A processing method for anti-interference electronic wiring harness

Abstract

The present application relates to the technical field of electronic harnesses, and specifically provides a method for processing anti-interference electronic harnesses, comprising: generating an impedance distribution curve through time domain waveform analysis, wherein the accuracy of the impedance distribution curve reaches the ohm level, identifying the reflection peak of the discontinuous point and the waveform baseline noise level, and determining the specific location of the impedance mutation; adjusting the connector size and via spacing in the bus structure according to the reflection peak of the discontinuous point in the impedance distribution curve, optimizing the path length for the signal propagation delay time, and reducing the attenuation rate of the reflected signal; constructing a virtual model of the bus structure using three-dimensional modeling software, importing the adjusted connector size and via spacing, setting the geometric parameter grid density to thousands of points per millimeter, and generating a digital twin; and using a frequency domain analysis method to calculate the input signal amplitude range and vibration mode frequency range of the optimized bus structure, and verifying whether the electromagnetic field coupling strength meets the matching requirements.

Description

Technical Field

[0001] The present application relates to the technical field of electronic wiring harnesses, and in particular to a method for processing anti-interference electronic wiring harnesses. Background Art

[0002] The high level of integration and intelligence in automotive electronic systems places higher demands on the performance of electronic wiring harnesses. As a key component connecting various electronic control units, the signal transmission quality of the wiring harness directly impacts the reliability and safety of the entire vehicle's electronic systems. However, with the surge in the number of onboard electronic devices, the dense arrangement of high-frequency signal lines in the wiring harness has led to an increasing problem of electromagnetic interference, a serious threat to signal integrity.

[0003] Conventional methods such as shielding and insulation are currently widely used to suppress interference, but these methods are limited in high-frequency, dense signal environments. This is especially true at the confluence of wiring harnesses, where complex structures and the convergence of multiple signal lines make interference more likely, making conventional methods difficult to effectively address.

[0004] The core challenges facing interference-resistant electronic wiring harnesses lie in the design and processing of the busbar. First, at high temperatures, an insulating oxide layer easily forms on the conductor contact surface at the busbar, leading to unstable contact resistance. Second, uneven thermal curing of the insulation material within the busbar connector can cause variations in insulation performance and signal leakage. Furthermore, poor continuity of the shielding mesh at the busbar can generate electromagnetic interference. These factors combine to create impedance discontinuities at the busbar, causing signal reflections and standing waves, compromising signal integrity.

[0005] Therefore, how to comprehensively consider factors such as conductor contact, insulation materials, and shielding networks at the junction of electronic wiring harnesses, effectively suppress electromagnetic interference by optimizing structural design and processing technology, and ensure the transmission quality of high-frequency intensive signals has become a key issue in improving the reliability and safety of automotive electronic systems. Summary of the Invention

[0006] The present invention provides a method for processing an anti-interference electronic wire harness, which mainly includes the following steps:

[0007] A time-domain reflectometer is used to input a step signal into the confluence structure. The rise time of the step signal is controlled at the picosecond level, and the returned time-domain waveform is recorded. The sampling rate of the time-domain waveform is set to 1,000 times per nanosecond to obtain the reflected signal attenuation rate and signal propagation delay time.

[0008] Generate an impedance distribution curve through time domain waveform analysis. The impedance distribution curve has an ohm-level accuracy, identifies the reflection peak of the discontinuity point and the waveform baseline noise level, and determines the specific location of the impedance mutation.

[0009] According to the reflection peak of the discontinuous point in the impedance distribution curve, the connector size and via spacing in the bus structure are adjusted to optimize the path length based on the signal propagation delay time and reduce the attenuation rate of the reflected signal;

[0010] A virtual model of the busbar structure was constructed using 3D modeling software. The adjusted connector dimensions and via spacing were imported, and the geometric parameter grid density was set to 1,000 points per millimeter to generate a digital twin.

[0011] A sensor data interface is embedded in the digital twin, with a sensor data refresh rate of 100 times per second. This allows for real-time acquisition of the temperature field distribution resolution and vibration modal frequency range of the confluence structure, enabling virtual-real mapping.

[0012] A thermoelectric coupling simulation was conducted to determine the temperature field distribution resolution and electromagnetic field coupling strength in the digital twin. The simulation time step was set to the microsecond level to obtain the stress concentration area and electromagnetic field distribution.

[0013] Based on the range of stress concentration areas and vibration modal frequency ranges, the via spacing and connector size in the digital twin are adjusted, the geometric parameter mesh density is optimized for the thermoelectric coupling boundary conditions, and the dynamic stability of the confluence structure is determined;

[0014] The frequency domain analysis method is used to calculate the input signal amplitude range and vibration modal frequency range of the optimized confluence structure to verify whether the electromagnetic field coupling strength meets the matching requirements;

[0015] The step signal is input again through the time domain reflectometer. The rise time of the step signal remains the same. The optimized time domain waveform is recorded. The baseline noise level of the new waveform and the reflection peak value of the discontinuity point are obtained to determine the degree of improvement in the accuracy of the impedance distribution curve.

[0016] Optionally, the method of using a time domain reflectometer to input a step signal to the confluence structure, wherein the rise time of the step signal is controlled at the picosecond level, and the returned time domain waveform is recorded, wherein the sampling rate of the time domain waveform is set to 1,000 times per nanosecond, and the attenuation rate of the reflected signal and the signal propagation delay time are obtained, includes:

[0017] Generate a step signal using a time domain reflectometer, control the rise time to the picosecond level, inject it into the confluence structure, and record the return waveform;

[0018] Use high-precision sampling equipment to set the sampling rate to thousands of times per nanosecond to obtain return waveform data;

[0019] Extract the reflected signal from the returned waveform and calculate the attenuation rate value;

[0020] Analyze the signal propagation path for the reflected signal and determine the delay time;

[0021] If the attenuation rate exceeds the preset threshold, the frequency components of the reflected signal are separated by Fourier transform to determine the propagation abnormality point;

[0022] Obtain the propagation characteristics of each section in the confluence structure based on the delay time and signal propagation path;

[0023] By comparing the attenuation rate value with the delay time, the preset model is used to determine the defect location of the confluence structure.

[0024] Optionally, the generating of an impedance distribution curve by time domain waveform analysis, wherein the impedance distribution curve has an ohm-level accuracy, identifying the reflection peak of the discontinuity point and the waveform baseline noise level, and determining the specific location of the impedance mutation includes:

[0025] By collecting time domain waveform data and processing it with fast Fourier transform, frequency domain characteristic parameters are obtained;

[0026] Extract impedance distribution information from frequency domain characteristic parameters and generate an initial distribution curve;

[0027] For the initial distribution curve, Gaussian filtering is applied to smooth the curve to obtain a smooth distribution curve;

[0028] If there is a significant mutation in the smooth distribution curve, the reflection peak position is identified by differential calculation to determine the coordinates of the discontinuity point;

[0029] By comparing the coordinates of the discontinuous points with the smooth distribution curve, the baseline noise level is obtained and the noise impact range is determined;

[0030] The support vector machine algorithm is used to classify the reflection peak and noise level to determine the specific location of the impedance mutation;

[0031] By matching the impedance mutation position with the time domain waveform, the timestamp of the mutation point is obtained.

[0032] Optionally, adjusting the connector size and via spacing in the bus structure according to the reflection peak value of the discontinuous point in the impedance distribution curve, optimizing the path length according to the signal propagation delay time, and reducing the reflected signal attenuation rate includes:

[0033] Obtain the discontinuity point location through impedance distribution curve analysis and determine the reflection peak data;

[0034] Extract reflection signal features from reflection peak data to determine delay time changes during signal propagation;

[0035] Obtain bus structure parameters, adjust connector size and via spacing, and optimize path length values;

[0036] Calculate the delay time variation trend through the path length value and determine the adjusted reflected signal strength;

[0037] The attenuation rate calculation formula is used to evaluate the reflected signal strength and the result after the attenuation rate is reduced is obtained;

[0038] If the decay rate exceeds the preset threshold, the path length is adjusted through an iterative optimization algorithm to obtain the final optimization parameters;

[0039] The confluence structure data is updated according to the final optimized parameters to determine the matching result between the signal propagation delay time and the attenuation rate.

[0040] Optionally, the method of constructing a virtual model of the bus structure using 3D modeling software, importing the adjusted connector size and via spacing, setting the geometric parameter grid density to 1,000 points per millimeter, and generating a digital twin includes:

[0041] Obtain the confluence structure data through 3D modeling software and construct an initial virtual model;

[0042] Extract connector dimensions and via spacing from the initial virtual model and import adjusted parameter values;

[0043] Set the geometric parameters according to the adjusted parameter values ​​and determine the grid density as 1,000 points per millimeter;

[0044] Finite element analysis algorithm is used to process mesh density and generate optimized virtual models;

[0045] Obtain optimized virtual model features to determine whether they meet the digital twin accuracy requirements. If not, adjust the density settings and regenerate;

[0046] The first formula is as follows:

[0047]

[0048] Among them, α represents the digital twin mapping strength, V i represents the volume of the virtual model, D i represents the model density, R i represents the mapping radius, β represents the correction coefficient, and M represents the material parameter;

[0049] The first formula is used to calculate the basic mapping relationship from the virtual model to the digital twin;

[0050] Generate a digital twin through the optimized virtual model to obtain a complete confluence structure mapping;

[0051] Verify the consistency of the confluence structure and geometric parameters based on the digital twin and output the final digital map.

[0052] Optionally, a sensor data interface is implanted in the digital twin, and the sensor data refresh rate reaches 100 times per second, so as to obtain the temperature field distribution resolution and vibration modal frequency range of the confluence structure in real time and perform virtual-real mapping, including:

[0053] The original data stream of the confluence structure is obtained through the sensor data interface, and the initial data set of temperature field distribution and vibration mode is obtained using a preset sampling method;

[0054] For the initial data set, the fast Fourier transform algorithm is used to extract the frequency range of the vibration mode and determine the frequency eigenvalue;

[0055] According to the temperature field distribution data, the resolution accuracy on the spatial grid is calculated to obtain a high-resolution temperature field distribution map;

[0056] If the frequency characteristic value exceeds the preset threshold, the vibration modal data is adjusted by interpolation method to obtain a smooth frequency range sequence;

[0057] After obtaining the smoothed frequency range sequence, combined with the high-resolution temperature field distribution map, the virtual-real mapping result of the confluence structure is achieved through the digital twin mapping algorithm;

[0058] Extract the dynamic response characteristics of the confluence structure from the virtual-real mapping results, determine the correlation between the temperature field distribution and the vibration mode, and obtain real-time status data;

[0059] Real-time status data is used to update the parameters of the digital twin to obtain an optimized virtual-reality mapping model.

[0060] Optionally, the temperature field distribution resolution and electromagnetic field coupling strength in the digital twin are simulated by performing a thermoelectric coupling simulation, with the simulation time step set to microseconds, to obtain the stress concentration area range and electromagnetic field distribution, including:

[0061] The temperature field distribution and electromagnetic field intensity were calculated through thermoelectric coupling simulation, with a time step of microseconds to obtain preliminary stress concentration data;

[0062] Based on the preliminary stress concentration data, determine the area corresponding to the stress concentration and obtain the electromagnetic distribution characteristics in the area;

[0063] Based on the electromagnetic distribution characteristics within the region, determine whether the coupling strength value exceeds the preset threshold. If so, adjust the simulation step size and recalculate the temperature field distribution;

[0064] Obtain the adjusted temperature field distribution and determine whether the resolution level meets the digital twin requirements. If not, improve the resolution level through interpolation algorithms to obtain the optimized temperature field distribution.

[0065] By optimizing the temperature field distribution, the stress concentration change trend under thermoelectric coupling is calculated to obtain the area range after the change;

[0066] According to the changed area, the finite element analysis algorithm is used to obtain the spatial correspondence between electromagnetic distribution and stress concentration;

[0067] Through the spatial correspondence, the coupling stability of the electromagnetic field intensity and temperature field distribution in the digital twin is judged to obtain the final simulation results.

[0068] Optionally, adjusting the via spacing and connector size in the digital twin according to the stress concentration area range and the vibration modal frequency range, optimizing the geometric parameter mesh density for the thermoelectric coupling boundary condition, and determining the dynamic stability of the confluence structure include:

[0069] The stress concentration area range and vibration modal frequency range are obtained through finite element analysis to obtain initial distribution data;

[0070] Adjust the via spacing and connector size in the digital twin based on the initial distribution data and determine the adjusted parameter set;

[0071] The thermoelectric coupling simulation is used to process the adjusted parameter set for the boundary conditions to obtain the thermoelectric coupling distribution;

[0072] Optimize the mesh density of geometric parameters through meshing technology and determine the optimized mesh model;

[0073] If the stress of the confluence structure in the optimized mesh model exceeds the preset threshold, the mesh density is adjusted to obtain a stable mesh model;

[0074] Through dynamic simulation analysis of the stable grid model, the dynamic stability distribution of the confluence structure is determined;

[0075] Use machine learning regression algorithm to process dynamic stability distribution and determine the final optimization parameters;

[0076] The second formula is as follows:

[0077]

[0078] Among them, E(y) represents the predicted value of system stability, β0 represents the intercept term, β i represents the linear coefficient, β ij represents the coefficient of the quadratic term, x i and x j represents the input variable;

[0079] The second formula is used to predict the system stability.

[0080] Optionally, the frequency domain analysis method is used to calculate the input signal amplitude range and vibration modal frequency range of the optimized confluence structure to verify whether the electromagnetic field coupling strength meets the matching requirements, including:

[0081] The input signal data is acquired through frequency domain analysis method, the amplitude range is calculated, and the preliminary signal characteristics are obtained;

[0082] The fast Fourier transform algorithm is used to process vibration modal data, determine the frequency range, and obtain modal distribution characteristics;

[0083] Obtain the optimized parameters of the confluence structure, calculate the electromagnetic field intensity, and obtain the field intensity distribution data;

[0084] Compare the field intensity distribution data with the coupling strength standard to determine whether the coupling strength meets the matching requirements and obtain the matching judgment result;

[0085] If the matching judgment result exceeds the preset threshold, the amplitude range is recalculated by adjusting the input signal parameters to obtain the updated signal characteristics;

[0086] Based on the updated signal characteristics and frequency range data, the support vector machine algorithm is used to analyze the trend of coupling strength changes and determine the performance of the optimized confluence structure;

[0087] By making a final comparison between the performance data and the matching requirements, it is determined whether the electromagnetic field coupling strength meets the business objectives and the final judgment result is obtained.

[0088] Optionally, the step signal is input again through the time domain reflectometer, the rise time of the step signal remains consistent, the optimized time domain waveform is recorded, the baseline noise level of the new waveform and the reflection peak value of the discontinuity point are obtained, and the degree of improvement in the accuracy of the impedance distribution curve is determined, including:

[0089] Input a step signal through a time domain reflectometer to keep the rise time consistent and obtain optimized time domain waveform data;

[0090] Extracting the baseline noise level from the acquired time domain waveform data, and processing the noise using a filtering algorithm to obtain smoothed waveform data;

[0091] For the smoothed waveform data, detect the position of the discontinuity point and obtain the corresponding reflection peak height;

[0092] According to the reflection peak height, the impedance distribution curve is calculated and the continuity of the curve is optimized using the interpolation algorithm;

[0093] If the discreteness of the impedance distribution curve exceeds the preset threshold, the filter parameters are adjusted and the waveform data is reprocessed;

[0094] By comparing the impedance distribution curves before and after adjustment, the degree of accuracy improvement can be determined and the final distribution results can be obtained.

[0095] Extract key features from the final distribution results and generate optimized time domain reflectometry data.

[0096] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0097] The present invention discloses a processing method for anti-interference electronic wire harnesses, and specifically relates to a method for optimizing a confluence structure based on time domain reflection and digital twins. The method first uses a time domain reflectometer to input a step signal to the confluence structure, generates a high-precision impedance distribution curve by analyzing the returned time domain waveform, identifies discontinuities and adjusts structural parameters. Subsequently, a digital twin of the confluence structure is constructed, a real-time sensor data interface is implanted, and thermoelectric coupling simulation analysis is performed. Based on the simulation results, the geometric parameters of the digital twin are optimized to improve the dynamic stability of the structure. Finally, the optimization effect is verified by frequency domain analysis, and the time domain reflectometer is used again to verify the improvement in the accuracy of the impedance distribution curve. The present invention achieves high-precision optimization of the confluence structure by combining time domain reflection technology and digital twin technology, effectively improving the electromagnetic performance and dynamic stability of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 The present invention is a flowchart of a method for processing an anti-interference electronic wire harness.

[0099] Figure 2 It is a schematic diagram of a method for processing an anti-interference electronic wire harness according to the present invention.

[0100] Figure 3 This is another schematic diagram of a method for processing an anti-interference electronic wire harness according to the present invention. DETAILED DESCRIPTION

[0101] To further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and examples. The present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0102] like Figure 1-3 In this embodiment, a method for processing an anti-interference electronic wire harness may specifically include:

[0103] In step S101, a time domain reflectometer is used to input a step signal to the confluence structure. The rise time of the step signal is controlled at the picosecond level, and the returned time domain waveform is recorded. The time domain waveform sampling rate is set to 1,000 times per nanosecond to obtain the reflected signal attenuation rate and signal propagation delay time.

[0104] A step signal is generated using a time-domain reflectometer, with the rise time controlled to the picosecond level. This signal is then injected into the confluence structure and the return waveform recorded. High-precision sampling equipment is used to set the sampling rate to 1,000 times per nanosecond to acquire the return waveform data. The reflected signal is extracted from the return waveform, and the attenuation value is calculated. The signal propagation path of the reflected signal is analyzed to determine the delay time. If the attenuation value exceeds a preset threshold, the frequency component of the reflected signal is separated using a Fourier transform to identify the propagation anomaly. Based on the delay time and signal propagation path, the propagation characteristics of each section in the confluence structure are determined. By comparing the attenuation value and delay time with a preset model, the location of the confluence structure defect is determined.

[0105] Specifically, when a step signal is input to the bus structure using a time-domain reflectometer, the rise time of the step signal is controlled to within 10 picoseconds to ensure that the high-frequency components of the signal accurately reflect the characteristics of the bus structure. Through precise circuit design and signal generation algorithms, the rising edge of the step signal is strictly controlled within 10 picoseconds and output using a high-speed signal generator.

[0106]

[0107] Among them, t r Indicates the signal rise time, V dd Indicates the power supply voltage, f c represents the cutoff frequency, R L Represents the load resistance, C L represents the load capacitance. This formula is used to calculate the rise time of a step signal. The time domain waveform of the reflected signal is recorded using a high-speed sampling system with a sampling rate of 1000 times per nanosecond, or a sampling interval of 1 picosecond, to ensure high-resolution waveform capture. The sampling system uses a high-precision analog-to-digital converter, combined with a digital signal processing algorithm, to acquire and store the returned waveform in real time. The attenuation rate of the reflected signal is determined by calculating the peak change of the waveform. The specific algorithm compares the maximum amplitude of the reflected waveform with the initial amplitude of the input signal to obtain the attenuation rate.

[0108] For example, if the initial amplitude of the input signal is 1 volt and the maximum amplitude of the reflected waveform is 8 volts, the attenuation rate is 20%. The signal propagation delay is determined by calculating the time difference between the input signal and the reflected signal, using a cross-correlation algorithm to accurately measure the time delay. Assuming that the input signal is emitted at time 0 and the reflected signal is recorded at time 2 nanoseconds, the propagation delay is 2 nanoseconds. Through multiple measurements and statistical analysis, the attenuation rate and delay time measurement results are ensured to have high reliability and low error. Ultimately, this data is used to analyze the impedance matching and signal transmission characteristics of the confluence structure, providing a basis for subsequent optimization design.

[0109] Step S102 , generating an impedance distribution curve through time domain waveform analysis, wherein the impedance distribution curve has an ohm-level accuracy, identifying the reflection peak of the discontinuity point and the waveform baseline noise level, and determining the specific location of the impedance mutation.

[0110] By collecting time-domain waveform data and processing it using fast Fourier transform, frequency-domain characteristic parameters are obtained. Impedance distribution information is extracted from the frequency-domain characteristic parameters to generate an initial distribution curve. Gaussian filtering is applied to the initial distribution curve to smooth it out, resulting in a smoothed distribution curve. If there is a significant mutation in the smoothed distribution curve, the reflection peak position is identified through differential calculation, and the coordinates of the discontinuity point are determined. By comparing the coordinates of the discontinuity point with the smoothed distribution curve, the baseline noise level is obtained and the noise impact range is determined. A support vector machine algorithm is used to classify the reflection peak and noise level to determine the specific location of the impedance mutation. By matching the impedance mutation location with the time-domain waveform, the mutation point timestamp is obtained.

[0111] Specifically, in time domain waveform analysis, the signal waveform is first acquired at a rate of 100 million sampling points per second through high-speed sampling equipment to ensure accurate capture of waveform details.

[0112]

[0113] Here, X(k) represents the frequency domain signal, x(n) represents the time domain signal, N represents the number of sampling points, k represents the frequency point number, and n represents the time point number. This is the basic FFT transformation formula. The collected waveform data is processed using a fast Fourier transform (FFT) algorithm to convert the time domain signal into a frequency domain signal for further analysis. Next, the impedance distribution curve is calculated using the frequency domain data, and a least squares fitting algorithm is used to ensure ohm-level accuracy of the impedance curve. After the impedance distribution curve is generated, a peak detection algorithm is used to identify the reflection peak at the discontinuity point. The threshold is set at three times the baseline noise level. That is, when the reflection peak exceeds three times the baseline noise level, it is identified as an impedance discontinuity point. The baseline noise level is determined by calculating the standard deviation of the waveform data, with a standard deviation of 0.2 ohms. Finally, an interpolation algorithm is used to determine the specific location of the impedance discontinuity, with an interpolation accuracy of 0.1 meter, ensuring precise identification of the discontinuity location. The entire process is completed through an automated data processing system, requiring no human intervention, ensuring accurate and consistent analysis results.

[0114] Step S103 , adjusting the connector size and via spacing in the bus structure according to the reflection peak value of the discontinuous point in the impedance distribution curve, optimizing the path length according to the signal propagation delay time, and reducing the attenuation rate of the reflected signal.

[0115] The impedance distribution curve is analyzed to determine the location of discontinuities and the reflection peak data. Reflection signal characteristics are extracted from the reflection peak data to determine changes in signal propagation delay. Confluence structure parameters are obtained, and connector dimensions and via spacing are adjusted to optimize the path length. The path length value is used to calculate the delay trend and determine the adjusted reflection signal strength. The attenuation rate calculation formula is used to evaluate the reflection signal strength and determine the result after the attenuation rate is reduced. If the attenuation rate exceeds the preset threshold, the path length is adjusted using an iterative optimization algorithm to obtain the final optimized parameters. The confluence structure data is updated based on the final optimized parameters to determine the matching between the signal propagation delay time and the attenuation rate.

[0116] Specifically, the presence of a reflection peak at a discontinuity in an impedance distribution curve is typically associated with a sudden impedance change in the signal propagation path. To optimize signal propagation delay and reduce reflected signal attenuation, time domain reflectometry (TDR) is first used to measure the reflection coefficient and locate the specific location of the discontinuity. Assuming a reflection peak of -15dB at 10GHz, the corresponding impedance change is 30mm from the source. Next, based on the magnitude of the reflection peak, finite element analysis (FEA) is used to simulate the effects of connector size and via spacing in the busbar structure.

[0117] For example, by adjusting the connector diameter from 1mm to 8mm and reducing the via pitch from 2mm to 5mm, simulations revealed that the reflection peak dropped to -20dB, reducing signal propagation delay by 5ps. Furthermore, using transmission line theory to calculate the optimal path length, assuming the signal propagation speed is 60% of the speed of light, by shortening the path length from 50mm to 45mm, the reflected signal attenuation rate decreased from 8dB / m to 6dB / m. Finally, impedance matching was analyzed using a Smith chart to ensure that the adjusted structure achieved optimal matching at 10GHz, with the reflection coefficient kept below -25dB, thereby achieving a comprehensive improvement in signal integrity.

[0118] In step S104, a virtual model of the bus structure is constructed using 3D modeling software, the adjusted connector size and via spacing are imported, and the geometric parameter grid density is set to 1,000 points per millimeter to generate a digital twin.

[0119] Using 3D modeling software, we acquire the data for the confluence structure and construct an initial virtual model. We extract the connector dimensions and via spacing from the initial virtual model and import the adjusted parameter values. We then set the geometric parameters based on these adjusted parameter values, determining a mesh density of 1,000 points per millimeter. We then use a finite element analysis algorithm to process the mesh density and generate an optimized virtual model. We then determine whether the optimized virtual model features meet the digital twin accuracy requirements. If not, we adjust the density settings and regenerate the model.

[0120] The first formula is as follows:

[0121]

[0122] Among them, α represents the digital twin mapping strength, V i represents the volume of the virtual model, D i represents the model density, R i The first formula is used to calculate the basic mapping relationship between the virtual model and the digital twin. The digital twin is generated from the optimized virtual model to obtain a complete confluence structure mapping. The confluence structure and geometric parameters are verified based on the digital twin, and the final digital mapping is output.

[0123] Specifically, first, a virtual model of the confluence structure is constructed using 3D modeling software such as SolidWorks or AutoCAD. The initial design parameters include a connector diameter of 5 mm and a via spacing of 10 mm. By importing the adjusted connector dimensions, its diameter is optimized to 4 mm and the via spacing is adjusted to 8 mm to improve the compactness of the structure. The geometric parameter grid density is set to 1,000 points per millimeter, and the finite element analysis algorithm is used to mesh the model, ensuring that the size of each grid unit is 0.01 mm to accurately capture the detailed features of the structure. During the generation of the digital twin, computational fluid dynamics (CFD) simulation is combined to analyze the flow characteristics of the fluid in the confluence structure. By iteratively solving the Navier-Stokes equations, the structural design is optimized to ensure the uniformity of the fluid flow and reduce turbulence and pressure drop. Ultimately, the generated high-precision digital twin can be used to monitor and predict the performance of the confluence structure in real time, providing data support for subsequent optimization and manufacturing.

[0124] In step S105, a sensor data interface is implanted in the digital twin, and the sensor data refresh rate reaches 100 times per second. The temperature field distribution resolution and vibration modal frequency range of the confluence structure are obtained in real time to perform virtual-real mapping.

[0125] The raw data stream of the confluence structure is acquired through the sensor data interface, and the initial data set of the temperature field distribution and vibration mode is obtained using a preset sampling method. For the initial data set, the fast Fourier transform algorithm is used to extract the frequency range of the vibration mode and determine the frequency eigenvalue. Based on the temperature field distribution data, the resolution accuracy on the spatial grid is calculated to obtain a high-resolution temperature field distribution map. If the frequency eigenvalue exceeds the preset threshold, the vibration mode data is adjusted using an interpolation method to obtain a smooth frequency range sequence. After obtaining the smooth frequency range sequence, combined with the high-resolution temperature field distribution map, the virtual-to-real mapping result of the confluence structure is achieved through the digital twin mapping algorithm. The dynamic response characteristics of the confluence structure are extracted from the virtual-to-real mapping results, and the correlation between the temperature field distribution and the vibration mode is determined to obtain real-time status data. The real-time status data is used to update the parameters of the digital twin to obtain an optimized virtual-to-real mapping model.

[0126] Specifically, to implant a sensor data interface in a digital twin, it is first necessary to design an efficient data acquisition system that can support a data refresh rate of hundreds of times per second.

[0127] For example, an FPGA-based hardware accelerator uses parallel processing technology to achieve high-speed data acquisition, ensuring that each sensor data point can be captured and transmitted to the digital twin within 10 milliseconds. Next, a Kalman filter algorithm is used to process the sensor data in real time to eliminate noise and improve data accuracy.

[0128] For example, the temperature distribution of a confluence structure is captured by a temperature sensor array. After Kalman filtering, the temperature field resolution can reach 1°C, ensuring that the temperature field in the digital twin is highly consistent with the physical entity. Furthermore, to obtain the vibration modal frequency range, a fast Fourier transform (FFT) algorithm is used to perform frequency domain analysis on the vibration sensor data, extracting the primary vibration frequency components.

[0129] For example, FFT analysis can accurately identify the vibration modes of the confluence structure within the 0 to 1000 Hz range, with a frequency resolution of 1 Hz. Finally, the processed sensor data is mapped to the virtual model in the digital twin, and the temperature field and vibration modes of the confluence structure are simulated using finite element analysis (FEA) algorithms, ensuring that the digital twin reflects the state of the physical entity in real time.

[0130] For example, through FEA simulation, the stress distribution of the confluence structure under different temperature gradients and the dynamic response at different vibration frequencies can be predicted, providing data support for structural optimization and fault diagnosis.

[0131] In step S106, a thermoelectric coupling simulation is performed based on the temperature field distribution resolution and electromagnetic field coupling strength in the digital twin. The simulation time step is set to microseconds to obtain the stress concentration area range and electromagnetic field distribution.

[0132] The temperature field distribution and electromagnetic field intensity are calculated through thermoelectric coupling simulation, using a microsecond time step to obtain preliminary stress concentration data. Based on this preliminary stress concentration data, the region corresponding to the stress concentration is determined, and the electromagnetic distribution characteristics within the region are obtained. Based on the electromagnetic distribution characteristics within the region, the coupling strength value is determined to see if it exceeds the preset threshold. If so, the simulation step size is adjusted and the temperature field distribution is recalculated. After obtaining the adjusted temperature field distribution, it is determined whether the resolution level meets the digital twin requirements. If not, the resolution level is increased through an interpolation algorithm to obtain an optimized temperature field distribution. By optimizing the temperature field distribution, the stress concentration trend under thermoelectric coupling is calculated, and the region after the change is obtained. Based on the region after the change, a finite element analysis algorithm is used to obtain the spatial correspondence between the electromagnetic distribution and the stress concentration. Based on this spatial correspondence, the coupling stability of the electromagnetic field intensity and temperature field distribution within the digital twin is determined to obtain the final simulation results.

[0133] Specifically, when performing thermoelectric coupling simulation in a digital twin, a high-resolution temperature field distribution model must first be established. Finite element analysis (FEM) was used to divide the model mesh into 1 million elements, each 1 micron in size, to ensure accurate capture of the temperature field. The diffusion of heat within the material was simulated using heat conduction equations and boundary conditions, with an initial temperature of 300K, a heat source power of 50W, and a simulation time step of 1 microsecond. During the simulation, the thermoelectric coupling module in ANSYS software was used to couple the temperature field with the electromagnetic field. The electromagnetic field simulation employed Maxwell's equations, with a frequency of 1 GHz, an electric field strength of 10 kV / m, and a magnetic field strength of 1 T. Through iterative calculations, the distribution of the electromagnetic field within the material was determined, and its interaction with the temperature field was analyzed. The simulation results identified areas of stress concentration, primarily concentrated near the material boundaries and heat sources, with stress values ​​reaching 500 MPa. Furthermore, the electromagnetic field distribution within the material exhibited a distinct gradient, with the maximum field intensity occurring at the center of the heat source. Comparative analysis revealed a correlation between stress concentration areas and regions of high electromagnetic field intensity, providing important insights for optimizing material design and improving device performance. During the simulation, parallel computing technology was employed to distribute the computational tasks across 100 computing nodes, each equipped with a 32-core CPU, ensuring the simulation was completed within 100 hours. Through high-resolution simulation and precise coupling calculations, reliable thermoelectric coupling distribution and the extent of stress concentration areas were determined, providing strong support for subsequent material optimization and device design.

[0134] Step S107 , according to the range of the stress concentration area and the vibration modal frequency range, adjust the via spacing and connector size in the digital twin, optimize the geometric parameter mesh density based on the thermoelectric coupling boundary conditions, and determine the dynamic stability of the confluence structure.

[0135] Finite element analysis is used to determine the stress concentration region and vibration modal frequency range, generating initial distribution data. Based on this initial distribution data, the via spacing and connector dimensions in the digital twin are adjusted to determine the adjusted parameter set. Thermoelectric coupling simulation is used to process the adjusted parameter set based on boundary conditions, generating a thermoelectric coupling distribution. Meshing techniques are used to optimize the mesh density of geometric parameters and determine the optimized mesh model. If the stress of the confluence structure in the optimized mesh model exceeds a preset threshold, the mesh density is adjusted to obtain a stable mesh model. Dynamic simulation is then used to analyze the stable mesh model and determine the dynamic stability distribution of the confluence structure. A machine learning regression algorithm is used to process the dynamic stability distribution and determine the final optimized parameters.

[0136] The second formula is as follows:

[0137]

[0138] Among them, E(y) represents the predicted value of system stability, β0 represents the intercept term, β i represents the linear coefficient, β ij represents the coefficient of the quadratic term, x i and x j The second formula is used to predict the stability of the system.

[0139] Specifically, in the digital twin, finite element analysis was first used to determine the extent of stress concentration areas. For example, a stress concentration radius of 5 mm was found around the vias. Based on this, a genetic algorithm was used to optimize the via spacing, adjusting the initial spacing from 10 mm to 8 mm to reduce the stress concentration factor. Simultaneously, modal analysis determined that the vibration modal frequency range was 100 to 500 Hz. The connector diameter was adjusted from 6 mm to 8 mm to improve structural stiffness and avoid resonant frequencies. To address the thermoelectric coupling boundary conditions, adaptive meshing technology was used to increase the mesh density from 10 elements per square millimeter to 20 elements per square millimeter in the high-temperature region to ensure accurate calculation of the temperature and stress fields. Finally, transient dynamic analysis was used to evaluate the dynamic stability of the confluence structure. Under a dynamic load of 1000 Newtons, the calculated maximum displacement was 2 mm, meeting the design requirements. The entire optimization process utilized a multi-physics coupled simulation platform, enabling the coordinated optimization of geometric parameters, mesh density, and dynamic stability.

[0140] In step S108 , a frequency domain analysis method is used to calculate the input signal amplitude range and vibration modal frequency range of the optimized confluence structure to verify whether the electromagnetic field coupling strength meets the matching requirements.

[0141] The input signal data is acquired through frequency domain analysis, the amplitude range is calculated, and preliminary signal characteristics are obtained. The fast Fourier transform algorithm is used to process the vibration modal data, determine the frequency range, and obtain the modal distribution characteristics. The optimized parameters of the confluence structure are obtained, the electromagnetic field strength is calculated, and the field strength distribution data is obtained. The field strength distribution data is compared with the coupling strength standard to determine whether the coupling strength meets the matching requirements and obtain the matching judgment result. If the matching judgment result exceeds the preset threshold, the amplitude range is recalculated by adjusting the input signal parameters to obtain the updated signal characteristics. Based on the updated signal characteristics and frequency range data, the support vector machine algorithm is used to analyze the coupling strength change trend and determine the performance of the optimized confluence structure. A final comparison is made between the performance data and the matching requirements to determine whether the electromagnetic field coupling strength meets the business objectives and obtain the final judgment result.

[0142] Specifically, in the frequency domain analysis method, the input signal amplitude range of the confluence structure must be determined first. Then, the time domain signal is converted into a frequency domain signal through Fourier transform, and its spectral characteristics are analyzed.

[0143] For example, if the input signal is a 5V sine wave with a frequency range of 10Hz to 1000Hz, Fast Fourier Transform (FFT) analysis can be used to obtain its spectrum distribution and determine that the signal amplitude range is 1V to 5V. Next, modal analysis is used to calculate the vibration modal frequency range of the confluence structure.

[0144]

[0145] Where ω_n represents the natural frequency of the structure, k represents the structural stiffness, m represents the mass, and c represents the damping coefficient. This formula is used to calculate the natural frequency of a simple harmonic vibration system.

[0146] Finite element analysis software was used to build a three-dimensional model of the confluence structure, setting the material properties to aluminum alloy, a density of 2700kg / m³, and an elastic modulus of 70GPa. Modal analysis was performed, and the first six modal frequencies were found to be 15Hz, 45Hz, 80Hz, 120Hz, 180Hz, and 250Hz, respectively. The electromagnetic field coupling strength was then verified to ensure that it met the matching requirements. By calculating the electromagnetic field coupling coefficient, the electromagnetic field distribution was solved using Maxwell's equations. Assuming an electromagnetic field strength of 5T and a coupling coefficient of 8, it was determined whether it was within the matching range of 7 to 9. Finally, data analysis software was used to plot frequency spectra and modal vibration shape diagrams to visually display the analysis results and ensure that the optimized confluence structure met the design requirements.

[0147] In step S109, a step signal is input again through the time domain reflectometer, the rise time of the step signal remains consistent, the optimized time domain waveform is recorded, the baseline noise level of the new waveform and the reflection peak value of the discontinuity point are obtained, and the degree of improvement of the impedance distribution curve accuracy is determined.

[0148] A step signal is input through the time domain reflectometer to maintain a consistent rise time and obtain optimized time domain waveform data. The baseline noise level is extracted from the acquired time domain waveform data, and the noise is processed using a filtering algorithm to obtain smoothed waveform data. For the smoothed waveform data, the position of the discontinuity point is detected to obtain the corresponding reflection peak height. Based on the reflection peak height, the impedance distribution curve is calculated, and the continuity of the curve is optimized using an interpolation algorithm. If the degree of discreteness of the impedance distribution curve exceeds the preset threshold, the filtering parameters are adjusted and the waveform data is reprocessed. By comparing the impedance distribution curves before and after adjustment, the degree of accuracy improvement is determined and the final distribution result is obtained. Key features are extracted from the final distribution result to generate optimized time domain reflection analysis data.

[0149] Specifically, a step signal is re-input through the time domain reflectometer to ensure that the rise time of the step signal remains consistent, for example, set to 1 nanosecond to maintain consistency in test conditions. In the optimized time domain waveform record, a fast Fourier transform algorithm is used to analyze the waveform and extract the baseline noise level of the new waveform. Assume that the measured noise level is 0.2 volts. Simultaneously, a peak detection algorithm is used to identify the reflection peak at the discontinuity point. Assume that the measured reflection peak is 5 volts. Based on this data, the impedance calculation formula Z = V / I is used, where V is the reflection peak and I is the input current. Assuming the input current is 1 ampere, the calculated impedance is 5 ohms. By comparing the impedance distribution curves before and after optimization, the root mean square error algorithm is used to calculate the degree of improvement in the curve's accuracy. Assuming that the root mean square error before optimization is 1 ohm, it is reduced to 0.5 ohms after optimization, indicating that the accuracy of the impedance distribution curve has improved by 50%. This process ensures the accuracy and reliability of the test results through data processing and analysis.

[0150] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A method for processing an anti-interference electronic harness, characterized in that: The method comprises the following steps: A time-domain reflectometer is used to input a step signal into the confluence structure. The rise time of the step signal is controlled at the picosecond level, and the returned time-domain waveform is recorded. The sampling rate of the time-domain waveform is set to 1,000 times per nanosecond to obtain the reflected signal attenuation rate and signal propagation delay time. Generate an impedance distribution curve through time domain waveform analysis. The impedance distribution curve has an ohm-level accuracy, identifies the reflection peak of the discontinuity point and the waveform baseline noise level, and determines the specific location of the impedance mutation. According to the reflection peak of the discontinuous point in the impedance distribution curve, the connector size and via spacing in the bus structure are adjusted to optimize the path length based on the signal propagation delay time and reduce the attenuation rate of the reflected signal; A virtual model of the busbar structure was constructed using 3D modeling software. The adjusted connector dimensions and via spacing were imported, and the geometric parameter grid density was set to 1,000 points per millimeter to generate a digital twin. A sensor data interface is embedded in the digital twin, with a sensor data refresh rate of 100 times per second. This allows for real-time acquisition of the temperature field distribution resolution and vibration modal frequency range of the confluence structure, enabling virtual-real mapping. A thermoelectric coupling simulation was conducted to determine the temperature field distribution resolution and electromagnetic field coupling strength in the digital twin. The simulation time step was set to the microsecond level to obtain the stress concentration area and electromagnetic field distribution. Based on the range of stress concentration areas and vibration modal frequency ranges, the via spacing and connector size in the digital twin are adjusted, the geometric parameter mesh density is optimized for the thermoelectric coupling boundary conditions, and the dynamic stability of the confluence structure is determined; The frequency domain analysis method is used to calculate the input signal amplitude range and vibration modal frequency range of the optimized confluence structure to verify whether the electromagnetic field coupling strength meets the matching requirements; This step also includes: The input signal data is acquired through frequency domain analysis method, the amplitude range is calculated, and the preliminary signal characteristics are obtained; The fast Fourier transform algorithm is used to process vibration modal data, determine the frequency range, and obtain modal distribution characteristics; Obtain the optimized parameters of the confluence structure, calculate the electromagnetic field intensity, and obtain the field intensity distribution data; The step signal is input again through the time domain reflectometer. The rise time of the step signal remains the same. The optimized time domain waveform is recorded. The baseline noise level of the new waveform and the reflection peak value of the discontinuity point are obtained to determine the degree of improvement in the accuracy of the impedance distribution curve.

2. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The time domain reflectometer is used to input a step signal to the confluence structure, the rise time of the step signal is controlled at the picosecond level, and the returned time domain waveform is recorded. The time domain waveform sampling rate is set to 1,000 times per nanosecond to obtain the reflected signal attenuation rate and signal propagation delay time, including: Generate a step signal using a time domain reflectometer, control the rise time to the picosecond level, inject it into the confluence structure, and record the return waveform; Use high-precision sampling equipment to set the sampling rate to thousands of times per nanosecond to obtain return waveform data; Extract the reflected signal from the returned waveform and calculate the attenuation rate value; Analyze the signal propagation path for the reflected signal and determine the delay time; If the attenuation rate exceeds the preset threshold, the frequency components of the reflected signal are separated by Fourier transform to determine the propagation abnormality point; Obtain the propagation characteristics of each section in the confluence structure based on the delay time and signal propagation path; By comparing the attenuation rate value with the delay time, the preset model is used to determine the defect location of the confluence structure.

3. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The impedance distribution curve is generated by time domain waveform analysis, the impedance distribution curve accuracy reaches the ohm level, the reflection peak of the discontinuity point and the waveform baseline noise level are identified, and the specific location of the impedance mutation is determined, including: By collecting time domain waveform data and processing it with fast Fourier transform, frequency domain characteristic parameters are obtained; Extract impedance distribution information from frequency domain characteristic parameters and generate an initial distribution curve; For the initial distribution curve, Gaussian filtering is applied to smooth the curve to obtain a smooth distribution curve; If there is a significant mutation in the smooth distribution curve, the reflection peak position is identified by differential calculation to determine the coordinates of the discontinuity point; By comparing the coordinates of the discontinuous points with the smooth distribution curve, the baseline noise level is obtained and the noise impact range is determined; The support vector machine algorithm is used to classify the reflection peak and noise level to determine the specific location of the impedance mutation; By matching the impedance mutation position with the time domain waveform, the timestamp of the mutation point is obtained.

4. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The method of adjusting the connector size and via spacing in the bus structure according to the reflection peak value of the discontinuous point in the impedance distribution curve, optimizing the path length according to the signal propagation delay time, and reducing the attenuation rate of the reflected signal includes: Obtain the discontinuity point location through impedance distribution curve analysis and determine the reflection peak data; Extract reflection signal features from reflection peak data to determine delay time changes during signal propagation; Obtain bus structure parameters, adjust connector size and via spacing, and optimize path length values; Calculate the delay time variation trend through the path length value and determine the adjusted reflected signal strength; The attenuation rate calculation formula is used to evaluate the reflected signal strength and the result after the attenuation rate is reduced is obtained; If the decay rate exceeds the preset threshold, the path length is adjusted through an iterative optimization algorithm to obtain the final optimization parameters; The confluence structure data is updated according to the final optimized parameters to determine the matching result between the signal propagation delay time and the attenuation rate.

5. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The method uses 3D modeling software to build a virtual model of the busbar structure, imports the adjusted connector size and via spacing, sets the geometric parameter grid density to 1,000 points per millimeter, and generates a digital twin, including: Obtain the confluence structure data through 3D modeling software and construct an initial virtual model; Extract connector dimensions and via spacing from the initial virtual model and import adjusted parameter values; Set the geometric parameters according to the adjusted parameter values ​​and determine the grid density as 1,000 points per millimeter; Finite element analysis algorithm is used to process mesh density and generate optimized virtual models; Obtain optimized virtual model features to determine whether they meet the digital twin accuracy requirements. If not, adjust the density settings and regenerate; The first formula is as follows: ; where α represents the digital twin mapping strength, V i represents the volume of the virtual model, D i represents the model density, R i represents the mapping radius, β represents the correction coefficient, and M represents the material parameter; The first formula is used to calculate the basic mapping relationship from the virtual model to the digital twin; Generate a digital twin through the optimized virtual model to obtain a complete confluence structure mapping; Verify the consistency of the confluence structure and geometric parameters based on the digital twin and output the final digital map.

6. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The sensor data interface is implanted in the digital twin, and the sensor data refresh rate reaches 100 times per second. The temperature field distribution resolution and vibration modal frequency range of the confluence structure are obtained in real time, and virtual-real mapping is performed, including: The original data stream of the confluence structure is obtained through the sensor data interface, and the initial data set of temperature field distribution and vibration mode is obtained using a preset sampling method; For the initial data set, the fast Fourier transform algorithm is used to extract the frequency range of the vibration mode and determine the frequency eigenvalue; According to the temperature field distribution data, the resolution accuracy on the spatial grid is calculated to obtain a high-resolution temperature field distribution map; If the frequency characteristic value exceeds the preset threshold, the vibration modal data is adjusted by interpolation method to obtain a smooth frequency range sequence; After obtaining the smoothed frequency range sequence, combined with the high-resolution temperature field distribution map, the virtual-real mapping result of the confluence structure is achieved through the digital twin mapping algorithm; Extract the dynamic response characteristics of the confluence structure from the virtual-real mapping results, determine the correlation between the temperature field distribution and the vibration mode, and obtain real-time status data; Real-time status data is used to update the parameters of the digital twin to obtain an optimized virtual-reality mapping model.

7. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The thermoelectric coupling simulation is carried out for the temperature field distribution resolution and electromagnetic field coupling strength in the digital twin. The simulation time step is set to the microsecond level to obtain the stress concentration area range and electromagnetic field distribution, including: The temperature field distribution and electromagnetic field intensity were calculated through thermoelectric coupling simulation, with a time step of microseconds to obtain preliminary stress concentration data; Based on the preliminary stress concentration data, determine the area corresponding to the stress concentration and obtain the electromagnetic distribution characteristics in the area; Based on the electromagnetic distribution characteristics within the region, determine whether the coupling strength value exceeds the preset threshold. If so, adjust the simulation step size and recalculate the temperature field distribution; Obtain the adjusted temperature field distribution and determine whether the resolution level meets the digital twin requirements. If not, improve the resolution level through interpolation algorithms to obtain the optimized temperature field distribution. By optimizing the temperature field distribution, the stress concentration change trend under thermoelectric coupling is calculated to obtain the area range after the change; According to the changed area, the finite element analysis algorithm is used to obtain the spatial correspondence between electromagnetic distribution and stress concentration; Through the spatial correspondence, the coupling stability of the electromagnetic field intensity and temperature field distribution in the digital twin is judged to obtain the final simulation results.

8. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The method adjusts the via spacing and connector size in the digital twin according to the stress concentration area range and vibration modal frequency range, optimizes the geometric parameter mesh density based on the thermoelectric coupling boundary conditions, and determines the dynamic stability of the confluence structure, including: The stress concentration area range and vibration modal frequency range are obtained through finite element analysis to obtain initial distribution data; Adjust the via spacing and connector size in the digital twin based on the initial distribution data and determine the adjusted parameter set; The thermoelectric coupling simulation is used to process the adjusted parameter set for the boundary conditions to obtain the thermoelectric coupling distribution; Optimize the mesh density of geometric parameters through meshing technology and determine the optimized mesh model; If the stress of the confluence structure in the optimized mesh model exceeds the preset threshold, the mesh density is adjusted to obtain a stable mesh model; Through dynamic simulation analysis of the stable grid model, the dynamic stability distribution of the confluence structure is determined; Use machine learning regression algorithm to process dynamic stability distribution and determine the final optimization parameters; The second formula is as follows: ; Where E(y) represents the predicted value of system stability, β0 represents the intercept term, β i represents the linear coefficient, β ij represents the coefficient of the quadratic term, x i and x j represents the input variable; The second formula is used to predict the system stability.

9. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The frequency domain analysis method is used to calculate the input signal amplitude range and vibration mode frequency range of the optimized confluence structure to verify whether the electromagnetic field coupling strength meets the matching requirements, including: Compare the field intensity distribution data with the coupling strength standard to determine whether the coupling strength meets the matching requirements and obtain the matching judgment result; If the matching judgment result exceeds the preset threshold, the amplitude range is recalculated by adjusting the input signal parameters to obtain the updated signal characteristics; Based on the updated signal characteristics and frequency range data, the support vector machine algorithm is used to analyze the trend of coupling strength changes and determine the performance of the optimized confluence structure; By making a final comparison between the performance data and the matching requirements, it is determined whether the electromagnetic field coupling strength meets the business objectives and the final judgment result is obtained.

10. The method for processing an anti-interference electronic wire harness according to claim 1, characterized in that: The step signal is input again through the time domain reflectometer, the rise time of the step signal remains the same, the optimized time domain waveform is recorded, the baseline noise level of the new waveform and the reflection peak value of the discontinuity point are obtained, and the degree of improvement of the impedance distribution curve accuracy is determined, including: Input a step signal through a time domain reflectometer to keep the rise time consistent and obtain optimized time domain waveform data; Extracting the baseline noise level from the acquired time domain waveform data, and processing the noise using a filtering algorithm to obtain smoothed waveform data; For the smoothed waveform data, detect the position of the discontinuity point and obtain the corresponding reflection peak height; According to the reflection peak height, the impedance distribution curve is calculated and the continuity of the curve is optimized using the interpolation algorithm; If the discreteness of the impedance distribution curve exceeds the preset threshold, the filter parameters are adjusted and the waveform data is reprocessed; By comparing the impedance distribution curves before and after adjustment, the degree of accuracy improvement can be determined and the final distribution results can be obtained. Extract key features from the final distribution results and generate optimized time domain reflectometry data.

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