Anti-interference method and anti-interference system for mainboard

Abstract

The present invention provides an anti-interference method and an anti-interference system for a mainboard, which relate to the technical field of mainboards. The method comprises the following steps: obtaining a mixed signal in an environment in which the mainboard is located, and performing multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, wherein the mixed signal includes an interference signal and a useful signal; performing signal classification processing based on a spectrum diagram of the signal component at each frequency scale to obtain a spectrum diagram of the interference signal at each frequency scale; matching a corresponding mask strategy from a preset strategy library and performing mask processing according to the frequency level of the frequency scale to which the spectrum diagram of the interference signal belongs; performing inverse transformation on the spectrum diagram of the interference signal after mask processing to obtain a denoised interference signal; and performing multi-scale weighted reconstruction on the useful signal and the denoised interference signal to obtain a denoised mixed signal. The present invention achieves effective suppression of complex interference signals in the environment in which the mainboard is located, thereby improving the stability and anti-interference capability of the mainboard.

Description

Technical Field

[0001] The present invention relates to the technical field of motherboards, and in particular to an anti-interference method and an anti-interference system for a motherboard. Background Art

[0002] In modern electronic systems, the motherboard, as the core component, has a crucial impact on overall performance due to its stability and anti-interference capabilities. With the rapid development of electronic technology, the motherboard's operating environment has become increasingly complex, with electromagnetic interference and noise pollution becoming increasingly prominent. These interfering signals often mix into the useful signals received by the motherboard, causing signal degradation and even leading to system errors or failures.

[0003] Traditional anti-interference methods mostly rely on hardware filtering or simple digital signal processing algorithms. These methods are often ineffective when dealing with complex and variable interference signals, making it difficult to effectively extract pure, useful signals. This is especially true at different frequency scales, such as high, medium, and low frequencies, where the characteristics and impact of interference signals vary. Traditional single anti-interference strategies are unable to cope with this multi-scale interference problem.

[0004] Therefore, it is necessary to provide an anti-interference method and an anti-interference system for a mainboard to solve the above technical problems. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides an anti-interference method and anti-interference system for a motherboard. Through multi-scale decomposition, spectrum analysis, signal classification, mask processing, multi-scale reconstruction and post-processing, it achieves effective suppression of complex interference signals in the environment where the motherboard is located, and improves the stability and anti-interference ability of the motherboard.

[0006] The present invention provides an anti-interference method for a mainboard, the anti-interference method comprising the following steps:

[0007] Acquire a mixed signal in the environment where the motherboard is located, and perform multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, wherein the mixed signal includes an interference signal and a useful signal;

[0008] Performing signal classification processing based on the spectrum of the signal component at each frequency scale to obtain the spectrum of the interference signal at each frequency scale;

[0009] According to the frequency level of the frequency scale to which the spectrum diagram of the interference signal belongs, matching the corresponding mask strategy from a preset strategy library and performing mask processing, wherein the frequency level includes high frequency, medium frequency and low frequency;

[0010] Performing an inverse transformation on the spectrum of the interference signal after masking to obtain the denoised interference signal;

[0011] Multi-scale weighted reconstruction is performed on the useful signal and the denoised interference signal to obtain a denoised mixed signal.

[0012] Preferably, the acquiring of a mixed signal in the environment where the mainboard is located and performing multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales includes:

[0013] A sensor array mounted on the mainboard collects mixed signals in the environment in real time, wherein the sensor array includes at least one sensor for detecting electromagnetic interference and one sensor for detecting the operating status of the mainboard;

[0014] Preprocessing the collected mixed signal, wherein the preprocessing includes signal amplification, filtering, and analog-to-digital conversion;

[0015] The variational mode decomposition (VMD) method is used to perform multi-scale decomposition on the preprocessed mixed signal to obtain signal components containing different frequency components, each of which corresponds to a frequency scale.

[0016] Preferably, the signal classification processing is performed based on the spectrum of the signal component at each frequency scale to obtain the spectrum of the interference signal at each frequency scale, including:

[0017] Applying a fast Fourier transform to convert the signal components at each frequency scale from the time domain to the frequency domain, obtaining a frequency spectrum of the signal components at each frequency scale, and determining the amplitude of each frequency point in the frequency spectrum;

[0018] Based on the signal statistics of the signal components at each frequency scale and combined with context awareness, a dynamic threshold is determined for each frequency point;

[0019] The amplitude of each frequency point in the spectrum diagram is compared with the corresponding dynamic threshold, and the frequency points whose amplitude exceeds the dynamic threshold are determined as interference signals, and the remaining frequency points are determined as useful signals, so as to obtain the spectrum diagram of the interference signal at each frequency scale.

[0020] Preferably, the determining of a dynamic threshold for each frequency point based on the signal statistics of the signal components at each frequency scale and in combination with context perception includes:

[0021] Calculate the short-time energy and entropy of the signal component at each frequency scale, where the calculation formulas for the short-time energy and entropy are:

[0022]

[0023] Where, E j represents the short-time energy at scale j, N represents the time window length, s j(i) represents the signal value at the i-th time point on scale j, H represents the entropy under scale j, and p k represents the probability density of the signal on frequency band k, where K represents the number of frequency bands;

[0024] Calculate the statistics of adjacent frequency points of each frequency point, the statistics including the median and standard deviation of the adjacent frequency points, wherein the calculation formulas of the median and standard deviation are respectively:

[0025] m=median(D f )

[0026] σ=std(D f )

[0027] Where D f The formula is:

[0028] D f ={S(f-1), S(f+1)}

[0029] Where m represents the median, σ represents the standard deviation, S(f-1) and S(f+1) represent the amplitudes of the frequency points adjacent to frequency point f, S(f) represents the amplitude of frequency point f in the spectrum graph, and D f Represents the amplitude distribution of adjacent frequency points of frequency point f;

[0030] The dynamic threshold is calculated by combining the short-time energy, entropy and statistics of adjacent frequency points of the signal component at each frequency scale, where the calculation formula of the dynamic threshold T is:

[0031] T=α·E j +β·H j +γ·m+δ·σ

[0032] Wherein, α, β, γ and δ are respectively set first weight coefficients.

[0033] Preferably, matching a corresponding mask strategy from a preset strategy library and performing mask processing according to the frequency level of the frequency scale to which the spectrum diagram of the interference signal belongs includes:

[0034] Performing frequency level division on the spectrum diagram of the interference signal at each frequency scale to determine the frequency level to which each frequency scale belongs, wherein the frequency levels include high frequency, medium frequency, and low frequency;

[0035] According to the determined frequency level, a corresponding masking strategy is selected from a set of masking strategies pre-designed and stored in a strategy library, wherein the masking strategies in the strategy library are preset for different frequency levels;

[0036] The selected masking strategy is applied to perform masking processing on the spectrum diagram of the interference signal to suppress the influence of the interference signal.

[0037] Preferably, the frequency level division step is:

[0038] Based on the preset frequency range threshold, the frequency scales of the signal components are divided into primary levels;

[0039] Combining the short-time energy and entropy of the signal components at each frequency scale, the division boundaries of the preliminary divided frequency levels are dynamically adjusted to obtain the final frequency levels.

[0040] Preferably, the step of dynamically adjusting the boundaries of the preliminarily divided frequency levels by combining the short-time energy and entropy of the signal components at each frequency scale to obtain the final frequency levels comprises:

[0041] Based on the short-time energy and entropy, combined with a preset second weight coefficient, calculating a comprehensive evaluation index of the signal component at each frequency scale, wherein the comprehensive evaluation index represents the properties of the signal component at different frequency scales;

[0042] Adjusting the boundaries of the preliminarily divided frequency levels according to the comprehensive evaluation index;

[0043] The final frequency level division result is obtained through iterative optimization, wherein the iterative optimization includes adjusting the second weight coefficient according to the current division result and recalculating the comprehensive evaluation index until a preset stop condition is met.

[0044] Preferably, the inverse transformation of the spectrum of the interference signal after masking is performed to obtain the denoised interference signal is specifically:

[0045] An inverse fast Fourier transform is applied to the spectrum of the masked interference signal to convert the frequency domain signal back to the time domain signal, thereby obtaining the time domain representation of the denoised interference signal.

[0046] Preferably, performing multi-scale weighted reconstruction on the useful signal and the denoised interference signal to obtain a denoised mixed signal includes:

[0047] Determining a third weight coefficient of the signal component at each frequency scale in the reconstruction process according to the final frequency level;

[0048] Using the third weight coefficient, weighted summing is performed on the useful signal and the denoised interference signal at each frequency scale to perform multi-scale weighted reconstruction;

[0049] The reconstructed signal is post-processed, wherein the post-processing includes signal smoothing and normalization processing to obtain a final denoised mixed signal.

[0050] The present invention also provides an anti-interference system for a motherboard, which is used to execute an anti-interference method for a motherboard. The anti-interference system includes:

[0051] a signal decomposition module, configured to obtain a mixed signal in the environment in which the motherboard is located, and perform multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, wherein the mixed signal includes an interference signal and a useful signal;

[0052] A signal classification module is used to perform signal classification processing based on the spectrum of the signal component at each frequency scale to obtain the spectrum of the interference signal at each frequency scale;

[0053] a mask processing module, configured to match a corresponding mask strategy from a preset strategy library and perform mask processing according to a frequency level of a frequency scale to which the spectrum diagram of the interference signal belongs, wherein the frequency level includes high frequency, medium frequency, and low frequency;

[0054] A frequency domain to time domain conversion module, configured to perform an inverse transformation on the spectrum of the interference signal after masking to obtain the denoised interference signal;

[0055] The signal reconstruction module is used to perform multi-scale weighted reconstruction on the useful signal and the denoised interference signal to obtain a denoised mixed signal.

[0056] Compared with related technologies, the anti-interference method and anti-interference system for a motherboard provided by the present invention have the following beneficial effects:

[0057] This invention uses multi-scale decomposition technology to decompose the mixed signals in the motherboard's environment into signal components at different frequency scales, effectively separating interference signals from useful signals. Furthermore, the invention utilizes spectrogram analysis and signal classification techniques to perform refined processing on the signal components at each frequency scale, accurately identifying interference signals and performing masking based on their frequency level using a preset masking strategy library.

[0058] During masking, the present invention not only considers the frequency characteristics of the interference signal but also incorporates statistical information such as its short-term energy and entropy, as well as the statistics of adjacent frequency points. By dynamically adjusting the frequency level division boundaries, the present invention achieves precise suppression of the interference signal. Finally, through multi-scale reconstruction technology, the present invention performs a weighted summation of the useful signal and the denoised interference signal to obtain the final denoised mixed signal. Post-processing steps such as signal smoothing and normalization further enhance the signal quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of an anti-interference method for a mainboard provided by the present invention;

[0060] Figure 2 This is a module structure diagram of an anti-interference system of a mainboard provided by the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all of the structures. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with each other unless there is a conflict.

[0062] It should also be noted that, for ease of description, only the part relevant to the present invention, rather than all of the content, is shown in the accompanying drawings. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processing or methods depicted as flow charts. Although the flow charts describe each operation (or step) as being processed sequentially, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. When its operation is completed, the processing can be terminated, but can also have additional steps not included in the accompanying drawings. The processing can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0063] Example 1

[0064] The present invention provides an anti-interference method for a mainboard, the anti-interference method comprising the following steps:

[0065] S1: Acquire a mixed signal in the environment where the motherboard is located, and perform multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, wherein the mixed signal includes an interference signal and a useful signal.

[0066] Specifically, step S1 includes the following steps:

[0067] S11: The mixed signals in the environment are collected in real time through a sensor array installed on the mainboard, wherein the sensor array includes at least one sensor for detecting electromagnetic interference and one sensor for detecting the operating status of the mainboard.

[0068] In modern electronic devices, motherboards, as core components, face a complex electromagnetic environment. Electromagnetic interference (EMI) not only originates from external sources like wireless communication devices and power lines, but can also arise from interference between other electronic components within the motherboard. To ensure stable motherboard operation, real-time monitoring and analysis of mixed signals in the environment is crucial for anti-interference design.

[0069] During anti-interference design, an array of multiple sensors is installed on the motherboard, including electromagnetic interference sensors and motherboard operation status sensors. Electromagnetic interference sensors refer to devices that can detect changes in electromagnetic field intensity, including but not limited to fluxgate sensors and Hall effect sensors. These sensors are used to capture electromagnetic fluctuations in the environment.

[0070] The motherboard operating status sensors include but are not limited to temperature sensors, current sensors, and voltage sensors, which are used to monitor the operating status of the motherboard itself. For example, excessively high temperatures may cause signal distortion.

[0071] S12: Preprocessing the collected mixed signal, wherein the preprocessing includes signal amplification, filtering, and analog-to-digital conversion.

[0072] In this embodiment, since the signal received by the sensor may contain a large amount of noise and the signal strength varies, it must be preprocessed before performing more complex analysis to improve the signal-to-noise ratio and make the signal adapt to the requirements of subsequent algorithms.

[0073] Among them, preprocessing usually includes the following aspects:

[0074] Signal amplification: Use a preamplifier to increase signal strength, making weak signals more noticeable.

[0075] Filtering: Use filters to remove unnecessary frequency components and retain useful signals. Filters include low-pass filters, high-pass filters, and band-pass filters.

[0076] Analog-to-digital conversion (ADC): Converts a continuous analog signal into a discrete digital signal so that a computer can process it.

[0077] S13: The preprocessed mixed signal is decomposed into multiple scales using the variational mode decomposition (VMD) method to obtain signal components containing different frequency components, where each signal component corresponds to a frequency scale.

[0078] In practical applications, mixed signals contain a large number of frequency components, including both useful signal and interference components. Traditional single-scale processing methods struggle to effectively separate these components. Multiscale analysis techniques, particularly variational mode decomposition (VMD), can decompose mixed signals into multiple modal components, each corresponding to a different frequency range, facilitating subsequent signal processing.

[0079] In this application, the specific working process of variational mode decomposition is:

[0080] First, determine the target number of modes to be decomposed, which is set according to the complexity of the signal and the expected resolution. In addition, set the convergence criterion, penalty factor (used to control the bandwidth of the modal component), and number of inner loop iterations of the VMD algorithm as required.

[0081] Next, a Lagrangian function is constructed. The VMD algorithm finds the optimal modal components by constructing a Lagrangian function. Given a signal, it hopes to find a set of modal components so that their Hilbert transforms are concentrated as much as possible around a center frequency in the frequency domain.

[0082] The alternating direction method of multipliers (ADMM) is then used to minimize the Lagrangian function. ADMM is a method for solving constrained optimization problems that gradually approaches the optimal solution by iteratively updating the modal components and center frequencies, as well as the Lagrangian multipliers and dual variables.

[0083] In each iteration, the center frequency is first fixed and the modal components are updated, then the modal components are fixed and the center frequency is updated.

[0084] Finally, the updated modal components are checked to see if they meet the convergence conditions. If the predetermined error limit or number of iterations is reached, the iteration is stopped. Once the algorithm converges, a set of modal components is obtained. These components are sorted according to their respective center frequencies to form a multi-scale signal representation.

[0085] Through VMD's multi-scale decomposition, complex mixed signals can be split into several relatively simple modal components, each with different frequency characteristics. This decomposition not only facilitates subsequent signal classification processing but also enables better identification and suppression of interfering signals while retaining useful signal components.

[0086] S2: Perform signal classification processing based on the spectrum of the signal component at each frequency scale to obtain the spectrum of the interference signal at each frequency scale.

[0087] Specifically, step S2 includes the following steps:

[0088] S21: Apply fast Fourier transform to convert the signal components at each frequency scale from the time domain to the frequency domain, obtain a spectrum diagram of the signal components at each frequency scale, and determine the amplitude of each frequency point in the spectrum diagram.

[0089] The Fast Fourier Transform (FFT) is an efficient algorithm used to convert signals from the time domain to the frequency domain, allowing analysis of the signal's different components in the frequency domain. In interference mitigation methods, the FFT converts signal components at different frequency scales into a spectrum, making it easier to distinguish between interfering and desired signals.

[0090] In implementation, an FFT algorithm is applied to the signal components at each frequency scale. FFT can quickly calculate the discrete Fourier transform (DFT), thereby converting the signal from the time domain to the frequency domain.

[0091] Calculating the spectrum of each signal component yields complex values at each frequency point. The amplitude of these complex values represents the intensity of the corresponding frequency component, while the phase reflects the phase information of the signal.

[0092] The amplitude of each frequency point is calculated from the complex number result obtained by FFT. The amplitude can be obtained by calculating the modulus of the complex number.

[0093] Through FFT transformation, we can intuitively see the distribution of the signal at different frequencies, thus providing a basis for the next step of signal classification. In addition, by calculating the amplitude, we can highlight those frequency components with higher intensity, which is particularly important for identifying interference signals.

[0094] S22: Determine a dynamic threshold for each frequency point based on the signal statistics of the signal components at each frequency scale and in combination with context awareness.

[0095] In this embodiment, in order to accurately distinguish interference signals from useful signals, a dynamic threshold is determined based on signal statistics and context awareness. This dynamic threshold helps determine which frequency components belong to interference signals and which belong to useful signals.

[0096] During implementation, the dynamic threshold is calculated by calculating the short-time energy and entropy of the signal components at each frequency scale, and calculating the statistics of the adjacent frequency points of each frequency point, including the median and standard deviation, and combining the short-time energy, entropy and statistics.

[0097] Wherein, step S22 specifically includes the following steps:

[0098] S22a: Calculate the short-time energy and entropy of the signal component at each frequency scale. The calculation formulas for short-time energy and entropy are:

[0099]

[0100] Where, E j represents the short-time energy at scale j, N represents the time window length, s j (i) represents the signal value at the i-th time point on scale j, H j represents the entropy at scale j, p k represents the probability density of the signal on frequency band k, where K represents the number of frequency bands.

[0101] In signal processing, short-term energy and entropy are important metrics for measuring signal strength and randomness. Short-term energy helps determine the signal's activity, while entropy reflects its complexity or uncertainty. When identifying interference signals, these two metrics can provide crucial information about the signal's characteristics.

[0102] During implementation, an appropriate time window length is selected according to requirements. This length should be long enough to capture the characteristics of the signal, but not too long to lose instantaneous changes. For each signal value at each frequency scale, the short-time energy is calculated within the selected time window.

[0103] In the process of entropy calculation, the probability density of the signal can be estimated by the histogram method, and then the entropy can be calculated using the probability density.

[0104] S22b: Calculate the statistics of the adjacent frequency points of each frequency point. The statistics include the median and standard deviation of the adjacent frequency points. The calculation formulas for the median and standard deviation are:

[0105] m=median(D f )

[0106] σ=std(D f )

[0107] Where D f The formula is:

[0108] D f ={S(f-1), S(f+1)}

[0109] Where m represents the median, σ represents the standard deviation, S(f-1) and S(f+1) represent the amplitudes of the frequency points adjacent to frequency point f, S(f) represents the amplitude of frequency point f in the spectrum graph, and D f Represents the amplitude distribution of adjacent frequency points of frequency point f.

[0110] In the frequency domain, the statistical characteristics of adjacent frequency points help understand the changing trends of the signal. By calculating the median and standard deviation of adjacent frequency points, we can understand the local changes in the signal, which is particularly important for identifying abnormal interference signals.

[0111] S22c: Calculate the dynamic threshold by combining the short-time energy and entropy of the signal component at each frequency scale and the statistics of adjacent frequency points. The dynamic threshold T is calculated as follows:

[0112] T=α·E j +β·H j +γ·m+δ·σ

[0113] Wherein, α, β, γ and δ are respectively set first weight coefficients.

[0114] In this embodiment, the dynamic threshold is a threshold that is dynamically adjusted based on the statistical characteristics of the signal and is used to distinguish the interference part from the useful part of the signal. Combining short-term energy, entropy, and statistics of adjacent frequency points can more accurately identify interference signals.

[0115] Set four first weight coefficients α, β, γ and δ. These coefficients are used to adjust the contribution ratio of short-time energy, entropy, median and standard deviation. Combined with short-time energy, entropy, median and standard deviation, the dynamic threshold is calculated by the above formula. The dynamic threshold can more accurately identify which frequency points are affected by interference, adapt to signal changes, avoid misjudgment problems that may be caused by fixed thresholds, and thus improve the accuracy of interference identification.

[0116] S23: Compare the amplitude of each frequency point in the spectrum diagram with the corresponding dynamic threshold, determine the frequency points whose amplitude exceeds the dynamic threshold as interference signals, and determine the remaining frequency points as useful signals, so as to obtain spectrum diagrams of interference signals at various frequency scales.

[0117] After the dynamic threshold is determined, the signals are classified according to the relationship between the amplitude of the frequency point and the threshold, that is, which are interference signals and which are useful signals.

[0118] During implementation, the amplitude of each frequency point is first compared with the corresponding dynamic threshold. If the amplitude exceeds the dynamic threshold, the frequency point is considered to be an interference signal. If the amplitude is lower than or equal to the dynamic threshold, the frequency point is considered to be a useful signal.

[0119] According to the above determination results, a spectrum diagram of the interference signal at each frequency scale is generated.

[0120] S3: According to the frequency level of the frequency scale to which the spectrum diagram of the interference signal belongs, a corresponding mask strategy is matched from a preset strategy library and mask processing is performed, wherein the frequency level includes high frequency, medium frequency and low frequency.

[0121] Specifically, step S3 includes the following steps:

[0122] S31: dividing the frequency spectrum of the interference signal at each frequency scale into frequency levels, and determining the frequency level to which each frequency scale belongs. The frequency levels include high frequency, medium frequency, and low frequency.

[0123] When combating interference, different interference signals may exist in different frequency ranges. For example, high-frequency interference may come from fast-switching circuits, while low-frequency interference may come from power supply ripple or other slowly varying signals. To effectively handle these interferences, it is necessary to classify the signal components according to their frequency characteristics so that targeted treatment measures can be taken.

[0124] In practice, a primary classification is performed, where signal components are divided into three levels: high, medium, and low frequencies, based on preset frequency range thresholds. These thresholds can typically be adjusted based on the application scenario. For example, high frequencies might be defined as greater than 100kHz, medium frequencies as between 10kHz and 100kHz, and low frequencies as less than 10kHz.

[0125] A preliminary partitioning can be done by setting fixed frequency limits and using histogram analysis to determine the main frequency distribution areas of the signal.

[0126] The boundaries of the initially divided frequency levels are then dynamically adjusted by combining the short-term energy and entropy of the signal components at each frequency scale. Short-term energy reflects the strength of the signal over a short period of time, while entropy measures the randomness or uncertainty of the signal.

[0127] Dynamic adjustment is achieved by calculating a comprehensive evaluation index for each frequency component. The comprehensive evaluation index is a value calculated based on short-term energy and entropy, which can be used to assess the "interference level" of the signal component.

[0128] The calculation formula of the comprehensive evaluation index is to perform weighted summation of short-time energy and entropy according to a preset second weight coefficient.

[0129] Finally, through multiple iterations, the second weight coefficient is continuously adjusted and the comprehensive evaluation index is recalculated until the preset stopping condition is met. The stopping condition is that the set number of iterations is reached.

[0130] S32: According to the determined frequency level, a corresponding masking strategy is selected from a set of masking strategies pre-designed and stored in a strategy library, wherein the masking strategies in the strategy library are preset for different frequency levels.

[0131] After completing the spectrum division of the interference signal, the next step is to select the appropriate masking strategy based on the frequency level. Masking strategy refers to a method of signal processing in the frequency domain. By masking the spectrum of the interference signal, the impact of the interference signal can be effectively suppressed.

[0132] During implementation, the spectrum of the interference signal is divided into three levels: high frequency, medium frequency and low frequency according to the frequency level classification in step S31. This step has determined which frequency components belong to high frequency interference, medium frequency interference and low frequency interference.

[0133] A set of mask strategies pre-designed and stored in the strategy library. These strategies are preset for different frequency levels, namely high-frequency mask strategy, medium-frequency mask strategy, and low-frequency mask strategy. Specifically:

[0134] High-frequency mask strategy:

[0135] For high-frequency interference signals, a high-frequency masking strategy (narrowband masking strategy) is employed. Specifically, a Gaussian function or a Hamming window function is used as the masking function. These functions are characterized by strong suppression near the center frequency and gradual recovery at locations away from the center frequency. By applying these functions to the spectrum graph in the high-frequency band, high-frequency interference signals can be effectively suppressed.

[0136] IF mask strategy:

[0137] For IF interference signals, a wideband masking strategy is employed. Specifically, a rectangular window function or a triangular window function is used as the masking function. These functions completely suppress signals within a certain frequency range while fully preserving signals in other frequency ranges. By applying these functions to the spectrum of the IF band, IF interference signals can be effectively suppressed.

[0138] Low-frequency mask strategy:

[0139] For low-frequency interference signals, a broadband masking strategy is employed. Specifically, a Hanning window or a Heiman window is used as the masking function. These functions provide smooth suppression across the entire low-frequency band, effectively covering interference components within the low-frequency range. By applying these functions to the spectrum of the low-frequency band, low-frequency interference signals can be effectively suppressed.

[0140] S33: Apply the selected masking strategy to perform masking processing on the spectrum diagram of the interference signal to suppress the influence of the interference signal.

[0141] After determining the masking strategies, the next step is to apply these strategies to process the interference signals. The purpose of masking is to weaken or eliminate the interference signals in the frequency domain, thereby improving the signal quality.

[0142] During implementation, the selected masking strategy is applied to the interfering signal's spectrum. Specifically, the signal's amplitude is modified in the frequency domain, and the signal strength within the specified frequency range is reduced by multiplying it by the masking function.

[0143] The mask function can be a 0 / 1 binary function or a decay function, depending on the selected masking strategy.

[0144] By masking, the impact of interfering signals can be significantly reduced, thereby improving signal quality and reliability. This method is very effective in practical applications, especially in situations where precise control of signal characteristics is required.

[0145] S4: Inversely transform the spectrum of the interference signal after masking to obtain a denoised interference signal.

[0146] In the previous steps, interference signals have been identified and suppressed through multi-scale decomposition of the mixed signal, signal classification processing, and masking. However, the signal is still in the frequency domain. To return the signal to its original time domain form for further processing or transmission, the signal in the frequency domain needs to be converted back to the time domain. This process is usually completed through the Inverse Fast Fourier Transform (IFFT). After the IFFT, the time domain signal is obtained with the interference components removed. The inverse transform converts the signal in the frequency domain back to the time domain, achieving complete signal recovery.

[0147] S5: Perform multi-scale weighted reconstruction on the useful signal and the denoised interference signal to obtain a denoised mixed signal.

[0148] Specifically, step S5 includes the following steps:

[0149] S51: Determine a third weight coefficient of the signal component at each frequency scale during the reconstruction process according to the final frequency level.

[0150] After identifying and suppressing interfering signals, the next step is signal reconstruction, which involves recombining the desired signal with the signal from which the interference has been removed. This process requires determining the weights of signal components at different frequency scales to ensure the quality of the reconstructed signal.

[0151] During implementation, a third weight coefficient is assigned to each signal component at each frequency scale according to the frequency level determined in the previous step. The determination of the third weight coefficient can be adjusted according to the importance and reliability of the signal. Low-frequency signals contain more useful information and have higher weights, while high-frequency signals are more affected by interference and have lower weights.

[0152] Specifically, the third weight coefficient is a normalized value of the comprehensive evaluation index of the signal component at each frequency scale.

[0153] S52: Using the third weight coefficient, perform weighted summation on the useful signal and the denoised interference signal at each frequency scale to perform multi-scale weighted reconstruction.

[0154] In the field of signal processing, multi-scale reconstruction is a commonly used signal recovery technique. By adding signal components at different frequency scales according to weights, a new signal that is closer to the original useful signal can be obtained.

[0155] During implementation, the third weight coefficient determined in step S51 is used to perform weighted summation on the useful signal and the denoised interference signal at each frequency scale. Specifically, the signal components at each frequency scale are multiplied by their corresponding third weight coefficients, and then all the components are added together, while gradually adding higher frequency components starting from the lowest frequency until all components are added.

[0156] S53: Post-processing the reconstructed signal, wherein the post-processing includes signal smoothing and normalization processing to obtain a final denoised mixed signal.

[0157] In this embodiment, although most interference has been removed from the reconstructed signal, some edge effects or discontinuities may still exist, and therefore further post-processing is required to improve the signal quality.

[0158] During implementation, a smoothing filter is used to eliminate spikes and discontinuities in the signal, making the signal curve smoother. The reconstructed signal is normalized so that its amplitude is within a fixed range, thus avoiding problems caused by signals that are too strong or too weak.

[0159] Example 2

[0160] The present invention also provides an anti-interference system for a motherboard, which is used to execute an anti-interference method for a motherboard. Figure 2 As shown, the anti-interference system includes:

[0161] The signal decomposition module 100 is used to obtain a mixed signal in the environment where the mainboard is located, and perform multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, wherein the mixed signal includes an interference signal and a useful signal.

[0162] The signal classification module 200 is configured to perform signal classification processing based on the spectrum of the signal component at each frequency scale, and obtain the spectrum of the interference signal at each frequency scale.

[0163] The mask processing module 300 is used to match a corresponding mask strategy from a preset strategy library and perform mask processing according to the frequency level of the frequency scale to which the spectrum diagram of the interference signal belongs, wherein the frequency level includes high frequency, medium frequency and low frequency.

[0164] The frequency domain to time domain conversion module 400 is configured to perform an inverse transformation on the spectrum of the interference signal that has been masked to obtain a denoised interference signal.

[0165] The signal reconstruction module 500 is used to perform multi-scale weighted reconstruction on the useful signal and the denoised interference signal to obtain a denoised mixed signal.

[0166] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0168] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A motherboard anti-interference method, characterized in that: The anti-interference method comprises the following steps: Acquire a mixed signal in the environment where the motherboard is located, and perform multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, wherein the mixed signal includes an interference signal and a useful signal; Performing signal classification processing based on the spectrum of the signal component at each frequency scale to obtain the spectrum of the interference signal at each frequency scale; According to the frequency level of the frequency scale to which the spectrum diagram of the interference signal belongs, a corresponding mask strategy is matched from a preset strategy library and mask processing is performed, wherein the frequency level includes high frequency, medium frequency and low frequency. This step specifically includes: Based on the preset frequency range threshold, the frequency scales of the signal components are divided into primary levels; Combined with the short-time energy and entropy of the signal components at each frequency scale, the boundaries of the frequency levels initially divided are dynamically adjusted to determine the frequency level to which each frequency scale belongs; According to the determined frequency level, a masking strategy matching the frequency level is selected from a masking strategy set in a strategy library, and the selected masking strategy is applied to perform masking processing on the spectrum graph of the interference signal; Performing an inverse transformation on the spectrum of the interference signal after masking to obtain the denoised interference signal; Multi-scale weighted reconstruction is performed on the useful signal and the denoised interference signal to obtain a denoised mixed signal.

2. The anti-interference method of a motherboard according to claim 1, characterized in that: The obtaining of a mixed signal in the environment where the mainboard is located, and performing multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, includes: A sensor array mounted on the mainboard collects mixed signals in the environment in real time, wherein the sensor array includes at least one sensor for detecting electromagnetic interference and one sensor for detecting the operating status of the mainboard; Preprocessing the collected mixed signal, wherein the preprocessing includes signal amplification, filtering, and analog-to-digital conversion; The variational mode decomposition (VMD) method is used to perform multi-scale decomposition on the preprocessed mixed signal to obtain signal components containing different frequency components, each of which corresponds to a frequency scale.

3. The anti-interference method of a motherboard according to claim 2, characterized in that: The signal classification processing is performed based on the spectrum of the signal component at each frequency scale to obtain the spectrum of the interference signal at each frequency scale, including: Applying a fast Fourier transform to convert the signal components at each frequency scale from the time domain to the frequency domain, obtaining a frequency spectrum of the signal components at each frequency scale, and determining the amplitude of each frequency point in the frequency spectrum; Based on the signal statistics of the signal components at each frequency scale and combined with context awareness, a dynamic threshold is determined for each frequency point; The amplitude of each frequency point in the spectrum diagram is compared with the corresponding dynamic threshold, and the frequency points whose amplitude exceeds the dynamic threshold are determined as interference signals, and the remaining frequency points are determined as useful signals, so as to obtain the spectrum diagram of the interference signal at each frequency scale.

4. The anti-interference method of a motherboard according to claim 3, characterized in that: The signal statistics of the signal components at each frequency scale are determined based on context perception for each frequency point, including: Calculate the short-time energy and entropy of the signal component at each frequency scale, where the calculation formulas for the short-time energy and entropy are: Where, Representation scale The short-term energy under Indicates the length of the time window, Indicated on scale Previous The signal value at a time point, Representation scale The entropy under Indicates signal frequency band The probability density on Indicates the number of frequency bands; Calculate the statistics of adjacent frequency points of each frequency point, the statistics including the median and standard deviation of the adjacent frequency points, wherein the calculation formulas of the median and standard deviation are respectively: Where, The formula is: in, represents the median, represents the standard deviation, Respectively represent frequency points The amplitude of adjacent frequency points, Indicates the frequency points in the spectrum graph The amplitude of Indicates frequency The amplitude distribution of adjacent frequency points; Combine the short-time energy, entropy and statistics of adjacent frequency points of the signal component at each frequency scale to calculate the dynamic threshold, where the dynamic threshold The calculation formula is: in, 、 、 and are the first weight coefficients set respectively.

5. The anti-interference method of a mainboard according to claim 4, characterized in that: The method combines the short-time energy and entropy of the signal components at each frequency scale to dynamically adjust the boundaries of the preliminary divided frequency levels to obtain the final frequency levels, including: Based on the short-time energy and entropy, combined with a preset second weight coefficient, calculating a comprehensive evaluation index of the signal component at each frequency scale, wherein the comprehensive evaluation index represents the properties of the signal component at different frequency scales; Adjusting the boundaries of the preliminarily divided frequency levels according to the comprehensive evaluation index; The final frequency level division result is obtained through iterative optimization, wherein the iterative optimization includes adjusting the second weight coefficient according to the current division result and recalculating the comprehensive evaluation index until a preset stop condition is met.

6. The anti-interference method of a mainboard according to claim 5, characterized in that: The inverse transformation of the spectrum of the interference signal after masking is performed to obtain the denoised interference signal is specifically: An inverse fast Fourier transform is applied to the spectrum of the masked interference signal to convert the frequency domain signal back to the time domain signal, thereby obtaining the time domain representation of the denoised interference signal.

7. The anti-interference method of a mainboard according to claim 6, characterized in that: The performing multi-scale weighted reconstruction on the useful signal and the denoised interference signal to obtain a denoised mixed signal includes: Determining a third weight coefficient of the signal component at each frequency scale in the reconstruction process according to the final frequency level; Using the third weight coefficient, weighted summing is performed on the useful signal and the denoised interference signal at each frequency scale to perform multi-scale weighted reconstruction; The reconstructed signal is post-processed, wherein the post-processing includes signal smoothing and normalization processing to obtain a final denoised mixed signal.

8. An anti-interference system for a motherboard, used to execute the anti-interference method for a motherboard according to any one of claims 1 to 7, characterized in that: The anti-interference system comprises: a signal decomposition module, configured to obtain a mixed signal in the environment in which the motherboard is located, and perform multi-scale decomposition on the mixed signal to obtain signal components at different frequency scales, wherein the mixed signal includes an interference signal and a useful signal; A signal classification module is used to perform signal classification processing based on the spectrum of the signal component at each frequency scale to obtain the spectrum of the interference signal at each frequency scale; a mask processing module, configured to match a corresponding mask strategy from a preset strategy library and perform mask processing according to a frequency level of a frequency scale to which the spectrum diagram of the interference signal belongs, wherein the frequency level includes high frequency, medium frequency, and low frequency; A frequency domain to time domain conversion module, configured to perform an inverse transformation on the spectrum of the interference signal after masking to obtain the denoised interference signal; The signal reconstruction module is used to perform multi-scale weighted reconstruction on the useful signal and the denoised interference signal to obtain a denoised mixed signal.

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