Efficient electrochemical signal data processing method and system

Abstract

According to the efficient electrochemical signal data processing method provided by the invention, the noise interference is effectively inhibited by reasonably selecting the wavelet basis function and the number of decomposition layers and adopting methods such as threshold value processing and the like; the processing quality of the electrochemical signal is improved and the accurate identification of a real peak value in the electrochemical signal is realized according to the steps of acquisition, pretreatment, wavelet decomposition, signal denoising, peak value feature extraction, signal reconstruction and the like; meanwhile, an embedded system is developed and designed according to the method, and the system has the advantages of being small in size, integrated, high in precision, small in size, easy to carry and the like.

Description

Technical Field

[0001] This invention relates to an efficient data processing method and system, and particularly to a method and system for processing electrochemical signal data. Background Technology

[0002] Electrochemical signals suffer from low signal-to-noise ratios and baseline drift, hindering measurement accuracy. Traditional methods, such as moving average filtering and Gaussian fitting, are susceptible to noise and baseline drift, and fail to identify singularities. Wavelet analysis, a powerful tool following Fourier analysis, is widely used in signal processing and pattern recognition. Compared to traditional wavelet transforms, the fast wavelet transform (Mallat algorithm) based on discrete signals offers superior resolution for analyzing non-stationary signals like electrochemical signals, enabling noise filtering and peak potential detection.

[0003] To address the aforementioned problems, this invention combines electrochemical signal characteristics and wavelet analysis, providing a new approach to achieving automated, rapid, and accurate electrochemical analysis and detection. Summary of the Invention

[0004] The purpose of this invention is to provide a highly efficient electrochemical signal data processing method, which mainly includes the following steps:

[0005] S1) Electrochemical signal acquisition

[0006] Cyclic voltammetry was used to detect solutions containing specific concentrations of substances. Cyclic voltammetric signals were acquired using a three-electrode system consisting of a working electrode, a reference electrode, and an auxiliary electrode.

[0007] S2) Preprocessing of cyclic voltammetric signals

[0008] The cyclic voltammetric signal is preprocessed using a mean-reduction method, which involves subtracting the mean from the acquired cyclic voltammetric signal data values ​​to obtain a zero-mean signal.

[0009] S3) Wavelet decomposition of the zero-mean signal

[0010] The zero-mean signal is decomposed into a third-order wavelet by the Mallat algorithm based on the Daubechies wavelet basis function. The structure of the decomposed signal is as follows:

[0011] x(n) = A3 + D1 + D2 + D3

[0012] In the formula:

[0013] A3 is a low-frequency approximation signal (overall signal trend), with a scale of 6 and the lowest resolution;

[0014] D1 represents high-frequency detail signals (noise and sharp peaks), scale 2, with the highest resolution;

[0015] D2 represents mid-to-high frequency detail signals (core effective features), scale 4, with medium resolution;

[0016] D3 represents the mid-to-low frequency detail signal (between the baseline and the peak).

[0017] S4) Signal Denoising Processing

[0018] Noise suppression is performed based on thresholding of wavelet coefficients. Specifically, a wavelet threshold is first determined, then the wavelet threshold is used to remove high-frequency noise, and a threshold function is applied to the detail coefficients. The formula for the threshold function is as follows:

[0019]

[0020] In the formula:

[0021] D′ j [k]: The j-th wavelet detail coefficient (k-th value) after processing, output after denoising;

[0022] D j [k]: The j-th wavelet detail coefficient (k-th value) before processing, and the input after decomposition;

[0023] γ: Wavelet threshold, the critical value that distinguishes noise from signal;

[0024] σ: Standard deviation of noise, reflecting noise intensity;

[0025] N: Total number of sampling points for the original signal, ensuring that the threshold matches the amount of data.

[0026] S5) Extract signal peak features

[0027] In the wavelet domain, the peak value represents the extreme point of the coefficient. The peak value corresponds to the local maximum point. Further, the peak position and amplitude are selected from the denoised coefficients.

[0028] S6) Signal Reconstruction

[0029] The wavelet coefficients, after noise reduction and peak feature extraction, are subjected to inverse wavelet transform. A third-order reconstruction algorithm is then used to reconstruct the processed electrochemical signal. The reconstruction formula for the electrochemical signal is as follows:

[0030]

[0031] In the formula:

[0032] a j(k): Low-frequency approximation coefficients at the j-th scale after reconstruction (the core of the final output signal);

[0033] a j-1 (k): Low-frequency approximation coefficients at the (j-1)th scale (fine-scale trend of the input);

[0034] d j-1 (k): High-frequency approximation coefficients at the (j-1)th scale (denoising fine-scale details);

[0035] h j (k-2n): Low-frequency reconstruction filter coefficients, recovering low-frequency components;

[0036] g j (k-2n): High-frequency reconstruction filter coefficients, recovering high-frequency components;

[0037] 2n: Upsampling characteristics of binary wavelets, and inverse downsampling operation for reconstruction;

[0038] k-2n: Convolution position, reflecting the sliding weighting relationship between the filter and the extended signal.

[0039] Preferably, the calculation formula for the mean-removal method in step S2 is as follows:

[0040] x′(t)=x(t)-μ

[0041]

[0042] In the formula:

[0043] x′(t): The zero-mean signal (target output);

[0044] x(t): Original signal (input);

[0045] μ: The mean of the original signal x(t) (reflecting the baseline drift level);

[0046] N: The number of sampling points (total data length) of the original signal x(t);

[0047] x(i): The i-th sampled value (discrete data point) of the original signal.

[0048] Preferably, the Mallat algorithm formula in step S3 is as follows:

[0049]

[0050] In the formula:

[0051] a j (k): Low-frequency approximation coefficients at the j-th scale, and input coarse-scale low-frequency data;

[0052] a j-1 (k), d j-1 (k): representing the near-low frequency similarity coefficients and high frequency detail coefficients at the (j-1)th scale, respectively;

[0053] h j (k-2n), g j (k-2n): These are the low-pass filter coefficients and high-pass filter coefficients, respectively.

[0054] Preferably, the algorithm formula for the third-order wavelet decomposition in step S3 is as follows:

[0055]

[0056] In the formula:

[0057] a j : Low-frequency approximation coefficients at the j-th scale;

[0058] a j-1 a j-2 a j-3 : Low-frequency approximation coefficients for the (j-1), (j-2), and (j-3)th digits;

[0059] d j-1 d j-2 d j-3 High-frequency detail coefficients at various scales;

[0060] g': High-pass filter coefficient, used to extract high-frequency detail components of the signal;

[0061] h': Low-pass filter coefficient, used to extract the low-frequency approximation components of the signal;

[0062] Double downsampling.

[0063] Preferably, the search for the peak point in step S5 must satisfy the following formula:

[0064] d j (k)>d j-1 (k-1),d j (k)<d j+1 (k-1)

[0065] In the formula:

[0066] d j (k): Wavelet detail coefficients with index k at the j-th scale (level), the core judgment object, usually j corresponds to the D2 layer (mid-to-high frequency details);

[0067] d j-1(k-1): Wavelet detail coefficients with index k-1 at the (j-1)th scale (1 level coarser than the current scale j), ensuring that the peak has relative prominence at the coarse scale.

[0068] d j+1 (k-1): Wavelet detail coefficients with index k-1 at the (j+1)th scale (1 level finer than the current scale j), ensuring that the peak value conforms to the peak value propagation law at fine scales.

[0069] Preferably, the formula for the third-order reconstruction algorithm in step S6 is as follows:

[0070]

[0071] In the formula:

[0072] a j-3 : Low-frequency approximation coefficients at the (j-3)th scale;

[0073] d j-1 d j-2 d j-3 High-frequency detail coefficients at various scales;

[0074] a j : The reconstructed coarse-scale low-frequency approximation coefficients;

[0075] g k High-pass reconstruction filter, used to extract high-frequency detail components of a signal;

[0076] h k Low-pass reconstruction filter, used to extract the low-frequency approximation components of a signal;

[0077] Double upsampling.

[0078] The present invention also discloses a high-efficiency electrochemical signal data processing system, which is designed to implement the above-described electrochemical signal data processing method.

[0079] Preferably, the high-efficiency electrochemical signal data processing system includes a signal acquisition module, a signal processing module, and a signal output module.

[0080] Preferably, the signal processing module includes an electrochemical signal preprocessing module, a wavelet decomposition module, a threshold processing module, and a wavelet reconstruction module.

[0081] Preferably, the high-efficiency electrochemical signal data processing system is an embedded system, and the embedded system can also be integrated into a miniaturized portable device.

[0082] The signal processing method and system of the present invention have strong versatility and flexibility. The system is simple to implement and has high computational efficiency. It can quickly process a large amount of signal data in application scenarios with high real-time requirements, thus meeting the needs of practical engineering applications.

[0083] It should be understood that the foregoing general description and the subsequent detailed description are illustrative and explanatory, and should not be used as limitations on the content claimed in this invention. Attached Figure Description

[0084] Referring to the accompanying drawings, further objects, functions, and advantages of the present invention will be clarified through the following description of embodiments of the invention, wherein:

[0085] Figure 1 The schematic diagram illustrates the modules of the high-efficiency electrochemical signal data processing system of the present invention.

[0086] Figure 2 A schematic diagram illustrating the flow chart of the electrochemical signal data processing method of the present invention is shown.

[0087] Figure 3(a) schematically shows the voltammetric curves of the present invention after detection and pretreatment of an ionic solution with a sample concentration of 50 ppm;

[0088] Figure 3(b) schematically shows the voltammetric curves of the present invention after detection and pretreatment of an ionic solution with a sample concentration of 30 ppm;

[0089] Figure 4(a) schematically shows the voltammetric curves after detection and wavelet decomposition of an ionic solution with a sample concentration of 50 ppm according to the present invention;

[0090] Figure 4(b) schematically shows the voltammetric curves after detection and wavelet decomposition of an ionic solution with a sample concentration of 30 ppm according to the present invention. Detailed Implementation

[0091] The objects and functions of the present invention, as well as the methods for achieving these objects and functions, will be clarified by referring to exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed below; it can be implemented in various forms. The purpose of this specification is merely to help those skilled in the art to comprehensively understand the specific details of the invention.

[0092] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0093] Figure 1 and Figure 2 This invention schematically illustrates a highly efficient electrochemical signal data processing method and system, wherein the method includes the following steps:

[0094] S1) Electrochemical signal acquisition

[0095] To obtain the raw cyclic voltammetric signal to be processed and provide a basic data source for subsequent data processing, this patent uses cyclic voltammetry to detect solutions containing substances of a specific concentration. The cyclic voltammetric signal is acquired through a three-electrode system consisting of a working electrode, a reference electrode, and an auxiliary electrode.

[0096] The sampling frequency, scan range, and scan rate can be set according to actual needs. The electrochemical signal acquisition process needs to combine hardware signal chains and software control logic to ensure the accuracy, real-time performance, and stability of signal acquisition.

[0097] S2) Preprocessing of cyclic voltammetric signals

[0098] Electrochemical signals are often affected by factors such as electrode polarization and solution resistance, resulting in slowly changing baseline drift (low-frequency noise). This invention employs mean removal to preprocess the electrochemical signal, adjusting the baseline to zero, making the true signal more prominent. The signal energy is concentrated in the AC component (fluctuation part), allowing wavelet decomposition to focus more on peak and abrupt change features, reducing interference from approximate components in detail extraction, and facilitating subsequent peak detection or extraction.

[0099] The operating principle is to preprocess the cyclic voltammetry signal using the mean-reduction method, that is, to subtract the mean from the collected cyclic voltammetry signal data values ​​to obtain a zero-mean signal.

[0100] The calculation formula for the mean-removal method in step S2 is as follows:

[0101] x'(t)=x(t)-μ

[0102]

[0103] In the formula:

[0104] x′(t): The zero-mean signal (target output);

[0105] x(t): Original signal (input);

[0106] μ: The mean of the original signal x(t) (reflecting the baseline drift level);

[0107] N: The number of sampling points (total data length) of the original signal x(t);

[0108] x(i): The i-th sampled value (discrete data point) of the original signal.

[0109] S3) Wavelet decomposition of the zero-mean signal

[0110] The zero-mean signal is split into different components according to frequency to achieve multi-resolution analysis, distinguish different features such as baseline trend, noise, and peak value, and prepare for subsequent denoising and peak value extraction.

[0111] The operating principle employs a third-order wavelet decomposition of the zero-mean signal based on the Daubechies wavelet basis function and the Mallat algorithm. The signal decomposition structure formula is as follows:

[0112] x(n) = A3 + D1 + D2 + D3

[0113] In the formula:

[0114] A3 is a low-frequency approximation signal (overall signal trend), with a scale of 6 and the lowest resolution;

[0115] D1 represents high-frequency detail signals (noise and sharp peaks), scale 2, with the highest resolution;

[0116] D2 represents mid-to-high frequency detail signals (core effective features), scale 4, with medium resolution;

[0117] D3 represents the mid-to-low frequency detail signal (between the baseline and the peak).

[0118] The Mallat algorithm formula is as follows.

[0119]

[0120] In the formula:

[0121] a j (k): Low-frequency approximation coefficients at the j-th scale, and input coarse-scale low-frequency data;

[0122] a j-1 (k),d j-1 (k): representing the near-low frequency similarity coefficients and high frequency detail coefficients at the (j-1)th scale, respectively;

[0123] h j (k-2n), g j (k-2n): These are the low-pass filter coefficients and high-pass filter coefficients, respectively.

[0124] Based on the above formula, with the approximate coefficient a of scale j j Using the input as the basis, the components at each scale are progressively decomposed through a loop of filtering and superimposing the corresponding filters at double downsampling, ultimately yielding the low-frequency approximation coefficients 'a' for each scale. j-1 a j-2 a j-3 and high-frequency detail coefficient d j-1 d j-2 d j-3 The procedure for third-order wavelet decomposition of electrochemical signals is as follows:

[0125]

[0126] In the formula:

[0127] a j : Low-frequency approximation coefficients at the j-th scale;

[0128] a j-1 a j-2 a j-3 : Low-frequency approximation coefficients for the (j-1), (j-2), and (j-3)th digits;

[0129] d j-1 d j-2 d j-3 High-frequency detail coefficients at various scales;

[0130] g': High-pass filter coefficient, used to extract high-frequency detail components of the signal;

[0131] h': Low-pass filter coefficient, used to extract the low-frequency approximation components of the signal;

[0132] Double downsampling;

[0133] S4) Signal Denoising Processing

[0134] Noise (mainly in layer D1) in the high-frequency detail components after wavelet decomposition is removed, while effective peak details (mainly in layer D2) are preserved, thereby improving the signal-to-noise ratio.

[0135] The operating principle is based on wavelet coefficient thresholding. A soft thresholding function is applied to the detail coefficients (D1, D2, D3), and the threshold is set according to the noise statistical characteristics to distinguish between noise coefficients and effective signal coefficients.

[0136] The formula for the soft threshold function is as follows:

[0137]

[0138]

[0139] In the formula:

[0140] D′ j [k]: The j-th wavelet detail coefficient (k-th value) after processing, output after denoising;

[0141] D j [k]: The j-th wavelet detail coefficient (k-th value) before processing, and the input after decomposition;

[0142] γ: Wavelet threshold, the critical value that distinguishes noise from signal;

[0143] σ: Standard deviation of noise, reflecting noise intensity;

[0144] N: Total number of sampling points for the original signal, ensuring that the threshold matches the amount of data.

[0145] Specifically, appropriate thresholds are determined based on the statistical characteristics of the noise and the signal-to-noise ratio of the signal. The high-frequency layer D1 mainly contains noise, so its threshold can be set relatively high. D2 contains peak details, so its threshold is appropriately lowered to preserve features. Local large-value enhancement is applied to the denoised detail coefficients, and combined with the approximate component A3, to highlight the difference between the peak and the baseline.

[0146] S5) Extract signal peak features

[0147] In the wavelet domain, the peak value represents the extreme point of the coefficient. The peak value corresponds to the local maximum point. Further, the peak position and amplitude are selected from the denoised coefficients.

[0148] According to the three-layer decomposition proposed in this invention, the approximate component A3 reflects the overall trend of the signal (such as the baseline of the electrochemical signal), while the detail components D1, D2, and D3 correspond to different high-frequency components (such as noise and fast-response signals). Peak positions and amplitudes are selected from the denoised coefficients. For example, the detail features corresponding to the peak current of the electrochemical signal curve are proposed in layer D2. The search for the above peak points must satisfy the following formula:

[0149] d j (k)>d j-1 (k-1),d j (k) <d j+1 (k-1)

[0150] In the formula:

[0151] d j (k): Wavelet detail coefficients with index k at the j-th scale (level), the core judgment object, usually j corresponds to the D2 layer (mid-to-high frequency details);

[0152] d j-1 (k-1): Wavelet detail coefficients with index k-1 at the (j-1)th scale (1 level coarser than the current scale j), ensuring that the peak has relative prominence at the coarse scale.

[0153] d j+1 (k-1): Wavelet detail coefficients with index k-1 at the (j+1)th scale (1 level finer than the current scale j), ensuring that the peak value conforms to the peak value propagation law at fine scales.

[0154] The peak position detected by the D2 layer is projected onto the original signal domain to determine the peak position. The coordinates of the peak in the original signal are recorded, the amplitude of the peak point is extracted, the net peak value is calculated, and the peak shape parameters are analyzed.

[0155] S6) Signal Reconstruction

[0156] The denoised detail coefficients and the retained approximation coefficients are reconstructed using inverse wavelet transform to reconstruct the processed complete electrochemical signal, completing the closed loop from the original signal through feature extraction to the reconstructed signal, and providing the final data for subsequent output.

[0157] The operating principle is to use inverse wavelet transform + third-order reconstruction algorithm, based on Mallat reconstruction logic, to merge components of each scale.

[0158] The reconstruction formula is as follows:

[0159]

[0160] In the formula:

[0161] a j (k): Low-frequency approximation coefficients at the j-th scale after reconstruction (the core of the final output signal);

[0162] a j-1 (k): Low-frequency approximation coefficients at the (j-1)th scale (fine-scale trend of the input);

[0163] d j-1 (k): High-frequency approximation coefficients at the (j-1)th scale (denoising fine-scale details);

[0164] h j (k-2n): Low-frequency reconstruction filter coefficients, recovering low-frequency components;

[0165] g j (k-2n): High-frequency reconstruction filter coefficients, recovering high-frequency components;

[0166] 2n: Upsampling characteristics of binary wavelets, and inverse downsampling operation for reconstruction;

[0167] k-2n: Convolution position, reflecting the sliding weighting relationship between the filter and the extended signal.

[0168] Based on the above reconstruction formula, the electrochemical signal flow after third-order reconstruction is as follows: using the finest scale j-3 approximation coefficient a j-3 and detail coefficients d at various scales j-1 d j-2 d j-3 As input, the components at each scale are progressively merged through a loop of filtering and superimposing the corresponding filters at double upsampling, ultimately yielding an approximate coefficient a for the coarse scale j. j (That is, the reconstructed complete signal). The flowchart of electrochemical signal reconstruction is as follows:

[0169]

[0170] In the formula:

[0171] a j-3 : Low-frequency approximation coefficients at the (j-3)th scale;

[0172] d j-1 d j-2 d j-3 High-frequency detail coefficients at various scales;

[0173] a j : The reconstructed coarse-scale low-frequency approximation coefficients;

[0174] g k High-pass reconstruction filter, used to extract high-frequency detail components of a signal;

[0175] h k Low-pass reconstruction filter, used to extract the low-frequency approximation components of a signal;

[0176] Double upsampling.

[0177] The present invention also discloses a high-efficiency electrochemical signal data processing system, which is designed to implement the above-mentioned high-efficiency electrochemical signal data processing method.

[0178] This high-efficiency electrochemical signal data processing system includes a signal acquisition module, a signal processing module, and a signal output module. The signal processing module includes an electrochemical signal preprocessing module, a wavelet decomposition module, a thresholding module, and a wavelet reconstruction module.

[0179] This high-efficiency electrochemical signal data processing system is an embedded system. Furthermore, this embedded system can be integrated into a miniaturized, all-in-one portable device, which is convenient to carry and use directly on site.

[0180] Furthermore, this invention also discloses an example of a comparative test experiment, which uses both conventional methods and the method of this invention to monitor single-ion solutions of different concentrations, as detailed below:

[0181] The comparative test experiment used the voltammetric method to test Fe at 30 ppm and 50 ppm respectively. 3+ / Fe 2+ Detection is performed using a single ion solution. The prepared standard sample is dropped into the sample application area of ​​the paper-based microfluidic chip. After the sample passes through the flow path and completely wets the detection area, the detection is performed.

[0182] Following the scanning parameters: scanning voltage 0.0V, -1.1V, sensitivity 1e-3, and scanning rate 50mV / s, an electrochemical experiment was conducted, yielding the preprocessed voltammetric curves shown in Figure 3. The 500 points were then decomposed using a 3-level wavelet decomposition method (db4) and reconstructed, resulting in the curve shown in Figure 4. A comparison of the curves in Figures 3 and 4 reveals that the curve in Figure 3 suffers from signal-to-noise interference, resulting in coarse lines and a lack of clear peak characteristics, making accurate identification impossible. In contrast, the curve in Figure 4 is smooth and exhibits clear peak characteristics, demonstrating the advantage of accurate identification.

[0183] Test results show that the electrochemical signal processing scheme using wavelet transform performs exceptionally well in both signal quality optimization and feature recognition efficiency. Its core advantages are reflected in the following two aspects:

[0184] (1) Significantly enhanced signal smoothness and noise suppression: Through software-level signal preprocessing and wavelet denoising, high-frequency interference in the original data (such as power frequency noise and circuit glitches during electrochemical signal acquisition) is effectively filtered out. After processing, the signal curve presented on the LCD interface has no obvious jitter or data breaks, and the continuity and smoothness fully meet the requirements of high-precision electrochemical detection scenarios; compared with traditional unfiltered or single low-pass filter schemes, the noise suppression effect is quantitatively improved by about 30%.

[0185] (2) Feature values ​​are prominent and highly recognizable: For key feature parameters (such as peak current Ip and peak potential Ep of electrochemical signals), a visual enhancement design is adopted. In the LCD display interface, the feature values ​​are superimposed on the corresponding positions of the curve in the form of "data annotation + visual enhancement", which improves the feature information extraction efficiency by 60% and avoids visual misjudgment.

[0186] In summary, the wavelet analysis algorithm of this invention can perform localized analysis of signals in both the time and frequency domains simultaneously. It has a good processing effect on non-stationary signals, can accurately capture the transient changes and frequency characteristics of signals, and improve the accuracy and reliability of signal analysis.

[0187] Furthermore, this invention, through the logic of "acquisition-preprocessing-wavelet decomposition-signal denoising-peak feature extraction-signal reconstruction," rationally selects wavelet basis functions and the number of decomposition layers, and employs threshold processing and other methods to effectively suppress noise interference. While removing noise, it retains the useful information of the signal to the greatest extent, reduces signal distortion, and distributes noise and peak values ​​at different frequency levels. Then, through a cross-scale verification mechanism, it enhances the reliability of peak detection, realizes the accurate identification of the true peak value in the electrochemical signal, and improves the quality of signal processing.

[0188] Finally, the efficient electrochemical signal data processing method and system of this invention has strong versatility and flexibility. The system is simple to implement, has high computational efficiency, significantly improves the accuracy and reliability of signal analysis, and can quickly process large amounts of signal data in application scenarios with high real-time requirements, meeting the needs of practical engineering applications.

[0189] The accompanying drawings are illustrative only and are not drawn to scale. Although the invention has been described in conjunction with preferred embodiments, it should be understood that the scope of protection of the invention is not limited to the embodiments described herein.

[0190] Other embodiments of the invention will be readily conceived and understood by those skilled in the art in conjunction with the description and practice of the invention disclosed herein. The descriptions and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are defined by the claims.

Claims

1. A highly efficient electrochemical signal data processing method, comprising the following steps: S1) Electrochemical signal acquisition Cyclic voltammetry was used to detect solutions containing specific concentrations of substances. Cyclic voltammetric signals were acquired using a three-electrode system consisting of a working electrode, a reference electrode, and an auxiliary electrode. S2) Preprocessing of cyclic voltammetric signals The cyclic voltammetric signal is preprocessed by subtracting the mean from the collected cyclic voltammetric signal data values ​​to obtain a zero-mean signal. S3) Wavelet decomposition of the zero-mean signal The zero-mean signal is decomposed into a third-order wavelet by the Mallat algorithm based on the Daubechies wavelet basis function. The structure of the decomposed signal is as follows: x(n) = A3 + D1 + D2 + D3 In the formula: A3 is a low-frequency approximation signal (overall signal trend), with a scale of 6 and the lowest resolution; D1 represents high-frequency detail signals (noise and sharp peaks), scale 2, with the highest resolution; D2 represents mid-to-high frequency detail signals (core effective features), scale 4, with medium resolution; D3 represents the mid-to-low frequency detail signal (between the baseline and the peak value); S4) Signal Denoising Processing Noise suppression is performed based on thresholding of wavelet coefficients. Specifically, a wavelet threshold is first determined, then the wavelet threshold is used to remove high-frequency noise, and a threshold function is applied to the detail coefficients. The formula for the threshold function is as follows: In the formula: D′ j [k]: The j-th wavelet detail coefficient (k-th value) after processing, output after denoising; D j [k]: The j-th wavelet detail coefficient (k-th value) before processing, and the input after decomposition; γ: Wavelet threshold, the critical value that distinguishes noise from signal; σ: Standard deviation of noise, reflecting noise intensity; N: Total number of sampling points for the original signal, ensuring that the threshold matches the amount of data; S5) Extract signal peak features In the wavelet domain, the peak value represents the extreme point of the coefficient, and the peak value corresponds to the local maximum point. Further selection of peak position and amplitude is performed in the denoised coefficients. S6) Signal Reconstruction The wavelet coefficients, after noise reduction and peak feature extraction, are subjected to inverse wavelet transform. A third-order reconstruction algorithm is then used to reconstruct the processed electrochemical signal. The reconstruction formula for the electrochemical signal is as follows: In the formula: a j (k): Low-frequency approximation coefficients at the j-th scale after reconstruction (the core of the final output signal); a j-1 (k): Low-frequency approximation coefficients at the (j-1)th scale (fine-scale trend of the input); d j-1 (k): High-frequency approximation coefficients at the (j-1)th scale (denoising fine-scale details); h j (k-2n): Low-frequency reconstruction filter coefficients, recovering low-frequency components; g j (k-2n): High-frequency reconstruction filter coefficients, recovering high-frequency components; 2n: Upsampling characteristics of binary wavelets, and inverse downsampling operation for reconstruction; k-2n: Convolution position, reflecting the sliding weighting relationship between the filter and the extended signal.

2. The efficient electrochemical signal data processing method according to claim 1, characterized in that: The calculation formula for the mean-removal method in step S2 is as follows: x′(t)=x(t)-μ In the formula: x′(t): The zero-mean signal (target output); x(t): Original signal (input); μ: The mean of the original signal x(t) (reflecting the baseline drift level); N: The number of sampling points (total data length) of the original signal x(t); x(i): The i-th sampled value (discrete data point) of the original signal.

3. The efficient electrochemical signal data processing method according to claim 1, characterized in that: The Mallat algorithm formula in step S3 is as follows. In the formula: a j (k): Low-frequency approximation coefficients at the j-th scale, and input coarse-scale low-frequency data; a j-1 (k), d j-1 (k): representing the near-low frequency similarity coefficients and high frequency detail coefficients at the (j-1)th scale, respectively; h j (k-2n), g j (k-2n): These are the low-pass filter coefficients and high-pass filter coefficients, respectively.

4. The efficient electrochemical signal data processing method according to claim 1, characterized in that: The algorithm formula for the third-order wavelet decomposition in step S3 is as follows. In the formula: a j : Low-frequency approximation coefficients at the j-th scale; a j-1 a j-2 a j-3 : Low-frequency approximation coefficients for the (j-1), (j-2), and (j-3)th digits; d j-1 d j-2 d j-3 High-frequency detail coefficients at various scales; g': High-pass filter coefficient, used to extract high-frequency detail components of the signal; h': Low-pass filter coefficient, used to extract the low-frequency approximation components of the signal; Double downsampling.

5. The efficient electrochemical signal data processing method according to claim 1, characterized in that: The search for peak points in step S5 must satisfy the following formula. d j (k)>d j-1 (k-1),d j (k)<d j+1 (k-1) In the formula: d j (k): Wavelet detail coefficients with index k at the j-th scale (level), the core judgment object, usually j corresponds to the D2 layer (mid-to-high frequency details); d j-1 (k-1): Wavelet detail coefficients with index k-1 at the (j-1)th scale (1 level coarser than the current scale j), ensuring that the peak has relative prominence at the coarse scale. d j+1 (k-1): Wavelet detail coefficients with index k-1 at the (j+1)th scale (1 level finer than the current scale j), ensuring that the peak value conforms to the peak value propagation law at fine scales.

6. The efficient electrochemical signal data processing method according to claim 1, characterized in that: The formula for the third-order reconstruction algorithm in step S6 is as follows. In the formula: a j-3 : Low-frequency approximation coefficients at the (j-3)th scale; d j-1 d j-2 d j-3 High-frequency detail coefficients at various scales; a j : The reconstructed coarse-scale low-frequency approximation coefficients; g k High-pass reconstruction filter, used to extract high-frequency detail components of a signal; h k Low-pass reconstruction filter, used to extract the low-frequency approximation components of a signal; Double upsampling.

7. A high-efficiency electrochemical signal data processing system designed for implementing the method according to any one of claims 1 to 6.

8. The high-efficiency electrochemical signal data processing system according to claim 7, characterized in that: The high-efficiency electrochemical signal data processing system includes a signal acquisition module, a signal processing module, and a signal output module.

9. The high-efficiency electrochemical signal data processing system according to claim 8, characterized in that: The signal processing module includes an electrochemical signal preprocessing module, a wavelet decomposition module, a threshold processing module, and a wavelet reconstruction module.

10. The high-efficiency electrochemical signal data processing system according to claim 9, characterized in that: The high-efficiency electrochemical signal data processing system is an embedded system, which can also be integrated into a miniaturized portable device.

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