Mass spectrometer calibration method, system, intelligent terminal and storage medium

Abstract

This invention relates to the field of materials testing technology, and discloses a mass spectrometer calibration method, system, intelligent terminal, and storage medium. The method includes: subtracting background signal from the raw mass spectrometry data to obtain standard mass spectrometry data; performing dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, thereby constructing a multidimensional feature vector; performing weighted similarity evaluation on the multidimensional feature vector and a preset standard feature library to obtain a similarity value; performing threshold screening on the similarity value, and comparing the screened candidate feature vectors with the corresponding standard feature vectors in the standard feature library using multidimensional deviation to obtain a systematic deviation parameter; performing non-uniform interpolation fitting on the systematic deviation parameter to obtain a continuous calibration curve; performing point-by-point correction on the standard mass spectrometry data, and outputting the calibrated mass spectrometry data. This invention can improve the calibration efficiency of mass spectrometers.

Description

Technical Field

[0001] This invention relates to the field of materials testing technology, and in particular to a mass spectrometer calibration method, system, smart terminal, and storage medium. Background Technology

[0002] In the field of materials testing technology, mass spectrometers are core testing equipment, and the accuracy of their detection data directly affects the reliability of analytical results. Calibration is crucial for ensuring detection accuracy. However, existing mass spectrometer calibration methods do not thoroughly process the background signal in the raw mass spectrometry data during the data preprocessing stage, making them susceptible to baseline noise interference. This results in insufficient accuracy in subsequent feature extraction, making it difficult to accurately capture the true characteristic peak information of the mass spectrometer. Furthermore, traditional calibration methods often use fixed thresholds for peak detection, which cannot adapt to differences in intensity distribution within different mass-to-charge ratio windows. This can easily lead to the omission or misjudgment of characteristic peaks, creating potential problems for subsequent bias analysis.

[0003] Existing technologies have significant shortcomings in deviation assessment and calibration curve construction, making it difficult to achieve efficient and accurate calibration. Traditional methods typically use uniform interpolation to generate calibration curves, failing to consider the differences in data distribution density of systematic deviation parameters. This leads to large fitting deviations in sparse data regions, resulting in insufficient continuity and accuracy of the calibration curves. Furthermore, existing calibration methods lack differentiated weighting of feature dimensions during feature matching and do not incorporate targeted evaluation of the signal-to-noise ratio and intensity stability of feature peaks. This results in inaccurate similarity judgments, affecting the calculation accuracy of systematic deviation parameters and ultimately leading to low calibration efficiency of mass spectrometers, failing to meet the practical needs of high-precision material detection. Summary of the Invention

[0004] This disclosure provides a mass spectrometer calibration method, system, smart terminal, and storage medium.

[0005] In a first aspect, this disclosure provides a mass spectrometer calibration method, including: S1. During the standard sample testing cycle of the mass spectrometer, the background signal of the raw mass spectrometry data of the mass spectrometer is subtracted to obtain the standard mass spectrometry data of the mass spectrometer. S2. Perform dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, and construct the multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks. S3. Based on the signal-to-noise ratio and intensity stability of the feature peaks, perform a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library to obtain the similarity value of the multidimensional feature vector. S4. Threshold screening is performed on the similarity values, and the selected candidate feature vectors are compared with the corresponding standard feature vectors in the standard feature library to obtain the systematic deviation parameters of the mass spectrometer. S5. Based on the data distribution density of the systematic deviation parameter, perform non-uniform interpolation fitting on the systematic deviation parameter to obtain the continuous calibration curve of the mass spectrometer. S6. Based on the continuous calibration curve, the standard mass spectrometry data is corrected point by point, and the calibrated mass spectrometry data of the mass spectrometer is output.

[0006] In a preferred embodiment, the step of subtracting the background signal from the raw mass spectrometry data of the mass spectrometer during the standard sample testing cycle of the mass spectrometer to obtain the standard mass spectrometry data of the mass spectrometer includes: During the standard test cycle of the mass spectrometer, the background spectrum of the mass spectrometer under the condition of no standard injection is acquired, and the background spectrum is determined as the baseline signal; The raw mass spectrometry data of the mass spectrometer is obtained, and the mass-to-charge ratio coordinate intensity value corresponding to the baseline signal in the raw mass spectrometry data is subtracted point by point to obtain the background-subtracted mass spectrometry data of the mass spectrometer. The intensity sequence of the mass spectrometry data after background subtraction is subjected to time-domain filtering to obtain the smoothed mass spectrometry data of the mass spectrometer. Based on the total ion current intensity of the sampling points within the standard test cycle, the intensity value of the smoothed mass spectrometry data is linearly scaled to obtain the standard mass spectrometry data of the mass spectrometer.

[0007] In a preferred embodiment, the step of performing dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, and constructing a multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks, includes: Based on the intensity distribution of the standard mass spectrometry data within the current mass-to-charge ratio window, the dynamic detection threshold of the current mass-to-charge ratio window is determined; Mass spectrometry peaks in the standard mass spectrometry data whose intensity values ​​exceed the dynamic detection threshold and which are local maxima within the current mass-to-charge ratio window are selected as candidate characteristic peaks of the mass spectrometer. The peak intensity of the candidate characteristic peaks is compared with the noise level within the current mass-to-charge ratio window to perform signal-to-noise ratio threshold filtering, thereby obtaining the characteristic peaks of the mass spectrometer; Extract the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks to construct the mass-to-charge ratio characteristic sequence and relative intensity characteristic sequence of the mass spectrometer; Based on the detection order of the characteristic peaks, the mass-to-charge ratio characteristic sequence and the relative intensity characteristic sequence are structurally recombined to obtain the multidimensional feature vector of the mass spectrometer.

[0008] In a preferred embodiment, the step of performing a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library based on the signal-to-noise ratio and intensity stability of the feature peaks to obtain a similarity value for the multidimensional feature vector includes: The multidimensional feature vector is aligned element-wise with the candidate standard feature vectors in the preset standard feature library to establish the feature dimension correspondence of the mass spectrometer. Based on the signal-to-noise ratio of the feature peaks, assign signal-to-noise ratio weight coefficients to the mass-to-charge ratio feature dimension in the multidimensional feature vector; Based on the intensity stability of the feature peak, intensity stability weight coefficients are assigned to the relative intensity feature dimensions in the multidimensional feature vector; By combining the signal-to-noise ratio weight coefficient and the intensity stability weight coefficient, the multidimensional feature vector and the candidate standard feature vector are weighted and normalized to obtain the weighted inner product result and the weighted magnitude parameter of the multidimensional feature vector; Based on the weighted inner product result and the weighted modulus parameter, the similarity value of the multidimensional feature vector relative to the candidate standard feature vector is evaluated, wherein the similarity value is calculated using the following formula: ; In the formula, This represents the similarity value. This represents the total number of dimensions of the multidimensional feature vector. This indicates that the multidimensional feature vector is at the th... The preset comprehensive weighting coefficients for each dimension This indicates that the multidimensional feature vector is at the th... Feature values ​​in each dimension The candidate standard feature vector is represented in the th... Feature values ​​in each dimension.

[0009] In a preferred embodiment, the step of thresholding the similarity values ​​and comparing the selected candidate feature vectors with the corresponding standard feature vectors in the standard feature library using multidimensional deviation analysis to obtain the systematic deviation parameters of the mass spectrometer includes: By selecting multidimensional feature vectors whose similarity values ​​are better than a preset quality qualification threshold, candidate feature vectors of the mass spectrometer are obtained. The candidate feature vectors are mapped to the standard feature library to obtain the standard feature vectors corresponding to the candidate feature vectors; In terms of mass-to-charge ratio and relative intensity, the candidate feature vector and the standard feature vector are subjected to element-level deviation determination to obtain the mass-to-charge ratio deviation component and intensity deviation component of the mass spectrometer. By performing cross-modal statistical induction on the mass-to-charge ratio deviation component and the intensity deviation component, the systematic deviation parameters of the mass spectrometer are obtained.

[0010] In a preferred embodiment, the step of performing non-uniform interpolation fitting on the systematic deviation parameter based on the data distribution density of the systematic deviation parameter to obtain the continuous calibration curve of the mass spectrometer includes: Based on the frequency of occurrence and degree of clustering of the systematic deviation parameters on the mass-to-charge ratio coordinate, a density spectrum of the systematic deviation parameters is generated; Based on the local peak characteristics of the density spectrum, the range of the mass-to-charge ratio coordinates is divided into a data-dense region and a data-sparse region. Based on the division results of the data-dense region and the data-sparse region, non-uniform interpolation node allocation is performed on the mass-to-charge ratio coordinates. Based on the allocation results, using the systematic deviation parameter as known data points and the interpolation node as the position to be determined, the systematic deviation parameter is asymptotically fitted to obtain the continuous calibration curve of the mass spectrometer.

[0011] In a preferred embodiment, the step of performing point-by-point correction on the standard mass spectrometry data based on the continuous calibration curve and outputting the calibrated mass spectrometry data of the mass spectrometer includes: The coordinate offset distribution of the continuous calibration curve in the mass-to-charge ratio dimension and the scale factor distribution in the intensity dimension are extracted to obtain the calibration parameters of the standard mass spectrometry data. The mass-to-charge ratio coordinates of the mass spectral peaks in the standard mass spectrometry data are corrected for coordinate offset distribution to obtain the mass-to-charge ratio corrected data of the mass spectrometer. The intensity values ​​of the mass spectrometry peaks in the standard mass spectrometry data are calibrated with the response values ​​of the scaling factor distribution to obtain the intensity-corrected data of the mass spectrometer. Based on the original mass spectrum peak correspondence, the mass-to-charge ratio corrected data and the intensity corrected data are structurally reconstructed to obtain the calibrated mass spectrometry data of the mass spectrometer.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention effectively eliminates noise interference and ensures the purity of standard mass spectrometry data by preprocessing the background signal during the standard sample testing period, including background signal subtraction, time-domain filtering, and linear scaling. It uses dynamic threshold peak detection to accurately capture characteristic peaks, and combines weighted similarity evaluation of signal-to-noise ratio and intensity stability to achieve accurate matching between multidimensional feature vectors and standard feature libraries. Then, it obtains accurate systematic deviation parameters through multidimensional deviation comparison, and generates high-fidelity continuous calibration curves based on non-uniform interpolation fitting. The entire process from data preprocessing to curve fitting ensures calibration accuracy.

[0013] 2. This invention achieves automated operation of each step through structured processing and intelligent algorithms, requiring minimal manual intervention; non-uniform interpolation fitting optimizes node allocation based on data distribution density, avoiding invalid calculations; point-by-point correction directly applies to standard mass spectrometry data, rapidly outputting calibration results, significantly shortening the calibration cycle, and meeting the practical needs of efficient calibration in the field of materials testing. Attached Figure Description

[0014] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings: Figure 1 A flowchart illustrating the process of a mass spectrometer calibration method according to Embodiment 1 of the present invention is shown. Figure 2 The diagram shows a functional block diagram of a mass spectrometer calibration system according to Embodiment 2 of the present invention.

[0015] Figure 3 The diagram shows the structural composition of a smart terminal that implements the mass spectrometer calibration method according to Embodiment 3 of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0017] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0018] Example 1 Figure 1 This is a schematic flowchart illustrating a mass spectrometer calibration method provided in an embodiment of this disclosure. Figure 1 As shown, a mass spectrometer calibration method includes: S1. During the standard sample testing cycle of the mass spectrometer, the background signal of the raw mass spectrometry data of the mass spectrometer is subtracted to obtain the standard mass spectrometry data of the mass spectrometer. In this embodiment of the invention, the step of subtracting the background signal from the raw mass spectrometry data of the mass spectrometer during the standard sample testing cycle of the mass spectrometer to obtain the standard mass spectrometry data of the mass spectrometer includes: During the standard test cycle of the mass spectrometer, the background spectrum of the mass spectrometer under the condition of no standard injection is acquired, and the background spectrum is determined as the baseline signal; The raw mass spectrometry data of the mass spectrometer is obtained, and the mass-to-charge ratio coordinate intensity value corresponding to the baseline signal in the raw mass spectrometry data is subtracted point by point to obtain the background-subtracted mass spectrometry data of the mass spectrometer. The intensity sequence of the mass spectrometry data after background subtraction is subjected to time-domain filtering to obtain the smoothed mass spectrometry data of the mass spectrometer. Based on the total ion current intensity of the sampling points within the standard test cycle, the intensity value of the smoothed mass spectrometry data is linearly scaled to obtain the standard mass spectrometry data of the mass spectrometer.

[0019] During the standard test cycle of the mass spectrometer, the mass spectrometer is kept in a state of no standard injection. The spectrum acquisition program of the mass spectrometer is started, and all signal spectra detected by the mass spectrometer in this state are continuously collected. The collected spectrum is directly determined as the baseline signal.

[0020] The raw mass spectrometry data generated after injecting the standard sample during the standard sample testing cycle is obtained. According to the one-to-one correspondence of mass-to-charge ratio coordinates, the intensity value corresponding to each mass-to-charge ratio coordinate in the raw mass spectrometry data is extracted one by one. At the same time, the intensity value corresponding to the same mass-to-charge ratio coordinate in the baseline signal is extracted. The intensity value of the corresponding baseline signal is subtracted from the intensity value of the mass-to-charge ratio coordinate in the raw mass spectrometry data to complete the point-by-point subtraction operation of the raw mass spectrometry data. The data set obtained after subtracting all mass-to-charge ratio coordinates is determined as the mass spectrometry data after background subtraction of the mass spectrometer.

[0021] Temporal filtering was performed on the complete intensity sequence contained in the mass spectrometry data after background subtraction. Multiple adjacent intensity values ​​continuously distributed in the intensity sequence were selected as processing units. The intensity values ​​in each processing unit were integrated and analyzed to identify and remove abnormal intensity values ​​that deviated from the overall trend of the intensity sequence. Intensity values ​​that conformed to the overall pattern were retained. Then, the intensity values ​​of each unit after processing were connected in the original order to form a continuous and stable intensity sequence, and finally the smoothed mass spectrometry data of the mass spectrometer was obtained.

[0022] The total ion current intensity corresponding to all sampling points within the standard sample testing period is statistically analyzed. Based on the actual situation of the total ion current intensity, a uniform adjustment ratio is determined. The intensity value corresponding to each mass-to-charge ratio coordinate in the smoothed mass spectrometry data is adjusted according to this ratio so that the adjusted intensity value can meet the standard requirements of the standard sample test. The data set obtained after completing the linear scaling operation of all intensity values ​​is the standard mass spectrometry data of the mass spectrometer.

[0023] The beneficial effects include eliminating background signal interference, removing outliers, obtaining stable and accurate standard mass spectrometry data, and improving the accuracy and reliability of standard sample test results.

[0024] S2. Perform dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, and construct the multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks. In this embodiment of the invention, the step of performing dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, and constructing a multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks, includes: Based on the intensity distribution of the standard mass spectrometry data within the current mass-to-charge ratio window, the dynamic detection threshold of the current mass-to-charge ratio window is determined; Mass spectrometry peaks in the standard mass spectrometry data whose intensity values ​​exceed the dynamic detection threshold and which are local maxima within the current mass-to-charge ratio window are selected as candidate characteristic peaks of the mass spectrometer. The peak intensity of the candidate characteristic peaks is compared with the noise level within the current mass-to-charge ratio window to perform signal-to-noise ratio threshold filtering, thereby obtaining the characteristic peaks of the mass spectrometer; Extract the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks to construct the mass-to-charge ratio characteristic sequence and relative intensity characteristic sequence of the mass spectrometer; Based on the detection order of the characteristic peaks, the mass-to-charge ratio characteristic sequence and the relative intensity characteristic sequence are structurally recombined to obtain the multidimensional feature vector of the mass spectrometer.

[0025] The standard mass spectrometry data is analyzed to cover all intensity values ​​within the current mass-to-charge ratio window. The specific state of each intensity value is recorded one by one. The distribution of these intensity values ​​within the window is sorted out, and the intervals of concentrated and discrete intensity values ​​are identified. Based on the distribution state, the boundary that can clearly distinguish the effective signal from the background noise is defined. The intensity value corresponding to the boundary is directly determined as the dynamic detection threshold of the current mass-to-charge ratio window.

[0026] All mass spectrometry peaks in the standard mass spectrometry data with intensity values ​​higher than the dynamic detection threshold are screened. The specific positions of these mass spectrometry peaks within the current mass-to-charge ratio window are marked one by one. At the same time, the intensity performance of each marked mass spectrometry peak in its local range is checked. The intensity values ​​of the mass spectrometry peak are compared with those of the surrounding adjacent mass spectrometry peaks to confirm that its intensity value is the maximum value in its local range. All mass spectrometry peaks that simultaneously meet the conditions of intensity value higher than the dynamic detection threshold and local maximum intensity value are selected as candidate characteristic peaks of the mass spectrometer.

[0027] The intensity values ​​below the dynamic detection threshold within the current mass-to-charge ratio window are statistically analyzed. These intensity values ​​are then aggregated and integrated to calculate the average level. This average level is used to define the noise level within the window. The peak intensity of each candidate characteristic peak is extracted one by one. The difference between the peak intensity of each candidate characteristic peak and the noise level is compared. Candidate characteristic peaks that meet the difference requirements are selected according to preset judgment criteria. The selected candidate characteristic peaks are directly determined as the characteristic peaks of the mass spectrometer.

[0028] Extract the mass-to-charge ratio coordinate information and relative intensity value information corresponding to each characteristic peak. Arrange the mass-to-charge ratio coordinate information of all characteristic peaks one by one according to the detection order of the characteristic peaks. Integrate the arranged mass-to-charge ratio coordinate information into an ordered set. This set is determined as the mass-to-charge ratio characteristic sequence of the mass spectrometer. At the same time, arrange the relative intensity value information of all characteristic peaks one by one according to the same detection order. Integrate the arranged relative intensity value information into an ordered set. This set is determined as the relative intensity characteristic sequence of the mass spectrometer.

[0029] According to the order in which the characteristic peaks are detected, the corresponding intensity value information in the relative intensity characteristic sequence is matched for each coordinate information in the mass-to-charge ratio characteristic sequence, a one-to-one correspondence between the two sets of sequences is established, and the corresponding information of the two sets of sequences is systematically combined and arranged to form a data structure containing two core dimensions of mass-to-charge ratio and relative intensity. This data structure is determined as the multidimensional feature vector of the mass spectrometer.

[0030] The beneficial effects are that by determining the dynamic detection threshold, the effective signal and background noise can be accurately separated, the candidate feature peaks that meet the conditions can be screened, the accurate feature peaks can be obtained by combining the signal-to-noise ratio threshold, and the feature peak correlation sequence can be extracted and recombined to form a multi-dimensional feature vector, thereby improving the accuracy and effectiveness of mass spectrometry data feature extraction.

[0031] S3. Based on the signal-to-noise ratio and intensity stability of the feature peaks, perform a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library to obtain the similarity value of the multidimensional feature vector. In this embodiment of the invention, the step of performing a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library based on the signal-to-noise ratio and intensity stability of the feature peak to obtain a similarity value for the multidimensional feature vector includes: The multidimensional feature vector is aligned element-wise with the candidate standard feature vectors in the preset standard feature library to establish the feature dimension correspondence of the mass spectrometer. Based on the signal-to-noise ratio of the feature peaks, assign signal-to-noise ratio weight coefficients to the mass-to-charge ratio feature dimension in the multidimensional feature vector; Based on the intensity stability of the feature peak, intensity stability weight coefficients are assigned to the relative intensity feature dimensions in the multidimensional feature vector; By combining the signal-to-noise ratio weight coefficient and the intensity stability weight coefficient, the multidimensional feature vector and the candidate standard feature vector are weighted and normalized to obtain the weighted inner product result and the weighted magnitude parameter of the multidimensional feature vector; Based on the weighted inner product result and the weighted modulus parameter, the similarity value of the multidimensional feature vector relative to the candidate standard feature vector is evaluated, wherein the similarity value is calculated using the following formula: ; In the formula, This represents the similarity value. This represents the total number of dimensions of the multidimensional feature vector. This indicates that the multidimensional feature vector is at the th... The preset comprehensive weighting coefficients for each dimension This indicates that the multidimensional feature vector is at the th... Feature values ​​in each dimension The candidate standard feature vector is represented in the th... Feature values ​​in each dimension.

[0032] All parameters of the similarity value are direct products of the previous mass spectrometry data processing workflow. The similarity value is a quantitative result of the degree of fit between the multidimensional feature vector and the candidate standard feature vector. The total dimension is the total number of feature dimensions contained in the multidimensional feature vector. The comprehensive weight coefficient is obtained by combining the signal-to-noise ratio weight coefficient assigned to the mass-to-charge ratio feature dimension and the intensity stability weight coefficient assigned to the relative intensity feature dimension. The feature values ​​of the multidimensional feature vector in the corresponding dimension are derived from the mass-to-charge ratio feature sequence and the relative intensity feature sequence composed of the mass-to-charge ratio coordinates and relative intensity values ​​of the extracted feature peaks. The feature values ​​of the candidate standard feature vector in the corresponding dimension are derived from the standard feature data stored in the preset standard feature library.

[0033] This formula is used to measure the similarity between the multidimensional feature vector generated by the mass spectrometer and the candidate standard feature vector in the preset standard feature library. By comprehensively adjusting the feature values ​​of different dimensions through weighting coefficients, and then eliminating the dimensional differences of feature values ​​of different dimensions through normalization calculation, a quantitative index that can directly reflect the degree of fit between the two vectors is finally obtained.

[0034] When the corresponding eigenvalues ​​of the multidimensional feature vector and the candidate standard feature vector tend to be consistent, the similarity value will approach the maximum value. When the difference between the corresponding eigenvalues ​​of the two vectors increases, the similarity value will gradually decrease. When the two vectors are completely unrelated, the similarity value will approach the minimum value.

[0035] Extract all feature elements contained in the multidimensional feature vector, and clarify the feature dimension attribute corresponding to each feature element. At the same time, extract all feature elements contained in the candidate standard feature vector in the preset standard feature library, and similarly clarify the feature dimension attribute corresponding to each feature element. Match the feature elements of the two vectors one by one according to the same feature dimension attribute to ensure that each feature element in the multidimensional feature vector can find a corresponding element with the same feature dimension attribute in the candidate standard feature vector. The feature dimension correspondence of the mass spectrometer is established through such one-by-one matching operation.

[0036] The signal-to-noise ratio (SNR) obtained after the characteristic peaks are statistically analyzed and the degree of separation between the characteristic peak signal and noise reflected by the SNR is clarified. Based on the degree of separation, the reliability of the mass-to-charge ratio (MTR) feature dimension is judged. According to the reliability of the MTR feature dimension, the corresponding weight coefficient is assigned to the MTR feature dimension in the multidimensional feature vector. The weight coefficient assigned according to the SNR is directly determined as the SNR weight coefficient.

[0037] The variation range of the relative intensity value of the feature peak is statistically analyzed during repeated detections under the same test conditions. The intensity stability of the feature peak is measured based on the variation range. The smaller the variation range, the higher the intensity stability, and the larger the variation range, the lower the intensity stability. According to the intensity stability, a corresponding weight coefficient is assigned to the relative intensity feature dimension in the multidimensional feature vector. The weight coefficient assigned according to the intensity stability is directly determined as the intensity stability weight coefficient.

[0038] The signal-to-noise ratio weight coefficients already assigned to the mass-to-charge ratio feature dimension and the intensity stability weight coefficients already assigned to the relative intensity feature dimension are retrieved. These two weight coefficients are then applied to the corresponding feature dimension elements of the multidimensional feature vector and the candidate standard feature vector, respectively. The corresponding elements of the same feature dimension in the two vectors are simultaneously adjusted and normalized. After the adjustment and normalization operation of all feature dimension elements is completed, the weighted inner product result of the multidimensional feature vector is calculated, and the weighted magnitude parameter of the multidimensional feature vector is also calculated.

[0039] Retrieve the weighted inner product result and weighted modulus parameter obtained through weighted normalization operation, analyze the degree of feature correlation between the multidimensional feature vector and the candidate standard feature vector reflected by the two parameters, determine the degree of fit between the two vectors based on the degree of correlation, convert the degree of fit into a specific metric, and determine the specific metric as the similarity value of the multidimensional feature vector relative to the candidate standard feature vector.

[0040] The beneficial effects are that by establishing a correspondence between feature dimensions, accurate vector alignment is achieved; by combining signal-to-noise ratio and intensity stability to assign corresponding weight coefficients, the feature vectors are weighted and normalized; and the similarity value is evaluated based on the weighted inner product result and the weighted modulus parameter, thereby improving the accuracy and reliability of mass spectrometry data feature matching.

[0041] S4. Threshold screening is performed on the similarity values, and the selected candidate feature vectors are compared with the corresponding standard feature vectors in the standard feature library to obtain the systematic deviation parameters of the mass spectrometer. In this embodiment of the invention, the step of thresholding the similarity value and comparing the selected candidate feature vectors with the corresponding standard feature vectors in the standard feature library to obtain the systematic deviation parameters of the mass spectrometer includes: By selecting multidimensional feature vectors whose similarity values ​​are better than a preset quality qualification threshold, candidate feature vectors of the mass spectrometer are obtained. The candidate feature vectors are mapped to the standard feature library to obtain the standard feature vectors corresponding to the candidate feature vectors; In terms of mass-to-charge ratio and relative intensity, the candidate feature vector and the standard feature vector are subjected to element-level deviation determination to obtain the mass-to-charge ratio deviation component and intensity deviation component of the mass spectrometer. By performing cross-modal statistical induction on the mass-to-charge ratio deviation component and the intensity deviation component, the systematic deviation parameters of the mass spectrometer are obtained.

[0042] Extract all calculated similarity values, compare each similarity value directly with a preset quality pass threshold, retain all multidimensional feature vectors whose similarity values ​​exceed the threshold, and uniformly determine these retained multidimensional feature vectors as candidate feature vectors for the mass spectrometer.

[0043] The candidate feature vector is matched one by one with all the standard feature vectors stored in the preset standard feature library. The standard feature vector that best matches the candidate feature vector is found through the correspondence of the feature elements. The standard feature vector obtained by the matching is directly determined as the standard feature vector corresponding to the candidate feature vector.

[0044] In the mass-to-charge ratio dimension, the element values ​​at corresponding positions of the candidate feature vector and the standard feature vector are extracted one by one, the difference between the two values ​​is calculated, and the difference is directly determined as the mass-to-charge ratio deviation component. Similarly, in the relative intensity dimension, the element values ​​at corresponding positions of the candidate feature vector and the standard feature vector are extracted one by one, the difference between the two values ​​is calculated, and the difference is directly determined as the intensity deviation component.

[0045] All mass-to-charge ratio deviation components and intensity deviation components are collected. Cross-modal overall statistical summarization is performed on the two types of deviation components to sort out the overall trend and concentrated distribution of the deviations, identify the common patterns in the deviations, and directly determine the overall deviation characteristics obtained from the summarization as the systematic deviation parameters of the mass spectrometer.

[0046] The beneficial effects are that by screening multidimensional feature vectors that meet the similarity requirements, candidate feature vectors are obtained, and corresponding standard feature vectors are mapped and matched to determine the deviation components of mass-to-charge ratio and intensity dimension. The systematic deviation parameters are then summarized, thereby accurately assessing the detection deviation of the mass spectrometer and improving the accuracy and reliability of mass spectrometry detection results.

[0047] S5. Based on the data distribution density of the systematic deviation parameter, perform non-uniform interpolation fitting on the systematic deviation parameter to obtain the continuous calibration curve of the mass spectrometer. In this embodiment of the invention, the step of performing non-uniform interpolation fitting on the systematic deviation parameter based on the data distribution density of the systematic deviation parameter to obtain the continuous calibration curve of the mass spectrometer includes: Based on the frequency of occurrence and degree of clustering of the systematic deviation parameters on the mass-to-charge ratio coordinate, a density spectrum of the systematic deviation parameters is generated; Based on the local peak characteristics of the density spectrum, the range of the mass-to-charge ratio coordinates is divided into a data-dense region and a data-sparse region. Based on the division results of the data-dense region and the data-sparse region, non-uniform interpolation node allocation is performed on the mass-to-charge ratio coordinates. Based on the allocation results, using the systematic deviation parameter as known data points and the interpolation node as the position to be determined, the systematic deviation parameter is asymptotically fitted to obtain the continuous calibration curve of the mass spectrometer.

[0048] The number of times the systematic deviation parameters appear on each mass-to-charge ratio coordinate is counted. At the same time, the distribution and clustering of these systematic deviation parameters within the mass-to-charge ratio coordinate interval are analyzed to clarify the density of deviation parameters at different mass-to-charge ratio coordinate positions. The statistically obtained frequency of occurrence and the analyzed clustering degree are combined and processed to transform into a visual spectrum that can intuitively reflect the distribution of deviation parameters. This visual spectrum is directly determined as the density spectrum of the systematic deviation parameters.

[0049] By observing the local peak positions and the variation of peak heights on the density spectrum, and taking the distribution range of the peaks and the density of the corresponding deviation parameters as the core basis, the regions with concentrated peaks and a large number of deviation parameters in the mass-to-charge ratio coordinate range are defined as data-dense regions, and the regions with dispersed peaks and a small number of deviation parameters in the mass-to-charge ratio coordinate range are defined as data-sparse regions.

[0050] Based on the division of the data-dense and data-sparse regions, more interpolation nodes are allocated to the data-dense regions to ensure that the interpolation nodes in this region can fully cover the distribution details of the deviation parameters. Fewer interpolation nodes are allocated to the data-sparse regions to reduce redundant node settings while meeting the basic fitting requirements, thus completing the non-uniform interpolation node allocation for the entire mass-to-charge ratio coordinate range.

[0051] Using all the interpolation nodes that have been assigned as the locations to be calculated, and the systematic deviation parameters as known reference data points, the discrete systematic deviation parameters are connected in an orderly manner using an asymptotic fitting method, so that the fitted curve can fit the overall trend of the deviation parameters and form a continuous and smooth curve. This continuous and smooth curve is directly determined as the continuous calibration curve of the mass spectrometer.

[0052] The beneficial effects are that by generating a density spectrum of systematic deviation parameters, the data dense region and sparse region are divided, non-uniform interpolation node allocation is carried out and asymptotic fitting is performed to obtain a continuous calibration curve, thereby achieving accurate calibration of the mass spectrometer detection deviation and improving the consistency and accuracy of the detection data.

[0053] S6. Based on the continuous calibration curve, the standard mass spectrometry data is corrected point by point, and the calibrated mass spectrometry data of the mass spectrometer is output.

[0054] In this embodiment of the invention, the step of performing point-by-point correction on the standard mass spectrometry data based on the continuous calibration curve and outputting the calibrated mass spectrometry data of the mass spectrometer includes: The coordinate offset distribution of the continuous calibration curve in the mass-to-charge ratio dimension and the scale factor distribution in the intensity dimension are extracted to obtain the calibration parameters of the standard mass spectrometry data. The mass-to-charge ratio coordinates of the mass spectral peaks in the standard mass spectrometry data are corrected for coordinate offset distribution to obtain the mass-to-charge ratio corrected data of the mass spectrometer. The intensity values ​​of the mass spectrometry peaks in the standard mass spectrometry data are calibrated with the response values ​​of the scaling factor distribution to obtain the intensity-corrected data of the mass spectrometer. Based on the original mass spectrum peak correspondence, the mass-to-charge ratio corrected data and the intensity corrected data are structurally reconstructed to obtain the calibrated mass spectrometry data of the mass spectrometer.

[0055] The offset values ​​corresponding to all coordinate positions covered by the continuous calibration curve in the mass-to-charge ratio dimension are extracted, and the magnitude of the offset at each coordinate position is recorded. The distribution pattern and trend of these offset values ​​in the entire mass-to-charge ratio coordinate range are analyzed. At the same time, the scale factor corresponding to all coordinate positions covered by the continuous calibration curve in the intensity dimension is extracted, and the magnitude of the scale factor at each coordinate position is recorded. The distribution pattern and trend of these scale factors in the entire mass-to-charge ratio coordinate range are analyzed. The obtained coordinate offset distribution and scale factor distribution are integrated and jointly determined as the calibration parameters of the standard mass spectrometry data.

[0056] The mass-to-charge ratio coordinates of all mass spectral peaks in the standard mass spectrometry data are retrieved to determine the specific position of each mass spectral peak on the mass-to-charge ratio coordinates. Based on this specific position, the values ​​corresponding to the coordinate offset distribution in the calibration parameters are matched one by one. The mass-to-charge ratio coordinates of each mass spectral peak are adjusted and corrected in a targeted manner in combination with the coordinate offset of the corresponding position to ensure that the mass-to-charge ratio coordinates of each mass spectral peak can be accurately calibrated. After the correction operation of the mass-to-charge ratio coordinates of all mass spectral peaks is completed, the resulting data set is determined as the mass-to-charge ratio calibrated data of the mass spectrometer.

[0057] The intensity values ​​of all mass spectrometry peaks in the standard mass spectrometry data are retrieved, and the specific magnitude of the intensity value of each mass spectrometry peak is determined. Based on the specific position of each mass spectrometry peak on the mass-to-charge ratio coordinate, the values ​​corresponding to the proportional factor distribution in the calibration parameters are matched one by one. The intensity value of each mass spectrometry peak is adjusted and calibrated in a targeted manner with the proportional factor at the corresponding position to ensure that the intensity value of each mass spectrometry peak can be accurately calibrated. After completing the calibration operation of the intensity values ​​of all mass spectrometry peaks, the resulting data set is determined as the intensity-corrected data of the mass spectrometer.

[0058] The original correspondence between the mass-to-charge ratio coordinates and intensity values ​​of each mass peak in the standard mass spectrometry data is analyzed, and the intensity value information matched for each mass-to-charge ratio coordinate is clarified. According to the original correspondence, each mass-to-charge ratio coordinate in the mass-to-charge ratio corrected data is matched one-to-one with each intensity value in the intensity corrected data to ensure that each corrected mass-to-charge ratio coordinate corresponds to a unique corrected intensity value. The structured recombination of the two sets of data is completed, and the data set obtained after recombination is determined as the calibrated mass spectrometry data of the mass spectrometer.

[0059] The beneficial effect is that by extracting the coordinate offset and scale factor distribution of the continuous calibration curve to obtain calibration parameters, the mass-to-charge ratio coordinates and intensity values ​​are accurately corrected respectively. Then, the data is recombined according to the original correspondence to obtain the calibrated mass spectrometry data, which effectively improves the accuracy and consistency of mass spectrometry detection data.

[0060] Example 2 like Figure 2As shown in the figure, this embodiment also provides a functional block diagram of a mass spectrometer calibration system.

[0061] The mass spectrometer calibration system 100 described in this embodiment can be installed in a smart terminal. Depending on the functions implemented, the mass spectrometer calibration system 100 may include a data preprocessing module 101, a feature vector construction module 102, a feature matching module 103, a deviation comparison module 104, a curve fitting module 105, and a data correction module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the smart terminal processor and perform a fixed function, stored in the smart terminal's memory.

[0062] In this embodiment, the functions of each module / unit are as follows: The data preprocessing module 101 is used to subtract the background signal from the raw mass spectrometry data of the mass spectrometer during the standard sample test cycle of the mass spectrometer to obtain the standard mass spectrometry data of the mass spectrometer. The feature vector construction module 102 is used to perform dynamic threshold peak detection on the standard mass spectrometry data to obtain the feature peaks of the mass spectrometer, and construct the multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the feature peaks. The feature matching module 103 is used to perform a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library based on the signal-to-noise ratio and intensity stability of the feature peak, so as to obtain the similarity value of the multidimensional feature vector. The deviation comparison module 104 is used to perform threshold filtering on the similarity value, and to perform multi-dimensional deviation comparison between the selected candidate feature vector and the corresponding standard feature vector in the standard feature library to obtain the systematic deviation parameters of the mass spectrometer. The curve fitting module 105 is used to perform non-uniform interpolation fitting on the systematic deviation parameter based on the data distribution density of the systematic deviation parameter to obtain the continuous calibration curve of the mass spectrometer. The data correction module 106 is used to perform point-by-point correction on the standard mass spectrometry data based on the continuous calibration curve, and output the calibrated mass spectrometry data of the mass spectrometer.

[0063] In detail, each module in the mass spectrometer calibration system 100 described in this embodiment of the invention uses the same technical means as the mass spectrometer calibration method described in Embodiment 1 and Embodiment 2, and can produce the same technical effect, which will not be repeated here.

[0064] Example 3 like Figure 3As shown, this embodiment also provides a smart terminal, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a program for determining the moisture content of saline soil.

[0065] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the smart terminal, connecting various components of the smart terminal via various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a program to determine the moisture content of saline soil) and calls data stored in the memory 11 to perform various functions and process data for the smart terminal.

[0066] The memory 11 includes at least one type of medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of a smart terminal, such as the portable hard drive of the smart terminal. In other embodiments, the memory 11 can also be an external storage device of the smart terminal, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the smart terminal. Furthermore, the memory 11 can include both internal storage units and external storage devices of the smart terminal. The memory 11 can be used not only to store application software and various types of data installed on the smart terminal, such as the code for a program to determine the moisture content of saline soil, but also to temporarily store data that has been output or will be output.

[0067] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0068] The communication interface 13 is used for communication between the aforementioned smart terminal and other smart terminals, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish a communication connection between the smart terminal and other smart terminals. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the smart terminal and to display a visual user interface.

[0069] The figure only shows a smart terminal with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the smart terminal, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0070] For example, although not shown, the smart terminal may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The smart terminal may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be elaborated here.

[0071] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0072] The program for determining the moisture content of saline soil stored in the memory 11 of the smart terminal is a combination of multiple instructions. When run in the processor 10, it can achieve the following: S1. During the standard sample testing cycle of the mass spectrometer, the background signal of the raw mass spectrometry data of the mass spectrometer is subtracted to obtain the standard mass spectrometry data of the mass spectrometer. S2. Perform dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, and construct the multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks. S3. Based on the signal-to-noise ratio and intensity stability of the feature peaks, perform a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library to obtain the similarity value of the multidimensional feature vector. S4. Threshold screening is performed on the similarity values, and the selected candidate feature vectors are compared with the corresponding standard feature vectors in the standard feature library to obtain the systematic deviation parameters of the mass spectrometer. S5. Based on the data distribution density of the systematic deviation parameter, perform non-uniform interpolation fitting on the systematic deviation parameter to obtain the continuous calibration curve of the mass spectrometer. S6. Based on the continuous calibration curve, the standard mass spectrometry data is corrected point by point, and the calibrated mass spectrometry data of the mass spectrometer is output.

[0073] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0074] Furthermore, if the modules / units integrated in the smart terminal are implemented as software functional units and sold or used as independent products, they can be stored in a medium. The medium can be volatile or non-volatile. For example, the medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0075] In the several embodiments provided by this invention, it should be understood that the disclosed smart terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0076] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0078] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0079] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method of calibrating a mass spectrometer, characterized by, The method includes: S1. During the standard sample testing cycle of the mass spectrometer, the background signal of the raw mass spectrometry data of the mass spectrometer is subtracted to obtain the standard mass spectrometry data of the mass spectrometer. S2. Perform dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, and construct the multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks. S3. Based on the signal-to-noise ratio and intensity stability of the feature peaks, perform a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library to obtain the similarity value of the multidimensional feature vector. S4. Threshold screening is performed on the similarity values, and the selected candidate feature vectors are compared with the corresponding standard feature vectors in the standard feature library to obtain the systematic deviation parameters of the mass spectrometer. S5. Based on the data distribution density of the systematic deviation parameter, perform non-uniform interpolation fitting on the systematic deviation parameter to obtain the continuous calibration curve of the mass spectrometer. S6. Based on the continuous calibration curve, the standard mass spectrometry data is corrected point by point, and the calibrated mass spectrometry data of the mass spectrometer is output.

2. A mass spectrometer calibration method as claimed in claim 1, characterised in that, The step of subtracting the background signal from the raw mass spectrometry data during the standard sample testing cycle of the mass spectrometer to obtain the standard mass spectrometry data of the mass spectrometer includes: During the standard test cycle of the mass spectrometer, the background spectrum of the mass spectrometer under the condition of no standard injection is acquired, and the background spectrum is determined as the baseline signal; The raw mass spectrometry data of the mass spectrometer is obtained, and the mass-to-charge ratio coordinate intensity value corresponding to the baseline signal in the raw mass spectrometry data is subtracted point by point to obtain the background-subtracted mass spectrometry data of the mass spectrometer. The intensity sequence of the mass spectrometry data after background subtraction is subjected to time-domain filtering to obtain the smoothed mass spectrometry data of the mass spectrometer. Based on the total ion current intensity of the sampling points within the standard test cycle, the intensity value of the smoothed mass spectrometry data is linearly scaled to obtain the standard mass spectrometry data of the mass spectrometer.

3. The mass spectrometer calibration method as described in claim 1, characterized in that, The process of performing dynamic threshold peak detection on the standard mass spectrometry data to obtain the characteristic peaks of the mass spectrometer, and constructing a multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks, includes: Based on the intensity distribution of the standard mass spectrometry data within the current mass-to-charge ratio window, the dynamic detection threshold of the current mass-to-charge ratio window is determined; Mass spectrometry peaks in the standard mass spectrometry data whose intensity values ​​exceed the dynamic detection threshold and which are local maxima within the current mass-to-charge ratio window are selected as candidate characteristic peaks of the mass spectrometer. The peak intensity of the candidate characteristic peaks is compared with the noise level within the current mass-to-charge ratio window to perform signal-to-noise ratio threshold filtering, thereby obtaining the characteristic peaks of the mass spectrometer; Extract the mass-to-charge ratio coordinates and relative intensity values ​​of the characteristic peaks to construct the mass-to-charge ratio characteristic sequence and relative intensity characteristic sequence of the mass spectrometer; Based on the detection order of the characteristic peaks, the mass-to-charge ratio characteristic sequence and the relative intensity characteristic sequence are structurally recombined to obtain the multidimensional feature vector of the mass spectrometer.

4. A mass spectrometer calibration method as claimed in claim 3, characterised in that, The step of evaluating the similarity between the multidimensional feature vector and a preset standard feature library based on the signal-to-noise ratio and intensity stability of the feature peaks to obtain the similarity value of the multidimensional feature vector includes: The multidimensional feature vector is aligned element-wise with the candidate standard feature vectors in the preset standard feature library to establish the feature dimension correspondence of the mass spectrometer. Based on the signal-to-noise ratio of the feature peaks, assign signal-to-noise ratio weight coefficients to the mass-to-charge ratio feature dimension in the multidimensional feature vector; Based on the intensity stability of the feature peak, intensity stability weight coefficients are assigned to the relative intensity feature dimensions in the multidimensional feature vector; By combining the signal-to-noise ratio weight coefficient and the intensity stability weight coefficient, the multidimensional feature vector and the candidate standard feature vector are weighted and normalized to obtain the weighted inner product result and the weighted magnitude parameter of the multidimensional feature vector; Based on the weighted inner product result and the weighted modulus parameter, the similarity value of the multidimensional feature vector relative to the candidate standard feature vector is evaluated, wherein the similarity value is calculated using the following formula: ； In the formula, This represents the similarity value. This represents the total number of dimensions of the multidimensional feature vector. This indicates that the multidimensional feature vector is at the th... The pre-set comprehensive weighting coefficients for each dimension This indicates that the multidimensional feature vector is at the th... Feature values ​​in each dimension The candidate standard feature vector is represented in the th... Feature values ​​in each dimension.

5. A mass spectrometer calibration method as described in claim 1, characterized in that, The process of thresholding the similarity values ​​and comparing the selected candidate feature vectors with the corresponding standard feature vectors in the standard feature library using multidimensional deviation analysis yields the systematic deviation parameters of the mass spectrometer, including: By selecting multidimensional feature vectors whose similarity values ​​are better than a preset quality qualification threshold, candidate feature vectors of the mass spectrometer are obtained. The candidate feature vectors are mapped to the standard feature library to obtain the standard feature vectors corresponding to the candidate feature vectors; In terms of mass-to-charge ratio and relative intensity, the candidate feature vector and the standard feature vector are subjected to element-level deviation determination to obtain the mass-to-charge ratio deviation component and intensity deviation component of the mass spectrometer. By performing cross-modal statistical induction on the mass-to-charge ratio deviation component and the intensity deviation component, the systematic deviation parameters of the mass spectrometer are obtained.

6. A method of calibrating a mass spectrometer as defined in claim 1, characterized in that, The method of performing non-uniform interpolation fitting on the systematic deviation parameter based on the data distribution density to obtain the continuous calibration curve of the mass spectrometer includes: Based on the frequency of occurrence and degree of clustering of the systematic deviation parameters on the mass-to-charge ratio coordinate, a density spectrum of the systematic deviation parameters is generated; Based on the local peak characteristics of the density spectrum, the range of the mass-to-charge ratio coordinates is divided into a data-dense region and a data-sparse region. Based on the division results of the data-dense region and the data-sparse region, non-uniform interpolation node allocation is performed on the mass-to-charge ratio coordinates. Based on the allocation results, using the systematic deviation parameter as known data points and the interpolation node as the position to be determined, the systematic deviation parameter is asymptotically fitted to obtain the continuous calibration curve of the mass spectrometer.

7. The mass spectrometer calibration method as described in claim 1, characterized in that, The step of performing point-by-point correction on the standard mass spectrometry data based on the continuous calibration curve and outputting the calibrated mass spectrometry data of the mass spectrometer includes: The coordinate offset distribution of the continuous calibration curve in the mass-to-charge ratio dimension and the scale factor distribution in the intensity dimension are extracted to obtain the calibration parameters of the standard mass spectrometry data. The mass-to-charge ratio coordinates of the mass spectral peaks in the standard mass spectrometry data are corrected for coordinate offset distribution to obtain the mass-to-charge ratio corrected data of the mass spectrometer. The intensity values ​​of the mass spectrometry peaks in the standard mass spectrometry data are calibrated with the response values ​​of the scaling factor distribution to obtain the intensity-corrected data of the mass spectrometer. Based on the original mass spectrum peak correspondence, the mass-to-charge ratio corrected data and the intensity corrected data are structurally reconstructed to obtain the calibrated mass spectrometry data of the mass spectrometer.

8. A mass spectrometer calibration system characterized by, include: The data preprocessing module is used to subtract the background signal from the raw mass spectrometry data of the mass spectrometer during the standard sample testing cycle of the mass spectrometer to obtain the standard mass spectrometry data of the mass spectrometer. The feature vector construction module is used to perform dynamic threshold peak detection on the standard mass spectrometry data to obtain the feature peaks of the mass spectrometer, and construct the multidimensional feature vector of the mass spectrometer based on the mass-to-charge ratio coordinates and relative intensity values ​​of the feature peaks. The feature matching module is used to perform a weighted similarity evaluation between the multidimensional feature vector and a preset standard feature library based on the signal-to-noise ratio and intensity stability of the feature peak, so as to obtain the similarity value of the multidimensional feature vector. The deviation comparison module is used to perform threshold filtering on the similarity value and perform multi-dimensional deviation comparison between the selected candidate feature vectors and the corresponding standard feature vectors in the standard feature library to obtain the systematic deviation parameters of the mass spectrometer. The curve fitting module is used to perform non-uniform interpolation fitting on the systematic deviation parameter based on the data distribution density of the systematic deviation parameter, so as to obtain the continuous calibration curve of the mass spectrometer. The data correction module is used to perform point-by-point correction on the standard mass spectrometry data based on the continuous calibration curve, and output the calibrated mass spectrometry data of the mass spectrometer.

9. An intelligent terminal comprising a memory, a processor, and a computer program stored on the memory, wherein, The processor executes the computer program to implement the steps of the mass spectrometer calibration method according to any one of claims 1 to 7.

10. A storage medium having stored thereon a computer program, characterized in that When executed by a processor, the computer program implements the steps of a mass spectrometer calibration method according to any one of claims 1 to 7.

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