Electronic wire harness anti-interference optimization method and system

Abstract

The invention provides an electronic wire harness anti-interference optimization method and system, and the method comprises the steps: S2, blocking a DC component and low-frequency interference through employing a band elimination filter according to the initial spectrum distribution, and extracting a core frequency band of high-frequency signal transmission from a filtered signal; s4, acquiring demand data of voltage grade separation from the signal output, designing an electrical isolation layer through a multi-layer shielding structure, and determining isolation boundaries among different voltage grades; s5, according to the isolation boundary, a space optimization algorithm is adopted to adjust wire harness arrangement in the compact layout, and structural parameters considering isolation and coupling balance are obtained; and S8, according to the final scheme, a time domain reflection method is adopted to detect the distortion rate in signal transmission, the actual effect of improving the anti-interference capability is judged from the detection result, and optimized operation parameters are obtained. The anti-interference capability and the signal transmission quality of the electronic wire harness in a complex electromagnetic environment are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to an electronic wire harness anti-interference optimization method and system. Background Technology

[0002] Electronic wire harness classification and management, as a core research direction in modern electrical engineering, is crucial for ensuring the stability and reliability of system operation. With the increasing complexity of electronic equipment, wire harness management involves not only optimizing physical wiring but also precise control of electrical performance and interference suppression, directly impacting application efficiency in key industries such as communications, energy, and industrial automation. In this field, proper classification and isolation techniques have become critical factors determining system performance. However, the limitations of existing methods are becoming increasingly apparent. Traditional wire harness separation methods rely heavily on physical isolation or simple shielding materials. While these can reduce interference to some extent, they often fall short in complex and variable high-frequency signal transmission scenarios, especially when different voltage levels coexist and signal integrity must be maintained, making it difficult to achieve efficient electrical isolation and interference suppression.

[0003] In-depth analysis reveals that the core challenges of electronic wire harness classification and management mainly lie in several key technical factors: first, how to ensure the effective transmission of high-frequency signals while blocking DC and low-frequency interference; second, how to achieve electrical isolation between different voltage levels within a limited space through reasonable structural design; and third, how to balance anti-interference capability with signal transmission efficiency. These technical factors have not been fully addressed, leading to unique technical challenges in high-frequency communication or complex circuit environments where the system is susceptible to power supply noise and ground loop interference, resulting in signal distortion or system instability. In particular, when the wire harness separation structure needs to simultaneously meet isolation and coupling requirements within a compact layout, existing solutions often struggle to achieve a dynamic balance between the two.

[0004] Therefore, how to effectively block low-frequency DC interference, accurately transmit high-frequency signals, and reliably isolate different voltage levels in electronic wire harness classification and management through innovative separation technology has become a key problem that this research urgently needs to overcome. Solving this problem will directly promote the improvement of wire harness management technology in high-frequency circuits and communication equipment. Summary of the Invention

[0005] To address the technical problems mentioned in the background art, a first aspect of the present invention provides an electronic wire harness anti-interference optimization method, the method comprising:

[0006] S1, acquire the characteristic data of DC interference and high-frequency signal in the electronic wire bundle, separate the DC component and high-frequency component through Fourier transform, and obtain the initial spectrum distribution of DC interference blocking;

[0007] S2, based on the initial spectrum distribution, a band-stop filter is used to block the DC component and low-frequency interference, and the core frequency band for high-frequency signal transmission is extracted from the filtered signal;

[0008] S3, for the core frequency band, determines the magnitude of the power supply noise impact. If the magnitude exceeds the preset impact magnitude threshold, the signal amplitude is adjusted through an adaptive equalization algorithm to obtain a signal output with improved anti-interference capability.

[0009] S4: Obtain the required data for voltage level separation from the signal output, design the electrical isolation layer through a multi-layer shielding structure, and determine the isolation boundary between different voltage levels;

[0010] S5. Based on the isolation boundary, a spatial optimization algorithm is used to adjust the arrangement of wire harnesses in the compact layout to obtain structural parameters that balance isolation and coupling.

[0011] S6. Based on the structural parameters, determine the current distribution of ground loop interference. If the current distribution is uneven, optimize the grounding path through the grounding grid reconstruction algorithm to obtain a layout scheme for interference suppression.

[0012] S7: Extract evaluation indicators of high-frequency signal transmission efficiency from the layout scheme, optimize the thickness of the isolation layer and coupling layer through dynamic balance adjustment algorithm, and determine the final wire harness classification management scheme.

[0013] S8. According to the final scheme, the distortion rate in signal transmission is detected by the time domain reflection method. The actual effect of the improved anti-interference capability is judged from the detection results, and the optimized operating parameters are obtained.

[0014] S9 obtains system stability data under high-frequency communication environment through optimized operating parameters, adjusts the harness structure through iterative update algorithm, and determines the long-term operating configuration that meets dynamic balance.

[0015] Optionally, step S1, acquiring characteristic data of DC interference and high-frequency signals in the electronic wire bundle, separating the DC component and high-frequency component through Fourier transform, and obtaining the initial spectral distribution of DC interference blocking, includes:

[0016] Step S11: Acquire signal data from the electronic wire bundle, and obtain the time-domain signal by acquiring the original waveform through the sensor;

[0017] Step S12: Process the time-domain signal through Fourier transform to decompose the DC component and high-frequency component to obtain the initial spectrum distribution;

[0018] Step S13: Use the spectrum analysis tool in Matlab to separate the DC interference in the initial spectrum, extract high-frequency signal features, and determine the interference boundary;

[0019] Step S14: If the DC component exceeds the preset DC component threshold, the interference is blocked by a filter to obtain a clean spectrum.

[0020] Step S15: Calculate the amplitude distribution of high-frequency components based on the pure spectrum and determine the changing trend of signal characteristics;

[0021] Step S16: Obtain statistical data on the changing trend, classify the abnormal signals using the support vector machine in Scikit-learn, and determine the interference blocking effect;

[0022] Step S17: Compare the original spectrum with the clean spectrum using the spectrum comparison function in Matplotlib to quantify the degree of separation of DC interference and obtain the final spectrum characteristics.

[0023] Optionally, in step S14, if the DC component exceeds a preset DC component threshold, the interference is blocked by a filter to obtain a clean spectrum, including the filter being a Butterworth filter.

[0024] Optionally, the upper and lower cutoff frequencies of the filter are set to 0.5Hz and 10Hz, respectively.

[0025] Optionally, step S2, based on the initial spectral distribution, uses a band-stop filter to block DC components and low-frequency interference, and extracts the core frequency band for high-frequency signal transmission from the filtered signal, including:

[0026] Step S21: Obtain spectral distribution data from the initial spectrum and use spectral analysis to determine the range of DC component and low-frequency interference;

[0027] Step S22: For the range of the low-frequency interference, use a fourth-order Butterworth band-stop filter to block the frequency band and obtain the filtered signal;

[0028] Step S23: Perform a Hanning windowed Fast Fourier Transform on the filtered signal, with the number of sampling points matching the length of the filtered signal, to obtain the frequency distribution of the high-frequency signal;

[0029] Step S24: Determine the characteristic frequency band of the transmission core based on the frequency points in the frequency distribution whose amplitude exceeds the maximum amplitude.

[0030] Step S25: Determine the upper and lower boundaries of the characteristic frequency band by using a preset cumulative energy threshold;

[0031] Step S26: For the upper and lower boundaries of the characteristic frequency band, an FIR bandpass filter is used to extract the effective frequency band to obtain the effective frequency band signal;

[0032] Step S27: Calculate the ratio of the effective frequency band signal energy to the total initial spectrum energy. If the ratio is lower than a preset energy ratio threshold, then re-acquire the spectrum distribution data from the initial spectrum.

[0033] Step S28: Output core frequency band data that meets the energy ratio requirement.

[0034] Optionally, step S21, which involves obtaining spectral distribution data from the initial spectrum and using spectral analysis to determine the range of DC component and low-frequency interference, includes setting the range of low-frequency interference to 0–10 Hz.

[0035] Optionally, step S23 involves performing a Hanning window-plus-window fast Fourier transform on the filtered signal, with the number of sampling points matching the length of the filtered signal, to obtain the frequency distribution of the high-frequency signal, including: the high-frequency signal being a signal with a frequency greater than or equal to 10Hz.

[0036] Optionally, step S4 involves obtaining the required data for voltage level separation from the signal output, designing an electrical isolation layer through a multi-layer shielding structure, and determining the isolation boundary between different voltage levels, including:

[0037] Step S41: Obtain the raw data stream by acquiring signals through the sensor, and process the raw data stream using Butterworth low-pass filter to obtain the denoised voltage waveform data;

[0038] Step S42: Extract peak-valley value sequences from the voltage waveform data, distinguish voltage levels using K-means clustering, and obtain characteristic values ​​for high-voltage and low-voltage areas;

[0039] Step S43: Construct an initial double-layer shielding structure based on the characteristic difference between the high-voltage zone characteristic value and the low-voltage zone characteristic value, and generate initial shielding distribution data;

[0040] Step S44: The electric field strength of the initial shielding distribution data is calculated using the finite element analysis method. If the local field strength exceeds the field strength threshold, the interlayer spacing in that region is proportionally increased to obtain optimized shielding distribution data.

[0041] Step S45: Extract the coordinates of the shielding nodes from the optimized shielding distribution data, generate a three-dimensional isolation mesh through Delaunay triangulation, and determine the isolation boundary surface;

[0042] Step S46: Import the isolated boundary surface into the simulation environment, apply a voltage twice the characteristic value for simulation, detect the field strength of the entire region, and determine whether the field strength of the entire region is lower than the field strength threshold.

[0043] Optionally, step S43 involves constructing an initial double-layer shielding structure based on the characteristic difference between the high-pressure zone characteristic value and the low-pressure zone characteristic value, and generating initial shielding distribution data, including setting the inner layer spacing to half of the characteristic difference and the outer layer spacing to one-quarter of the characteristic difference.

[0044] A second aspect of the present invention provides an electronic wire harness anti-interference optimization system, which optimizes the anti-interference of electronic wire harnesses using the method described above, the system comprising:

[0045] The spectrum analysis module is used to acquire characteristic data of DC interference and high-frequency signals in the electronic wire bundle. It separates the DC component and the high-frequency component through Fourier transform to obtain the initial spectrum distribution of DC interference blocking.

[0046] The band-stop filter module is used to block DC components and low-frequency interference based on the initial spectrum distribution, and to extract the core frequency band for high-frequency signal transmission from the filtered signal.

[0047] The adaptive equalization module is used to determine the magnitude of power supply noise impact on the core frequency band. If the magnitude exceeds the preset impact magnitude threshold, the signal amplitude is adjusted through the adaptive equalization algorithm to obtain a signal output with improved anti-interference capability.

[0048] The isolation design module is used to obtain the voltage level separation requirement data from the signal output, design the electrical isolation layer through a multi-layer shielding structure, and determine the isolation boundary between different voltage levels.

[0049] The space optimization module is used to adjust the arrangement of wire harnesses in a compact layout based on the isolation boundary using a space optimization algorithm, so as to obtain structural parameters that balance isolation and coupling.

[0050] The grounding optimization module is used to determine the current distribution of ground loop interference based on structural parameters. If the current distribution is uneven, the grounding path is optimized through the grounding grid reconstruction algorithm to obtain a layout scheme for interference suppression.

[0051] The dynamic balancing module is used to extract evaluation indicators of high-frequency signal transmission efficiency from the layout scheme, optimize the thickness of the isolation layer and coupling layer through the dynamic balancing adjustment algorithm, and determine the final wire harness classification and management scheme.

[0052] The performance testing module is used to detect the distortion rate in signal transmission using the time-domain reflectometry method according to the final scheme, judge the actual effect of the anti-interference capability improvement from the test results, and obtain optimized operating parameters.

[0053] The iterative configuration module is used to obtain stability data of the system under high-frequency communication environment through optimized operating parameters, adjust the harness structure through iterative update algorithm, and determine the long-term operating configuration that meets dynamic balance.

[0054] The technical solutions provided by the embodiments of the present invention have the following beneficial effects:

[0055] This invention discloses an electronic wire harness anti-interference optimization method and system. It separates DC and high-frequency components using Fourier transform, employs a band-stop filter to block DC interference, extracts the core frequency band of the high-frequency signal, adjusts the signal amplitude using an adaptive equalization algorithm to address power supply noise, designs a multi-layer shielding structure to achieve electrical isolation based on voltage level requirements, adjusts the wire harness arrangement using a spatial optimization algorithm to balance isolation and coupling, optimizes the grounding path using a ground grid reconstruction algorithm to suppress ground loop interference, uses a dynamic balance adjustment algorithm to optimize the thickness of the isolation and coupling layers, determines the wire harness classification and management scheme, and finally uses a time-domain reflectometry method to detect signal distortion rate, evaluate the anti-interference effect, and adjusts the wire harness structure through an iterative update algorithm to achieve stable operation in a high-frequency communication environment.

[0056] This invention effectively improves the anti-interference capability and signal transmission quality of electronic wire harnesses in complex electromagnetic environments. Attached Figure Description

[0057] Figure 1 This is a flowchart of an electronic wire harness anti-interference optimization method according to the present invention.

[0058] Figure 2 This is a schematic diagram of the structure of an electronic wire harness anti-interference optimization system according to the present invention. Detailed Implementation

[0059] 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.

[0060] like Figure 1 As shown, in a first aspect, the present invention provides an electronic wire harness anti-interference optimization method, the method comprising:

[0061] S1: Obtain the characteristic data of DC interference and high-frequency signal in the electronic wire bundle, separate the DC component and high-frequency component through Fourier transform, and obtain the initial spectral distribution of DC interference blocking.

[0062] Optionally, this step also includes:

[0063] Step S11: Acquire signal data from the electronic wire bundle by acquiring the original waveform using a sensor to obtain the time-domain signal. Step S12: Process the time-domain signal using Fourier transform to decompose the DC component and high-frequency components, obtaining the initial spectral distribution. Step S13: Use the spectrum analysis tool in Matlab to separate the DC interference in the initial spectrum, extract high-frequency signal features, and determine the interference boundary. Step S14: If the DC component exceeds a preset DC component threshold, block the interference part using a Butterworth filter to obtain a clean spectrum. Step S15: Calculate the amplitude distribution of the high-frequency components based on the clean spectrum to determine the trend of signal characteristic changes. Step S16: Obtain statistical data on the changing trends, classify abnormal signals using a support vector machine in Scikit-learn, and determine the interference blocking effect. Step S17: Compare the original spectrum with the clean spectrum using the spectrum comparison function in Matplotlib to quantify the degree of DC interference separation and obtain the final spectral characteristics.

[0064] Specifically, when acquiring signal data from an electronic wire harness, the original waveform is typically collected by a sensor to obtain a time-domain signal.

[0065] For example, in automotive wiring harness testing, sensors can be installed at critical nodes to collect waveform data of voltage or current changes over time. This waveform may contain noise, DC offset, and high-frequency interference, reflecting the state of the wiring harness during actual operation.

[0066] It should be noted that the choice of sensor must be determined based on the signal frequency range and sensitivity requirements to ensure data accuracy. This provides a reliable foundation for subsequent analysis.

[0067] In one possible implementation, the time-domain signal is processed by Fourier transform, decomposing it into DC and high-frequency components to obtain the initial spectral distribution.

[0068] For example, assuming the acquired time-domain signal is a waveform with a period of 1 second, a Fourier transform reveals that the DC component is concentrated around 0 Hz, while the high-frequency components are distributed above 50 Hz. This decomposition helps identify the main components of the signal, laying the foundation for interference separation. Its beneficial effect lies in transforming complex time-domain signals into frequency-domain signals, facilitating analysis.

[0069] Specifically, the DC interference in the initial spectrum is separated using the spectrum analysis tools in Matlab, high-frequency signal features are extracted, and the interference boundary is determined.

[0070] For example, analyzing a signal with a 5V DC offset reveals a clear peak at 0Hz in the spectrum. By setting a frequency threshold, the DC component is separated, while high-frequency characteristics such as 100Hz harmonics are preserved. This method clearly distinguishes between interference and valid signals, improving analysis accuracy.

[0071] In one embodiment, if the DC component exceeds the DC component threshold, for example, greater than 2V, the interference is blocked by a Butterworth filter to obtain a clean spectrum.

[0072] Preferably, the cutoff frequency of the filter can be set to 10Hz to filter out low-frequency interference while retaining high-frequency signals.

[0073] For example, after filtering, the peak near 0Hz disappears, while the high-frequency components remain intact. This processing effectively removes unwanted DC offset, ensuring signal quality. The amplitude distribution of the high-frequency components is calculated based on the clean spectrum to determine the changing trend of signal characteristics.

[0074] For example, analyzing whether the amplitude at 50Hz and 100Hz increases over time can reflect aging of the wiring harness or poor contact.

[0075] Understandably, this trend analysis can help predict potential failures and improve system reliability. After obtaining statistical data on the changing trends, the abnormal signals are classified using a support vector machine in Scikit-learn to determine the effectiveness of interference blocking.

[0076] In one embodiment, assuming the normal signal amplitude is below 0.5V and the abnormal signal exceeds 1V, a support vector machine can distinguish between the two using training data. The classification result indicates whether the interference has been effectively blocked. Its advantage lies in automatically identifying anomalies and improving efficiency. The degree of separation of DC interference is quantified by comparing the original spectrum with the clean spectrum using the spectrum comparison function in Matplotlib.

[0077] For example, the original spectrum shows a DC peak of 5V, while the clean spectrum drops to 0.1V, achieving a separation rate of 98%. This visual comparison directly reflects the processing effect and provides a basis for optimizing the final spectral characteristics. Its technical advantage lies in facilitating the verification of algorithm effectiveness and ensuring the accuracy of signal analysis.

[0078] S2, based on the initial spectrum distribution, uses a band-stop filter to block DC components and low-frequency interference, and extracts the core frequency band for high-frequency signal transmission from the filtered signal.

[0079] Optionally, this step also includes:

[0080] Step S21: Obtain spectral distribution data from the initial spectrum and use spectral analysis to determine the range (0-10Hz) of the DC component and low-frequency interference. Step S22: For the range of low-frequency interference, use a fourth-order Butterworth band-stop filter to block the frequency band, obtaining a filtered signal. Step S23: Perform a Hanning windowed Fast Fourier Transform on the filtered signal, with the number of sampling points matching the length of the filtered signal, to obtain the frequency distribution of the high-frequency signal (greater than or equal to 10Hz). Step S24: Determine the characteristic frequency band of the transmission core based on the frequency points in the frequency distribution whose amplitude exceeds the maximum amplitude. Step S25: Determine the upper and lower boundaries of the characteristic frequency band using a preset cumulative energy threshold (90%). Step S26: For the upper and lower boundaries of the characteristic frequency band, use an FIR bandpass filter to extract the effective frequency band, obtaining the effective frequency band signal. Step S27: Calculate the ratio of the effective frequency band signal energy to the total energy of the initial spectrum. If the ratio is lower than a preset energy ratio threshold (70%), re-obtain spectral distribution data from the initial spectrum. Step S28: Output core frequency band data that meets the energy ratio requirement.

[0081] Specifically, after obtaining spectral distribution data through the initial spectrum, spectral analysis is the key to determining the range of DC components and low-frequency interference.

[0082] For example, in automotive wiring harness signal testing, the initial spectrum may show a DC peak at 0Hz, while low-frequency noise exists in the 0-10Hz range, which is usually caused by power supply fluctuations or environmental interference. During analysis, the boundary between the DC component and the low-frequency interference can be clearly identified by observing the areas of concentrated energy in the spectrum.

[0083] Specifically, if the spectrum shows a peak voltage of 3V at 0Hz and a relatively dense energy distribution in the 0-10Hz range, then this range can be preliminarily identified as an interference area. For blocking interference in the 0-10Hz range, a fourth-order Butterworth band-stop filter is a common choice.

[0084] It should be noted that the fourth-order design provides a steeper attenuation curve, ensuring effective filtering of low-frequency components while preserving signals above 10Hz as much as possible.

[0085] In one possible implementation, the upper and lower cutoff frequencies of the filter are set to 0.5Hz and 10Hz, respectively.

[0086] For example, assuming the original signal contains a 5V DC offset and 8Hz low-frequency noise, the 0-10Hz portion of the filtered spectrum will be significantly attenuated, with the peak value potentially dropping below 0.2V, while the higher frequency bands remain intact. Applying a Hanning-windowed Fast Fourier Transform to the filtered signal can further optimize the spectral resolution.

[0087] Understandably, the Hanning window reduces spectral leakage by smoothing signal edges, making it particularly suitable for line harness signals with low periodicity.

[0088] Preferably, the number of sampling points is consistent with the signal length. For example, 1024 points are taken for a 1-second signal, and after transformation, the frequency distribution above 10Hz can be clearly distinguished.

[0089] For example, the transformation result may show a significant amplitude at 20Hz, while there is no significant energy below 10Hz. When determining the core characteristic frequency band of transmission based on the frequency distribution, the frequency point where the amplitude exceeds 5% of the maximum amplitude is an important basis.

[0090] In one embodiment, if the maximum amplitude is 1V, then frequencies with amplitudes higher than 0.05V are of interest.

[0091] For example, the analysis revealed amplitudes of 0.8V, 0.6V, and 0.3V at 20Hz, 50Hz, and 100Hz, respectively, which can be considered as candidates for characteristic frequency bands. Next, the boundaries were determined by setting a cumulative energy threshold of 90%.

[0092] Specifically, energy is accumulated from low to high frequencies. When the accumulated energy reaches 90% of the total energy, assuming the boundary falls at 80Hz, the core frequency band is defined as 10–80Hz. For the core frequency band, an FIR bandpass filter can accurately extract the effective signal.

[0093] In one embodiment, the bandpass range is set to 10–80 Hz, and only this band of signal is retained after filtering.

[0094] For example, the original spectrum may contain 150Hz noise, which is completely removed after filtering, leaving a frequency band that better matches the transmission characteristics of the wire harness. Calculating the ratio of the effective frequency band energy to the total energy of the initial spectrum is a key step in verifying the effect.

[0095] For example, if the initial total energy is 100J and the effective frequency band energy is 60J, the ratio is 60%, which is lower than the energy ratio threshold set at 70%. Therefore, the data needs to be acquired again. This iteration ensures data quality. The final output core frequency band data must meet the energy ratio requirements.

[0096] For example, after adjustment, the ratio is increased to 75%, and the data includes amplitude distribution in the 10–80 Hz range, which can be directly used for subsequent analysis. This method, through multi-step processing, from interference separation to feature extraction, progressively optimizes the signal, providing reliable support for harness condition assessment.

[0097] S3 determines the magnitude of power supply noise impact on the core frequency band. If the magnitude exceeds the preset impact threshold, the signal amplitude is adjusted through an adaptive equalization algorithm to obtain a signal output with improved anti-interference capability.

[0098] Optionally, this step also includes:

[0099] Step S31: Obtain the influence amplitude from the power supply noise and determine whether it exceeds a preset influence amplitude threshold. Step S32: If the influence amplitude exceeds the preset influence amplitude threshold, adjust the signal amplitude using the minimum mean square error algorithm to obtain a preliminary processed signal. Step S33: Calculate the improvement in signal-to-noise ratio based on the preliminary processed signal to determine the adjusted signal characteristics. Step S34: Perform frequency domain analysis using Fast Fourier Transform on the adjusted signal characteristics to optimize the noise influence and obtain an optimized signal. Step S35: Extract the power spectral density of the core frequency band from the optimized signal to assess the actual performance of the anti-interference capability. Step S36: Based on the actual performance of the anti-interference capability, verify whether the signal output meets the requirements by judging the influence amplitude to obtain the final signal output.

[0100] Specifically, when obtaining the magnitude of the impact from power supply noise, it is usually necessary to clarify the specific degree of interference of the noise on the signal.

[0101] For example, in automotive wiring harness signal testing, power supply noise may originate from voltage fluctuations or electromagnetic interference, and the magnitude of its impact directly affects signal quality. One possible approach is to acquire the original signal using an oscilloscope and observe the noise waveform superimposed on the valid signal.

[0102] For example, assuming the normal signal amplitude is 2V, while the peak value introduced by power supply noise reaches 0.8V, the impact amplitude can be initially recorded as 0.8V.

[0103] It should be noted that the impact amplitude threshold is usually set based on the system's tolerance. For example, if the impact amplitude threshold is set to 0.5V, and the actual impact amplitude is 0.8V, it exceeds this threshold and requires further processing. This method quantifies the noise amplitude to provide a basis for subsequent adjustments. If the impact amplitude exceeds the threshold, adjusting the signal amplitude using the least mean square error algorithm is a common choice.

[0104] Specifically, the algorithm reduces the deviation of the signal by noise through iterative optimization.

[0105] In one embodiment, assuming the original signal contains 0.8V of noise, the algorithm can predict the ideal signal shape based on historical data and gradually reduce the noise amplitude to below 0.3V.

[0106] Preferably, an adaptive step size can be introduced during the adjustment process to ensure both convergence speed and accuracy.

[0107] Understandably, this method effectively smooths the signal, laying the foundation for subsequent analysis. When calculating the improvement in signal-to-noise ratio based on the initially processed signal, it is necessary to pay attention to the changes in signal quality before and after the adjustment.

[0108] For example, the initial signal-to-noise ratio may be 10dB, which is then adjusted to 15dB, indicating a significant reduction in the impact of noise.

[0109] In one possible implementation, this improvement can be quantified by power spectrum comparison to determine whether the signal characteristics are more stable. This analysis helps to determine the actual effect of the preprocessing. Frequency domain analysis using Fast Fourier Transform can further optimize the impact of noise.

[0110] For example, applying a transformation to the adjusted signal clearly reveals the distribution of each frequency component. Assuming the original signal contains 30Hz noise, the amplitude of its impact decreases from 0.6V to 0.1V after the transformation, indicating that the noise is effectively suppressed.

[0111] It should be noted that windowing techniques, such as the Hanning window, can be used in conjunction with other methods to reduce spectral leakage and improve analysis accuracy. This step highlights the effective frequency band and weakens interference components. When extracting the power spectral density of the core frequency band from the optimized signal, the focus is on evaluating its anti-interference capability.

[0112] In one embodiment, assuming the core frequency band is 20–60 Hz, the calculated power spectral density within this range is 0.5 W / Hz, while the interference frequency band is only 0.05 W / Hz, indicating that the signal has strong anti-interference capability.

[0113] Specifically, this characteristic can be visually presented through a frequency band energy distribution map. This quantification method provides data support for subsequent verification. Based on the actual performance of the anti-interference capability, the amplitude of the impact is used to determine whether the verification signal output meets the requirements.

[0114] For example, assuming the system requires the output signal amplitude to be stable above 1.5V and the noise amplitude to be below 0.2V, if the optimized signal displays a main signal of 1.8V and noise of 0.1V, then it meets the standard.

[0115] In one possible implementation, the test can be repeated multiple times to ensure consistent results.

[0116] Understandably, this verification ensures signal reliability and directly supports the accuracy of harness condition assessment.

[0117] S4 obtains the required data for voltage level separation from the signal output, and determines the isolation boundary between different voltage levels by designing an electrical isolation layer through a multi-layer shielding structure.

[0118] Optionally, this step also includes:

[0119] Step S41: Acquire the raw data stream by collecting signals through sensors, and process the raw data stream using Butterworth low-pass filtering to obtain denoised voltage waveform data. Step S42: Extract peak-valley value sequences from the voltage waveform data, and distinguish voltage levels using K-means clustering to obtain high-voltage and low-voltage characteristic values. Step S43: Construct an initial double-layer shielding structure based on the characteristic difference between the high-voltage and low-voltage characteristic values. The inner layer spacing is set to half of the characteristic difference, and the outer layer spacing is set to one-quarter of the characteristic difference, generating initial shielding distribution data. Step S44: Calculate the electric field strength of the initial shielding distribution data using finite element analysis. If the local field strength exceeds the field strength threshold (10kV / mm), proportionally increase the layer spacing in that region to obtain optimized shielding distribution data. Step S45: Extract the shielding node coordinates from the optimized shielding distribution data, generate a three-dimensional isolation mesh using Delaunay triangulation, and determine the isolation boundary surface. Step S46: Import the isolated boundary surface into the simulation environment, apply a voltage twice the characteristic value for simulation, detect the field strength of the entire region, and determine whether the field strength of the entire region is lower than the field strength threshold (10kV / mm).

[0120] Specifically, acquiring raw data streams by collecting signals through sensors is the foundation of voltage waveform analysis.

[0121] For example, in automotive wiring harness testing, a Hall effect sensor can be selected, which directly contacts the wiring harness conductor to collect voltage change data, typically in the frequency range of 0–100 Hz. The raw data stream may contain high-frequency interference or environmental noise, such as 50 Hz fluctuations during engine operation.

[0122] It should be noted that the sensor sampling rate needs to be more than twice the signal frequency, such as 200Hz, to ensure data integrity. Using a Butterworth low-pass filter to process the data can effectively remove high-frequency noise.

[0123] In one embodiment, the cutoff frequency can be set to 80Hz to ensure that the core signal is preserved.

[0124] In one embodiment, the raw data contains a 2V valid signal and 0.5V high-frequency interference. After filtering, the interference is reduced to below 0.1V, resulting in a smoother voltage waveform.

[0125] Specifically, Butterworth filtering, due to its flat passband characteristics, avoids signal distortion and is suitable for harness voltage analysis. Extracting the peak-valley sequence from the voltage waveform data is crucial for subsequent classification.

[0126] For example, local maxima and minima can be found by traversing the waveform, such as a peak of 2.2V and a valley of 1.0V, forming a sequence. One possible implementation uses a sliding window to detect extreme points, with the window width set to 10 sampling points to ensure accuracy.

[0127] Understandably, peak and trough values ​​reflect the range of voltage fluctuations, providing a basis for clustering. K-means clustering can be used to distinguish voltage levels, quickly separating high-voltage and low-voltage areas.

[0128] In one embodiment, assuming the peak-valley value sequence includes 1.0V, 1.5V, 2.0V, and 2.2V, and the number of clusters is set to 2, the characteristic value of the high-voltage region after clustering is approximately 2.1V, and the characteristic value of the low-voltage region is 1.2V.

[0129] Specifically, K-means iteratively optimizes the centroids to group the data, making it suitable for rapidly processing large numbers of samples. A key design feature is the establishment of a double-layer shielding structure based on the characteristic differences between high-pressure and low-pressure areas.

[0130] Preferably, the inner layer spacing is set to half of the feature difference, and the outer layer spacing is set to one-quarter of the feature difference.

[0131] For example, if the feature difference is 0.9V, the inner layer spacing is set to 0.45V and the outer layer spacing is 0.225V.

[0132] Preferably, the inner shielding layer can be made of a highly conductive material such as copper foil, and the outer layer can be made of an insulating layer such as polyethylene, forming a gradient protection.

[0133] It should be noted that this distribution effectively disperses electric field stress. The electric field strength is calculated using the finite element method, providing a quantitative basis.

[0134] In one possible implementation, the initial shielding distribution is imported into the mesh model. If the local field strength reaches 12kV / mm, exceeding the field strength threshold of 10kV / mm, the interlayer spacing is increased to 1.2 times the original, and the local field strength is reduced to 9kV / mm.

[0135] For example, this adjustment can balance the field strength distribution and enhance stability. Extracting node coordinates from the optimized shielding distribution and generating a 3D isolation mesh is the core of spatial modeling.

[0136] For example, after extracting the coordinates of 100 nodes, Delaunay triangulation can generate a regular triangular mesh with smooth and non-overlapping boundary surfaces.

[0137] Specifically, this method ensures mesh uniformity, facilitating simulation analysis. The isolated boundary surface is imported into ANSYS Maxwell for simulation to ensure design feasibility.

[0138] Preferably, twice the characteristic voltage, such as 4.2V, is applied to detect the field strength distribution.

[0139] For example, the field strength in the entire area is controlled below 8kV / mm, which is lower than the field strength threshold of 10kV / mm.

[0140] Understandably, this verification confirms the isolation effect and improves the reliability of wire harness protection.

[0141] S5. Based on the isolation boundary, a spatial optimization algorithm is used to adjust the arrangement of wire harnesses in the compact layout to obtain structural parameters that balance isolation and coupling.

[0142] Optionally, this step also includes:

[0143] Step S51: Obtain initial coordinate data for the wire harness arrangement through the isolation boundary, process the initial coordinates using simulated annealing algorithm, and output the optimized wire harness position matrix. Step S52: Extract the minimum bounding rectangle from the optimized position matrix as the compact layout boundary, and calculate the electromagnetic coupling strength of adjacent wire harnesses. Step S53: If the coupling strength exceeds a preset coupling strength threshold of 0.5, adjust the wire harness spacing using gradient descent and output the adjusted position matrix. Step S54: For the adjusted position matrix, calculate the ratio of layout space utilization to average coupling strength, and use K-means clustering to analyze the density distribution of wire harness coordinates. Step S55: Re-divide the isolation region boundaries based on the cluster center coordinates to generate a new position matrix containing the isolation spacing parameter. Step S56: Determine whether the standard deviation of the coupling strength in each region of the new matrix is ​​less than the standard deviation threshold of 0.1. If so, output the final wire harness arrangement.

[0144] Specifically, obtaining the initial coordinate data of the wire harness arrangement by isolating the boundary is the starting point for layout optimization.

[0145] For example, in automotive wiring harness design, isolation boundaries may be regions defined based on voltage levels. Initial coordinate data can be obtained by scanning the wiring harness position using sensors. Assume the starting coordinates of a wiring harness segment are (10, 20, 5). These coordinates reflect the initial distribution of the wiring harness in three-dimensional space. Using a simulated annealing algorithm to process the initial coordinates can effectively optimize the wiring harness position.

[0146] For example, simulated annealing involves randomly perturbing the coordinates, such as adjusting (10, 20, 5) to (12, 19, 6), and evaluating whether the change is acceptable based on an objective function. The objective function can be set as the optimal sum of the harness spacing and length. This method simulates the physical annealing process, avoiding getting trapped in local optima, and ultimately outputs an optimized position matrix, for example, a set of coordinates containing 50 harness nodes. Extracting the minimum bounding rectangle from the optimized position matrix as a compact layout boundary simplifies subsequent analysis.

[0147] In one possible implementation, assuming the coordinate range of the matrix is ​​x: 10–15, y: 19–25, and z: 5–8, the circumscribed rectangle boundary is (10, 19, 5) to (15, 25, 8). This boundary determination method ensures a compact layout and compliance with spatial constraints. Calculating the electromagnetic coupling strength of adjacent wire harnesses is crucial for assessing interference.

[0148] Specifically, the coupling value can be obtained by measuring the influence of the electric field between two wire bundles. Suppose that the coupling strength of an adjacent wire bundle pair is 0.6, which is higher than the coupling strength threshold of 0.5, indicating that the interference exceeds the standard.

[0149] It should be noted that the coupling strength is closely related to the wire harness spacing and current intensity. If the coupling strength exceeds the coupling strength threshold, the wire harness spacing is adjusted using the gradient descent method.

[0150] For example, increasing the spacing between two wire harnesses from 2mm to 3mm may reduce the coupling strength to 0.4. This adjustment is based on gradient descent to gradually approximate the optimal solution, outputting an adjusted position matrix that ensures controllable interference. For the adjusted position matrix, the ratio of layout space utilization to average coupling strength is calculated, reflecting design efficiency.

[0151] Preferably, the space utilization rate is defined as the ratio of the volume occupied by the wire harness to the volume of the circumscribed rectangle, assumed to be 0.8. With an average coupling strength of 0.3, the ratio is 2.67. This metric quantifies the balance of the layout. Using K-means clustering analysis to analyze the density distribution of the wire harness coordinates can further optimize the area division.

[0152] In one embodiment, 50 coordinate points are clustered into 3 clusters with center coordinates of (12,22,6), (14,20,7), and (11,24,5), reflecting high-density areas. The boundaries of the isolation regions are redefined based on the cluster centers to form a new matrix containing an isolation spacing parameter such as 2.5 mm. Determining whether the standard deviation of the coupling strength of each region in the new matrix is ​​less than the standard deviation threshold of 0.1 is the basis for verifying uniformity.

[0153] For example, the coupling strength values ​​in a certain region are 0.35, 0.38, and 0.36, with a standard deviation of approximately 0.015, which meets the requirements.

[0154] Understandably, this uniformity reduces the risk of local interference, resulting in a more reliable final output harness arrangement.

[0155] S6. Based on the structural parameters, determine the current distribution of ground loop interference. If the current distribution is uneven, optimize the grounding path through the grounding grid reconstruction algorithm to obtain a layout scheme for interference suppression.

[0156] Optionally, this step also includes:

[0157] Step S61: Extract ground loop impedance parameters using COMSOL and calculate the current density values ​​of each node. Step S62: Determine the distribution uniformity based on the comparison between the current density standard deviation and the current density threshold of 0.15. Step S63: When uniformity is not met, mark areas where the current density deviates from the mean by more than 30% as adjustment ranges. Step S64: Regenerate grounding grid nodes in the marked areas using the Delaunay triangulation algorithm to generate optimized grounding paths. Step S65: Import the new paths into Altium Designer to adjust the PCB layout and obtain preliminary suppression results. Step S66: Use MATLAB to perform a Fast Fourier Transform on the ground loop noise spectrum before and after suppression. When the amplitude at the 50Hz frequency point does not decrease by more than 20dB, initiate the parameter update process. Step S67: Iteratively correct the trace width and interlayer spacing parameters based on the gradient descent method to generate a new layout scheme. Step S68: Verify the noise attenuation across the entire frequency band using ANSYS Maxwell simulation. Output the final solution when the interference intensity at each frequency point within 1MHz is below 40dBμV / m.

[0158] Specifically, when extracting ground loop impedance parameters using COMSOL, it is understood that this process relies on finite element simulation technology, which can accurately capture the electromagnetic characteristics of the ground loop.

[0159] For example, in a typical PCB design, an engineer might use COMSOL to build a 3D model of the ground loop, set material properties such as the conductivity of copper being 5.8 × 10^7 S / m, and then apply boundary conditions to simulate the actual current input and output impedance distribution.

[0160] Preferably, the impedance parameters are derived in matrix form for subsequent current density calculations. When calculating the current density values ​​at each node, for example, the current distribution at each node can be analyzed based on the impedance distribution diagram and Kirchhoff's current law.

[0161] Specifically, in a multilayer PCB, if the input current is 1A, a certain node may only share 0.2A due to its higher impedance, while the neighboring node shares 0.8A, reflecting the uneven distribution.

[0162] It should be noted that the standard deviation can be quickly calculated using MATLAB. Assuming the calculated value is 0.18, which exceeds the preset current density threshold of 0.15, subsequent adjustments will be triggered.

[0163] When determining the uniformity of distribution, in one possible implementation, marking areas where the current density deviates from the mean by more than 30% may be key.

[0164] For example, if the average current density is 0.5 A / cm², and a certain area reaches 0.65 A / cm², it is marked as a high-density area. This method can quickly locate problem areas, facilitate optimization of grounding design, and effectively reduce the risk of localized overheating.

[0165] When regenerating grounding grid nodes using the Delaunay triangulation algorithm, it is understandable that the algorithm ensures uniform node distribution through geometric optimization.

[0166] For example, in a 10cm x 10cm PCB area, the original nodes are randomly distributed. After partitioning, 50 uniform triangular units can be generated, with the node spacing within each unit controlled at approximately 2cm. This method can improve the connectivity of the grounding path and reduce impedance concentration.

[0167] When importing new paths into Altium Designer to adjust the PCB layout, specifically, the width of the ground trace can be increased from 0.5mm to 0.8mm using manual or automatic routing tools to increase current carrying capacity.

[0168] Preferably, the adjusted preliminary suppression results can be verified by the built-in simulation module to observe the noise reduction trend and provide a basis for subsequent spectrum analysis.

[0169] When using MATLAB to perform Fast Fourier Transform analysis of the noise spectrum, in one embodiment, the amplitude at the 50Hz frequency point may drop from -10dB to -25dB, but does not reach the target of 20dB.

[0170] For example, the spectrum analysis shows that before adjustment, noise was concentrated in the low-frequency band, while after adjustment, high-frequency interference was reduced. This step visually reflects the noise suppression effect and provides data support for parameter updates.

[0171] When iteratively correcting trace width and interlayer spacing using the gradient descent method, for example, the initial trace width can be set to 1mm and the interlayer spacing to 0.2mm, and after 10 iterations, it can be optimized to 1.2mm and 0.25mm.

[0172] Specifically, this fine-tuning can gradually balance current density and electromagnetic shielding requirements, improving the stability of the overall layout.

[0173] When verifying the noise attenuation across the entire frequency band using ANSYS Maxwell simulation, one possible implementation is to set the frequency range to 10Hz to 1MHz and observe the interference intensity at each frequency point.

[0174] For example, after adjustment, the interference at 500kHz decreased from 50dBμV / m to 35dBμV / m, meeting the requirement of less than 40dBμV / m. This full-band verification ensures the reliability of the design in various operating scenarios, helping to improve product performance and electromagnetic compatibility.

[0175] S7 extracts evaluation indicators of high-frequency signal transmission efficiency from the layout scheme, optimizes the thickness of the isolation layer and coupling layer through a dynamic balance adjustment algorithm, and determines the final wire harness classification and management scheme.

[0176] Optionally, this step also includes:

[0177] Step S71: Obtain high-frequency signal data through the layout scheme, and extract the transmission efficiency evaluation index using the support vector machine algorithm to obtain the quantization result. Step S72: Determine the dynamic balance state based on the quantization result. If the transmission efficiency is lower than the transmission efficiency threshold, calculate the adjustment value of the isolation layer thickness using an adjustment algorithm to determine the optimization parameters. Step S73: Calculate the adjustment value of the coupling layer thickness using an adjustment algorithm to obtain a complete layer thickness adjustment scheme and obtain the adjusted layer thickness data. Step S74: Extract signal optimization features from the adjusted layer thickness data, use a clustering algorithm to classify the wire harnesses into categories, and determine the classification result. Step S75: Obtain the structure of the management scheme based on the classification result, use a decision tree algorithm to optimize the wire harness classification allocation rules, and obtain the final scheme. Step S76: Extract updated transmission efficiency data from the final scheme, and determine whether the layer thickness adjustment meets expectations through information processing to obtain the verification result. Step S77: Determine the stability of the signal optimization based on the verification result. If the stability reaches the stability threshold, determine the final output of the wire harness classification management and obtain the complete solution.

[0178] Specifically, when acquiring high-frequency signal data through layout schemes, it is understandable that this process typically relies on simulation tools or test equipment to capture signal characteristics.

[0179] For example, in PCB design, engineers may use an oscilloscope to measure high-frequency signals on transmission lines and record waveform data at a frequency of 10 GHz for subsequent analysis.

[0180] When using the support vector machine algorithm to extract evaluation metrics for transmission efficiency, for example, signal amplitude and attenuation rate can be used as input features, and the efficiency value, such as 85%, can be output after training the model.

[0181] Specifically, this method distinguishes between efficient and inefficient transmission states by classifying boundaries, and the quantification results intuitively reflect signal quality.

[0182] When judging the state of dynamic equilibrium based on the quantification results, in one possible implementation, if the transmission efficiency is lower than the transmission efficiency threshold of 90%, it indicates that the system is unbalanced.

[0183] For example, if the transmission efficiency in a certain design is 80%, it indicates that optimization is needed.

[0184] When calculating the adjustment value of the isolation layer thickness using the adjustment algorithm, it is preferable to adjust the thickness based on an empirical model.

[0185] For example, if the initial isolation layer thickness is 0.3mm, the algorithm predicts that increasing it to 0.35mm can improve the shielding effect.

[0186] Specifically, the adjustment value for the coupling layer thickness can be determined through electromagnetic simulation when calculating the adjustment value.

[0187] For example, the coupling layer thickness is adjusted from 0.5 mm to 0.55 mm to ensure minimal interlayer interference, and the layer thickness data is updated after obtaining the complete scheme. Then, features for signal optimization are extracted from the adjusted layer thickness data.

[0188] It should be noted that features may include signal integrity and crosstalk level.

[0189] For example, the crosstalk was reduced from 20mV to 15mV after adjustment.

[0190] When using clustering algorithms to classify wire harnesses, in one embodiment, the K-means algorithm can be used to classify wire harnesses into three categories based on current load, such as high load, medium load, and low load, resulting in clear classification results.

[0191] When determining the structure of a management solution based on the classification results, for example, a hierarchical management model can be designed, with each type of wire harness corresponding to specific wiring rules.

[0192] When using the decision tree algorithm to optimize the allocation rules for wire harness classification, branching conditions can be set according to the wire harness length and load, for example.

[0193] For example, items longer than 5cm are classified as high-load items, resulting in a more reasonable final allocation.

[0194] When extracting updated data on transmission efficiency from the final solution, specifically, if the transmission efficiency may increase from 80% to 92%, the information processing stage will determine whether the adjustment meets expectations.

[0195] For example, the verification showed that signal loss was reduced after the display layer thickness was optimized, and the effect met the requirements.

[0196] When judging the stability of signal optimization based on the verification results, in one possible implementation, if the transmission efficiency fluctuation is less than 2%, it is considered stable.

[0197] For example, the efficiency in multiple tests is between 91% and 93%, which meets the transmission efficiency threshold requirement.

[0198] When determining the final output of wire harness classification management, it is preferable to generate a complete wiring diagram and parameter table.

[0199] For example, the diagram clearly indicates the type and location of the wiring harness, providing a comprehensive solution. This approach improves design efficiency, ensures reliable signal transmission and system stability, and lays a solid foundation for subsequent production.

[0200] S8. According to the final scheme, the distortion rate in signal transmission is detected by the time-domain reflectometry method. The actual effect of the improved anti-interference capability is judged from the detection results, and the optimized operating parameters are obtained.

[0201] Optionally, this step also includes:

[0202] Step S81: Signal transmission is detected using the time-domain reflectometry method to obtain distortion rate data. Step S82: The degree of abnormality in the detection results is determined by calculating the deviation between the distortion rate and a distortion rate threshold. The distortion rate threshold is determined based on historical data statistics. Step S83: The improvement in anti-interference capability is determined based on the trend of the detection results. The trend is determined through linear regression analysis. Step S84: If the anti-interference capability is lower than expected, a new parameter set is generated by adjusting the operating parameters, including signal frequency and power. Step S85: The new parameter set is classified using a support vector machine algorithm to obtain an optimized parameter combination. The support vector machine algorithm uses a radial basis function as the kernel function. Step S86: Signal transmission is re-detected using the optimized parameter combination to obtain new distortion rate data. The re-detection uses the same time-domain reflectometry method. Step S87: The improvement effect is determined by comparing the new distortion rate data with the initial data. The comparison method uses mean square error calculation.

[0203] Specifically, when using the time-domain reflectometry method to detect signal transmission, anomalies in the transmission path can be analyzed by sending pulse signals and receiving reflected waves.

[0204] For example, in a high-frequency signal transmission line, a pulse signal will be reflected when it encounters an impedance mismatch point. By measuring the time delay and amplitude of the reflected wave, the distortion rate can be calculated.

[0205] For example, if the distortion rate in the initial detection is 5%, while the distortion rate threshold in historical data statistics is 3%, then the deviation is 2%, indicating that there is a certain anomaly.

[0206] In one possible implementation, after calculating the deviation between the distortion rate and the distortion rate threshold, a distortion rate distribution map can be drawn using visualization tools to intuitively determine the degree of anomaly.

[0207] Specifically, if the distortion rate of a certain section of the line consistently exceeds the distortion rate threshold, it may indicate that there are loose joints or aging materials in that section. This method facilitates rapid location of the problem area and improves detection efficiency.

[0208] It should be noted that the trend of the test results can be determined through linear regression analysis.

[0209] For example, the distortion rates for 10 consecutive tests were 5%, 4.8%, 4.5%, 4.7%, and 4.3%, respectively. A decreasing curve was fitted by linear regression, and the slope of the curve reflected the improvement in anti-interference ability.

[0210] In one embodiment, a slope of -0.2 indicates a gradual improvement in robustness. This analysis helps predict long-term performance. If robustness is lower than expected, operating parameters are adjusted to generate a new parameter set.

[0211] Preferably, the signal frequency can be adjusted from 10MHz to 12MHz, while the power is increased from 5W to 6W.

[0212] For example, this adjustment might improve transmission quality because the increased frequency reduces the superposition of external interference, while the increased power enhances the signal strength.

[0213] Understandably, when classifying a new parameter set, the support vector machine algorithm uses radial basis functions as kernel functions, which can effectively handle nonlinear relationships.

[0214] For example, inputting various combinations of frequency and power into the algorithm might yield a set of optimized parameters, such as a frequency of 11.5MHz and a power of 5.8W. This classification method can filter out the optimal solution from multidimensional data, improving the scientific rigor of parameter tuning.

[0215] In one embodiment, after re-detection by optimizing the parameter combination, the new distortion rate may be reduced to 2.5%, which is below the distortion rate threshold of 3%.

[0216] Specifically, the re-detection still uses the time-domain reflectometry method to ensure consistent detection conditions and facilitate comparison before and after. This consistency helps verify the effectiveness of the adjustment.

[0217] For example, when comparing the new distortion rate with the initial data, the mean square error calculation can reflect the improvement effect. The small mean square error between the initial distortion rate of 5% and the new distortion rate of 2.5% indicates that the parameter adjustment significantly optimized the signal transmission.

[0218] Preferably, this comparison can also reveal potential stability issues; if the fluctuations in multiple test results are small, it proves the reliability of the solution. This method not only improves the anti-interference capability but also provides data support for subsequent optimization.

[0219] S9 obtains system stability data under high-frequency communication environment through optimized operating parameters, adjusts the harness structure through iterative update algorithm, and determines the long-term operating configuration that meets dynamic balance.

[0220] Optionally, this step also includes:

[0221] Step S91: Obtain stability data from the high-frequency communication environment, and use the support vector machine algorithm to perform preliminary classification of the data to obtain an initial stability distribution. Step S92: Based on the initial stability distribution, determine the initial values ​​of the harness structure parameters, adjust the harness structure features using the gradient descent algorithm, and determine the intermediate dynamic equilibrium state. Step S93: Obtain operating data under the intermediate dynamic equilibrium state, determine whether the data fluctuation exceeds the data fluctuation threshold, and if so, use the random forest algorithm to optimize the harness structure to obtain the adjusted structure configuration. Step S94: Simulate long-term operation under the high-frequency communication environment using the adjusted structure configuration, obtain system response data, and determine the long-term stability trend. Step S95: Based on the long-term stability trend, analyze the impact of the system environment on dynamic equilibrium, calculate the deviation value using statistical tools, and obtain the environmental adaptability distribution. Step S96: Based on the environmental adaptability distribution, adjust the matching relationship between the operating parameters and the harness structure to determine the final configuration that satisfies dynamic equilibrium. Step S97: Obtain operating data under the final configuration, compare the initial stability distribution with the final data, determine the long-term stability of the system under the high-frequency communication environment, and obtain the optimized operating scheme.

[0222] Specifically, when acquiring stability data in a high-frequency communication environment, it can be achieved by monitoring key indicators in the signal transmission process in real time.

[0223] In one possible implementation, the acquired data includes signal amplitude, phase shift, and noise level. This data is recorded 1000 times per second using a high-speed sampling device to ensure that subtle changes in the high-frequency environment are captured. When the support vector machine algorithm performs initial classification of the data, it can divide the data into "stable" and "unstable" categories based on stability characteristics.

[0224] For example, if a signal amplitude fluctuation is less than 0.5 volts and noise is below -60 dB, it is classified as "stable," and vice versa, thus forming an initial stability distribution. This distribution intuitively reflects the system's performance baseline under the current environment.

[0225] When determining the initial values ​​of the wire harness structural parameters based on the initial stability distribution, it is preferable to set the insulation thickness of the wire harness to 1.2 mm and the conductor spacing to 0.8 mm.

[0226] When adjusting the harness structure characteristics using the gradient descent algorithm, it's understandable to gradually reduce the conductor spacing to 0.6 mm and observe whether the system response becomes smoother. If, during the intermediate dynamic equilibrium state, the fluctuations in the operating data exceed the data fluctuation threshold (e.g., amplitude changes exceeding 1 volt), further optimization is required.

[0227] Specifically, the random forest algorithm can analyze 50 sets of historical wiring harness configuration data to select a combination of conductor spacing of 0.7 mm and insulation thickness of 1.5 mm as the adjusted structural configuration. This configuration exhibits lower noise interference in simulations.

[0228] In one embodiment, during long-term operation in a simulated high-frequency communication environment, the system can be set to run continuously for 72 hours, recording system response data, such as the signal attenuation rate decreasing from an initial 2% to 1.5%. The long-term stability trend is plotted using these data, and analysis shows that the system tends to stabilize after 48 hours.

[0229] When using statistical tools to calculate deviation values, for example, using standard deviation analysis to analyze the dispersion of response data, it was found that the deviation value decreased from 0.8 to 0.3, indicating enhanced environmental adaptability.

[0230] When adjusting the operating parameters and matching the harness structure to adapt to environmental conditions, it is preferable to fine-tune the signal frequency from 2.4GHz to 2.45GHz, so that the final configuration is more adaptable to dynamic balance.

[0231] After obtaining the operating data under the final configuration, comparing the initial stability distribution with the final data reveals that the initial instability rate was 30%, which eventually decreased to 10%. This improvement indicates a significant enhancement in the long-term stability of the system under high-frequency communication environments.

[0232] For example, in practical applications, the communication interruption rate decreased from 5 times per hour to 1 time per hour, and the optimized operating scheme effectively improved reliability. This method, through multi-level analysis and adjustment, ensured the adaptability and practicality of the scheme, providing data support for subsequent system design.

[0233] like Figure 2 As shown, in a second aspect, the present invention provides an electronic wire harness anti-interference optimization system, which optimizes the electronic wire harness for anti-interference using the method described above. The system includes:

[0234] The spectrum analysis module is used to acquire characteristic data of DC interference and high-frequency signals in the electronic wire bundle. It separates the DC component and the high-frequency component through Fourier transform to obtain the initial spectrum distribution of DC interference blocking.

[0235] The band-stop filter module is used to block DC components and low-frequency interference based on the initial spectrum distribution, and to extract the core frequency band for high-frequency signal transmission from the filtered signal.

[0236] The adaptive equalization module is used to determine the magnitude of power supply noise impact on the core frequency band. If the magnitude exceeds the preset impact magnitude threshold, the signal amplitude is adjusted through the adaptive equalization algorithm to obtain a signal output with improved anti-interference capability.

[0237] The isolation design module is used to obtain the voltage level separation requirement data from the signal output, design the electrical isolation layer through a multi-layer shielding structure, and determine the isolation boundary between different voltage levels.

[0238] The space optimization module is used to adjust the arrangement of wire harnesses in a compact layout based on the isolation boundary using a space optimization algorithm, so as to obtain structural parameters that balance isolation and coupling.

[0239] The grounding optimization module is used to determine the current distribution of ground loop interference based on structural parameters. If the current distribution is uneven, the grounding path is optimized through the grounding grid reconstruction algorithm to obtain a layout scheme for interference suppression.

[0240] The dynamic balancing module is used to extract evaluation indicators of high-frequency signal transmission efficiency from the layout scheme, optimize the thickness of the isolation layer and coupling layer through the dynamic balancing adjustment algorithm, and determine the final wire harness classification and management scheme.

[0241] The performance testing module is used to detect the distortion rate in signal transmission using the time-domain reflectometry method according to the final scheme, judge the actual effect of the anti-interference capability improvement from the test results, and obtain optimized operating parameters.

[0242] The iterative configuration module is used to obtain stability data of the system under high-frequency communication environment through optimized operating parameters, adjust the harness structure through iterative update algorithm, and determine the long-term operating configuration that meets dynamic balance.

[0243] The above description of the embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing anti-interference of electronic wire bundles, characterized in that, The method includes: S1, acquire the characteristic data of DC interference and high-frequency signal in the electronic wire bundle, separate the DC component and high-frequency component through Fourier transform, and obtain the initial spectrum distribution of DC interference blocking; S2, based on the initial spectrum distribution, uses a band-stop filter to block DC components and low-frequency interference, and extracts the core frequency band for high-frequency signal transmission from the filtered signal; S3, for the core frequency band, determines the magnitude of the power supply noise impact. If the magnitude exceeds the preset impact magnitude threshold, the signal amplitude is adjusted through an adaptive equalization algorithm to obtain a signal output with improved anti-interference capability. S4: Obtain the required data for voltage level separation from the signal output, design the electrical isolation layer through a multi-layer shielding structure, and determine the isolation boundary between different voltage levels; S5. Based on the isolation boundary, a spatial optimization algorithm is used to adjust the arrangement of wire harnesses in the compact layout to obtain structural parameters that balance isolation and coupling. S6. Based on the structural parameters, determine the current distribution of ground loop interference. If the current distribution is uneven, optimize the grounding path through the grounding grid reconstruction algorithm to obtain a layout scheme for interference suppression. S7. Extract evaluation indicators of high-frequency signal transmission efficiency from the layout scheme, optimize the thickness of the isolation layer and coupling layer through dynamic balance adjustment algorithm, and determine the final wire harness classification management scheme. S8. According to the final scheme, the distortion rate in signal transmission is detected by the time domain reflection method. The actual effect of the improved anti-interference capability is judged from the detection results, and the optimized operating parameters are obtained. S9 obtains system stability data under high-frequency communication environment through optimized operating parameters, adjusts the harness structure through iterative update algorithm, and determines the long-term operating configuration that meets dynamic balance.

2. The method according to claim 1, characterized in that, Step S1 involves acquiring characteristic data of DC interference and high-frequency signals in the electronic wire bundle, separating the DC component and high-frequency component using Fourier transform, and obtaining the initial spectral distribution for DC interference blocking, including: Step S11: Acquire signal data from the electronic wire bundle, and obtain the time-domain signal by acquiring the original waveform through the sensor; Step S12: Process the time-domain signal through Fourier transform to decompose the DC component and high-frequency component to obtain the initial spectrum distribution; Step S13: Use the spectrum analysis tool in Matlab to separate the DC interference in the initial spectrum, extract high-frequency signal features, and determine the interference boundary; Step S14: If the DC component exceeds the preset DC component threshold, the interference is blocked by a filter to obtain a clean spectrum. Step S15: Calculate the amplitude distribution of high-frequency components based on the pure spectrum and determine the changing trend of signal characteristics; Step S16: Obtain statistical data on the changing trend, classify the abnormal signals using the support vector machine in Scikit-learn, and determine the interference blocking effect; Step S17: Compare the original spectrum with the clean spectrum using the spectrum comparison function in Matplotlib to quantify the degree of separation of DC interference and obtain the final spectrum characteristics.

3. The method according to claim 2, characterized in that, In step S14, if the DC component exceeds a preset DC component threshold, the interference is blocked by a filter to obtain a clean spectrum, including the Butterworth filter.

4. The method according to claim 3, characterized in that, The upper and lower cutoff frequencies of the filter are set to 0.5Hz and 10Hz, respectively.

5. The method according to claim 1, characterized in that, Step S2, based on the initial spectral distribution, uses a band-stop filter to block DC components and low-frequency interference, and extracts the core frequency band for high-frequency signal transmission from the filtered signal, including: Step S21: Obtain spectral distribution data from the initial spectrum and use spectral analysis to determine the range of DC component and low-frequency interference; Step S22: For the range of the low-frequency interference, use a fourth-order Butterworth band-stop filter to block the frequency band and obtain the filtered signal; Step S23: Perform a Hanning windowed Fast Fourier Transform on the filtered signal, with the number of sampling points matching the length of the filtered signal, to obtain the frequency distribution of the high-frequency signal; Step S24: Determine the characteristic frequency band of the transmission core based on the frequency points in the frequency distribution whose amplitude exceeds the maximum amplitude. Step S25: Determine the upper and lower boundaries of the characteristic frequency band by using a preset cumulative energy threshold; Step S26: For the upper and lower boundaries of the characteristic frequency band, an FIR bandpass filter is used to extract the effective frequency band to obtain the effective frequency band signal; Step S27: Calculate the ratio of the effective frequency band signal energy to the total initial spectrum energy. If the ratio is lower than a preset energy ratio threshold, then re-acquire the spectrum distribution data from the initial spectrum. Step S28: Output core frequency band data that meets the energy ratio requirement.

6. The method according to claim 5, characterized in that, Step S21 involves obtaining spectral distribution data from the initial spectrum and using spectral analysis to determine the range of DC component and low-frequency interference, including setting the range of low-frequency interference to 0–10 Hz.

7. The method according to claim 6, characterized in that, Step S23 involves performing a Hanning windowed Fast Fourier Transform on the filtered signal, with the number of sampling points matching the length of the filtered signal, to obtain the frequency distribution of the high-frequency signal, including: the high-frequency signal being a signal with a frequency greater than or equal to 10Hz.

8. The method according to claim 1, characterized in that, Step S4 involves obtaining the required data for voltage level separation from the signal output, designing an electrical isolation layer using a multi-layer shielding structure, and determining the isolation boundaries between different voltage levels, including: Step S41: Obtain the raw data stream by acquiring signals through the sensor, and process the raw data stream using Butterworth low-pass filter to obtain the denoised voltage waveform data; Step S42: Extract peak-valley value sequences from the voltage waveform data, distinguish voltage levels using K-means clustering, and obtain characteristic values ​​for high-voltage and low-voltage areas; Step S43: Construct an initial double-layer shielding structure based on the characteristic difference between the high-voltage zone characteristic value and the low-voltage zone characteristic value, and generate initial shielding distribution data; Step S44: The electric field strength of the initial shielding distribution data is calculated using the finite element analysis method. If the local field strength exceeds the field strength threshold, the interlayer spacing in that region is proportionally increased to obtain optimized shielding distribution data. Step S45: Extract the coordinates of the shielding nodes from the optimized shielding distribution data, generate a three-dimensional isolation mesh through Delaunay triangulation, and determine the isolation boundary surface; Step S46: Import the isolated boundary surface into the simulation environment, apply a voltage twice the characteristic value for simulation, detect the field strength of the entire region, and determine whether the field strength of the entire region is lower than the field strength threshold.

9. The method according to claim 8, characterized in that, Step S43 involves constructing an initial double-layer shielding structure based on the characteristic difference between the high-pressure zone characteristic value and the low-pressure zone characteristic value, and generating initial shielding distribution data, including setting the inner layer spacing to half of the characteristic difference and the outer layer spacing to one-quarter of the characteristic difference.

10. An electronic wire harness anti-interference optimization system, characterized in that, The system optimizes the anti-interference performance of an electronic wire harness using the method described in any one of claims 1-9, wherein the system comprises: The spectrum analysis module is used to acquire characteristic data of DC interference and high-frequency signals in the electronic wire bundle. It separates the DC component and the high-frequency component through Fourier transform to obtain the initial spectrum distribution of DC interference blocking. The band-stop filter module is used to block DC components and low-frequency interference based on the initial spectrum distribution, and to extract the core frequency band for high-frequency signal transmission from the filtered signal. The adaptive equalization module is used to determine the magnitude of power supply noise impact on the core frequency band. If the magnitude exceeds the preset impact magnitude threshold, the signal amplitude is adjusted through the adaptive equalization algorithm to obtain a signal output with improved anti-interference capability. The isolation design module is used to obtain the voltage level separation requirement data from the signal output, design the electrical isolation layer through a multi-layer shielding structure, and determine the isolation boundary between different voltage levels. The space optimization module is used to adjust the arrangement of wire harnesses in a compact layout based on the isolation boundary using a space optimization algorithm, so as to obtain structural parameters that balance isolation and coupling. The grounding optimization module is used to determine the current distribution of ground loop interference based on structural parameters. If the current distribution is uneven, the grounding path is optimized through the grounding grid reconstruction algorithm to obtain a layout scheme for interference suppression. The dynamic balancing module is used to extract evaluation indicators of high-frequency signal transmission efficiency from the layout scheme, optimize the thickness of the isolation layer and coupling layer through the dynamic balancing adjustment algorithm, and determine the final wire harness classification and management scheme. The performance testing module is used to detect the distortion rate in signal transmission using the time-domain reflectometry method according to the final scheme, judge the actual effect of the anti-interference capability improvement from the test results, and obtain optimized operating parameters. The iterative configuration module is used to obtain stability data of the system under high-frequency communication environment through optimized operating parameters, adjust the harness structure through iterative update algorithm, and determine the long-term operating configuration that meets dynamic balance.

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