Detection signal noise reduction method based on electrochemical workstation

Abstract

The invention discloses a detection signal noise reduction method based on an electrochemical workstation, and relates to the technical field of signal processing. According to the detection signal noise reduction method provided by the invention, according to a normal distribution rule of thermal noise generated during working of the electrochemical workstation and independent analysis and processing of each group of electrochemical experiment data, the noise is reduced; corresponding reverse noise is randomly generated by adopting a similar normal distribution rule to carry out independent noise reduction on each group of electrochemical experiment data, and the experiment data after noise reduction is displayed in real time, so that real-time efficient noise reduction processing on the electrochemical experiment data can be realized, and meanwhile, the real characteristics of an original acquisition signal and a noise signal can be greatly reserved; therefore, the electrochemical measurement precision and reliability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a detection signal noise reduction method based on an electrochemical workstation. Background Art

[0002] The signal characteristics of an electrochemical workstation are usually manifested as the superposition of low-frequency signals and high-frequency noise, in which low-frequency signals and high-frequency noise are independent of each other and do not affect each other. The low-frequency signal part is the useful information generated during the electrochemical reaction, and its frequency ranges from millihertz to several thousand hertz, representing the electrochemical characteristics of the electrode reaction, such as changes in current, voltage and other parameters; on the other hand, high-frequency noise mainly comes from external environmental interference and the electronic noise of the equipment itself, and its frequency is high, generally exceeding megahertz, exceeding the highest sampling frequency of the instrument. This signal characteristic leads to the need to adopt effective noise reduction methods in the process of signal detection and data analysis to enhance the identifiability and accuracy of the signal, while retaining the integrity of the original signal, that is, to improve the signal-to-noise ratio of the electrochemical workstation.

[0003] In the application scenarios of electrochemical workstations, the real-time display function of data is crucial. It gives experimenters the ability to monitor the dynamic process of electrochemical reactions in real time, ensuring the keen capture and timely adjustment of changes in experimental states. In view of this demand, the noise reduction strategy provided by related technologies, that is, the technical solution of centralized noise reduction processing after complete data acquisition, obviously conflicts with the strict real-time requirements of electrochemical workstations. Related technologies are limited by their inherent post-processing computational complexity, which often consumes a lot of time and resources, making it difficult to achieve the goal of instant processing and feedback, thereby weakening the timeliness of data-guided experiments; in addition, the smoothed data loses the characteristics of noise, and the comparison of the characteristics of noise signals and real signals is often an important means of judging the authenticity of signals. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a detection signal noise reduction method based on an electrochemical workstation, which performs real-time independent noise identification and noise reduction processing on each data point collected by the electrochemical workstation to reduce the thermal noise and white noise levels of its random distribution characteristics, while ensuring that the inherent characteristics of the noise are effectively retained, which can significantly improve the signal-to-noise ratio, thereby enhancing the accuracy and reliability of electrochemical measurements. The technical solution provided by the present invention is as follows:

[0005] According to one aspect of an embodiment of the present invention, a detection signal denoising method based on an electrochemical workstation is provided, wherein the detection signal denoising method comprises:

[0006] S10: obtaining the original independent variables and the original dependent variables corresponding to the first n groups of electrochemical experimental data, and calculating the standard deviation σ according to the original dependent variables of the first n groups of electrochemical experimental data, wherein the value of n increases dynamically and is self-updated as the number of times the electrochemical experimental data is obtained increases;

[0007] S20: Perform linear regression fitting on the first n groups of electrochemical experimental data, calculate the slope a and intercept b of the linear regression equation, and use the original independent variables of the n+1th group of electrochemical experimental data to predict the corresponding predicted dependent variable y according to the linear regression equation predicted , predict the dependent variable y predicted The expression is as follows:

[0008] y predicted =a·x n+1 +b Formula 1 Among them, x n+1 is the original independent variable corresponding to the n+1th group of electrochemical experimental data;

[0009] S30: randomly generating a noise value z corresponding to the reverse noise in the range [-5σ, 5σ], wherein the generation rule of the noise value z obeys the normal distribution characteristics;

[0010] S40: Calculate the probability density f(z) corresponding to the noise value z and the maximum probability density f according to the probability density function f(x) max , and calculate the ratio r between the two; the probability density function f(x) is expressed as formula 2:

[0011]

[0012] Where, μ = 0;

[0013] Maximum probability density f max is the value of f(x) when x=μ=0;

[0014] The ratio r is expressed as formula 3:

[0015]

[0016] S50: Generate a random number in the range [0, 1]. If it is detected that the random number is less than r, determine the noise value z as effective noise; if it is detected that the random number is greater than r, re-execute the step of randomly generating a noise value z corresponding to the reverse noise in the range [-5σ, 5σ] in S30;

[0017] S60: Add the noise value z determined as effective noise to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data measured , get the denoised dependent variable y new , denoising dependent variable y newThe expression is as follows:

[0018] y new =y measured +z formula 4;

[0019] S70: Calculate the original dependent variable y measured and predict the dependent variable y predicted The first absolute distance, and the denoised dependent variable y new and predict the dependent variable y predicted If the second absolute distance is less than the first absolute distance, the noise reduction dependent variable y new Determine the real dependent variable corresponding to the original independent variable of the n+1th group of electrochemical experimental data, output and display it, then update the value of n, and execute the step of obtaining the original independent variable and the original dependent variable corresponding to the first n groups of electrochemical experimental data described in S10; if the second absolute distance is greater than the first absolute distance, re-execute the step of randomly generating a noise value z corresponding to the reverse noise within the range of [-5σ, 5σ] described in S30.

[0020] Preferably, the initial value of n is a fixed preset value m, and both m and n are positive integers greater than 1.

[0021] Preferably, the method comprises:

[0022] After outputting and displaying the true dependent variables corresponding to the original independent variables of each group of electrochemical experimental data after the mth group, for each group of electrochemical experimental data, the true dependent variable corresponding to the electrochemical experimental data is updated to the original dependent variable ymeasured, n is updated to m, and steps S10 to S70 are re-executed to achieve successive noise reduction for each group of electrochemical experimental data.

[0023] Preferably, the method comprises:

[0024] When it is detected that the absolute value of the difference between the standard deviation σ corresponding to the first n-2 groups of electrochemical experimental data and the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data is less than the preset difference, after executing the step of obtaining the original independent variables and the original dependent variables corresponding to the first n groups of electrochemical experimental data in S10, the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data is directly determined as the standard deviation σ corresponding to the first n groups of electrochemical experimental data.

[0025] Preferably, the noise value z corresponding to the reverse noise is similar in amplitude to the noise value corresponding to the original thermal noise generated when the electrochemical workstation is operating, and has an opposite phase.

[0026] Preferably, the method further comprises:

[0027] Analyzing other types of noise included in the operation of the electrochemical workstation;

[0028] For each other noise type, determining a distribution feature corresponding to the other noise type, and generating corresponding effective reverse noise according to the distribution feature corresponding to the other noise type;

[0029] In executing S60, the noise value z determined as effective noise is added to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data. measured , get the denoised dependent variable y new In the step, the noise value of the effective reverse noise corresponding to each noise type is added to the noise reduction dependent variable y new And update the denoised dependent variable y new .

[0030] Preferably, the linear regression fitting algorithm adopts the least squares method.

[0031] Preferably, when it is not detected that the electrochemical workstation has acquired the latest set of electrochemical experimental data, the operation of the detection signal noise reduction method is terminated.

[0032] Preferably, the step of successive noise reduction is performed at least three times.

[0033] Preferably, the original independent variable is the horizontal coordinate corresponding to the electrochemical experimental data before the noise reduction process, and the original dependent variable is the vertical coordinate corresponding to the electrochemical experimental data before the noise reduction process.

[0034] Compared with the prior art, the detection signal noise reduction method based on the electrochemical workstation provided by the present invention has the following advantages:

[0035] The present invention provides a detection signal denoising method based on an electrochemical workstation, which comprises obtaining the original independent variables and the original dependent variables corresponding to the first n groups of electrochemical experimental data, calculating the standard deviation σ according to the original dependent variables of the first n groups of electrochemical experimental data; performing linear regression fitting on the first n groups of electrochemical experimental data, and calculating the slope of the linear regression equation.

[0036] and intercept b, and according to the linear regression equation, use the original independent variables of the n+1 group of electrochemical experimental data to predict its corresponding predicted dependent variable y predicted ; Randomly generate a noise value z corresponding to the reverse noise in the range [-5σ, 5σ], and the generation rule of the noise value z obeys the normal distribution characteristics; Calculate the probability density f(z) corresponding to the noise value z and the maximum probability density f according to the probability density function f(x) max, and calculate the ratio r of the two; generate a random number in the range [0, 1], if the random number is detected to be less than r, the noise value z is determined as effective noise; if the random number is detected to be greater than r, re-execute the step of randomly generating the noise value z corresponding to the reverse noise in the range [-5σ, 5σ]; add the noise value z determined as effective noise to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data measured , get the denoised dependent variable y new ; Calculate the original dependent variable y measured and predict the dependent variable y predicted The first absolute distance, and the denoised dependent variable y new and predict the dependent variable y predicted If the second absolute distance is less than the first absolute distance, the noise reduction dependent variable y new The real dependent variable corresponding to the original independent variable of the n+1th group of electrochemical experimental data is determined to be output and displayed, then the value of n is updated, and the steps of obtaining the original independent variable and the original dependent variable corresponding to the first n groups of electrochemical experimental data are executed; if the second absolute distance is greater than the first absolute distance, the step of randomly generating a noise value z corresponding to the reverse noise within the range [-5σ, 5σ] is re-executed; according to the normal distribution law of the thermal noise generated when the electrochemical workstation is working, and the independent analysis and processing of each group of electrochemical experimental data, the present invention adopts a similar normal distribution law to randomly generate the corresponding reverse noise to independently reduce the noise of each group of electrochemical experimental data and displays the experimental data after noise reduction in real time, while realizing real-time and efficient noise reduction processing of the electrochemical experimental data, the real characteristics of the original acquisition signal and the noise signal are greatly retained, thereby enhancing the accuracy and reliability of electrochemical measurements. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0038] Figure 1 The figure is a method flow chart showing a method for reducing noise of a detection signal based on an electrochemical workstation according to an exemplary embodiment of the present invention.

[0039] Figure 2 FIG. 1 is a schematic diagram showing distribution of generated noise values ​​according to an exemplary embodiment of the present invention.

[0040] Figure 3 It is a schematic diagram of the initial cyclic voltammetry curve corresponding to Example 1 of the present invention.

[0041] Figure 4 It is a schematic diagram of the change of the cyclic voltammetric characteristic curve after noise reduction corresponding to Example 1 of the present invention.

[0042] Figure 5 It is a schematic diagram of the initial noise curve corresponding to Example 1 of the present invention.

[0043] Figure 6 It is a schematic diagram of the change of the noise curve after noise reduction corresponding to Example 1 of the present invention.

[0044] Figure 7 3 is a curve diagram showing the change of the noise standard deviation corresponding to the first embodiment of the present invention with the number of noise reduction times.

[0045] Figure 8 It is a schematic diagram of the initial timing current curve corresponding to Example 2 of the present invention.

[0046] Fig. 9 It is a schematic diagram of the change of the initial curve of the timing current after noise reduction corresponding to Example 2 of the present invention.

[0047] Fig.10 Schematic diagram of the initial noise curve corresponding to Example 2 of the present invention.

[0048] Fig.11 It is a schematic diagram of the change of the noise curve after noise reduction corresponding to Example 2 of the present invention.

[0049] Fig.12 It is a curve diagram showing the change of the noise standard deviation corresponding to the second embodiment of the present invention with the number of noise reduction times.

[0050] Fig.13 It is a schematic diagram of the initial cyclic voltammetry curve corresponding to Example 3 of the present invention.

[0051] Fig.14 It is a schematic diagram of the change of the cyclic voltammetric characteristic curve after noise reduction corresponding to Example 3 of the present invention.

[0052] Fig.15 Schematic diagram of the initial noise curve corresponding to Example 3 of the present invention.

[0053] Fig.16 It is a schematic diagram of the change of the noise curve after noise reduction corresponding to Example 3 of the present invention.

[0054] Fig.17 3 is a curve diagram showing the change of the noise standard deviation corresponding to the embodiment 3 of the present invention with the number of noise reduction times. DETAILED DESCRIPTION

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 is a method flow chart of a detection signal noise reduction method based on an electrochemical workstation according to an exemplary embodiment of the present invention, such as Figure 1 As shown, the detection signal noise reduction method includes:

[0057] S10: Obtain the original independent variables and original dependent variables corresponding to the first n groups of electrochemical experimental data, and calculate the standard deviation σ based on the original dependent variables of the first n groups of electrochemical experimental data. The value of n increases dynamically and is self-updated as the number of times the electrochemical experimental data is obtained increases.

[0058] The timing for obtaining and calculating the standard deviation σ corresponding to the first n groups of electrochemical experimental data in S10 may be: when obtaining the latest n+1 group of electrochemical experimental data. That is, when obtaining a new group of electrochemical experimental data, the present invention performs fine noise reduction processing on the currently obtained n+1 group of electrochemical experimental data separately according to the signal characteristics presented by the previous n groups of electrochemical experimental data, thereby realizing real-time dynamic noise reduction of the detection signal.

[0059] The value of n increases dynamically and is self-updated as the number of times the electrochemical experimental data is acquired increases, that is, the value of n is self-updated to n+1 each time new electrochemical experimental data is acquired.

[0060] Preferably, the original independent variable is the horizontal coordinate corresponding to the electrochemical experimental data before the noise reduction process, and the original dependent variable is the vertical coordinate corresponding to the electrochemical experimental data before the noise reduction process.

[0061] For example, the horizontal axis is time or voltage, and the vertical axis is the measured analog quantity. When a potential is applied in the electrochemical workstation, the current is measured, and when a current is applied, the potential is measured.

[0062] For ease of understanding, for example, when n is 4, the electrochemical experimental data of each group shown in Example A are shown as follows:

[0063] The first set of electrochemical experimental data is (6.01650×10 -1 ,7.73106×10 -6 );

[0064] The second set of electrochemical experimental data is (6.01339×10 -1 ,7.75108×10 -6 );

[0065] The third group of electrochemical experimental data is (6.01171×10 -1 ,3.91377×10 -6 );

[0066] The fourth group of electrochemical experimental data is (6.00784×10 -1,1.96271×10 -6 );

[0067] The fifth group of electrochemical experimental data is (6.00289×10 -1 ,1.45374×10 -6 ).

[0068] After obtaining the above-mentioned first four groups of electrochemical experimental data, the standard deviation σ is calculated according to the original dependent variable, i.e., the ordinate value, and the standard deviation σ value is: σ=2.885×10 -6 .

[0069] S20: Perform linear regression fitting on the first n groups of electrochemical experimental data, calculate the slope a and intercept b of the linear regression equation, and use the original independent variables of the n+1th group of electrochemical experimental data to predict the corresponding predicted dependent variable y according to the linear regression equation predicted .

[0070] Predict the dependent variable y predicted The expression is as follows:

[0071] y predicted =a·x n+1 +b Formula 1

[0072] Among them, x n+1 is the original independent variable corresponding to the n+1th group of electrochemical experimental data.

[0073] Preferably, the linear regression fitting algorithm adopts the least squares method.

[0074] Preferably, the initial value of n is a fixed preset value m, and both m and n are positive integers greater than 1.

[0075] Among them, m is a preset standard parameter, which is used to define the starting point for calculating the prediction dependent variable from the mth group of electrochemical experimental data. This setting means that the first m-1 groups of electrochemical experimental data are used as the benchmark set to perform the linear fitting process. Due to the inherent logic of linear fitting, the first m-1 groups of electrochemical experimental data themselves do not participate in the direct generation of the predicted value, which leads to the blank of this data segment in the noise reduction process, that is, its noise reduction step is naturally bypassed.

[0076] In a feasible implementation, in the scenario of cyclic voltammetry experiment, in view of the characteristics of the curve retracement, the first set of data in the retrace period can be used as the starting point of the new sequence, and then the first m-1 sets of data following it can be used for linear fitting to accurately predict the values ​​of subsequent data points. It should be noted that in the retracement stage, the first m-1 sets of data in this sequence are also used as the basis for fitting and do not involve noise reduction processing.

[0077] Continuing with the electrochemical experimental data of each group shown in Example A above, a linear regression fitting is performed based on the abscissa values ​​and ordinate values ​​of the first four groups of electrochemical experimental data in Example A, and the slope a of the corresponding linear regression equation is obtained as a=0.0073, and the intercept b=-0.0044, that is, the linear regression equation is:

[0078] y predicted =0.0073·x n+1 -0.0044

[0079] The current n value is 4, so the horizontal coordinate value of the fifth group of electrochemical experimental data is 6.00289×10 -1 Substitute into the above linear regression equation to get the current predicted dependent variable y predicted is: y predicted =-1.589×10 -6 .

[0080] S30: Randomly generate a noise value z corresponding to the reverse noise in the range [-5σ, 5σ], and the generation rule of the noise value z obeys the normal distribution characteristics.

[0081] The above scheme generates a distribution diagram of noise values ​​as follows: Figure 2 As shown by Figure 2 It can be seen that the generated noise is in a normal distribution, which conforms to the basic characteristics of noise.

[0082] The present invention conducts an in-depth analysis of various types of noise generated during electrochemical experiments in an electrochemical workstation and finds that the thermal noise therein strictly follows the normal distribution law. According to this law, a reverse noise that obeys the normal distribution characteristics is designed to perform targeted noise reduction processing on the thermal noise in the detection signal.

[0083] Continuing with the electrochemical experimental data of each group shown in Example A, the standard deviation σ corresponding to the first four groups of electrochemical experimental data in Example A is calculated as follows: -6 , then in the range [-1.4425×10 -5 , 1.4425×10 -5 ] randomly generates a reverse noise with normal distribution characteristics. The noise value z=-2.274983×10 -7 .

[0084] S40: Calculate the probability density f(z) corresponding to the noise value z and the maximum probability density f according to the probability density function f(x) max , and calculate the ratio r between the two.

[0085] The probability density function f(x) is expressed as formula 2:

[0086]

[0087] Where, μ = 0;

[0088] Maximum probability density f max is the value of f(x) when x=μ=0;

[0089] The ratio r is expressed as formula 3:

[0090]

[0091] Continuing with the electrochemical experimental data of each group shown in Example A, the standard deviation σ corresponding to the first four groups of electrochemical experimental data in Example A is calculated as follows: -6 , the randomly generated reverse noise value z = -2.274983×10 -7 , then according to the expression of probability density function f(x) shown in formula 2, the above calculated μ=0, σ=2.885×10 -6 , z = x = -2.274983 × 10 -7 Substituting into equation 2, we can calculate the probability density f(z)=137852.29 corresponding to the current noise value z. Then let x=μ=0 in equation 2 to get the maximum probability density f max =138281.55, and then divide the probability density f(z) corresponding to the current noise value z by the maximum probability density f according to formula 3 max , the calculated ratio r is 0.9969.

[0092] It should be noted that, in further experimental research, it was found that white noise also has the same distribution law, so this method can also perform noise reduction on the white noise in the detection signal.

[0093] S50: Generate a random number in the range [0, 1]. If it is detected that the random number is less than r, determine the noise value z as effective noise; if it is detected that the random number is greater than r, re-execute the step of randomly generating a noise value z corresponding to the reverse noise in the range [-5σ, 5σ] in S30.

[0094] Continuing with the electrochemical experimental data of each group shown in Example A, the above calculation results in the ratio r corresponding to the current noise value z being 0.9969, and the random number generated in the range [0, 1] being 0.702839. If the random number is detected to be less than the ratio r, the noise value z is set to -2.274983×10 -7 Determined as effective noise.

[0095] S60: Add the noise value z determined as effective noise to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data measured , get the denoised dependent variable y new.

[0096] Denoise the dependent variable y new The expression is as follows:

[0097] y new =y measured +z Formula 4.

[0098] The original dependent variable y measured The unprocessed electrochemical experimental data were measured by an electrochemical workstation.

[0099] Continuing with the electrochemical experimental data of each group shown in Example A, the noise value z of the effective noise determined above is calculated according to Formula 4 = -2.274983×10 -7 Added to the original dependent variable y corresponding to the fifth set of electrochemical experimental data measured =1.45374×10 -6 , get the denoised dependent variable y new =1.2262417×10 -6 .

[0100] S70: Calculate the original dependent variable y measured and predict the dependent variable y predicted The first absolute distance, and the denoised dependent variable y new and predict the dependent variable y predicted If the second absolute distance is less than the first absolute distance, the noise reduction dependent variable y new Determine the real dependent variable corresponding to the original independent variable of the n+1th group of electrochemical experimental data, output and display it, then update the value of n, and execute the step of obtaining the original independent variable and the original dependent variable corresponding to the first n groups of electrochemical experimental data described in S10; if the second absolute distance is greater than the first absolute distance, re-execute the step of randomly generating a noise value z corresponding to the reverse noise within the range of [-5σ, 5σ] described in S30.

[0101] Through the above steps S10 to S70, the present invention dynamically adjusts the intensity and phase of the generated noise according to the characteristics of the noise, thereby ensuring that the introduced noise is close to the real value after being added to the original noise.

[0102] Continuing with the electrochemical experimental data of each group shown in Example A above, the original dependent variable y corresponding to the electrochemical experimental data of the fifth group has been obtained. measured =1.45374×10 -6 , the current predicted dependent variable y predicted =-1.589×10 -6 , denoising dependent variable y new =1.2262417×10 -6, then the original dependent variable y is calculated measured and predict the dependent variable y predicted The first absolute distance Δy 1 =3.04274×10 -6 , denoising dependent variable y new and predict the dependent variable y predicted The second absolute distance Δy 2 =2.8152417×10 -6 , the second absolute distance Δy is detected 2 =2.8152417×10 -6 Less than the first absolute distance Δy 1 =3.04274×10 -6 , that is, the denoised dependent variable y new The value is closer to the predicted dependent variable y predicted The value of , then the noise reduction dependent variable y new =1.2262417×10 -6 The real dependent variable corresponding to the independent variable of the fifth group of electrochemical experimental data is determined to be output and displayed on the display screen, and then the value of n is updated to 5, and the steps of obtaining the original independent variables and original dependent variables corresponding to the first five groups of electrochemical experimental data in S10 are continued.

[0103] Preferably, the method comprises:

[0104] After the real dependent variables corresponding to the original independent variables of each group of electrochemical experimental data after the mth group are output and displayed, for each group of electrochemical experimental data, the real dependent variable corresponding to the electrochemical experimental data is updated to the original dependent variable y measured , update n to m, and re-execute steps S10 to S70 to achieve successive noise reduction of each group of electrochemical experimental data.

[0105] It should be noted that m is a fixed preset value, and the value of n increases dynamically with the number of times the electrochemical experimental data is obtained. When the real dependent variables corresponding to the original independent variables of each group of electrochemical experimental data after the mth group are output and displayed, that is, when each group of electrochemical experimental data obtained in this round of electrochemical experiments has been subjected to a noise reduction process by the detection signal noise reduction method provided by the present invention, each group of electrochemical experimental data after the noise reduction process can be used as a benchmark, and the detection signal noise reduction method provided by the present invention can be used again to perform multiple rounds of noise reduction processes on each group of electrochemical experimental data after the noise reduction process.

[0106] The above method can realize multiple noise reduction of the detection signal, and maximize the purity and accuracy of the signal. It should be noted that the first m-1 groups of electrochemical experimental data are used as the benchmark set to perform the linear fitting process. Due to the inherent logic of linear fitting, the first m-1 groups of electrochemical experimental data themselves do not participate in the direct generation of the prediction value, and do not need to be subjected to noise reduction processing in the process of successive noise reduction.

[0107] Preferably, the method comprises:

[0108] When it is detected that the absolute value of the difference between the standard deviation σ corresponding to the first n-2 groups of electrochemical experimental data and the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data is less than the preset difference, after executing the step of obtaining the original independent variables and the original dependent variables corresponding to the first n groups of electrochemical experimental data in S10, the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data is directly determined as the standard deviation σ corresponding to the first n groups of electrochemical experimental data.

[0109] That is, when it is detected that the absolute value of the difference between the standard deviation σ corresponding to the first n-2 groups of electrochemical experimental data and the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data is less than the preset difference, it is determined that the characteristics of the noise have been fully acquired, and the corresponding reverse noise can be generated based on the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data for noise reduction.

[0110] Preferably, the noise value z corresponding to the reverse noise is similar in amplitude to the noise value corresponding to the original thermal noise generated when the electrochemical workstation is operating, and has an opposite phase.

[0111] The noise value z corresponding to the reverse noise has a similar amplitude and an opposite phase to the noise value corresponding to the original thermal noise, so as to achieve the best noise suppression effect.

[0112] Preferably, the method further comprises:

[0113] Analyzing other types of noise included in the operation of the electrochemical workstation;

[0114] For each other noise type, determining a distribution feature corresponding to the other noise type, and generating corresponding effective reverse noise according to the distribution feature corresponding to the other noise type;

[0115] In executing S60, the noise value z determined as effective noise is added to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data. measured , get the denoised dependent variable y new In the step, the noise value of the effective reverse noise corresponding to each noise type is added to the noise reduction dependent variable y new And update the denoised dependent variable y new .

[0116] The above has explained how to improve the purity and accuracy of the detection signal by performing multiple rounds of single noise type noise reduction processing on the electrochemical experimental data. The above further improves the purity and accuracy of the detection signal by performing multiple rounds of multiple noise type noise reduction processing on the electrochemical experimental data. It should be noted that each time the reverse noise is generated, it is adjusted based on the residual noise characteristics after the previous noise reduction, and for different types of noise distribution, different description and processing methods are required to simulate and generate reverse noise.

[0117] Preferably, when it is not detected that the electrochemical workstation has acquired the latest set of electrochemical experimental data, the operation of the detection signal noise reduction method is terminated.

[0118] Furthermore, as a specific termination condition, the operation of the detection signal denoising method may be terminated when it is not detected within a preset time period that the electrochemical workstation acquires the latest set of electrochemical experimental data.

[0119] Preferably, the step of successive noise reduction is performed at least three times.

[0120] In order to better illustrate the beneficial effects of a detection signal noise reduction method based on an electrochemical workstation provided by the present invention, the following embodiments 1-3 are shown for illustration:

[0121] Example 1

[0122] Glassy carbon electrode in 1mmol / L K 3 Fe(CN) 6 The cyclic voltammetry experiment scene in the +0.1mol / L KCl system, the sampling frequency is 1kHz, and the schematic diagram of the initial cyclic voltammetry curve obtained by the electrochemical workstation is as follows Figure 3 shown.

[0123] By observation Figure 3 It can be seen that the initial cyclic voltammetry curve is significantly disturbed by noise, which directly hinders the accurate reading of the redox peak current and peak potential.

[0124] After the detection signal noise reduction method based on the electrochemical workstation provided by the present invention is used to perform six rounds of continuous noise reduction processing on each electrochemical experimental data in the cyclic voltammetry experiment scene, a schematic diagram of the change of the cyclic voltammetry characteristic curve after noise reduction processing is obtained as shown in the figure. Figure 4 As shown, in Figure 4In the figure, a is a schematic diagram of the cyclic voltammetric characteristic curve after the first round of noise reduction treatment, b is a schematic diagram of the cyclic voltammetric characteristic curve after the second round of noise reduction treatment, c is a schematic diagram of the cyclic voltammetric characteristic curve after the third round of noise reduction treatment, d is a schematic diagram of the cyclic voltammetric characteristic curve after the fourth round of noise reduction treatment, e is a schematic diagram of the cyclic voltammetric characteristic curve after the fifth round of noise reduction treatment, and f is a schematic diagram of the cyclic voltammetric characteristic curve after the sixth round of noise reduction treatment.

[0125] according to Figure 4 It can be seen that by adopting the detection signal denoising method provided by the present invention to implement one to six consecutive denoising processes, each round of denoising process achieves significant suppression of noise.

[0126] It should be emphasized that the number of noise reduction processes is not unlimited. Although excessive noise reduction can greatly smooth the curve, it may lose the original noise characteristics of the curve due to excessive processing, resulting in information distortion. Therefore, while pursuing the noise reduction effect, the number of noise reduction processes must be carefully controlled to ensure the integrity and accuracy of the curve information.

[0127] In order to further demonstrate the beneficial effects of the detection signal noise reduction method based on the electrochemical workstation provided by the present invention, Figure 3 A schematic diagram of the initial noise curve corresponding to the initial cyclic voltammetry curve shown is presented separately in Figure 5 ,exist Figure 5 In the figure, the noise characteristics of the initial cyclic voltammetry curve are presented separately, and its distribution range is clearly defined above and below the zero point, specifically in the range of -12.5μA to 10μA.

[0128] After the above six rounds of continuous noise reduction processing, the noise curve after noise reduction is shown in the figure below. Figure 6 As shown, in Figure 6 In the figure, a is a schematic diagram of the noise curve after the first round of noise reduction processing, b is a schematic diagram of the noise curve after the second round of noise reduction processing, c is a schematic diagram of the noise curve after the third round of noise reduction processing, d is a schematic diagram of the noise curve after the fourth round of noise reduction processing, e is a schematic diagram of the noise curve after the fifth round of noise reduction processing, and f is a schematic diagram of the noise curve after the sixth round of noise reduction processing.

[0129] according to Figure 6 It can be seen that by gradually increasing the number of times random reverse noise is introduced, the resulting image clearly shows the effect of suppressing noise. Although the noise is still concentrated near the zero point, its amplitude has been significantly reduced. In particular, after the sixth round of reverse noise is introduced, the original noise range is greatly reduced to the range of -2μA to 2μA, indicating that the present invention can effectively suppress noise while successfully retaining the original characteristics of the noise and avoiding excessive distortion of information. Obviously, the detection signal denoising method based on an electrochemical workstation provided by the present invention is an effective means to reduce the noise of cyclic voltammetry curves and improve the quality of electrochemical experimental data.

[0130] Furthermore, a curve schematic diagram showing the change of the noise standard deviation of the detection signal noise reduction method provided by the present invention with the number of noise reduction times in this cyclic voltammetry experiment scenario is shown as follows: Figure 7 .exist Figure 7 In the figure, the correlation between the number of denoising times and the standard deviation of noise is intuitively demonstrated. As the number of denoising times increases, the standard deviation of noise shows a downward trend, but this process does not follow a strict linear law. Specifically, as the denoising operation continues, the reduction in the standard deviation of noise gradually becomes flat, and after the first denoising, the standard deviation is reduced to about 54.7% of the initial state; the second denoising is further reduced to 58.6% of the standard deviation after the first denoising; until the sixth denoising, the standard deviation is only reduced by about 28.3% compared with the fifth denoising, that is, about 71.7% of the standard deviation after the fifth denoising remains. This trend clearly shows that as the number of denoising times accumulates, its noise suppression effect gradually weakens, that is, in practical applications, the number of denoising times needs to be reasonably controlled to balance the denoising effect and computational cost.

[0131] Example 2

[0132] Glassy carbon electrode in 2mmol / L K 3 Fe(CN) 6 The chronoamperometric experimental scene in the +0.1mol / L KCl system, the sampling frequency is 1kHz, and the schematic diagram of the initial chronoamperometric curve obtained by the electrochemical workstation is as follows Figure 8 shown.

[0133] By observation Figure 8 It can be seen that the initial chronocurrent curve is significantly disturbed by noise, which directly hinders the accurate reading of data at specific points.

[0134] After the detection signal noise reduction method based on the electrochemical workstation provided by the present invention is used to perform six rounds of continuous noise reduction processing on the electrochemical experimental data in the chronoamperometric experiment scenario, a schematic diagram of the change of the cyclic voltammetric characteristic curve after noise reduction is obtained as shown in the figure. Fig. 9 As shown, in Fig. 9 In the figure, a is a schematic diagram of the timing current curve after the first round of noise reduction, b is a schematic diagram of the timing current curve after the second round of noise reduction, c is a schematic diagram of the timing current curve after the third round of noise reduction, d is a schematic diagram of the timing current curve after the fourth round of noise reduction, e is a schematic diagram of the timing current curve after the fifth round of noise reduction, and f is a schematic diagram of the timing current curve after the sixth round of noise reduction.

[0135] according to Fig. 9 As shown, by adopting the detection signal denoising method provided by the present invention to implement continuous denoising processing from one to six times, each denoising process achieves significant suppression of noise, especially when the number of denoising reaches six times, the curve presents an extremely smooth state, which fully verifies the effectiveness and superiority of the denoising method provided by the present invention.

[0136] In order to further demonstrate the beneficial effects of the detection signal noise reduction method based on the electrochemical workstation provided by the present invention, Figure 8 A schematic diagram of the initial noise curve corresponding to the initial chronoamperometric curve is presented separately in Fig.10 ,exist Fig.10 In the figure, the noise characteristics of the initial curve of the timing current are presented separately, and its distribution range is clearly defined above and below the zero point, specifically in the range of -0.09μA to 0.08μA.

[0137] After the above six rounds of continuous noise reduction processing, the noise curve after noise reduction is shown in the figure below. Fig.11 As shown, in Fig.11 In the figure, a is a schematic diagram of the noise curve after the first round of noise reduction processing, b is a schematic diagram of the noise curve after the second round of noise reduction processing, c is a schematic diagram of the noise curve after the third round of noise reduction processing, d is a schematic diagram of the noise curve after the fourth round of noise reduction processing, e is a schematic diagram of the noise curve after the fifth round of noise reduction processing, and f is a schematic diagram of the noise curve after the sixth round of noise reduction processing.

[0138] according to Fig.11 It can be seen that by gradually increasing the number of times random reverse noise is introduced, the resulting image clearly shows the noise suppression effect. Although the noise is still concentrated near the zero point, its amplitude has been significantly reduced. In particular, after the sixth round of reverse noise introduction, the original noise range is greatly reduced to -0.015μA to 0.01μA, indicating that the present invention not only significantly reduces the noise level of the chronoamperometric curve, but also cleverly retains the original characteristics of the noise, ensuring the authenticity and integrity of the data, and providing a solid foundation for subsequent accurate analysis.

[0139] Furthermore, a curve diagram showing the variation of the noise standard deviation of the detection signal noise reduction method provided by the present invention with the number of noise reduction times in the current timing experiment scenario is shown as follows: Fig.12 .exist Fig.12 In the experiment, the correlation between the number of noise reduction and the standard deviation of noise is intuitively demonstrated. The chronoamperometry and cyclic voltammetry show similar trends, that is, as the number of noise reduction increases, the standard deviation of noise gradually decreases. However, this process does not follow a simple linear law. Specifically, as the noise reduction operation continues, the reduction in the standard deviation of noise gradually slows down. Specifically, after the first noise reduction, the standard deviation is reduced to about 54.8% of the initial state; the second noise reduction is further reduced to 57.8% of the standard deviation after the first noise reduction; until the sixth noise reduction, the standard deviation is only 33.5% of the remaining standard deviation compared with the fifth noise reduction, that is, about 66.5% of the standard deviation after the fifth noise reduction remains. This phenomenon also shows that as the number of noise reduction increases, the noise reduction effect gradually weakens.

[0140] Example 3

[0141] The cyclic voltammetry experiment scene of the 6800μF capacitor, the sampling frequency is 2kHz, and the schematic diagram of the initial cyclic voltammetry curve obtained by the electrochemical workstation is as follows Fig.13 shown.

[0142] By observation Fig.13 It can be seen that the initial cyclic voltammetry curve is significantly disturbed by noise.

[0143] After the detection signal noise reduction method based on the electrochemical workstation provided by the present invention is used to perform six rounds of continuous noise reduction processing on each electrochemical experimental data in the cyclic voltammetry experiment scene, a schematic diagram of the change of the cyclic voltammetry characteristic curve after noise reduction is obtained as shown in the figure. Fig.14 As shown, in Fig.14 In the figure, a is a schematic diagram of the cyclic voltammetric characteristic curve after the first round of noise reduction, b is a schematic diagram of the cyclic voltammetric characteristic curve after the second round of noise reduction, c is a schematic diagram of the cyclic voltammetric characteristic curve after the third round of noise reduction, d is a schematic diagram of the cyclic voltammetric characteristic curve after the fourth round of noise reduction, e is a schematic diagram of the cyclic voltammetric characteristic curve after the fifth round of noise reduction, and f is a schematic diagram of the cyclic voltammetric characteristic curve after the sixth round of noise reduction.

[0144] according to Fig.14 It can be seen that by adopting the detection signal denoising method provided by the present invention to implement one to six consecutive denoising processes, each denoising process achieves further significant suppression of noise.

[0145] In order to further demonstrate the beneficial effects of the detection signal noise reduction method based on the electrochemical workstation provided by the present invention, Fig.13 A schematic diagram of the initial noise curve corresponding to the initial cyclic voltammetry curve shown is presented separately in Fig.15 ,exist Fig.15 In the figure, the noise characteristics of the initial cyclic voltammetry curve are presented separately, and its distribution range is also clearly defined above and below the zero point, specifically in the range of -3μA to 3μA.

[0146] After the above six rounds of continuous noise reduction processing, the noise curve after noise reduction is shown in the figure below. Fig.16 As shown, in Fig.16 In the figure, a is a schematic diagram of the noise curve after the first round of noise reduction processing, b is a schematic diagram of the noise curve after the second round of noise reduction processing, c is a schematic diagram of the noise curve after the third round of noise reduction processing, d is a schematic diagram of the noise curve after the fourth round of noise reduction processing, e is a schematic diagram of the noise curve after the fifth round of noise reduction processing, and f is a schematic diagram of the noise curve after the sixth round of noise reduction processing.

[0147] according to Fig.16It can be seen that after gradually increasing the number of times the random reverse noise is introduced, the noise curve after each round of noise reduction processing clearly demonstrates the effective noise suppression effect brought by the detection signal noise reduction method. Although the noise is still concentrated near the zero point, its amplitude has been significantly reduced. In particular, after six times of reverse noise introduction, the original noise range is greatly reduced to the range of -0.3μA to 0.2μA, indicating that the detection signal noise reduction method provided by the present invention can achieve effective noise suppression while greatly retaining the original characteristics of the noise, thereby avoiding excessive distortion of the collected detection signal.

[0148] Furthermore, a curve schematic diagram showing the change of the noise standard deviation of the detection signal noise reduction method provided by the present invention with the number of noise reduction times in this cyclic voltammetry experiment scenario is shown as follows: Fig.17 .exist Fig.17 In the figure, the correlation between the number of noise reduction and the standard deviation of noise is intuitively demonstrated. As the number of noise reduction increases, the standard deviation of noise shows a downward trend, but this process does not follow a strict linear law. As the noise reduction operation continues, the reduction of the standard deviation of noise gradually tends to be flat. Specifically, after the first round of noise reduction, the standard deviation is reduced to about 56.6% of the initial state; the second round of noise reduction is further reduced to 59.1% of the standard deviation after the first round of noise reduction; until the sixth round of noise reduction, the standard deviation is only reduced by about 8.0% compared with the fifth round of noise reduction, that is, about 92.0% of the standard deviation after the fifth round of noise reduction remains. This trend also shows that as the number of noise reduction accumulates, its noise suppression effect gradually weakens.

[0149] Comparative analysis of Example 1 and Example 3 shows that although the two use different sampling frequencies to carry out the same experimental method and detection signal noise reduction method, the noise curves of the two do not show significant differences in the noise reduction effect after each round of noise reduction by the detection signal noise reduction method. This comparative experiment strongly proves that the detection signal noise reduction method based on the electrochemical workstation provided by the present invention has a high degree of versatility and adaptability, its application efficiency is not limited to the specific value of the sampling frequency, and can retain the frequency characteristics of the noise. Obviously, the detection signal noise reduction method provided by the present invention has strong independence and stability, and can achieve efficient and stable noise reduction effects under different sampling conditions.

[0150] In summary, the present invention provides a detection signal denoising method based on an electrochemical workstation, which includes obtaining the original independent variables and the original dependent variables corresponding to the first n groups of electrochemical experimental data, and calculating the standard deviation σ according to the original dependent variables of the first n groups of electrochemical experimental data; performing linear regression fitting on the first n groups of electrochemical experimental data, calculating the slope a and the intercept b of the linear regression equation, and predicting the corresponding predicted dependent variable y using the original independent variables of the n+1th group of electrochemical experimental data according to the linear regression equation. predicted; Randomly generate a noise value z corresponding to the reverse noise in the range [-5σ, 5σ], and the generation rule of the noise value z obeys the normal distribution characteristics; Calculate the probability density f(z) corresponding to the noise value z and the maximum probability density f according to the probability density function f(x) max , and calculate the ratio r of the two; generate a random number in the range [0, 1], if the random number is detected to be less than r, the noise value z is determined as effective noise; if the random number is detected to be greater than r, re-execute the step of randomly generating the noise value z corresponding to the reverse noise in the range [-5σ, 5σ]; add the noise value z determined as effective noise to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data measured , get the denoised dependent variable y new ; Calculate the original dependent variable y measured and predict the dependent variable y predicted The first absolute distance, and the denoised dependent variable y new and predict the dependent variable y predicted If the second absolute distance is less than the first absolute distance, the noise-reduced dependent variable y new The real dependent variable corresponding to the original independent variable of the n+1th group of electrochemical experimental data is determined to be output and displayed, then the value of n is updated, and the steps of obtaining the original independent variable and the original dependent variable corresponding to the first n groups of electrochemical experimental data are executed; if the second absolute distance is greater than the first absolute distance, the step of randomly generating a noise value z corresponding to the reverse noise within the range [-5σ, 5σ] is re-executed; according to the normal distribution law of the thermal noise generated when the electrochemical workstation is working, and the independent analysis and processing of each group of electrochemical experimental data, the present invention adopts a similar normal distribution law to randomly generate the corresponding reverse noise to independently reduce the noise of each group of electrochemical experimental data and displays the experimental data after noise reduction in real time, while realizing real-time and efficient noise reduction processing of the electrochemical experimental data, the real characteristics of the original acquisition signal and the noise signal are greatly retained, thereby enhancing the accuracy and reliability of electrochemical measurements.

[0151] Although the present invention has been described in detail above by means of general description, specific implementation methods and tests, it is obvious to those skilled in the art that modifications or improvements can be made based on the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection claimed by the present invention.

[0152] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. It should be understood that the present invention is not limited to the precise structure that has been described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof.

Claims

1. A detection signal noise reduction method based on an electrochemical workstation, characterized in that: The detection signal noise reduction method comprises: Si0: Obtain the original independent variables and original dependent variables corresponding to the first n groups of electrochemical experimental data, and calculate the standard deviation σ based on the original dependent variables of the first n groups of electrochemical experimental data. The value of n increases dynamically and is self-updated as the number of electrochemical experimental data acquisition increases; S20: Perform linear regression fitting on the first n groups of electrochemical experimental data, calculate the slope α and intercept b of the linear regression equation, and use the original independent variables of the n+1th group of electrochemical experimental data to predict the corresponding predicted dependent variable y according to the linear regression equation predicted , predict the dependent variable y predicted The expression is as follows: y predicted =a·x n+1 +b Formula 1 Among them, x n+1 is the original independent variable corresponding to the n+1th group of electrochemical experimental data; S30: randomly generating a noise value z corresponding to the reverse noise in the range [-5σ, 5σ], wherein the generation rule of the noise value z obeys the normal distribution characteristics; S40: Calculate the probability density f(z) corresponding to the noise value z and the maximum probability density f according to the probability density function f(x) max , and calculate the ratio r between the two; the probability density function f(x) is expressed as formula 2: Where, μ = 0; Maximum probability density f max is the value of f(x) when x=μ=0; The ratio r is expressed as formula 3: S50: Generate a random number in the range [0, 1]. If it is detected that the random number is less than r, determine the noise value z as effective noise; if it is detected that the random number is greater than r, re-execute the step of randomly generating a noise value z corresponding to the reverse noise in the range [-5σ, 5σ] in S30; S60: Add the noise value z determined as effective noise to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data measured , get the denoised dependent variable y new , denoising dependent variable y new The expression is as follows: y new = y measured + z Equation 4; S70: Calculate the original dependent variable y measured and predict the dependent variable y predicted The first absolute distance, and the denoised dependent variable y new and predict the dependent variable y predicted If the second absolute distance is less than the first absolute distance, the noise reduction dependent variable y new Determine the real dependent variable corresponding to the original independent variable of the n+1th group of electrochemical experimental data, output and display it, then update the value of n, and execute the step of obtaining the original independent variable and the original dependent variable corresponding to the first n groups of electrochemical experimental data described in S10; if the second absolute distance is greater than the first absolute distance, re-execute the step of randomly generating a noise value z corresponding to the reverse noise within the range of [-5σ, 5σ] described in S30.

2. The detection signal denoising method according to claim 1, characterized in that: The initial value of n is a fixed preset value m, and both m and n are positive integers greater than 1.

3. The detection signal denoising method according to claim 2, characterized in that: The method comprises: After the real dependent variables corresponding to the original independent variables of each group of electrochemical experimental data after the mth group are output and displayed, for each group of electrochemical experimental data, the real dependent variable corresponding to the electrochemical experimental data is updated to the original dependent variable ymeasure d , update n to m, and re-execute steps S10 to S70 to achieve successive noise reduction of each group of electrochemical experimental data.

4. The detection signal denoising method according to claim 1, characterized in that: The method comprises: When it is detected that the absolute value of the difference between the standard deviation σ corresponding to the first n-2 groups of electrochemical experimental data and the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data is less than the preset difference, after executing the step of obtaining the original independent variables and the original dependent variables corresponding to the first n groups of electrochemical experimental data in S10, the standard deviation σ corresponding to the first n-1 groups of electrochemical experimental data is directly determined as the standard deviation σ corresponding to the first n groups of electrochemical experimental data.

5. The detection signal denoising method according to claim 1, characterized in that: The noise value z corresponding to the reverse noise is similar in amplitude to the noise value corresponding to the original thermal noise generated when the electrochemical workstation is working, and has an opposite phase.

6. The detection signal denoising method according to claim 1, characterized in that: The method further comprises: Analyzing other types of noise included in the operation of the electrochemical workstation; For each other noise type, determining a distribution feature corresponding to the other noise type, and generating corresponding effective reverse noise according to the distribution feature corresponding to the other noise type; In executing S60, the noise value z determined as effective noise is added to the original dependent variable y corresponding to the n+1th group of electrochemical experimental data. measured , get the denoised dependent variable y new In the step, the noise value of the effective reverse noise corresponding to each noise type is added to the noise reduction dependent variable y new And update the denoised dependent variable y new .

7. The detection signal denoising method according to claim 1, characterized in that: The linear regression fitting algorithm uses the least squares method.

8. The detection signal denoising method according to claim 1, characterized in that: When it is detected that the electrochemical workstation has not acquired the latest set of electrochemical experimental data, the operation of the detection signal noise reduction method is terminated.

9. The detection signal denoising method according to claim 3, characterized in that: The steps of successive denoising were performed at least three times.

10. The detection signal noise reduction method according to claim 1, characterized in that: The original independent variable is the horizontal coordinate corresponding to the electrochemical experimental data before noise reduction, and the original dependent variable is the vertical coordinate corresponding to the electrochemical experimental data before noise reduction.

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