75: Obtain the quantitative result of the risk value by comparing the damage accumulation mode with the current parameters;

[0046] Step S76: If the risk value exceeds the risk value threshold, adjust the parameters of the prediction model, use the support vector machine algorithm to optimize the fatigue damage prediction accuracy, and obtain the final risk assessment value.

[0047] Step S77: Determine the fatigue damage distribution trend of the wiring harness during long-term use based on the final risk assessment value.

[0048] Optionally, in step S8, if the fatigue damage risk value exceeds a preset damage risk threshold, a regression analysis of the equipment parameters and vibration characteristic data is performed using a machine learning algorithm to determine a further optimized parameter adjustment scheme, including:

[0049] Step S81: If the fatigue damage risk value exceeds the damage risk threshold, the equipment parameters and vibration characteristic data are obtained through the sensors to obtain the original dataset.

[0050] Step S82: Extract key feature values ​​from the original dataset and use principal component analysis to determine the main influencing factors;

[0051] Step S83: The relationship between the main influencing factors and fatigue damage is modeled by linear regression to obtain the parameter influence model;

[0052] Step S84: Calculate the adjustment range of each equipment parameter based on the parameter influence model, and determine the preliminary optimization scheme;

[0053] Step S85: If the adjustment range in the preliminary optimization scheme exceeds the equipment limit, the adjustment range is iteratively optimized using the gradient descent method to obtain the final parameter scheme.

[0054] Step S86: Generate corresponding vibration characteristic prediction values ​​for the final parameter scheme, and determine whether the prediction values ​​are lower than the damage risk threshold.

[0055] Step S87: Verify the applicability of the final parameter scheme using historical data to determine the basis for optimization and adjustment.

[0056] Optionally, step S9, based on a further optimized parameter adjustment scheme, dynamically adjusts the twisting device through a closed-loop control system to obtain the final vibration characteristics and geometric accuracy data, and determines whether the product quality meets the target requirements, including:

[0057] Step S91: Collect the operating status data of the twisting device through the closed-loop control system, obtain vibration characteristics and geometric accuracy information, and determine the initial dataset;

[0058] Step S92: Extract vibration characteristic parameters from the initial dataset, including amplitude, frequency and phase, and use Fast Fourier Transform (FFT) to analyze the frequency distribution and obtain the trend of vibration characteristic changes.

[0059] Step S93: Based on the trend of vibration characteristic change, compare it with the preset vibration characteristic change threshold. If it exceeds the vibration characteristic change threshold, adjust the control parameters and generate an adjustment command.

[0060] Step S94: Dynamically adjust the twisting device according to the adjustment command. By adjusting the motor speed and torque, obtain the adjusted vibration characteristics and geometric accuracy data, and determine the updated dataset.

[0061] Step S95: Extract geometric accuracy parameters, including straightness and roundness, from the updated dataset, calculate the deviation value using root mean square error (RMSE), and determine whether the accuracy meets the target requirements.

[0062] Step S96: If the geometric accuracy deviation value exceeds the preset range, new control parameters are generated through the closed-loop control system, and the dynamic adjustment process is repeated.

[0063] Step S97: After multiple adjustments to the updated dataset, the final vibration characteristics and geometric accuracy data are obtained to determine whether the product quality meets the target requirements.

[0064] A second aspect of the present invention provides a vibration suppression and quality optimization system for copper-aluminum composite wire harness twisting equipment. The system employs the method described above to suppress vibration and optimize the quality of the copper-aluminum composite wire harness twisting equipment. The system includes: a data acquisition module for acquiring physical property data of the copper and aluminum wires, including density and elastic modulus, and calculating the natural vibration frequency range of both during the twisting process through numerical analysis to obtain a frequency distribution reference value; a vibration matching module for determining the degree of matching between the current vibration frequency and the natural frequency by collecting real-time vibration characteristic data of the copper and aluminum materials through sensors during the operation of the twisting equipment, based on the frequency distribution reference value; a resonance analysis module for determining whether a resonance frequency region exists by analyzing the spectral characteristics of the vibration characteristic data through Fourier transform if the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold; and a parameter optimization module for extracting the current rotational speed and torque values ​​from the equipment operating parameters based on the determined resonance frequency region, calculating the parameter adjustment amount using an adaptive filtering algorithm, and obtaining an optimized set of operating parameters. The system comprises four modules: a real-time adjustment module and a closed-loop control module. The real-time adjustment module uses optimized operating parameters to control the speed and torque of the twisting device, acquiring vibration characteristic data and determining whether vibration deviation amplification is suppressed. The geometric detection module, after acquiring the adjusted vibration characteristic data, analyzes the diameter and twist angle of the wire harness using a geometric detection algorithm, calculating the trend of geometric accuracy changes and determining whether quality consistency has improved. The fatigue prediction module extracts a fatigue damage prediction model corresponding to the current parameters from historical operating data based on the trend of geometric accuracy changes, and uses finite element analysis to calculate the stress distribution of the wire harness during long-term use, obtaining a fatigue damage risk value. The regression optimization module performs regression analysis on the equipment parameters and vibration characteristic data using a machine learning algorithm if the fatigue damage risk value exceeds a preset damage risk threshold, determining a further optimized parameter adjustment scheme. The closed-loop control module dynamically adjusts the twisting device through a closed-loop control system based on the further optimized parameter adjustment scheme, acquiring the final vibration characteristics and geometric accuracy data to determine whether product quality meets target requirements.

[0065] The technical solutions provided by the embodiments of the present invention have the following beneficial effects:

[0066] The method and system for vibration suppression and quality optimization of copper-aluminum composite wire harness twisting equipment provided by this invention first acquires the physical characteristic data of copper and aluminum wires and calculates their natural vibration frequency range. During the twisting process, real-time vibration data is collected by sensors to determine whether there is a resonance frequency region. If so, the rotational speed and torque values ​​are extracted from the operating parameters, and an adaptive filtering algorithm is used to calculate the parameter adjustment amount to obtain optimized operating parameters. Based on the optimized parameters, the rotational speed and torque of the equipment are adjusted in real time to suppress the amplification of vibration deviation. At the same time, the wire harness diameter and twisting angle are analyzed by geometric detection to evaluate the quality consistency.

[0067] Furthermore, this invention utilizes fatigue damage prediction models and finite element analysis to calculate the stress distribution of the wire harness during long-term use, ensuring that the product quality meets the target requirements. In addition, this invention can effectively suppress vibration during the twisting process, improving product quality consistency and service life. Attached Figure Description

[0068] Figure 1 This is a flowchart of the vibration suppression and quality optimization method for the copper-aluminum composite wire harness twisting device of the present invention.

[0069] Figure 2 This is a schematic diagram of the vibration suppression and quality optimization system of the copper-aluminum composite wire harness twisting device of the present invention. Detailed Implementation

[0070] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0071] like Figure 1 As shown, in a first aspect, the present invention provides a method for vibration suppression and quality optimization of a copper-aluminum composite wire harness twisting device, the method comprising:

[0072] S1. Obtain physical property data of copper and aluminum wires, including density and elastic modulus. Calculate the natural vibration frequency range of the two wires during the twisting process through numerical analysis to obtain the frequency distribution reference value.

[0073] Optionally, this step also includes:

[0074] Step S11: Obtain the density and elastic modulus data of copper and aluminum wires from the material database, import the data into MATLAB for numerical analysis and processing, and obtain the original values ​​of physical properties.

[0075] Step S12: Use ANSYS software to simulate the twisting process based on the finite element analysis algorithm and calculate the inherent vibration parameters of the copper and aluminum wires.

[0076] Step S13: Based on the inherent vibration parameters, use the FFT tool in MATLAB to perform a fast Fourier transform on the vibration signal during the twisting process to obtain the frequency distribution characteristic values.

[0077] Step S14: If the frequency distribution characteristic value exceeds the preset distribution characteristic threshold, adjust the rotational speed and tension parameters during the twisting process, recalculate the natural vibration parameters using ANSYS, and perform FFT analysis again to determine the stability of the frequency distribution characteristic value.

[0078] Step S15: Use statistical analysis tools in MATLAB to perform stability analysis on the frequency distribution characteristic values ​​and determine the reference values ​​for the frequency distribution of copper and aluminum wires.

[0079] Step S16: Based on the frequency distribution benchmark value, the difference in vibration characteristics between copper wire and aluminum wire is quantified using the statistical toolbox in MATLAB to obtain the final frequency distribution comparison value.

[0080] Specifically, obtaining density and elastic modulus data for copper and aluminum wires from a materials database is the foundation of the entire analysis process.

[0081] For example, suppose the density of copper wire is found to be 8.96 g / cm³, and its elastic modulus is 110 GPa; while the density of aluminum wire is 2.70 g / cm³, and its elastic modulus is 70 GPa. These data reflect the differences in the physical properties of the two materials: copper wire is heavier and more rigid, while aluminum wire is lighter and more flexible. Importing this data into MATLAB allows for the generation of initial physical property curves through simple numerical processing, such as a density versus elastic modulus comparison graph, facilitating a visual understanding of the material differences. This step provides reliable data support for subsequent simulations.

[0082] When using ANSYS software to perform finite element analysis of the twisting process, the stress distribution and vibration behavior of copper and aluminum wires during the twisting process can be simulated.

[0083] In one possible implementation, the twisting device is set to rotate at 500 rpm and the tension to 10 N. Models are then created for copper and aluminum wires with a diameter of 1 mm. Simulation results may show that the natural vibration frequency of the copper wire is 150 Hz, while that of the aluminum wire is 120 Hz. This difference stems from the different densities and elastic moduli of the materials. ANSYS's finite element algorithm ensures the accuracy of the results through mesh generation and boundary condition calculations. This lays the foundation for subsequent vibration signal analysis.

[0084] Specifically, by using MATLAB's FFT tool to perform a fast Fourier transform on the vibration signal during the twisting process, the time-domain signal can be converted into frequency-domain features.

[0085] For example, vibration signals collected from sensors show that the dominant frequency of copper wire is concentrated in the 145-155Hz range, while that of aluminum wire is concentrated in the 115-125Hz range. FFT analysis can clearly extract these frequency distribution characteristics. The advantage of this method is that it can quickly identify the main frequency components in the vibration signal, providing a basis for parameter adjustment.

[0086] It should be noted that if the frequency distribution characteristic value exceeds the preset distribution characteristic threshold, for example, the copper wire frequency exceeds 160Hz, it indicates excessive vibration, which may lead to material fatigue. In this case, the rotation speed can be adjusted to 450rpm and the tension to 8N, and the simulation can be repeated in ANSYS to obtain a new natural vibration frequency, such as the copper wire frequency dropping to 148Hz. An FFT analysis can then be performed again to verify whether the frequency is stable within a safe range. This iterative adjustment improves the stability of the twisting process and reduces the risk of material damage.

[0087] Preferably, the stability analysis of the frequency distribution characteristic values ​​can be performed using MATLAB's statistical analysis tools, which can calculate the mean and standard deviation of multiple experiments.

[0088] For example, the average frequency of 10 measurements on the copper wire was 150 Hz with a standard deviation of 2 Hz; the average frequency of the aluminum wire was 120 Hz with a standard deviation of 1.5 Hz. These benchmark values ​​reflect the stability of the vibration characteristics of the two materials during the twisting process, laying the foundation for subsequent comparisons.

[0089] In one embodiment, the difference in vibration characteristics between copper and aluminum wires is quantified using the MATLAB statistical toolbox based on a frequency distribution benchmark.

[0090] For example, calculating the frequency distribution comparison of the two wires reveals that the copper wire's vibration frequency is approximately 25% higher than that of the aluminum wire. This difference may be due to the higher density of the copper wire leading to greater inertia, or its higher elastic modulus resulting in greater rigidity. Visualizing the comparison results through a bar chart clearly illustrates the differences. This quantitative analysis helps optimize twisting process parameters and improve product quality.

[0091] Understandably, each step of the above method is closely interconnected, forming a complete closed-loop process from data acquisition to simulation analysis, parameter adjustment, and characteristic comparison. The advantage of this method lies in its ability to not only reveal the differences in vibration characteristics between copper and aluminum wires but also to improve the reliability and efficiency of the process through iterative optimization, providing technical support for industrial production.

[0092] S2, based on the frequency distribution benchmark value, real-time vibration characteristic data of copper and aluminum materials are collected by sensors during the operation of the twisting device to determine the degree of matching between the current vibration frequency and the natural frequency.

[0093] Optionally, this step also includes:

[0094] Step S21: Collect real-time vibration characteristic data of copper and aluminum materials during the operation of the twisting device using sensors to obtain the original vibration signal.

[0095] Step S22: Calculate the frequency distribution using Fast Fourier Transform based on the original vibration signal, and extract the frequency with the largest amplitude from the frequency distribution as the current vibration frequency value.

[0096] Step S23: Obtain the natural frequency range of the corresponding material from the pre-established natural frequency database of copper and aluminum materials.

[0097] Step S24: Compare the current vibration frequency value with the natural frequency range. If the current vibration frequency value is within the natural frequency range, the matching degree is determined to be high, and the matching state is obtained.

[0098] Step S25: Based on the matching status, the vibration signal is processed by mean filtering with a window size of 10 to obtain smoothed characteristic data.

[0099] Step S26: Calculate the standard deviation of the frequency distribution using the smoothed characteristic data. If the standard deviation of the frequency distribution is less than the frequency distribution standard deviation threshold of 5, the frequency distribution is determined to be stable, and the final matching result is determined.

[0100] Step S27: Generate vibration characteristic analysis data including vibration frequency, matching status and stability index based on the final matching result, and output complete evaluation content.

[0101] Specifically, collecting real-time vibration characteristic data of copper and aluminum materials during the operation of the twisting device using sensors is the foundation of the entire analysis.

[0102] For example, suppose an accelerometer is installed at a critical location on the twisting device, with the acquisition frequency set to 1000 Hz to ensure that high-frequency vibration details are captured. The resulting raw vibration signal may be an acceleration waveform in the time domain, reflecting the dynamic response of the material during device operation.

[0103] In one possible implementation, the raw vibration signal is input into analysis software and converted into frequency domain data using a Fast Fourier Transform. It's conceivable that the vibration signal of the copper wire shows the frequency with the largest amplitude at 140Hz, while that of the aluminum wire is at 110Hz. This frequency with the largest amplitude is extracted as the current vibration frequency value, intuitively reflecting the main vibration characteristics of the material under the current operating conditions.

[0104] It should be noted that the pre-established database of natural frequencies of copper and aluminum materials is derived from multiple experiments and simulations.

[0105] For example, the database records the natural frequency range of copper wire as 130-150Hz and that of aluminum wire as 100-120Hz. These ranges are determined based on the material's physical properties and process parameters, and are valuable for reference. When comparing the current vibration frequency value with the database, assuming that the copper wire's 140Hz falls within the 130-150Hz range, the match is high; similarly, if the aluminum wire's 110Hz is also within the 100-120Hz range, it indicates a good match.

[0106] Specifically, when using mean filtering to process vibration signals based on the matching state, a window size of 10 means taking 10 consecutive data points to calculate the average value.

[0107] For example, filtering the raw signal from the copper wire weakens high-frequency noise in the waveform, and the smoothed characteristic data more clearly shows the main trend. This smoothing process helps reduce transient interference and facilitates subsequent analysis.

[0108] In one embodiment, when calculating the standard deviation of the frequency distribution using smoothed characteristic data, it is assumed that the standard deviation of the frequency distribution for copper wire is 3 and for aluminum wire it is 2.5. Since both are less than the preset standard deviation threshold of 5, the frequency distribution can be determined to be stable. This stability indicates that the vibration characteristics are reliable under the current operating conditions, which helps in the optimization and adjustment of process parameters.

[0109] Preferably, when generating vibration characteristic analysis data from the final matching results, specific numerical values ​​may be included.

[0110] For example, the output report shows that the copper wire's vibration frequency is 140Hz, the matching status is "high," and the stability index is "standard deviation 3"; the aluminum wire's is 110Hz, the matching status is "high," and the stability index is "standard deviation 2.5." This comprehensive assessment provides a clear basis for process monitoring.

[0111] It is understandable that the process from signal acquisition to output analysis data forms a closed-loop system.

[0112] For example, if the frequency of the copper wire suddenly jumps to 160Hz, exceeding the database range and indicating a low matching degree, it may indicate a device malfunction. Further verification through smoothing and calculation of the standard deviation of the frequency distribution can quickly pinpoint the problem. The advantage of this method lies in its real-time performance and accuracy, providing reliable support for the production process.

[0113] For example, from multiple perspectives, the real-time nature of sensor acquisition ensures the dynamic nature of the data, the frequency values ​​extracted by Fourier transform provide the accuracy of the features, and database comparison and filtering enhance the reliability of the results. These elements support each other, jointly ensuring the rigor of vibration characteristic analysis and contributing to improved product quality and equipment operating efficiency.

[0114] S3. If the difference between the current vibration frequency and the natural frequency is less than the preset frequency difference threshold, the spectral characteristics of the vibration characteristic data are analyzed by Fourier transform to determine whether there is a resonance frequency region.

[0115] Optionally, this step also includes:

[0116] Step S31: If the difference between the vibration frequency and the natural frequency is less than a preset frequency difference threshold, then obtain vibration characteristic data.

[0117] Step S32: Perform Fourier transform on the vibration characteristic data to obtain the spectral characteristics.

[0118] Step S33: Extract the transformation result from the spectral features and determine whether there are resonant frequency features in the transformation result.

[0119] Step S34: If the transformation result contains resonant frequency characteristics, the specific value of the resonant frequency is determined through data analysis.

[0120] Step S35: Based on the specific value of the resonant frequency, obtain the frequency difference between the resonant frequency and the natural frequency. By comparing the frequency difference with the frequency difference threshold, determine whether to adjust the vibration characteristics.

[0121] Step S36: Using the adjusted vibration characteristic data, perform a Fourier transform to obtain new spectral characteristics.

[0122] Step S37: Extract the transformation result from the new spectral features and determine whether the resonant frequency region has disappeared.

[0123] Step S38: If the resonant frequency region still exists, adjust the natural frequency through data analysis to obtain a new region judgment result.

[0124] Specifically, when analyzing vibration characteristic data, the first step is to determine whether the difference between the vibration frequency and the natural frequency is within an acceptable range.

[0125] For example, suppose the vibration frequency of copper is 145Hz, while its natural frequency range is 130-150Hz, and the preset frequency difference threshold is 10Hz. In this case, the frequency difference between the vibration frequency and the natural frequency is 5Hz, which is less than the frequency difference threshold, so vibration characteristic data can continue to be acquired. This judgment method ensures the validity of the basic data for subsequent analysis. Next, the acquired vibration characteristic data is processed.

[0126] In one possible implementation, the vibration signal in the time domain is converted into spectral data through Fourier transform.

[0127] For example, after the vibration signal of copper is transformed, the spectrum shows multiple frequency peaks, with one significant peak appearing at 145Hz. This spectral characteristic reflects the vibration pattern of the material under the current operating conditions, providing a clear basis for subsequent analysis.

[0128] It should be noted that when extracting transformation results from spectral features, the key is to identify whether resonant frequency features exist.

[0129] Specifically, if a peak at 145Hz is unusually prominent in the frequency spectrum and its amplitude far exceeds that of other frequencies, it may indicate resonance. In this case, by comparing it with the known natural frequency range, a preliminary judgment can be made as to whether this frequency is related to resonance. This identification process helps to detect potential problems in a timely manner.

[0130] In one embodiment, if a resonant frequency characteristic is confirmed, its specific value is further analyzed.

[0131] For example, the spectrum data shows that the resonant frequency is stable at 145Hz. By comparing it with the natural frequency range, the calculated frequency difference between the resonant frequency and the natural frequency is still 5Hz, which is below the frequency difference threshold of 10Hz. At this point, the resonance effect can be considered small, but it is still necessary to pay attention to whether the vibration characteristics need to be adjusted to optimize the operating state.

[0132] Preferably, if it is decided to adjust the vibration characteristics, new vibration data can be generated by changing parameters such as the equipment speed or tension.

[0133] For example, the vibration frequency of the copper material was adjusted to 142 Hz. A Fourier transform was performed again to obtain new spectral characteristics. The new spectrum showed a reduced peak at 145 Hz, indicating that the adjustment effectively reduced the resonance effect. This iterative adjustment method improves the applicability of the data.

[0134] Understandably, when extracting the transformation results from the new spectral features, it is necessary to determine whether the resonant frequency region has disappeared.

[0135] For example, if the adjusted spectrum shows that the 145Hz peak is no longer prominent and the overall frequency distribution tends to be flatter, it indicates that the resonance has been controlled. This verification process ensures the effectiveness of the adjustment measures.

[0136] In one possible implementation, if the resonant region still does not disappear, the natural frequency needs to be adjusted.

[0137] For example, by changing the material thickness or tension, the natural frequency range of copper can be changed to 135-155Hz. After reanalysis, the vibration frequency of 145Hz falls into the new range, and the spectral characteristics are stable. This method fundamentally eliminates the potential for resonance by optimizing the natural frequency.

[0138] For example, from multiple perspectives, determining the frequency difference ensures the accuracy of data screening, Fourier transform provides intuitive spectral analysis, and the identification and adjustment of resonance characteristics enhances process stability. These progressive steps collectively support the integrity of vibration characteristic analysis, providing reliable assurance for equipment operation.

[0139] Optionally, step S33, extracting the transformation result from the spectral features and determining whether there are resonant frequency features in the transformation result, further includes:

[0140] Step S331: Obtain the transformation result from the spectral features and use the transformation result as input data.

[0141] Step S332: Perform a fast Fourier transform on the transformation result to generate frequency distribution data.

[0142] Step S333: Extract the characteristic value of the resonant frequency from the frequency distribution data and calculate the amplitude of the characteristic value.

[0143] Step S334: Determine whether the feature value amplitude exceeds the preset feature value amplitude threshold. The feature value amplitude threshold is determined by statistical analysis of historical data.

[0144] Step S335: If the characteristic value amplitude exceeds the preset characteristic value amplitude threshold, the vibration characteristic data is smoothed and filtered to generate adjusted vibration characteristic data.

[0145] Step S336: Perform a fast Fourier transform on the adjusted vibration characteristic data to obtain new spectral features.

[0146] Step S337: Extract the trend of resonant frequency change from the new spectral features and calculate the rate of change of resonant frequency.

[0147] Step S338: Determine whether the frequency region is stable based on the rate of change of the resonant frequency.

[0148] Step S339: If the frequency region is stable, extract the characteristic value of the resonant frequency from the stable region as the final output result.

[0149] Specifically, the transformation results are obtained from the spectral features and used as input data. The core of this process is to ensure the accuracy of the data.

[0150] In one possible implementation, assuming spectral features are extracted from the vibration signal of mechanical equipment, the transformation result may manifest as multiple frequency peaks, which reflect the operating status of the equipment. A specific implementation method could be to perform preliminary cleaning of the acquired raw signal using a digital signal processor to remove noise before using it as input. A Fast Fourier Transform (FFT) is then performed on the transformation result to generate frequency distribution data; the key to this step is capturing the frequency distribution patterns.

[0151] For example, in the monitoring of a rotating machine, the Fast Fourier Transform can convert the vibration signal in the time domain into the frequency domain. The generated data may show frequency components such as 50Hz and 120Hz, which are closely related to the rotational speed of the equipment.

[0152] Specifically, the sampling rate can be adjusted, for example, to 1024Hz, to ensure a sufficiently high frequency resolution. Extracting the characteristic values ​​of the resonant frequency from the frequency distribution data and calculating its amplitude is an important step in determining the vibration characteristics.

[0153] In one embodiment, assuming a significant peak appears at 120Hz in the frequency distribution data, with an amplitude of 0.8m / s², it can be determined whether this is a resonant frequency by comparing it with historical data. In terms of implementation, a peak detection algorithm can be used to pinpoint the target frequency, and then its amplitude can be calculated.

[0154] Determine whether the amplitude of the feature value exceeds the preset feature value amplitude threshold, which is usually determined based on historical statistical data.

[0155] For example, if historical data indicates that the characteristic value amplitude does not exceed 0.5 m / s² during normal operation, while the current characteristic value amplitude is 0.8 m / s², then the current characteristic value amplitude is determined to exceed the characteristic value amplitude threshold. This judgment helps to identify potential problems in a timely manner. In practice, a dynamic characteristic value amplitude threshold can be set and gradually adjusted according to the equipment's operating time. If the characteristic value amplitude exceeds the standard, the vibration characteristic data is smoothed and filtered to generate adjusted data; this process aims to reduce interference.

[0156] Preferably, a moving average filter can be used to process the raw data.

[0157] For example, setting the window to 10 sampling points significantly reduces data fluctuations after smoothing, which is helpful for subsequent analysis. Performing a Fast Fourier Transform again on the adjusted data to obtain new spectral characteristics is a step that verifies the adjustment effect.

[0158] In one possible implementation, after smoothing and transformation, the peak characteristic amplitude at 120Hz may decrease to 0.6m / s², indicating some mitigation of interference. Specifically, the same sampling parameters as the initial transformation can be maintained to ensure consistency. Extracting the trend of resonant frequency variation from the new spectral characteristics and calculating the rate of change are crucial for evaluating system stability.

[0159] For example, if the eigenvalue amplitude at 120Hz decreases from 0.8m / s² to 0.6m / s², the rate of change of the resonant frequency is 25%, indicating that the adjustment is effective. In practice, the rate of change of the resonant frequency can be calculated through trend analysis of multiple consecutive samples. Determining whether the frequency range is stable based on the rate of change of the resonant frequency directly impacts subsequent decisions.

[0160] In one embodiment, if the rate of change of the resonant frequency is less than 10%, the frequency region is considered to be stable.

[0161] Specifically, a stability standard can be set, such as confirming stability when the rate of change of the resonant frequency is less than a certain value in three consecutive measurements. If the frequency range is stable, the resonant frequency characteristic value is extracted from the stable range as the final output, and this result can be used for equipment maintenance.

[0162] For example, the stabilized 120Hz characteristic value can be used as a reference to guide the adjustment of equipment parameters.

[0163] It should be noted that the advantage of this method is that it improves the reliability of the data, providing a basis for subsequent optimization.

[0164] S4. By determining the obtained resonance frequency region, the current speed and torque values ​​are extracted from the equipment operating parameters. An adaptive filtering algorithm is used to calculate the parameter adjustment amount to obtain the optimized set of operating parameters.

[0165] Optionally, this step also includes:

[0166] Step S41: Determine the frequency range of the device by analyzing the resonant frequency.

[0167] Step S42: Obtain the current speed and torque values ​​from the equipment operation data, and determine the equipment operating status by combining the frequency range.

[0168] Step S43: Use an adaptive filtering algorithm to process the current speed and torque values, calculate the parameter adjustment amount, and obtain the adjusted operating parameters.

[0169] Step S44: If the adjusted operating parameters exceed the preset operating parameter threshold, the adjusted operating parameters are processed by secondary filtering to determine stable parameter values.

[0170] Step S45: Compare the stable parameter values ​​with the frequency range, calculate the parameter deviation value, and determine the optimization direction.

[0171] Step S46: Adjust and optimize the parameters by adjusting the parameter deviation value to obtain the optimized set of operating parameters.

[0172] Step S47: For the optimized set of operating parameters, determine its consistency with the equipment operating status, and determine the final set of parameters.

[0173] Step S48: Extract key operating parameters from the final parameter set and generate an adjustment instruction set.

[0174] Specifically, determining the frequency range of the equipment by analyzing the resonant frequency is a key step in vibration analysis.

[0175] For example, assuming a device's vibration data identifies a resonant frequency of 200Hz, and considering the device's material and structural characteristics, its frequency range can be inferred to be 190-210Hz. This range provides a basis for subsequent judgments.

[0176] In one possible implementation, the current speed and torque values ​​are obtained from the equipment's operating data, for example, the speed is 3000 rpm and the torque is 50 N·m. Combined with the frequency range of 190-210 Hz, if the frequency corresponding to the current speed is 195 Hz, it can be preliminarily determined that the equipment is operating near the resonance region.

[0177] It should be noted that the purpose of using the adaptive filtering algorithm to process speed and torque values ​​is to smooth out data noise.

[0178] For example, the engine speed data may change abruptly due to sensor fluctuations. Adaptive filtering can adjust it to a stable value, such as 2980 rpm, and the torque can be adjusted to 52 N·m. After calculating the parameter adjustment, the adjusted operating parameters are obtained, such as the engine speed increasing to 3050 rpm.

[0179] In one embodiment, if the frequency corresponding to the adjusted rotational speed is 215Hz, which exceeds the preset operating parameter threshold of 210Hz, a secondary filtering process is required. After the secondary filtering, the rotational speed stabilizes at 3020rpm, corresponding to a frequency of 208Hz, which is taken as the stable parameter value.

[0180] Specifically, comparing the stable parameter value of 208Hz with the frequency range of 190-210Hz, the parameter deviation is -2Hz, indicating that the operating state is close to the upper limit.

[0181] Preferably, the parameters are adjusted and optimized by adjusting the parameter deviation value, for example, reducing the speed to 3000 rpm and making the frequency drop back to 205 Hz, thereby generating an optimized set of operating parameters.

[0182] Understandably, when judging the consistency between the set of optimized parameters and the operating status of the equipment, if the equipment load is stable, the set of parameters can be applied directly.

[0183] For example, if the optimized speed of 3000 rpm and the torque of 53 N·m match the current operating conditions, then the final parameter set is determined.

[0184] In one embodiment, key operating parameters, such as a rotational speed of 3000 rpm, are extracted from the final parameter set to generate an adjustment instruction set. The instruction set may include steps for gradually adjusting the rotational speed, such as reducing it from 3020 rpm to 3000 rpm, to ensure a smooth transition for the equipment.

[0185] For example, from multiple perspectives, speed adjustment can prevent increased vibration caused by resonance, while torque optimization ensures output efficiency. This method ensures more stable equipment operation through defining the frequency range, smoothing data processing, and iterative parameter optimization.

[0186] In one possible implementation, if the initial speed is too high, such as 3100 rpm, which corresponds to a frequency of 220 Hz, then filtering and adjustment are used to gradually return it to a safe range, demonstrating the flexibility of the solution.

[0187] It should be noted that the implementation of each step is closely centered around vibration characteristics.

[0188] For example, adaptive filtering reduces data errors, secondary filtering ensures parameter reliability, and parameter deviation calculation points the way for optimization. These steps are progressive, forming a complete technology chain.

[0189] In one embodiment, if the parameter deviation is large, such as -10Hz, the device structural parameters can be further adjusted, such as by increasing damping, to broaden the frequency range. This extended solution enriches application scenarios and ensures adaptability for long-term operation.

[0190] S5, based on the optimized set of operating parameters, adjusts the speed and torque of the twisting device in real time through the control system, obtains the adjusted vibration characteristic data, and determines whether the vibration deviation amplification is suppressed.

[0191] Optionally, this step also includes:

[0192] Step S51: Based on the preset set of operating parameters, the speed and torque of the twisting device are adjusted in real time to obtain the adjusted operating status data.

[0193] Step S52: Extract vibration characteristic information, including vibration frequency and vibration amplitude, from the adjusted operating status data.

[0194] Step S53: Determine the trend of vibration characteristic changes based on vibration frequency and vibration amplitude.

[0195] Step S54: Based on the trend of vibration characteristic changes, use the support vector machine classifier in Scikit-learn, with vibration frequency and vibration amplitude as features, to determine whether vibration deviation amplification has occurred.

[0196] In step S55, if the vibration deviation is amplified, the gradient descent method is used to optimize the operating parameters and generate a new set of operating parameters.

[0197] Step S56: Based on the new set of operating parameters, the rotational speed and torque of the twisting device are adjusted in real time to obtain updated vibration characteristic data.

[0198] Step S57: Calculate the mean value of vibration amplitude using the updated vibration characteristic data to determine the degree of suppression of vibration deviation amplification.

[0199] Step S58: Based on the degree of suppression of vibration deviation amplification, determine whether the parameter optimization has reached the preset optimization threshold, and obtain the final adjustment result.

[0200] Specifically, when adjusting the speed and torque of the twisting device in real time according to a preset set of operating parameters.

[0201] Understandably, the core of this process lies in ensuring the dynamic adaptation of the equipment's operating status.

[0202] For example, in the operation of a twisting machine, assuming the initial speed is set to 1200 rpm and the torque to 50 N·m, real-time adjustments may be made to increase the speed to 1300 rpm and decrease the torque to 45 N·m to cope with load changes. The adjusted operating status data will reflect the current stability of the equipment. Vibration characteristic information is then extracted from the adjusted operating status data.

[0203] Specifically, the vibration frequency might be 60 Hz, and the vibration amplitude might be 0.5 mm / s. This data is typically acquired using sensors mounted on the equipment.

[0204] In one possible implementation, technicians would focus on whether the vibration frequency is close to the device's natural frequency range to avoid the risk of resonance.

[0205] Regarding the determination of the vibration characteristic change trend, for example, continuous monitoring for 10 minutes showed that the vibration frequency gradually increased from 60Hz to 65Hz, and the vibration amplitude increased from 0.5mm / s to 0.7mm / s. This trend indicates that the equipment may be affected by external interference or internal parameter imbalance.

[0206] It should be noted that trend analysis can help provide early warnings of potential problems.

[0207] Preferably, when using a support vector machine classifier to determine vibration deviation amplification, vibration frequency and vibration amplitude are used as input features.

[0208] For example, when the vibration frequency rises to 70Hz and the vibration amplitude reaches 0.9mm / s, the classifier may identify signs of amplified vibration deviation. The advantage of this method is its ability to quickly distinguish between normal fluctuations and abnormal states. If amplified vibration deviation occurs, optimizing operating parameters using gradient descent is a common choice in this implementation.

[0209] For example, the initial speed of 1300 rpm can be adjusted to 1250 rpm, and the torque can be fine-tuned from 45 N·m to 47 N·m. After the new parameter set is generated, the equipment operation tends to be more stable, and the amplitude is expected to decrease. This method improves the accuracy of parameter adjustment.

[0210] In one embodiment, when the vibration characteristic data is obtained after readjusting the rotational speed and torque, the vibration frequency may drop back to 62 Hz, and the vibration amplitude may decrease to 0.6 mm / s. This change indicates that the optimization direction is correct.

[0211] Understandably, real-time monitoring data feedback is essential for successful optimization.

[0212] In one embodiment, when calculating the average vibration amplitude using the updated vibration characteristic data, five measurements were taken: 0.6, 0.61, 0.59, 0.62, and 0.58 mm / s, with an average of 0.6 mm / s. This indicates that the vibration deviation amplification has been somewhat suppressed.

[0213] Specifically, mean calculation provides a quantitative basis for evaluation.

[0214] In one embodiment, when judging whether parameter optimization meets the standard based on the degree of suppression of vibration deviation amplification, if the preset optimization threshold is that the vibration amplitude does not exceed 0.65 mm / s, and the average value of the current vibration amplitude is 0.6 mm / s, it means that the optimization has met expectations.

[0215] In one embodiment, this determination can also provide reference data for subsequent equipment maintenance.

[0216] Ideally, stable operating parameters can extend equipment life and improve production efficiency.

[0217] S6. After obtaining the adjusted vibration characteristic data, the diameter and torsion angle of the wire harness are analyzed through a geometric detection algorithm to calculate the trend of geometric accuracy change and determine whether the quality consistency has improved.

[0218] Optionally, this step also includes:

[0219] Step S61: Obtain vibration characteristic data, input the vibration characteristic data into a preset adjustment method for processing, and obtain adjusted vibration characteristic data.

[0220] Step S62: Use a geometric detection tool to analyze the adjusted vibration characteristic data, calculate the values ​​of the wire harness diameter and torsion angle, and obtain the geometric detection results.

[0221] Step S63: Process the geometric inspection results using the accuracy calculation formula, calculate the geometric accuracy of the wire harness diameter and the twist angle, and obtain the geometric accuracy data.

[0222] Step S64: Process the geometric accuracy data using a trend analysis tool to determine the trend of geometric accuracy changes and obtain geometric accuracy trend data.

[0223] Step S65: If the geometric accuracy change trend data exceeds the preset change trend threshold, the consistency judgment tool is used to process the geometric accuracy change trend data to determine whether the quality consistency has been improved, and a consistency judgment result is obtained.

[0224] Step S66: Obtain the consistency judgment result, use a data comparison tool to analyze the difference between the result and historical data, determine the stability of quality consistency, and obtain stability data.

[0225] Step S67: Based on the stability data, use a classification algorithm to process the stability data, determine the overall quality consistency of the harness, and obtain the final judgment result.

[0226] Specifically, acquiring vibration characteristic data and inputting it into a preset adjustment method for processing is the first step in ensuring the operational status analysis of the twisting equipment.

[0227] Understandably, vibration characteristic data usually contains key information such as vibration frequency and vibration amplitude. By adjusting the data, smoothing or filtering can reduce noise interference.

[0228] For example, the original vibration frequency collected by the sensor may be 62Hz and the vibration amplitude may be 0.6mm / s. After processing by adjustment methods, more stable data may be obtained, which is convenient for subsequent analysis.

[0229] In one possible implementation, the adjustment method could be a time-series smoothing algorithm, which effectively preserves the core features of the data. When analyzing the adjusted vibration characteristic data using geometric inspection tools, the focus is on extracting the geometric parameters of the harness.

[0230] For example, a technician might use optical measuring equipment to inspect the diameter and twist angle of the wire harness, assuming that adjusted data analysis shows a diameter of 5.2 mm and a twist angle of 15 degrees. This inspection process relies on high-precision tools to ensure the reliability of the data.

[0231] Specifically, geometric inspection tools can also improve the representativeness of results through multi-point sampling. For example, diameters measured at different locations are 5.19mm, 5.21mm, and 5.20mm, and angles are 14.8 degrees, 15.1 degrees, and 15.0 degrees. When processing geometric inspection results using accuracy calculation formulas, the goal is to quantify the geometric accuracy of the wire harness.

[0232] Preferably, accuracy can be reflected by statistical deviation.

[0233] For example, based on multiple diameter measurements, the average diameter was calculated to be 5.2 mm, with a diameter deviation within ±0.01 mm. The average twist angle was 15 degrees, with a twist angle deviation of ±0.1 degrees. This method directly reflects the processing quality of the wire harness.

[0234] In one embodiment, accuracy data can also provide a basis for subsequent process improvements. When processing geometric accuracy data using trend analysis tools, the focus is on long-term stability.

[0235] It should be noted that continuous monitoring may show the diameter gradually changing from 5.2mm to 5.23mm, and the angle increasing from 15 degrees to 15.3 degrees. This trend indicates that the equipment may have a slight offset.

[0236] For example, analysis tools can clearly show the dynamic process of change by plotting time-value curves, helping technicians identify potential problems. If the geometric accuracy change trend data exceeds the preset change trend threshold, such as a diameter deviation exceeding ±0.02mm or an angle deviation exceeding ±0.2 degrees, then a consistency judgment tool is used for further analysis.

[0237] In one embodiment, the tool may determine whether the data is clustered based on statistical distribution; for example, if diameter data are clustered around 5.2 mm, it indicates good consistency. This method can quickly confirm whether quality has improved. After obtaining the consistency assessment results, using data comparison tools to analyze the differences with historical data is a key step in evaluating stability.

[0238] Specifically, if the historical average diameter is 5.18 mm, while the current average is 5.2 mm, the difference is 0.02 mm, indicating that the stability may be affected.

[0239] In one possible implementation, the comparison tool can also highlight differences through visual charts for easier intuitive understanding. When using a classification algorithm to determine the overall quality consistency of the harness based on stability data, the algorithm takes geometric accuracy and stability data as input.

[0240] For example, if the diameter deviation is controlled within ±0.01mm and the angle deviation is within ±0.1 degrees, the classification result may be "high consistency".

[0241] Preferably, this method can support production decisions.

[0242] Understandably, consistent quality helps improve product reliability and reduce rework rates.

[0243] S7. Based on the changing trend of geometric accuracy, extract the fatigue damage prediction model corresponding to the current parameters from historical operating data, and use finite element analysis to calculate the stress distribution of the harness during long-term use to obtain the fatigue damage risk value.

[0244] Optionally, this step also includes:

[0245] Step S71: Obtain the trend of geometric precision changes through historical data, and use data extraction technology to determine the dataset corresponding to the current parameters.

[0246] Step S72: Extract features from the changing trend of geometric precision, use the random forest algorithm to build a prediction model, and obtain a preliminary assessment value of fatigue damage.

[0247] Step S73: Based on the preliminary evaluation values, use finite element analysis to calculate the stress distribution of the wire harness during use and obtain the stress concentration area.

[0248] Step S74: Determine the distribution characteristics of fatigue damage based on the stress concentration area, and determine the damage accumulation mode during long-term use.

[0249] Step S75: Obtain the quantitative result of the risk value by comparing the damage accumulation mode with the current parameters.

[0250] Step S76: If the risk value exceeds the risk value threshold, adjust the parameters of the prediction model, use the support vector machine algorithm to optimize the fatigue damage prediction accuracy, and obtain the final risk assessment value.

[0251] Step S77: Determine the fatigue damage distribution trend of the wiring harness during long-term use based on the final risk assessment value.

[0252] Specifically, when obtaining the trend of changes in geometric precision through historical data, data extraction techniques can be used to filter out datasets related to the current parameters.

[0253] For example, technicians might extract data on the diameter and twist angle of wire harnesses from production records over the past year. Assuming the current parameters are a diameter of 5.2 mm and a twist angle of 15 degrees, the extracted dataset might contain historical data under similar operating conditions, such as diameters ranging from 5.18 mm to 5.23 mm. This method can quickly pinpoint the object of analysis, ensuring the relevance of subsequent predictions.

[0254] In one possible implementation, when extracting features from trends, one can focus on the patterns of data fluctuation.

[0255] For example, historical data might show that the diameter slowly increased from 5.2 mm to 5.23 mm over a period of time, and the angle increased from 15 degrees to 15.3 degrees. When building a predictive model using the random forest algorithm, these features are input into the model to generate preliminary assessments of fatigue damage.

[0256] Preferably, the model may output a numerical value, such as a fatigue damage value of 0.3, indicating the potential damage risk of the wiring harness. This approach improves the comprehensiveness of the assessment through multi-dimensional feature analysis. When performing finite element analysis on the preliminary assessment values, the focus is on calculating the stress distribution of the wiring harness during use.

[0257] In one embodiment, technicians may simulate the performance of the wire harness under tensile or bending conditions and find that stress is concentrated in areas with a smaller diameter, such as a location with a stress of 120 MPa at 5.19 mm, while other areas only experience stress of 80 MPa. This analysis can visually reveal the areas of stress concentration, providing a basis for subsequent judgment.

[0258] When judging the distribution characteristics of fatigue damage based on stress concentration areas, the damage accumulation mode can be determined.

[0259] Specifically, if stress concentration areas consistently appear at the same location in the wire harness, such as a twist point near the end, it may indicate that damage accumulates at that location over time.

[0260] For example, after 1000 consecutive uses, the damage in this area may be 20% higher than in other areas. This pattern helps predict weaknesses that may develop over long-term use. The quantification of risk values ​​is particularly crucial when comparing the cumulative damage pattern with current parameters.

[0261] In one possible implementation, if the current diameter is 5.2 mm and the diameter of the concentrated damage area in historical data is 5.19 mm, the difference is only 0.01 mm, which might yield a risk value of 0.4, close to the warning line. This comparison can effectively measure the potential problems in the current state. If the risk value exceeds a preset risk threshold, such as 0.5, the prediction model parameters are adjusted, and a support vector machine algorithm is used to optimize the prediction accuracy.

[0262] Understandably, support vector machines can optimize the risk value from 0.4 to 0.38 by reclassifying historical data, making it closer to reality. This optimization improves the credibility of the assessment and provides more reliable support for decision-making. When determining the fatigue damage distribution trend based on the final risk assessment value, technicians may find that the damage is mainly concentrated in a specific area and gradually spreads with the extension of usage time.

[0263] For example, initial damage is concentrated at a diameter of 5.19 mm, expanding to around 5.20 mm after 1000 uses. This trend analysis can help plan maintenance strategies in advance and extend the lifespan of the wiring harness.

[0264] S8. If the fatigue damage risk value exceeds the preset damage risk threshold, a regression analysis of the equipment parameters and vibration characteristic data is performed using a machine learning algorithm to determine a further optimized parameter adjustment scheme.

[0265] Optionally, this step also includes:

[0266] Step S81: If the fatigue damage risk value exceeds the preset damage risk threshold, the device parameters and vibration characteristic data are obtained through the sensor to obtain the original dataset.

[0267] Step S82: Extract key feature values ​​from the original dataset and use principal component analysis to determine the main influencing factors.

[0268] Step S83: The relationship between the main influencing factors and fatigue damage is modeled by linear regression to obtain the parameter influence model.

[0269] Step S84: Calculate the adjustment range of each equipment parameter based on the parameter influence model, and determine the preliminary optimization scheme.

[0270] Step S85: If the adjustment range in the preliminary optimization scheme exceeds the equipment limit, the adjustment range is iteratively optimized using the gradient descent method to obtain the final parameter scheme.

[0271] Step S86: Generate corresponding vibration characteristic prediction values ​​for the final parameter scheme, and determine whether the prediction values ​​are lower than the damage risk threshold.

[0272] Step S87: Verify the applicability of the final parameter scheme using historical data to determine the basis for optimization and adjustment.

[0273] Specifically, if the fatigue damage risk value exceeds the preset damage risk threshold, technicians can use sensors to collect equipment parameters and vibration characteristic data in real time.

[0274] For example, the sensor may detect that the temperature of the harness is 75 degrees Celsius, the vibration frequency is 50 Hz, and the amplitude is 0.2 mm during operation. These data constitute the raw dataset.

[0275] In one possible implementation, sensors can be deployed at multiple points during data acquisition, such as the ends and middle sections of a harness, to ensure data comprehensiveness. This approach provides more reliable raw information for subsequent analysis. When extracting key feature values ​​from the raw dataset, principal component analysis can be used to screen for major influencing factors.

[0276] For example, technicians may find from multiple variables such as temperature, frequency, and amplitude that vibration frequency and amplitude account for more than 70% of the impact on fatigue damage.

[0277] Specifically, principal component analysis transforms these variables into several principal components, highlighting the weights of frequency and amplitude. This method effectively reduces data dimensionality and focuses on key factors. When modeling with linear regression, technicians can quantify the relationship between major influencing factors and fatigue damage.

[0278] In one embodiment, it is assumed that historical data shows that for every 10 Hz increase in vibration frequency, the damage value increases by 0.1, while for every 0.1 mm increase in amplitude, the damage value increases by 0.15. A linear regression model could then establish a simple relationship to predict damage levels under different parameters. This modeling approach intuitively reflects the impact of parameter changes.

[0279] When calculating the adjustment range based on the parameter influence model, a preliminary optimization scheme can be determined.

[0280] For example, the model might suggest reducing the vibration frequency from 50 Hz to 45 Hz and the amplitude from 0.2 mm to 0.15 mm to reduce the risk of damage.

[0281] Preferably, technicians will assess the feasibility of these adjustments based on the equipment's operational requirements. This approach can quickly identify areas for improvement. If the adjustment exceeds equipment limitations, such as a minimum frequency reduction to 46Hz, iterative optimization using gradient descent is necessary.

[0282] In one possible implementation, the initial adjustment range could be gradually modified, such as adjusting the frequency from 45Hz back to 46Hz while fine-tuning the amplitude to 0.14mm, ultimately reducing the risk of damage to an acceptable range. This iterative process balances equipment limitations with optimization goals.

[0283] When generating vibration characteristic prediction values ​​for the final parameter scheme, it can be determined whether they are below the damage risk threshold.

[0284] For example, after the adjusted vibration frequency of 46 Hz and amplitude of 0.14 mm are input into the model, the predicted damage value may be 0.35, which is lower than the damage risk threshold of 0.5.

[0285] Understandably, such predictions can intuitively verify the effectiveness of a solution and provide a basis for adjustments. When verifying the applicability of a solution using historical data, technicians may compare operational records under similar parameters in the past.

[0286] For example, historical data may show that when the vibration frequency is 46Hz and the amplitude is 0.14mm, the average lifespan of the wiring harness reaches 1500 hours, which is in line with expectations.

[0287] It should be noted that this verification confirms the reliability of the solution under actual operating conditions, providing support for its implementation. This method strengthens the basis for decision-making through historical experience.

[0288] S9, based on the further optimized parameter adjustment scheme, dynamically adjusts the twisting equipment through the closed-loop control system to obtain the final vibration characteristics and geometric accuracy data, and judges whether the product quality meets the target requirements.

[0289] Optionally, this step also includes:

[0290] Step S91: Collect the operating status data of the twisting device through the closed-loop control system, obtain vibration characteristics and geometric accuracy information, and determine the initial dataset.

[0291] Step S92: Extract vibration characteristic parameters from the initial dataset, including amplitude, frequency and phase, and use Fast Fourier Transform (FFT) to analyze the frequency distribution and obtain the trend of vibration characteristic changes.

[0292] Step S93: Based on the trend of vibration characteristic change, compare it with the preset vibration characteristic change threshold. If it exceeds the vibration characteristic change threshold, adjust the control parameters and generate an adjustment command.

[0293] Step S94: Dynamically adjust the twisting device according to the adjustment command. By adjusting the motor speed and torque, obtain the adjusted vibration characteristics and geometric accuracy data, and determine the updated dataset.

[0294] Step S95: Extract geometric accuracy parameters, including straightness and roundness, from the updated dataset, calculate the deviation value using root mean square error (RMSE), and determine whether the geometric accuracy meets the target requirements.

[0295] In step S96, if the geometric accuracy deviation value exceeds the preset range, new control parameters are generated through the closed-loop control system, and the dynamic adjustment process is repeated.

[0296] Step S97: After multiple adjustments to the updated dataset, the final vibration characteristics and geometric accuracy data are obtained to determine whether the product quality meets the target requirements.

[0297] Specifically, when collecting the operating status data of the twisting equipment through the closed-loop control system, the key indicators of the equipment can be monitored in real time.

[0298] For example, a technician may use an accelerometer and a displacement sensor to obtain vibration characteristics and geometric accuracy information, respectively.

[0299] In one embodiment, the sensor records vibration data of the twisting device during operation, such as an amplitude of 0.3 mm and a frequency of 60 Hz, while geometric accuracy data shows a straightness deviation of 0.02 mm. These data together constitute the initial dataset, laying the foundation for subsequent analysis.

[0300] It should be noted that the real-time nature of the closed-loop system ensures dynamic data updates, facilitating timely anomaly detection. The Fast Fourier Transform (FFT) is a commonly used analytical method when extracting vibration characteristic parameters from the initial dataset.

[0301] Specifically, technicians input the collected vibration signals into an FFT analysis tool to extract a frequency distribution map.

[0302] For example, the analysis might show that the main vibration frequencies are concentrated between 55Hz and 65Hz, with a peak amplitude of 0.35mm and relatively stable phase changes. This method can clearly demonstrate the changing trend of vibration characteristics, providing support for subsequent determination of vibration characteristic change thresholds.

[0303] Understandably, the efficiency of FFT analysis lies in its ability to quickly separate key frequency components and improve data processing efficiency. When comparing the trend of vibration characteristic changes with the preset vibration characteristic change threshold, if the amplitude is found to exceed the upper limit of the vibration characteristic change threshold of 0.3 mm, the control parameters need to be adjusted.

[0304] In one possible implementation, the system automatically generates adjustment commands, such as reducing the motor speed by 10% or reducing the torque by 5 N·m.

[0305] Preferably, this comparison process incorporates historical operating data to ensure the rationality of the adjustments.

[0306] For example, technicians may refer to past records of excessive vibration to confirm the feasibility of the current adjustment direction. After dynamically adjusting the twisting device according to the adjustment instructions, updated data can be obtained.

[0307] For example, when the motor speed is reduced from 1000 rpm to 900 rpm and the torque is adjusted from 20 N·m to 18 N·m, the amplitude decreases to 0.25 mm, and the frequency stabilizes at 58 Hz. Geometric accuracy data is also updated synchronously, with the straightness deviation reduced to 0.015 mm. This dynamic adjustment enables rapid response to abnormal situations and maintains the stability of equipment operation. When extracting geometric accuracy parameters from the updated dataset, the root mean square error is often used to quantify data deviation.

[0308] For example, the target straightness value is 0.01mm, the actual value is 0.015mm, and the straightness deviation is small after RMSE calculation but still exceeds the range.

[0309] Specifically, technicians may compare multiple sets of data and discover that the roundness deviation also slightly exceeds the standard. This analytical method intuitively reflects the geometric accuracy status, facilitating subsequent optimization. If the geometric accuracy deviation exceeds the preset range, the closed-loop control system will iteratively generate new parameters.

[0310] In one embodiment, the system may further fine-tune the rotational speed to 880 rpm while adjusting the tension control parameters, ultimately reducing the straightness deviation to 0.012 mm.

[0311] It should be noted that this iterative adjustment gradually approaches the target requirements, improving the stability of product quality. When assessing product quality using updated datasets after multiple adjustments, technical personnel will comprehensively evaluate the final data.

[0312] For example, the amplitude stabilized at 0.22 mm, the frequency at 57 Hz, the straightness deviation at 0.01 mm, and the roundness met the expected standard. This multi-dimensional verification ensures that the product meets design requirements and provides reliable assurance for production.

[0313] Understandably, the continuous optimization capability of closed-loop control significantly improves the accuracy and consistency of the process.

[0314] like Figure 2As shown, in a second aspect, the present invention provides a vibration suppression and quality optimization system for a copper-aluminum composite wire harness twisting device. The system employs the method described above to suppress vibration and optimize the quality of the copper-aluminum composite wire harness twisting device. The system mainly includes: a data acquisition module, used to acquire physical property data of the copper and aluminum wires, including density and elastic modulus, and calculate the natural vibration frequency range of both during the twisting process through numerical analysis to obtain a frequency distribution reference value; a vibration matching module, used to determine the degree of matching between the current vibration frequency and the natural frequency by collecting real-time vibration characteristic data of the copper and aluminum materials through sensors during the operation of the twisting device, based on the frequency distribution reference value; a resonance analysis module, used to analyze the spectral characteristics of the vibration characteristic data through Fourier transform to determine whether a resonance frequency region exists if the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold; and a parameter optimization module, used to extract the current rotational speed and torque values ​​from the device operating parameters based on the determined resonance frequency region, and calculate the parameter adjustment amount using an adaptive filtering algorithm to obtain an optimized set of operating parameters. The system comprises four modules: a real-time adjustment module and a closed-loop control module. The real-time adjustment module uses optimized operating parameters to control the speed and torque of the twisting device, acquiring vibration characteristic data and determining whether vibration deviation amplification is suppressed. The geometric detection module, after acquiring the adjusted vibration characteristic data, analyzes the diameter and twist angle of the wire harness using a geometric detection algorithm, calculating the trend of geometric accuracy changes and determining whether quality consistency has improved. The fatigue prediction module, based on the trend of geometric accuracy changes, extracts a fatigue damage prediction model corresponding to the current parameters from historical operating data, uses finite element analysis to calculate the stress distribution of the wire harness during long-term use, and obtains a fatigue damage risk value. The regression optimization module, if the fatigue damage risk value exceeds a preset damage risk threshold, uses machine learning algorithms to perform regression analysis on the equipment parameters and vibration characteristic data to determine further optimized parameter adjustment schemes. The closed-loop control module, based on the further optimized parameter adjustment scheme, dynamically adjusts the twisting device through a closed-loop control system, acquiring final vibration characteristic and geometric accuracy data to determine whether product quality meets target requirements.

[0315] The method and system for vibration suppression and quality optimization of copper-aluminum composite wire harness twisting equipment provided by this invention first acquires the physical characteristic data of copper and aluminum wires and calculates their natural vibration frequency range. During the twisting process, real-time vibration data is collected by sensors to determine whether there is a resonance frequency region. If so, the rotational speed and torque values ​​are extracted from the operating parameters, and an adaptive filtering algorithm is used to calculate the parameter adjustment amount to obtain optimized operating parameters. Based on the optimized parameters, the rotational speed and torque of the equipment are adjusted in real time to suppress the amplification of vibration deviation. At the same time, the wire harness diameter and twisting angle are analyzed by geometric detection to evaluate the quality consistency.

[0316] Furthermore, this invention utilizes fatigue damage prediction models and finite element analysis to calculate the stress distribution of the wire harness during long-term use, ensuring that product quality meets target requirements. Moreover, this invention can effectively suppress vibration during the twisting process, improving product quality consistency and service life. The preferred embodiments of this invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Obviously, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. This invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for vibration suppression and quality optimization of copper-aluminum composite wire harness twisting equipment, characterized in that, The method includes: S1. Acquire physical property data of copper and aluminum wires, including density and elastic modulus. Calculate the natural vibration frequency range of both during the twisting process using numerical analysis to obtain a frequency distribution baseline value. S2. Based on the frequency distribution baseline value, collect real-time vibration characteristic data of the copper and aluminum materials using sensors during the twisting equipment operation to determine the degree of matching between the current vibration frequency and the natural frequency. S3. If the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold, analyze the spectral characteristics of the vibration characteristic data using Fourier transform to determine if a resonance frequency region exists. S4. Based on the identified resonance frequency region, extract the current rotational speed and torque values ​​from the equipment operating parameters. Use an adaptive filtering algorithm to calculate the parameter adjustment amount to obtain an optimized set of operating parameters. S5. Based on the optimized set of operating parameters, adjust the rotational speed and torque of the twisting equipment in real-time using the control system. Obtain the adjusted vibration characteristic data to determine if vibration deviation amplification has been suppressed. S6. After obtaining the adjusted vibration characteristic data, analyze the diameter and twist angle of the wire bundle using a geometric detection algorithm to calculate the trend of geometric accuracy changes and determine if quality consistency has improved. S7. Based on the changing trend of geometric accuracy, extract the fatigue damage prediction model corresponding to the current parameters from historical operating data, and use finite element analysis to calculate the stress distribution of the wire harness during long-term use to obtain the fatigue damage risk value; S8. If the fatigue damage risk value exceeds the preset damage risk threshold, then use machine learning algorithms to perform regression analysis on the equipment parameters and vibration characteristic data to determine a further optimized parameter adjustment scheme; S9. Based on the further optimized parameter adjustment scheme, use a closed-loop control system to dynamically adjust the twisting equipment, obtain the final vibration characteristics and geometric accuracy data, and determine whether the product quality meets the target requirements.

2. The method according to claim 1, characterized in that, Step S1 involves acquiring physical property data of the copper and aluminum wires, including density and elastic modulus, and calculating the natural vibration frequency range of both during the twisting process through numerical analysis to obtain a frequency distribution reference value, including: Step S11: Obtain the density and elastic modulus data of copper wire and aluminum wire from the material database, import the data into MATLAB for numerical analysis and processing, and obtain the original values ​​of physical properties. Step S12: Use ANSYS software to simulate the twisting process based on the finite element analysis algorithm and calculate the inherent vibration parameters of the copper wire and aluminum wire. Step S13: Based on the inherent vibration parameters, use the FFT tool in MATLAB to perform a fast Fourier transform on the vibration signal during the twisting process to obtain the frequency distribution characteristic values. Step S14: If the frequency distribution characteristic value exceeds the preset distribution characteristic threshold, adjust the rotational speed and tension parameters during the twisting process, recalculate the natural vibration parameters using ANSYS, and perform FFT analysis again to determine the stability of the frequency distribution characteristic value. Step S15: Use statistical analysis tools in MATLAB to perform stability analysis on the frequency distribution characteristic values ​​and determine the reference values ​​for the frequency distribution of copper wire and aluminum wire. Step S16: Based on the frequency distribution benchmark value, the difference in vibration characteristics between copper wire and aluminum wire is quantified using the statistical toolbox in MATLAB to obtain the final frequency distribution comparison value.

3. The method according to claim 1, characterized in that, Step S2, based on the frequency distribution reference value, involves collecting real-time vibration characteristic data of the copper and aluminum materials using sensors during the operation of the twisting device to determine the degree of matching between the current vibration frequency and the natural frequency, including: Step S21: Collect real-time vibration characteristic data of copper and aluminum materials during the operation of the twisting device using sensors to obtain the original vibration signal; Step S22: Calculate the frequency distribution using Fast Fourier Transform based on the original vibration signal, and extract the frequency with the largest amplitude from the frequency distribution as the current vibration frequency value. Step S23: Obtain the natural frequency range of the corresponding material from the pre-established natural frequency database of copper and aluminum materials; Step S24: Compare the current vibration frequency value with the natural frequency range. If the current vibration frequency value is within the natural frequency range, the matching degree is determined to be high, and the matching state is obtained. Step S25: Perform mean filtering on the vibration signal according to the matching state to obtain smoothed characteristic data; Step S26: Calculate the standard deviation of the frequency distribution using the smoothed characteristic data. If the standard deviation of the frequency distribution is less than the standard deviation threshold of the frequency distribution, the frequency distribution is determined to be stable, and the final matching result is determined. Step S27: Generate vibration characteristic analysis data including vibration frequency, matching status and stability index based on the final matching result, and output complete evaluation content.

4. The method according to claim 3, characterized in that, The standard deviation threshold for the frequency distribution is set to 5.

5. The method according to claim 1, characterized in that, In step S3, if the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold, the spectral characteristics of the vibration characteristic data are analyzed by Fourier transform to determine whether a resonance frequency region exists, including: Step S31: If the difference between the vibration frequency and the natural frequency is less than the frequency difference threshold, then obtain the vibration characteristic data. Step S32: Perform Fourier transform on the vibration characteristic data to obtain the spectral characteristics; Step S33: Extract the transformation result from the spectral features and determine whether there are resonant frequency features in the transformation result; Step S34: If the transformation result contains a resonant frequency feature, then the specific value of the resonant frequency is determined through data analysis. Step S35: Based on the specific value of the resonant frequency, obtain the frequency difference between the resonant frequency and the natural frequency, and determine whether to adjust the vibration characteristics by comparing the frequency difference with the frequency difference threshold. Step S36: Using the adjusted vibration characteristic data, perform Fourier transform to obtain new spectral characteristics; Step S37: Extract the transformation result from the new spectral features and determine whether the resonant frequency region has disappeared; Step S38: If the resonant frequency region still exists, adjust the natural frequency through data analysis to obtain a new region judgment result.

6. The method according to claim 5, characterized in that, Step S33 involves extracting the transformation result from the spectral features and determining whether the transformation result contains resonant frequency features, including: Step S331: Obtain the transformation result from the spectral features and use the transformation result as input data; Step S332: Perform a Fast Fourier Transform on the transformation result to generate frequency distribution data; Step S333: Extract the characteristic value of the resonant frequency from the frequency distribution data and calculate the amplitude of the characteristic value; Step S334: Determine whether the amplitude of the characteristic value exceeds the characteristic value amplitude threshold; Step S335: If the amplitude of the characteristic value exceeds the characteristic value amplitude threshold, perform smoothing filtering on the vibration characteristic data to generate adjusted vibration characteristic data; Step S336: Perform a Fast Fourier Transform on the adjusted vibration characteristic data to obtain new spectral features; Step S337: Extract the trend of resonant frequency change from the new spectral features and calculate the rate of change of resonant frequency; Step S338: Determine whether the frequency region is stable based on the rate of change of resonant frequency; Step S339: If the frequency region is stable, extract the characteristic value of the resonant frequency from the stable region as the final output result.

7. The method according to claim 1, characterized in that, Step S7 involves extracting a fatigue damage prediction model corresponding to the current parameters from historical operating data based on the changing trend of geometric accuracy. Finite element analysis is then used to calculate the stress distribution of the harness during long-term use to obtain the fatigue damage risk value, including: Step S71: Obtain the trend of geometric precision change through historical data, and use data extraction technology to determine the dataset corresponding to the current parameters; Step S72: Extract features from the changing trend of geometric precision, use the random forest algorithm to build a prediction model, and obtain a preliminary assessment value of fatigue damage; Step S73: Based on the preliminary evaluation values, use finite element analysis to calculate the stress distribution of the wire harness during use and obtain the stress concentration area; Step S74: Determine the distribution characteristics of fatigue damage based on the stress concentration area, and determine the damage accumulation mode during long-term use. Step S75: Obtain the quantitative result of the risk value by comparing the damage accumulation mode with the current parameters; Step S76: If the risk value exceeds the risk value threshold, adjust the parameters of the prediction model, use the support vector machine algorithm to optimize the fatigue damage prediction accuracy, and obtain the final risk assessment value. Step S77: Determine the fatigue damage distribution trend of the wiring harness during long-term use based on the final risk assessment value.

8. The method according to claim 1, characterized in that, In step S8, if the fatigue damage risk value exceeds a preset damage risk threshold, a regression analysis of the equipment parameters and vibration characteristic data is performed using a machine learning algorithm to determine a further optimized parameter adjustment scheme, including: Step S81: If the fatigue damage risk value exceeds the damage risk threshold, the equipment parameters and vibration characteristic data are obtained through the sensors to obtain the original dataset. Step S82: Extract key feature values ​​from the original dataset and use principal component analysis to determine the main influencing factors; Step S83: The relationship between the main influencing factors and fatigue damage is modeled by linear regression to obtain the parameter influence model; Step S84: Calculate the adjustment range of each equipment parameter based on the parameter influence model, and determine the preliminary optimization scheme; Step S85: If the adjustment range in the preliminary optimization scheme exceeds the equipment limit, the adjustment range is iteratively optimized using the gradient descent method to obtain the final parameter scheme. Step S86: Generate corresponding vibration characteristic prediction values ​​for the final parameter scheme, and determine whether the prediction values ​​are lower than the damage risk threshold. Step S87: Verify the applicability of the final parameter scheme using historical data to determine the basis for optimization and adjustment.

9. The method according to claim 1, characterized in that, Step S9, based on a further optimized parameter adjustment scheme, dynamically adjusts the twisting device through a closed-loop control system to obtain the final vibration characteristics and geometric accuracy data, and determines whether the product quality meets the target requirements, including: Step S91: Collect the operating status data of the twisting device through the closed-loop control system, obtain vibration characteristics and geometric accuracy information, and determine the initial dataset; Step S92: Extract vibration characteristic parameters from the initial dataset, including amplitude, frequency and phase, and use Fast Fourier Transform (FFT) to analyze the frequency distribution and obtain the trend of vibration characteristic changes. Step S93: Based on the trend of vibration characteristic change, compare it with the preset vibration characteristic change threshold. If it exceeds the vibration characteristic change threshold, adjust the control parameters and generate an adjustment command. Step S94: Dynamically adjust the twisting device according to the adjustment command. By adjusting the motor speed and torque, obtain the adjusted vibration characteristics and geometric accuracy data, and determine the updated dataset. Step S95: Extract geometric accuracy parameters, including straightness and roundness, from the updated dataset, calculate the deviation value using root mean square error (RMSE), and determine whether the accuracy meets the target requirements. Step S96: If the geometric accuracy deviation value exceeds the preset range, new control parameters are generated through the closed-loop control system, and the dynamic adjustment process is repeated. Step S97: After multiple adjustments to the updated dataset, the final vibration characteristics and geometric accuracy data are obtained to determine whether the product quality meets the target requirements.

10. A vibration suppression and quality optimization system for copper-aluminum composite wire harness twisting equipment, characterized in that, The method described in any one of claims 1-9 is used to suppress vibration and optimize the quality of a copper-aluminum composite wire harness twisting device. The system comprises: a data acquisition module for acquiring physical property data of the copper and aluminum wires, including density and elastic modulus, and calculating the natural vibration frequency range of both during the twisting process through numerical analysis to obtain a frequency distribution benchmark value; a vibration matching module for determining the degree of matching between the current vibration frequency and the natural frequency by collecting real-time vibration characteristic data of the copper and aluminum materials through sensors during the operation of the twisting device, based on the frequency distribution benchmark value; a resonance analysis module for determining whether a resonance frequency region exists by analyzing the spectral characteristics of the vibration characteristic data through Fourier transform if the difference between the current vibration frequency and the natural frequency is less than a preset frequency difference threshold; and a parameter optimization module for extracting the current rotational speed and torque values ​​from the device operating parameters based on the determined resonance frequency region, calculating the parameter adjustment amount using an adaptive filtering algorithm, and obtaining an optimized set of operating parameters. The system comprises four modules: a real-time adjustment module and a closed-loop control module. The real-time adjustment module uses optimized operating parameters to control the speed and torque of the twisting device, acquiring vibration characteristic data and determining whether vibration deviation amplification is suppressed. The geometric detection module, after acquiring the adjusted vibration characteristic data, analyzes the diameter and twist angle of the wire harness using a geometric detection algorithm, calculating the trend of geometric accuracy changes and determining whether quality consistency has improved. The fatigue prediction module extracts a fatigue damage prediction model corresponding to the current parameters from historical operating data based on the trend of geometric accuracy changes, and uses finite element analysis to calculate the stress distribution of the wire harness during long-term use, obtaining a fatigue damage risk value. The regression optimization module performs regression analysis on the equipment parameters and vibration characteristic data using a machine learning algorithm if the fatigue damage risk value exceeds a preset damage risk threshold, determining a further optimized parameter adjustment scheme. The closed-loop control module dynamically adjusts the twisting device through a closed-loop control system based on the further optimized parameter adjustment scheme, acquiring the final vibration characteristics and geometric accuracy data to determine whether product quality meets target requirements.

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