Construction of Multi-component Characteristic Chromatogram of Tianyuanzhi Tong Granules and Quality Control Method

Abstract

The present application relates to the technical field of quality control, in particular to a method for constructing a multi-component characteristic chromatogram of Tianyuan Zhitong granules and quality control, comprising the following steps: obtaining chromatogram information of multiple batches of reference samples, extracting common peak data to build an initial data set, calculating the area peak height ratio variation coefficient to screen standard peaks, constructing a standard proportion order structure, determining the quality grade of the chromatogram, verifying the consistency of the proportion order structure, and obtaining stability information. In the present application, the relative area proportion value of the standard characteristic peak is extracted and a deviation tolerance interval is introduced to realize proportion quantitative control. The proportion order structure is constructed to identify the content order relationship between components. The proportion order structure consistency state of the key index components is used to enhance the structure stability judgment power between batches, improve the identification efficiency and response accuracy of the multi-component proportion structure in quality control, and solve the problem of insufficient proportion structure representation ability between components in the traditional chromatogram evaluation mode.

Description

Technical Field

[0001] This invention relates to the field of quality control technology, and in particular to the construction of multi-component characteristic maps and quality control methods for Tianyuan Zhitong granules. Background Technology

[0002] The field of quality control technology encompasses quality evaluation methods, testing technologies, standard setting, and data comparison methods for products such as pharmaceuticals, food, cosmetics, and chemical raw materials throughout their production and use. Core aspects of this field include, but are not limited to, raw material quality assessment, consistency control of the preparation process, evaluation of finished product quality stability, and its correlation with clinical efficacy. In the field of traditional Chinese medicine preparations, the key to quality control lies in constructing a repeatable, traceable, and quantifiable evaluation system tailored to the characteristics of complex multi-component, multi-target systems. This ensures that drugs maintain a consistent efficacy base and safety across different batches and process conditions. In recent years, with the development of analytical techniques, quality control has gradually transitioned from traditional single-indicator detection to comprehensive evaluation methods based on multi-component detection, fingerprint analysis, and characteristic spectrum construction. These methods reveal the overall compositional characteristics of traditional Chinese medicine compound preparations, providing a scientific basis for product quality.

[0003] The traditional method for constructing and controlling the multi-component characteristic chromatograms of Tianyuan Zhitong granules refers to the extraction of representative active and excipient components from Tianyuan Zhitong granule-type traditional Chinese medicine preparations. This involves qualitative and quantitative detection using chromatographic analysis, and the establishment of a chromatographic system reflecting the quality characteristics of the finished product based on the detection data. This method typically includes the following technical aspects: selecting representative target components such as gastrodin, paeonol, and ligustilide as quality evaluation objects; using high-performance liquid chromatography (HPLC) to separate and detect samples, establishing chromatograms for multiple batches of samples by comparing parameters such as peak area and retention time; constructing characteristic chromatograms based on the number of common chromatographic peaks and their relative retention times; using thin-layer chromatography (TLC) to qualitatively identify major components such as chuanxiong, angelica, and corydalis (processed with vinegar) to verify the source of the components; and finally, confirming the attribution of the obtained chromatographic information data with the identified components to form a chromatographic model that characterizes the product's quality stability. This method uses standard references and typical chromatographic behavior as a basis for sample comparison to complete a comprehensive evaluation of drug quality.

[0004] Traditional control methods compare batch-to-batch quality using chromatograms of representative components, relying on parameters such as the number of common peaks, retention time, and peak area for overall matching. However, these methods lack a detailed characterization of the structural stability of multiple components and fail to model and quantify the relative content ratios of each component. In practical applications, fluctuations in the content of some components are easily masked by global matching. This is especially true when there are slight process deviations or differences in raw materials among multiple batches of samples. Traditional methods struggle to reveal structural imbalances between components, affecting the effective identification of batch consistency and resulting in insufficient dimensions for quality evaluation. Consequently, they are unable to support batch quality grading management under high standards. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules, including the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules, comprising the following steps: S1: Obtain chromatographic detection information of reference samples from multiple batches of Tianyuan Zhitong granules, extract the retention time, area and peak height of common peaks of gastrodin, paeonol and ligustilide, and establish an initial liquid chromatography dataset; S2: Calculate the common peak area-to-peak-height ratio sequence of the initial liquid chromatography dataset, calculate the coefficient of variation using the standard deviation and the mean, screen common peaks with a preset stability threshold for the coefficient of variation, and determine the standard characteristic chromatographic peaks. S3: Calculate the relative area ratio of the standard characteristic chromatographic peaks, construct the standard deviation tolerance range, and generate the standard proportion sequence structure based on the relative area ratio of key components; S4: Collect the liquid chromatography detection data of the sample to be tested, calculate the target relative area ratio of the standard characteristic chromatographic peaks corresponding to the liquid chromatography detection data and the difference from the standard deviation tolerance range, and determine the quality level of the chromatogram. S5: Extract the peak area of ​​key components from the liquid chromatography detection data of the sample to be tested, generate a target proportion sequence structure based on the proportion of the peak area of ​​key components, verify the consistency between the target proportion sequence structure and the standard proportion sequence structure, and obtain the multi-component structure stability information between batches.

[0006] As a further aspect of the present invention, the initial liquid chromatography dataset includes sample batch index number, original chromatographic response signal and full-band spectral scanning matrix; the standard characteristic chromatographic peaks include common peak identification number, characteristic peak spectral fingerprint and standard peak height ratio reference value; the standard proportional ordinal structure includes key component sorting index, relative area weight vector and ordinal hierarchy correlation matrix; the chromatographic quality level includes deviation interval location identifier, component fluctuation risk index and final compliance judgment label; and the batch-to-batch multi-component structural stability information includes ordinal consistency check code, structural variability score and batch stability confidence level.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Based on reference samples of multiple batches of Tianyuan Zhitong granules, the chromatographic column temperature is set to be constant and the flow rate is controlled to be stable. The absorbance change signal of the effluent is continuously collected at the preset detection wavelength to generate the original chromatographic response signal sequence including the light signal intensity at each time point. S102: Perform baseline smoothing and correction on the original chromatographic response signal sequence, locate the start and end points of the chromatographic peaks, identify common peaks including gastrodin, paeonol and ligustilide based on the retention time range window of the standard, and extract the retention time, integral area and peak response height of each common peak through integral calculation to obtain the set of peak morphology parameters of key components. S103: For the set of peak morphology parameters of the key components, add corresponding sample batch index labels, perform structured mapping and association on the retention time, integral area and vertex height data of all common peaks in the same batch, remove abnormal outliers and aggregate them according to the sample collection order to establish an initial liquid chromatography dataset.

[0008] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: For each common peak indexed in the initial liquid chromatography dataset, extract its integral area value and vertex response height data in multiple detection batches, divide the integral area value of the same common peak in each batch by the corresponding vertex response height data, obtain the ratio value, and arrange them in batch order to generate a peak area to peak height ratio sequence. S202: Based on the peak area and peak height ratio sequence, the arithmetic mean and the overall standard deviation of the numerical distribution in the sequence are calculated using statistical algorithms. The overall standard deviation is divided by the arithmetic mean to obtain the coefficient of variation index. S203: Call the coefficient of variation index of each common peak, call the preset stability threshold, compare the coefficient of variation index with the preset stability threshold, filter out common peaks whose coefficient of variation index is less than the preset stability threshold, lock the retention time index and corresponding spectral fingerprint data of the common peaks, and determine the standard characteristic chromatographic peaks.

[0009] As a further aspect of the present invention, the stability threshold is specifically set by arranging the coefficient of variation data of all common peaks in the reference sample in ascending order, constructing a coefficient of variation distribution curve, calculating the first derivative of the coefficient of variation distribution curve, identifying the inflection point where the derivative value undergoes a step change, and determining the coefficient of variation value corresponding to the inflection point as the stability threshold.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Call the integral area data of the standard characteristic chromatographic peaks, perform an accumulation operation on the integral area data of all the standard characteristic chromatographic peaks in the same spectrum, obtain the total response area representing the total amount of substances, divide the integral area data of each standard characteristic chromatographic peak by the total response area, quantify the relative proportion weight of the characteristic peak in the overall spectrum, and generate a relative area ratio value. S302: Summarize the relative area ratio values ​​of multiple batches in the reference sample set, classify and statistically analyze the numerical distribution pattern of each characteristic peak in the historical batches according to the peak index, calculate the arithmetic mean and discrete standard deviation of the relative area ratio values ​​of each peak using statistical methods, construct a dynamic floating area with the arithmetic mean as the central benchmark value and covering a preset multiple of the standard deviation range, define the allowable fluctuation boundary of each peak in the qualified samples, and establish the standard deviation tolerance interval; S303: Select data items corresponding to the preset key components of gastrodin, paeonol and ligustilide from the relative area ratio values, sort the relative area ratio values ​​of the selected key components in descending order according to the numerical value, determine the relative content hierarchy of the key components in the compound preparation system, construct a benchmark logical topology that characterizes the intrinsic quantitative dependence relationship between multiple components, and generate a standard proportion sequence structure.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Collect liquid chromatography detection data of the sample to be tested, retrieve the corresponding chromatographic peaks in the data based on the retention time and spectral characteristics of the standard characteristic chromatographic peaks, perform peak area integration calculation and accumulate the areas of all corresponding chromatographic peaks to obtain the total response value, divide the area value of each corresponding chromatographic peak by the total response value, calculate the relative proportion weight of each characteristic component in the chromatographic system of the sample to be tested, and generate the target relative area ratio. S402: For each characteristic peak, call the target relative area ratio and introduce the standard deviation tolerance interval. Extract the central reference value and range boundary parameters of the standard deviation tolerance interval. Analyze the deviation of the target relative area ratio from the central reference value through subtraction operation, or calculate the overflow amount of its exceeding the range boundary parameters. Perform absolute value and standardization processing on the calculated deviation value or overflow amount to quantify the difference between the sample characteristics and the standard model and generate characteristic deviation measurement data. S403: Obtain the feature deviation measurement data and load the preset dynamic segmentation interval. Compare the feature deviation measurement data with the boundary value of each segmentation interval to identify the numerical range segment into which the data falls. Based on the quality evaluation logic and compliance definition of segment mapping, determine the specific quality level of the sample under test in the multi-component feature distribution and generate the spectrum quality level.

[0012] As a further aspect of the present invention, the process of setting the dynamic segmentation interval is specifically as follows: extracting the arithmetic mean of the relative area ratio data of the corresponding characteristic peaks in the reference sample set as the central benchmark, calculating the overall standard deviation of the data, and using the integer multiple of the overall standard deviation as the boundary threshold of each segment, dividing a number of continuous and non-overlapping numerical range regions on the number axis with the central benchmark as the origin as the dynamic segmentation interval.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Perform full-spectrum scanning and characteristic peak matching on the liquid chromatography detection data of the sample to be tested, locate the chromatographic peak positions corresponding to the preset key indicator components using the retention time window, extract the chromatographic peak areas of key indicator components such as gastrodin, paeonol and ligustilide and perform addition operations to obtain the total area of ​​key components, divide the independent chromatographic peak area of ​​each key indicator component by the total area of ​​key components to generate a key component proportion feature set; S502: Call the weight values ​​in the key component proportion feature set, sort the weight data of each component in descending order according to the value, parse the actual content hierarchy of each key indicator component in the sample to be tested, construct a dynamic topological sequence that maps the correspondence between the component name and its current position, convert the physical sorting features of each component into a mathematical ordinal index, and establish the target proportion ordinal structure. S503: Obtain the target proportional sequence structure and introduce the standard proportional sequence structure. Map and compare the component arrangement order in the two structures position by position. Verify the relative hierarchical relationship of each key component in the sample under test and the consistency constraint state of the standard model. Calculate the sequence overlap degree or inversion number index to quantify the internal structural differences. Based on the specific state of the difference index falling into the preset judgment logic, evaluate the fluctuation attributes of the sample's internal quality system and determine the stability information of the multi-component structure between batches.

[0014] As a further aspect of the present invention, the process of verifying the consistency constraint state between the relative hierarchical relationship of each key component in the sample to be tested and the standard model, calculating the order overlap degree or inversion number index, and quantifying the internal structural differences specifically involves: setting the standard proportional order structure as the benchmark reference sequence, extracting the inherent hierarchical position of each key component in the standard proportional order structure as the benchmark index value; reorganizing the benchmark index values ​​corresponding to the key components into a digital sequence to be tested according to the actual arrangement order of each key component in the target proportional order structure; performing a full sequence scan on the digital sequence to be tested, counting the number of element pairs whose positions are earlier but whose values ​​are greater than those of later positions, and generating an inversion number index; The process of assessing the fluctuation attributes of the sample's intrinsic quality system and determining the inter-batch multi-component structural stability information based on the specific state of the difference index falling into the preset judgment logic is as follows: A preset order structure variation tolerance threshold is invoked. If the inversion number index is zero, it is confirmed that the multi-component arrangement of the sample to be tested completely coincides with the standard model, and the inter-batch multi-component structural stability information is determined to be strictly consistent. If the inversion number index is within the open interval between zero and the order structure variation tolerance threshold, the inter-batch multi-component structural stability information is determined to be relatively stable within the allowable deviation range. If the inversion number index is greater than or equal to the order structure variation tolerance threshold, the inter-batch multi-component structural stability information is determined to be structurally abnormal.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a stability screening mechanism based on the coefficient of variation of the ratio of peak area to peak height of key components is constructed. Characteristic peaks with stable component expression are clearly identified as spectral benchmarks. By extracting the relative area ratio of standard characteristic peaks and introducing a deviation tolerance interval, quantitative control of the ratio is achieved. The content order relationship between components is identified by constructing a proportional sequence structure to determine the integrity of the component structure. The quality grade of the spectral sample is divided by setting dynamic segment intervals. The consistency of the proportional sequence structure of key indicator components is compared to enhance the judgment of structural stability between batches. This improves the identification efficiency and response accuracy of multi-component proportional structure in quality control and solves the problem of insufficient representation ability of proportional structure between components in traditional spectral evaluation modes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] Please see Figure 1 This invention provides a method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules, including the following steps: S1: Obtain chromatographic detection information of reference samples from multiple batches of Tianyuan Zhitong granules, extract the retention time, peak area and peak height data of all common peaks of gastrodin, paeonol and ligustilide in the chromatographic detection information, and establish an initial liquid chromatography dataset; S2: Calculate the peak area to peak height ratio sequence for each common peak in the initial liquid chromatography dataset, calculate the coefficient of variation index of the peak area to peak height ratio sequence based on the standard deviation and average value, screen common peaks with a coefficient of variation index lower than the preset stability threshold, and determine the standard characteristic chromatographic peaks. S3: Calculate the relative area ratio of the peak area of ​​the standard characteristic chromatographic peak to the total area of ​​all standard characteristic chromatographic peaks, construct the standard deviation tolerance intervals distinguished by peak position based on the relative area ratio values, and generate the standard ratio sequence structure according to the order of the relative area ratio values ​​of the key components. S4: Collect the liquid chromatography detection data of the sample to be tested, calculate the target relative area ratio of the corresponding standard characteristic chromatographic peak in the liquid chromatography detection data, calculate the difference data between the target relative area ratio and the standard deviation tolerance interval, and determine the chromatographic quality grade of the sample to be tested based on the specific position of the difference data falling into the preset dynamic segmented interval. S5: Extract the chromatographic peak area of ​​the preset key indicator components from the liquid chromatography detection data of the sample to be tested, generate the target proportion sequence structure according to the proportion of the chromatographic peak area to the total area of ​​the key indicator components, and determine the batch-to-batch multi-component structural stability information of the sample to be tested by verifying the consistency constraint state between the target proportion sequence structure and the standard proportion sequence structure.

[0021] The initial liquid chromatography dataset includes sample batch index numbers, raw chromatographic response signals, and a full-band spectral scanning matrix. Standard characteristic chromatographic peaks include common peak identification numbers, characteristic peak spectral fingerprints, and standard peak height ratio reference values. The standard proportional ordinal structure includes key component sorting indexes, relative area weight vectors, and ordinal hierarchy correlation matrices. The chromatographic quality level includes deviation interval location markers, component fluctuation risk indices, and final compliance judgment labels. The inter-batch multi-component structural stability information includes ordinal consistency check codes, structural variability scores, and batch stability confidence levels.

[0022] Please see Figure 2 The specific steps of S1 are as follows: S101: Based on reference samples of multiple batches of Tianyuan Zhitong granules, the chromatographic column temperature is set to be constant and the flow rate is controlled to be stable. The absorbance change signal of the effluent is continuously collected at the preset detection wavelength to generate the original chromatographic response signal sequence including the light signal intensity at each time point. Physical pretreatment was performed on reference samples from multiple batches of Tianyuan Zhitong granules. The ground granule powder was weighed and added to methanol solvent. After ultrasonic extraction, cooling, weight replenishment, and filtration through a microporous membrane, a test solution was prepared. The operating environment of a high-performance liquid chromatograph (HPLC) was then configured. An octadecylsilane-bonded silica column was selected as the packing material, and the column oven temperature was kept constant at 30°C to ensure the thermodynamic stability of the chromatographic separation process. Simultaneously, a binary mobile phase system consisting of acetonitrile and 0.1% phosphoric acid aqueous solution was constructed, and a flow rate controller was set to ensure the mobile phase flowed through the column at a rate of 1.0 mL per minute. During the detection phase, a diode array detector or ultraviolet detector was activated, and the detection wavelength was locked at 260 nm, which corresponds to the common maximum absorption band of key components such as gastrodin, paeonol, and ligustilide. During the gradient elution process of the mobile phase, the absorbance change signal of the effluent is continuously acquired at a sampling frequency of 10 Hz, that is, 10 light signal intensity values ​​are recorded per second. Each discrete moment on the time axis is mapped one-to-one with its corresponding light signal intensity value to generate a raw chromatographic response signal sequence containing timestamps and absorbance response values. For example, if the acquisition process lasts for 60 minutes, a sequence containing 36,000 data points will be generated, where the 1200th data point records an absorbance of 150 mAU at 2 minutes.

[0023] S102: Perform baseline smoothing and correction on the original chromatographic response signal sequence, locate the start and end points of the chromatographic peaks, identify common peaks including gastrodin, paeonol and ligustilide based on the retention time range window of the standard, and extract the retention time, integral area and peak response height of each common peak through integral calculation to obtain the set of peak morphology parameters of key components. The Savitsky-Gauley smoothing filter was applied to denoise the signal, with a window width of 15 data points and a third-order polynomial fitting to remove high-frequency random noise interference. Subsequently, the first and second derivatives of the smoothed signal were calculated. The zero-crossing point where the first derivative changes from positive to negative was identified as the peak position, and the inflection point of the peak was determined using the extreme points of the second derivative. The starting and ending points of the peak were then determined based on the slope threshold. Furthermore, the retention time range windows of standards were used; for example, the retention time window for gastrodin was set to 14.5 to 15.5 minutes, for paeonol to 35.0 to 36.0 minutes, and for ligustilide to 45.0 to 46.0 minutes. Common peaks falling within these windows were automatically matched and identified. For each identified common peak, the trapezoidal integral method is used to calculate the integral area by summing the products of the absorbance values ​​of all sampling points from the start point to the end point and the sampling time interval. Simultaneously, the absorbance value at the vertex position is directly read as the vertex response height. For example, for a chromatographic peak with a retention time of 15.2 minutes, time slices within the peak's range are traversed, and the integral area is calculated to be 125,000 microvolt-seconds. The vertex response height is then extracted as 1200 microvolts. This process gathers the morphology data of all common peaks, resulting in a set of key component peak morphology parameters.

[0024] S103: For the set of peak morphology parameters of key components, add corresponding sample batch index labels, perform structured mapping and association of the retention time, integral area and vertex height data of all common peaks in the same batch, remove abnormal outliers and aggregate them according to the sample collection order to establish an initial liquid chromatography dataset. Each data set is assigned a unique sample batch index label, such as "Batch_20240101_01," to establish a traceability identifier for the data. Next, a multidimensional data mapping table is constructed, arranging all common peaks identified within the same batch according to their retention time, and writing the retention time, integral area, and peak height of each peak into the corresponding attribute columns. Before data aggregation, outlier data cleaning is performed, calculating the average and standard deviation of the retention time of each common peak, and removing chromatographic peaks whose retention time deviates from the average by more than three times the standard deviation to eliminate misidentification caused by instrument fluctuations or impurities. The cleaned data is then row-level aggregated according to the sample collection time order or batch number order to form a structured two-dimensional matrix table. As shown in Table 1, this dataset clearly records the quantitative indicators of each characteristic peak in different batches, laying the foundation for subsequent statistical analysis and establishing the initial liquid chromatography dataset.

[0025] Table 1 Examples of Initial Liquid Chromatography Dataset Fragments Sample Batch Index Common Peak ID Retention time (min) Integral area (μV*s) Vertex response height (μV) Batch_001 Peak_01 (Gastrodin) 15.12 124500 1180 Batch_001 Peak_02 (Ligustilide) 22.45 89000 950 Batch_001 Peak_03 (Paeonol) 35.50 450200 5600 Batch_002 Peak_01 (Gastrodin) 15.15 126000 1195 Batch_002 Peak_03 (Paeonol) 35.48 448000 5580 Please see Figure 3 The specific steps of S2 are as follows: S201: For each common peak indexed in the initial liquid chromatography dataset, extract its integral area value and vertex response height data in multiple detection batches one by one. Divide the integral area value of the same common peak in each batch by the corresponding vertex response height data to obtain the ratio value describing the peak width characteristics and arrange them in batch order to generate a peak area to peak height ratio sequence. For a selected common peak, iterate through each batch of data, using the integral area value of that batch as the dividend and the corresponding vertex response height data as the divisor, and perform a division operation. This ratio physically reflects the "half-peak width" characteristic or peak sharpness of the chromatographic peak. The calculated ratio values ​​of all batches are arranged into a one-dimensional array according to the batch index. For example, for Peak_01 (gastrodin) in Table 1, in Batch_001, dividing the integral area 124500 by the vertex height 1180 yields a ratio of approximately 105.51; in Batch_002, dividing the integral area 126000 by the vertex height 1195 yields a ratio of approximately 105.44. This logic is applied to all batches, generating a numerical sequence for Peak_01 [105.51, 105.44, ...]. This operation is repeated for all common peaks, generating corresponding peak area and peak height ratio sequences respectively.

[0026] S202: Based on the peak area and peak height ratio sequence, the arithmetic mean and the population standard deviation of the numerical distribution in the sequence are calculated by statistical algorithms. The population standard deviation is divided by the arithmetic mean to construct a dimensionless discrete metric parameter, which quantifies the relative fluctuation of the morphological characteristics of each common peak among multiple batches of samples, and obtains the coefficient of variation index. The arithmetic mean is calculated by summing all values ​​in the sequence and dividing the sum by the total number of elements in the sequence. This value characterizes the central tendency of the common peak's morphological characteristics. Then, the difference between each value in the sequence and the arithmetic mean is calculated. All differences are squared, summed, and then divided by the total number of elements minus one. Finally, the square root of the result is taken to obtain the population standard deviation, which reflects the dispersion of the data. Finally, the population standard deviation is used as the numerator, and the arithmetic mean as the denominator, and division is performed to obtain the coefficient of variation (CV). For example, if the mean of a ratio sequence of a common peak is 105.5 and the standard deviation is 1.055, then dividing 1.055 by 105.5 yields a coefficient of variation of 0.01 (i.e., 1%). This index eliminates the influence of dimensions and can objectively evaluate the morphological stability of different peak positions in multiple batches of production. The smaller the value, the higher the consistency of the peak shape between batches, thus obtaining the coefficient of variation index.

[0027] S203: Call the coefficient of variation index of each common peak, call the preset stability threshold, compare the coefficient of variation index with the preset stability threshold, filter out common peaks with coefficient of variation index less than the preset stability threshold, lock the retention time index and corresponding spectral fingerprint data of the common peak, and determine the standard characteristic chromatographic peak. The stability threshold is set by sorting the coefficient of variation data of all common peaks in the reference sample in ascending order, constructing a coefficient of variation distribution curve, calculating the first derivative of the coefficient of variation distribution curve, identifying the inflection point where the derivative value changes abruptly, and determining the coefficient of variation value corresponding to the inflection point as the stability threshold. Extract the coefficient of variation (COP) data of all common peaks from the reference sample and sort them in ascending order to generate a monotonically increasing COP distribution curve. Next, use the finite difference method to calculate the difference in COP between adjacent points on the curve, approximating the first derivative. Scan this derivative sequence to identify the position where the derivative value first shows a significant surge (e.g., the derivative value exceeds 5 times the derivative of the previous point), defining this position as the "inflection point of a step change." Set the COP value corresponding to this inflection point as a preset stability threshold. For example, if the sorted COPs are [0.005, 0.006, 0.008, 0.045, 0.050...], where there is a significant jump between 0.008 and 0.045, determine 0.008 as the upper bound of the stable interval, and set the stability threshold to 0.01. Subsequently, compare the COP of each common peak with this threshold of 0.01, retaining common peaks with COPs less than 0.01. Lock in the retention time index (such as Peak_01, Peak_03, etc.) and corresponding spectral fingerprint data of these preferred common peaks, establish them as the benchmark for subsequent quality control, and determine the standard characteristic chromatographic peaks.

[0028] Please see Figure 4 The specific steps of S3 are as follows: S301: Call the integrated area data of the standard characteristic chromatographic peaks, perform cumulative calculation on the integrated area data of all standard characteristic chromatographic peaks in the same spectrum, obtain the total response area representing the total amount of substances, divide the integrated area data of each standard characteristic chromatographic peak by the total response area, perform normalization processing, and eliminate the influence of injection volume differences and instrument response fluctuations on the absolute response value, quantify the relative proportion weight of the characteristic peaks in the overall spectrum, and generate a relative area ratio value. The process identifies all peaks marked as "standard characteristic peaks" in the same chromatogram and extracts their integrated area values. These values ​​are then summed to obtain the total response area of ​​the stable components in the chromatogram. Subsequently, for each standard characteristic peak, its individual integrated area value is used as the numerator, and the total response area is used as the denominator, and a division operation is performed. For example, if there are three standard characteristic peaks in the chromatogram with areas of 100,000, 200,000, and 700,000 respectively, the total response area is 1,000,000. After normalization, the relative area ratio of the first peak is 0.1, the second is 0.2, and the third is 0.7. This processing step converts absolute area values ​​into relative weightings, making chromatograms with different concentrations and injection volumes comparable and generating relative area ratio values.

[0029] S302: Summarize the relative area ratio values ​​of multiple batches in the reference sample set, classify and statistically analyze the numerical distribution pattern of each characteristic peak in the historical batches according to the peak index, calculate the arithmetic mean and discrete standard deviation of the relative area ratio value of each peak using statistical methods, construct a dynamic floating area with the arithmetic mean as the central benchmark value and covering the preset multiple standard deviation range, define the allowable fluctuation boundary of each peak in qualified samples, and establish the standard deviation tolerance interval. For each specific standard characteristic chromatographic peak (such as Peak_01 for gastrodin), its relative area ratio values ​​in all historical qualified batches are collected to form a sample set. Using statistical methods, the arithmetic mean (Mean) and standard deviation (SD) of this set are calculated. Based on this, a dynamic fluctuation range is constructed: the lower limit is set as "arithmetic mean minus 3 times the standard deviation," and the upper limit is set as "arithmetic mean plus 3 times the standard deviation." For example, for Peak_01, if the average ratio of historical data is 0.15 and the standard deviation is 0.005, then the lower limit is 0.135 and the upper limit is 0.165. This closed interval [0.135, 0.165] defines the allowable normal fluctuation range of this component in qualified products. Any value falling within this interval is considered to meet the standard deviation tolerance requirement, thus establishing the standard deviation tolerance range.

[0030] S303: Select data items corresponding to the preset key components of gastrodin, paeonol and ligustilide from the relative area ratio values, sort the relative area ratio values ​​of the selected key components in descending order according to the numerical value, determine the relative content hierarchy of the key components in the compound preparation system, construct the benchmark logical topology that characterizes the intrinsic quantitative dependence relationship between multiple components, and generate the standard ratio sequence structure. In a standard reference spectrum, the relative area ratios of ligustilide (0.45), paeonol (0.30), and gastrodin (0.15) are defined. Based on these values, a descending order is performed, resulting in the following sequence: ligustilide first, paeonol second, and gastrodin third. This specific order (ligustilide > paeonol > gastrodin) is solidified into a baseline logical topology, and the relative proportions of each component are recorded (e.g., ligustilide is approximately 3 times that of gastrodin). This structure is not merely a numerical record but also a fingerprint characteristic of the synergistic effects of multiple components within the compound preparation, used for subsequent verification of the structural integrity of the test sample and generation of a standard proportion sequence structure.

[0031] Please see Figure 5 The specific steps of S4 are as follows: S401: Collect liquid chromatography detection data of the sample to be tested, retrieve the corresponding chromatographic peaks in the data based on the retention time and spectral characteristics of the standard characteristic chromatographic peaks, perform peak area integration calculation and accumulate the areas of all corresponding chromatographic peaks to obtain the total response value, divide the area value of each corresponding chromatographic peak by the total response value, calculate the relative proportion weight of each characteristic component in the chromatographic system of the sample to be tested, and generate the target relative area ratio. Based on the retention time and spectral characteristics of standard characteristic chromatographic peaks, corresponding chromatographic peaks are retrieved and matched in the chromatogram of the sample to be tested. The integrated areas of these matched peaks are extracted, and their sum is calculated to obtain the total response value of the sample to be tested. Then, the area value of each corresponding chromatographic peak is divided by the total response value to obtain the proportion of each characteristic component in the sample to be tested. For example, if the sample to be tested contains ligustilide with a peak area of ​​400,000, paeonol with 300,000, gastrodin with 200,000, other standard peaks with 100,000, and a total response value of 1,000,000, then the target relative area proportions for ligustilide are calculated to be 0.40, paeonol with 0.30, and gastrodin with 0.20. This data accurately reflects the current component distribution state of the sample to be tested, generating the target relative area proportions.

[0032] S402: For each characteristic peak, call the target relative area ratio and introduce the standard deviation tolerance interval. Extract the center reference value and range boundary parameters of the standard deviation tolerance interval. Analyze the deviation of the target relative area ratio from the center reference value through subtraction operation, or calculate the overflow amount of its exceeding the range boundary parameters. Perform absolute value and standardization processing on the calculated deviation value or overflow amount to quantify the difference between the sample characteristics and the standard model and generate characteristic deviation measurement data. Extract the center reference value and range boundary parameters corresponding to the peak position. Calculate the algebraic difference between the target value and the center value, and take its absolute value to obtain the deviation value; or determine whether the target value exceeds the boundary. If it does, calculate the difference between it and the nearest boundary as the overflow amount. Then, divide this deviation value or overflow amount by the standard deviation and perform Z-score standardization to obtain a dimensionless deviation metric score. For example, if the target proportion of Peak_01 is 0.17, and the center of the standard interval is 0.15 with a standard deviation of 0.005, then the deviation value is |0.17-0.15|=0.02. Dividing it by the standard deviation of 0.005, the standardized characteristic deviation metric data is 4.0. This value intuitively represents that the component of the sample deviates from the center value by 4 standard deviations, generating characteristic deviation metric data.

[0033] S403: Acquire feature deviation measurement data and load preset dynamic segmented intervals. Compare the feature deviation measurement data with the boundary values ​​of each segmented interval to identify the numerical range segment into which the data falls. Based on the quality evaluation logic and compliance definition of segment mapping, determine the specific quality level of the sample under test on the multi-component feature distribution and generate the spectral quality level. The process of setting dynamic segmentation intervals is as follows: extract the arithmetic mean of the relative area ratio data of the corresponding characteristic peaks in the reference sample set as the central benchmark, calculate the overall standard deviation of the data, and use the integer multiple of the overall standard deviation as the boundary threshold of each segment. Divide several continuous and non-overlapping numerical range regions on the number axis with the central benchmark as the origin as the dynamic segmentation intervals.

[0034] The pre-defined dynamic segmentation interval rules are as follows: 0 to 1 standard deviation is "Excellent", 1 to 2 standard deviations is "Good", 2 to 3 standard deviations is "Acceptable", and greater than 3 standard deviations is "Abnormal". The characteristic deviation measurement data (e.g., 4.0) is compared with these boundary values ​​(1, 2, 3). Since 4.0 is greater than 3, the data is identified as falling into the "Abnormal" segment. Combining the segmentation points of all characteristic peaks, if all peaks are within 3, the spectrum quality is deemed acceptable; if any peak falls into the "Abnormal" segment, an alert is triggered. This step, through a segmentation mapping mechanism, transforms complex continuous numerical values ​​into discrete quality level labels, such as "Level 1 (Excellent)", "Level 2 (Acceptable)", or "Unacceptable", generating the spectrum quality level.

[0035] Please see Figure 6 The specific steps of S5 are as follows: S501: Perform full-spectrum scanning and characteristic peak matching on the liquid chromatography detection data of the sample to be tested. Use the retention time window to locate the chromatographic peak position corresponding to the preset key indicator components. Extract the chromatographic peak area of ​​key indicator components such as gastrodin, paeonol and ligustilide and perform addition operation to obtain the total area of ​​key components. Divide the independent chromatographic peak area of ​​each key indicator component by the total area of ​​key components. Quantify the relative weight distribution of the monomer components within the key component set through normalization calculation to generate the key component proportion feature set. Only three key indicator components were identified: gastrodin, paeonol, and ligustilide. Their chromatographic peak areas were extracted and set as follows: gastrodin 200,000, paeonol 300,000, and ligustilide 400,000. An addition operation was performed to calculate the total area of ​​the key components as 200,000 + 300,000 + 400,000 = 900,000. Subsequently, local normalization was performed: gastrodin proportion = 200,000 / 900,000 ≈ 0.222; paeonol proportion = 300,000 / 900,000 ≈ 0.333; ligustilide proportion = 400,000 / 900,000 ≈ 0.444. This set of values ​​eliminates interference from background components and purely reflects the relative proportions of the core active ingredients, generating a key component proportion feature set.

[0036] S502: Call the weight values ​​in the key component proportion feature set, sort the weight data of each component in descending order according to the value, analyze the actual content hierarchy of each key indicator component in the sample to be tested, construct a dynamic topological sequence that maps the component name and its current position, transform the physical ordering features of each component into a mathematical ordinal index, digitally characterize the relative strength pattern and dependency structure among multiple component material groups inside the sample, and establish the target proportion ordinal structure. The calculated weight values ​​(0.222, 0.333, 0.444) were compared and sorted in descending order. The sorting result was: 0.444 (ligustilide) > 0.333 (paeonol) > 0.222 (gastrodin). Based on this, a component position mapping for the sample was established: ligustilide is the first, paeonol is the second, and gastrodin is the third. This physical order was converted into a numerical index sequence, for example, represented by component IDs as [ID_Lig, ID_Pae, ID_Gas]. This sequence structurally expresses the strength distribution pattern of key components in the sample, establishing a target proportion order structure.

[0037] S503: Obtain the target proportion sequence structure and introduce the standard proportion sequence structure. Map and compare the component arrangement order in the two structures position by position. Verify the relative hierarchical relationship of each key component in the sample to be tested and the consistency constraint state of the standard model. Calculate the sequence overlap degree or inversion number index to quantify the internal structural differences. According to the specific state of the difference index falling into the preset judgment logic, evaluate the fluctuation attributes of the sample's internal quality system and determine the stability information of the multi-component structure between batches. The process of verifying the consistency between the relative hierarchical relationship of each key component in the sample and the standard model, and calculating the order overlap or inversion number index to quantify the internal structural differences is as follows: The standard proportional order structure is set as the benchmark reference sequence; the inherent hierarchical position of each key component in the standard proportional order structure is extracted as the benchmark index value; according to the actual arrangement of each key component in the target proportional order structure, the benchmark index values ​​corresponding to the key components are reorganized into a digital sequence to be tested; a full sequence scan is performed on the digital sequence to be tested, and the number of element pairs whose positions are earlier but whose values ​​are greater than those of later positions is counted to generate the inversion number index. Based on the specific state of the difference index falling into the preset judgment logic, the process of assessing the fluctuation attributes of the sample's intrinsic quality system and determining the batch-to-batch multi-component structural stability information is as follows: A preset order structure variation tolerance threshold is invoked. If the inversion number index is zero, it confirms that the multi-component arrangement of the sample to be tested completely coincides with the standard model, and the batch-to-batch multi-component structural stability information is determined to be strictly consistent. If the inversion number index is within the open interval between zero and the order structure variation tolerance threshold, the batch-to-batch multi-component structural stability information is determined to be relatively stable within the allowable deviation range. If the inversion number index is greater than or equal to the order structure variation tolerance threshold, the batch-to-batch multi-component structural stability information is determined to be structurally abnormal. Set the standard proportional sequence structure as the baseline, for example, the standard order is "Gastrodin (1)-Paeonol (2)-Ligusticol (3)" (the standard is set in ascending order or a specific order, which must be consistent with the previous logic. The previous standard is set as Ligusticol > Paeonol > Gastrodin, i.e., sequence ABC). Extract the target proportional sequence structure of the sample to be tested, and set the actual arrangement of the sample to be tested as "Paeonol-Ligusticol-Gastrodin". Replace the components in the sequence to be tested with the baseline index values ​​in the standard sequence. If the standard is A>B>C (index 1, 2, 3), and the sample to be tested is B>A>C, then the sequence of numbers to be tested is [2, 1, 3]. Next, calculate the number of inversions in the sequence: examine (2, 1), 2>1, constituting 1 inversion; examine (2, 3), 2<3, normal; examine (1, 3), 1<3, normal. The total number of inversions is 1. Finally, according to the preset judgment logic: if the number of inversions is 0, it is judged as "strictly consistent"; if the number of inversions is 1 (between 0 and the tolerance threshold of 2), it is judged as "relatively stable"; if the number of inversions is greater than or equal to 2, it is judged as "structural anomaly". Through this mathematical topological comparison, the inversion of the relative relationship between components can be keenly captured, thereby determining the structural stability information of multi-components between batches.

[0038] Table 2. Example of logic for determining the stability of multi-component structures between batches. Sample number to be tested Target proportion order structure (components from high to low) Corresponding benchmark index sequence Inversion number calculation result Stability determination conclusion Sample_001 Ligustilide > Paeonol > Gastrodin [1，2，3] 0 Strict consistency Sample_002 Ligustilide > Gastrodin > Paeonol [1，3，2] 1 Relatively stable Sample_003 Gastrodin > Ligustilide > Paeonol [3，1，2] 2 Structural variation The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules, characterized in that, Includes the following steps: S1: Obtain chromatographic detection information of reference samples from multiple batches of Tianyuan Zhitong granules, extract the retention time, area and peak height of common peaks of gastrodin, paeonol and ligustilide, and establish an initial liquid chromatography dataset; S2: Calculate the common peak area-to-peak-height ratio sequence of the initial liquid chromatography dataset, calculate the coefficient of variation using the standard deviation and the mean, screen common peaks with a preset stability threshold for the coefficient of variation, and determine the standard characteristic chromatographic peaks. S3: Calculate the relative area ratio of the standard characteristic chromatographic peaks, construct the standard deviation tolerance range, and generate the standard proportion sequence structure based on the relative area ratio of key components; S4: Collect the liquid chromatography detection data of the sample to be tested, calculate the target relative area ratio of the standard characteristic chromatographic peaks corresponding to the liquid chromatography detection data and the difference from the standard deviation tolerance range, and determine the quality level of the chromatogram. S5: Extract the peak area of ​​key components from the liquid chromatography detection data of the sample to be tested, generate a target proportion sequence structure based on the proportion of the peak area of ​​key components, verify the consistency between the target proportion sequence structure and the standard proportion sequence structure, and obtain the multi-component structure stability information between batches.

2. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 1, characterized in that, The initial liquid chromatography dataset includes sample batch index number, original chromatographic response signal and full-band spectral scanning matrix. The standard characteristic chromatographic peaks include common peak identification number, characteristic peak spectral fingerprint and standard peak height ratio reference value. The standard proportional ordinal structure includes key component sorting index, relative area weight vector and ordinal hierarchy correlation matrix. The chromatographic quality level includes deviation interval location identifier, component fluctuation risk index and final compliance judgment label. The batch-to-batch multi-component structural stability information includes ordinal consistency check code, structural variability score and batch stability confidence level.

3. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Based on reference samples of multiple batches of Tianyuan Zhitong granules, the chromatographic column temperature is set to be constant and the flow rate is controlled to be stable. The absorbance change signal of the effluent is continuously collected at the preset detection wavelength to generate the original chromatographic response signal sequence including the light signal intensity at each time point. S102: Perform baseline smoothing and correction on the original chromatographic response signal sequence, locate the start and end points of the chromatographic peaks, identify common peaks including gastrodin, paeonol and ligustilide based on the retention time range window of the standard, and extract the retention time, integral area and peak response height of each common peak through integral calculation to obtain the set of peak morphology parameters of key components. S103: For the set of peak morphology parameters of the key components, add corresponding sample batch index labels, perform structured mapping and association on the retention time, integral area and vertex height data of all common peaks in the same batch, remove abnormal outliers and aggregate them according to the sample collection order to establish an initial liquid chromatography dataset.

4. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: For each common peak indexed in the initial liquid chromatography dataset, extract its integral area value and vertex response height data in multiple detection batches, divide the integral area value of the same common peak in each batch by the corresponding vertex response height data, obtain the ratio value, and arrange them in batch order to generate a peak area to peak height ratio sequence. S202: Based on the peak area and peak height ratio sequence, the arithmetic mean and the overall standard deviation of the numerical distribution in the sequence are calculated using statistical algorithms. The overall standard deviation is divided by the arithmetic mean to obtain the coefficient of variation index. S203: Call the coefficient of variation index of each common peak, call the preset stability threshold, compare the coefficient of variation index with the preset stability threshold, filter out common peaks whose coefficient of variation index is less than the preset stability threshold, lock the retention time index and corresponding spectral fingerprint data of the common peaks, and determine the standard characteristic chromatographic peaks.

5. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 4, characterized in that, The stability threshold is set by arranging the coefficient of variation data of all common peaks in the reference sample in ascending order, constructing a coefficient of variation distribution curve, calculating the first derivative of the coefficient of variation distribution curve, identifying the inflection point where the derivative value undergoes a step change, and determining the coefficient of variation value corresponding to the inflection point as the stability threshold.

6. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Call the integral area data of the standard characteristic chromatographic peaks, perform an accumulation operation on the integral area data of all the standard characteristic chromatographic peaks in the same spectrum, obtain the total response area representing the total amount of substances, divide the integral area data of each standard characteristic chromatographic peak by the total response area, quantify the relative proportion weight of the characteristic peak in the overall spectrum, and generate a relative area ratio value. S302: Summarize the relative area ratio values ​​of multiple batches in the reference sample set, classify and statistically analyze the numerical distribution pattern of each characteristic peak in the historical batches according to the peak index, calculate the arithmetic mean and discrete standard deviation of the relative area ratio values ​​of each peak using statistical methods, construct a dynamic floating area with the arithmetic mean as the central benchmark value and covering a preset multiple of the standard deviation range, define the allowable fluctuation boundary of each peak in the qualified samples, and establish the standard deviation tolerance interval; S303: Select data items corresponding to the preset key components of gastrodin, paeonol and ligustilide from the relative area ratio values, sort the relative area ratio values ​​of the selected key components in descending order according to the numerical value, determine the relative content hierarchy of the key components in the compound preparation system, construct a benchmark logical topology that characterizes the intrinsic quantitative dependence relationship between multiple components, and generate a standard proportion sequence structure.

7. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Collect liquid chromatography detection data of the sample to be tested, retrieve the corresponding chromatographic peaks in the data based on the retention time and spectral characteristics of the standard characteristic chromatographic peaks, perform peak area integration calculation and accumulate the areas of all corresponding chromatographic peaks to obtain the total response value, divide the area value of each corresponding chromatographic peak by the total response value, calculate the relative proportion weight of each characteristic component in the chromatographic system of the sample to be tested, and generate the target relative area ratio. S402: For each characteristic peak, call the target relative area ratio and introduce the standard deviation tolerance interval. Extract the central reference value and range boundary parameters of the standard deviation tolerance interval. Analyze the deviation of the target relative area ratio from the central reference value through subtraction operation, or calculate the overflow amount of its exceeding the range boundary parameters. Perform absolute value and standardization processing on the calculated deviation value or overflow amount to quantify the difference between the sample characteristics and the standard model and generate characteristic deviation measurement data. S403: Obtain the feature deviation measurement data and load the preset dynamic segmentation interval. Compare the feature deviation measurement data with the boundary value of each segmentation interval to identify the numerical range segment into which the data falls. Based on the quality evaluation logic and compliance definition of segment mapping, determine the specific quality level of the sample under test in the multi-component feature distribution and generate the spectrum quality level.

8. The method for constructing and quality controlling the multi-component feature map of Tianyuan Zhitong granules according to claim 7, wherein the process of setting the dynamic segmentation interval is as follows: extracting the arithmetic mean of the relative area ratio data of the corresponding feature peaks in the reference sample set as the central benchmark, calculating the overall standard deviation of the data, and using the integer multiple of the overall standard deviation as the boundary threshold of each segment, dividing a number of continuous and non-overlapping numerical range regions on the number axis with the central benchmark as the origin as the dynamic segmentation interval.

9. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Perform full-spectrum scanning and characteristic peak matching on the liquid chromatography detection data of the sample to be tested, locate the chromatographic peak positions corresponding to the preset key indicator components using the retention time window, extract the chromatographic peak areas of key indicator components such as gastrodin, paeonol and ligustilide and perform addition operations to obtain the total area of ​​key components, divide the independent chromatographic peak area of ​​each key indicator component by the total area of ​​key components to generate a key component proportion feature set; S502: Call the weight values ​​in the key component proportion feature set, sort the weight data of each component in descending order according to the value, parse the actual content hierarchy of each key indicator component in the sample to be tested, construct a dynamic topological sequence that maps the correspondence between the component name and its current position, convert the physical sorting features of each component into a mathematical ordinal index, and establish the target proportion ordinal structure. S503: Obtain the target proportional sequence structure and introduce the standard proportional sequence structure. Map and compare the component arrangement order in the two structures position by position. Verify the relative hierarchical relationship of each key component in the sample under test and the consistency constraint state of the standard model. Calculate the sequence overlap degree or inversion number index to quantify the internal structural differences. Based on the specific state of the difference index falling into the preset judgment logic, evaluate the fluctuation attributes of the sample's internal quality system and determine the stability information of the multi-component structure between batches.

10. The method for constructing and quality controlling the multi-component characteristic spectrum of Tianyuan Zhitong granules according to claim 9, characterized in that, The process of verifying the consistency constraint state between the relative hierarchical relationship of each key component in the test sample and the standard model, calculating the order overlap degree or inversion number index, and quantifying the internal structural differences is as follows: the standard proportional order structure is set as the benchmark reference sequence, and the inherent hierarchical position of each key component in the standard proportional order structure is extracted as the benchmark index value. According to the actual arrangement of each key component in the target proportional sequence structure, the benchmark index values ​​corresponding to the key components are reorganized into a sequence of numbers to be inspected; a full sequence scan is performed on the sequence of numbers to be inspected, and the number of pairs of elements whose positions are earlier but whose values ​​are greater than those of the later positions is counted to generate an inversion index. The process of assessing the fluctuation attributes of the sample's intrinsic quality system and determining the inter-batch multi-component structural stability information based on the specific state of the difference index falling into the preset judgment logic is as follows: A preset order structure variation tolerance threshold is invoked. If the inversion number index is zero, it is confirmed that the multi-component arrangement of the sample to be tested completely coincides with the standard model, and the inter-batch multi-component structural stability information is determined to be strictly consistent. If the inversion number index is within the open interval between zero and the order structure variation tolerance threshold, the inter-batch multi-component structural stability information is determined to be relatively stable within the allowable deviation range. If the inversion number index is greater than or equal to the order structure variation tolerance threshold, the inter-batch multi-component structural stability information is determined to be structurally abnormal.

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