A method for monitoring and analyzing the quality of Chinese medicine raw materials

Abstract

The present invention relates to the technical field of drug analysis, specifically a method for monitoring and analyzing the quality of Chinese medicine raw materials, comprising the following steps: merging intervals with consistent fluctuation directions based on storage environment data, extracting component extraction rates to identify trend values, screening overlapping segments of concentration and impurity changes to generate abnormal batch indexes, tracking humidity and efficacy deviation segments to cluster early warning intervals, and analyzing grade label fluctuations to determine a quality stability list. In the present invention, by merging intervals with consistent directions of temperature, humidity, and light data sampled at equal intervals, the stability of environmental influences is dynamically defined, the accuracy of the starting point of quality analysis is enhanced, the concentration difference and duration are quantified in combination with the extraction rate fluctuation trend, the identifiability of component changes is improved, the overlapping concentration and impurity fluctuations are used to construct a pointer list and fuse the sampling distribution, the batch anomaly positioning is strengthened, the quality label change path and mutation point are analyzed, the multi-source data stability logic chain is reconstructed, and the efficiency and accuracy of Chinese medicine raw material quality screening are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drug analysis, and in particular to a method for monitoring and analyzing the quality of Chinese medicine raw materials. Background Art

[0002] The field of pharmaceutical analysis technology includes technical methods for researching, testing and quality assessment of pharmaceutical raw materials and their preparations through various analytical means. The core content of this field is to ensure the safety, effectiveness and stability of drugs, including qualitative and quantitative analysis of traditional Chinese medicine components, impurities and active ingredients. Pharmaceutical analysis technology is widely used in drug research and development, production, quality control and related fields. It aims to ensure that the quality of drugs meets standards and meets usage requirements through scientific and precise analytical means. Common analytical methods include chromatography, spectral analysis, mass spectrometry and other technologies, which can provide accurate component analysis and quantitative evaluation.

[0003] Among them, the quality monitoring and analysis methods for Chinese medicine raw materials refer to methods for comprehensively monitoring and evaluating the quality of Chinese medicine raw materials through a series of analytical techniques. The technical matters targeted include monitoring the active ingredients and their content in Chinese medicine raw materials, identifying and analyzing impurities, and evaluating quality stability. By using technical means such as high-performance liquid chromatography, gas chromatography, and mass spectrometry, combined with standardized quality control methods for Chinese medicine raw materials, the Chinese medicine raw materials are accurately analyzed for composition, content determination, and purity verification. This method effectively solves the problem of quality fluctuations in Chinese medicine raw materials during the production and distribution process, ensuring that the quality of Chinese medicine products meets the specified standards.

[0004] Although existing technologies provide qualitative and quantitative analysis methods, they have obvious shortcomings in dealing with the quality changes of Chinese medicine raw materials caused by time and environmental fluctuations. They lack the ability to identify the dynamic interaction between environmental parameters and quality indicators, and rely only on sampling and analysis at fixed time points, which cannot cover the delayed and cumulative effects of environmental changes on the extraction effect of components. In practical applications, traditional methods often use high-performance liquid chromatography, gas chromatography and other technologies to directly measure the levels of components or impurities, ignoring the continuity of the process and its inherent connection with batch fluctuations, resulting in difficulty in accurately attributing the analysis results, affecting the formulation of quality control strategies. For example, when the same batch of Chinese medicine raw materials shows differences in extraction rate under different storage conditions, conventional technologies cannot locate the impact of environmental intervention based on time period characteristics, nor can they achieve traceability and clustering of quality anomalies between batches, further causing uncertainty in the standardization process of Chinese medicine and one-sided quality assessment. This limitation of lacking modeling of temporal relationships and the joint effects of multiple parameters can easily lead to errors in identifying quality fluctuations and deviations in stability evaluation, reducing the scientific nature and operability of overall quality supervision. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method for monitoring and analyzing the quality of Chinese medicine raw materials.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for monitoring and analyzing the quality of Chinese medicine raw materials, comprising the following steps:

[0007] S1: Based on the storage area information of traditional Chinese medicine raw materials, the storage temperature probe data, humidity monitor data and light receiver values ​​are extracted. Each data item is grouped into equal interval groups according to the sampling time node. The fluctuation direction of the values ​​in each group is determined to be consistent. The common interval features with consistent directions are extracted to obtain the stable interval of environmental impact;

[0008] S2: Based on the stable period of environmental impact, extract the effective ingredient extraction rate data, identify the interval of unidirectional fluctuation in the continuous time period, and record its concentration difference and duration, identify the trend indicator, and obtain the concentration fluctuation trend value of the traditional Chinese medicine component;

[0009] S3: Call the concentration fluctuation trend value of the traditional Chinese medicine component, screen the overlapping sections with high concentration changes and increased impurity ratios, build a pointer list based on the frequency of overlapping occurrences and batch numbers, and record the distribution characteristics of the sampling point sequence of the processing section to generate an index table of quality abnormal batches;

[0010] S4: Based on the quality abnormality batch index table, track the humidity curve and efficacy maintenance time of each batch, identify the deviation distance, and cluster similar deviation segments by batch, determine the time axis span interval, and obtain the batch fluctuation warning segment interval.

[0011] As a further solution of the present invention, the environmental impact stable segment interval includes a data fluctuation consistency feature segment, a temperature, humidity and light combined stable interval, and a stable value within an equally spaced sampling group; the Chinese medicine ingredient concentration fluctuation trend value includes a concentration change amplitude, a fluctuation duration, and a unidirectional change direction; the quality abnormality batch index table includes a high-amplitude concentration fluctuation segment, an impurity ratio abnormal overlapping segment, a batch abnormality frequency pointer, and a processing sampling sequence feature; the batch fluctuation warning segment interval includes a humidity curve deviation, a drug efficacy maintenance time difference segment, a batch clustering feature group, and a time span aggregation segment.

[0012] As a further solution of the present invention, the steps for obtaining the environmental impact stable segment are specifically as follows:

[0013] S111: Based on the information of the storage area of ​​Chinese medicinal raw materials, extract the temperature probe data, humidity monitor data and light receiver value, synchronize them according to the sampling time node, divide the data into time periods at uniform intervals, and identify the direction of the value change of the three data in each period. Select the time period intervals with the same direction to generate the environmental trend coordination interval segment;

[0014] S112: Call the environmental trend collaborative interval segment, extract the quality inspection values ​​of the corresponding batches of Chinese medicine raw materials in the interval, analyze the corresponding differences between the environmental trend and the quality parameters in the segment based on the time mapping relationship between the interval and the raw material batches, identify the time period set where the quality remains stable under the continuous trend, and obtain the environmental impact stable segment interval.

[0015] As a further solution of the present invention, the step of obtaining the concentration fluctuation trend value of the traditional Chinese medicine component is specifically as follows:

[0016] S211: Based on the environmental impact stable interval, extract the extraction rate data of the active ingredients in the traditional Chinese medicine raw materials, identify the unidirectional fluctuation segments of the ingredients in the time dimension, select the time intervals in which the extraction rate continuously increases and decreases, record the start and end times of the intervals and the corresponding extraction rates, calculate the extraction rate change and duration of each segment, and generate a unidirectional fluctuation extraction feature segment;

[0017] S212: Call the extraction rate change and duration of each segment in the one-way fluctuation extraction feature segment, combine the fluctuation frequency and change intensity of the Chinese medicine ingredients in the corresponding segment, analyze the concentration trend structure relationship, extract the composite trend index of the fluctuation in the time and component attribute dimensions, and obtain the concentration fluctuation trend value of the Chinese medicine ingredients.

[0018] As a further solution of the present invention, the steps for obtaining the quality abnormality batch index table are specifically as follows:

[0019] S311: Calling the concentration fluctuation trend value of the traditional Chinese medicine component, setting the concentration fluctuation amplitude threshold and the impurity ratio change rate threshold, performing interval scanning on the time series data of each batch, calculating the segment abnormality index, and obtaining the overlapping segment identification value;

[0020] S312: Based on the overlapping segment identification value, extract the batch number and overlap frequency within the corresponding segment, analyze the mapping relationship between the batch number and the frequency, sort the batches whose frequency exceeds a preset benchmark by timestamp, and generate a batch overlap frequency mapping table;

[0021] S313: Call the batch overlap frequency mapping table, associate the spatial sequence of the processing section sampling points, identify the distribution density and the overlap rate of the overlapping area, extract the supercritical batches and sort them by priority, and generate an index table for batches with abnormal quality.

[0022] As a further solution of the present invention, the step of obtaining the batch fluctuation warning section interval is specifically as follows:

[0023] S411: Based on the quality abnormal batch index table, collect the time series data of the humidity curve and the duration of drug efficacy of each batch, calculate the humidity-duration deviation, and perform index mapping based on the batch number to generate the humidity-drug efficacy deviation;

[0024] S412: calling the humidity-drug efficacy deviation, identifying the deviation difference between adjacent batches on the time axis, dividing batches with a difference value less than a set clustering threshold into the same group, and calculating the timestamp coverage of each group to obtain a time span clustering result;

[0025] S413: Based on the time span clustering results, the earliest storage timestamp and the latest drug expiration timestamp of each group are extracted, the groups whose time difference exceeds the standard storage period are marked as abnormal, and the time intervals of the abnormal units are integrated to obtain the batch fluctuation warning section interval.

[0026] As a further embodiment of the present invention, the method further comprises step S5:

[0027] S5: Call the batch fluctuation warning segment interval, extract the processing record number, focus on the change rate of the quality grade label value within the number segment, identify the rising or falling path of the grade label within the fluctuation segment, determine whether there is a mutation point that deviates from the normal trend, and obtain a list of Chinese medicine raw material quality stability judgments;

[0028] The Chinese medicine raw material quality stability determination list includes quality grade change paths, grade mutation nodes, and trend deviation identification results.

[0029] As a further solution of the present invention, the steps for obtaining the Chinese medicine raw material quality stability determination list are specifically as follows:

[0030] S511: Call the batch fluctuation warning segment interval, extract all processing record numbers within the interval, locate the start and end index values ​​of the number segment, identify the quality data screening range, and obtain the quality data extraction index interval;

[0031] S512: Extracting an index interval based on the quality data, extracting the quality grade labels of the Chinese medicinal raw materials within the interval, analyzing the variation range of adjacent label values, identifying the label variation trend path through the variation direction and continuity, and obtaining the quality variation trend path of the Chinese medicinal raw materials;

[0032] S513: Call the quality change trend path of the Chinese medicinal raw materials, identify the reversal node of the change direction of any continuous label value, count the frequency of mutation points and compare it with the set trend mutation frequency threshold, determine whether there is a structural deviation in the current quality trend, and obtain a Chinese medicinal raw material quality stability judgment list.

[0033] Compared with the prior art, the advantages and positive effects of the present invention are:

[0034] In the present invention, by merging the fluctuation directions of temperature, humidity and light data within equally spaced sampling times and extracting intervals with consistent directions, a dynamic definition of the stability of environmental influences is achieved, so that the subsequent screening of quality influencing factors has an accurate starting point, avoiding interference with analysis results due to unstable time periods. The extraction rate of effective ingredients is linked to environmental segments, continuous unidirectional fluctuations are identified and their concentration differences and durations are quantified, which enhances the recognizability of component change trends and promotes the clarity of concentration fluctuation directions in the time dimension. On the basis of identifying overlapping segments of high-amplitude concentration changes and rising impurity ratios, a pointer list is constructed by integrating frequency and batch numbers, driving the improvement of the accuracy of abnormal sample identification, and strengthening the spatial correlation of data with the distribution characteristics of sampling points, effectively supporting the structured archiving of batch anomalies, tracking the humidity curve of the incoming warehouse and the efficacy maintenance interval, and constructing a deviation trend map with deviation measurement. Clustering and integration are performed based on similar segments between batches to effectively establish a causal logical relationship between time span segments and quality fluctuations. In the analysis of the change rate of quality label values, we focus on the path trend within the numbered paragraphs, combine mutation point identification to enhance the fluctuation identification capability, and achieve accurate judgment of the label path trend deviation, forming a stability judgment system based on multi-source data logical path reconstruction, which significantly improves the efficiency and accuracy of quality stability screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0036] Figure 2 This is a flow chart for obtaining the environmental impact stable period in the present invention;

[0037] Figure 3 This is a flow chart for obtaining the concentration fluctuation trend value of the traditional Chinese medicine ingredients in the present invention;

[0038] Figure 4 This is a flow chart for obtaining the quality abnormality batch index table in the present invention;

[0039] Figure 5 This is a flow chart for obtaining the batch fluctuation warning section interval in the present invention;

[0040] Figure 6 The figure is a flow chart for obtaining the quality stability determination list of Chinese medicinal raw materials in the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0043] Example 1:

[0044] See also Figure 1 The present invention provides a technical solution: a method for monitoring and analyzing the quality of Chinese medicine raw materials, comprising the following steps:

[0045] S1: Based on the storage area information of traditional Chinese medicine raw materials, the storage temperature probe data, humidity monitor data and light receiver values ​​are extracted. Each data item is grouped into equal interval groups according to the sampling time node. The fluctuation direction of the values ​​in each group is determined to be consistent. The common interval features with consistent directions are extracted to obtain the stable interval of environmental impact;

[0046] S2: Based on the stable interval of environmental impact, extract the effective ingredient extraction rate data, identify the interval of unidirectional fluctuation in the continuous time period, and record its concentration difference and duration, identify the trend indicator, and obtain the concentration fluctuation trend value of the traditional Chinese medicine ingredients;

[0047] S3: Call the concentration fluctuation trend value of traditional Chinese medicine ingredients, screen the overlapping sections with high concentration changes and increased impurity ratios, build a pointer list based on the frequency of overlap and batch number, and record the distribution characteristics of the sampling point sequence in the processing section to generate an index table for quality abnormal batches;

[0048] S4: Based on the quality abnormal batch index table, track the humidity curve and efficacy maintenance time of each batch, identify the deviation distance, and cluster similar deviation segments by batch, determine the time axis span, and obtain the batch fluctuation warning segment interval;

[0049] S5: Call the batch fluctuation warning segment interval, extract the processing record number, focus on the change rate of the quality grade label value within the number segment, identify the rising or falling path of the grade label within the fluctuation segment, determine whether there is a mutation point that deviates from the normal trend, and obtain a list of quality stability judgments for Chinese medicine raw materials.

[0050] The environmental impact stability segment includes the data fluctuation consistency feature segment, the temperature, humidity and light combined stability segment, and the stable value within the equally spaced sampling group. The concentration fluctuation trend value of Chinese medicine ingredients includes the concentration change amplitude, fluctuation duration, and unidirectional change direction. The quality abnormality batch index table includes the high-amplitude concentration fluctuation segment, the impurity ratio abnormal overlapping segment, the batch abnormality frequency pointer, and the processing sampling sequence characteristics. The batch fluctuation warning segment includes the humidity curve deviation, the efficacy maintenance time difference segment, the batch clustering feature group, and the time span aggregation segment. The Chinese medicine raw material quality stability judgment list includes the quality grade change path, grade mutation node, and trend deviation identification result.

[0051] See also Figure 2 , the specific steps for obtaining the environmental impact stable segment are:

[0052] S111: Based on the information of the storage area of ​​Chinese medicinal raw materials, extract the temperature probe data, humidity monitor data and light receiver value, synchronize them according to the sampling time node, divide the data into time periods at uniform intervals, and identify the direction of the value change of the three data in each period. Select the time period intervals with the same direction to generate the environmental trend coordination interval segment;

[0053] Clarify the time and space mapping relationship between the physical deployment location of various sensing devices and the corresponding raw material batches. Call the layout map or management database of the storage area to extract the raw material batch number and the storage time interval corresponding to each storage location. Combined with the installation numbers of the temperature probe, humidity monitor, and light receiver, extract the corresponding data records within the time interval. For example, a storage location A001 stored astragalus from May 1 to May 4. Its probe equipment numbers are T01, H01, and L01. Accordingly, retrieve the continuous monitoring values ​​within the corresponding time periods of T01, H01, and L01 from each data source. Then, it is necessary to unify the sampling frequency of each data. The common frequency is once every 5 minutes. For example, if the humidity meter samples every 10 minutes and the temperature meter samples every 5 minutes, it is necessary to interpolate and fill in the humidity data to ensure that the three types of data have corresponding items at each moment, forming a three-item synchronous vector. Each data vector is divided into intervals according to a uniform interval (such as every hour), and the direction of change of the value within each hour is calculated. Specifically, the sign of the difference between the end point and the starting point is determined. If all three are positive or negative, the directions are consistent, and the start and end indexes of the time period are stored. For example, from 8:00 to 9:00, the temperature rises from 24.1℃ to 24.7℃, the humidity rises from 48.5% to 50.3%, and the light rises from 110lx to 142lx. The directions are consistent, and this section will be recorded as a unified environmental trend section to form an environmental trend collaborative interval section.

[0054] S112: Calling the environmental trend coordination interval segment, extracting the quality test values ​​of the corresponding batches of Chinese medicinal raw materials within the interval, analyzing the corresponding differences between the environmental trend and the quality parameters within the segment based on the time mapping relationship between the interval and the raw material batches, identifying the set of time periods where the quality remains stable under the continuous trend, and obtaining the environmental impact stable segment interval;

[0055] Trace back the batches of Chinese medicine raw materials and their quality inspection data involved in the corresponding time period. First, extract the batch numbers and storage start and end times actually involved in the consistent sections in each direction from the storage raw material records, and extract the quality inspection parameter values ​​corresponding to the batch, including property identification items, moisture content, active ingredient content (such as astragalus polysaccharide or glycyrrhizic acid, etc.), and match the corresponding sections with time as the primary key. For example, the astragalus batch B001 corresponds to a test data in the consistent direction interval from 8:00 to 9:00. The glycyrrhizic acid content in the test is 1.35%, the moisture content is 11.2%, and the color grade is Grade I. Then compare the result with the environmental The segment information is bound and multi-segment comparison is performed. If the test values ​​of the corresponding batches in multiple consecutive segments with the same direction are all within the quality standard range (such as glycyrrhizic acid 1.2%-1.6%, moisture content <12%), it is considered that the environmental fluctuation in this segment has no abnormal impact on the quality, and the segment is further determined to be a stable quality segment. In this process, the range of each quality control indicator must be defined in accordance with the national pharmacopoeia and enterprise standards, and the difference in indicators of the same batch at different time points must be calculated. If the differences between consecutive segments are all less than the threshold (such as glycyrrhizic acid fluctuation <0.1%, moisture content fluctuation <0.5%), the consecutive segment is classified as an environmental impact stable segment.

[0056] See also Figure 3 The specific steps for obtaining the concentration fluctuation trend value of traditional Chinese medicine ingredients are as follows:

[0057] S211: Based on the environmental impact stable interval, extract the extraction rate data of the active ingredients in the traditional Chinese medicine raw materials, identify the unidirectional fluctuation segments of the ingredients in the time dimension, select the time intervals where the extraction rate continuously increases and decreases, record the start and end times of the intervals and the corresponding extraction rates, calculate the extraction rate change and duration of each segment, and generate the unidirectional fluctuation extraction feature segment;

[0058] All active ingredient data involved in the continuous extraction process of traditional Chinese medicine raw materials are obtained, and the extraction rate value of each ingredient at each sampling time point is extracted according to the time series. The process is based on the actual extraction production records. For example, in a certain production batch, the extraction rate of tanshinone IIA is recorded every 10 minutes, for a total of 8 times, which are 1.12, 1.13, 1.16, 1.21, 1.25, 1.24, 1.20, and 1.18, forming a time-extraction rate sequence. On this basis, whether the change direction of adjacent values ​​is consistent is judged in turn, and the sections with continuous increase and decrease in extraction rate are marked as unidirectional fluctuation section intervals. The judgment basis is the continuity of the sign of the difference between adjacent data pairs. If the signs of continuous differences are consistent, it is determined to be a unidirectional trend section. The start and end time points of the extraction section are the first and last sampling time points of the trend section. At this time, the extraction rate difference corresponding to the start and end points is calculated. For example, from 1.12 to 1.25, the change amount The difference between the two segments is 0.13, the start and end time is 0 to 50 minutes, and the duration is 50 minutes. Subsequently, all identified fluctuation segments are subjected to difference and time extraction and organized into a segment feature data table. The corresponding fields include "extracted component name", "fluctuation direction", "start time", "end time", "difference", "duration", etc. The data structure is organized by segment. For example, two consecutive rising segments and one falling segment are identified in the stable segment of Tanshinone IIA. The data structure of each segment is as follows: segment 1 is 0-50 minutes, the difference is 0.13, segment 2 is 60-80 minutes, and the difference is -0.06. The above method obtains fluctuation segments with time continuity and direction consistency, and records their concentration changes and time spans. This operation can be performed in the automatic sampling and data preprocessing module. By setting the extraction rate fluctuation judgment logic, the unidirectional trend segments of all components are screened and archived, and finally a unidirectional fluctuation extraction feature segment is generated.

[0059] S212: Call the extraction rate change and duration of each segment in the unidirectional fluctuation feature segment, combine the fluctuation frequency and change intensity of the Chinese medicinal ingredients in the corresponding segment, analyze the concentration trend structure relationship, and use the formula:

[0060] ;

[0061] Extract the composite trend index of fluctuation in time and ingredient attribute dimensions to obtain the concentration fluctuation trend value of traditional Chinese medicine ingredients;

[0062] in, Represents the concentration fluctuation trend value of Chinese medicine ingredients, Representative The change in the extraction rate of Chinese medicinal ingredients in the segment Representative The duration of the Chinese herbal ingredients, Representative The fluctuation frequency of Chinese herbal medicine ingredients, Representative The intensity level of the change of Chinese medicinal ingredients in this section, It indicates the total number of unidirectional fluctuation extraction sections during the extraction process of Chinese herbal medicine raw materials;

[0063] The concentration fluctuation trend value of Chinese herbal medicine components is a comprehensive indicator used to quantify the degree of change in the extraction rate of the active ingredients of Chinese herbal medicine raw materials within a certain time period. This value comprehensively considers multiple factors such as the variation amplitude of the component extraction rate, the duration of the fluctuation, the frequency of the change, and the intensity change per unit time. After normalization, the dimensions are unified to make the various components comparable under different fluctuation characteristics. The higher the value, the more drastic the fluctuation of the extraction concentration of the component in the analyzed section and the worse the stability; conversely, the lower the value, the relatively stable the extraction concentration. It can serve as an important basis for determining whether the extraction process of Chinese herbal medicine raw materials is in a stable and controlled state. It can also be used to identify and warn of abnormal fluctuations that may exist in the extraction process of Chinese herbal medicine, and assist in quality monitoring and regulation.

[0064] The extraction rate change and duration information extracted by the one-way fluctuation feature segment are called. It is necessary to perform trend modeling on the extraction of each Chinese medicine raw material component in the continuous fluctuation segment. The process first organizes the data of each segment into the extraction rate change value. , duration , Fluctuation frequency and the intensity of change per unit time For structured records, To obtain , we directly use the difference in the extraction rate values ​​corresponding to the first and last time points of the fluctuation segment. For example, if the extraction rate in a certain segment changes from 1.12 to 1.25, ;

[0065] for , that is, the difference between the beginning and end time of the segment. If the sampling time points are 0min and 50min, then ;

[0066] The number of times the direction of the extraction rate change in this segment is reversed is determined by whether the positive and negative signs of the differences between adjacent sampling points are reversed. For example, if the extraction rate sequence in a segment is 1.12, 1.15, 1.13, and 1.14, the reversal occurs at points 2 to 3 and 3 to 4, a total of 2 times. ;

[0067] for , then it is defined by the sum of the absolute differences between the sampling points divided by the duration of the segment. For example, if the extraction rate sequence is 1.12, 1.15, 1.13, and 1.14, then the adjacent differences are 0.03, 0.02, and 0.01, and the total is 0.06. ,but ;

[0068] In order to eliminate the interference of dimensional differences between participating items on the calculation results, it is necessary to 、 、 、 Normalization is performed. Normalization uses the minimum-maximum normalization method, that is, for each item Convert to , to ensure that all parameters fall between 0 and 1, e.g. in all segments The maximum value is 0.18 and the minimum value is 0.06. , after normalization , and so on to obtain the normalized values ​​of each parameter;

[0069] Taking a batch of Chinese medicine raw material "baicalin" as an example, the extracted data are as follows: , , , , its maximum and minimum values ​​are as follows: , ; , ; , ; , , calculate the normalized value as follows:

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] Substituting into the formula we get: ;

[0075] pass and The product of the trend strength is measured by and Adjusting the impact of fluctuation frequency and change intensity on trend values ​​enhances the ability of trend calculation to identify short-term violent fluctuation segments, which helps to screen out sensitive segments of quality changes from time series. The results show that the quality fluctuation trend value of baicalin in this extraction segment of traditional Chinese medicine ingredients is 0.773, which is in the preset high-risk range of fluctuation trend (above the threshold of 0.7), and can be determined as a segment with strong volatility. Combined with the trend intensity grading standard set in actual monitoring (0-0.4 is stable, 0.4-0.7 is moderate fluctuation, and 0.7-1 is strong fluctuation), this segment can be identified as a key segment for quality monitoring.

[0076] See also Figure 4 The specific steps for obtaining the quality abnormal batch index table are as follows:

[0077] S311: Call the concentration fluctuation trend value of the traditional Chinese medicine component, set the concentration fluctuation amplitude threshold and the impurity ratio change rate threshold, and perform interval scanning on the time series data of each batch using the formula:

[0078] ;

[0079] Calculate the segment abnormality index and filter The overlapping section coordinates are obtained to obtain the overlapping section identification value;

[0080] in, represents the segment abnormality index, Represents the maximum concentration value of a single segment, Represents the minimum concentration value of a single segment, Represents the absolute value of the impurity ratio change rate, Representative The concentration value of each sampling point, represents the batch average concentration, To dynamically adjust the threshold;

[0081] The segment anomaly index is a comprehensive indicator used to measure the concentration fluctuations and impurity ratio changes in the production process of a certain batch. The larger the value, the more significant the concentration fluctuations and impurity ratio changes in the segment, indicating the presence of quality anomalies or unstable production processes. Specifically, the segment anomaly index combines the concentration fluctuation amplitude, the impurity ratio change rate, and the degree of concentration deviation. It determines whether there is an anomaly by calculating the combined influence of factors. The concentration fluctuation amplitude is obtained by determining the maximum and minimum concentrations of each segment, and the impurity ratio changes within the segment are measured. The deviations of the concentration values ​​of all sampling points in the batch from the average concentration value are squared and summed, and the square root is taken to finally obtain the anomaly index θ for the segment. When the value of θ exceeds the preset threshold, the segment is considered abnormal and requires special treatment or further quality inspection. The segment anomaly index is an effective tool for monitoring quality fluctuations in the production process, helping production managers to identify potential problems in a timely manner and take appropriate control measures.

[0082] Call the fluctuation trend value data of the concentration of traditional Chinese medicine ingredients, set the concentration fluctuation amplitude threshold and the impurity ratio change rate threshold, and then perform interval scanning on the time series data of each batch. First, calculate the concentration value of each batch to obtain its maximum concentration value in each time interval ( ) and the minimum concentration value ( ), concentration values ​​are collected and recorded in real time by laboratory analytical equipment such as spectrometers and chromatographs. For example, if the concentration data of a batch in a time period is: [2.1, 3.5, 4.8, 3.1, 2.9], then is 4.8, is 2.1, and the value is used to determine the concentration fluctuation range ( - ), that is, 4.8-2.1=2.7;

[0083] Impurity ratio change rate ( ) also needs to be calculated, which is obtained by real-time detection of the ratio change of impurity components. Assuming that within a section, the impurity ratio changes over time, increasing from 0.03 to 0.05, then , in order to ensure the consistency of different unit dimensions, The values ​​and concentration fluctuations need to be normalized so that they can be compared in the same dimension. The normalization process can be achieved by dividing each value by its maximum value to standardize the range of each parameter. For example, the concentration fluctuation of 2.7 is normalized to 2.7 / 4.8≈0.5625, and the impurity ratio change rate of 0.02 is normalized to 0.02 / 0.05=0.4, thus obtaining a standardized value on a unified scale.

[0084] By calculating the fluctuation of concentration at each sampling point in each batch, the abnormal index (θ) of each segment is calculated using the formula;

[0085] in, and are the maximum and minimum concentrations of a single segment, is the impurity ratio change rate, is the concentration value of the kth sampling point in the batch, is the average concentration value of the batch. In this calculation, the concentration variation range and the standardized value of the impurity ratio variation are combined to reflect the abnormality of each segment. Assuming that the concentration data of the batch is [2.1, 3.5, 4.8, 3.1, 2.9], then , calculate the concentration deviation of each sampling point ( ):

[0086] For C1=2.1: ;

[0087] For C2=3.5: ;

[0088] For C3=4.8: ;

[0089] For C4=3.1: ;

[0090] For C5=2.9: ;

[0091] Calculating the sum of squared deviations yields:

[0092] ;

[0093] Substitute into the formula to calculate the value of θ: ;

[0094] Assuming that the preset anomaly index threshold Γ is 0.5, then θ=0.5325 is greater than Γ, so the segment is identified as an abnormal segment. It will be screened according to the threshold and the identification value of the abnormal segment will be extracted as the basis for subsequent processing.

[0095] S312: Based on the overlapping segment identification value, extract the batch number and overlap frequency within the corresponding segment, analyze the mapping relationship between the batch number and frequency, sort the batches whose frequency exceeds the preset benchmark by timestamp, and generate a batch overlap frequency mapping table;

[0096] Extracting the batch number within each segment and the frequency of overlap within that segment first involves extracting the batch number. The corresponding batch number is found based on the identification value of each segment, and the frequency of the batch appearing in all segments is counted. Batches with higher frequencies of overlapping segments indicate more unstable factors in the production process, and therefore require special attention. All batches are sorted by segment frequency, and batches with higher frequencies are marked as priority processing objects. For example, if a batch overlaps 8 times in 10 segments, while the batch overlaps only in 3 segments, then the batch has a higher priority and requires more attention. A batch overlap frequency mapping table is generated, which lists each batch number and its corresponding overlap frequency. Through this mapping table, production managers can sort batches according to timestamps and arrange inspection and adjustment plans accordingly to ensure that batches with higher frequencies are processed first.

[0097] S313: Call the batch overlap frequency mapping table, associate the spatial sequence of the processing section sampling points, identify the distribution density and the overlap rate of the overlapping area, extract the supercritical batches and sort them by priority, and generate an index table for batches with abnormal quality;

[0098] After processing the batch overlap frequency mapping table, the spatial coordinate sequence of the processing section sampling points is further calculated, and the spatial overlap rate with the overlapping section is analyzed. This step mainly involves the processing of spatial data, in which the spatial coordinate data of the sampling points is obtained by positioning equipment. The location of the sampling points is monitored in real time through the factory's internal geographic information system or sensor network. Combined with the identification information of the overlapping section, the spatial overlap rate between each sampling point and the overlapping section is calculated, that is, the proportion of the sampling point falling within the overlapping section. Assuming that the area of ​​an overlapping section is 100 square meters, and the area of ​​the area where a certain sampling point is located is 10 square meters, and this 10 square meter area is completely within the overlapping section, then the spatial overlap rate of the sampling point is 100%. Through this method, it is possible to determine which batches have a high degree of overlap with the overlapping section and give them priority. All batches with an overlap rate higher than the critical value will be sorted according to priority to generate a quality abnormality batch index table. This table lists all batches with high overlap rates and sorts them from high to low priority. The batches will be marked as potential quality problem batches and given priority for inspection and adjustment.

[0099] See also Figure 5 The specific steps for obtaining the batch fluctuation warning segment interval are as follows:

[0100] S411: Based on the quality abnormal batch index table, collect the time series data of the humidity curve of each batch of warehouse entry and the duration of drug efficacy maintenance, using the formula:

[0101] ;

[0102] Calculate the humidity-duration deviation, combine it with the batch number for index mapping, and generate the humidity-efficacy deviation;

[0103] in, Represents the humidity-duration deviation, is the environmental factor correction coefficient, represents the humidity monitoring value at time τ, is the standard humidity threshold, The number of days the drug's effectiveness lasts. is the air permeability coefficient of the storage container;

[0104] Humidity-duration deviation is a quantitative indicator used to measure the degree of deviation between humidity changes and efficacy maintenance time during the storage of medicinal materials. It is calculated by combining multiple factors such as humidity monitoring values, standard humidity thresholds, efficacy maintenance days, and air permeability of storage containers. Specifically, the larger the value of the humidity-duration deviation, the more significant the deviation between humidity changes and efficacy maintenance time, that is, the medicinal materials cannot maintain the expected efficacy maintenance time under specific humidity conditions. The indicator can effectively reflect the accuracy of humidity control and the suitability of the medicinal materials storage environment, which is of great significance for ensuring the effectiveness and quality of medicinal materials. In practical applications, humidity-duration deviation is used to indicate and identify potential problems in the storage process, and to help take corresponding adjustment measures to ensure the stable quality of medicinal materials during storage;

[0105] First, we collected the time series data of each batch's incoming humidity and the duration of its efficacy. Taking humidity as an example, each batch generates time series data during storage, recording the changes in humidity at different time points. The duration of efficacy refers to the length of time the medicinal material maintains its efficacy under specific humidity conditions. This is to further quantitatively describe the relationship between humidity and duration of efficacy.

[0106] Environmental factor correction factor ( ): Environmental factor correction coefficient It depends on factors such as the warehouse's geographical location and climatic conditions. For example, if the warehouse is located in a humid area with relatively stable humidity changes, the environmental factors Set to 0.8. If the warehouse is located in a dry area with drastic humidity changes, It should be set to 1.2. The correction factor is derived from long-term environmental monitoring data and can be further confirmed by the standard deviation of humidity changes.

[0107] Humidity monitoring value ( ): Humidity monitoring value It is the specific value measured by the humidity sensor at different time points during the storage process. For example, in the humidity record of a batch, at time point , the humidity sensor reads a humidity value of 62%, then %, monitoring data comes from precise humidity sensing equipment, ensuring the accuracy of humidity data;

[0108] Standard humidity threshold for medicinal materials ( ): Standard humidity threshold for medicinal materials It varies according to the characteristics of different medicinal materials. For example, for a certain type of Chinese medicinal material, the optimal storage humidity is 60%±5%. The value is 60%, which is obtained through experimental research or relevant literature;

[0109] The duration of drug effect ( ):Days the drug effect lasts It refers to the number of days that medicinal materials can maintain their effective efficacy under specific humidity conditions. This value is determined through experiments. For example, the experimental results of a batch of medicinal materials show that under 60% humidity conditions, the batch of medicinal materials can maintain their efficacy for 30 days. ;

[0110] The permeability coefficient of the storage container ( ): Air permeability coefficient of storage container It depends on the material and structure of the container. Containers with high air permeability, such as wooden boxes, For containers with large values ​​and poor air permeability, such as plastic barrels, Smaller, assuming a standard plastic container is used, its permeability coefficient Set to 0.5;

[0111] Dimension unification and normalization: Since the parameters involved in the formula have different dimensions (for example, humidity is a percentage and the duration of drug efficacy is a number of days), it is necessary to unify the dimensions and normalize the parameters. A common practice is to convert the humidity value into a standardized ratio. For example, convert the humidity from 60% to 0.6 and the duration of drug efficacy is 0.6. The original units can be retained and do not need to be converted to units, but it is necessary to ensure that the dimensions are consistent with the parameters when multiplied. To ensure unit consistency during the calculation process, all parameters can be normalized as follows;

[0112] The humidity The value is converted to decimal form, for example, 60% becomes 0.6;

[0113] How many days does the drug last? It can be normalized according to the actual situation, such as converting it into a value within the range of days during calculation, for example, 30 days can be directly used as Input value of

[0114] Assume that the humidity monitoring value of a batch The standard humidity threshold for medicinal materials is 62%. 60%, the duration of drug efficacy For 30 days, the air permeability coefficient of the storage container 0.5, environmental factor correction coefficient is 1.0;

[0115] Then substitute the specific values ​​into the formula for calculation: ;

[0116] Moisture-efficacy deviation of this batch The value is 4.14, indicating that there is a certain deviation in the relationship between humidity and efficacy duration for this batch. The value can be used for subsequent clustering and anomaly analysis, thereby providing more accurate early warning for medicinal material storage. It can ensure that the relationship between humidity and efficacy duration is calculated in detail, and the acquisition process of each parameter and its dimensionality are fully explained to ensure that the final calculated result is accurate and meaningful.

[0117] S412: Calling the humidity-drug efficacy deviation to identify the deviation difference between adjacent batches on the time axis, grouping batches with a difference value less than a set clustering threshold into the same group, and calculating the timestamp coverage of each group to obtain the time span clustering result;

[0118] Based on the calculation results of the humidity-efficacy deviation, the deviation differences between adjacent batches on the time axis are further analyzed. The humidity changes of adjacent batches are affected by multiple factors such as the environment, storage containers, and the type of medicinal materials. By calculating the deviation differences between batches on the time axis, the law of humidity and efficacy changes can be revealed. When calculating, it is first necessary to use the set clustering threshold to determine whether the difference between batches is less than the set value. If the difference is small, the batches are divided into the same group. The setting of the threshold depends on the actual application scenario. If the humidity difference is less than 5%, it can be considered that the humidity changes of the two batches belong to a similar group. During the clustering process, the timestamp range of each batch is determined, that is, the storage period of the batch. Suppose there are two batches. The humidity of one batch is maintained within the range of 60% ± 5%, and the efficacy is maintained for 30 days, while the humidity of the other batch is maintained within the range of 55% ± 5%, and the efficacy is maintained for 28 days. The humidity difference between the two batches is within 5%, so they can be classified into the same group in the clustering process. Through this type of cluster analysis, the time span clustering results are obtained, that is, the batch storage period of each group. In this way, a more in-depth analysis of the changes in humidity and efficacy is carried out.

[0119] S413: Based on the time span clustering results, extract the earliest storage timestamp and the latest drug expiration timestamp of each group, mark the group whose time difference exceeds the standard storage period as abnormal, and integrate the time intervals of the abnormal units to obtain the batch fluctuation warning segment interval;

[0120] After extracting the earliest entry timestamp and the latest expiration timestamp of each group based on the time span clustering results, the groups whose timestamp difference exceeds the standard storage period can be marked as abnormal units. For example, assuming the standard storage period is 30 days, if the earliest entry timestamp of a batch is January 1st and the latest expiration timestamp of the efficacy is February 15th, and the humidity of the batch during this period is always maintained within the range of 60% ± 5%, and the efficacy is maintained for 35 days, then because its storage period exceeds the standard range of 30 days, the batch will be marked as an abnormal unit. This method can further track and process abnormal batches to avoid problems such as excessive humidity or reduced efficacy. After the time interval coordinates of all abnormal units are integrated, the batch fluctuation warning segment interval is obtained. The interval can effectively reflect the abnormal fluctuations in the storage process of medicinal materials, thereby providing timely warning information in practical applications.

[0121] See also Figure 6 The specific steps for obtaining the Chinese medicine raw material quality stability determination list are as follows:

[0122] S511: Call the batch fluctuation warning segment interval, extract all processing record numbers within the interval, locate the start and end index values ​​of the number segment, identify the quality data screening range, and obtain the quality data extraction index interval;

[0123] First, the start and end index values ​​corresponding to the number are determined, and then the area where the number is located is precisely located. During execution, all processing record numbers for the batch data are obtained from the database and filtered according to certain rules (such as time range, batch number, etc.). The filtering result will give all processing record numbers that meet the conditions. The numbers are further matched with their corresponding data indexes, setting the starting index to the earliest record and the ending index to the last record. The index values ​​can be obtained through database queries or code traversal operations to obtain the quality data filtering range, that is, the interval range of the relevant data corresponding to the number. The specific method includes obtaining the data range corresponding to each record number through the program, and then aggregating the start and end data of all records to obtain the final filtering range. For example, if a processing record number is between January 1 and January 3, through a series of operations, the quality data extraction index range is obtained, which will be used for precise screening and analysis of subsequent data.

[0124] S512: Extracting index intervals based on the quality data, extracting quality grade labels of the Chinese medicinal raw materials within the intervals, analyzing the variation ranges of adjacent label values, identifying label variation trend paths through variation directions and continuity, and obtaining the quality variation trend paths of the Chinese medicinal raw materials;

[0125] The quality grade labels of the Chinese medicine raw materials are extracted from this interval. The labels are annotated according to certain testing standards, such as through color, texture, composition, and other data assessments. Then, the quality change trend is further identified by analyzing the fluctuation range of adjacent label values. By calculating the fluctuation range of the label value, the numerical range of the fluctuation range is determined, and a change threshold is set. If the change value exceeds the threshold, it means that the label value has changed significantly. For example, if the quality grade of a batch changes from grade A to grade B, and this fluctuation range is greater than the set threshold, it means that the quality has fluctuated. The direction of change is further determined and trend analysis is performed. Data analysis tools, such as the rolling window method or the sliding average method, are used to identify the trend path of label changes, analyze the changing trend of labels in the time series, and finally draw the trend path of the quality change of Chinese medicine raw materials. For example, if it is found that the quality of Chinese medicine raw materials changes from grade A to grade B and then to grade C within a certain period of time, and the change trend is stable, a relatively stable quality change trend can be obtained, thereby providing a decision-making basis for subsequent quality control and improvement.

[0126] S513: Call the trend path of the quality change of traditional Chinese medicine raw materials, identify the reversal node of the change direction of any continuous label value, count the frequency of mutation points and compare it with the set trend mutation frequency threshold, determine whether there is a structural deviation in the current quality trend, and obtain a list of traditional Chinese medicine raw material quality stability judgments;

[0127] Identify the reversal node of the change direction of any continuous label value in the path. A reversal node refers to a point in time when the quality label that was originally rising or falling suddenly reverses direction. For example, the label of a batch continuously decreases from grade A to grade B, and then suddenly rises back to grade A. This sudden turn is a reversal node. Count the frequency of reversal nodes. Through frequency statistics, it can be determined whether frequent quality fluctuations occur during the entire quality change process, and compare the frequency with the preset trend mutation frequency threshold. If the frequency exceeds the threshold, it means that the quality fluctuation is abnormal and further attention should be paid to the quality problem of this batch. This threshold can be set according to actual historical data. For example, the threshold is set to 5 reversals. If a batch is counted to have more than 5 reversal nodes, it is determined that there is a structural deviation in its quality trend. If the frequency is lower than the set threshold, it can be considered that the quality fluctuation of this batch is within a reasonable range. Finally, a list of quality stability judgments of traditional Chinese medicine raw materials is obtained through data analysis, which is used for quality control and monitoring management to ensure the long-term stability of product quality.

[0128] Table 1: Example of data changes on quality labels for Chinese medicine raw materials

[0129] ;

[0130] As shown in Table 1, the table shows detailed data on the quality label changes of four different processing record numbers. Through data such as the initial value, final value, change amplitude, change direction, and reversal node frequency of each number, the quality fluctuation trend of each batch of Chinese medicine raw materials can be further judged and its stability can be identified. With the support of data, the quality change trend path has been further confirmed and evaluated.

[0131] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for monitoring and analyzing the quality of Chinese medicine raw materials, characterized in that: The following steps are involved: S1: Based on the storage area information of traditional Chinese medicine raw materials, the storage temperature probe data, humidity monitor data and light receiver values ​​are extracted. Each data item is grouped into equal interval groups according to the sampling time node. The fluctuation direction of the values ​​in each group is determined to be consistent. The common interval features with consistent directions are extracted to obtain the stable interval of environmental impact; S2: Based on the stable period of environmental impact, extract the effective ingredient extraction rate data, identify the interval of unidirectional fluctuation in the continuous time period, and record its concentration difference and duration, identify the trend indicator, and obtain the concentration fluctuation trend value of the traditional Chinese medicine component; S3: Call the concentration fluctuation trend value of the traditional Chinese medicine component, screen the overlapping sections with high concentration changes and increased impurity ratios, build a pointer list based on the frequency of overlapping occurrences and batch numbers, and record the distribution characteristics of the sampling point sequence of the processing section to generate an index table of quality abnormal batches; S4: Based on the quality abnormality batch index table, track the humidity curve and efficacy maintenance time of each batch, identify the deviation distance, and cluster similar deviation segments by batch, determine the time axis span interval, and obtain the batch fluctuation warning segment interval.

2. The method for monitoring and analyzing the quality of Chinese medicinal raw materials according to claim 1, wherein: The environmental impact stability segment includes the data fluctuation consistency feature segment, the temperature, humidity and light combined stability segment, and the stable value within the equally spaced sampling group. The Chinese medicine ingredient concentration fluctuation trend value includes the concentration change amplitude, fluctuation duration, and unidirectional change directionality. The quality abnormality batch index table includes the high-amplitude concentration fluctuation segment, the impurity ratio abnormal overlapping segment, the batch abnormality frequency pointer, and the processing sampling sequence feature. The batch fluctuation warning segment includes the humidity curve deviation, the drug efficacy maintenance time difference segment, the batch clustering feature group, and the time span aggregation segment.

3. The method for monitoring and analyzing the quality of Chinese medicinal raw materials according to claim 1, wherein: The steps for obtaining the environmental impact stable segment are specifically as follows: S111: Based on the information of the storage area of ​​Chinese medicinal raw materials, extract the temperature probe data, humidity monitor data and light receiver value, synchronize them according to the sampling time node, divide the data into time periods at uniform intervals, and identify the direction of the value change of the three data in each period. Select the time period intervals with the same direction to generate the environmental trend coordination interval segment; S112: Call the environmental trend collaborative interval segment, extract the quality inspection values ​​of the corresponding batches of Chinese medicine raw materials in the interval, analyze the corresponding differences between the environmental trend and the quality parameters in the segment based on the time mapping relationship between the interval and the raw material batches, identify the time period set where the quality remains stable under the continuous trend, and obtain the environmental impact stable segment interval.

4. The method for monitoring and analyzing the quality of Chinese medicinal raw materials according to claim 3, wherein: The steps for obtaining the concentration fluctuation trend value of the traditional Chinese medicine component are specifically as follows: S211: Based on the environmental impact stable interval, extract the extraction rate data of the active ingredients in the traditional Chinese medicine raw materials, identify the unidirectional fluctuation segments of the ingredients in the time dimension, select the time intervals in which the extraction rate continuously increases and decreases, record the start and end times of the intervals and the corresponding extraction rates, calculate the extraction rate change and duration of each segment, and generate a unidirectional fluctuation extraction feature segment; S212: Call the extraction rate change and duration of each segment in the one-way fluctuation extraction feature segment, combine the fluctuation frequency and change intensity of the Chinese medicine ingredients in the corresponding segment, analyze the concentration trend structure relationship, extract the composite trend index of the fluctuation in the time and component attribute dimensions, and obtain the concentration fluctuation trend value of the Chinese medicine ingredients.

5. The method for monitoring and analyzing the quality of Chinese medicinal raw materials according to claim 4, wherein: The specific steps for obtaining the quality abnormal batch index table are: S311: Calling the concentration fluctuation trend value of the traditional Chinese medicine component, setting the concentration fluctuation amplitude threshold and the impurity ratio change rate threshold, performing interval scanning on the time series data of each batch, calculating the segment abnormality index, and obtaining the overlapping segment identification value; S312: Based on the overlapping segment identification value, extract the batch number and overlap frequency within the corresponding segment, analyze the mapping relationship between the batch number and the frequency, sort the batches whose frequency exceeds a preset benchmark by timestamp, and generate a batch overlap frequency mapping table; S313: Call the batch overlap frequency mapping table, associate the spatial sequence of the processing section sampling points, identify the distribution density and the overlap rate of the overlapping area, extract the supercritical batches and sort them by priority, and generate an index table for batches with abnormal quality.

6. The method for monitoring and analyzing the quality of Chinese medicinal raw materials according to claim 5, wherein: The steps for obtaining the batch fluctuation warning segment interval are specifically as follows: S411: Based on the quality abnormal batch index table, collect the time series data of the humidity curve and the duration of drug efficacy of each batch, calculate the humidity-duration deviation, and perform index mapping based on the batch number to generate the humidity-drug efficacy deviation; S412: calling the humidity-drug efficacy deviation, identifying the deviation difference between adjacent batches on the time axis, dividing batches with a difference value less than a set clustering threshold into the same group, and calculating the timestamp coverage of each group to obtain a time span clustering result; S413: Based on the time span clustering results, the earliest storage timestamp and the latest drug expiration timestamp of each group are extracted, the groups whose time difference exceeds the standard storage period are marked as abnormal, and the time intervals of the abnormal units are integrated to obtain the batch fluctuation warning section interval.

7. The method for monitoring and analyzing the quality of Chinese medicinal raw materials according to claim 1, wherein: The method further comprises step S5: S5: Call the batch fluctuation warning segment interval, extract the processing record number, focus on the change rate of the quality grade label value within the number segment, identify the rising or falling path of the grade label within the fluctuation segment, determine whether there is a mutation point that deviates from the normal trend, and obtain a list of Chinese medicine raw material quality stability judgments; The Chinese medicine raw material quality stability determination list includes quality grade change paths, grade mutation nodes, and trend deviation identification results.

8. The method for monitoring and analyzing the quality of Chinese medicinal raw materials according to claim 7, wherein: The specific steps for obtaining the Chinese medicine raw material quality stability determination list are as follows: S511: Call the batch fluctuation warning segment interval, extract all processing record numbers within the interval, locate the start and end index values ​​of the number segment, identify the quality data screening range, and obtain the quality data extraction index interval; S512: Extracting an index interval based on the quality data, extracting the quality grade labels of the Chinese medicinal raw materials within the interval, analyzing the variation range of adjacent label values, identifying the label variation trend path through the variation direction and continuity, and obtaining the quality variation trend path of the Chinese medicinal raw materials; S513: Call the quality change trend path of the Chinese medicinal raw materials, identify the reversal node of the change direction of any continuous label value, count the frequency of mutation points and compare it with the set trend mutation frequency threshold, determine whether there is a structural deviation in the current quality trend, and obtain a Chinese medicinal raw material quality stability judgment list.

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