Chromatograph baseline real-time automatic correction method and system and electronic equipment

Abstract

The invention discloses a chromatographic instrument baseline real-time automatic correction method and system and electronic equipment, and the method comprises the steps: obtaining real-time data in the detection process of a chromatographic instrument, and carrying out the real-time correction of the real-time data according to initial conditions; calculating a real-time data first-order derivative based on a real-time correction process, obtaining a correction coefficient by judging whether the data is located at concave points or not, whether the data is gentle or not and the distance between the concave points, and calculating a baseline drift trend; performing linear fitting on the concave point data based on the baseline drift trend; and updating the correction coefficient in real time. The method provided by the invention has the beneficial effects that the signal noise can be well eliminated when the chromatograph works, a chromatographic signal can be effectively reproduced, the form of a peak spectrum is kept unchanged, the characteristics such as the amplitude and the area of the peak spectrum are not distorted, a baseline can be kept not to drift when the instrument is in temperature fluctuation, and the detection accuracy is improved. Therefore, the spectrogram is more complete, and the accuracy of peak spectrum detection and analysis is improved.

Description

Technical Field

[0001] The invention relates to the technical field of chromatograph instrument signals, in particular to a chromatograph baseline real-time automatic correction method. Background Art

[0002] Chromatograph is a separation and detection tool. It is a process in which samples composed of different components to be separated are repeatedly distributed between the stationary phase and the mobile phase, and finally the components are mutually diverted, so as to make qualitative judgment and quantitative calculation of the sample components. Due to the objective environmental conditions in the separation process, such as the influence or superposition effect of factors such as impurities in the mobile phase, instrument contamination, temperature fluctuations, etc., the problem of baseline drift will occur, showing that the baseline changes upward or downward or irregularly, making it difficult for subsequent spectra to achieve the desired effect, causing certain difficulties in spectrum analysis, and reducing the accuracy of peak detection and analysis. In general, baseline drift appears at the bottom of the final chromatogram, which will seriously affect the calculation of peak height and area.

[0003] In addition, eliminating signal noise and baseline drift is a basic task of chromatographic signal preprocessing. In order to effectively reproduce the chromatographic signal, it is required to maintain the shape of the peak spectrum, including amplitude, area and other characteristics, without distortion while eliminating noise. This is also a key factor in completing qualitative and quantitative analysis.

[0004] The existing data processing methods for dealing with the above technical problems, especially the real-time signal baseline drift processing, cannot achieve ideal results. Summary of the invention

[0005] The object of the present invention is to provide a method for real-time automatic correction of a chromatograph baseline to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for automatically correcting a chromatograph baseline in real time, comprising:

[0008] Acquire real-time data during the chromatograph detection process, and perform real-time correction on the real-time data according to initial conditions;

[0009] Based on the real-time correction process, the first-order derivative of the real-time data is calculated, and the correction coefficient is obtained by judging whether the data is located at a concave point, whether the data is flat, and the spacing between concave points, and the baseline drift trend is calculated;

[0010] Linear fit is performed on the concave point data based on the baseline drift trend;

[0011] Update the correction coefficients in real time.

[0012] Preferably, the real-time data acquired during the chromatograph detection process and the real-time data corrected in real time according to the initial conditions include: acquiring real-time signal data of multiple sensors, including temperature, pressure and chromatographic signals;

[0013] Input real-time signal data into a multivariate adaptive filtering algorithm;

[0014] Use filtering algorithms to identify and eliminate high-frequency noise;

[0015] Output the processed clean signal.

[0016] Preferably, the first-order derivative of the real-time data is calculated based on the real-time correction process, and the correction coefficient is obtained by judging whether the data is located at a concave point, whether the data is flat, and the distance between the concave points, and calculating the baseline drift trend, including:

[0017] Real-time collection of data points uploaded by the chromatograph T i , S i ;

[0018] The real-time sampling signal S i Perform linear correction based on the initial coefficient to obtain the real-time correction value SR i , for each latest sampling point S i First, we need to remove the accumulated error of the last segmentation point, and then remove the interval baseline error, i.e. the drift of the shadow part, according to the slope prediction value obtained in the previous interval. After the two parts of error correction, we can get a straight baseline.

[0019] By implementing the correction value SR i Determine the update time of the correction coefficient;

[0020] The above steps are repeated until the collection is completed.

[0021] Preferably, the real-time correction value SR i The calculation formula is:

[0022] SR i =S i -(S Ki -SR Ki )-(iK i )*(cK old +K).

[0023] Preferably, Ti is the current moment, and Si is the detector signal value corresponding to the current moment.

[0024] Preferably, the updating time of the correction coefficient is determined by implementing the correction value SRi, including:

[0025] Determine whether the current data point is a concave point. If it is a concave point, proceed to the next step of judgment. The judgment criterion is that N consecutive points fall and then N consecutive points rise;

[0026] Determine whether the previous data interval of the current concave point is flat, if so, record it for the next step of determination;

[0027] Determine the interval between the current flat concave point and the previous flat concave point. If the interval exceeds 5S, it is adopted as the latest fitting point and recorded in the new N-order array K i At the end, delete the first data in the array and perform fitting update calculation;

[0028] K i And the corresponding original data Ak i Perform N-point linear fitting to obtain the latest fitting slope K, and store the previous interval value of K in K old .

[0029] In a second aspect, an embodiment of the present invention provides a chromatograph baseline real-time automatic correction system, which is used to implement any of the methods described in the above embodiments, and the system comprises:

[0030] An acquisition module, the acquisition module is used to acquire real-time data during the chromatograph detection process, and to perform real-time correction on the real-time data according to initial conditions;

[0031] A calculation module, wherein the calculation module calculates the first-order derivative of the real-time data based on the real-time correction process, obtains the correction coefficient by judging whether the data is located at a concave point, whether the data is flat, and the distance between the concave points, and calculates the baseline drift trend;

[0032] A fitting module, wherein the fitting module performs linear fitting on the concave point data based on the baseline drift trend;

[0033] The updating module is used to update the correction coefficient in real time.

[0034] The third invention, an embodiment of the present invention provides an electronic device, which includes at least one processor, and the processor is communicatively connected to at least one memory, wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the methods described in the above embodiments.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The method provided by the present invention can effectively eliminate signal noise when the chromatograph is working, can effectively reproduce the chromatographic signal, keep the shape of the peak spectrum unchanged, and prevent the peak spectrum amplitude, area and other characteristics from being distorted;

[0037] 2. The method provided by the present invention can effectively maintain the stability of the chromatograph baseline and can keep the baseline from drifting when the instrument is subject to temperature fluctuations, thereby making the spectrum more complete and improving the accuracy of peak spectrum detection and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0039] Figure 2 is a sub-flow chart of step S100 of an embodiment of the present invention;

[0040] Figure 3 This is a sub-flow chart of step S200 of an embodiment of the present invention;

[0041] Figure 4 This is a sub-flow chart of step S230 of an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of a chromatograph baseline correction method according to an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of the system modules of an embodiment of the present invention;

[0044] Figure 7 A data valley point judgment diagram according to an embodiment of the present invention;

[0045] Figure 8 Schematic diagram of an electronic device module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1 The present invention provides a technical solution: a method for real-time automatic correction of a chromatograph baseline, the method comprising the following steps:

[0048] S100: acquiring real-time data during the chromatograph detection process, and performing real-time correction on the real-time data according to initial conditions;

[0049] S200: Calculate the first-order derivative of the real-time data based on the real-time correction process, obtain the correction coefficient by judging whether the data is located at a concave point, whether the data is flat, and the distance between the concave points, and calculate the baseline drift trend;

[0050] S300: performing linear fitting on the concave point data based on the baseline drift trend;

[0051] S400: Update the correction coefficient in real time.

[0052] Through these steps, the present invention effectively solves the problems of baseline drift caused by the simultaneous peaking of multiple substances under complex sample conditions, signal instability caused by detector temperature fluctuations under high temperature conditions, and baseline noise caused by column pressure changes under high pressure conditions. Specifically, it means that the baseline data drifts upward, downward, or fluctuates irregularly due to the comprehensive factors of the chromatographic instrument. By controlling the threshold of the first-order derivative of the data, the stable concave points in the collected data are judged, and the baseline drift data is determined by the sliding linear fitting algorithm, so as to realize real-time correction processing of the original data, and will not affect the qualitative judgment and quantitative calculation of the spectrum peak.

[0053] The first step is to obtain real-time data during the chromatograph detection process and perform real-time correction on the real-time data according to the initial conditions. This means that the real-time output signal of the chromatographic column is converted into a digital signal through the sensor and acquisition system inside the instrument and transmitted to the processing module. This step is the basis of the entire baseline correction method and ensures the accuracy of the data source for subsequent processing. For example, during a chromatographic analysis, real-time data may include the retention time and peak intensity of different substances, and these data are continuously recorded through an efficient data acquisition card.

[0054] The second step is to calculate the first-order derivative of the real-time data based on the real-time correction process, obtain the correction coefficient by judging whether the data is located at a concave point, whether the data is flat, and the spacing between the concave points, and calculate the baseline drift trend. This step obtains the logic that leads to frequent corrections through the correction coefficient. Determine whether the current data is a concave point or a valley point, and decide whether to proceed to the next step based on the judgment result. Perform preliminary processing on the real-time data through the algorithm to eliminate high-frequency noise. High-frequency noise is an important factor affecting the accuracy and reliability of chromatographic data, especially in the analysis of complex samples, where background interference is particularly obvious. The present invention adopts a multivariate adaptive filtering algorithm and uses the algorithm to smooth the data, thereby eliminating or reducing the impact of these high-frequency noises. In one embodiment, if an abnormally high noise peak is detected during the experiment, the adaptive filtering algorithm can quickly identify and remove this peak, thereby improving the purity of the data.

[0055] The third step is to perform a linear fit on the concave point data based on the baseline drift trend. The positive and negative sign and size of the first-order derivative can reflect the drift direction and speed of the data. If the first-order derivative is continuously positive and has a large value, it means that the baseline is drifting upward; if the first-order derivative is continuously negative and has a large value, it means that the baseline is drifting downward. Calculate the baseline drift trend through baseline drift data: The drift data over a period of time can be analyzed, for example, using a linear regression method. If the drift data changes roughly linearly, a linear drift trend equation y=mx+b can be obtained, where m is the drift speed and b is the initial offset.

[0056] Linear fitting of concave point data is a common problem in chromatographic analysis. Baseline drift is particularly prominent under complex sample conditions. Through machine learning models, a baseline drift prediction model can be established by learning from historical data, and baseline drift can be predicted and corrected in real time during the detection process. For example, after a lot of training, the model can identify baseline change data in a specific mode and make adjustments in advance to reduce the possibility of baseline drift.

[0057] The fourth step is to update the correction coefficient in real time. After the previous steps, the fitted baseline data needs to be updated and output in real time to correct the correction coefficient so that the correction coefficient can be automatically corrected. This step is usually implemented through a software interface, and the corrected data will be displayed on a real-time chart for the operator to monitor. Specifically, when the chromatographic analysis is performed, the user can see the baseline after real-time correction on the interface to ensure that the baseline remains within a predetermined range during the analysis process, thereby improving the reliability and accuracy of the entire analysis process and ensuring the stability of the data. Through the above steps, the present invention not only effectively solves the problems of baseline drift and signal instability, but also significantly improves the overall performance of the chromatographic analysis.

[0058] In one embodiment of the present invention, step S100 specifically includes:

[0059] S110: Acquire real-time signal data of multiple sensors, including temperature, pressure and chromatographic signals.

[0060] This step is to collect various key environmental and operating parameters that affect the performance of the chromatograph in order to comprehensively evaluate and correct the baseline drift problem. These sensors can be high-precision temperature sensors, pressure sensors, and chromatographic detectors. For example, the temperature change of the detector is monitored in real time by a temperature sensor, the pressure change of the chromatographic column is monitored by a pressure sensor, and the chromatographic signal comes directly from the output of the chromatograph.

[0061] S120: Input the real-time signal data into the multivariate adaptive filtering algorithm.

[0062] The multivariate adaptive filtering algorithm is an algorithm that can process multidimensional data and automatically adjust the filtering parameters. The algorithm uses an adaptive mechanism to continuously optimize the filtering parameters to better handle complex noise signals. Specifically, after preprocessing, the input multivariate data will be converted into a form suitable for algorithm processing. In this process, normalization or standardization methods can be used to process data from different sensors to ensure that they are analyzed at the same level. In one embodiment, the chromatograph monitors in real time that the temperature sensor reading is 35°C, the pressure sensor reading is 150bar, and the chromatographic signal intensity is 500mV.

[0063] S130: Using a filtering algorithm to identify and eliminate high-frequency noise.

[0064] The filtering algorithm identifies and distinguishes the useful components and noise components in the signal, and then filters the noise through mathematical operations. For example, the least mean square error (LMS) algorithm or the recursive least squares (RLS) algorithm in the adaptive filtering algorithm can be used. Taking the LMS algorithm as an example, its objective function is to minimize the square error between the actual output and the expected output. Taking the LMS algorithm as an example, its objective function is to minimize the square error between the actual output and the expected output. Let x(n) be the input signal vector, d(n) be the expected signal, w(n) be the filter weight vector, and y(n) = w(n) T x(n) is the output signal of the filter, e(n)=d(n)-y(n) is the error signal, and the update formula of the LMS algorithm is: w(n+1)=w(n)+μe(n)x(n)

[0065] Among them, μ is the step size parameter, which determines the convergence speed of the filter. Its value range is usually between 0 and 1. The optimal value needs to be adjusted according to the actual situation. If it is too large, the filter will be unstable, and if it is too small, the convergence speed will be too slow.

[0066] In this embodiment, step S130 includes:

[0067] First, obtain the real-time data of the temperature sensor T1 and the pressure sensor P1. This is achieved by installing the temperature sensor T1 and the pressure sensor P1 in the key parts of the chromatograph. The temperature sensor T1 is used to monitor the temperature changes in the chromatographic column, while the pressure sensor P1 monitors the pressure fluctuations in the chromatographic column. These real-time data can reflect the changes in the experimental environment, thereby providing important reference information for subsequent data processing. For example, in a typical chromatographic analysis process, changes in temperature and pressure may cause baseline drift or peak shape changes, so real-time acquisition of this data helps to improve the accuracy and reliability of chromatographic signals.

[0068] Then, the data of the temperature sensor T1 and the pressure sensor P1 are combined with the chromatographic signal S into a multivariate signal vector V = [T1, P1, S]. This step integrates the data of multiple sensors into one vector for subsequent comprehensive processing. Each element in the multivariate signal vector V represents the instantaneous value of temperature, pressure and chromatographic signal. Specifically, in chromatographic analysis, the chromatographic signal S is usually a signal output by the detector, which is used to characterize the changes of sample components over time; while temperature T1 and pressure P1 are important parameters that affect the chromatographic separation effect.

[0069] Next, the noise weight W is calculated based on the multivariate signal vector V, where W = a*T1+b*P1+c*S), and a, b, and c are the influence coefficients of temperature, pressure, and chromatographic signals, respectively. The noise weight W reflects the relative intensity of the noise in the current environment and is used to guide the parameter adjustment of the filter. Here, the influence coefficients of a, b, and c can be optimized according to the experimental conditions and equipment characteristics. Generally speaking, the value range of a is 0.1 to 0.5, the value range of b is 0.2 to 0.6, and the value range of c is 0.3 to 0.7. The selection of the optimal value needs to be verified by a large amount of experimental data. The multiplication operation in the formula represents the degree of influence of each variable on its weight, while the addition operation comprehensively considers the overall influence of all variables. For example, in a specific chromatographic analysis scenario, if the experimental environment temperature fluctuates greatly, the value of a can be appropriately increased to enhance the impact of temperature changes on the noise weight.

[0070] Finally, the filter parameters are adjusted according to the noise weight W to ensure the stability and accuracy of the signal. The key to this step is to dynamically adjust the smoothness of the filter to cope with different environmental changes. When the noise weight W is high, the smoothness of the filter should be appropriately enhanced, otherwise it should be weakened. In this way, the noise caused by environmental factors can be effectively eliminated and the quality of the chromatographic signal can be improved. Specifically, during the operation of the chromatograph, if the temperature and pressure fluctuations cause a significant increase in the noise weight, the impact of noise on the chromatographic baseline can be reduced by increasing the smoothing parameters of the filter, thereby ensuring the stability and recognizability of the chromatographic peak.

[0071] S140: Outputs the processed clean signal.

[0072] Ensure that the baseline drift problem caused by the simultaneous peaking of multiple substances under complex sample conditions is effectively solved, the signal instability problem caused by detector temperature fluctuation under high temperature conditions is improved, and the baseline noise problem caused by column pressure changes under high pressure conditions is alleviated. Specifically, the temperature, pressure and chromatographic signals processed by the filtering algorithm will be more stable and reliable. For example, in an experiment, the processed chromatographic signal showed a smoother baseline, the baseline drift was significantly reduced, and the detected peak shape was more accurate, which greatly improved the measurement accuracy and reliability of the chromatograph.

[0073] In one embodiment of the present invention, step S200 includes:

[0074] S210: Real-time collection of data points T uploaded by the chromatograph i , S i .

[0075] In this embodiment, the number of linear fitting data is determined according to the instrument sampling frequency (generally a 20 Hz sampling frequency, which may be 7 by default); the associated variables are initialized and set to zero by default.

[0076] Specifically, T i is the current moment, S i is the detector signal value corresponding to the current moment.

[0077] S220: real-time sampling signal S i Perform linear correction based on the initial coefficient to obtain the real-time correction value SR i , for each latest sampling point S i First, we need to remove the accumulated error S of the previous segmentation point. ki -SR ki , and then the interval baseline error, i.e. the drift of the shadow part, is removed according to the slope prediction value obtained in the previous interval. After the two parts of error correction, a straight baseline is obtained.

[0078] In this embodiment, SR i The value of is given by the formula:

[0079] SR i =S i -(S Ki -SR Ki )-(iK i )*(cK old +K)

[0080] Get, where S i represents the original value at the i-th time point, S ki Indicates time point K i The corresponding original value, SR ki Indicates time point Ki The corresponding corrected value.

[0081] S230: Determine the update time of the correction coefficient by implementing the correction value SRi.

[0082] In this embodiment, the specific process of this step is as follows:

[0083] S2310: Determine whether the current data point is a concave point. If it is a concave point, proceed to the next judgment. The judgment criterion is that after N consecutive points decrease, N consecutive points increase.

[0084] In this step, specifically, as shown in the appendix Figure 7 Determine whether the current data point is a concave point, i.e., a valley point. If it is a concave point, proceed to the next judgment. The judgment criterion is that after N consecutive points decrease, N consecutive points increase (N can be changed according to actual conditions, default is 2 or 3 points). In the figure: Si - 3 > Si - 2 > Si - 1 > Si < Si + 1 < Si + 2 < Si + 3.

[0085] S2320: Determine whether the previous data interval of the current concave point is flat. If it is flat, record it and proceed to the next judgment.

[0086] In this step, specifically, the judgment criterion is the first - derivative method, as follows: Take the derivative of all data points within 8 seconds before this point, and the maximum value of the absolute value of the obtained derivative is not greater than the set threshold Threshold, that is, Max{S i}} < Threshold, then it indicates that the data before this sampling point is flat.

[0087] S2330: Determine the interval between the current flat concave point and the previous flat concave point. If the interval exceeds 5S, then adopt it as the latest fitting point, record it at the end of the N - order new array K i tail, and at the same time delete the first data of this array for fitting update calculation.

[0088] In this step, specifically, with a time interval of 5S, if the interval between the current flat concave point and the previous flat concave point exceeds 5S, then it is judged that the interval is far, and adopt it as the latest fitting point, record it at the end of the N - order new array Ki, and at the same time delete the first data of this array for fitting update calculation. If the interval time is less than 5S, then it is judged that the interval is near. A large number of flat concave points may appear at a certain moment, resulting in frequent fluctuations of the correction slope in a short period of time, leading to distortion of the baseline correction and affecting the spectral peak situation.

[0089] S240: Loop through the above steps 210 to 230 until the acquisition ends.

[0090] In the present invention, step S300 includes:

[0091] Ki and the corresponding original data Ak i Perform N-point linear fitting to obtain the latest fitting slope K, and store the previous interval value of K in K old .

[0092] In this step, specifically, K i And the corresponding original data A ki Perform N-point linear fitting to obtain the latest fitting slope K, and store the previous interval value of K in K old The updated slope is K old +K. Introducing K old This is to solve the residual error problem caused by the inadequate first-order correction, so as to make past corrections. The linear fitting slope calculation formula is as follows:

[0093]

[0094] During the operation of the chromatograph, changes in environmental conditions such as temperature and pressure will significantly affect the stability of the baseline. Therefore, this method monitors these environmental parameters in real time and dynamically adjusts the baseline correction parameters according to the environmental conditions. For example, under high temperature conditions, the temperature compensation parameters are adjusted to reduce the impact of detector temperature fluctuations on the signal; under high pressure conditions, the pressure compensation parameters are adjusted to reduce the baseline noise caused by column pressure changes.

[0095] In an embodiment of the present invention, after the above steps, new baseline data will be obtained, and the last step is to overwrite the last correction coefficient with the new baseline data to correct the baseline drift deviation. For example, if it is found that the baseline drift trend is accelerating, the correction coefficient may need to be increased; if it is found that the local data changes greatly after the concave point data is linearly fitted, the correction coefficient also needs to be adjusted accordingly. After the correction coefficient is updated, it is applied to the subsequent real-time data processing to continuously correct the baseline of the chromatograph to ensure the accuracy of the test results.

[0096] Also, see Figure 6 The embodiment of the present invention further provides a chromatograph baseline real-time automatic correction system, which is used to implement the method of the above embodiment, including:

[0097] An acquisition module 100, which is used to acquire real-time data during the chromatograph detection process and to perform real-time correction on the real-time data according to initial conditions;

[0098] The calculation module 200 calculates the first-order derivative of the real-time data based on the real-time correction process, obtains the correction coefficient by judging whether the data is located at a concave point, whether the data is flat, and the distance between the concave points, and calculates the baseline drift trend;

[0099] A fitting module 300, the fitting module 300 performs linear fitting on the concave point data based on the baseline drift trend;

[0100] The updating module 400, the output module 400 is used to update the correction coefficient in real time.

[0101] See also Figure 8 , Figure 8 A schematic diagram of an electronic device 20 that can implement an embodiment of the present invention is shown, and the electronic device is intended to represent various forms of control devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0102] The electronic device 20 includes at least one processor 21, and a memory connected to the at least one processor 21, such as a read-only memory (ROM) 22, a random access memory (RAM) 23, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 22 or the computer program loaded from the storage unit 28 to the random access memory (RAM) 13. In the RAM 23, various programs and data required for the operation of the electronic device 20 can also be stored. The processor 21, ROM 22 and RAM 23 are connected to each other through a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0103] A number of components in the electronic device 20 are connected to the I / O interface 25, including: an input unit 26, such as a keyboard, a mouse, etc.; an output unit 27, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 29, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 29 allows the electronic device 20 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0104] The processor 21 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 21 performs the various methods and processes described above.

[0105] In some embodiments, the method of the above embodiment may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 28. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 20 via the ROM 22 and / or the communication unit 29. When the computer program is loaded into the RAM 23 and executed by the processor 21, one or more steps in the method described above may be performed. Alternatively, in other embodiments, the processor 21 may be configured to perform the method of the above embodiment in any other appropriate manner (e.g., by means of firmware).

[0106] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0108] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and

[0110] Input from the user may be received in any form, including acoustic input, voice input, or tactile input.

[0111] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0112] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0113] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0114] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for real-time automatic correction of a chromatograph baseline, characterized in that: include: Acquire real-time data during the chromatograph detection process, and perform real-time correction on the real-time data according to initial conditions; Based on the real-time correction process, the first-order derivative of the real-time data is calculated, and the correction coefficient is obtained by judging whether the data is located at a concave point, whether the data is flat, and the spacing between concave points, and the baseline drift trend is calculated; Linear fit is performed on the concave point data based on the baseline drift trend; Update the correction coefficients in real time.

2. A method for real-time automatic correction of chromatograph baseline according to claim 1, characterized in that: The step of acquiring real-time data during the chromatograph detection process and correcting the real-time data in real time according to initial conditions comprises: Acquire real-time signal data from multiple sensors, including temperature, pressure, and chromatographic signals; Input real-time signal data into a multivariate adaptive filtering algorithm; Use filtering algorithms to identify and eliminate high-frequency noise; Output the processed clean signal.

3. A method for real-time automatic correction of chromatograph baseline according to claim 2, characterized in that: The first-order derivative of the real-time data is calculated in the real-time correction process, and the correction coefficient is obtained by judging whether the data is located at a concave point, whether the data is flat, and the distance between the concave points, and calculating the baseline drift trend, including: Real-time collection of data points uploaded by the chromatograph T i , S i ; The real-time sampling signal S i According to the initial coefficient, linear correction is performed to obtain the real-time correction value SR i , for each latest sampling point S i First, we need to remove the accumulated error of the last segmentation point, and then remove the interval baseline error, i.e. the drift of the shadow part, according to the slope prediction value obtained in the previous interval. After the two parts of error correction, we can get a straight baseline. By implementing the correction value SR i Determine the update time of the correction coefficient; The above steps are repeated until the collection is completed.

4. A method for real-time automatic correction of chromatograph baseline according to claim 3, characterized in that: The real-time correction value SR i The calculation formula is: SR i =S i -(S Ki -SR Ki )-(iK i )*(cK old +K).

5. A method for real-time automatic correction of chromatograph baseline according to claim 4, characterized in that: The T i is the current moment, S i is the detector signal value corresponding to the current moment.

6. A method for real-time automatic correction of chromatograph baseline according to claim 1, characterized in that: The correction value SR is implemented i Determine the update time of the correction coefficient, including: Determine whether the current data point is a concave point. If it is a concave point, proceed to the next step of judgment. The judgment criterion is that N consecutive points fall and then N consecutive points rise; Determine whether the previous data interval of the current concave point is flat, if so, record it for the next step of determination; Determine the interval between the current flat concave point and the previous flat concave point. If the interval exceeds 5S, adopt it as the latest fitting point and record it at the end of the N-order new array Ki. At the same time, delete the first data in the array and perform fitting update calculation; Ki and the corresponding original data Ak i Perform N-point linear fitting to obtain the latest fitting slope K, and store the previous interval value of K in K old .

7. A real-time automatic correction system for a chromatograph baseline, used to implement any of the methods described in claims 1 to 6, characterized in that: The system comprises: An acquisition module, the acquisition module is used to acquire real-time data during the chromatograph detection process, and to perform real-time correction on the real-time data according to initial conditions; A calculation module, wherein the calculation module calculates the first-order derivative of the real-time data based on the real-time correction process, obtains the correction coefficient by judging whether the data is located at a concave point, whether the data is flat, and the distance between the concave points, and calculates the baseline drift trend; A fitting module, wherein the fitting module performs linear fitting on the concave point data based on the baseline drift trend; The updating module is used to update the correction coefficient in real time.

8. An electronic device, characterized in that: The method comprises at least one processor, wherein the processor is communicatively connected to at least one memory, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 6.

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