A chromatograph component separation method, system, terminal and storage medium

Abstract

The present disclosure relates to the technical field of chromatographic analysis, and discloses a chromatograph component separation method, system, terminal and storage medium. The method comprises: performing standardization processing on original component data of a chromatograph to obtain regular chromatograph data of the chromatograph, and performing preliminary peak detection on the regular chromatograph data to obtain candidate peak intervals of the chromatograph; performing importance weighting analysis on time-frequency domain joint features of the chromatograph to obtain a weight mapping relationship, and determining an adaptive decision threshold of the chromatograph according to the weight mapping relationship; performing significance determination on the candidate peak intervals according to the adaptive decision threshold to obtain final chromatograph peaks; performing overlap degree evaluation on the final chromatograph peaks to obtain overlapping signals of the chromatograph, and performing parameter collaborative estimation on the overlapping signals to obtain sub-peak parameters of the chromatograph; performing parameter decoupling on the sub-peak parameters to obtain component quantitative information of the chromatograph, and outputting a component separation report of the chromatograph; and the present application can improve the efficiency of chromatograph component separation.

Description

Technical Field

[0001] This invention relates to the field of chromatographic analysis technology, and in particular to a chromatographic component separation method, system, terminal, and storage medium. Background Technology

[0002] In existing chromatographic analysis techniques, the separation of components in complex mixtures often relies on preset fixed thresholds or peak identification methods based on single-domain characteristics. These methods are poorly adaptable to baseline drift, noise fluctuations, and changes in signal morphology, easily leading to missed detection of weak peaks or misjudgment of noise, and making it difficult to maintain stable analytical performance in multi-sample, high-noise environments.

[0003] Furthermore, traditional methods, when faced with severely overlapping chromatographic peaks, often employ simplified mathematical models or empirical fitting, lacking the synergistic utilization of time-frequency domain characteristics and dynamic weight evaluation. This leads to inaccurate estimation of sub-peak parameters and significant deviations in quantitative results, making it difficult to meet the requirements of high-precision and high-resolution separation. Therefore, improving the efficiency of component separation in chromatographs has become an urgent problem to be solved. Summary of the Invention

[0004] This disclosure provides a chromatographic component separation method, system, terminal, and storage medium.

[0005] In a first aspect, this disclosure provides a chromatographic component separation method, comprising: S1. Standardize the raw component data of the chromatograph to obtain regular chromatographic data of the chromatograph, and perform preliminary peak detection on the regular chromatographic data to obtain the candidate peak range of the chromatograph. S2. Perform importance-weighted analysis on the time-frequency domain joint features of the chromatograph to obtain the weight mapping relationship of the time-frequency domain joint features, and determine the adaptive decision threshold of the chromatograph based on the weight mapping relationship; S3. Determine the significance of the candidate peak intervals based on the adaptive decision threshold to obtain the final chromatographic peak; S4. Evaluate the overlap of the final chromatographic peaks to obtain the overlap signal of the chromatograph, and perform parameter co-estimation on the overlap signal to obtain the sub-peak parameters of the chromatograph; S5. Decouple the sub-peak parameters to obtain the component quantification information of the chromatograph and output the component separation report of the chromatograph.

[0006] In a preferred embodiment, the standardization of the raw component data of the chromatograph to obtain regularized chromatographic data of the chromatograph, and the preliminary peak detection of the regularized chromatographic data to obtain candidate peak ranges of the chromatograph, includes: Obtain raw component data from the chromatograph; Spline interpolation is performed on the raw component data of the chromatograph to obtain smoothed data of the chromatograph; The smoothed data is corrected for baseline drift to obtain the regularized chromatographic data of the chromatograph. The coordinates of the regularized chromatographic data are calibrated to obtain the potential peak coordinates of the regularized chromatographic data; Based on the potential peak position coordinates, the regular chromatographic data is divided into target peak segments to obtain the candidate peak intervals of the chromatograph.

[0007] In a preferred embodiment, the step of performing importance-weighted analysis on the joint time-frequency features of the chromatograph to obtain a weighted mapping relationship of the joint time-frequency features, and determining the adaptive decision threshold of the chromatograph based on the weighted mapping relationship, includes: The regularized chromatographic data are subjected to time-frequency transformation to obtain the time-frequency spectrum sequence of the chromatograph; Multi-scale feature extraction is performed on the time-frequency spectrum sequence to obtain the joint time-frequency domain features of the chromatograph; The contribution of the joint time-frequency domain features is evaluated to obtain the weight mapping relationship of the joint time-frequency domain features; Based on the weight mapping relationship, threshold adaptation is performed on the joint time-frequency domain features to obtain the adaptive decision threshold of the chromatograph.

[0008] In a preferred embodiment, the step of evaluating the contribution of the joint time-frequency features to obtain the weight mapping relationship of the joint time-frequency features includes: The time-frequency domain joint features are decomposed into time dimension sub-features and frequency dimension sub-features. A morphological dispersion analysis is performed on the time-dimensional sub-features to obtain the distribution entropy value of the time-dimensional sub-features; Cluster analysis is performed on the frequency dimension sub-features to obtain the cohesion index of the frequency dimension sub-features; The contribution of the time-frequency domain joint feature is obtained by nonlinearly coupling the distribution entropy value and the cohesion index. Based on the contribution, a dynamic weighting factor is calculated for fusing the time-dimension sub-features and the frequency-dimension sub-features, wherein the formula for the dynamic weighting factor is as follows: ; in, For the first The dynamic weighting factor of the time-frequency domain sub-features, For the first The distribution entropy value of the item feature, For the first Cohesion index of item characteristics This is an adjustment parameter for the degree of influence of the cohesion index. The non-zero smoothing parameter of the dynamic weighting factor is, The total number of terms in the time-frequency domain sub-features; Based on the dynamic weighting factor, the time dimension sub-features and the frequency dimension sub-features are weighted and coupled to obtain the weight mapping relationship of the time-frequency domain joint features.

[0009] In a preferred embodiment, the step of determining the significance of the candidate peak interval based on the adaptive decision threshold to obtain the final chromatographic peak includes: Based on the adaptive decision threshold, peak intensity comparison is performed on the candidate peak intervals to obtain the preliminary significant peaks of the candidate peak intervals; The signal-to-noise ratio of the preliminary significant peak is evaluated to obtain the effective peak of the preliminary significant peak; Based on the effective peak, the candidate peak range is adapted and optimized to obtain the final chromatographic peak of the chromatograph.

[0010] In a preferred embodiment, the step of evaluating the overlap of the final chromatographic peak to obtain the overlap signal of the chromatograph, and performing parameter co-estimation on the overlap signal to obtain the sub-peak parameters of the chromatograph, includes: Peak region localization is performed on the final chromatographic peak to obtain the overlapping peak region in the final chromatographic peak; The overlapping signal of the chromatograph is obtained by extracting the overlapping signal from the overlapping peak region; The parameters of the overlapping signals are analyzed to obtain the initial sub-peak parameters of the overlapping signals; Based on the initial sub-peak parameters, the overlapping signals are calibrated and optimized to obtain the sub-peak parameters of the chromatograph.

[0011] In a preferred embodiment, the step of decoupling the sub-peak parameters to obtain the component quantification information of the chromatograph and outputting the component separation report of the chromatograph includes: Independent component analysis is performed on the sub-peak parameters to obtain the component parameters of the sub-peak parameters; Based on the independent component parameters, quantitative inversion is performed on the regular chromatographic data to obtain the component quantitative information of the chromatograph; The quantitative information of the components and the final chromatographic peaks are structurally integrated to obtain the comprehensive data of the chromatograph; The aggregated data is output as a component separation report for the chromatograph.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves accurate identification of chromatographic peaks under different noise levels and baseline drift conditions by performing joint time-frequency domain feature analysis on chromatographic signals and dynamically generating adaptive decision thresholds based on feature contribution. This method effectively overcomes the limitations of traditional fixed thresholds or single-domain feature criteria, significantly improves the sensitivity and specificity of peak detection, reduces the risk of missed detection of weak peaks and false noise judgments in complex matrices, thereby enhancing the reliability and applicability of chromatographic data processing.

[0013] 2. This invention, by introducing a strategy combining parameter co-estimation and independent component analysis in the overlapping peak resolution stage, can accurately extract sub-peak parameters from highly overlapping signals and obtain precise component content information through quantitative inversion. This process enhances the resolution capability for multi-component co-elution scenarios, improves the accuracy and repeatability of qualitative and quantitative analysis, and simultaneously achieves full automation and structured output from data preprocessing to report generation, significantly improving the overall efficiency and usability of chromatographic analysis results. 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 chromatographic component separation method according to Embodiment 1 of the present invention is shown. Figure 2 This diagram shows a functional block diagram of a chromatograph component separation system according to Embodiment 2 of the present invention; Figure 3 The diagram shows the structural composition of a terminal for implementing the chromatograph component separation method according to Embodiment 3 of the present invention. Detailed Implementation

[0015] 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.

[0016] 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.

[0017] Example 1 Figure 1 This is a schematic flowchart of a chromatographic component separation method provided in an embodiment of this disclosure. Figure 1 As shown, a chromatographic component separation method includes: S1. Standardize the raw component data of the chromatograph to obtain regular chromatographic data of the chromatograph, and perform preliminary peak detection on the regular chromatographic data to obtain the candidate peak range of the chromatograph. In this embodiment of the invention, the standardization of the raw component data of the chromatograph to obtain regularized chromatographic data of the chromatograph, and the preliminary peak detection of the regularized chromatographic data to obtain the candidate peak range of the chromatograph, includes: Obtain raw component data from the chromatograph; Spline interpolation is performed on the raw component data of the chromatograph to obtain smoothed data of the chromatograph; The smoothed data is corrected for baseline drift to obtain the regularized chromatographic data of the chromatograph. The coordinates of the regularized chromatographic data are calibrated to obtain the potential peak coordinates of the regularized chromatographic data; Based on the potential peak position coordinates, the regular chromatographic data is divided into target peak segments to obtain the candidate peak intervals of the chromatograph.

[0018] Obtain raw component data from the chromatograph. The raw signal is directly acquired from the detector of the chromatograph. This signal exists in the form of a sequence with time on the x-axis and intensity on the y-axis. It completely records the response values ​​generated by each component in the sample as it flows through the detector. These unprocessed intensity value sequences are the raw component data of the chromatograph.

[0019] Spline interpolation is performed on the raw component data from the chromatograph to obtain smoothed data. This process is achieved by constructing a cubic spline function. Specifically, a smooth curve is fitted piecewise between adjacent raw data points, ensuring the continuity of the first and second derivatives at the junctions of each segment. This eliminates high-frequency random noise in the signal and preserves the true chromatographic peak shape trend, ultimately generating a continuous and smooth curve showing the intensity change over time. This curve represents the smoothed data from the chromatograph.

[0020] Baseline drift correction is performed on the smoothed data to obtain the regularized chromatographic data of the chromatograph. This step uses a polynomial fitting method to estimate the signal baseline. Specifically, the least squares method is used to fit the smoothed data into a low-order polynomial curve, which represents the background drift trend of the signal. Then, the polynomial fitting value at the corresponding time point is subtracted from each intensity value of the smoothed data to remove the basic signal shift caused by instrument baseline drift or background interference, thereby obtaining an intensity curve with a stable baseline near zero. This processed curve is the regularized chromatographic data of the chromatograph.

[0021] Coordinate calibration is performed on regular chromatographic data to obtain the potential peak coordinates. This operation involves systematically scanning each data point in the regular chromatographic data and strictly comparing its intensity value with the intensity values ​​of the preceding and following data points. When the intensity value of a point is greater than the intensity values ​​of both the preceding and following points, the point is identified as a local maximum, and its corresponding time and intensity coordinates are recorded as a potential peak coordinate. All identified local maxima constitute a set of potential peak coordinates.

[0022] Based on the coordinates of potential peak positions, the regular chromatographic data is divided into target peak segments to obtain the candidate peak intervals for the chromatograph. This process uses each potential peak position coordinate as the center point and searches along the time axis to its left and right. During the left-hand search, the peak's starting point is determined when the intensity value drops to a specific ratio of the difference between the baseline intensity and the peak height. The same rule is used to determine the peak's ending point during the right-hand search. The baseline intensity is determined by the average intensity of the peak-free region in the regular chromatographic data. Thus, each potential peak position is divided into a continuous time interval from its starting point to its ending point; these independent time intervals constitute the candidate peak intervals for the chromatograph.

[0023] The beneficial effects are that regular chromatographic data are generated through spline interpolation and baseline drift correction, which improves data quality. Then, candidate peak intervals are accurately extracted through coordinate calibration and target peak segment division, laying the foundation for subsequent peak significance determination and overlapping peak analysis, thereby improving the efficiency and accuracy of chromatographic peak identification and optimizing the overall performance of the component separation process.

[0024] S2. Perform importance-weighted analysis on the time-frequency domain joint features of the chromatograph to obtain the weight mapping relationship of the time-frequency domain joint features, and determine the adaptive decision threshold of the chromatograph based on the weight mapping relationship; In this embodiment of the invention, the step of performing importance-weighted analysis on the joint time-frequency features of the chromatograph to obtain the weight mapping relationship of the joint time-frequency features, and determining the adaptive decision threshold of the chromatograph based on the weight mapping relationship, includes: The regularized chromatographic data are subjected to time-frequency transformation to obtain the time-frequency spectrum sequence of the chromatograph; Multi-scale feature extraction is performed on the time-frequency spectrum sequence to obtain the joint time-frequency domain features of the chromatograph; The contribution of the joint time-frequency domain features is evaluated to obtain the weight mapping relationship of the joint time-frequency domain features; Based on the weight mapping relationship, threshold adaptation is performed on the joint time-frequency domain features to obtain the adaptive decision threshold of the chromatograph.

[0025] The step of evaluating the contribution of the joint time-frequency features to obtain the weight mapping relationship of the joint time-frequency features includes: The time-frequency domain joint features are decomposed into time dimension sub-features and frequency dimension sub-features. A morphological dispersion analysis is performed on the time-dimensional sub-features to obtain the distribution entropy value of the time-dimensional sub-features; Cluster analysis is performed on the frequency dimension sub-features to obtain the cohesion index of the frequency dimension sub-features; The contribution of the time-frequency domain joint feature is obtained by nonlinearly coupling the distribution entropy value and the cohesion index. Based on the contribution, a dynamic weighting factor is calculated for fusing the time-dimension sub-features and the frequency-dimension sub-features, wherein the formula for the dynamic weighting factor is as follows: ; in, For the first The dynamic weighting factor of the time-frequency domain sub-features, For the first The distribution entropy value of the item feature, For the first Cohesion index of item characteristics This is an adjustment parameter for the degree of influence of the cohesion index. The non-zero smoothing parameter of the dynamic weighting factor is, The total number of terms in the time-frequency domain sub-features; Based on the dynamic weighting factor, the time dimension sub-features and the frequency dimension sub-features are weighted and coupled to obtain the weight mapping relationship of the time-frequency domain joint features.

[0026] Regular chromatographic data is divided into continuous and fixed-length time segments according to time sequence. For each time segment, the various frequency components contained therein and the intensity value of each frequency component are analyzed one by one. The frequency component types and intensity values ​​of each time segment are presented in the form of images. All such images corresponding to all time segments are arranged in chronological order to form a time-spectrum sequence.

[0027] Multiple different analysis scales are set, each corresponding to a specific range of detail capture. The small scale is used to capture subtle changes in frequency components and short-term time node features in the time spectrum sequence. The medium scale is used to extract the frequency distribution patterns and time duration features of medium range. The large scale is used to grasp the overall frequency change trend and the feature patterns of long time span. For each analysis scale, the corresponding feature information is extracted from the time spectrum sequence, including the occurrence time, duration, and intensity change amplitude of different frequency components. The feature information extracted from all scales is systematically integrated to obtain the time-frequency domain joint feature that simultaneously covers information in both the time and frequency dimensions.

[0028] All feature information in the joint time-frequency domain features is comprehensively reviewed and classified according to the attributes reflected by the feature information. Time-related feature parts and frequency-related feature parts are distinguished. The time-related feature parts include the specific time when each frequency component appears, the duration, and the intensity fluctuation at different time points. This part of the feature is extracted separately as the time dimension sub-feature. The frequency-related feature parts include the types of frequencies, the specific intensity values ​​of each frequency, and the proportional relationship between different frequencies. This part of the feature is extracted separately as the frequency dimension sub-feature.

[0029] The specific values ​​of each feature in the time dimension sub-features are statistically analyzed one by one to determine the overall distribution range of all feature values. The difference between each feature value and the average value of all feature values ​​is calculated. All differences are summarized and the distribution of these differences is analyzed to determine the dispersion state of each feature value in the overall distribution. Based on the specific manifestation of this dispersion state, a distribution entropy value that can accurately quantify the degree of dispersion is formed.

[0030] The feature values ​​in the frequency dimension sub-features are classified according to attribute similarity. Feature values ​​with similar attributes are grouped into the same set. The number of feature values ​​in each set is counted. The similarity between all feature values ​​within each set is calculated. The difference between feature values ​​between different sets is analyzed. Based on the concentration of feature values ​​within each set and the separation between different sets, a cohesion index that can accurately reflect the degree of clustering of frequency dimension sub-features is determined.

[0031] This study delves into the interrelationship between the dispersion of time-dimensional sub-features reflected by distribution entropy values ​​and the aggregation of frequency-dimensional sub-features reflected by cohesion indices. It fully considers the quantitative information of both and their mutual influence. In the integration process, it emphasizes both the differences in time-dimensional features reflected by distribution entropy values ​​and the concentration trend of frequency-dimensional features reflected by cohesion indices. Through this comprehensive integration, it forms a comprehensive reflection of the contribution of time-dimensional and frequency-dimensional sub-features to the joint time-frequency domain features.

[0032] Based on the contribution of each time dimension sub-feature and its corresponding frequency dimension sub-feature, the influence of different sub-features in the overall fusion process is clarified. The sub-feature with a higher contribution has a greater proportion in the fusion result. Taking into account the contribution of all sub-features, the weight value corresponding to each sub-feature is reasonably determined. These weight values ​​will be adjusted accordingly as the contribution of the sub-features changes, and finally a dynamic weighting factor is formed for fusing time dimension sub-features and frequency dimension sub-features.

[0033] The joint time-frequency domain features are decomposed into time-dimensional and frequency-dimensional sub-features. Morphological dispersion analysis is performed on the time-dimensional sub-features, which quantifies the distribution entropy by statistically analyzing the distribution differences of the time-dimensional sub-features across data points. Cluster analysis is performed on the frequency-dimensional sub-features, grouping them according to similarity and calculating the clustering density within each group to obtain a cohesion index. The adjustment parameter is a pre-set value used to adjust the influence of the cohesion index on the overall calculation. The non-zero smoothing parameter is a pre-set value used to avoid zero values ​​or abnormal fluctuations in the dynamic weighting factor. The total number of terms is the total number of results obtained after statistically analyzing all sub-features in the time-frequency domain.

[0034] The contribution of distribution entropy and cohesion index is obtained through nonlinear coupling. This nonlinear coupling combines the distribution entropy and cohesion index according to specific mathematical logic, rather than a simple linear operation. It constructs a nonlinear relationship based on their characteristics to reflect the combined effect of sub-features. The dynamic weighting factor is calculated by first processing the distribution entropy value of each sub-feature, then multiplying the result by the power of the cohesion index's adjustment parameter, and finally adding a non-zero smoothing parameter to obtain the weighted calculation term for that sub-feature. The weighted calculation terms of all time-frequency domain sub-features are summed, and the weighted calculation term of a single sub-feature is divided by the sum of all sub-feature weighted calculation terms to obtain the dynamic weighting factor for that sub-feature. Through this series of calculations, corresponding weights are assigned to time-dimensional and frequency-dimensional sub-features with different contributions, and then the weighted coupling of the two yields the weighted mapping relationship of the joint time-frequency domain features.

[0035] When the distribution entropy value of a time-dimensional sub-feature increases, the calculated result of the distribution entropy value will also increase, and the weighted calculation term of that sub-feature will increase accordingly. With the sum of the weighted calculation terms of all sub-features remaining constant, the dynamic weighting factor of that sub-feature will increase accordingly. When the cohesion index of a frequency-dimensional sub-feature increases, the result of the power operation of the cohesion index adjustment parameter will increase, the corresponding weighted calculation term will increase, and the dynamic weighting factor will also increase accordingly. When the adjustment parameter increases, the influence of the cohesion index on the weighted calculation term will strengthen. If the cohesion index is positive, it will further increase the weighted calculation term, which may lead to an increase in the dynamic weighting factor. When the non-zero smoothing parameter increases, the weighted calculation terms of all sub-features will increase synchronously. However, because the increase in the weighted calculation terms of each sub-feature is consistent, the relative proportion of the dynamic weighting factor of each sub-feature changes relatively little, mainly playing a role in stabilizing the value of the dynamic weighting factor and avoiding abnormal fluctuations. When the total number of items increases, if the weighted calculation item of the newly added sub-feature is not zero, the sum of the weighted calculation items of all sub-features will increase. If the weighted calculation items of the original sub-features remain unchanged, the dynamic weighting factor of the original sub-features will decrease accordingly. If the weighted calculation item of the newly added sub-feature is large, the decrease in the dynamic weighting factor of the original sub-features will be more obvious.

[0036] Each sub-feature is assigned a weight based on its corresponding dynamic weighting factor and the corresponding frequency sub-feature. Sub-features with higher weights play a more prominent role in the integration process. The weighted time and frequency sub-features are organically combined to ensure that the integrated features not only fully retain the key information of the time and frequency dimensions, but also clearly reflect the differences in importance of different sub-features through weight allocation. This relationship, which includes the weight allocation and interrelationship of sub-features, is the weight mapping relationship of the time-frequency domain joint features.

[0037] Based on the weight values ​​of different sub-features in the weight mapping relationship, the criteria for dividing key features and secondary features in the joint features of the time and frequency domains are clarified. Focusing on the feature value range corresponding to the key features, a reasonable judgment criterion is determined in combination with the actual data features. This judgment criterion can accurately identify the feature information with significant meaning, while effectively eliminating the interference brought by secondary features. This judgment criterion is the adaptive decision threshold. Its specific value will be adjusted accordingly with the change of the weight mapping relationship to ensure a high degree of fit with the actual situation of the joint features of the time and frequency domains.

[0038] The beneficial effects are that through a systematic and meticulous time-frequency domain feature processing workflow, key information can be accurately extracted from regular chromatographic data. The resulting adaptive decision threshold is highly consistent with the actual characteristics of the data, effectively improving the accuracy of subsequent candidate peak interval significance determination. At the same time, it allows each sub-feature in the time-frequency domain joint feature to play its role according to its own importance, reducing the interference of invalid information on subsequent processing, thereby improving the overall efficiency and reliability of chromatograph component separation.

[0039] S3. Determine the significance of the candidate peak intervals based on the adaptive decision threshold to obtain the final chromatographic peak; In this embodiment of the invention, the step of determining the significance of the candidate peak interval based on the adaptive decision threshold to obtain the final chromatographic peak includes: Based on the adaptive decision threshold, peak intensity comparison is performed on the candidate peak intervals to obtain the preliminary significant peaks of the candidate peak intervals; The signal-to-noise ratio of the preliminary significant peak is evaluated to obtain the effective peak of the preliminary significant peak; Based on the effective peak, the candidate peak range is adapted and optimized to obtain the final chromatographic peak of the chromatograph.

[0040] Extract the intensity data of all peaks within each candidate peak interval, compare the intensity value of each peak with the adaptive decision threshold one by one, and filter out the peaks with intensity values ​​higher than the adaptive decision threshold. These filtered peaks are the preliminary significant peaks of the candidate peak interval.

[0041] The signal strength and background noise intensity of each preliminary significant peak are determined. The signal strength is judged by the peak height of the preliminary significant peak. The background noise intensity is obtained by statistically analyzing the signal fluctuation values ​​in the non-interference area around the preliminary significant peak and calculating its average value. Then, the ratio of the signal strength to the background noise intensity of each preliminary significant peak is calculated. Preliminary significant peaks whose ratios meet the preset qualified standards are retained. These peaks are the valid peaks among the preliminary significant peaks.

[0042] The peak shape characteristics, peak position coordinates, and peak width data of all valid peaks are analyzed. Based on the characteristic parameters of these valid peaks, the original candidate peak intervals are adjusted, and redundant peak segments that are not determined to be valid peaks within the candidate peak intervals are eliminated. At the same time, the peak segments where the valid peaks are located are calibrated to ensure that the start and end positions of the peak intervals accurately match the actual coverage range of the valid peaks. All peak segments corresponding to the calibrated valid peaks are integrated to form the final chromatographic peaks of the chromatograph.

[0043] The beneficial effects are as follows: by standardizing the raw component data of the chromatograph through spline interpolation and baseline drift correction, and by constructing a weight mapping relationship by combining the dimensional decomposition, contribution evaluation and dynamic weighted coupling of joint time-frequency domain features, the adaptive decision threshold is accurately determined, and the candidate peak interval is efficiently and significantly determined to obtain high-quality final chromatographic peaks. Then, by locating overlapping peak regions, extracting overlapping signals and estimating parameters to split overlapping signals, accurate sub-peak parameters are obtained. Through parameter decoupling and quantitative inversion of independent component analysis, accurate component quantitative information is obtained. Finally, the component separation report is output in a structured and integrated manner, which comprehensively improves the efficiency, accuracy and completeness of component separation of the chromatograph and provides reliable data support for component analysis.

[0044] S4. Evaluate the overlap of the final chromatographic peaks to obtain the overlap signal of the chromatograph, and perform parameter co-estimation on the overlap signal to obtain the sub-peak parameters of the chromatograph; In this embodiment of the invention, the step of evaluating the overlap of the final chromatographic peak to obtain the overlap signal of the chromatograph, and performing parameter co-estimation on the overlap signal to obtain the sub-peak parameters of the chromatograph, includes: Peak region localization is performed on the final chromatographic peak to obtain the overlapping peak region in the final chromatographic peak; The overlapping signal of the chromatograph is obtained by extracting the overlapping signal from the overlapping peak region; The parameters of the overlapping signals are analyzed to obtain the initial sub-peak parameters of the overlapping signals; Based on the initial sub-peak parameters, the overlapping signals are calibrated and optimized to obtain the sub-peak parameters of the chromatograph.

[0045] Obtain complete data information for the final chromatographic peaks, including the retention time range, peak height, peak width, and complete peak shape characteristics for each peak, ensuring coverage of all signal data from start to finish for each peak. Each final chromatographic peak is analyzed individually, with a focus on comparing the retention time intervals of adjacent peaks to determine if there is any temporal overlap. Simultaneously, the peak shape curves are analyzed to observe whether there is partial or complete overlap. A peak overlap determination criterion is established: when the overlap ratio of the retention time intervals of two adjacent peaks exceeds a preset threshold, or when the cross-coverage area of ​​the peak shape curves reaches a certain level, the region is marked as an overlapping peak region, precisely pinpointing the specific range of all peak overlaps in the final chromatographic peaks.

[0046] For the identified overlapping peak regions, signal extraction techniques are used to accurately extract all original signal data within these regions, ensuring that no signal information related to the overlapping peaks is missed. Targeted signal filtering algorithms are employed to filter out irrelevant signals and environmental noise interference outside the overlapping peak regions, focusing on the composite signal formed by the superposition of multiple sub-peaks within these regions. Signal decomposition techniques are then used to separate the signals corresponding to each overlapping sub-peak in the composite signal based on the differences in signal characteristics, eliminating signal interference from non-overlapping parts, ultimately obtaining a high-purity chromatograph overlapping signal containing only the contribution of the overlapping peaks.

[0047] Using commonly used peak shape models in chromatographic analysis, such as Gaussian peak models, Lorentz peak models, or mixed peak models, as a reference for parameter analysis, a detailed analysis of the waveform profile of the overlapping signals is conducted. By identifying inflection points, peak points, and signal intensity variation trends in the signal waveform, the approximate boundary range of each sub-peak is preliminarily delineated, and the start and end signal points of each sub-peak are determined. Based on the delineated sub-peak boundaries, the core parameters of each sub-peak are preliminarily estimated, including the retention time corresponding to the sub-peak apex, the signal intensity at the highest point of the sub-peak (peak height), the span of the sub-peak on the baseline (peak width), and the peak width at half the peak height (half-peak width). These preliminarily estimated parameters are the initial sub-peak parameters of the overlapping signals.

[0048] The obtained initial sub-peak parameters are substituted into a preset peak shape fitting model. The model generates simulated overlapping signal waveforms, ensuring that the generated simulated waveforms strictly adhere to the initial sub-peak parameter settings. The simulated overlapping signal waveforms are compared point-by-point with the actually detected overlapping signal waveforms. The deviation between the simulated and actual signal intensities at each corresponding time point is calculated, and the overall deviation is statistically analyzed, such as by calculating the sum of squares or the average deviation. Based on the magnitude and distribution of the deviations, the initial parameters of each sub-peak are adjusted accordingly: if the vertex position of the simulated peak deviates from the actual peak, the retention time of the sub-peak is adjusted; if the height of the simulated peak does not match the actual peak, the peak height parameter is corrected; if the peak width is inconsistent, the peak width and half-peak width parameters are adjusted. This iterative process of fitting, comparing, and adjusting is repeated. After each adjustment, the simulated waveform is regenerated and the deviation is calculated until the deviation between the simulated waveform and the actual overlapping signal waveform is less than a preset acceptable threshold. The sub-peak parameters obtained at this point are the sub-peak parameters of the chromatograph after multiple calibrations and optimizations, achieving the required accuracy.

[0049] The beneficial effects include: by comprehensively collecting final chromatographic peak data and accurately comparing peak shape and retention time characteristics, precise location of overlapping peak regions was achieved, avoiding omissions or misjudgments of overlapping peaks; through targeted signal extraction, filtering, and decomposition techniques, pure overlapping signals were effectively extracted, eliminating irrelevant signals and noise interference, providing a high-quality signal foundation for subsequent parameter analysis; using classic peak shape models for parameter analysis, combined with iterative fitting comparisons and parameter adjustments, precise optimization of initial sub-peak parameters was achieved, significantly improving the accuracy and reliability of sub-peak parameters, ensuring that key indicators such as retention time, peak height, and peak width of each sub-peak highly match the actual situation. These advantages not only provide solid and accurate data support for subsequent sub-peak parameter decoupling and component quantification information acquisition, but also further guarantee the overall quality of chromatograph component separation, improve the reliability of component analysis results, and meet the needs of precise separation and analysis of multiple components in complex samples.

[0050] S5. Decouple the sub-peak parameters to obtain the component quantification information of the chromatograph and output the component separation report of the chromatograph.

[0051] In this embodiment of the invention, the step of decoupling the sub-peak parameters to obtain the component quantification information of the chromatograph and outputting the component separation report of the chromatograph includes: Independent component analysis is performed on the sub-peak parameters to obtain the component parameters of the sub-peak parameters; Based on the independent component parameters, quantitative inversion is performed on the regular chromatographic data to obtain the component quantitative information of the chromatograph; The quantitative information of the components and the final chromatographic peaks are structurally integrated to obtain the comprehensive data of the chromatograph; The aggregated data is output as a component separation report for the chromatograph.

[0052] All calibrated and optimized sub-peak parameters were collected, including key information such as retention time, peak height, peak width, and half-maximum width for each sub-peak, ensuring complete characteristic data for each sub-peak was covered. Independent component analysis (ICA) was then used to process these sub-peak parameters. This algorithm, by mining the statistical independence of the signals in the sub-peak parameters, decomposes the mixed and interfering sub-peak signals into uncorrelated independent signal components, eliminating cross-interference factors between different sub-peaks, and finally obtaining component parameters that can individually and clearly reflect the inherent characteristics of each component.

[0053] The standardized chromatographic data obtained earlier, having eliminated noise and baseline drift interference, is retrieved, ensuring stable data quality. Independent component parameters are correlated and matched with the standardized chromatographic data to clarify the intrinsic correspondence between each indicator in the independent component parameters and the content of the corresponding component in the sample. For example, a mathematical correlation model is established between parameters such as peak height and peak width and component concentration. Based on this correlation model, quantitative inversion calculations are performed. The actual content of each component in the sample, including key quantitative indicators such as component concentration and purity, is derived from the specific values ​​of the independent component parameters. These indicators constitute the component quantitative information of the chromatograph.

[0054] This process involves compiling comprehensive quantitative information for each component, including its name, content, and purity. It also includes organizing relevant characteristic data for the final chromatographic peaks, covering peak coordinates, shape parameters, and retention time ranges. Following a unified logical structure and data format, the quantitative information is integrated with the peak characteristics. For example, using the retention time of each component as a link, the quantitative data is matched one-to-one with the corresponding peak characteristics, forming comprehensive chromatographic data that includes all information about sample component types, quantitative indicators, and peak characteristics. This ensures the data is systematic and interconnected.

[0055] Based on industry standards and practical application needs, a structured framework for the component separation report is designed. This framework must include core modules such as data source descriptions, an overview of the data processing workflow, component analysis results, and details of chromatographic peak characteristics. The comprehensive data is then filled in and formatted according to the report framework, presenting a clear classification, such as displaying basic sample information, processing steps, quantitative results for each component, and corresponding chromatographic peak annotations in separate chapters. The data in the report is verified to ensure accuracy and standardized presentation. The final result is a complete, standardized, and easily interpretable component separation report for the chromatograph, readily available for users to use directly for experimental analysis, result archiving, or subsequent applications.

[0056] The beneficial effects include: precise processing of peak parameters through independent component analysis, effectively separating mutually interfering signal components, significantly improving the purity and independence of component parameters, and providing highly reliable basic data for subsequent quantitative analysis; quantitative inversion based on independent component parameters and regular chromatographic data fully utilizes the advantages of high-quality data, significantly improving the accuracy and precision of component quantitative information, ensuring that it can truly reflect the actual content of each component in the sample; the structured integration of component quantitative information with the final chromatographic peaks makes the comprehensive data have both quantitative indicators and peak shape characteristics, resulting in stronger data integrity and correlation; the final output structured component separation report is not only clear and comprehensive, but also simplifies the user's interpretation and usage of the analysis results, comprehensively improving the practicality and analytical efficiency of chromatograph component separation, and providing strong support for component analysis in various scientific research and production scenarios.

[0057] Example 2 like Figure 2 As shown in the figure, this embodiment also provides a functional block diagram of a chromatograph component separation system.

[0058] The chromatographic component separation system 100 described in this embodiment can be installed in a terminal. Depending on the functions implemented, the chromatographic component separation system 100 may include a data processing module 101, a weighted mapping relationship generation module 102, a peak significance determination module 103, an overlapping peak analysis module 104, and a report generation module 105. 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 terminal processor and perform a fixed function, stored in the terminal's memory.

[0059] In this embodiment, the functions of each module / unit are as follows: The data processing module 101 is used to standardize the raw component data of the chromatograph to obtain the regular chromatographic data of the chromatograph, and to perform preliminary peak detection on the regular chromatographic data to obtain the candidate peak range of the chromatograph. The weight mapping relationship generation module 102 is used to perform importance weighted analysis on the time-frequency domain joint features of the chromatograph, obtain the weight mapping relationship of the time-frequency domain joint features, and determine the adaptive decision threshold of the chromatograph based on the weight mapping relationship. The peak significance determination module 103 is used to determine the significance of the candidate peak interval according to the adaptive decision threshold to obtain the final chromatographic peak. The overlapping peak analysis module 104 is used to evaluate the overlap of the final chromatographic peak, obtain the overlapping signal of the chromatograph, and perform parameter co-estimation on the overlapping signal to obtain the sub-peak parameters of the chromatograph. The report generation module 105 is used to decouple the sub-peak parameters, obtain the component quantification information of the chromatograph, and output the component separation report of the chromatograph.

[0060] In detail, each module of the chromatographic component separation system 100 described in the embodiments of the present invention uses the same technical means as the chromatographic component separation method described in Embodiment 1 and Embodiment 2, and can produce the same technical effect, which will not be repeated here.

[0061] Example 3 like Figure 3 As shown, this embodiment also provides a computer 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 executable on the processor 10, such as a chromatograph component separation program.

[0062] 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 terminal, connecting various components of the terminal via various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a chromatograph component separation program) and calls data stored in the memory 11 to perform various functions of the terminal and process data.

[0063] 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, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the terminal, such as the portable hard drive of the terminal. In other embodiments, the memory 11 can also be an external storage device of the terminal, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal storage units and external storage devices of the terminal. The memory 11 can be used not only to store application software and various types of data installed on the terminal, such as the code of a chromatograph component separation program, but also to temporarily store data that has been output or will be output.

[0064] 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.

[0065] The communication interface 13 is used for communication between the aforementioned terminal and other 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 communication connections between the terminal and other 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 terminal and to display a visual user interface.

[0066] The figure only shows a terminal with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the terminal and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0067] For example, although not shown, the 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 system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, a recharging system, a power fault detection circuit, a power converter or inverter, a power status indicator, or any other components. The terminal may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0068] 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.

[0069] The memory 11 in the terminal stores a chromatograph component separation program, which is a combination of multiple instructions. When run in the processor 10, it can achieve the following: S1. Standardize the raw component data of the chromatograph to obtain regular chromatographic data of the chromatograph, and perform preliminary peak detection on the regular chromatographic data to obtain the candidate peak range of the chromatograph. S2. Perform importance-weighted analysis on the time-frequency domain joint features of the chromatograph to obtain the weight mapping relationship of the time-frequency domain joint features, and determine the adaptive decision threshold of the chromatograph based on the weight mapping relationship; S3. Determine the significance of the candidate peak intervals based on the adaptive decision threshold to obtain the final chromatographic peak; S4. Evaluate the overlap of the final chromatographic peaks to obtain the overlap signal of the chromatograph, and perform parameter co-estimation on the overlap signal to obtain the sub-peak parameters of the chromatograph; S5. Decouple the sub-peak parameters to obtain the component quantification information of the chromatograph and output the component separation report of the chromatograph.

[0070] 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.

[0071] Furthermore, if the modules / units integrated into the 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 system 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).

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed terminals, systems, and methods can be implemented in other ways. For example, the system 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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 chromatographic component separation method, characterized in that, The method includes: S1. Standardize the raw component data of the chromatograph to obtain regular chromatographic data of the chromatograph, and perform preliminary peak detection on the regular chromatographic data to obtain the candidate peak range of the chromatograph. S2. Perform importance-weighted analysis on the time-frequency domain joint features of the chromatograph to obtain the weight mapping relationship of the time-frequency domain joint features, and determine the adaptive decision threshold of the chromatograph based on the weight mapping relationship; S3. Determine the significance of the candidate peak intervals based on the adaptive decision threshold to obtain the final chromatographic peak; S4. Evaluate the overlap of the final chromatographic peaks to obtain the overlap signal of the chromatograph, and perform parameter co-estimation on the overlap signal to obtain the sub-peak parameters of the chromatograph; S5. Decouple the sub-peak parameters to obtain the component quantification information of the chromatograph and output the component separation report of the chromatograph.

2. The chromatographic component separation method according to claim 1, characterized in that, The raw component data of the chromatograph is standardized to obtain regularized chromatographic data of the chromatograph, and preliminary peak detection is performed on the regularized chromatographic data to obtain candidate peak ranges of the chromatograph, including: Obtain raw component data from the chromatograph; Spline interpolation is performed on the raw component data of the chromatograph to obtain smoothed data of the chromatograph; The smoothed data is corrected for baseline drift to obtain the regularized chromatographic data of the chromatograph. The coordinates of the regularized chromatographic data are calibrated to obtain the potential peak coordinates of the regularized chromatographic data; Based on the potential peak position coordinates, the regular chromatographic data is divided into target peak segments to obtain the candidate peak intervals of the chromatograph.

3. The chromatographic component separation method according to claim 1, characterized in that, The step of performing importance-weighted analysis on the joint time-frequency features of the chromatograph to obtain the weight mapping relationship of the joint time-frequency features, and determining the adaptive decision threshold of the chromatograph based on the weight mapping relationship, includes: The regularized chromatographic data are subjected to time-frequency transformation to obtain the time-frequency spectrum sequence of the chromatograph; Multi-scale feature extraction is performed on the time-frequency spectrum sequence to obtain the joint time-frequency domain features of the chromatograph; The contribution of the joint time-frequency domain features is evaluated to obtain the weight mapping relationship of the joint time-frequency domain features; Based on the weight mapping relationship, threshold adaptation is performed on the joint time-frequency domain features to obtain the adaptive decision threshold of the chromatograph.

4. The chromatographic component separation method as described in claim 3, characterized in that, The step of evaluating the contribution of the joint time-frequency features to obtain the weight mapping relationship of the joint time-frequency features includes: The time-frequency domain joint features are decomposed into time dimension sub-features and frequency dimension sub-features. A morphological dispersion analysis is performed on the time-dimensional sub-features to obtain the distribution entropy value of the time-dimensional sub-features; Cluster analysis is performed on the frequency dimension sub-features to obtain the cohesion index of the frequency dimension sub-features; The contribution of the time-frequency domain joint feature is obtained by nonlinearly coupling the distribution entropy value and the cohesion index. Based on the contribution, a dynamic weighting factor is calculated for fusing the time-dimension sub-features and the frequency-dimension sub-features, wherein the formula for the dynamic weighting factor is as follows: ； in, For the first The dynamic weighting factor of the time-frequency domain sub-features, For the first The distribution entropy value of the item feature, For the first Cohesion index of item characteristics This is an adjustment parameter for the degree of influence of the cohesion index. The non-zero smoothing parameter of the dynamic weighting factor is, The total number of terms in the time-frequency domain sub-features; Based on the dynamic weighting factor, the time dimension sub-features and the frequency dimension sub-features are weighted and coupled to obtain the weight mapping relationship of the time-frequency domain joint features.

5. The chromatographic component separation method according to claim 1, characterized in that, The step of determining the significance of the candidate peak interval based on the adaptive decision threshold to obtain the final chromatographic peak includes: Based on the adaptive decision threshold, peak intensity comparison is performed on the candidate peak intervals to obtain the preliminary significant peaks of the candidate peak intervals; The signal-to-noise ratio of the preliminary significant peak is evaluated to obtain the effective peak of the preliminary significant peak; Based on the effective peak, the candidate peak range is adapted and optimized to obtain the final chromatographic peak of the chromatograph.

6. The chromatographic component separation method according to claim 1, characterized in that, The process of evaluating the overlap of the final chromatographic peak to obtain the overlap signal of the chromatograph, and performing parameter co-estimation on the overlap signal to obtain the sub-peak parameters of the chromatograph, includes: Peak region localization is performed on the final chromatographic peak to obtain the overlapping peak region in the final chromatographic peak; The overlapping signal of the chromatograph is obtained by extracting the overlapping signal from the overlapping peak region; The parameters of the overlapping signals are analyzed to obtain the initial sub-peak parameters of the overlapping signals; Based on the initial sub-peak parameters, the overlapping signals are calibrated and optimized to obtain the sub-peak parameters of the chromatograph.

7. The chromatographic component separation method according to claim 1, characterized in that, The process of decoupling the sub-peak parameters to obtain the component quantification information of the chromatograph and outputting the component separation report of the chromatograph includes: Independent component analysis is performed on the sub-peak parameters to obtain the component parameters of the sub-peak parameters; Based on the independent component parameters, quantitative inversion is performed on the regular chromatographic data to obtain the component quantitative information of the chromatograph; The quantitative information of the components and the final chromatographic peaks are structurally integrated to obtain the comprehensive data of the chromatograph; The aggregated data is output as a component separation report for the chromatograph.

8. A chromatographic component separation system for implementing the chromatographic component separation method of claim 1, characterized in that, The system includes: The data processing module is used to standardize the raw component data of the chromatograph to obtain the regular chromatographic data of the chromatograph, and to perform preliminary peak detection on the regular chromatographic data to obtain the candidate peak range of the chromatograph. The weight mapping relationship generation module is used to perform importance weighting analysis on the time-frequency domain joint features of the chromatograph, obtain the weight mapping relationship of the time-frequency domain joint features, and determine the adaptive decision threshold of the chromatograph based on the weight mapping relationship. The peak significance determination module is used to determine the significance of the candidate peak intervals based on the adaptive decision threshold to obtain the final chromatographic peak; The overlapping peak resolution module is used to evaluate the overlap of the final chromatographic peaks, obtain the overlapping signal of the chromatograph, and perform parameter co-estimation on the overlapping signal to obtain the sub-peak parameters of the chromatograph. The report generation module is used to decouple the sub-peak parameters, obtain the component quantification information of the chromatograph, and output the component separation report of the chromatograph.

9. A computer terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

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

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