A data drift correction method and system based on linear function

Abstract

The present invention discloses a data drift correction method and system based on linear function, which relates to the field of data processing technology, including: obtaining the measurement data of samples to be corrected based on inductively coupled plasma spectroscopy; obtaining standard substances according to the measurement data of samples to be corrected; constructing a standard curve using the standard substances; correcting the measurement data of samples to be corrected using the standard curve according to the standard substances to obtain calibration sample data; and detecting and obtaining drift correction results according to the calibration sample data. Under the condition of constructing a standard curve once and ensuring high precision and high accuracy of analytical data, the present invention realizes the determination of large batches of samples and improves work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data drift correction method and system based on linear functions. Background Art

[0002] The existing data correction method generally uses the standard curve method for direct correction, that is, when the instrument signal is unstable, the analysis data will have a large deviation, and it is necessary to repeatedly draw the standard curve for signal drift correction, and repeat the analysis of the last measured sample until the instrument signal is stable and the deviation is within the required range. However, due to the uncertainty of instrument signal drift, the above method has a large amount of repetitive work, and the number of samples that can be analyzed by measuring a standard curve is basically 10 to 20 samples, which has low work efficiency and is not suitable for large-scale sample analysis.

[0003] Therefore, there is an urgent need for a data drift correction method and system based on linear functions to solve the problems of low data analysis precision and accuracy in the prior art, as well as the inability to perform large-scale sample measurements under conditions of high-precision and high-accuracy analysis data, and the problems of data analysis stability and efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a data drift correction method and system based on linear function to achieve the determination of large batches of samples and improve work efficiency.

[0005] On the one hand, to achieve the above object, the present invention provides a data drift correction method based on a linear function, comprising the following steps:

[0006] S1. Obtaining measurement data of a sample to be calibrated based on inductively coupled plasma spectroscopy;

[0007] S2. Acquire a standard substance according to the measurement data of the sample to be calibrated;

[0008] S3, constructing a standard curve using the standard substance;

[0009] S4, correcting the measurement data of the sample to be corrected using the standard curve according to the standard substance to obtain calibration sample data;

[0010] S5. Perform detection according to the calibration sample data to obtain a drift correction result.

[0011] Optionally, obtaining the measurement data of the sample to be calibrated based on inductively coupled plasma spectroscopy includes:

[0012] S1-1, collecting the first sample to be calibrated;

[0013] S1-2, obtaining first sample data to be calibrated measured by inductively coupled plasma spectrometry according to the first sample to be calibrated;

[0014] S1-3, using the first sample data to be corrected to draw a scatter plot to obtain a correlation coefficient of the first sample data to be corrected;

[0015] S1-4, determining whether the correlation coefficient of the first sample data to be calibrated meets the correlation coefficient threshold, if so, obtaining the first sample data to be calibrated as the initial sample data to be calibrated, and executing S1-5, otherwise, executing S1-1;

[0016] S1-5. According to the initial sample data to be calibrated, outliers are removed and mean filtering is performed in sequence to obtain the sample data to be calibrated measured by the inductively coupled plasma spectrometry method.

[0017] Optionally, obtaining a standard substance according to the measurement data of the sample to be calibrated includes:

[0018] S2-1, using the measurement data of the sample to be calibrated to perform feature extraction to obtain characteristics of the measurement data of the sample to be calibrated;

[0019] S2-2, obtaining a first standard substance and characteristics of the first standard substance according to the measurement data of the sample to be calibrated and the corresponding historical measurement data of the sample to be calibrated;

[0020] S2-3, determining whether the characteristics of the measurement data of the sample to be calibrated are consistent with the characteristics of the first standard substance, if so, executing S2-4, otherwise, returning to S2-2;

[0021] S2-4, obtaining a concentration range of first standard substance data by measuring the first standard substance using inductively coupled plasma spectrometry;

[0022] S2-5, using the measurement data of the sample to be calibrated to obtain the concentration range of the measurement data of the sample to be calibrated;

[0023] S2-6. Determine whether the concentration range of the first standard substance data completely covers the concentration range of the measurement data of the sample to be calibrated. If so, use the first standard substance as the standard substance. Otherwise, execute S2-2.

[0024] Optionally, performing feature extraction using the measurement data of the sample to be calibrated to obtain characteristics of the measurement data of the sample to be calibrated includes:

[0025] S2-1-1, determine whether the corresponding historical measurement data of the sample to be calibrated completely contains the measurement data of the sample to be calibrated, if so, execute S2-1-2, otherwise, return to S1-5;

[0026] S2-1-2, obtaining initial data features of the measurement data of the sample to be calibrated according to the measurement data of the sample to be calibrated as first initial data features, second initial data features and third initial data features of the measurement data of the sample to be calibrated respectively;

[0027] S2-1-3, determining whether the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated are completely consistent, if so, obtaining the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated as the measurement data characteristics of the sample to be calibrated, otherwise, executing S1-2-4;

[0028] S2-1-4. Determine whether the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated are consistent with each other. If so, obtain the consistent initial data features of the measurement data of the sample to be calibrated as the characteristics of the measurement data of the sample to be calibrated. Otherwise, update the measurement data of the sample to be calibrated according to the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated, and return to S2-1-1.

[0029] Optionally, constructing a standard curve using the standard substance includes:

[0030] S3-1 obtaining initial standard material data by measuring the standard material using an inductively coupled plasma method;

[0031] S3-2, using the initial standard material data to sequentially remove outliers and perform mean filtering to obtain first standard material data, second standard material data, and third standard material data;

[0032] S3-3, performing linear fitting on the first standard substance data, the second standard substance data, and the third standard substance data to obtain a first standard curve and a second standard curve;

[0033] S3-4, determine whether the first standard curve is the same as the second standard curve, if so, use the first standard curve and the second standard curve as the standard curve and execute S4, otherwise, return to S3-1.

[0034] Optionally, correcting the sample measurement data to be corrected using the standard curve according to the standard substance to obtain calibration sample data includes:

[0035] S4-1, obtaining the standard substance as a monitoring sample;

[0036] S4-2, inserting the monitoring sample into the monitoring sample according to the measurement data of the sample to be calibrated by using a preset sequence number, measuring and analyzing in sequence to obtain first analysis data and second analysis data corresponding to the monitoring sample;

[0037] S4-3, obtaining first standard monitoring sample data and second standard monitoring sample data using the standard curve according to the first analysis data and the second analysis data corresponding to the monitoring sample;

[0038] S4-4, obtaining a data deviation using the first analysis data, the second analysis data, the first standard monitoring sample data, and the second standard monitoring sample data;

[0039] S4-5. Correct the measurement data of the sample to be corrected using a linear correction function according to the data deviation to obtain the calibration sample data.

[0040] Optionally, obtaining the data deviation using the first analysis data, the second analysis data, the first standard monitoring sample data, and the second standard monitoring sample data includes:

[0041] S4-4-1. Obtain a first data deviation according to the first analysis data and the first standard monitoring sample data;

[0042] S4-4-2. Obtain a second data deviation according to the second analysis data and the second standard monitoring sample data;

[0043] S4-4-3, determine whether the first data deviation and the second data deviation are both positive deviations, if so, take the first data deviation and the second data deviation as the data deviations, and directly execute S4-5, otherwise, execute S4-4-4;

[0044] S4-4-4. Determine whether the first data deviation and the second data deviation are both negative deviations. If so, use the first data deviation and the second data deviation as the data deviations and execute S4-5. Otherwise, return to S4-2.

[0045] Optionally, correcting the sample measurement data to be corrected using a linear correction function according to the data deviation to obtain the calibration sample data includes:

[0046] S4-5-1, determine whether the data deviation is a positive deviation, if so, obtain a decreasing function as a linear correction function, and execute S4-5-2, otherwise, obtain an increasing function as a linear correction function, and execute S4-5-2;

[0047] S4-5-2, using the measurement data of the sample to be calibrated to input the linear calibration function to obtain first calibration data and second calibration data;

[0048] S4-5-3, constructing a measurement data curve of the sample to be calibrated by linear fitting according to the first calibration data and the second calibration data;

[0049] S4-5-3, using the sample to be calibrated measurement data curve and the standard curve to obtain a curve relative error;

[0050] S4-5-4, obtaining a curve relative error threshold value according to the curve relative error and the corresponding historical curve relative error;

[0051] S4-5-5, judging whether the relative error of the curve meets the relative error threshold of the curve, if so, taking the first correction data and the second correction data as calibration sample data and directly executing S5, otherwise, returning to S4-2;

[0052] The decreasing function is a linear correction function when the data deviation is a positive deviation, and the increasing function is a linear correction function when the data deviation is a negative deviation.

[0053] Optionally, performing detection according to the calibration sample data to obtain a drift correction result includes:

[0054] S5-1, obtaining a correction data deviation according to the calibration sample data and the measurement data of the sample to be corrected;

[0055] S5-2, determining whether the correction data deviation is positively correlated with the first data deviation and the second data deviation, if so, obtaining the calibration sample data as the first correction sample data, otherwise, executing S5-3;

[0056] S5-3, determining whether the correction data deviation is a negative deviation from the first data deviation and the second data deviation, if so, obtaining the calibration sample data as the first correction sample data, otherwise, executing S5-4;

[0057] S5-4, obtaining the certified value of the standard substance according to the standard substance and the corresponding historical standard substance;

[0058] S5-5. Determine whether the difference between the first calibration sample data and the certified value of the standard substance meets the target threshold value. If so, obtain the first calibration sample data as the drift correction result. Otherwise, return to S4-2.

[0059] On the other hand, to achieve the above-mentioned object, the present invention provides a data drift correction system based on a linear function, comprising: a first data acquisition module, a second data acquisition module, a curve construction module, a data correction module and a data detection module;

[0060] The first data acquisition module is used to acquire the measurement data of the sample to be calibrated based on inductively coupled plasma spectroscopy;

[0061] The second data acquisition module is used to acquire the standard substance according to the measurement data of the sample to be calibrated;

[0062] The curve construction module is used to construct a standard curve according to the standard substance;

[0063] The data correction module is used to correct the measurement data of the sample to be corrected using the standard curve according to the standard substance to obtain calibration sample data;

[0064] The data detection module is used to perform detection according to the calibration sample data to obtain a drift correction result.

[0065] Compared with the closest prior art, the present invention has the following beneficial effects:

[0066] The present invention can realize stable repetition of analysis data and eliminate analysis errors caused by instrument signal drift by constructing a linear correction function. The influence of instrument signal drift can be ignored in the analysis process. By making a standard curve once, more than 100 samples can be analyzed, which greatly improves work efficiency. Moreover, by monitoring quality control samples, the stability of analysis data can be improved and the correctness of analysis data can be guaranteed. The present invention constructs a linear correction function to correct inductively coupled plasma spectrometry measurement data and eliminates positive or negative deviations caused by signal drift. At the same time, under the condition of constructing a standard curve once and ensuring high precision and high accuracy of analysis data, the measurement of large batches of samples is realized, thereby improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 This is a flow chart of a data drift correction method based on a linear function according to an embodiment of the present invention;

[0069] Figure 2 The structure diagram of a data drift correction system based on a linear function according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the specific embodiments of the present invention and the corresponding drawings. 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.

[0071] The terms used in the embodiments of the present invention are only used to explain the specific embodiments of the present invention and are not intended to limit the present invention.

[0072] The commonly used calibration methods for instrumental analysis include internal standard calibration and external standard calibration. Internal standard calibration generally requires that the content of the internal standard element used is much higher than the content of the existing elements in the sample, and the calibration wavelength needs to be close to the wavelength of the element being analyzed in order to achieve accurate calibration. The introduction of internal standard elements may also introduce background concentrations of other elements, causing analysis errors; on the other hand, when multiple elements are measured simultaneously, different internal standard elements may need to be used for online calibration, which seriously affects work efficiency. External standard calibration generally draws a standard curve based on the concentration of a known standard solution to detect the concentration of an unknown sample. If the instrument signal drifts, it is necessary to repeatedly draw a standard curve for repeated detection until the instrument signal is stable, which results in low work efficiency.

[0073] Therefore, the present invention constructs a linear correction function in an Excel table to correct the inductively coupled plasma optical / mass spectrometry data and eliminate the positive or negative deviation caused by signal drift. Under the condition of constructing a standard curve once, under the condition of ensuring high precision and high accuracy of the analysis data, the determination of large batches of samples is achieved and the work efficiency is improved.

[0074] like Figure 1 As shown, the present invention provides a data drift correction method based on a linear function, comprising the following steps:

[0075] S1. Obtaining measurement data of a sample to be calibrated based on inductively coupled plasma spectroscopy;

[0076] S2. Obtaining a standard substance according to the measurement data of the sample to be calibrated;

[0077] S3, constructing a standard curve using standard substances;

[0078] S4, correcting the measurement data of the sample to be corrected using the standard curve according to the standard substance to obtain calibration sample data;

[0079] S5. Perform detection according to the calibration sample data to obtain a drift correction result.

[0080] S1 specifically includes:

[0081] S1-1, collecting the first sample to be calibrated;

[0082] S1-2, obtaining first sample data to be calibrated measured by inductively coupled plasma spectrometry according to the first sample to be calibrated;

[0083] S1-3, using the first sample data to be corrected to draw a scatter plot to obtain a correlation coefficient of the first sample data to be corrected;

[0084] S1-4, determining whether the correlation coefficient of the first sample data to be calibrated meets the correlation coefficient threshold, if so, obtaining the first sample data to be calibrated as the initial sample data to be calibrated, and executing S1-5, otherwise, executing S1-1, wherein the value range of the correlation coefficient is between -1 and 1, and the correlation coefficient threshold is set to be greater than 0.98 in this embodiment;

[0085] S1-5. According to the initial sample data to be calibrated, outliers are removed and mean filtering is performed in sequence to obtain the sample data to be calibrated measured by inductively coupled plasma spectrometry.

[0086] S2 specifically includes:

[0087] S2-1, extracting features from the measurement data of the sample to be calibrated to obtain characteristics of the measurement data of the sample to be calibrated;

[0088] S2-2, obtaining a first standard substance and characteristics of the first standard substance according to the measurement data of the sample to be calibrated and the historical measurement data of the sample to be calibrated;

[0089] S2-3, determine whether the characteristics of the measured data of the sample to be calibrated are consistent with the characteristics of the first standard substance, if so, execute S2-4, otherwise, return to S2-2;

[0090] S2-4, obtaining a concentration range of the first standard substance data by measuring the first standard substance using inductively coupled plasma spectrometry;

[0091] S2-5, using the measurement data of the sample to be calibrated to obtain the concentration range of the measurement data of the sample to be calibrated;

[0092] S2-6. Determine whether the concentration range of the first standard substance data completely covers the concentration range of the sample measurement data to be calibrated. If so, use the first standard substance as the standard substance. Otherwise, execute S2-2.

[0093] S2-1 specifically includes:

[0094] S2-1-1, determine whether the corresponding historical sample measurement data to be calibrated completely contains the sample measurement data to be calibrated, if so, execute S2-1-2, otherwise, return to S1-5;

[0095] S2-1-2, obtaining initial data features of the measurement data of the sample to be calibrated according to the measurement data of the sample to be calibrated as first initial data features, second initial data features and third initial data features of the measurement data of the sample to be calibrated respectively;

[0096] S2-1-3, determine whether the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated are completely consistent, if so, obtain the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated as the measurement data characteristics of the sample to be calibrated, otherwise, execute S1-2-4;

[0097] S2-1-4. Determine whether the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated are consistent with each other. If so, obtain the consistent initial data features of the measurement data of the sample to be calibrated as the characteristics of the measurement data of the sample to be calibrated. Otherwise, update the measurement data of the sample to be calibrated according to the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated, and return to S2-1-1.

[0098] S3 specifically includes:

[0099] S3-1 Obtaining initial standard material data by measuring the standard material using the inductively coupled plasma method;

[0100] S3-2, using the initial standard material data to sequentially remove outliers and perform mean filtering to obtain first standard material data, second standard material data, and third standard material data;

[0101] S3-3, performing linear fitting on the first standard material data, the second standard material data, and the third standard material data to obtain a first standard curve and a second standard curve;

[0102] S3-4, determine whether the first standard curve is the same as the second standard curve. If so, use the first standard curve and the second standard curve as standard curves and execute S4. Otherwise, return to S3-1.

[0103] S4 specifically includes:

[0104] S4-1. Obtain standard substances as monitoring samples;

[0105] S4-2, inserting monitoring samples according to the measurement data of the sample to be calibrated using a preset sequence number, measuring and analyzing them in sequence, and obtaining first analysis data and second analysis data corresponding to the monitoring samples;

[0106] S4-3, obtaining first standard monitoring sample data and second standard monitoring sample data using a standard curve according to the first analysis data and the second analysis data corresponding to the monitoring sample;

[0107] S4-4, obtaining a data deviation using the first analysis data, the second analysis data, the first standard monitoring sample data, and the second standard monitoring sample data;

[0108] S4-5. Correct the measurement data of the sample to be corrected using a linear correction function according to the data deviation to obtain calibration sample data.

[0109] S4-4 specifically includes:

[0110] S4-4-1. Obtaining a first data deviation according to first analysis data and first standard monitoring sample data;

[0111] S4-4-2, obtaining a second data deviation according to the second analysis data and the second standard monitoring sample data;

[0112] S4-4-3, determine whether the first data deviation and the second data deviation are both positive deviations, if so, take the first data deviation and the second data deviation as data deviations and directly execute S4-5, otherwise, execute S4-4-4;

[0113] S4-4-4, determine whether the first data deviation and the second data deviation are both negative deviations, if so, take the first data deviation and the second data deviation as data deviations and execute S4-5, otherwise, return to S4-2.

[0114] S4-5 specifically includes:

[0115] S4-5-1, determine whether the data deviation is a positive deviation, if so, obtain a decreasing function as a linear correction function, and execute S4-5-2, otherwise, obtain an increasing function as a linear correction function, and execute S4-5-2, wherein an increasing function and a decreasing function are obtained as linear correction functions according to the sample data to be corrected, the first analysis data, and the second analysis data, and the decreasing function is used as a linear correction function. and the increment function They are:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] In the formula, is the sample measurement data to be calibrated, is the sequence number of the sample measurement data to be calibrated, , is the empirical coefficient, The first analysis data corresponding to the monitoring sample, To monitor the second analysis data corresponding to the sample, is the sequence number of the first analysis data corresponding to the monitored sample, is the sequence number of the second analysis data corresponding to the monitoring sample, is the certified value of the standard substance, Not multiplied by The previous and corrected data, i.e. the data after the preliminary correction of the measured data of the sample to be corrected;

[0122] S4-5-2, using the measurement data of the sample to be calibrated to input the linear calibration function to obtain the first calibration data and the second calibration data;

[0123] S4-5-3, constructing a measurement data curve of the sample to be calibrated by linear fitting according to the first calibration data and the second calibration data;

[0124] S4-5-3. Use the sample to be calibrated measurement data curve and the standard curve to obtain the relative error of the curve, specifically:

[0125] According to the standard substance, the sample data curve to be calibrated and the standard curve are used to obtain the calibrated data value and the corresponding value of the standard curve, and then the relative error of the curve is calculated. The formula is:

[0126] ;

[0127] S4-5-4, obtaining a curve relative error threshold according to the curve relative error and the corresponding historical curve relative error. In this embodiment, the curve relative error threshold is set to be less than 0.05;

[0128] S4-5-5, judging whether the relative error of the curve meets the relative error threshold of the curve, if so, taking the first correction data and the second correction data as calibration sample data and directly executing S5, otherwise, returning to S4-2;

[0129] The decreasing function is a linear correction function when the data deviation is a positive deviation, and the increasing function is a linear correction function when the data deviation is a negative deviation.

[0130] S5 specifically includes:

[0131] S5-1, obtaining a correction data deviation according to the calibration sample data and the measurement data of the sample to be corrected;

[0132] S5-2, determining whether the correction data deviation, the first data deviation and the second data deviation are all positive deviations, if so, obtaining the calibration sample data as the first correction sample data, otherwise, executing S5-3;

[0133] S5-3, determining whether the correction data deviation and the first data deviation and the second data deviation are all negative deviations, if so, obtaining the calibration sample data as the first correction sample data, otherwise, executing S5-4;

[0134] S5-4. Obtain the certified value of the standard material based on the standard material and the corresponding historical standard material;

[0135] S5-5. Determine whether the difference between the first calibration sample data and the certified value of the standard substance meets the target threshold. If so, obtain the first calibration sample data as the drift correction result. Otherwise, return to S4-2. In this embodiment, the target threshold is set to be less than 1.

[0136] like Figure 2 As shown, the present invention provides a data drift correction system based on a linear function, comprising: a first data acquisition module, a second data acquisition module, a curve construction module, a data correction module and a data detection module;

[0137] A first data acquisition module, used for acquiring measurement data of a sample to be calibrated based on inductively coupled plasma spectroscopy;

[0138] A second data acquisition module is used to acquire a standard substance according to the measurement data of the sample to be calibrated;

[0139] A curve building module, used to build a standard curve based on standard substances;

[0140] A data correction module, used to correct the measurement data of the sample to be corrected using a standard curve according to the standard substance to obtain calibration sample data;

[0141] The data detection module is used to detect and obtain drift correction results based on the calibration sample data.

[0142] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A data drift correction method based on linear function, characterized in that: The specific steps include: S1. Obtaining measurement data of a sample to be calibrated based on inductively coupled plasma spectroscopy; S2. Acquire a standard substance according to the measurement data of the sample to be calibrated; Acquiring a standard substance according to the measurement data of the sample to be calibrated comprises: S2-1, using the measurement data of the sample to be calibrated to perform feature extraction to obtain characteristics of the measurement data of the sample to be calibrated; S2-2, obtaining a first standard substance and characteristics of the first standard substance according to the measurement data of the sample to be calibrated and the corresponding historical measurement data of the sample to be calibrated; S2-3, determining whether the characteristics of the measurement data of the sample to be calibrated are consistent with the characteristics of the first standard substance, if so, executing S2-4, otherwise, returning to S2-2; S2-4, obtaining a concentration range of first standard substance data by measuring the first standard substance using inductively coupled plasma spectrometry; S2-5, using the measurement data of the sample to be calibrated to obtain the concentration range of the measurement data of the sample to be calibrated; S2-6, determining whether the concentration range of the first standard substance data completely covers the concentration range of the measurement data of the sample to be calibrated, if so, using the first standard substance as the standard substance, otherwise, executing S2-2; S3, constructing a standard curve using the standard substance; S4, correcting the measurement data of the sample to be corrected using the standard curve according to the standard substance to obtain calibration sample data; Correcting the sample measurement data to be corrected using the standard curve according to the standard substance to obtain calibration sample data includes: S4-1, obtaining the standard substance as a monitoring sample; S4-2, inserting the monitoring sample into the monitoring sample using a preset sequence number according to the measurement data of the sample to be calibrated, measuring and analyzing in sequence, and obtaining first analysis data and second analysis data corresponding to the monitoring sample; S4-3, obtaining first standard monitoring sample data and second standard monitoring sample data using the standard curve according to the first analysis data and the second analysis data corresponding to the monitoring sample; S4-4, obtaining a data deviation using the first analysis data, the second analysis data, the first standard monitoring sample data, and the second standard monitoring sample data; S4-5, correcting the sample measurement data to be corrected using a linear correction function according to the data deviation to obtain the calibration sample data; Obtaining data deviation using the first analysis data, the second analysis data, the first standard monitoring sample data, and the second standard monitoring sample data includes: S4-4-1. Obtain a first data deviation according to the first analysis data and the first standard monitoring sample data; S4-4-2. Obtain a second data deviation according to the second analysis data and the second standard monitoring sample data; S4-4-3, determine whether the first data deviation and the second data deviation are both positive deviations, if so, take the first data deviation and the second data deviation as the data deviations, and directly execute S4-5, otherwise, execute S4-4-4; S4-4-4, determining whether the first data deviation and the second data deviation are both negative deviations, if so, taking the first data deviation and the second data deviation as the data deviations, and executing S4-5, otherwise, returning to S4-2; Correcting the sample measurement data to be corrected by using a linear correction function according to the data deviation to obtain the calibration sample data comprises: S4-5-1, determine whether the data deviation is a positive deviation, if so, obtain a decreasing function as a linear correction function, and execute S4-5-2, otherwise, obtain an increasing function as a linear correction function, and execute S4-5-2; S4-5-2, using the measurement data of the sample to be calibrated to input the linear calibration function to obtain first calibration data and second calibration data; S4-5-3, constructing a measurement data curve of the sample to be calibrated by linear fitting according to the first calibration data and the second calibration data; S4-5-3, using the sample to be calibrated measurement data curve and the standard curve to obtain a curve relative error; S4-5-4, obtaining a curve relative error threshold value according to the curve relative error and the corresponding historical curve relative error; S4-5-5, judging whether the relative error of the curve meets the relative error threshold of the curve, if so, taking the first correction data and the second correction data as calibration sample data and directly executing S5, otherwise, returning to S4-2; Wherein, the decreasing function is a linear correction function when the data deviation is a positive deviation, and the increasing function is a linear correction function when the data deviation is a negative deviation; The decreasing function M 递增 and the increasing function M 递减 They are: Among them, C i is the sample measurement data to be calibrated, A i is the sequence number of the sample measurement data to be calibrated, K1 and K2 are empirical coefficients, C i1 is the first analysis data corresponding to the monitoring sample, C i2 A is the second analysis data corresponding to the monitoring sample. i1 A is the sequence number of the first analysis data corresponding to the monitored sample, i2 is the sequence number of the second analysis data corresponding to the monitoring sample, C 认定 is the certified value of the standard substance, M 初步 The data after preliminary correction of the measured data of the sample to be corrected; S5, performing detection according to the calibration sample data to obtain a drift correction result; Detecting and obtaining drift correction results according to the calibration sample data includes: S5-1, obtaining a correction data deviation according to the calibration sample data and the measurement data of the sample to be corrected; S5-2, determining whether the correction data deviation is positively correlated with the first data deviation and the second data deviation, if so, obtaining the calibration sample data as the first correction sample data, otherwise, executing S5-3; S5-3, determining whether the correction data deviation is a negative deviation from the first data deviation and the second data deviation, if so, obtaining the calibration sample data as the first correction sample data, otherwise, executing S5-4; S5-4, obtaining the certified value of the standard substance according to the standard substance and the corresponding historical standard substance; S5-5. Determine whether the difference between the first calibration sample data and the certified value of the standard substance meets the target threshold value. If so, obtain the first calibration sample data as the drift correction result. Otherwise, return to S4-2.

2. The data drift correction method based on linear function according to claim 1, characterized in that: The data of the sample to be calibrated based on inductively coupled plasma spectroscopy include: S1-1, collecting the first sample to be calibrated; S1-2, obtaining first sample data to be calibrated measured by inductively coupled plasma spectrometry according to the first sample to be calibrated; S1-3, using the first sample data to be corrected to draw a scatter plot to obtain a correlation coefficient of the first sample data to be corrected; S1-4, determining whether the correlation coefficient of the first sample data to be calibrated meets the correlation coefficient threshold, if so, obtaining the first sample data to be calibrated as the initial sample data to be calibrated, and executing S1-5, otherwise, executing S1-1; S1-5. According to the initial sample data to be calibrated, outliers are removed and mean filtering is performed in sequence to obtain the sample measurement data to be calibrated measured by the inductively coupled plasma spectrometry method.

3. The data drift correction method based on linear function according to claim 2, characterized in that: Using the measurement data of the sample to be calibrated to extract features to obtain the characteristics of the measurement data of the sample to be calibrated includes: S2-1-1, determine whether the corresponding historical measurement data of the sample to be calibrated completely contains the measurement data of the sample to be calibrated, if so, execute S2-1-2, otherwise, return to S1-5; S2-1-2, obtaining initial data features of the measurement data of the sample to be calibrated according to the measurement data of the sample to be calibrated as first initial data features, second initial data features and third initial data features of the measurement data of the sample to be calibrated respectively; S2-1-3, determining whether the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated are completely consistent, if so, obtaining the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated as the measurement data characteristics of the sample to be calibrated, otherwise, executing S1-2-4; S2-1-4. Determine whether the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated are consistent with each other. If so, obtain the consistent initial data features of the measurement data of the sample to be calibrated as the characteristics of the measurement data of the sample to be calibrated. Otherwise, update the measurement data of the sample to be calibrated according to the first initial data feature, the second initial data feature and the third initial data feature of the measurement data of the sample to be calibrated, and return to S2-1-1.

4. The data drift correction method based on linear function according to claim 1, characterized in that: Constructing a standard curve using the standard substance includes: S3-1 obtaining initial standard material data by measuring the standard material using an inductively coupled plasma method; S3-2, using the initial standard material data to sequentially remove outliers and perform mean filtering to obtain first standard material data, second standard material data, and third standard material data; S3-3, performing linear fitting on the first standard substance data, the second standard substance data, and the third standard substance data to obtain a first standard curve and a second standard curve; S3-4, determine whether the first standard curve is the same as the second standard curve, if so, use the first standard curve and the second standard curve as the standard curve and execute S4, otherwise, return to S3-1.

5. A data drift correction system based on a linear function, implementing the method according to any one of claims 1 to 4, characterized in that: include: A first data acquisition module, a second data acquisition module, a curve construction module, a data correction module and a data detection module; The first data acquisition module is used to acquire the measurement data of the sample to be calibrated based on inductively coupled plasma spectroscopy; The second data acquisition module is used to acquire the standard substance according to the measurement data of the sample to be calibrated; The curve construction module is used to construct a standard curve according to the standard substance; The data correction module is used to correct the measurement data of the sample to be corrected using the standard curve according to the standard substance to obtain calibration sample data; The data detection module is used to perform detection according to the calibration sample data to obtain a drift correction result.

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