Traditional Chinese medicinal material radix angelicae pubescentis quality evaluation method based on data analysis

Abstract

The invention discloses a traditional Chinese medicinal material radix angelicae pubescentis quality evaluation method based on data analysis, relates to the field of traditional Chinese medicinal material quality evaluation, and is used for solving the problem that the accuracy of traditional Chinese medicinal material quality evaluation is hindered due to the fact that screening quality standards do not have unified specifications. Collecting the target growth longitude and latitude similarity, the target growth cycle, the target root diameter and the target root drug component proportion to establish a data analysis model, obtaining a quality evaluation coefficient, performing comparative analysis on the quality evaluation coefficient and a preset quality threshold value to obtain a comparison result, and performing sorting from large to small according to the comparison result to obtain an optimal radix angelicae pubescentis plant. After the optimal radix angelicae pubescentis plants are screened out, the remaining optimal plants are determined, through measurement and evaluation, the pesticide residues after harvesting and the root volume similarity after harvesting are obtained, a set of fuzzy rules are formulated for fuzzy reasoning, a high-quality radix angelicae pubescentis plant screening scheme is determined, the radix angelicae pubescentis quality screening efficiency is improved, and the unified specification of the quality standard is formed; and the accuracy of quality evaluation of traditional Chinese medicinal materials is improved.

Description

Technical Field

[0001] The present invention relates to the field of quality evaluation of Chinese medicinal materials, and more specifically, to a quality evaluation method of the Chinese medicinal material Angelica dahurica based on data analysis. Background Art

[0002] Angelica dahurica is a traditional Chinese medicinal material, widely used in the field of traditional Chinese medicine. Its main effects include dispelling wind and dampness, relieving pain and dispersing cold pathogens, etc. Angelica dahurica has unique requirements for the growing environment, and its quality is closely related to the growing environment. After harvesting, the roots are generally washed, impurities are removed, and dried or baked to form Chinese medicinal materials. The roots of Angelica dahurica are rich in volatile oils and coumarin components, which are the main factors for its effects of dispelling wind and dampness, relieving pain and relieving pain.

[0003] The prior art has the following deficiencies:

[0004] At present, traditional quality evaluation methods mostly rely on subjective experience or single component analysis, lacking systematic evaluation standards. Especially for medicinal plants such as Angelica dahurica, it is difficult to accurately judge the overall quality of Angelica dahurica, resulting in no unified standard for screening quality standards, which hinders the accuracy of quality evaluation of Chinese herbal medicines. Therefore, a quality evaluation method for Chinese herbal medicine Angelica dahurica based on data analysis is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a quality evaluation method of the Chinese medicinal material Angelica dahurica based on data analysis, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for evaluating the quality of Chinese medicinal material Angelica dahurica based on data analysis, comprising:

[0008] S1: Before picking Angelica dahurica, collect field agronomic index information and drug component information, and perform preprocessing to obtain the target growth longitude and latitude similarity, target growth cycle, target root diameter, and target root drug component ratio;

[0009] S2: Obtain the target growth longitude and latitude similarity, target growth period, target root diameter and target root drug component ratio, establish a data analysis model, and obtain the quality assessment coefficient;

[0010] S3: Obtaining a quality assessment coefficient, and comparing and analyzing it with a preset quality threshold to obtain a comparison result, and sorting the comparison results from large to small to obtain the best Angelica dahurica plant, and after screening out the best Angelica dahurica plant, determining the remaining preferred plants;

[0011] S4: Obtain the remaining preferred plants, harvest them, and then perform measurement and evaluation to obtain the pesticide residues after harvest and the root volume similarity after harvest;

[0012] S5: Obtain the pesticide residue after harvest and the root volume similarity after harvest and substitute them into fuzzy logic to determine the screening plan for high-quality Angelica dahurica plants.

[0013] In a preferred embodiment, the preprocessing of the field agronomic index information and the drug component information includes standardization processing;

[0014] Field agronomic index information includes target growth longitude and latitude similarity, target growth cycle, and target root diameter; drug component information includes the proportion of drug components in the target root;

[0015] Data was collected through GPS equipment to determine the longitude and latitude of each Angelica dahurica plant, and then the longitude and latitude of the Angelica dahurica plant was similarly calculated with the longitude and latitude of the standard Angelica dahurica plant to obtain the target growth longitude and latitude similarity Cs i ; where i is the i-th Angelica dahurica strain;

[0016] By recording the growth process of the Angelica dahurica plant, the start and end time of the plant's growth are determined, and the target growth period Ls is obtained. i ;

[0017] The target root diameter Rd was obtained by combining plant height and leaf area using a multi-factor regression model. i ;

[0018] Quantitative analysis was performed using chromatography and tandem mass spectrometry, and the concentration of each drug component detected was calculated and added to the dry weight of the root to obtain the target root drug component ratio Cr. i .

[0019] In a preferred embodiment, the target growth longitude and latitude similarity Cs is obtained. i , target growth period Ls i , Target root diameter Rd i And the target root drug component ratio Cr i , generate the quality assessment coefficient Qu i , according to the formula:

[0020]

[0021] Furthermore,

[0022] (Cs i >0 and Ls i <threshold value);

[0023] Furthermore,

[0024]

[0025] In the formula, Qu i is the quality assessment coefficient, the threshold is the preset target growth cycle threshold, as well as are the preset proportional coefficients of the target growth longitude and latitude similarity, target growth period, target root diameter, and target root drug component ratio, respectively, and as well as Both are greater than 0.

[0026] In a preferred embodiment, after obtaining the quality assessment coefficient, the quality assessment coefficient is compared and analyzed with the continuously iterated quality threshold;

[0027] If the quality assessment coefficient is greater than or equal to the quality threshold, the current Angelica dahurica quality is marked as a high-quality Angelica dahurica plant, and a high-quality signal is generated;

[0028] If the quality assessment coefficient is less than the quality threshold, the current Angelica dahurica quality is marked as a poor-quality Angelica dahurica plant and a poor-quality signal is generated.

[0029] In a preferred embodiment, inferior Angelica dahurica plants are screened out based on the labels of the comparison results;

[0030] Obtain the Angelica dahurica plants labeled as high-quality Angelica dahurica plants, obtain the corresponding quality assessment coefficients, and sort the quality assessment coefficients from large to small to obtain the ranking of high-quality Angelica dahurica plants;

[0031] The maximum value of the quality assessment coefficient corresponding to the Angelica dahurica plants labeled as high-quality Angelica dahurica plants is marked as the optimal Angelica dahurica plants;

[0032] The best Angelica dahurica plants are screened out, and the remaining Angelica dahurica plants labeled as high-quality Angelica dahurica plants are counted to determine the remaining preferred plants.

[0033] In a preferred embodiment, the remaining preferred plants after harvest are further analyzed by obtaining samples from the roots of the harvested Angelica dahurica, extracting the samples with an organic solvent, extracting pesticide residues from the roots, and identifying the amount of pesticide residues after harvest based on the chromatogram;

[0034] The volume of the harvested Angelica dahurica roots was obtained by three-dimensional scanning and the similarity with the root volume of the standard Angelica dahurica plant was calculated to obtain the similarity of the root volume after harvest.

[0035] In a preferred embodiment, the pesticide residue after harvest and the similarity of the root volume after harvest are defined as input variables and divided into different fuzzy sets respectively;

[0036] The screening results of high-quality Angelica dahurica plants are defined as output variables and divided into fuzzy sets;

[0037] Fuzzy rules were formulated to describe the effects of post-harvest pesticide residues and post-harvest root volume similarity on the screening results of high-quality Angelica dahurica plants;

[0038] Fuzzy reasoning was performed based on fuzzy rules to determine the screening plan for high-quality Angelica dahurica plants.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention establishes a data analysis model through the target growth longitude and latitude similarity, the target growth cycle, the target root diameter and the target root medicinal component ratio to obtain a quality assessment coefficient, and compares and analyzes it with a preset quality threshold to obtain a comparison result, and sorts the comparison results in descending order to obtain the optimal Angelica dahurica plant, and after screening out the optimal Angelica dahurica plant, determines the remaining preferred plants, increases the screening efficiency of Angelica dahurica quality, forms a unified specification of quality standards, and improves the accuracy of quality evaluation of Chinese medicinal materials.

[0041] 2. The present invention obtains the remaining preferred plants, obtains the pesticide residues after harvest and the similarity of the root volume after harvest through measurement and evaluation, formulates a set of fuzzy rules for fuzzy reasoning, determines the screening plan for high-quality Angelica dahurica plants, and re-evaluates the quality of the Angelica dahurica Chinese medicinal materials after harvest, completes a complete quality evaluation method for the Angelica dahurica Chinese medicinal materials, and improves the accuracy of the quality evaluation of Chinese medicinal materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The present invention is a method flow chart of a method for evaluating the quality of the Chinese medicinal material Angelica dahurica based on data analysis.

[0043] Figure 2 The present invention is a schematic diagram of a method for evaluating the quality of the Chinese medicinal material Angelica dahurica based on data analysis. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] The present invention discusses a method for evaluating the quality of the Chinese medicinal material Angelica dahurica in the Guanshan area of ​​eastern Gansu. Specifically, the altitude of the Guanshan area of ​​eastern Gansu is generally between 1500 meters and 2800 meters, with an average altitude of about 1800 meters to 2000 meters. The climate is continuously humid and cool. This altitude is conducive to the accumulation of effective ingredients of medicinal plants and provides a good natural environment for the high-quality production of Chinese medicinal materials. Therefore, environmental differences are not used as an influencing factor in the actual measurement process and are not substituted into the collection information range;

[0046] Example 1

[0047] See also Figure 1 to Figure 2 , a quality evaluation method for the Chinese medicinal material Angelica dahurica based on data analysis, the specific operation process is as follows:

[0048] S1: Before picking Angelica dahurica, collect field agronomic index information and drug component information, and perform preprocessing to obtain the target growth longitude and latitude similarity, target growth cycle, target root diameter, and target root drug component ratio;

[0049] Among them, the preprocessing process of field agronomic index information and drug component information includes standardization processing, and the specific standardization processing formula is expressed as:

[0050]

[0051] Where X is the original field agronomic index information and drug component information, μ is the mean value of the field agronomic index information and drug component information, σ is the standard deviation of the field agronomic index information and drug component information, and X* is the field agronomic index information and drug component information after standardization;

[0052] The numerical values ​​of different quality indicators are standardized to eliminate the dimensional differences between different indicators. The specific standardization operation is not limited, but is set by the experimenter based on the differences between the specific original data and is not limited here;

[0053] Specifically, the field agronomic index information includes the target growth longitude and latitude similarity, the target growth cycle, and the target root diameter; the drug component information includes the target root drug component ratio;

[0054] Among them, the target growth longitude and latitude similarity refers to the similarity between the precise geographic coordinates of the Angelica dahurica planting plot and the geographic coordinates of the standard Angelica dahurica. The acquisition logic is to collect data through GPS equipment, determine the longitude and latitude of each Angelica dahurica plant, and then calculate the similarity between the longitude and latitude of the Angelica dahurica plant and the longitude and latitude of the standard Angelica dahurica plant to obtain the target growth longitude and latitude similarity Cs i ; where i is the i-th Angelica dahurica strain;

[0055] It should be noted that standard Angelica dahurica refers to the best quality Angelica dahurica samples obtained in the historical period. The setting of standard Angelica dahurica is usually obtained by the experimenters based on historical quality data, production data and key environmental factors, so it will not be elaborated here.

[0056] The longitude and latitude are converted into vectors, and then the similarity is calculated based on the longitude and latitude of the specific Angelica dahurica plant and the longitude and latitude of the standard Angelica dahurica plant, and then the longitude and latitude similarity of the target growth is calculated based on the cosine similarity;

[0057] The longitude and latitude of the Angelica dahurica plant and the longitude and latitude of the standard Angelica dahurica plant are respectively converted into two-dimensional plane vector representations, wherein the longitude and latitude can be converted into radians, and then projected onto a two-dimensional plane according to the spherical coordinates of the earth;

[0058] Assume that the coordinates of the Angelica dahurica plant are (lat1, lon1) and the coordinates of the standard Angelica dahurica plant are (lat2, lon2), then the conversion method is as follows:

[0059]

[0060] in, is the vector of the Angelica dahurica plant, Vector 2 of the standard Angelica dahurica plant

[0061] Will and Substituting into the cosine similarity calculation formula, the specific expression is as follows:

[0062]

[0063] In the formula, Cs i is the target growth longitude and latitude similarity, and are the moduli of the corresponding vectors respectively;

[0064] The result of cosine similarity ranges between -1 and 1. A value close to 1 indicates that the geographical locations of the two Angelica dahurica plants are highly similar, whereas a value close to -1 indicates that the locations are completely opposite.

[0065] The target growth cycle refers to the entire growth cycle of the Angelica dahurica plant from sowing to picking. The acquisition logic is to record the growth process of the Angelica dahurica plant, determine the start and end time of the plant's growth, and obtain the target growth cycle Ls i ;

[0066] The threshold is set and compared with the target growth cycle. If it is greater than or equal to the threshold, it means that the current target growth cycle exceeds the normal growth cycle, which leads to a decrease in plant quality. Otherwise, it means that the current target growth cycle is in the normal growth cycle. The longer the growth cycle, the higher the plant quality.

[0067] It should be noted that the threshold value was set by the experimenters based on the growth law and characteristics of Angelica dahurica, and will not be elaborated here;

[0068] To realize the recording of the growth process of Angelica dahurica plants, it is usually set to install a sensor with a clock function in the field to automatically record the specific time of sowing and picking. The specific collection method is not limited, but is set by the experimenter according to the specific monitoring method, which is not limited here;

[0069] The target root diameter refers to the estimated value of the root diameter of the Angelica dahurica plant. Since the information is collected before picking, it is impossible to accurately and intuitively express the root diameter of the Angelica dahurica plant. The acquisition logic is to integrate plant height and leaf area, and use a multi-factor regression model to obtain the target root diameter Rd i ;

[0070] Obtain specific values ​​of Angelica dahurica plants at each growth stage, calculate the average, and substitute it into the multivariate linear regression model for calculation;

[0071] The Angelica dahurica plants were sampled at different growth stages, and the measured values ​​of plant height and leaf area of ​​each Angelica dahurica were recorded. In the multivariate analysis, a multivariate linear regression model was used. It was assumed that the linear combination of plant height and leaf area was related to the root diameter, and the model formula was established as follows:

[0072] Rd i =α×H i +β×A i +γ

[0073] In the formula, Rd i is the target root diameter, H i is the height of the i-th plant, A i is the leaf area of ​​the ith plant, α, β and γ are regression coefficients, which are obtained by fitting the experimental data;

[0074] The coefficients α, β, and Y are determined to fit the experimental data using the least squares method to minimize the error between the predicted target root diameter and the actual measured root diameter. Specifically, the loss function is used for fitting, and the specific formula is as follows:

[0075]

[0076] Where f is the loss function, D i , H i and A i are the root diameter, plant height, and leaf area of ​​the ith plant, respectively, and n is the total number of samples;

[0077] Use data analysis tools to perform regression fitting to find the specific values ​​of the export, β, and γ. Through the fitted model, the root diameter of unknown plants can be estimated before picking;

[0078] It should be noted that the setting of different growth stages was set by the experimenters according to the specific growth cycle of Angelica dahurica and is not limited here;

[0079] The target root drug component ratio refers to the content ratio of the key medicinal components in the root of the Angelica dahurica plant relative to the dry weight of the root. The acquisition logic is to perform quantitative analysis through chromatography and tandem mass spectrometry technology, and calculate the ratio of the concentration of each detected drug component to the dry weight of the root and add them up to obtain the target root drug component ratio Cri;

[0080] The samples were tested using chromatography and mass spectrometry techniques to quantitatively analyze the target medicinal components in the root of Angelica dahurica;

[0081] It should be noted that the scope of target drug ingredients includes but is not limited to volatile oils, polysaccharides, coumarin compounds, etc. The experimenters set the target drug ingredients based on the experimental results and actual needs, and no limitation is made here;

[0082] The extracts were separated using HPLC to isolate different compounds;

[0083] Perform mass spectrometry analysis on the separated compounds. Tandem mass spectrometry can confirm the structure of compounds at the molecular level; the specific steps include: entering the separated compounds into the mass spectrometry system, ionizing and decomposing them to obtain a mass spectrum, identifying the target components based on the characteristic fragment peaks in the mass spectrum and combining them with the mass spectrum data of the standard sample, and calculating the concentration of each drug component using the standard curve of peak intensity and known concentration;

[0084] The ratio of the detected drug component concentration to the root dry weight is calculated and added to obtain the target root drug component ratio:

[0085] The experimenters can understand that the target drug components do not only include a single drug component, but the root of Angelica dahurica contains multiple drug components. Therefore, for different drug components, the characteristic fragment peaks in the mass spectrum generated are also different; and the standard curve is set by the experimenters based on the known concentration and peak area data of the experimental standard, which is not limited here;

[0086] S2: Obtain the target growth longitude and latitude similarity, target growth period, target root diameter and target root drug component ratio, establish a data analysis model, and obtain the quality assessment coefficient;

[0087] Among them, the data analysis model refers to a weighted analysis model, which generates quality assessment coefficients through weighted calculation;

[0088] Get the target growth longitude and latitude similarity Cs i , target growth period Ls i , Target root diameter Rd i And the target root drug component ratio Cr i , generate the quality assessment coefficient Qu i , according to the formula:

[0089]

[0090] (Cs i >0 and Ls i <threshold value);

[0091] (Cs i <0 and Ls i ≥ threshold value);

[0092] In the formula, Qu i is the quality assessment coefficient, the threshold is the preset target growth cycle threshold, as well as are the preset proportional coefficients of the target growth longitude and latitude similarity, target growth period, target root diameter, and target root drug component ratio, respectively, and as well as All are greater than 0;

[0093] Among them, the target growth longitude and latitude similarity, target growth cycle, target root diameter and target root drug component ratio are all data-based manifestations that directly express the current quality of Angelica dahurica.

[0094] It can be seen from the above formula that when the target root diameter and the target root drug component ratio are larger, the maturity of the root of Angelica dahurica and the content of the active ingredient are higher, indicating that the medicinal value of Angelica dahurica is higher, and the quality assessment coefficient is larger, otherwise, the quality assessment coefficient is smaller;

[0095] S3: Obtaining a quality assessment coefficient, and comparing and analyzing it with a preset quality threshold to obtain a comparison result, and sorting the comparison results from large to small to obtain the best Angelica dahurica plant, and after screening out the best Angelica dahurica plant, determining the remaining preferred plants;

[0096] The logic for obtaining the quality threshold is to collect a historical set of quality evaluations of Angelica dahurica plants, including a high-quality set of Angelica dahurica plants and a low-quality set of Angelica dahurica plants, and then divide the data set into a training set and a test set, set the evaluation index and clustering algorithm, and in each round of cross-validation, train the model on the training set and evaluate the model performance on the test set, and then adjust the quality threshold according to the performance of the validation set. Therefore, the quality threshold has been continuously iterated and updated;

[0097] In the present invention, clustering algorithm is a kind of unsupervised learning algorithm, which is used to divide the data points in the data set into groups or clusters with similarity; a common one is K-means clustering, which divides the data points in the data set into K clusters, so that the distance between the data point of each curve and the center point (center of mass) of the cluster to which it belongs is minimized, and finally the effect of the adjusted quality threshold is measured by the Euclidean distance, so as to set the quality threshold;

[0098] After obtaining the quality assessment coefficient, compare and analyze the quality assessment coefficient with the iterative quality threshold 2

[0099] If the quality assessment coefficient is greater than or equal to the quality threshold, the current Angelica dahurica quality is marked as a high-quality Angelica dahurica plant, and a high-quality signal is generated;

[0100] If the quality assessment coefficient is less than the quality threshold, the current quality of the Angelica dahurica is marked as a poor-quality Angelica dahurica plant, and a poor-quality signal is generated;

[0101] Based on the labels of the comparison results, inferior Angelica dahurica plants are screened out;

[0102] Obtain the Angelica dahurica plants labeled as high-quality Angelica dahurica plants, obtain the corresponding quality assessment coefficients, and sort the quality assessment coefficients from large to small to obtain the ranking of high-quality Angelica dahurica plants;

[0103] The maximum value of the quality assessment coefficient corresponding to the Angelica dahurica plants labeled as high-quality Angelica dahurica plants is marked as the optimal Angelica dahurica plants;

[0104] It should be noted that the best Angelica dahurica plant is the one with the best quality obtained in this harvest, which will not be described in detail here;

[0105] The best Angelica dahurica plants are screened out, and the remaining Angelica dahurica plants with labels of high-quality Angelica dahurica plants are counted to determine the remaining preferred plants;

[0106] It should be noted that the remaining preferred plants refer to a batch of Angelica dahurica with higher quality that can be determined before harvesting, which will not be described in detail here;

[0107] The present invention establishes a data analysis model through the target growth longitude and latitude similarity, the target growth cycle, the target root diameter and the target root medicinal component ratio to obtain a quality assessment coefficient, and compares and analyzes with a preset quality threshold to obtain a comparison result, and sorts the comparison results in descending order to obtain the optimal Angelica dahurica plant, and after screening out the optimal Angelica dahurica plant, determines the remaining preferred plants, increases the screening efficiency of Angelica dahurica quality, forms a unified specification of quality standards, and improves the accuracy of quality evaluation of Chinese medicinal materials.

[0108] Example 2

[0109] In Example 1 of the present invention, examples are given to establish a data analysis model based on the similarity of target growth longitude and latitude, target growth cycle, target root diameter and target root drug component ratio, obtain a quality assessment coefficient, and compare and analyze with a preset quality threshold to obtain a comparison result, and sort the comparison results from large to small to obtain the optimal Angelica dahurica plant, and determine the operation strategy for the remaining preferred plants after the optimal Angelica dahurica plant is screened out; however, in Example 1, only the evaluation is performed before harvesting, and the Chinese medicinal materials after harvesting have not been analyzed. Obviously, although the quality of the Angelica dahurica can be determined before harvesting without destroying its growth environment and causing too many unstable factors, there are still a series of pharmaceutical processes after harvesting, resulting in changes in quality, and it is impossible to track the quality of the Chinese medicinal materials after they are made into medicines, thereby reducing the accuracy of the quality evaluation of the Chinese medicinal materials. 2 In view of the above problems, Example 2 of the present invention is further refined;

[0110] The Angelica dahurica plants labeled as high-quality Angelica dahurica plants are now harvested. This operation can be based on manual or machine, which will not be described in detail here;

[0111] The remaining selected plants after harvesting are further analyzed, and the specific steps are as follows:

[0112] S4: Obtain the remaining preferred plants, harvest them, and then perform measurement and evaluation to obtain the pesticide residues after harvest and the root volume similarity after harvest;

[0113] The remaining preferred plants are the best Angelica dahurica plants screened out as described in Example 1, and the remaining Angelica dahurica plants with the label of high-quality Angelica dahurica plants are counted;

[0114] Among them, the post-harvest pesticide residue refers to the pesticide content remaining in the roots of the Angelica dahurica plant. The acquisition logic is to obtain samples from the roots of the Angelica dahurica plant after harvest, extract the pesticide residues from the roots by using organic solvents to extract the samples, and then identify the post-harvest pesticide residues based on the chromatogram.

[0115] Among them, the root samples of Angelica dahurica were washed, dried and crushed to ensure uniform samples for easy detection;

[0116] It should be noted that organic solvents include but are not limited to acetonitrile, methanol, etc. The experimenters can understand that the corresponding organic solvent is used for extraction according to the type of pesticide sprayed during planting, which will not be described in detail here;

[0117] The root volume similarity after harvest refers to the similarity between the volume of the root of the harvested Angelica dahurica plant and the volume of the root of the standard Angelica dahurica plant. The acquisition logic is to perform a three-dimensional scan on the root of the harvested Angelica dahurica plant to obtain the volume and calculate the similarity with the root volume of the standard Angelica dahurica plant to obtain the root volume similarity after harvest;

[0118] The standard Angelica dahurica plant refers to the Angelica dahurica sample with the best quality obtained in the historical period. This has been described in Example 1 and will not be repeated here;

[0119] Volume similarity calculation is usually measured using Euclidean distance, which is obtained by analyzing the similarity of each point in the three-dimensional volume, and is not limited here;

[0120] S5: Obtain the pesticide residue after harvest and the root volume similarity after harvest and substitute them into fuzzy logic to determine the high-quality Angelica dahurica plant screening scheme 2

[0121] For example, "High", "Low", "Medium" for post-harvest pesticide residues, and "Closely", "Quite", "Moderately" for post-harvest root volume similarity:

[0122] Formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0123] The pesticide residue after harvest is marked as X, the root volume similarity after harvest is marked as U, and the screening results of high-quality Angelica dahurica plants are marked as C_results;

[0124] Then we can define:

[0125] Rule 1: IF(X is High)AND(U is Quite)THEN(C_results is Low)

[0126] Rule 2: IF(U is Low)AND(U is Closely)THEN(C_results is High)

[0127] …

[0128] Perform fuzzy reasoning based on fuzzy rules to determine the screening scheme for high-quality Angelica dahurica plants;

[0129] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although the present embodiment takes three fuzzy sets as examples, in fact, the post-harvest pesticide residues and the post-harvest root volume similarity can be divided into more than three sets to facilitate better precise adjustment according to different similarities.

[0130] For the judgment of the pesticide residue after harvest and the similarity of the root volume after harvest, the threshold value can be set according to the actual situation. For example, when the pesticide residue after harvest exceeds 55%, it is marked as "High", and when the similarity of the root volume after harvest is higher than 74%, it is marked as "Closely", etc., which will not be elaborated here.

[0131] The present invention obtains the remaining preferred plants, obtains the pesticide residues after harvest and the similarity of the root volume after harvest through measurement and evaluation, formulates a set of fuzzy rules for fuzzy reasoning, determines a high-quality Angelica dahurica plant screening plan, and re-evaluates the quality of the Angelica dahurica Chinese medicinal materials after harvest, completes a complete quality evaluation method for the Angelica dahurica Chinese medicinal materials, and improves the accuracy of Chinese medicinal material quality evaluation.

[0132] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0133] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0134] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0135] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0140] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0141] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating the quality of Chinese medicinal material Angelica dahurica based on data analysis, characterized in that: include: S1: Before picking Angelica dahurica, collect field agronomic index information and drug component information, and perform preprocessing to obtain the target growth longitude and latitude similarity, target growth cycle, target root diameter, and target root drug component ratio; S2: Obtain the target growth longitude and latitude similarity, target growth period, target root diameter and target root drug component ratio, establish a data analysis model, and obtain the quality assessment coefficient; S3: Obtaining a quality assessment coefficient, and comparing and analyzing it with a preset quality threshold to obtain a comparison result, and sorting the comparison results from large to small to obtain the best Angelica dahurica plant, and after screening out the best Angelica dahurica plant, determining the remaining preferred plants; S4: Obtain the remaining preferred plants, harvest them, and then perform measurement and evaluation to obtain the pesticide residues after harvest and the root volume similarity after harvest; S5: Obtain the pesticide residue after harvest and the root volume similarity after harvest and substitute them into fuzzy logic to determine the screening plan for high-quality Angelica dahurica plants.

2. A method for evaluating the quality of Chinese medicinal material Angelica dahurica based on data analysis according to claim 1, characterized in that: The preprocessing of field agronomic index information and drug component information includes standardization; Field agronomic index information includes target growth longitude and latitude similarity, target growth period, and target root diameter; Drug ingredient information includes the proportion of drug ingredients in the target root; Data was collected through GPS equipment to determine the longitude and latitude of each Angelica dahurica plant, and then the longitude and latitude of the Angelica dahurica plant was similarly calculated with the longitude and latitude of the standard Angelica dahurica plant to obtain the target growth longitude and latitude similarity Cs i ; where i is the i-th Angelica dahurica strain; By recording the growth process of the Angelica dahurica plant, the start and end time of the plant's growth are determined, and the target growth period Ls is obtained. i ; The target root diameter Rd was obtained by combining plant height and leaf area using a multi-factor regression model. i ; Quantitative analysis was performed using chromatography and tandem mass spectrometry, and the concentration of each drug component detected was calculated and added to the dry weight of the root to obtain the target root drug component ratio Cr. i .

3. A method for evaluating the quality of Chinese medicinal material Angelica dahurica based on data analysis according to claim 2, characterized in that: Get the target growth longitude and latitude similarity Cs i , target growth period Ls i , Target root diameter Rd i And the target root drug component ratio Cr i , generate the quality assessment coefficient Qu i , according to the formula: Furthermore, (Cs i >0 and Ls i <threshold value); Furthermore, (Cs i <0 and Ls i ≥ threshold value); In the formula, Qu i is the quality assessment coefficient, the threshold is the preset target growth cycle threshold, as well as are the preset proportional coefficients of the target growth longitude and latitude similarity, target growth period, target root diameter, and target root drug component ratio, respectively, and as well as Both are greater than 0.

4. A method for evaluating the quality of Chinese medicinal material Angelica dahurica based on data analysis according to claim 3, characterized in that: After obtaining the quality assessment coefficient, the quality assessment coefficient is compared and analyzed with the continuously iterated quality threshold; If the quality assessment coefficient is greater than or equal to the quality threshold, the current Angelica dahurica quality is marked as a high-quality Angelica dahurica plant, and a high-quality signal is generated; If the quality assessment coefficient is less than the quality threshold, the current Angelica dahurica quality is marked as a poor-quality Angelica dahurica plant and a poor-quality signal is generated.

5. A method for evaluating the quality of Chinese medicinal material Angelica dahurica based on data analysis according to claim 4, characterized in that: Based on the labels of the comparison results, inferior Angelica dahurica plants are screened out; Obtain the Angelica dahurica plants labeled as high-quality Angelica dahurica plants, obtain the corresponding quality assessment coefficients, and sort the quality assessment coefficients from large to small to obtain the ranking of high-quality Angelica dahurica plants; The maximum value of the quality assessment coefficient corresponding to the Angelica dahurica plants labeled as high-quality Angelica dahurica plants is marked as the optimal Angelica dahurica plants; The best Angelica dahurica plants are screened out, and the remaining Angelica dahurica plants labeled as high-quality Angelica dahurica plants are counted to determine the remaining preferred plants.

6. A method for evaluating the quality of the Chinese medicinal material Angelica dahurica based on data analysis according to claim 5, characterized in that: The remaining selected plants after harvesting are further analyzed by obtaining samples from the roots of the harvested Angelica dahurica, extracting the samples with organic solvents, extracting pesticide residues from the roots, and identifying the pesticide residues after harvesting based on the chromatogram; The volume of the harvested Angelica dahurica roots was obtained by three-dimensional scanning and the similarity was calculated with the root volume of the standard Angelica dahurica plant to obtain the similarity of the root volume after harvest.

7. A method for evaluating the quality of the Chinese medicinal material Angelica dahurica based on data analysis according to claim 6, characterized in that: The pesticide residue after harvest and the similarity of root volume after harvest were defined as input variables and divided into different fuzzy sets respectively; The screening results of high-quality Angelica dahurica plants are defined as output variables and divided into fuzzy sets; Fuzzy rules were formulated to describe the effects of post-harvest pesticide residues and post-harvest root volume similarity on the screening results of high-quality Angelica dahurica plants; Fuzzy reasoning was performed based on fuzzy rules to determine the screening plan for high-quality Angelica dahurica plants.

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