Extremely low frequency noise filtering method, system and device and storage medium

Abstract

The embodiment of the invention discloses an extremely-low-frequency noise filtering method, system and device and a storage medium, a wavelet coefficient can capture the instantaneous change and local features of a to-be-filtered signal, and the change characteristics of the to-be-filtered signal are reflected; the complex matrix has good energy calculation characteristics, and energy value distribution of the to-be-filtered signal in a preset frequency range can be accurately obtained by combining the wavelet coefficient and the complex matrix; statistical analysis and spectral analysis are performed on the energy value distribution, so that the extremely low frequency noise is identified, the extremely low frequency noise in the to-be-filtered signal is filtered, and the filtering effect of the to-be-filtered signal is improved.

Description

[Technical field]

[0001] The present invention relates to the technical field of data filtering and denoising, and in particular to an extremely low frequency noise filtering method, system, device and storage medium. [Background technology]

[0002] In the field of modern power systems and electromagnetic measurement, electric field sensors play a vital role and are widely used to monitor the status of power equipment, evaluate the electromagnetic environment, and ensure the safe and stable operation of power systems. However, in practical applications, the signals collected by electric field sensors are often seriously interfered by extremely low frequency noise, which comes from a wide range of sources, including but not limited to the operating noise of power equipment, geomagnetic fluctuations, and environmental electromagnetic interference. The presence of extremely low frequency noise not only reduces the signal-to-noise ratio of the signal, but may also lead to erroneous measurement results, thereby affecting the normal operation and maintenance of the power system. In order to effectively suppress extremely low frequency noise and improve the measurement accuracy and reliability of electric field sensors, a variety of digital filtering technologies have been proposed and applied to practical scenarios, including traditional filter design methods, such as Butterworth filters, Chebyshev filters, etc., as well as modern intelligent filtering algorithms, such as wavelet transforms and adaptive filtering. However, due to the complex and changeable characteristics of extremely low frequency noise and the harsh operating environment of the power system, the existing filtering methods often have poor filtering effects when dealing with such noise. [Summary of the invention]

[0003] In view of this, the present invention provides a very low frequency noise filtering method, system, device and storage medium.

[0004] The specific technical scheme of the first embodiment of the present invention is: a very low frequency noise filtering method, the method comprising: performing wavelet transform of different scales on the signal to be filtered to obtain the wavelet coefficients of the signal to be filtered at each scale; performing short-time Fourier transform on the signal to be filtered to obtain the complex matrix of the signal to be filtered at different time frames and different frequencies; using the wavelet coefficients and the complex matrix to obtain the energy value distribution of the signal to be filtered within a preset frequency range; performing statistical analysis and spectral analysis on the energy value distribution to obtain the very low frequency noise in the signal to be filtered; the very low frequency noise is a signal whose energy value exceeds a preset energy threshold; filtering out the very low frequency noise in the signal to be filtered to obtain a first optimized signal.

[0005] Preferably, filtering out the extremely low frequency noise in the signal to be filtered includes: obtaining the center frequency of the signal to be filtered; obtaining the bandwidth of the filter based on the center frequency and a first preset frequency range; constructing a transfer function of the filter using the center frequency and the bandwidth; and the filter filtering out the extremely low frequency noise in the signal to be filtered using the transfer function.

[0006] Preferably, the step of obtaining the first optimized signal further includes: performing wavelet decomposition on the first optimized signal to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal; obtaining a frequency range of the low-frequency approximate component; if the frequency range exceeds a second preset frequency range, taking the low-frequency approximate component as the first optimized signal, and returning to the step of performing wavelet decomposition on the first optimized signal to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal, until the frequency range of the low-frequency approximate component does not exceed the second preset frequency range, or the number of wavelet decompositions reaches a preset number threshold; obtaining a first target low-frequency approximate component and a second target low-frequency approximate component, and removing the second target low-frequency approximate component from the first target low-frequency approximate component to obtain a third target low-frequency approximate component; the first target low-frequency approximate component is a low-frequency approximate component obtained by the last wavelet decomposition, and the second target low-frequency approximate component is a low-frequency signal in the first target low-frequency approximate component whose frequency is less than or equal to a preset frequency threshold; and obtaining the second optimized signal using the third target low-frequency approximate component and all high-frequency detail components.

[0007] Preferably, the wavelet decomposition of the first optimized signal to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal includes: convolving the first optimized signal with a preset wavelet function to obtain a first convolution result; downsampling the first convolution result to obtain the high-frequency detail component; translating the first optimized signal according to a preset translation scale; convolving the translated first optimized signal with a preset scale function to obtain a second convolution result; and downsampling the second convolution result to obtain the low-frequency approximate component.

[0008] Preferably, the method of obtaining the second optimized signal by utilizing the third target low-frequency approximate component and all high-frequency detail components includes: reconstructing the third target low-frequency approximate component and all high-frequency detail components according to the inverse process of wavelet decomposition to obtain the second optimized signal.

[0009] Preferably, after obtaining the second optimization signal, the method further includes: obtaining a signal-to-noise ratio of the second optimization signal; comparing the signal-to-noise ratio with a preset signal-to-noise ratio range to obtain a signal-to-noise ratio comparison result; and optimizing the preset number threshold according to the signal-to-noise ratio comparison result.

[0010] Preferably, the signal-to-noise ratio comparison result includes that the signal-to-noise ratio is less than the minimum value of the preset signal-to-noise ratio range, the signal-to-noise ratio is within the preset signal-to-noise ratio range, and the signal-to-noise ratio is greater than the maximum value of the preset signal-to-noise ratio range; then optimizing the preset number threshold according to the signal-to-noise ratio comparison result includes: if the signal-to-noise ratio is less than the minimum value of the preset signal-to-noise ratio range, increasing the preset value of the preset number threshold; if the signal-to-noise ratio is within the preset signal-to-noise ratio range, not modifying the preset value of the preset number threshold; if the signal-to-noise ratio is greater than the maximum value of the preset signal-to-noise ratio range, reducing the preset value of the preset number threshold.

[0011] The specific technical scheme of the second embodiment of the present invention is: a very low frequency noise filtering system, the system comprising: a wavelet transform module, a Fourier transform module, an energy distribution acquisition module, an analysis module and an optimization module; the wavelet transform module is used to perform wavelet transforms of different scales on the signal to be filtered to obtain the wavelet coefficients of the signal to be filtered at each scale; the Fourier transform module is used to perform short-time Fourier transform on the signal to be filtered to obtain the complex matrix of the signal to be filtered at different time frames and different frequencies; the energy distribution acquisition module is used to use the wavelet coefficients and the complex matrix to obtain the energy value distribution of the signal to be filtered within a preset frequency range; the analysis module is used to perform statistical analysis and spectral analysis on the energy value distribution to obtain the very low frequency noise in the signal to be filtered; the very low frequency noise is a signal whose energy value exceeds a preset energy threshold; the optimization module is used to filter out the very low frequency noise in the signal to be filtered to obtain a first optimized signal.

[0012] The specific technical solution of the third embodiment of the present invention is: an extremely low frequency noise filtering device, comprising a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in any one of the first embodiments of the present application.

[0013] The specific technical solution of the fourth embodiment of the present invention is: a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method described in any one of the first embodiments of the present application.

[0014] Implementing the embodiments of the present invention will have the following beneficial effects:

[0015] The wavelet coefficients in the present invention can capture the instantaneous changes and local features of the signal to be filtered, and reflect the changing characteristics of the signal to be filtered; the complex matrix has good energy calculation characteristics, and by combining the wavelet coefficients and the complex matrix, the energy value distribution of the signal to be filtered within a preset frequency range can be accurately obtained; the energy value distribution is statistically analyzed and spectrally analyzed to identify extremely low frequency noise, and the extremely low frequency noise in the signal to be filtered is filtered out, thereby improving the filtering effect of the signal to be filtered.

Brief Description of the Drawings

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a flow chart of the steps of a very low frequency noise filtering method;

[0018] Figure 2 This is a schematic diagram of the results of wavelet decomposition;

[0019] Figure 3 It is a structural schematic diagram of an extremely low frequency noise filtering system;

[0020] Figure 4 A diagram of the internal structure of a computer device;

[0021] Among them, 201 is a wavelet transform module; 202 is a Fourier transform module; 203 is an energy distribution acquisition module; 204 is an analysis module; and 205 is an optimization module. [Specific implementation method]

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] The terms "first", "second", etc. in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally includes steps or modules that are not listed, or optionally includes other steps or modules that are inherent to these processes, methods, products or devices.

[0024] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] See also Figure 1 , is a flowchart of the steps of a very low frequency noise filtering method in the first embodiment of the present application, thereby improving the filtering effect of the signal to be filtered, the method comprising:

[0026] Step 101, performing wavelet transform of different scales on the signal to be filtered to obtain the wavelet coefficient of the signal to be filtered at each scale;

[0027] Step 102, performing short-time Fourier transform on the signal to be filtered to obtain a complex matrix of the signal to be filtered at different time frames and different frequencies;

[0028] Step 103, using the wavelet coefficients and the complex matrix to obtain the energy value distribution of the signal to be filtered within a preset frequency range;

[0029] Step 104: Perform statistical analysis and spectrum analysis on the energy value distribution to obtain extremely low frequency noise in the signal to be filtered; the extremely low frequency noise is a signal whose energy value exceeds a preset energy threshold;

[0030] Step 105: Filter out the extremely low frequency noise in the signal to be filtered to obtain a first optimized signal.

[0031] Specifically, the signal to be filtered may be an electric field signal collected by an electric field sensor. The electric field signal is decomposed at multiple scales to obtain the transformation coefficient c of the electric field signal at scale a and translation b during time period t. a,b (t), the specific formula is: Where ψ is the wavelet function. The window of wavelet transform is in coordinates (b, ±ω 0 / a) as the center, with a time domain window width of aΔψ and a frequency domain window width of Δψ / a. Therefore, the sampling window size can be adjusted to achieve multi-resolution analysis, and the value of b realizes the translation of the window. Use numerical integration method to solve c a,b (t), obtain the wavelet coefficient c of the electric field signal at each scale a,b , the specific formula is:

[0032] When processing time series signals, the short-time Fourier transform (STFT) uses a time-frequency localized window function. It is assumed that the window function used remains stable (or pseudo-stationary) in a shorter time period. First, a certain length of electric field signal segment is intercepted in the time domain, and a short-time Fourier transform is performed on this signal segment to obtain the local spectrum in a small time period near the time point t. By continuously moving the window function on the entire time axis, a set of local spectra is obtained for each time period. Therefore, the STFT can be regarded as a function that exhibits two-dimensional characteristics in both time and frequency. The STFT results at each time frame m and frequency ω, that is, the complex matrix X(m,ω), are calculated. The specific formula for obtaining X(m,ω) is: Wherein, R is the step size of the window function ψ(t) moving on the time axis, and j is the imaginary unit.

[0033] The wavelet coefficients of each scale obtained by wavelet transform are compared and analyzed with the complex matrix. Specifically, the wavelet coefficients and complex matrix of each scale are subjected to spectrum analysis to identify the characteristics of the electric field signal at different times and frequencies. The advantages of time localization of wavelet transform and frequency localization of STFT are combined to refine the distinction between the signal frequency band and the noise frequency band. Specifically, the frequency range of the electric field signal is set to 0.5Hz to 1Hz, covering all possible extremely low-frequency noise components. The wavelet coefficients and complex matrix are used to analyze the energy distribution of the electric field signal within this frequency range. The energy of the electric field signal and the complex matrix within the frequency range ω∈[0.5,1] is subjected to statistical spectrum analysis, and the average energy, maximum energy and minimum energy within this frequency range are calculated to identify the noise component. Specifically, the threshold method is used to identify the energy component above the threshold as extremely low-frequency noise. The extremely low-frequency noise in the electric field signal is filtered out to obtain the first optimized signal.

[0034] The wavelet coefficients in this embodiment can capture the instantaneous changes and local features of the signal to be filtered, and reflect the changing characteristics of the signal to be filtered; the complex matrix has good energy calculation characteristics, and by combining the wavelet coefficients and the complex matrix, the energy value distribution of the signal to be filtered within the preset frequency range can be accurately obtained; the energy value distribution is statistically analyzed and spectrally analyzed to identify extremely low frequency noise, and the extremely low frequency noise in the signal to be filtered is filtered out, thereby improving the filtering effect of the signal to be filtered.

[0035] In a specific embodiment, filtering out the extremely low frequency noise in the signal to be filtered includes: obtaining the center frequency of the signal to be filtered; obtaining the bandwidth of the filter based on the center frequency and a first preset frequency range; constructing the transfer function of the filter using the center frequency and the bandwidth; and the filter using the transfer function to filter out the extremely low frequency noise in the signal to be filtered.

[0036] Specifically, the center frequency f of the filter is set according to the identified extremely low frequency noise component. c and bandwidth B, where the center frequency f of the electric field signal c Set at around 0.75 Hz, the bandwidth B is adjusted according to the specific range of the noise to ensure that the filter can effectively suppress extremely low-frequency noise below 1 Hz while retaining useful signals. Using the selected center frequency and bandwidth, the transfer function of the bandpass filter is constructed to ensure that the transfer function is within the frequency range ω∈[f c -B / 2,f c +B / 2] is 1 and 0 in other frequency ranges.

[0037] In a specific embodiment, the step of obtaining the first optimized signal further includes: performing wavelet decomposition on the first optimized signal to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal; obtaining a frequency range of the low-frequency approximate component; if the frequency range exceeds a second preset frequency range, taking the low-frequency approximate component as the first optimized signal, and returning to the step of performing wavelet decomposition on the first optimized signal to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal, until the frequency range of the low-frequency approximate component does not exceed the second preset frequency range, or the number of wavelet decompositions reaches a preset number threshold; obtaining a first target low-frequency approximate component and a second target low-frequency approximate component, and removing the second target low-frequency approximate component from the first target low-frequency approximate component to obtain a third target low-frequency approximate component; the first target low-frequency approximate component is a low-frequency approximate component obtained by the last wavelet decomposition, and the second target low-frequency approximate component is a low-frequency signal in the first target low-frequency approximate component whose frequency is less than or equal to a preset frequency threshold; and obtaining the second optimized signal using the third target low-frequency approximate component and all high-frequency detail components.

[0038] For details, please refer to Figure 2 , for the first optimized signal S(f s =1000Hz) to obtain a first low-frequency approximate component A1 (frequency range is [125Hz, 250Hz]) and a first high-frequency detail component D1 (frequency range is [250Hz, 500Hz]) of the first optimized signal S; obtain the frequency range of the first low-frequency approximate component A1; if the frequency range exceeds the second preset frequency range, perform wavelet decomposition on the first low-frequency approximate component A1 to obtain a second low-frequency approximate component A2 (frequency range is [62.5Hz, 125Hz]) and a second high-frequency detail component D2 (frequency range is [125Hz, 250Hz]); similarly, compare the frequency range of the second low-frequency approximate component A2 with the second preset frequency range. If the frequency range exceeds the second preset frequency range, the second low-frequency approximate component A2 is subjected to wavelet decomposition to obtain a third low-frequency approximate component A3 (frequency range is [31.25 Hz, 62.5 Hz]) and a third high-frequency detail component D3 (frequency range is [62.5 Hz, 125 Hz]); if the third low-frequency approximate component A3 does not exceed the second preset frequency range, or the number of wavelet decompositions reaches a preset number threshold, such as 3 times, the third low-frequency approximate component A3 is the first target low-frequency approximate component, and the low-frequency signal in the third low-frequency approximate component A3 whose frequency is less than or equal to the preset frequency threshold is the second target low-frequency approximate component A4, wherein the preset frequency threshold is determined by the following formula: Wherein, λ is the preset frequency threshold, σ is the standard deviation of the noise, and N is the length of the electric field signal. The third target low-frequency approximate component A5 is obtained by removing the second target low-frequency approximate component A4 from the first target low-frequency approximate component A3, and the second optimized signal is obtained according to the third target low-frequency approximate component A5, the third high-frequency detail component D3, the second high-frequency detail component D2 and the first high-frequency detail component D1. The preset number threshold of the wavelet decomposition number can be comprehensively considered according to the length and sampling frequency of the signal, as well as the required frequency resolution and noise suppression effect.

[0039] In a specific embodiment, the wavelet decomposition of the first optimized signal is performed to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal, including: convolving the first optimized signal with a preset wavelet function to obtain a first convolution result; downsampling the first convolution result to obtain the high-frequency detail component; translating the first optimized signal according to a preset translation scale; convolving the translated first optimized signal with a preset scale function to obtain a second convolution result; and downsampling the second convolution result to obtain the low-frequency approximate component.

[0040] Specifically, the convolution result of the first optimized signal and the wavelet function is downsampled to obtain the high-frequency detail component, and the convolution result of the translated version of the signal and the scale function is downsampled to obtain the low-frequency approximate component. The decomposition formula is as follows. This process is repeated until it does not exceed the second preset frequency range, or the number of wavelet decompositions reaches the preset threshold, thereby constructing a wavelet transform decomposition tree. The specific decomposition formula is: Among them, a j,k is the sequence parameter, is the high-pass filter of the decomposition algorithm, m is the number of decompositions, Z is the total number of decompositions, d j,k is the wavelet coefficient of the first optimized signal, is the low-pass filter of the synthesis algorithm, c j+1,m is the scale factor, a j+1,m is the low-frequency approximate component, h m-2k is the high-pass filter of the synthesis algorithm, g m-2k is the low-pass filter for the decomposition algorithm.

[0041] In a specific embodiment, the method of obtaining the second optimized signal using the third target low-frequency approximate component and all high-frequency detail components includes: reconstructing the third target low-frequency approximate component and all high-frequency detail components according to the inverse process of wavelet decomposition to obtain the second optimized signal.

[0042] Specifically, the second target low-frequency approximate component that is less than or equal to the threshold is directly set to zero to remove irregular noise in the extremely low frequency range, while retaining the smooth part of the electric field signal as much as possible. According to the inverse process of wavelet decomposition, the processed wavelet coefficients and the unprocessed low-frequency approximate component are used to reconstruct the signal through the Mallat inverse algorithm, d jk is the processed wavelet coefficient, the specific formula is: Where f(t) is the second optimized signal, k is the kth direction, c J,K is the detail coefficient, φ J,k (t) is the wavelet basis function in the kth direction, j is the jth level, J is the number of layers of wavelet decomposition, φ j,k (t) is the wavelet basis function in the kth direction of the jth level.

[0043] In a specific embodiment, after obtaining the second optimization signal, it also includes: obtaining a signal-to-noise ratio of the second optimization signal; comparing the signal-to-noise ratio with a preset signal-to-noise ratio range to obtain a signal-to-noise ratio comparison result; and optimizing the preset number threshold according to the signal-to-noise ratio comparison result.

[0044] Specifically, the signal-to-noise ratio SNR is calculated according to the signal power and the noise power. The specific formula is: Among them, P 1is the signal power of the second optimized signal, P 2 For the second optimization signal noise power. Set a reasonable SNR threshold range [SNR min ,SNR max ], if SNR is lower than SNR min , indicating that the noise suppression effect is poor; if the SNR is higher than SNR max , the useful components in the signal may be over-suppressed. In this case, the preset number threshold is optimized according to the signal-to-noise ratio comparison result to adjust the signal suppression strength.

[0045] In a specific embodiment, the signal-to-noise ratio comparison result includes that the signal-to-noise ratio is less than the minimum value of the preset signal-to-noise ratio range, the signal-to-noise ratio is within the preset signal-to-noise ratio range, and the signal-to-noise ratio is greater than the maximum value of the preset signal-to-noise ratio range; then optimizing the preset number threshold according to the signal-to-noise ratio comparison result includes: if the signal-to-noise ratio is less than the minimum value of the preset signal-to-noise ratio range, increasing the preset value of the preset number threshold; if the signal-to-noise ratio is within the preset signal-to-noise ratio range, not modifying the preset value of the preset number threshold; if the signal-to-noise ratio is greater than the maximum value of the preset signal-to-noise ratio range, reducing the preset value of the preset number threshold.

[0046] Specifically, when the detected real-time SNR is not within the set threshold range, a feedback signal is generated, and the strength and direction of the feedback signal depend on the degree and direction of the deviation between the SNR and the threshold. The filtering strategy is adjusted according to the feedback signal. When the SNR is lower than the SNR min When , the preset value of the preset number threshold is increased, that is, the number of layers J of wavelet decomposition is increased to more finely separate the noise and signal components, increase the threshold to more strictly remove the noise, and try to replace a more suitable wavelet basis function to better adapt to the characteristics of the signal; when SNR is higher than SNR max When , the preset value of the preset number threshold is reduced, that is, the number of wavelet decomposition layers J is reduced. The optimization algorithm based on gradient descent is used to update the filtering parameters according to the deviation between SNR and the threshold.

[0047] The method in this embodiment can accurately identify the extremely low frequency noise component in the electric field signal and effectively suppress it through the advanced multi-channel adaptive noise modeling method and the spectrum fine control technology, thereby improving the measurement accuracy of the electric field signal and providing strong technical support for high-precision electric field measurement. By real-time monitoring and adjusting the noise model, it can adaptively cope with various complex noise environments to ensure the accuracy and reliability of the measurement results. The proposed real-time signal-to-noise ratio detection and feedback mechanism enables the filter to automatically adjust the filter parameters according to the actual signal-to-noise ratio, thereby achieving effective suppression of noise. This dynamic adjustment mechanism ensures the adaptability and stability of the filter in different noise environments, so that it always remains in the best working state. Through this real-time monitoring and optimization, the clarity and recognizability of the electric field signal can be ensured, and the quality of the data and the accuracy of the analysis can be improved. This embodiment is also applicable to fields facing complex noise environments, such as geological exploration, environmental monitoring, etc. In these fields, the accurate measurement of electric field signals is crucial for the reliability of data and the scientific nature of analysis.

[0048] In the specific embodiments, see Figure 3 , is a structural schematic diagram of an extremely low frequency noise filtering system in the second embodiment of the present application, the system comprising: a wavelet transform module 201, a Fourier transform module 202, an energy distribution acquisition module 203, an analysis module 204 and an optimization module 205; the wavelet transform module 201 is used to perform wavelet transforms of different scales on the signal to be filtered to obtain the wavelet coefficients of the signal to be filtered at each scale; the Fourier transform module 202 is used to perform short-time Fourier transform on the signal to be filtered to obtain the complex matrix of the signal to be filtered at different time frames and different frequencies; the energy distribution acquisition module 203 is used to obtain the energy value distribution of the signal to be filtered within a preset frequency range using the wavelet coefficients and the complex matrix; the analysis module 204 is used to perform statistical analysis and spectrum analysis on the energy value distribution to obtain the extremely low frequency noise in the signal to be filtered; the extremely low frequency noise is a signal whose energy value exceeds a preset energy threshold; the optimization module 205 is used to filter out the extremely low frequency noise in the signal to be filtered to obtain a first optimized signal.

[0049] In a specific embodiment, the third embodiment of the present application provides an extremely low frequency noise filtering device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method as described in any one of the first embodiments of the present application.

[0050] In a specific embodiment, the fourth embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method as described in any one of the first embodiments of the present application.

[0051] Figure 4 The internal structure of a computer device in one embodiment is shown. The computer device can be a terminal or a server. Figure 4 The computer device includes a processor, a memory, etc. connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the method in this embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the method in this embodiment. Those skilled in the art can understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0052] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0053] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A very low frequency noise filtering method, characterized in that: The method comprises: Perform wavelet transform of different scales on the signal to be filtered to obtain the wavelet coefficient of the signal to be filtered at each scale; Performing short-time Fourier transform on the signal to be filtered to obtain a complex matrix of the signal to be filtered at different time frames and different frequencies; Obtaining energy value distribution of the signal to be filtered within a preset frequency range using the wavelet coefficients and the complex matrix; Performing statistical analysis and spectrum analysis on the energy value distribution to obtain extremely low frequency noise in the signal to be filtered; the extremely low frequency noise is a signal whose energy value exceeds a preset energy threshold; The extremely low frequency noise in the signal to be filtered is filtered out to obtain a first optimized signal.

2. The very low frequency noise filtering method according to claim 1, characterized in that: The step of filtering out the extremely low frequency noise in the signal to be filtered includes: Obtaining the center frequency of the signal to be filtered; Acquire the bandwidth of the filter according to the center frequency and the first preset frequency range; constructing a transfer function of the filter using the center frequency and the bandwidth; The filter uses the transfer function to filter out the extremely low frequency noise in the signal to be filtered.

3. The very low frequency noise filtering method according to claim 1, characterized in that: The obtaining of the first optimization signal further comprises: Performing wavelet decomposition on the first optimized signal to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal; Acquire the frequency range of the low-frequency approximate component; If the frequency range exceeds the second preset frequency range, the low-frequency approximate component is used as the first optimized signal, and the step of performing wavelet decomposition on the first optimized signal to obtain the low-frequency approximate component and the high-frequency detail component of the first optimized signal is returned until the frequency range of the low-frequency approximate component does not exceed the second preset frequency range, or the number of wavelet decompositions reaches a preset number threshold; Acquire a first target low-frequency approximate component and a second target low-frequency approximate component, and remove the second target low-frequency approximate component from the first target low-frequency approximate component to obtain a third target low-frequency approximate component; the first target low-frequency approximate component is a low-frequency approximate component obtained by the last wavelet decomposition, and the second target low-frequency approximate component is a low-frequency signal in the first target low-frequency approximate component whose frequency is less than or equal to a preset frequency threshold; A second optimized signal is obtained by using the third target low-frequency approximation component and all high-frequency detail components.

4. The very low frequency noise filtering method according to claim 3, characterized in that: The performing wavelet decomposition on the first optimized signal to obtain a low-frequency approximate component and a high-frequency detail component of the first optimized signal includes: Convolving the first optimized signal with a preset wavelet function to obtain a first convolution result; Downsampling the first convolution result to obtain the high-frequency detail component; translating the first optimized signal according to a preset translation scale; Convolving the first optimized signal after translation with a preset scaling function to obtain a second convolution result; The second convolution result is downsampled to obtain the low-frequency approximation component.

5. The very low frequency noise filtering method according to claim 3, characterized in that: The method of obtaining a second optimized signal by using the third target low-frequency approximate component and all high-frequency detail components comprises: Signal reconstruction is performed on the third target low-frequency approximate component and all the high-frequency detail components according to the inverse process of wavelet decomposition to obtain the second optimized signal.

6. The very low frequency noise filtering method according to claim 3, characterized in that: After obtaining the second optimization signal, the method further includes: Acquiring a signal-to-noise ratio of the second optimized signal; Comparing the signal-to-noise ratio with a preset signal-to-noise ratio range to obtain a signal-to-noise ratio comparison result; The preset number threshold is optimized according to the signal-to-noise ratio comparison result.

7. The very low frequency noise filtering method according to claim 6, characterized in that: The signal-to-noise ratio comparison result includes that the signal-to-noise ratio is less than a minimum value of the preset signal-to-noise ratio range, the signal-to-noise ratio is within the preset signal-to-noise ratio range, and the signal-to-noise ratio is greater than a maximum value of the preset signal-to-noise ratio range; Then the step of optimizing the preset number threshold according to the signal-to-noise ratio comparison result includes: If the signal-to-noise ratio is less than the minimum value of the preset signal-to-noise ratio range, increasing the preset value of the preset number threshold; If the signal-to-noise ratio is within the preset signal-to-noise ratio range, the preset value of the preset number threshold is not modified; If the signal-to-noise ratio is greater than the maximum value of the preset signal-to-noise ratio range, the preset value of the preset number threshold is reduced.

8. An extremely low frequency noise filtering system, characterized in that: The system includes: a wavelet transform module, a Fourier transform module, an energy distribution acquisition module, an analysis module and an optimization module; The wavelet transform module is used to perform wavelet transforms of different scales on the signal to be filtered to obtain the wavelet coefficients of the signal to be filtered at each scale; The Fourier transform module is used to perform short-time Fourier transform on the signal to be filtered to obtain a complex matrix of the signal to be filtered at different time frames and different frequencies; The energy distribution acquisition module is used to obtain the energy value distribution of the signal to be filtered within a preset frequency range by using the wavelet coefficients and the complex matrix; The analysis module is used to perform statistical analysis and spectrum analysis on the energy value distribution to obtain extremely low frequency noise in the signal to be filtered; the extremely low frequency noise is a signal whose energy value exceeds a preset energy threshold; The optimization module is used to filter out the extremely low frequency noise in the signal to be filtered to obtain a first optimized signal.

9. An extremely low frequency noise filtering device, comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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