Chromatographic signal denoising method, device, equipment, medium and product

Abstract

The invention discloses a chromatographic signal de-noising method, device and equipment, a medium and a product, and relates to the technical field of signal de-noising, the method comprises the following steps: using a noisy chromatographic signal, and calculating to obtain an initial signal intensity threshold; based on the pulse characteristics of the noisy chromatographic signal, the noisy chromatographic signal and the initial signal intensity threshold are used to calculate a target signal intensity threshold; filtering the noisy chromatographic signal based on an initial sliding window and a maximum sliding window with preset sizes to obtain a filtered chromatographic signal; determining a mother wavelet basis function and a decomposition layer number; decomposing the filtered chromatographic signal by using a mother wavelet basis function and a decomposition layer number to obtain a low-frequency component and a high-frequency component; performing noise reduction on the high-frequency component based on a soft threshold to obtain a noise-reduced high-frequency component; and reconstructing the low-frequency component and the noise-reduced high-frequency component based on the mother wavelet basis function to obtain a reconstructed chromatographic signal, so that the noise of the chromatographic signal can be effectively removed, the degree of distortion of the chromatographic signal is reduced, and the denoising performance is improved.

Description

Technical Field

[0001] The present application relates to the technical field of signal denoising, and in particular to a chromatographic signal denoising method, device, equipment, medium and product. Background Art

[0002] Nucleic acid synthesizer is an experimental instrument widely used in biotechnology, genetic research, drug development, molecular diagnosis and other fields. It uses chemical synthesis to gradually combine nucleotides in a specific order to generate the required DNA or RNA sequence. In the nucleic acid synthesizer, spectral analysis is a key link to ensure the quality control of the synthesis process. By observing the shape, peak time, peak height and peak area of ​​the peak in the chromatogram, the efficiency and accuracy of the synthesis reaction can be accurately evaluated. These parameters not only reflect the purity of the synthetic nucleic acid, but also reveal its integrity and possible impurity contamination, providing reliable quality assurance for the subsequent application of synthetic nucleic acids.

[0003] However, during the acquisition process, chromatographic signals are often affected by noise from various factors such as instrument detection errors, environmental interference, and background signals. These factors not only reduce the signal-to-noise ratio (SNR) and affect the detection sensitivity, but may also mask or distort the real chromatographic peaks, resulting in inaccurate quantitative analysis results. Therefore, how to effectively remove the noise of chromatographic signals and reduce the distortion of chromatographic signals has become an urgent problem to be solved in order to improve the quality of chromatographic analysis. Summary of the invention

[0004] The purpose of this application is to provide a chromatographic signal denoising method, device, equipment, medium and product, which can effectively remove the noise of the chromatographic signal and reduce the distortion of the chromatographic signal.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a chromatographic signal denoising method, comprising:

[0007] Using the noisy chromatographic signal obtained by the nucleic acid synthesizer, an initial signal intensity threshold is calculated;

[0008] Based on the pulse characteristics of the noisy chromatographic signal, using the noisy chromatographic signal and the initial signal intensity threshold, a target signal intensity threshold is calculated;

[0009] Based on an initial sliding window and a maximum sliding window of a preset size, the noisy chromatographic signal is filtered to obtain a filtered chromatographic signal; wherein the maximum sliding window is determined according to the target signal strength threshold;

[0010] Determining a mother wavelet basis function and a number of decomposition levels that match the filtered chromatogram signal;

[0011] Decomposing the filtered chromatogram signal using the mother wavelet basis function and the decomposition level to obtain a low-frequency component and a high-frequency component;

[0012] De-noising the high-frequency component based on a soft threshold technology to obtain a de-noised high-frequency component;

[0013] The low-frequency component and the noise-reduced high-frequency component are reconstructed based on the mother wavelet basis function to obtain a reconstructed chromatographic signal.

[0014] Optionally, the using of the noisy chromatographic signal obtained by the nucleic acid synthesizer to calculate the initial signal intensity threshold specifically includes:

[0015] Acquire a data set from a noisy chromatographic signal obtained by a nucleic acid synthesizer, wherein the data set includes a plurality of data points, and the plurality of data points are sorted in chronological order;

[0016] Using the signal intensity of each data point and the total number of data points, calculate a first mean and a first standard deviation of the noisy chromatographic signal;

[0017] An initial signal strength threshold is calculated using the first mean, the first standard deviation, and a preset adjustment coefficient.

[0018] Optionally, the calculating of the target signal strength threshold based on the pulse feature of the noisy chromatographic signal using the noisy chromatographic signal and the initial signal strength threshold specifically includes:

[0019] Compare the signal strength of each data point with the initial signal strength threshold in chronological order to obtain Boolean data corresponding to each data point;

[0020] Combine the Boolean arrays corresponding to each data point in chronological order to obtain a Boolean array;

[0021] Calculating the difference between adjacent Boolean data in the Boolean array to obtain a difference array; wherein the difference array contains a plurality of difference data, each Boolean data corresponds to a difference data, and the plurality of difference data in the difference array are arranged in chronological order;

[0022] Determine the differential data with a value of 1 in the differential array as the starting differential data;

[0023] Determine the differential data with a value of -1 in the differential array as the termination differential data;

[0024] Determine a data point in the data group corresponding to the initial differential data as a pulse starting point;

[0025] Determine a data point in the data group corresponding to the termination differential data as a pulse endpoint;

[0026] Calculate a second mean and a second standard deviation of the noisy chromatographic signal using the signal intensity of each data point between the pulse start point and the pulse end point and the total number of data points in the data set;

[0027] The target signal strength threshold is calculated using the second mean, the second standard deviation, and the preset adjustment coefficient.

[0028] Optionally, filtering the noisy chromatogram signal based on an initial sliding window and a maximum sliding window of a preset size to obtain a filtered chromatogram signal specifically includes:

[0029] Based on an initial sliding window and a maximum sliding window of preset sizes, performing at least one filtering operation on the data points in the data group of the noisy chromatographic signal in sequence until the last data point in the data group completes the filtering operation, thereby obtaining a filtered chromatographic signal corresponding to the data group;

[0030] Among them, any filtering operation includes:

[0031] Get the current signal strength corresponding to the data point;

[0032] Determining the initial sliding window as a sliding window;

[0033] Based on the sliding window, determining the median signal strength corresponding to the data point;

[0034] If the median signal strength is equal to the current signal strength and the sliding window has not reached the maximum sliding window, the following steps are repeated:

[0035] Expanding the size of the sliding window to obtain an expanded sliding window; determining the median signal strength corresponding to the data point based on the expanded sliding window;

[0036] Until the median signal strength is not equal to the current signal strength or the sliding window reaches the maximum sliding window, the current signal strength is replaced by the median signal strength.

[0037] Optionally, the using the mother wavelet basis function and the decomposition level number to decompose the filtered chromatogram signal to obtain a low-frequency component and a high-frequency component specifically includes:

[0038] Based on the number of decomposition layers and the signal length of the filtered chromatogram signal, the mother wavelet basis function is converted to obtain a wavelet basis function;

[0039] Decomposing the filtered chromatogram signal into a first low-frequency component and a first high-frequency component based on the wavelet basis function;

[0040] Substituting the wavelet basis function into the first low-frequency component and the first high-frequency component to obtain a second low-frequency component and a second high-frequency component;

[0041] Performing variable transformation on the second low-frequency component and the second high-frequency component to obtain a third low-frequency component and a third high-frequency component;

[0042] Using a discrete wavelet basis function to discretize the third low-frequency component and the third high-frequency component to obtain a discrete low-frequency component and a discrete high-frequency component; wherein the discrete wavelet basis function is obtained by converting the mother wavelet basis function;

[0043] Performing low-pass filtering on the discrete low-frequency component to obtain a low-frequency component;

[0044] The discrete high-frequency component is subjected to high-pass filtering to obtain a high-pass component.

[0045] Optionally, reconstructing the low-frequency component and the denoised high-frequency component based on the mother wavelet basis function to obtain a reconstructed chromatographic signal specifically includes:

[0046] Using the discrete wavelet basis function and the low-frequency component, a low-frequency signal is calculated;

[0047] Using the discrete wavelet basis function and the noise reduction high-frequency component, a high-frequency signal is calculated;

[0048] A reconstructed chromatographic signal is calculated using the low-frequency signal and the high-frequency signal.

[0049] In a second aspect, the present application provides a chromatographic signal denoising device, comprising:

[0050] A first calculation unit is used to calculate an initial signal intensity threshold using a noisy chromatographic signal obtained by a nucleic acid synthesizer;

[0051] A second calculation unit is used to calculate a target signal strength threshold based on the pulse characteristics of the noisy chromatographic signal, using the noisy chromatographic signal and the initial signal strength threshold;

[0052] A filtering unit, configured to filter the noisy chromatographic signal based on an initial sliding window and a maximum sliding window of a preset size to obtain a filtered chromatographic signal; wherein the maximum sliding window is determined according to the target signal strength threshold;

[0053] A determination unit, used to determine a mother wavelet basis function and a number of decomposition layers matching the filtered chromatogram signal;

[0054] A decomposition unit, used to decompose the filtered chromatogram signal using the mother wavelet basis function and the decomposition level number to obtain a low-frequency component and a high-frequency component;

[0055] A noise reduction unit, used for performing noise reduction on the high-frequency component based on a soft threshold technology to obtain a noise-reduced high-frequency component;

[0056] A reconstruction unit is used to reconstruct the low-frequency component and the denoised high-frequency component based on the mother wavelet basis function to obtain a reconstructed chromatogram signal.

[0057] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the chromatographic signal denoising methods described above.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described chromatographic signal denoising methods.

[0059] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned chromatographic signal denoising methods.

[0060] In a sixth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction. When the processor executes the program or instruction, the steps of the chromatographic signal denoising method described in any one of the above-mentioned methods are implemented.

[0061] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0062] The present application provides a chromatographic signal denoising method, device, equipment, medium and product. By calculating the acquired noisy chromatographic signal, a target signal strength threshold can be obtained. Based on the obtained target signal strength threshold, the noisy chromatographic signal can be filtered to obtain a filtered chromatographic signal; by filtering the noisy chromatographic signal, most of the noise therein can be removed, which greatly reduces the calculation burden during wavelet transform processing and can more accurately distinguish signal coefficients from noise coefficients; then, based on the mother wavelet basis function and the number of decomposition layers matched to the filtered chromatographic signal, the filtered chromatographic signal can be subjected to wavelet transform to obtain a reconstructed chromatographic signal; by using a denoising method that combines filtering with wavelet transform, the noise of the chromatographic signal can be effectively removed, the distortion of the chromatographic signal can be reduced, and the denoising performance can be improved, thereby more completely retaining the details of the chromatographic signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 This is a flow chart of a chromatographic signal denoising method in one embodiment of the present application;

[0065] Figure 2 A schematic diagram of a simulated original chromatographic signal using a Gaussian function as a model provided in one embodiment of the present application;

[0066] Figure 3 For Figure 2 Schematic diagram of a noise-added signal with a signal-to-noise ratio of 5 dB corresponding to the original chromatographic signal simulated in FIG.

[0067] Figure 4 For Figure 3 Schematic diagram of the effect of using adaptive median filtering to reduce noise on the noisy signal in;

[0068] Figure 5 For Figure 3 Schematic diagram of the effect of using wavelet threshold method to reduce noise on the noisy signal in;

[0069] Figure 6 For Figure 3 A schematic diagram showing the effect of reducing the noise of the noisy signal using the chromatographic signal denoising method provided in the present application;

[0070] Figure 7 For Figure 2Schematic diagram of a noise-added signal with a signal-to-noise ratio of 10 dB corresponding to the original chromatographic signal simulated in FIG.

[0071] Figure 8 For Figure 7 Schematic diagram of the effect of using adaptive median filtering to reduce noise on the noisy signal in;

[0072] Fig. 9 For Figure 7 Schematic diagram of the effect of using wavelet threshold method to reduce noise on the noisy signal in;

[0073] Fig.10 For Figure 7 A schematic diagram showing the effect of reducing the noise of the noisy signal using the chromatographic signal denoising method provided in the present application;

[0074] Fig.11 For Figure 2 Schematic diagram of a noise-added signal with a signal-to-noise ratio of 20 dB corresponding to the original chromatographic signal simulated in FIG.

[0075] Fig.12 For Fig.11 Schematic diagram of the effect of using adaptive median filtering to reduce noise on the noisy signal in;

[0076] Fig.13 For Fig.11 Schematic diagram of the effect of using wavelet threshold method to reduce noise on the noisy signal in;

[0077] Fig.14 For Fig.11 A schematic diagram showing the effect of reducing the noise of the noisy signal using the chromatographic signal denoising method provided in the present application;

[0078] Fig.15 An original chromatogram of a local oligonucleotide chemical synthesis provided in one embodiment of the present application;

[0079] Fig.16 for Fig.15 A partial enlarged view of the dotted line frame;

[0080] Fig.17 A denoised local oligonucleotide chemical synthesis chromatogram provided in one embodiment of the present application;

[0081] Fig.18 for Fig.17 A partial enlarged view of the dotted line frame;

[0082] Fig.19 A schematic diagram of the functional modules of a chromatographic signal denoising device provided in one embodiment of the present application.

[0083] Fig. 20A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0085] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0086] In an exemplary embodiment, Figure 1 As shown, a chromatographic signal denoising method is provided, which is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In an embodiment of the present application, the method includes the following steps 101 to 107.

[0087] in:

[0088] Step 101: using the noisy chromatographic signal obtained by the nucleic acid synthesizer, calculate and obtain an initial signal intensity threshold.

[0089] In the embodiment of the present application, the noisy chromatographic signal may also be referred to as a noisy chromatographic signal, which contains noise that needs to be removed.

[0090] As an optional implementation, step 101 uses the noisy chromatographic signal obtained by the nucleic acid synthesizer to calculate the initial signal intensity threshold in a manner that may include:

[0091] Acquire a data set from a noisy chromatographic signal obtained by a nucleic acid synthesizer, wherein the data set includes a plurality of data points, and the plurality of data points are sorted in chronological order;

[0092] Using the signal intensity of each data point and the total number of data points, calculate a first mean and a first standard deviation of the noisy chromatographic signal;

[0093] An initial signal strength threshold is calculated using the first mean, the first standard deviation, and a preset adjustment coefficient.

[0094] Among them, by implementing this embodiment, it is possible to effectively extract a data group from the noisy chromatographic signal obtained by the nucleic acid synthesizer, and obtain an accurate first mean and first standard deviation by calculating the relationship between the signal intensity and the total number of data points. Further, combined with the preset adjustment coefficient, the initial signal intensity threshold can be determined more scientifically. This method not only improves the accuracy of the threshold calculation, but also enhances its adaptability, providing a more reliable basis for subsequent chromatographic signal analysis and processing, thereby optimizing data evaluation and quality control during nucleic acid synthesis.

[0095] In the embodiment of the present application, the first mean value m 1 The calculation formula can be:

[0096]

[0097] Among them, x i is the ith data point in the data set, and N is the total number of data points.

[0098] In the embodiment of the present application, the first standard deviation s 1 The calculation formula can be:

[0099]

[0100] In the embodiment of the present application, the calculation formula of the initial signal strength threshold λ may be:

[0101] λ=m 1 +ks 1

[0102] Wherein, k represents a preset adjustment coefficient, and the preset adjustment coefficient may be a positive integer.

[0103] Step 102: Based on the pulse characteristics of the noisy chromatographic signal, the target signal strength threshold is calculated using the noisy chromatographic signal and the initial signal strength threshold.

[0104] As an optional implementation, step 102 uses the noisy chromatographic signal and the initial signal strength threshold to calculate the target signal strength threshold, which may include:

[0105] Compare the signal strength of each data point with the initial signal strength threshold in chronological order to obtain Boolean data corresponding to each data point;

[0106] Combine the Boolean arrays corresponding to each data point in chronological order to obtain a Boolean array;

[0107] Calculating the difference between adjacent Boolean data in the Boolean array to obtain a difference array; wherein the difference array contains a plurality of difference data, each Boolean data corresponds to a difference data, and the plurality of difference data in the difference array are arranged in chronological order;

[0108] Determine the differential data with a value of 1 in the differential array as the starting differential data;

[0109] Determine the differential data with a value of -1 in the differential array as the termination differential data;

[0110] Determine a data point in the data group corresponding to the initial differential data as a pulse starting point;

[0111] Determine a data point in the data group corresponding to the termination differential data as a pulse endpoint;

[0112] Calculate a second mean and a second standard deviation of the noisy chromatographic signal using the signal intensity of each data point between the pulse start point and the pulse end point and the total number of data points in the data set;

[0113] The target signal strength threshold is calculated using the second mean, the second standard deviation, and the preset adjustment coefficient.

[0114] Among them, by implementing this implementation method, the pulse area in the signal, that is, the starting and ending points where the signal intensity changes significantly, can be accurately identified by comparing the signal intensity of each data point in the noisy chromatographic signal with the initial signal intensity threshold. This method not only effectively utilizes the data processing technology of Boolean arrays and differential arrays to improve the accuracy and efficiency of pulse identification, but also further calculates the second mean and second standard deviation of the data points in the pulse area, combined with the preset adjustment coefficient, to obtain a more accurate target signal intensity threshold. This meticulous processing and analysis process helps to reduce noise interference, improve the accuracy of signal processing, and provide strong support for subsequent chromatographic signal analysis, feature extraction and decision-making, thereby optimizing the reliability and accuracy of experimental results.

[0115] In the embodiment of the present application, the Boolean array may be: b(x), x={x 1 , x 2 , …x i , …, x n}, i, n∈R;

[0116] Each Boolean data in the Boolean array represents the relationship between the signal strength of the corresponding data point and the initial signal strength threshold. If the signal strength of the data point does not exceed the initial signal strength threshold, the Boolean data corresponding to the data point is 0; if the signal strength of the data point exceeds the initial signal strength threshold, the Boolean data corresponding to the data point is 1.

[0117] In the embodiment of the present application, the differential data y in the differential array i Can be:

[0118] y i =b(x i+1 )-b(x i ), i = 1, 2, ..., N-1

[0119] In the embodiment of the present application, the second mean m 2 The calculation formula can be:

[0120]

[0121] Among them, c i =b(i)-a(i) represents the pulse width, a(i) represents the pulse starting point, and b(i) represents the pulse end point; the pulse width, pulse starting point and pulse end point can all be considered as the pulse characteristics of the noisy chromatographic signal.

[0122] In the embodiment of the present application, the second standard deviation s 2 The calculation formula can be:

[0123]

[0124] In the embodiment of the present application, the target signal strength threshold The calculation formula can be:

[0125] Step 103: filtering the noisy chromatographic signal based on an initial sliding window and a maximum sliding window of preset sizes to obtain a filtered chromatographic signal.

[0126] In an embodiment of the present application, the maximum sliding window is determined according to the target signal strength threshold.

[0127] As an optional implementation, step 103 filters the noisy chromatogram signal based on an initial sliding window and a maximum sliding window of a preset size, and a method of obtaining a filtered chromatogram signal may include:

[0128] Based on an initial sliding window and a maximum sliding window of preset sizes, performing at least one filtering operation on the data points in the data group of the noisy chromatographic signal in sequence until the last data point in the data group completes the filtering operation, thereby obtaining a filtered chromatographic signal corresponding to the data group;

[0129] Among them, any filtering operation includes:

[0130] Get the current signal strength corresponding to the data point;

[0131] Determining the initial sliding window as a sliding window;

[0132] Based on the sliding window, determining the median signal strength corresponding to the data point;

[0133] If the median signal strength is equal to the current signal strength and the sliding window has not reached the maximum sliding window, the following steps are repeated:

[0134] Expanding the size of the sliding window to obtain an expanded sliding window; determining the median signal strength corresponding to the data point based on the expanded sliding window;

[0135] Until the median signal strength is not equal to the current signal strength or the sliding window reaches the maximum sliding window, the current signal strength is replaced by the median signal strength.

[0136] Among them, by implementing this implementation method, by gradually adjusting the size of the sliding window, a fine filtering process for each data point in the data set is achieved. During the filtering process, by continuously comparing the median signal intensity with the current signal intensity, and expanding the sliding window when the conditions are met, until the intensity value that best meets the signal characteristics is found for replacement, this method effectively reduces the impact of noise while retaining the key features of the signal. In addition, by setting the maximum sliding window, the efficiency and feasibility of the filtering operation are ensured, and signal distortion caused by excessive processing is avoided. In short, this filtering method not only improves the signal-to-noise ratio of the chromatographic signal, but also enhances the stability and reliability of the signal, laying a solid foundation for subsequent signal analysis and processing.

[0137] In the embodiment of the present application, the preset size of the initial sliding window may be 3. If the median signal strength is not equal to the current signal strength, it may indicate that the current signal strength is noise, and therefore the median signal strength limit needs to be used for filtering.

[0138] In the embodiment of the present application, when the size of the sliding window is expanded, the size of the window can be expanded by 2 each time, and the embodiment of the present application does not limit this.

[0139] For example, if the acquired data point is the 4th data point in the data group, the size of the initial sliding window is 3, and the maximum sliding window can be 7, then the signal strengths of the 3rd data point, the 4th data point, and the 5th data point in the data group can be acquired together, and the median signal strength can be determined from the signal strengths of these three data points, and the signal strength of the 4th data point can be determined as the current signal strength;

[0140] If the median signal strength is not equal to the current signal strength, the median signal strength is used to replace the current signal strength to complete the first median filtering of the fourth data point.

[0141] If the median signal strength is equal to the current signal strength and the sliding window has not reached the maximum sliding window, the initial sliding window is expanded by 2 windows to obtain a sliding window with a window of 5. At this time, the signal strengths of the 2nd to 6th data points in the data group can be obtained at the same time, and the median signal strength can be determined from the signal strengths of these five data points;

[0142] If the median signal strength is not equal to the current signal strength, the median signal strength is used to replace the current signal strength to complete the second median filtering of the 4th data point.

[0143] If the median signal strength is equal to the current signal strength and the sliding window has not reached the maximum sliding window, the sliding window is expanded by 2 windows to obtain a sliding window with a window of 7. At this time, the signal strengths of the 1st to 7th data points in the data group can be obtained at the same time, and the median signal strength can be determined from the signal strengths of these seven data points;

[0144] If the median signal strength is not equal to the current signal strength, the median signal strength is used to replace the current signal strength to complete the third median filtering of the fourth data point.

[0145] If the median signal strength is equal to the current signal strength and the sliding window reaches the maximum sliding window, the median signal strength is used to replace the current signal strength to complete the third median filtering of the 4th data point.

[0146] Step 104: determine a mother wavelet basis function and a decomposition level number that match the filtered chromatogram signal.

[0147] In the embodiment of the present application, the filtered chromatographic signal is denoised by testing different mother wavelet basis functions and decomposition levels to obtain a denoised signal, and the best combination scheme is found based on the signal-to-noise ratio (SNR) and mean-square error (MSE) of the obtained denoised signal.

[0148] The signal-to-noise ratio (SNR) and mean square error (MSE) are the criteria for judging the denoising effect. The expressions are as follows:

[0149]

[0150]

[0151] Where d(t) is the filtered chromatographic signal, is the denoised signal, the length of the filtered chromatographic signal is N, and t is a constant.

[0152] The larger the signal-to-noise ratio, the better the denoising effect; the smaller the mean square error, the better the signal restoration effect.

[0153] Therefore, through Matlab simulation experiments, after comparing the actual filtering effects of various mother wavelet functions and decomposition layers, the sym7 wavelet base and 5 decomposition layers were finally adopted.

[0154] Step 105: Decompose the filtered chromatogram signal using the mother wavelet basis function and the decomposition level to obtain a low-frequency component and a high-frequency component.

[0155] As an optional implementation manner, step 105 uses the mother wavelet basis function and the decomposition level number to decompose the filtered chromatogram signal to obtain low-frequency components and high-frequency components, which may include:

[0156] Based on the number of decomposition layers and the signal length of the filtered chromatogram signal, the mother wavelet basis function is converted to obtain a wavelet basis function;

[0157] Decomposing the filtered chromatogram signal into a first low-frequency component and a first high-frequency component based on the wavelet basis function;

[0158] Substituting the wavelet basis function into the first low-frequency component and the first high-frequency component to obtain a second low-frequency component and a second high-frequency component;

[0159] Performing variable transformation on the second low-frequency component and the second high-frequency component to obtain a third low-frequency component and a third high-frequency component;

[0160] Using a discrete wavelet basis function to discretize the third low-frequency component and the third high-frequency component to obtain a discrete low-frequency component and a discrete high-frequency component; wherein the discrete wavelet basis function is obtained by converting the mother wavelet basis function;

[0161] Performing low-pass filtering on the discrete low-frequency component to obtain a low-frequency component;

[0162] The discrete high-frequency component is subjected to high-pass filtering to obtain a high-pass component.

[0163] Among them, this implementation method is implemented, by flexibly using the mother wavelet basis function, and combining the parameter setting of the decomposition layer number and signal length, the wavelet basis function suitable for signal processing is effectively converted. On this basis, by decomposing the filtered chromatographic signal layer by layer, the preliminary low-frequency and high-frequency components are first obtained, and then the decomposition results are further refined through the substitution of the mother wavelet basis function and variable transformation, and a more accurate second low-frequency component and a second high-frequency component are obtained. Then, the decomposition results are discretized using the discrete wavelet basis function, which not only improves the accuracy of signal processing, but also provides convenience for subsequent filtering operations. Finally, by low-pass filtering the discrete low-frequency component and high-pass filtering the discrete high-frequency component, clear low-frequency components and high-frequency components are successfully extracted. This step effectively separates the different frequency components in the signal, providing a more accurate data basis for subsequent signal analysis and feature extraction, thereby improving the efficiency and accuracy of the entire signal processing process.

[0164] In the embodiment of the present application, the wavelet basis function Ψ j，k (t) can be obtained by translating and scaling the mother wavelet basis function Ψ(t), which can be specifically:

[0165]

[0166] Wherein, j=0, 1, 2, ..., L; k=0, 1, 2, ..., N; j and k are scale parameters and translation parameters respectively, and are both constants, L is the number of decomposition layers, and N is the signal length of the filtered chromatographic signal.

[0167] Based on the wavelet basis function, the filtered chromatographic signal is decomposed into the first low-frequency component A 1j (k) and the first high frequency component D 1j The formula for (k) can be:

[0168]

[0169] Among them, the first low-frequency component A 1j (k) and the first high frequency component D 1j (k) represent the low-frequency component and high-frequency component at scale j and position k, respectively.

[0170] Substitute the wavelet basis function into the first low-frequency component and the first high-frequency component to obtain the second low-frequency component A 2j (k) and the second high frequency component D 2j The formula for (k) can be:

[0171]

[0172] The method of performing variable transformation on the second low-frequency component and the second high-frequency component to obtain the third low-frequency component and the third high-frequency component may specifically be:

[0173]

[0174] That is, t = u·2 j +k·2 j , so dt = 2 j du.

[0175] Substituting t and dt into the second low-frequency component and the second high-frequency component, we can get the third low-frequency component A 3j (k) and the third high frequency component D 3j (k) is:

[0176]

[0177]

[0178] In the embodiment of the present application, u·2 j +k·2 j Discretized to index n, so the discrete wavelet basis function Ψ j，k The formula for (n) can be:

[0179]

[0180] Use discrete wavelet basis function to discretize the third low-frequency component and the third high-frequency component to obtain discrete low-frequency component A 4j (k) and discrete high frequency components D 4j (k) may be:

[0181]

[0182] In the embodiment of the present application, a low-pass filter h(n) can be used to perform low-pass filtering on the discrete low-frequency component to obtain a low-frequency component A j (k) may be:

[0183]

[0184] In the embodiment of the present application, a high-pass filter g(n) can be used to perform high-pass filtering on the discrete high-frequency component to obtain a high-pass component D j (k) may be:

[0185]

[0186] Among them, 2k represents downsampling, and the convolution result retains one data point every other sampling point.

[0187] Step 106: De-noise the high-frequency component based on a soft threshold technology to obtain a de-noised high-frequency component.

[0188] In the embodiment of the present application, the high frequency component D j (k) Perform soft threshold processing to obtain the denoised high-frequency component D′ after denoising j (k) is:

[0189]

[0190] Among them, t p Represents the preset threshold. According to the principle of soft threshold denoising, the high-frequency component D j (k) and t p Compare: If |D j (k)| is less than t p , then the high-frequency component is considered to be high-frequency noise, so that D′ j (k) = 0; if |D j (k)|greater than t p And D j (k)<0, then D′ j (k) = D j (k)+t p ; if D j (k)>0, then D′ j (k) = D j (k)-t p .

[0191] Step 107: reconstruct the low-frequency component and the denoised high-frequency component based on the mother wavelet basis function to obtain a reconstructed chromatographic signal.

[0192] As an optional implementation manner, in step 107, the low-frequency component and the denoised high-frequency component are reconstructed based on the mother wavelet basis function to obtain the reconstructed chromatographic signal, which may include:

[0193] Using the discrete wavelet basis function and the low-frequency component, a low-frequency signal is calculated;

[0194] Using the discrete wavelet basis function and the noise reduction high-frequency component, a high-frequency signal is calculated;

[0195] A reconstructed chromatographic signal is calculated using the low-frequency signal and the high-frequency signal.

[0196] Among them, the implementation method uses the calculation between the discrete wavelet basis function and the low-frequency component obtained by decomposition and the denoised high-frequency component to restore the low-frequency and high-frequency parts of the signal respectively. This step not only ensures the accuracy of signal reconstruction, but also effectively integrates the high-frequency information after denoising, thereby minimizing the interference of noise while ensuring signal integrity. The quality and clarity of the reconstructed chromatographic signal finally obtained have been significantly improved, providing more reliable data support for subsequent signal analysis, feature extraction and decision-making, greatly improving the efficiency and practicality of the entire signal processing process.

[0197] In the embodiment of the present application, the reconstructed chromatographic signal d′ j The calculation formula of (n) can be:

[0198]

[0199] By implementing the above-mentioned steps 101 to 107, by using a denoising method combining filtering with wavelet transform, the noise of the chromatographic signal can be effectively removed, the distortion of the chromatographic signal can be reduced, and the denoising performance can be improved, thereby retaining the details of the chromatographic signal more completely. In addition, the present application can also improve the accuracy of the initial signal intensity threshold calculation. In addition, the present application can also derive a more accurate target signal intensity threshold. In addition, the present application can also improve the signal-to-noise ratio of the chromatographic signal and enhance the stability and reliability of the signal. In addition, the present application can also successfully extract clear low-frequency components and high-frequency components. In addition, the present application can also minimize the interference of noise while ensuring signal integrity.

[0200] Please also read Figure 2 , Figure 2 A schematic diagram of a simulated original chromatographic signal based on a Gaussian function as a model is provided for one embodiment of the present application; under ideal conditions, the peak shape of the original chromatographic signal is very similar to the Gaussian distribution, showing symmetry, unimodality and a specific peak width.

[0201] The chromatographic signal denoising method proposed in the present invention achieves the expected denoising effect. In order to further verify the superiority of the proposed method and the feasibility of practical application, Matlab is used to simulate the original chromatographic signal and the local chromatographic signal of oligonucleotide chemical synthesis with Gaussian function as the model to verify its effectiveness.

[0202] Experiment 1: Experiment on denoising effect of original chromatographic signal simulated by Gaussian function as model

[0203] The signal expression modeled by Gaussian function is:

[0204]

[0205] Among them, g(t) is the original chromatographic signal of the simulation, S i ,t i are the peak area and retention time of the ith peak, Z is the total number of peaks, σ is the standard deviation of the Gaussian function, t is the time of a single sampling point, and N is the total signal length.

[0206] Add noise with signal-to-noise ratio of 5dB, 10dB and 20dB to the simulation signal one by one; Figure 3 , Figure 7 as well as Fig.11 shown.

[0207] Subsequently, three methods, namely, adaptive median filtering, wavelet threshold method and the chromatographic signal denoising method provided in the present application, are used to reduce the noise of the noisy signal; finally, a comparison result graph is generated.

[0208] Figure 4 , Figure 5 as well as Figure 6 For Figure 3 The noisy signal in the figure is subjected to denoising by three methods, namely, adaptive median filtering, wavelet threshold method, and the chromatographic signal denoising method provided in the present application, and the final effect is shown in the figure.

[0209] Figure 8 , Fig. 9 as well as Fig.10 For Figure 7 The noisy signal in the figure is subjected to denoising by three methods, namely, adaptive median filtering, wavelet threshold method, and the chromatographic signal denoising method provided in the present application, and the final effect is shown in the figure.

[0210] Fig.12 , Fig.13 as well as Fig.14 For Fig.11 The noisy signal in the figure is subjected to denoising by three methods, namely, adaptive median filtering, wavelet threshold method, and the chromatographic signal denoising method provided in the present application, and the final effect is shown in the figure.

[0211] The noisy signal is smooth after being denoised by the adaptive median filter method, the wavelet threshold method and the chromatographic signal denoising method provided in this application. By directly comparing the smoothing characteristics of these three curves and their similarities with the original signal, it can be found that: the signal after denoising by the adaptive median filter still has jagged noise; the signal curve deviation after denoising by the wavelet threshold method is large; the signal after denoising by the chromatographic signal denoising method provided in this application is smoother and the curve deviation is small, and the greater the noise, the better the denoising effect.

[0212] The signal-to-noise ratio and root mean square error results after denoising by different denoising methods are shown in Table 1.

[0213] Table 1: Comparison of signal-to-noise ratio and root mean square error after different denoising methods

[0214]

[0215]

[0216] Analysis of the experimental data in Table 1 shows that the signal-to-noise ratio of the noisy chromatographic signal after noise reduction by different methods has been significantly improved, and the signal error after noise reduction is within the normal range. It is worth noting that the signal-to-noise ratio value of the signal after noise reduction by the chromatographic signal denoising method provided in the present application is the largest and the mean square error is the smallest. Therefore, it can be seen from the simulation experiment comparison chart and the experimental data results that for noisy chromatographic signals under different signal-to-noise ratios, the chromatographic signal denoising method provided in the present application can better retain the integrity of the real signal while effectively reducing the noise signal.

[0217] Experiment 2: Experiment on the effect of noise reduction processing on chromatographic signals of oligonucleotide chemical synthesis.

[0218] Please also read Figure 15 to Figure 18 , Fig.15 An original chromatogram of a local oligonucleotide chemical synthesis provided in one embodiment of the present application; Fig.16 for Fig.15 A partial enlarged view of the dotted box in the middle; Fig.17 A denoised local oligonucleotide chemical synthesis chromatogram provided in one embodiment of the present application; Fig.18 for Fig.17 A partial enlarged view of the dotted box in the middle.

[0219] The oligonucleotide chemical synthesis chromatographic signal collected by the chromatograph is subjected to noise reduction processing using the method of the present invention. The processing result is as follows: Fig.17 and Fig.18 As shown in the figure, it can be seen that after the original noisy chromatogram is processed by the chromatographic signal denoising method provided by the present application, the signal becomes smoother and the original information of the signal is not reduced.

[0220] The present application also provides an application scenario, which applies the above-mentioned chromatographic signal denoising method. Specifically: The chromatographic signal denoising method provided in this embodiment can be applied in the denoising scenario of noisy chromatographic signals obtained by a nucleic acid synthesizer. The noisy chromatographic signal is obtained by a nucleic acid synthesizer, and a denoised reconstructed chromatographic signal is obtained by performing a series of denoising operations on the noisy chromatographic signal. The chromatographic signal denoising method provided in this embodiment belongs to a series of denoising operations on the noisy chromatographic signal.

[0221] Based on the same inventive concept, the embodiment of the present application also provides a chromatographic signal denoising device for implementing the chromatographic signal denoising method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more chromatographic signal denoising device embodiments provided below can refer to the limitations of the chromatographic signal denoising method above, and will not be repeated here.

[0222] In an exemplary embodiment, Fig.19 As shown, a chromatographic signal denoising device is provided, comprising:

[0223] The first calculation unit 1901 is used to calculate an initial signal intensity threshold using a noisy chromatographic signal obtained by a nucleic acid synthesizer;

[0224] A second calculation unit 1902 is used to calculate a target signal strength threshold based on the pulse feature of the noisy chromatographic signal, using the noisy chromatographic signal and the initial signal strength threshold;

[0225] A filtering unit 1903 is used to filter the noisy chromatogram signal based on an initial sliding window and a maximum sliding window of a preset size to obtain a filtered chromatogram signal; wherein the maximum sliding window is determined according to the target signal strength threshold;

[0226] A determination unit 1904 is used to determine a mother wavelet basis function and a decomposition level number that matches the filtered chromatogram signal;

[0227] A decomposition unit 1905 is used to decompose the filtered chromatogram signal using the mother wavelet basis function and the decomposition level to obtain a low-frequency component and a high-frequency component;

[0228] A noise reduction unit 1906, configured to reduce noise on the high frequency component based on a soft threshold technology to obtain a noise-reduced high frequency component;

[0229] The reconstruction unit 1907 is used to reconstruct the low-frequency component and the denoised high-frequency component based on the mother wavelet basis function to obtain a reconstructed chromatogram signal.

[0230] As an optional implementation, the first calculation unit 1901 uses the noisy chromatographic signal obtained by the nucleic acid synthesizer to calculate the initial signal intensity threshold in the following manner:

[0231] Acquire a data set from a noisy chromatographic signal obtained by a nucleic acid synthesizer, wherein the data set includes a plurality of data points, and the plurality of data points are sorted in chronological order;

[0232] Using the signal intensity of each data point and the total number of data points, calculate a first mean and a first standard deviation of the noisy chromatographic signal;

[0233] An initial signal strength threshold is calculated using the first mean, the first standard deviation, and a preset adjustment coefficient.

[0234] Among them, by implementing this embodiment, it is possible to effectively extract a data group from the noisy chromatographic signal obtained by the nucleic acid synthesizer, and obtain an accurate first mean and first standard deviation by calculating the relationship between the signal intensity and the total number of data points. Further, combined with the preset adjustment coefficient, the initial signal intensity threshold can be determined more scientifically. This method not only improves the accuracy of the threshold calculation, but also enhances its adaptability, providing a more reliable basis for subsequent chromatographic signal analysis and processing, thereby optimizing data evaluation and quality control during nucleic acid synthesis.

[0235] As an optional implementation, the second calculation unit 1902 calculates the target signal strength threshold based on the pulse feature of the noisy chromatographic signal using the noisy chromatographic signal and the initial signal strength threshold in the following manner:

[0236] Compare the signal strength of each data point with the initial signal strength threshold in chronological order to obtain Boolean data corresponding to each data point;

[0237] Combine the Boolean arrays corresponding to each data point in chronological order to obtain a Boolean array;

[0238] Calculating the difference between adjacent Boolean data in the Boolean array to obtain a difference array; wherein the difference array contains a plurality of difference data, each Boolean data corresponds to a difference data, and the plurality of difference data in the difference array are arranged in chronological order;

[0239] Determine the differential data with a value of 1 in the differential array as the starting differential data;

[0240] Determine the differential data with a value of -1 in the differential array as the termination differential data;

[0241] Determine a data point in the data group corresponding to the initial differential data as a pulse starting point;

[0242] Determine a data point in the data group corresponding to the termination differential data as a pulse endpoint;

[0243] Calculate a second mean and a second standard deviation of the noisy chromatographic signal using the signal intensity of each data point between the pulse start point and the pulse end point and the total number of data points in the data set;

[0244] The target signal strength threshold is calculated using the second mean, the second standard deviation, and the preset adjustment coefficient.

[0245] Among them, by implementing this implementation method, the pulse area in the signal, that is, the starting and ending points where the signal intensity changes significantly, can be accurately identified by comparing the signal intensity of each data point in the noisy chromatographic signal with the initial signal intensity threshold. This method not only effectively utilizes the data processing technology of Boolean arrays and differential arrays to improve the accuracy and efficiency of pulse identification, but also further calculates the second mean and second standard deviation of the data points in the pulse area, combined with the preset adjustment coefficient, to obtain a more accurate target signal intensity threshold. This meticulous processing and analysis process helps to reduce noise interference, improve the accuracy of signal processing, and provide strong support for subsequent chromatographic signal analysis, feature extraction and decision-making, thereby optimizing the reliability and accuracy of experimental results.

[0246] As an optional implementation, the filtering unit 1903 filters the noisy chromatogram signal based on an initial sliding window and a maximum sliding window of a preset size, and the manner of obtaining the filtered chromatogram signal may be:

[0247] Based on an initial sliding window and a maximum sliding window of preset sizes, performing at least one filtering operation on the data points in the data group of the noisy chromatographic signal in sequence until the last data point in the data group completes the filtering operation, thereby obtaining a filtered chromatographic signal corresponding to the data group;

[0248] Among them, any filtering operation includes:

[0249] Get the current signal strength corresponding to the data point;

[0250] Determining the initial sliding window as a sliding window;

[0251] Based on the sliding window, determining the median signal strength corresponding to the data point;

[0252] If the median signal strength is equal to the current signal strength and the sliding window has not reached the maximum sliding window, then repeat:

[0253] Expanding the size of the sliding window to obtain an expanded sliding window; determining the median signal strength corresponding to the data point based on the expanded sliding window;

[0254] Until the median signal strength is not equal to the current signal strength or the sliding window reaches the maximum sliding window, the current signal strength is replaced by the median signal strength.

[0255] Among them, by implementing this implementation method, by gradually adjusting the size of the sliding window, a fine filtering process for each data point in the data set is achieved. During the filtering process, by continuously comparing the median signal intensity with the current signal intensity, and expanding the sliding window when the conditions are met, until the intensity value that best meets the signal characteristics is found for replacement, this method effectively reduces the impact of noise while retaining the key features of the signal. In addition, by setting the maximum sliding window, the efficiency and feasibility of the filtering operation are ensured, and signal distortion caused by excessive processing is avoided. In short, this filtering method not only improves the signal-to-noise ratio of the chromatographic signal, but also enhances the stability and reliability of the signal, laying a solid foundation for subsequent signal analysis and processing.

[0256] As an optional implementation manner, the decomposition unit 1905 uses the mother wavelet basis function and the decomposition level number to decompose the filtered chromatogram signal to obtain the low-frequency component and the high-frequency component in the following manner:

[0257] Based on the number of decomposition layers and the signal length of the filtered chromatogram signal, the mother wavelet basis function is converted to obtain a wavelet basis function;

[0258] Decomposing the filtered chromatogram signal into a first low-frequency component and a first high-frequency component based on the wavelet basis function;

[0259] Substituting the wavelet basis function into the first low-frequency component and the first high-frequency component to obtain a second low-frequency component and a second high-frequency component;

[0260] Performing variable transformation on the second low-frequency component and the second high-frequency component to obtain a third low-frequency component and a third high-frequency component;

[0261] Using a discrete wavelet basis function to discretize the third low-frequency component and the third high-frequency component to obtain a discrete low-frequency component and a discrete high-frequency component; wherein the discrete wavelet basis function is obtained by converting the mother wavelet basis function;

[0262] Performing low-pass filtering on the discrete low-frequency component to obtain a low-frequency component;

[0263] The discrete high-frequency component is subjected to high-pass filtering to obtain a high-pass component.

[0264] Among them, this implementation method is implemented, by flexibly using the mother wavelet basis function, and combining the parameter setting of the decomposition layer number and signal length, the wavelet basis function suitable for signal processing is effectively converted. On this basis, by decomposing the filtered chromatographic signal layer by layer, the preliminary low-frequency and high-frequency components are first obtained, and then the decomposition results are further refined through the substitution of the mother wavelet basis function and variable transformation, and a more accurate second low-frequency component and a second high-frequency component are obtained. Then, the decomposition results are discretized using the discrete wavelet basis function, which not only improves the accuracy of signal processing, but also provides convenience for subsequent filtering operations. Finally, by low-pass filtering the discrete low-frequency component and high-pass filtering the discrete high-frequency component, clear low-frequency components and high-frequency components are successfully extracted. This step effectively separates the different frequency components in the signal, providing a more accurate data basis for subsequent signal analysis and feature extraction, thereby improving the efficiency and accuracy of the entire signal processing process.

[0265] As an optional implementation manner, the reconstruction unit 1907 reconstructs the low-frequency component and the denoised high-frequency component based on the mother wavelet basis function to obtain the reconstructed chromatographic signal in the following manner:

[0266] Using the discrete wavelet basis function and the low-frequency component, a low-frequency signal is calculated;

[0267] Using the discrete wavelet basis function and the noise reduction high-frequency component, a high-frequency signal is calculated;

[0268] A reconstructed chromatographic signal is calculated using the low-frequency signal and the high-frequency signal.

[0269] Among them, the implementation method uses the calculation between the discrete wavelet basis function and the low-frequency component obtained by decomposition and the denoised high-frequency component to restore the low-frequency and high-frequency parts of the signal respectively. This step not only ensures the accuracy of signal reconstruction, but also effectively integrates the high-frequency information after denoising, thereby minimizing the interference of noise while ensuring signal integrity. The quality and clarity of the reconstructed chromatographic signal finally obtained have been significantly improved, providing more reliable data support for subsequent signal analysis, feature extraction and decision-making, greatly improving the efficiency and practicality of the entire signal processing process.

[0270] By implementing the above-mentioned implementation mode, by using a denoising method combining filtering with wavelet transform, the noise of the chromatographic signal can be effectively removed, the distortion of the chromatographic signal can be reduced, and the denoising performance can be improved, thereby retaining the details of the chromatographic signal more completely. In addition, the present application can also improve the accuracy of the initial signal intensity threshold calculation. In addition, the present application can also derive a more accurate target signal intensity threshold. In addition, the present application can also improve the signal-to-noise ratio of the chromatographic signal and enhance the stability and reliability of the signal. In addition, the present application can also successfully extract clear low-frequency components and high-frequency components. In addition, the present application can also minimize the interference of noise while ensuring signal integrity.

[0271] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig. 20 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store chromatographic signal denoising data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a chromatographic signal denoising method is implemented.

[0272] Those skilled in the art will understand that Fig. 20 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0273] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0274] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0275] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0276] In an exemplary embodiment, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps in the above-mentioned method embodiments and achieve the same technical effects. To avoid repetition, they are not described here.

[0277] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0278] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0279] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0280] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0281] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0282] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A chromatographic signal denoising method, characterized in that: The chromatographic signal denoising method comprises: Using the noisy chromatographic signal obtained by the nucleic acid synthesizer, an initial signal intensity threshold is calculated; Based on the pulse characteristics of the noisy chromatographic signal, using the noisy chromatographic signal and the initial signal intensity threshold, a target signal intensity threshold is calculated; Based on an initial sliding window and a maximum sliding window of a preset size, the noisy chromatographic signal is filtered to obtain a filtered chromatographic signal; wherein the maximum sliding window is determined according to the target signal strength threshold; Determining a mother wavelet basis function and a number of decomposition levels that match the filtered chromatogram signal; Decomposing the filtered chromatogram signal using the mother wavelet basis function and the decomposition level to obtain a low-frequency component and a high-frequency component; De-noising the high-frequency component based on a soft threshold technology to obtain a de-noised high-frequency component; The low-frequency component and the noise-reduced high-frequency component are reconstructed based on the mother wavelet basis function to obtain a reconstructed chromatographic signal.

2. The chromatographic signal denoising method according to claim 1, characterized in that: The method of using the noisy chromatographic signal obtained by the nucleic acid synthesizer to calculate the initial signal intensity threshold specifically includes: Acquire a data set from a noisy chromatographic signal obtained by a nucleic acid synthesizer, wherein the data set includes a plurality of data points, and the plurality of data points are sorted in chronological order; Using the signal intensity of each data point and the total number of data points, calculate a first mean and a first standard deviation of the noisy chromatographic signal; An initial signal strength threshold is calculated using the first mean, the first standard deviation, and a preset adjustment coefficient.

3. The chromatographic signal denoising method according to claim 2, characterized in that: The step of calculating the target signal strength threshold based on the pulse feature of the noisy chromatographic signal and using the noisy chromatographic signal and the initial signal strength threshold specifically includes: Compare the signal strength of each data point with the initial signal strength threshold in chronological order to obtain Boolean data corresponding to each data point; Combine the Boolean arrays corresponding to each data point in chronological order to obtain a Boolean array; Calculating the difference between adjacent Boolean data in the Boolean array to obtain a difference array; wherein the difference array contains a plurality of difference data, each Boolean data corresponds to a difference data, and the plurality of difference data in the difference array are arranged in chronological order; Determine the differential data with a value of 1 in the differential array as the starting differential data; Determine the differential data with a value of -1 in the differential array as the termination differential data; Determine a data point in the data group corresponding to the initial differential data as a pulse starting point; Determine a data point in the data group corresponding to the termination differential data as a pulse endpoint; Calculate a second mean and a second standard deviation of the noisy chromatographic signal using the signal intensity of each data point between the pulse start point and the pulse end point and the total number of data points in the data set; The target signal strength threshold is calculated using the second mean, the second standard deviation, and the preset adjustment coefficient.

4. The chromatographic signal denoising method according to claim 2, characterized in that: The filtering of the noisy chromatographic signal based on the initial sliding window and the maximum sliding window of the preset size to obtain the filtered chromatographic signal specifically includes: Based on an initial sliding window and a maximum sliding window of preset sizes, performing at least one filtering operation on the data points in the data group of the noisy chromatographic signal in sequence until the last data point in the data group completes the filtering operation, thereby obtaining a filtered chromatographic signal corresponding to the data group; Among them, any filtering operation includes: Get the current signal strength corresponding to the data point; Determining the initial sliding window as a sliding window; Based on the sliding window, determining the median signal strength corresponding to the data point; If the median signal strength is equal to the current signal strength and the sliding window has not reached the maximum sliding window, the following steps are repeated: Expanding the size of the sliding window to obtain an expanded sliding window; determining the median signal strength corresponding to the data point based on the expanded sliding window; Until the median signal strength is not equal to the current signal strength or the sliding window reaches the maximum sliding window, the current signal strength is replaced by the median signal strength.

5. The chromatographic signal denoising method according to claim 1, characterized in that: Decomposing the filtered chromatogram signal using the mother wavelet basis function and the decomposition layer number to obtain a low-frequency component and a high-frequency component specifically includes: Based on the number of decomposition layers and the signal length of the filtered chromatogram signal, the mother wavelet basis function is converted to obtain a wavelet basis function; Decomposing the filtered chromatogram signal into a first low-frequency component and a first high-frequency component based on the wavelet basis function; Substituting the wavelet basis function into the first low-frequency component and the first high-frequency component to obtain a second low-frequency component and a second high-frequency component; Performing variable transformation on the second low-frequency component and the second high-frequency component to obtain a third low-frequency component and a third high-frequency component; Using a discrete wavelet basis function to discretize the third low-frequency component and the third high-frequency component to obtain a discrete low-frequency component and a discrete high-frequency component; wherein the discrete wavelet basis function is obtained by converting the mother wavelet basis function; Performing low-pass filtering on the discrete low-frequency component to obtain a low-frequency component; The discrete high-frequency component is subjected to high-pass filtering to obtain a high-pass component.

6. The chromatographic signal denoising method according to claim 5, characterized in that: The reconstructing the low-frequency component and the denoised high-frequency component based on the mother wavelet basis function to obtain a reconstructed chromatographic signal specifically includes: Using the discrete wavelet basis function and the low-frequency component, a low-frequency signal is calculated; Using the discrete wavelet basis function and the noise reduction high-frequency component, a high-frequency signal is calculated; A reconstructed chromatographic signal is calculated using the low-frequency signal and the high-frequency signal.

7. A chromatographic signal denoising device, characterized in that: The chromatographic signal denoising device comprises: A first calculation unit is used to calculate an initial signal intensity threshold using a noisy chromatographic signal obtained by a nucleic acid synthesizer; A second calculation unit is used to calculate a target signal strength threshold based on the pulse characteristics of the noisy chromatographic signal, using the noisy chromatographic signal and the initial signal strength threshold; A filtering unit, configured to filter the noisy chromatographic signal based on an initial sliding window and a maximum sliding window of a preset size to obtain a filtered chromatographic signal; wherein the maximum sliding window is determined according to the target signal strength threshold; A determination unit, used to determine a mother wavelet basis function and a number of decomposition layers matching the filtered chromatogram signal; A decomposition unit, used to decompose the filtered chromatogram signal using the mother wavelet basis function and the decomposition level number to obtain a low-frequency component and a high-frequency component; A noise reduction unit, used for performing noise reduction on the high-frequency component based on a soft threshold technology to obtain a noise-reduced high-frequency component; A reconstruction unit is used to reconstruct the low-frequency component and the denoised high-frequency component based on the mother wavelet basis function to obtain a reconstructed chromatogram signal.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the chromatographic signal denoising method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the chromatographic signal denoising method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the chromatographic signal denoising method described in any one of claims 1 to 6 are implemented.

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