Modeling method of traditional Chinese medicine data traceability model

Abstract

The invention relates to the technical field of traditional Chinese medicine data traceability, in particular to a modeling method of a traditional Chinese medicine data traceability model. The method comprises the following steps: carrying out traditional Chinese medicine collection on a target traditional Chinese medicine area to obtain traditional Chinese medicine collection field record data; performing traditional Chinese medicine identification processing according to the traditional Chinese medicine collection field record data to obtain traditional Chinese medicine identification data; performing traditional Chinese medicine quality detection according to the traditional Chinese medicine identification data to obtain quality detection data; performing data integration and data cleaning according to the quality detection data to obtain a traditional Chinese medicine data cleaning data set; key climate index extraction is carried out on the traditional Chinese medicine data cleaning data set to obtain a climate index data set; and performing quality index correlation analysis on the climate index data set to obtain a correlation analysis data set. By systematically analyzing the regional characteristics of the traditional Chinese medicine and deeply analyzing the quality influence factors of the traditional Chinese medicine, the defects of the modeling method of the traditional Chinese medicine data traceability model are overcome, and more accurate and more comprehensive quality traceability of the traditional Chinese medicine is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine data traceability, and in particular to a modeling method of a traditional Chinese medicine data traceability model. Background Art

[0002] With the growing demand for quality safety and traceability information of Chinese medicinal materials, the modeling methods of Chinese medicine data traceability models have also undergone continuous development. Early methods mainly relied on manual records and paper documents, which had problems such as low efficiency, error-proneness, and difficulty in tracing. In recent years, with the rapid development of information technology, technologies such as barcodes, QR codes, and RFID have been gradually applied to the field of Chinese medicine traceability, realizing automated information collection and management and improving traceability efficiency. However, the existing modeling methods of Chinese medicine data traceability models still have some shortcomings, which are mainly reflected in the following two aspects:

[0003] 1. Lack of systematic analysis of regional characteristics of traditional Chinese medicine:

[0004] The growth environment of Chinese medicinal materials has an important impact on their quality. Differences in climate, soil, hydrology and other conditions in different production areas will lead to significant differences in the quality of Chinese medicinal materials. Traditional Chinese medicine traceability methods mostly only focus on origin information, lack a systematic analysis of regional characteristics, and are difficult to reveal the underlying reasons for the differences in quality of Chinese medicinal materials in different production areas. It is impossible to guide the rational planting and production of Chinese medicinal materials, ensure the stability of the quality of Chinese medicinal materials, and meet consumers' demand for high-quality Chinese medicinal materials.

[0005] 2. Lack of in-depth analysis of factors affecting the quality of traditional Chinese medicine:

[0006] The quality of Chinese herbal medicines is affected by many factors, including climate conditions, soil environment, planting technology, processing technology, etc. Traditional Chinese medicine traceability methods mostly only record simple production information, lack of in-depth analysis of these influencing factors, and it is difficult to predict and control the quality changes of Chinese herbal medicines. It is difficult to effectively control quality, ensure the stability of the quality of Chinese herbal medicines, and meet consumers' demand for high-quality Chinese herbal medicines. Summary of the invention

[0007] Based on this, it is necessary to provide a modeling method for a traditional Chinese medicine data traceability model to solve at least one of the above technical problems.

[0008] To achieve the above purpose, a modeling method of a traditional Chinese medicine data traceability model includes the following steps:

[0009] Step S1: collecting Chinese medicine in the target Chinese medicine area to obtain Chinese medicine collection site record data; performing Chinese medicine identification processing according to the Chinese medicine collection site record data to obtain Chinese medicine identification data;

[0010] Step S2: Performing quality inspection of the traditional Chinese medicine according to the identification data of the traditional Chinese medicine to obtain quality inspection data;

[0011] Step S3: integrating and cleaning the data according to the quality inspection data to obtain a cleaned data set of traditional Chinese medicine data; extracting key climate indicators from the cleaned data set of traditional Chinese medicine data to obtain a climate indicator data set; performing quality indicator correlation analysis on the climate indicator data set to obtain a correlation analysis data set; performing influencing factor analysis based on the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data; constructing a quality prediction model based on the influencing factor analysis data to obtain a quality prediction model; performing timeliness impact analysis using the quality prediction model to obtain timeliness impact data;

[0012] Step S4: Perform quality and environment correlation analysis based on the timeliness impact data to obtain quality and environment correlation data; perform regional characteristic analysis based on the quality and environment correlation data to obtain a regional characteristic analysis report;

[0013] Step S5: constructing a traceability database based on the TCM identification data, quality inspection data, timeliness impact data, and regional feature analysis report using a preset traceability system architecture to obtain a traceability database;

[0014] Step S6: Optimize the quality prediction model according to the traceability database and the quality prediction model to obtain quality prediction model optimization data to realize the traditional Chinese medicine data traceability operation.

[0015] The present invention collects Chinese medicine in the target Chinese medicine area to obtain the on-site record data of Chinese medicine collection; performs Chinese medicine identification processing according to the on-site record data of Chinese medicine collection to obtain Chinese medicine identification data. This step can systematically collect and record Chinese medicine information, establish an identification coding system, and provide a comprehensive and reliable data foundation for subsequent quality inspection, influencing factor analysis and traceability application, which is the key starting point of the entire Chinese medicine data traceability model. Perform Chinese medicine quality inspection according to the Chinese medicine identification data to obtain quality inspection data. This step can systematically carry out the quality inspection of Chinese medicine, from the formulation of the inspection plan, sample pretreatment, inspection implementation to the result analysis, to form the quality inspection data of the whole process, and provide a solid data foundation for the subsequent influencing factor analysis and traceability application. According to the quality inspection data, data integration and data cleaning are performed to obtain the Chinese medicine data cleaning data set; key climate indicators are extracted from the Chinese medicine data cleaning data set to obtain the climate indicator data set; quality indicator correlation analysis is performed on the climate indicator data set to obtain the correlation analysis data set; influencing factor analysis is performed based on the climate indicator data set and the correlation analysis data set to obtain the influencing factor analysis data; quality prediction model is constructed based on the influencing factor analysis data to obtain the quality prediction model; timeliness impact analysis is performed using the quality prediction model to obtain timeliness impact data. This step can make full use of Chinese medicine quality inspection data, market data and climate data, and through systematic data integration, feature extraction and influencing factor analysis, a reliable quality prediction model can be constructed to provide accurate prediction basis and timeliness analysis for Chinese medicine quality traceability, laying a solid foundation for subsequent traceability applications. According to the timeliness impact data, quality and environment correlation analysis is performed to obtain quality and environment correlation data; regional characteristics analysis is performed based on the quality and environment correlation data to obtain a regional characteristics analysis report. This step can analyze the quality differences of Chinese medicines in different regions, determine the correlation between quality and environmental factors, extract the unique characteristics of each region, and finally form a comprehensive regional characteristic analysis report, which provides important regional characteristic basis for subsequent Chinese medicine quality traceability applications, and helps to achieve a more accurate and comprehensive traceability effect. The traceability database is constructed according to the Chinese medicine identification data, quality inspection data, timeliness impact data and regional characteristic analysis report using the preset traceability system architecture to obtain a traceability database. This step can utilize the preset traceability system architecture to organically integrate the various types of Chinese medicine data obtained above, and use blockchain technology to ensure data security, and finally construct a reliable Chinese medicine traceability system and database, providing a solid technical and data foundation for subsequent traceability applications. Therefore, the present invention provides a modeling method for a Chinese medicine data traceability model, which is used to solve the defects of the existing modeling method of the Chinese medicine data traceability model, which lacks a systematic analysis of the regional characteristics of Chinese medicine and lacks an in-depth analysis of the factors affecting the quality of Chinese medicine.By analyzing the correlation between the quality of traditional Chinese medicine and the environment, extracting regional characteristics and forming a regional characteristic analysis report, through a series of steps such as extraction of key climate indicators, correlation analysis of quality indicators, and analysis of influencing factors, the relationship between the quality of traditional Chinese medicine and factors such as climate and environment is deeply analyzed. The regional characteristics of traditional Chinese medicine and the factors affecting the quality of traditional Chinese medicine are systematically analyzed. Your method makes up for the shortcomings of the existing modeling method of the traditional Chinese medicine data traceability model and realizes more accurate and comprehensive traceability of traditional Chinese medicine quality.

[0016] Preferably, step S1 comprises the following steps:

[0017] Step S11: Analyze the Chinese medicine collection plan for the target Chinese medicine area to obtain Chinese medicine collection plan data;

[0018] Step S12: Record the on-site collection of Chinese medicine according to the Chinese medicine collection plan data to obtain on-site record data of Chinese medicine collection;

[0019] Step S13: performing Chinese medicine identification coding according to the data recorded at the Chinese medicine collection site to obtain Chinese medicine coding data;

[0020] Step S14: Entering Chinese medicine information into the Chinese medicine coding data and the Chinese medicine collection site record data to obtain Chinese medicine information data;

[0021] Step S15: Encrypt the Chinese medicine identification data of the Chinese medicine information data and associate the data with the label to obtain the Chinese medicine identification data.

[0022] The present invention obtains Chinese medicine collection plan data by analyzing the Chinese medicine collection plan of the target Chinese medicine area. This step can ensure the orderly progress of Chinese medicine collection work and improve collection efficiency and data integrity. By analyzing the Chinese medicine types, growth environment and other factors in the target area, a detailed collection plan is formulated to lay the foundation for subsequent steps. According to the Chinese medicine collection plan data, the Chinese medicine collection site record is recorded to obtain the Chinese medicine collection site record data. This step can comprehensively record the key information in the Chinese medicine collection process and provide a basis for Chinese medicine identification and quality inspection. Detailed collection site records are helpful to trace the source of Chinese medicine and improve data reliability. According to the Chinese medicine collection site record data, Chinese medicine identification coding is performed to obtain Chinese medicine coding data. This step can give each batch of Chinese medicine samples a unique coding identification to realize the digital management of Chinese medicine information. The identification coding is conducive to the subsequent entry of Chinese medicine information, quality inspection and traceability. Chinese medicine information is entered into the Chinese medicine coding data and the Chinese medicine collection site record data to obtain Chinese medicine information data. This step can associate the basic information of Chinese medicine at the collection site, such as type, properties, weight, etc., with the coding data to form a complete Chinese medicine information database. This provides necessary data support for subsequent quality inspections and influencing factor analysis. The TCM information data is encrypted and the data is associated with the label to obtain the TCM identification data. This step can ensure the security and integrity of the TCM information data, and at the same time associate the digitized TCM identification data with the physical TCM samples, laying the foundation for subsequent traceability work. Encryption and label association help prevent TCM information from being tampered with or lost.

[0023] Preferably, step S2 comprises the following steps:

[0024] Step S21: Formulate a quality inspection plan based on the TCM identification data to obtain quality inspection plan data;

[0025] Step S22: pre-process each batch of Chinese medicine samples according to the quality inspection plan data to obtain sample processing data;

[0026] Step S23 performs quality inspection on the sample processing data according to the quality inspection plan data to obtain quality inspection result data;

[0027] Step S24: performing test result analysis on the quality test result data and performing result analysis verification to obtain test result analysis data;

[0028] Step S25: generate quality inspection data based on the inspection result analysis data and archive the quality inspection data to obtain quality inspection data.

[0029] The present invention formulates a quality inspection scheme according to the Chinese medicine identification data to obtain quality inspection scheme data. This step can ensure that the quality inspection work is highly targeted and improve the targeting and effectiveness of the inspection. According to the information such as the Chinese medicine variety and source, a comprehensive and scientific quality inspection scheme is formulated to lay the foundation for subsequent inspection work. Each batch of Chinese medicine samples is pretreated according to the quality inspection scheme data to obtain sample processing data. This step can ensure that the Chinese medicine samples meet the requirements of quality inspection and improve the accuracy of the inspection results. Through necessary pretreatments such as washing, slicing, and crushing, interference factors are eliminated to create conditions for subsequent inspection work. According to the quality inspection scheme data, the sample processing data quality inspection is implemented to obtain quality inspection result data. This step can obtain the comprehensive quality inspection results of each batch of Chinese medicine samples, providing a basis for subsequent quality analysis. The samples are inspected strictly according to the inspection scheme to ensure the accuracy and reliability of the results. The quality inspection result data is analyzed and the result analysis is verified to obtain the inspection result analysis data. This step can conduct in-depth analysis and verification of the inspection results to improve the credibility of the data. The accuracy and integrity of the quality inspection results are ensured by repeated inspection, comparative analysis, and the like. The quality inspection data is generated and archived for the test result analysis data to obtain the quality inspection data. This step can organize the quality inspection results that have been analyzed and verified into standardized data and properly preserve them to provide reliable data support for subsequent traceability and analysis.

[0030] Preferably, step S3 comprises the following steps:

[0031] Step S31: collecting historical climate data of the target Chinese medicine region to obtain historical climate data; collecting Chinese medicine market data according to the Chinese medicine identification data to obtain Chinese medicine market data;

[0032] Step S32: integrating and preprocessing the traditional Chinese medicine market data, historical climate data, and quality inspection data to obtain a preprocessed comprehensive data set; performing outlier detection and data cleaning on the preprocessed comprehensive data set to obtain a traditional Chinese medicine data cleaning data set;

[0033] Step S33: extract key climate indicators from the cleaned data set of traditional Chinese medicine data to obtain a climate indicator data set; perform quality indicator correlation analysis on the climate indicator data set to obtain a correlation analysis data set;

[0034] Step S34: performing influencing factor analysis based on the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data;

[0035] Step S35: constructing a quality prediction model based on the influencing factor analysis data to obtain a quality prediction model;

[0036] Step S36: Use the quality prediction model to predict the quality of Chinese medicines in different seasons on the Chinese medicine data cleaning data set to obtain the Chinese medicine quality change trend data; perform timeliness impact analysis based on the Chinese medicine quality change trend data to obtain timeliness impact data.

[0037] The present invention obtains historical climate data by collecting historical climate data of the target Chinese medicine area; and obtains Chinese medicine market data according to the Chinese medicine identification data. This step can provide necessary data support for subsequent influencing factor analysis and quality prediction. The historical climate data of the target area and the sales, price and other information of Chinese medicine in the market are obtained to lay a foundation for analyzing the relationship between the quality of Chinese medicine and environmental factors. The Chinese medicine market data, historical climate data and quality inspection data are integrated and preprocessed to obtain a preprocessed comprehensive data set; the preprocessed comprehensive data set is detected for outliers and cleaned to obtain a Chinese medicine data cleaning data set. This step can ensure that the data used in subsequent analysis is complete and accurate. Data from different sources are integrated, preprocessed and cleaned to eliminate outliers and erroneous data, and improve data quality. Key climate indicators are extracted from the Chinese medicine data cleaning data set to obtain a climate indicator data set; quality indicator correlation analysis is performed on the climate indicator data set to obtain a correlation analysis data set. This step can identify key climate factors affecting the quality of Chinese medicine, and provide a basis for subsequent influencing factor analysis. By extracting key climate indicators and analyzing their correlation with quality indicators, the key connection between the quality of traditional Chinese medicine and the environment is revealed. The influencing factors are analyzed according to the climate indicator data set and the association analysis data set to obtain the influencing factor analysis data. This step can deeply explore the main influencing factors of the quality of traditional Chinese medicine and provide a basis for the construction of the quality prediction model. The key influencing factors of the quality of traditional Chinese medicine are analyzed by using the association analysis results of climate indicators and quality indicators to provide basic data for subsequent quality prediction. The quality prediction model is constructed according to the influencing factor analysis data to obtain the quality prediction model. This step can provide strong support for the prediction and traceability of the quality of traditional Chinese medicine. Based on the influencing factors obtained by the above analysis, a prediction model that can accurately predict the quality changes of traditional Chinese medicine is constructed to provide a reliable basis for quality prediction for traceability applications. The quality prediction model is used to predict the quality of traditional Chinese medicine in different seasons on the traditional Chinese medicine data cleaning data set to obtain the quality change trend data of traditional Chinese medicine; the timeliness impact analysis is performed according to the quality change trend data of traditional Chinese medicine to obtain the timeliness impact data. This step can predict the quality change trend of traditional Chinese medicine in different seasons and analyze the timeliness impact of traditional Chinese medicine quality, providing more comprehensive data support for subsequent traceability applications.

[0038] Preferably, step S33 includes the following steps:

[0039] Step S331: classify the Chinese medicine types on the Chinese medicine data cleansing data set to obtain a Chinese medicine classification data set; divide the Chinese medicine classification data set into groups according to key seasons to obtain a Chinese medicine seasonal grouping data set;

[0040] Step S332: dividing the Chinese medicine seasonal grouping data set into growth cycles to obtain a cycle-divided data set;

[0041] Step S333: extract key climate indicators from the period-divided data set to obtain a climate indicator data set;

[0042] Step S334: performing climate indicator statistical analysis on the climate indicator data set to obtain a climate statistical data set;

[0043] Step S335: Perform quality indicator correlation analysis on the climate statistical data set and the traditional Chinese medicine data cleaning data set to obtain a correlation analysis data set.

[0044] The present invention obtains a Chinese medicine classification data set by classifying the types of Chinese medicines on a Chinese medicine data cleaning data set; and obtains a Chinese medicine seasonal grouping data set by dividing the Chinese medicine classification data set into groups according to key seasons. This step can provide a data basis for subsequent climate index extraction and association analysis. By subdividing the Chinese medicine data according to types and growth seasons, detailed Chinese medicine classification and seasonal group data are established, laying a foundation for subsequent analysis work. The Chinese medicine seasonal grouping data set is divided into growth cycles to obtain a cycle division data set. This step can more accurately identify the growth law of Chinese medicine and provide a basis for climate index extraction. The Chinese medicine data is subdivided according to the characteristics of the growth cycle, such as the seedling stage, the stem and leaf growth stage, the flowering and fruiting stage, etc., to form a cycle division data set, which is conducive to the subsequent targeted extraction of key climate indicators. The key climate indicators are extracted from the cycle division data set to obtain a climate indicator data set. This step can determine the key climate factors that have the greatest impact on the quality of Chinese medicine, providing a data basis for subsequent association analysis. Key climate indicators related to the growth and quality of Chinese medicine, such as temperature, rainfall, sunshine duration, etc., are extracted from the cycle division data set to form a climate indicator data set. Perform climate indicator statistical analysis on the climate indicator data set to obtain a climate statistical data set. This step can provide data support for the relationship analysis between climate factors and the quality of traditional Chinese medicine. Perform statistical analysis on the climate indicator data set, including calculating the mean, standard deviation, correlation coefficient and other indicators, to form a climate statistical data set, laying the foundation for subsequent association analysis. Perform quality indicator association analysis on the climate statistical data set and the traditional Chinese medicine data cleaning data set to obtain an association analysis data set. This step can reveal the intrinsic connection between the quality of traditional Chinese medicine and climate factors, and provide a basis for the analysis of influencing factors. Conduct in-depth association analysis between climate statistical data and traditional Chinese medicine quality indicators to determine the degree of influence of key climate factors on the quality of traditional Chinese medicine and form an association analysis data set.

[0045] Preferably, step S34 includes the following steps:

[0046] Step S341: performing a significance test on the association analysis data set to obtain a significance test data set;

[0047] Step S342: sorting the influencing factors of the climate indicator dataset according to the significance test dataset to obtain an influencing factor sorting dataset;

[0048] Step S343: performing year difference analysis on the data set sorted according to the influencing factors to obtain a year difference analysis data set;

[0049] Step S344: quantifying the impact degree according to the year difference analysis data set and the climate indicator data set to obtain a quantitative data set of impact factors;

[0050] Step S345: Perform influencing factor analysis based on the climate indicator data set, the year difference analysis data set, and the influencing factor quantification data set to obtain influencing factor analysis data.

[0051] The present invention obtains a significance test data set by performing a significance test on an association analysis data set. This step can determine which climate indicators have a significant correlation with the quality of traditional Chinese medicine, and provide a reliable basis for subsequent analysis of influencing factors. By means of statistical test methods, such as t-test, variance analysis, etc., a significance analysis is performed on the association analysis data set to form a significance test data set. According to the significance test data set, the climate indicator data set is sorted for influencing factors to obtain an influencing factor sorting data set. This step can determine the main influencing factors of the quality of traditional Chinese medicine, and provide a basis for the construction of a quality prediction model. Based on the significance test results, the climate indicators are sorted, and the key factors that have the greatest impact on the quality of traditional Chinese medicine are identified to form an influencing factor sorting data set. According to the influencing factor sorting data set, year difference analysis is performed to obtain a year difference analysis data set. This step can analyze the difference between the quality of traditional Chinese medicine and climate factors in different years, and provide data support for timeliness impact analysis. For the influencing factor sorting data set, the differences between different years are analyzed to form a year difference analysis data set. The degree of impact is quantified according to the year difference analysis data set and the climate indicator data set to obtain an influencing factor quantification data set. This step can quantify the specific degree of impact of each climate indicator on the quality of traditional Chinese medicine, and provide data basis for subsequent analysis of influencing factors. Combined with the year difference analysis data and climate indicator data, the sensitivity and elasticity of each influencing factor are quantitatively analyzed to form a quantitative data set of influencing factors. The influencing factor analysis is performed based on the climate indicator data set, the year difference analysis data set, and the influencing factor quantitative data set to obtain the influencing factor analysis data. This step can deeply analyze the intrinsic relationship between the quality of traditional Chinese medicine and factors such as climate and environment, and provide a basis for the optimization of the quality prediction model. Comprehensively utilizing the aforementioned data sets, the main influencing factors of the quality of traditional Chinese medicine and their mechanisms of action are systematically analyzed to form the final influencing factor analysis data.

[0052] Preferably, step S4 comprises the following steps:

[0053] Step S41: performing quality difference analysis of Chinese medicines in different regions according to the timeliness impact data to obtain quality difference analysis data;

[0054] Step S42: performing quality and environment correlation analysis based on the quality difference analysis data to obtain quality and environment correlation data;

[0055] Step S43: performing regional comparison analysis based on the quality and environment correlation data to obtain regional comparison data;

[0056] Step S44: extracting regional features according to the regional comparison data to obtain regional feature data;

[0057] Step S45: performing relevant data association analysis on the regional characteristic data to obtain a regional characteristic analysis report.

[0058] The present invention obtains quality difference analysis data by performing quality difference analysis of Chinese medicines in different regions according to timeliness impact data. This step can identify the quality difference of the same Chinese medicine in different geographical regions, and provide a basis for subsequent regional characteristic analysis. By analyzing the timeliness impact data, the differences in the quality of Chinese medicines in different regions are revealed, laying a foundation for the next step of correlation analysis. Quality and environmental correlation analysis is performed according to the quality difference analysis data to obtain quality and environmental correlation data. This step can determine the correlation between the quality of Chinese medicines and regional environmental factors, and provide a basis for regional feature extraction. Combined with the quality difference analysis results and the regional environmental feature data, the correlation between the quality of Chinese medicines and factors such as climate, geography, and soil is analyzed to form quality and environmental correlation data. Regional comparative analysis is performed according to the quality and environmental correlation data to obtain regional comparative data. This step can compare the relationship differences between the quality of Chinese medicines in different regions and environmental factors, and reveal regional characteristics. By comparing and analyzing the quality and environmental correlation data of different regions, the differences between the quality of Chinese medicines and environmental factors in each region are determined, and regional comparative data are formed. Regional feature extraction is performed according to the regional comparative data to obtain regional feature data. This step can summarize the unique characteristics of Chinese medicine growth in each geographical region, and provide a basis for subsequent traceability applications. Based on the results of regional comparative analysis, the unique characteristics of the growth of Chinese medicine in each region, such as climate, geography, and soil, are extracted to form regional characteristic data. The regional characteristic data is subjected to relevant data association analysis to obtain a regional characteristic analysis report. This step can form a comprehensive regional characteristic analysis report, providing an important reference for the quality traceability of Chinese medicine. The regional characteristic data is deeply associated with other relevant data such as Chinese medicine quality and market, and the unique growth laws and quality characteristics of Chinese medicine in each region are summarized to form a comprehensive regional characteristic analysis report.

[0059] Preferably, step S42 includes the following steps:

[0060] Step S421: performing regional characteristic demand analysis based on the quality difference analysis data to obtain regional characteristic demand data;

[0061] Step S422: collecting regional characteristic data according to the regional characteristic demand data to obtain regional characteristic collection data;

[0062] Step S423: integrating the regional characteristic collection data and the timeliness impact data with the quality and environment relationship to obtain the quality and environment relationship integrated data;

[0063] Step S424: Use geographic information system to perform spatial analysis on the integrated data of the relationship between quality and environment to obtain spatial distribution data;

[0064] Step S425: Perform quality and environment correlation analysis based on the spatial distribution data and the quality and environment relationship integration data to obtain quality and environment correlation data.

[0065] The present invention obtains regional characteristic demand data by performing regional characteristic demand analysis according to quality difference analysis data. This step can determine the regional environmental characteristic factors that need to be focused on and collected, and provide a basis for subsequent data collection work. Through in-depth analysis of the quality difference analysis data, the key environmental factors that cause the quality differences of Chinese medicines in different regions are identified, and targeted regional characteristic demand data are formed. Regional characteristic data collection is performed according to the regional characteristic demand data to obtain regional characteristic collection data. This step can obtain regional environmental data that meets the quality and environmental correlation analysis requirements, and provide a basis for subsequent relationship integration. According to the aforementioned determined regional characteristic requirements, environmental data such as climate, geology, and soil in the target area are collected on the spot to form comprehensive regional characteristic collection data. The regional characteristic collection data and the timeliness impact data are integrated with the quality and environment relationship to obtain the quality and environment relationship integration data. This step can establish a correlation model between the quality of Chinese medicine and regional environmental factors, and provide a data basis for subsequent correlation analysis. The regional characteristic collection data and the timeliness impact data are organically integrated, and the intrinsic connection between the two is explored to form the quality and environment relationship integration data. The quality and environment relationship integration data are spatially analyzed using a geographic information system to obtain spatial distribution data. This step can intuitively present the spatial distribution relationship between the quality of traditional Chinese medicine and environmental factors, and provide visual support for subsequent correlation analysis. With the help of tools such as geographic information systems, the integrated data on the relationship between quality and environment are spatially visualized to form clear spatial distribution data. Quality and environmental correlation analysis is performed based on spatial distribution data and integrated data on the relationship between quality and environment to obtain quality and environmental correlation data. This step can deeply reveal the intrinsic connection between the quality of traditional Chinese medicine and regional environmental factors, and provide a basis for subsequent regional characteristic analysis. Comprehensively utilize spatial distribution data and integrated data on the relationship between quality and environment, systematically analyze the correlation between the two, and form the final quality and environmental correlation data.

[0066] Preferably, step S5 comprises the following steps:

[0067] Step S51: Designing a traceability system according to the TCM identification data, quality inspection data, timeliness impact data, and regional characteristic analysis report using a preset traceability system architecture to obtain traceability system design data;

[0068] Step S52: integrating the traceability system design data using blockchain technology to obtain blockchain integrated data;

[0069] Step S53: Generate a QR code and an RFID tag according to the Chinese medicine identification data to obtain identification tag data;

[0070] Step S5: embed the identification tag data into the blockchain integrated data to test the traceability system and obtain the traceability system test data;

[0071] Step S55: Generate a traceability database according to the traceability system test data to obtain a traceability database.

[0072] The present invention designs the traceability system according to the traditional Chinese medicine identification data, quality inspection data, timeliness impact data and regional feature analysis report by using the preset traceability system architecture to obtain the traceability system design data. This step can ensure that the design of the traceability system meets the actual needs and provide a reliable basis for the subsequent system construction. Based on the preset traceability system architecture, combined with the various types of traditional Chinese medicine data obtained above, a targeted system design is carried out to form a complete traceability system design data. The traceability system design data is integrated with blockchain technology to obtain blockchain integrated data. This step can improve the security and credibility of traditional Chinese medicine traceability data and lay the foundation for subsequent traceability applications. The traceability system design data is combined with blockchain technology, and the distributed ledger, anti-tampering and other characteristics of blockchain are used to ensure the security and integrity of traditional Chinese medicine traceability data and form blockchain integrated data. According to the traditional Chinese medicine identification data, a two-dimensional code and an RFID tag are generated to obtain identification tag data. This step can realize the association between traditional Chinese medicine information and physical samples, providing a basis for the subsequent traceability system construction. Based on the TCM identification data obtained above, generate QR codes and RFID tags, associate the digitized TCM information with the physical TCM samples, and form identification tag data. Embed the identification tag data into the blockchain integrated data for traceability system testing to obtain traceability system test data. This step can verify the feasibility and effectiveness of the traceability system and lay the foundation for subsequent formal applications. Associate the identification tag data with the blockchain integrated data, build a complete TCM traceability system, and conduct comprehensive testing and verification to form traceability system test data. Generate a traceability database based on the traceability system test data to obtain a traceability database. This step can provide reliable data support for TCM quality traceability applications. Based on the traceability system data that has passed the test, build a TCM traceability database, and systematically store information such as TCM identification data, quality inspection data, timeliness impact data, and regional characteristic analysis reports to form a final traceability database.

[0073] Preferably, step S6 comprises the following steps:

[0074] Step S61: performing decision support demand analysis according to the traceability database to obtain decision support demand data;

[0075] Step S62: Select a model according to the decision support demand data to obtain an initial model; extract model training data from the traceability database to obtain a model training data set;

[0076] Step S63: inputting the cleaned data set of traditional Chinese medicine data into the initial model for feature engineering processing to obtain a model to be trained;

[0077] Step S64: Perform model training on the model to be trained using the model training data set to obtain model training data;

[0078] Step S65: Optimize the quality prediction model according to the quality prediction model and the model training data to obtain quality prediction model optimization data to realize the traditional Chinese medicine data traceability operation.

[0079] The present invention obtains decision support demand data by performing decision support demand analysis according to the traceability database. This step can ensure that the subsequent quality prediction model can meet the actual needs of users and improve the pertinence and effectiveness of traceability applications. By analyzing the data characteristics and user needs in the traceability database, the decision support function that the quality prediction model needs to implement is determined to form decision support demand data. Model selection is performed according to the decision support demand data to obtain an initial model; model training data is extracted from the traceability database to obtain a model training data set. This step can lay the foundation for subsequent model training and optimization. According to the aforementioned determined decision support needs, a suitable prediction model is selected as the initial model, and at the same time, the data set required for model training is extracted from the traceability database to provide data support for subsequent model training work. The traditional Chinese medicine data cleaning data set is input into the initial model for feature engineering processing to obtain a model to be trained. This step can ensure the quality and effectiveness of the model input data and create conditions for model training. The traditional Chinese medicine data cleaning data set obtained above is input into the initial model, and feature engineering processing such as feature selection and data preprocessing is performed to form a model to be trained that can be further trained. The model training data set is used to perform model training on the model to be trained to obtain model training data. This step can improve the accuracy and reliability of the quality prediction model and provide strong support for subsequent traceability applications. Using the extracted model training data set, the model to be trained is trained using methods such as deep learning or machine learning to obtain the final model training data. The quality prediction model is optimized according to the quality prediction model and the model training data to obtain the quality prediction model optimization data to realize the traditional Chinese medicine data traceability operation. This step can continuously improve the performance of the quality prediction model and provide a more accurate and reliable prediction basis for the quality traceability application of traditional Chinese medicine. Combined with the quality prediction model and model training data obtained above, optimization and adjustment are carried out to make it better suitable for the quality prediction of traditional Chinese medicine, forming optimized quality prediction model data, which provides strong support for subsequent traceability applications.

[0080] The present invention collects Chinese medicine in the target Chinese medicine area to obtain the on-site record data of Chinese medicine collection; performs Chinese medicine identification processing according to the on-site record data of Chinese medicine collection to obtain Chinese medicine identification data. This step can systematically collect and record Chinese medicine information, establish an identification coding system, and provide a comprehensive and reliable data foundation for subsequent quality inspection, influencing factor analysis and traceability application, which is the key starting point of the entire Chinese medicine data traceability model. Perform Chinese medicine quality inspection according to the Chinese medicine identification data to obtain quality inspection data. This step can systematically carry out the quality inspection of Chinese medicine, from the formulation of the inspection plan, sample pretreatment, inspection implementation to the result analysis, to form the quality inspection data of the whole process, and provide a solid data foundation for the subsequent influencing factor analysis and traceability application. According to the quality inspection data, data integration and data cleaning are performed to obtain the Chinese medicine data cleaning data set; key climate indicators are extracted from the Chinese medicine data cleaning data set to obtain the climate indicator data set; quality indicator correlation analysis is performed on the climate indicator data set to obtain the correlation analysis data set; influencing factor analysis is performed based on the climate indicator data set and the correlation analysis data set to obtain the influencing factor analysis data; quality prediction model is constructed based on the influencing factor analysis data to obtain the quality prediction model; timeliness impact analysis is performed using the quality prediction model to obtain timeliness impact data. This step can make full use of Chinese medicine quality inspection data, market data and climate data, and through systematic data integration, feature extraction and influencing factor analysis, a reliable quality prediction model can be constructed to provide accurate prediction basis and timeliness analysis for Chinese medicine quality traceability, laying a solid foundation for subsequent traceability applications. According to the timeliness impact data, quality and environment correlation analysis is performed to obtain quality and environment correlation data; regional characteristics analysis is performed based on the quality and environment correlation data to obtain a regional characteristics analysis report. This step can analyze the quality differences of Chinese medicines in different regions, determine the correlation between quality and environmental factors, extract the unique characteristics of each region, and finally form a comprehensive regional characteristic analysis report, which provides important regional characteristic basis for subsequent Chinese medicine quality traceability applications, and helps to achieve a more accurate and comprehensive traceability effect. The traceability database is constructed according to the Chinese medicine identification data, quality inspection data, timeliness impact data and regional characteristic analysis report using the preset traceability system architecture to obtain a traceability database. This step can utilize the preset traceability system architecture to organically integrate the various types of Chinese medicine data obtained above, and use blockchain technology to ensure data security, and finally construct a reliable Chinese medicine traceability system and database, providing a solid technical and data foundation for subsequent traceability applications. Therefore, the present invention provides a modeling method for a Chinese medicine data traceability model, which is used to solve the defects of the existing modeling method of the Chinese medicine data traceability model, which lacks a systematic analysis of the regional characteristics of Chinese medicine and lacks an in-depth analysis of the factors affecting the quality of Chinese medicine.By analyzing the correlation between the quality of traditional Chinese medicine and the environment, extracting regional characteristics and forming a regional characteristic analysis report, through a series of steps such as extraction of key climate indicators, correlation analysis of quality indicators, and analysis of influencing factors, the relationship between the quality of traditional Chinese medicine and factors such as climate and environment is deeply analyzed. The regional characteristics of traditional Chinese medicine and the factors affecting the quality of traditional Chinese medicine are systematically analyzed. Your method makes up for the shortcomings of the existing modeling method of the traditional Chinese medicine data traceability model and realizes more accurate and comprehensive traceability of traditional Chinese medicine quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic diagram of the steps of a modeling method for a traditional Chinese medicine data traceability model;

[0082] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0083] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0084] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0085] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0086] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0087] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0088] To achieve this, please refer to Figures 1 to 3 A modeling method for a traditional Chinese medicine data traceability model comprises the following steps:

[0089] Step S1: collecting Chinese medicine in the target Chinese medicine area to obtain Chinese medicine collection site record data; performing Chinese medicine identification processing according to the Chinese medicine collection site record data to obtain Chinese medicine identification data;

[0090] Step S2: Performing quality inspection of the traditional Chinese medicine according to the identification data of the traditional Chinese medicine to obtain quality inspection data;

[0091] Step S3: integrating and cleaning the data according to the quality inspection data to obtain a cleaned data set of traditional Chinese medicine data; extracting key climate indicators from the cleaned data set of traditional Chinese medicine data to obtain a climate indicator data set; performing quality indicator correlation analysis on the climate indicator data set to obtain a correlation analysis data set; performing influencing factor analysis based on the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data; constructing a quality prediction model based on the influencing factor analysis data to obtain a quality prediction model; performing timeliness impact analysis using the quality prediction model to obtain timeliness impact data;

[0092] Step S4: Perform quality and environment correlation analysis based on the timeliness impact data to obtain quality and environment correlation data; perform regional characteristic analysis based on the quality and environment correlation data to obtain a regional characteristic analysis report;

[0093] Step S5: constructing a traceability database based on the TCM identification data, quality inspection data, timeliness impact data, and regional feature analysis report using a preset traceability system architecture to obtain a traceability database;

[0094] Step S6: Optimize the quality prediction model according to the traceability database and the quality prediction model to obtain quality prediction model optimization data to realize the traditional Chinese medicine data traceability operation.

[0095] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of the modeling method of the traditional Chinese medicine data traceability model of the present invention. In this example, the modeling method of the traditional Chinese medicine data traceability model includes the following steps:

[0096] Step S1: collecting Chinese medicine in the target Chinese medicine area to obtain Chinese medicine collection site record data; performing Chinese medicine identification processing according to the Chinese medicine collection site record data to obtain Chinese medicine identification data;

[0097] Step S2: Performing quality inspection of the traditional Chinese medicine according to the identification data of the traditional Chinese medicine to obtain quality inspection data;

[0098] Step S3: integrating and cleaning the data according to the quality inspection data to obtain a cleaned data set of traditional Chinese medicine data; extracting key climate indicators from the cleaned data set of traditional Chinese medicine data to obtain a climate indicator data set; performing quality indicator correlation analysis on the climate indicator data set to obtain a correlation analysis data set; performing influencing factor analysis based on the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data; constructing a quality prediction model based on the influencing factor analysis data to obtain a quality prediction model; performing timeliness impact analysis using the quality prediction model to obtain timeliness impact data;

[0099] Step S4: Perform quality and environment correlation analysis based on the timeliness impact data to obtain quality and environment correlation data; perform regional characteristic analysis based on the quality and environment correlation data to obtain a regional characteristic analysis report;

[0100] Step S5: constructing a traceability database based on the TCM identification data, quality inspection data, timeliness impact data, and regional feature analysis report using a preset traceability system architecture to obtain a traceability database;

[0101] Step S6: Optimize the quality prediction model according to the traceability database and the quality prediction model to obtain quality prediction model optimization data to realize the traditional Chinese medicine data traceability operation.

[0102] In the embodiment of the present invention, Chinese medicine is collected in the target Chinese medicine area to obtain the on-site record data of Chinese medicine collection; Chinese medicine identification processing is performed according to the on-site record data of Chinese medicine collection to obtain Chinese medicine identification data. First, for the target Chinese medicine area, a collection plan is formulated and on-site collection is carried out, and key information of the collection process is recorded to form on-site record data of Chinese medicine collection. Then, the Chinese medicine samples are coded and identified according to the collection records to obtain Chinese medicine identification data. The quality of Chinese medicine is tested according to the Chinese medicine identification data to obtain quality inspection data. Using the Chinese medicine identification data, a targeted quality inspection plan is formulated, and the Chinese medicine samples are tested and analyzed to obtain quality inspection data. Data integration and data cleaning are performed according to the quality inspection data to obtain a Chinese medicine data cleaning data set; key climate indicators are extracted from the Chinese medicine data cleaning data set to obtain a climate indicator data set; quality indicator correlation analysis is performed on the climate indicator data set to obtain a correlation analysis data set; influencing factor analysis is performed according to the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data; a quality prediction model is constructed according to the influencing factor analysis data to obtain a quality prediction model; and timeliness impact analysis is performed using the quality prediction model to obtain timeliness impact data. First, the quality inspection data, market data and historical climate data are integrated and cleaned to form a cleaned data set of Chinese medicine data. Then, key climate indicators are extracted from the data set, and the correlation analysis between them and quality indicators is performed. Based on this, the main influencing factors of Chinese medicine quality are analyzed, a quality prediction model is constructed, and the model is used to perform timeliness impact analysis to obtain timeliness impact data. According to the timeliness impact data, the quality and environment correlation analysis is performed to obtain quality and environment correlation data; according to the quality and environment correlation data, regional characteristics analysis is performed to obtain a regional characteristics analysis report. Using the timeliness impact data, the correlation between Chinese medicine quality and environmental factors in different regions is analyzed to form quality and environment correlation data. Further extract the unique characteristics of Chinese medicine growth in each region to obtain a regional characteristics analysis report. Using the preset traceability system architecture, the traceability database is constructed according to the Chinese medicine identification data, quality inspection data, timeliness impact data and regional characteristics analysis report to obtain a traceability database. Based on the preset traceability system architecture, the various types of Chinese medicine data obtained above are integrated to construct a traceability database. According to the traceability database and quality prediction model, the quality prediction model is optimized to obtain the quality prediction model optimization data to realize the traceability of traditional Chinese medicine data. Using the traceability database and quality prediction model, the prediction model is continuously optimized to improve the accuracy of traditional Chinese medicine quality prediction and finally realize the traceability application of traditional Chinese medicine data.

[0103] Preferably, step S1 comprises the following steps:

[0104] Step S11: Analyze the Chinese medicine collection plan for the target Chinese medicine area to obtain Chinese medicine collection plan data;

[0105] Step S12: Record the on-site collection of Chinese medicine according to the Chinese medicine collection plan data to obtain on-site record data of Chinese medicine collection;

[0106] Step S13: performing Chinese medicine identification coding according to the data recorded at the Chinese medicine collection site to obtain Chinese medicine coding data;

[0107] Step S14: Entering Chinese medicine information into the Chinese medicine coding data and the Chinese medicine collection site record data to obtain Chinese medicine information data;

[0108] Step S15: Encrypt the Chinese medicine identification data of the Chinese medicine information data and associate the data with the label to obtain the Chinese medicine identification data.

[0109] In the embodiment of the present invention, a Chinese medicine collection plan is analyzed for the target Chinese medicine area to obtain Chinese medicine collection plan data. First, a detailed Chinese medicine collection plan is formulated according to factors such as the type of Chinese medicine, the growing area, and the growing environment. This includes determining the collection point, collection time, collection method, and other contents to form Chinese medicine collection plan data. According to the Chinese medicine collection plan data, the Chinese medicine collection site record is performed to obtain Chinese medicine collection site record data. According to the collection plan formulated in advance, the Chinese medicine collection operation is actually performed, and the key information in the collection process is recorded in detail, such as the collection time, collection location, and collection personnel, to form Chinese medicine collection site record data. According to the Chinese medicine collection site record data, the Chinese medicine identification is coded to obtain Chinese medicine coding data. Each batch of Chinese medicine samples collected is given a unique coding identification for subsequent traceability and management. The coding information includes key information such as the collection location and collection time to form Chinese medicine coding data. Chinese medicine information is entered into the Chinese medicine coding data and the Chinese medicine collection site record data to obtain Chinese medicine information data. The basic information of Chinese medicine recorded on site, such as type, properties, weight, etc., is entered into the information system together with the coded data to form complete Chinese medicine information data. The Chinese medicine identification data is encrypted and the data is associated with the label to obtain the Chinese medicine identification data. Encryption technology is used to protect the Chinese medicine information data, and the encrypted data is associated with the physical Chinese medicine sample, such as printing a QR code or RFID tag, to form the final Chinese medicine identification data.

[0110] Preferably, step S2 comprises the following steps:

[0111] Step S21: Formulate a quality inspection plan based on the TCM identification data to obtain quality inspection plan data;

[0112] Step S22: pre-process each batch of Chinese medicine samples according to the quality inspection plan data to obtain sample processing data;

[0113] Step S23 performs quality inspection on the sample processing data according to the quality inspection plan data to obtain quality inspection result data;

[0114] Step S24: performing test result analysis on the quality test result data and performing result analysis verification to obtain test result analysis data;

[0115] Step S25: generate quality inspection data based on the inspection result analysis data and archive the quality inspection data to obtain quality inspection data.

[0116] In the embodiment of the present invention, a quality inspection scheme is formulated according to the Chinese medicine identification data to obtain quality inspection scheme data. First, a targeted quality inspection scheme is formulated according to the collected Chinese medicine identification data, such as information such as Chinese medicine varieties and places of origin. This includes determining the inspection items, inspection standards, inspection processes, etc., to form quality inspection scheme data. Pre-process each batch of Chinese medicine samples according to the quality inspection scheme data to obtain sample processing data. According to the quality inspection scheme, necessary pre-processing, such as washing, slicing, crushing, etc., is performed on each batch of collected Chinese medicine samples to meet the requirements of subsequent inspections, and sample processing data is formed. The sample processing data quality inspection is processed according to the quality inspection scheme data to obtain quality inspection result data. According to the quality inspection scheme, various quality indicators of the pre-treated Chinese medicine samples are tested, such as content determination, impurity inspection, etc., to obtain the quality inspection result data of each batch of Chinese medicine. The quality inspection result data is analyzed and the result analysis is verified to obtain the inspection result analysis data. The quality inspection results are deeply analyzed, and necessary verification measures, such as repeated inspections, are taken to ensure the accuracy and reliability of the inspection results, and the inspection result analysis data is formed. The quality inspection data is generated and archived for the test result analysis data to obtain the quality inspection data. The analyzed quality inspection results are sorted into standardized quality inspection data and properly archived to provide data support for subsequent traceability and analysis.

[0117] Preferably, step S3 comprises the following steps:

[0118] Step S31: collecting historical climate data of the target Chinese medicine region to obtain historical climate data; collecting Chinese medicine market data according to the Chinese medicine identification data to obtain Chinese medicine market data;

[0119] Step S32: integrating and preprocessing the traditional Chinese medicine market data, historical climate data, and quality inspection data to obtain a preprocessed comprehensive data set; performing outlier detection and data cleaning on the preprocessed comprehensive data set to obtain a traditional Chinese medicine data cleaning data set;

[0120] Step S33: extract key climate indicators from the cleaned data set of traditional Chinese medicine data to obtain a climate indicator data set; perform quality indicator correlation analysis on the climate indicator data set to obtain a correlation analysis data set;

[0121] Step S34: performing influencing factor analysis based on the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data;

[0122] Step S35: constructing a quality prediction model based on the influencing factor analysis data to obtain a quality prediction model;

[0123] Step S36: Use the quality prediction model to predict the quality of Chinese medicines in different seasons on the Chinese medicine data cleaning data set to obtain the Chinese medicine quality change trend data; perform timeliness impact analysis based on the Chinese medicine quality change trend data to obtain timeliness impact data.

[0124] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0125] Step S31: collecting historical climate data of the target Chinese medicine region to obtain historical climate data; collecting Chinese medicine market data according to the Chinese medicine identification data to obtain Chinese medicine market data;

[0126] In the embodiment of the present invention, historical climate data is collected for the target Chinese medicine area to obtain historical climate data; and Chinese medicine market data is collected according to the Chinese medicine identification data to obtain Chinese medicine market data. First, historical climate data of the target Chinese medicine area, such as temperature, rainfall, sunshine duration and other indicators, are collected to form historical climate data. At the same time, according to the Chinese medicine identification data, relevant information of Chinese medicine in the market, such as sales volume, price, etc., is collected to form Chinese medicine market data.

[0127] Step S32: integrating and preprocessing the traditional Chinese medicine market data, historical climate data, and quality inspection data to obtain a preprocessed comprehensive data set; performing outlier detection and data cleaning on the preprocessed comprehensive data set to obtain a traditional Chinese medicine data cleaning data set;

[0128] In the embodiment of the present invention, data integration and data preprocessing are performed on the traditional Chinese medicine market data, historical climate data and quality inspection data to obtain a preprocessed comprehensive data set; outlier detection and data cleaning are performed on the preprocessed comprehensive data set to obtain a traditional Chinese medicine data cleaning data set. The above three types of data are integrated to form a preprocessed comprehensive data set. Then, outlier detection and data cleaning are performed on the data set to eliminate invalid or erroneous data to obtain a final traditional Chinese medicine data cleaning data set.

[0129] Step S33: extract key climate indicators from the cleaned data set of traditional Chinese medicine data to obtain a climate indicator data set; perform quality indicator correlation analysis on the climate indicator data set to obtain a correlation analysis data set;

[0130] In an embodiment of the present invention, key climate indicators are extracted from a Chinese medicine data cleaning data set to obtain a climate indicator data set; quality indicator correlation analysis is performed on the climate indicator data set to obtain a correlation analysis data set. Key climate indicators related to the quality of Chinese medicine are extracted from the Chinese medicine data cleaning data set to form a climate indicator data set. Then, quality indicator correlation analysis is performed on the climate indicator data set to obtain a correlation analysis data set.

[0131] Step S34: performing influencing factor analysis based on the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data;

[0132] In the embodiment of the present invention, the influencing factor analysis is performed based on the climate index data set and the association analysis data set to obtain the influencing factor analysis data. The climate index data set and the association analysis data set are used to analyze the mutual influence relationship between the quality of traditional Chinese medicine and factors such as the climate environment, determine the main influencing factors of the quality of traditional Chinese medicine, and form the influencing factor analysis data.

[0133] Step S35: constructing a quality prediction model based on the influencing factor analysis data to obtain a quality prediction model;

[0134] In the embodiment of the present invention, a quality prediction model is constructed based on the influencing factor analysis data to obtain a quality prediction model. Based on the influencing factor analysis data obtained by the above analysis, a prediction model that can accurately predict the quality change of traditional Chinese medicine is constructed to obtain a quality prediction model.

[0135] Step S36: using the quality prediction model to predict the quality of Chinese medicines in different seasons on the Chinese medicine data cleaning data set, and obtaining the Chinese medicine quality change trend data; performing timeliness impact analysis based on the Chinese medicine quality change trend data, and obtaining timeliness impact data;

[0136] In the embodiment of the present invention, the quality prediction model is used to predict the quality of Chinese medicines in different seasons on the Chinese medicine data cleaning data set to obtain the quality change trend data of Chinese medicines; the timeliness impact analysis is performed based on the quality change trend data of Chinese medicines to obtain the timeliness impact data. The quality prediction model is used to predict the quality of Chinese medicines in different seasons to obtain the trend data of the quality of Chinese medicines changing over time. And based on the trend data, the timeliness impact of the quality of Chinese medicines is analyzed to obtain the timeliness impact data.

[0137] Preferably, step S33 includes the following steps:

[0138] Step S331: classify the Chinese medicine types on the Chinese medicine data cleansing data set to obtain a Chinese medicine classification data set; divide the Chinese medicine classification data set into groups according to key seasons to obtain a Chinese medicine seasonal grouping data set;

[0139] Step S332: dividing the Chinese medicine seasonal grouping data set into growth cycles to obtain a cycle-divided data set;

[0140] Step S333: extract key climate indicators from the period-divided data set to obtain a climate indicator data set;

[0141] Step S334: performing climate indicator statistical analysis on the climate indicator data set to obtain a climate statistical data set;

[0142] Step S335: Perform quality indicator correlation analysis on the climate statistical data set and the traditional Chinese medicine data cleaning data set to obtain a correlation analysis data set.

[0143] In an embodiment of the present invention, a Chinese medicine data cleaning data set is classified into Chinese medicine types to obtain a Chinese medicine classification data set; and a Chinese medicine classification data set is divided into groups according to key seasons to obtain a Chinese medicine seasonal grouping data set. First, the Chinese medicine information in the Chinese medicine data cleaning data set is classified, and the data is divided into different groups according to the types of Chinese medicine to form a Chinese medicine classification data set. Then, the Chinese medicine classification data set is further subdivided into groups according to key seasonal characteristics, such as spring, summer, autumn and winter, to obtain a Chinese medicine seasonal grouping data set. The Chinese medicine seasonal grouping data set is divided into growth cycles to obtain a cycle division data set. For the Chinese medicine seasonal grouping data set, the growth cycle characteristics of the Chinese medicine, such as the seedling stage, the stem and leaf growth stage, the flowering and fruiting stage, etc., are analyzed, and the data are divided into different growth cycle groups to form a cycle division data set. Key climate indicators are extracted from the cycle division data set to obtain a climate indicator data set. From the cycle division data set, key climate indicators related to the growth and quality of Chinese medicine, such as temperature, rainfall, sunshine duration, etc., are extracted to form a climate indicator data set. Climate indicator statistical analysis is performed on the climate indicator data set to obtain a climate statistical data set. The climate indicator data set was statistically analyzed, including the calculation of the mean, standard deviation, correlation coefficient and other indicators to form a climate statistical data set. The climate statistical data set and the Chinese medicine data cleaning data set were subjected to quality indicator correlation analysis to obtain a correlation analysis data set. The climate statistical data set and the Chinese medicine data cleaning data set were subjected to correlation analysis to determine the correlation between climate factors and Chinese medicine quality indicators to obtain a correlation analysis data set.

[0144] Preferably, step S34 includes the following steps:

[0145] Step S341: performing a significance test on the association analysis data set to obtain a significance test data set;

[0146] Step S342: sorting the influencing factors of the climate indicator dataset according to the significance test dataset to obtain an influencing factor sorting dataset;

[0147] Step S343: performing year difference analysis on the data set sorted according to the influencing factors to obtain a year difference analysis data set;

[0148] Step S344: quantifying the impact degree according to the year difference analysis data set and the climate indicator data set to obtain a quantitative data set of impact factors;

[0149] Step S345: Perform influencing factor analysis based on the climate indicator data set, the year difference analysis data set, and the influencing factor quantification data set to obtain influencing factor analysis data.

[0150] In the embodiment of the present invention, a significance test is performed on the association analysis data set to obtain a significance test data set. For the association analysis data set, a statistical method such as a t-test, variance analysis, etc. is used to perform a significance test on the correlation between the climate index and the quality index of traditional Chinese medicine, determine which climate index has a significant correlation with the quality of traditional Chinese medicine, and form a significance test data set. According to the significance test data set, the climate index data set is sorted by influencing factors to obtain an influencing factor sorting data set. Based on the significance test result, each indicator in the climate index data set is sorted to determine the key climate factors that have the greatest impact on the quality of traditional Chinese medicine, and form an influencing factor sorting data set. According to the influencing factor sorting data set, a year difference analysis is performed to obtain a year difference analysis data set. For the influencing factor sorting data set, the difference between the climate index and the quality of traditional Chinese medicine in different years is analyzed to form a year difference analysis data set. According to the year difference analysis data set and the climate index data set, the degree of influence is quantified to obtain an influencing factor quantification data set. The degree of influence of the climate index on the quality of traditional Chinese medicine is further quantified, including indicators such as sensitivity and elasticity, to form an influencing factor quantification data set. According to the climate index data set, the year difference analysis data set and the influencing factor quantification data set, the influencing factor analysis is performed to obtain the influencing factor analysis data. By comprehensively analyzing the above-mentioned data, we deeply explored the mutual influence between the quality of traditional Chinese medicine and factors such as climate and environment, and obtained the final influencing factor analysis data.

[0151] Preferably, step S4 comprises the following steps:

[0152] Step S41: performing quality difference analysis of Chinese medicines in different regions according to the timeliness impact data to obtain quality difference analysis data;

[0153] Step S42: performing quality and environment correlation analysis based on the quality difference analysis data to obtain quality and environment correlation data;

[0154] Step S43: performing regional comparison analysis based on the quality and environment correlation data to obtain regional comparison data;

[0155] Step S44: extracting regional features according to the regional comparison data to obtain regional feature data;

[0156] Step S45: performing relevant data association analysis on the regional characteristic data to obtain a regional characteristic analysis report.

[0157] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes:

[0158] Step S41: performing quality difference analysis of Chinese medicines in different regions according to the timeliness impact data to obtain quality difference analysis data;

[0159] In the embodiment of the present invention, the quality difference analysis of Chinese medicines in different regions is performed based on the timeliness impact data to obtain quality difference analysis data. The quality difference of the same Chinese medicine in different geographical regions is analyzed by using the timeliness impact data obtained above, and the difference in the quality of Chinese medicines in each region is determined to form quality difference analysis data.

[0160] Step S42: performing quality and environment correlation analysis based on the quality difference analysis data to obtain quality and environment correlation data;

[0161] In the embodiment of the present invention, the quality and environment correlation analysis is performed based on the quality difference analysis data to obtain the quality and environment correlation data. Based on the quality difference analysis data, combined with regional environmental characteristic data such as climate, soil, etc., the correlation between the quality of traditional Chinese medicine and environmental factors is analyzed to obtain the quality and environment correlation data.

[0162] Step S43: performing regional comparison analysis based on the quality and environment correlation data to obtain regional comparison data;

[0163] In the embodiment of the present invention, regional comparative analysis is performed based on the quality and environment correlation data to obtain regional comparative data. The quality and environment correlation data of Chinese medicine in different regions are compared and analyzed to determine the difference in the relationship between the quality of Chinese medicine and environmental factors in each region, and form regional comparative data.

[0164] Step S44: extracting regional features according to the regional comparison data to obtain regional feature data;

[0165] In the embodiment of the present invention, regional feature extraction is performed based on regional comparison data to obtain regional feature data. Based on the regional comparison data, the unique features of Chinese medicine growth in each geographical region, such as climate characteristics, soil conditions, geographical location, etc., are extracted to form regional feature data.

[0166] Step S45: performing relevant data association analysis on the regional characteristic data to obtain a regional characteristic analysis report;

[0167] In the embodiment of the present invention, the regional characteristic data is subjected to relevant data association analysis to obtain a regional characteristic analysis report. The regional characteristic data is associated with other relevant data obtained by the above analysis, such as the quality of traditional Chinese medicine, market information, etc., and a regional characteristic analysis report is summarized to provide a basis for subsequent traceability applications.

[0168] Preferably, step S42 includes the following steps:

[0169] Step S421: performing regional characteristic demand analysis based on the quality difference analysis data to obtain regional characteristic demand data;

[0170] Step S422: collecting regional characteristic data according to the regional characteristic demand data to obtain regional characteristic collection data;

[0171] Step S423: integrating the regional characteristic collection data and the timeliness impact data with the quality and environment relationship to obtain the quality and environment relationship integrated data;

[0172] Step S424: Use geographic information system to perform spatial analysis on the integrated data of the relationship between quality and environment to obtain spatial distribution data;

[0173] Step S425: Perform quality and environment correlation analysis based on the spatial distribution data and the quality and environment relationship integration data to obtain quality and environment correlation data.

[0174] In an embodiment of the present invention, regional characteristic demand analysis is performed based on the quality difference analysis data to obtain regional characteristic demand data. First, based on the aforementioned quality difference analysis data, the reasons for the differences in the quality of Chinese medicine in different regions are analyzed, and the regional environmental characteristic factors that need to be further collected and analyzed are determined to form regional characteristic demand data. Regional characteristic data collection is performed based on the regional characteristic demand data to obtain regional characteristic collection data. According to the regional characteristic demand data, relevant environmental data of the target area, such as climate, geology, soil, etc., are collected on the spot to form regional characteristic collection data. The regional characteristic collection data and the timeliness impact data are integrated with the relationship between quality and environment to obtain quality and environment relationship integrated data. The regional characteristic collection data and the aforementioned timeliness impact data are integrated and analyzed to explore the relationship between the quality of Chinese medicine and environmental factors to form quality and environment relationship integrated data. The regional characteristic collection data and the timeliness impact data are integrated with the relationship between quality and environment to obtain quality and environment relationship integrated data. The regional characteristic collection data and the timeliness impact data are integrated and analyzed to explore the relationship between the quality of Chinese medicine and environmental factors to form quality and environment relationship integrated data. The regional characteristic collection data and the aforementioned timeliness impact data are integrated and analyzed to explore the relationship between the quality of Chinese medicine and environmental factors to form quality and environment relationship integrated data. The quality and environment relationship integrated data are spatially analyzed using a geographic information system to obtain spatial distribution data. With the help of tools such as geographic information systems, spatial visualization analysis of the integrated data on the relationship between quality and environment was performed to obtain the spatial distribution data of the quality and environmental factors of traditional Chinese medicine in each region. Quality and environmental correlation analysis was performed based on the spatial distribution data and the integrated data on the relationship between quality and environment to obtain the quality and environmental correlation data. The spatial distribution data and the integrated data on the relationship between quality and environment were used to analyze the correlation between the quality of traditional Chinese medicine and environmental factors in depth, and the final quality and environmental correlation data were formed.

[0175] Preferably, step S5 comprises the following steps:

[0176] Step S51: Designing a traceability system according to the TCM identification data, quality inspection data, timeliness impact data, and regional characteristic analysis report using a preset traceability system architecture to obtain traceability system design data;

[0177] Step S52: integrating the traceability system design data using blockchain technology to obtain blockchain integrated data;

[0178] Step S53: Generate a QR code and an RFID tag according to the Chinese medicine identification data to obtain identification tag data;

[0179] Step S5: embed the identification tag data into the blockchain integrated data to test the traceability system and obtain the traceability system test data;

[0180] Step S55: Generate a traceability database according to the traceability system test data to obtain a traceability database.

[0181] In the embodiment of the present invention, the traceability system design is performed according to the Chinese medicine identification data, quality inspection data, timeliness impact data and regional characteristic analysis report using the preset traceability system architecture to obtain the traceability system design data. First, referring to the pre-designed traceability system architecture, combined with the various types of Chinese medicine data obtained above, such as identification data, quality inspection data, timeliness impact data and regional characteristic analysis report, the traceability system is specifically designed to form the traceability system design data. The traceability system design data is integrated with blockchain technology to obtain blockchain integrated data. The traceability system design data is integrated with blockchain technology, and the distributed ledger and non-tamperable characteristics of blockchain are used to ensure the security and credibility of Chinese medicine traceability data to obtain blockchain integrated data. According to the Chinese medicine identification data, a QR code and an RFID tag are generated to obtain identification tag data. Based on the Chinese medicine identification data obtained above, a QR code and an RFID tag suitable for Chinese medicine entities are generated to form identification tag data. The identification tag data is embedded into the blockchain integrated data to test the traceability system to obtain the traceability system test data. The generated identification tag data is associated with the blockchain integrated data to build a complete Chinese medicine traceability system, and the test verification is carried out to obtain the traceability system test data. The traceability database is generated according to the traceability system test data to obtain the traceability database. Based on the data that has passed the traceability system test, the Chinese medicine traceability database is constructed, and the Chinese medicine identification data, quality inspection data, timeliness impact data, and regional feature analysis reports are systematically stored to form the final Chinese medicine traceability database.

[0182] Preferably, step S6 comprises the following steps:

[0183] Step S61: performing decision support demand analysis according to the traceability database to obtain decision support demand data;

[0184] Step S62: Select a model according to the decision support demand data to obtain an initial model; extract model training data from the traceability database to obtain a model training data set;

[0185] Step S63: inputting the cleaned data set of traditional Chinese medicine data into the initial model for feature engineering processing to obtain a model to be trained;

[0186] Step S64: Perform model training on the model to be trained using the model training data set to obtain model training data;

[0187] Step S65: optimizing the quality prediction model according to the quality prediction model and the model training data to obtain the quality prediction model optimization data to realize the Chinese medicine data traceability operation;

[0188] In an embodiment of the present invention, decision support demand analysis is performed according to the traceability database to obtain decision support demand data. First, for the constructed Chinese medicine traceability database, the decision support needs of users in terms of quality prediction, risk control, etc. are analyzed to form decision support demand data. Model selection is performed according to the decision support demand data to obtain an initial model; model training data is extracted from the traceability database to obtain a model training data set. Based on the decision support demand data, a suitable prediction model is selected as the initial model, and the data set required for model training is extracted from the traceability database. The Chinese medicine data cleaning data set is input into the initial model for feature engineering processing to obtain a model to be trained. The aforementioned Chinese medicine data cleaning data set is input into the initial model, and feature engineering processing such as feature selection and data preprocessing is performed to obtain a model to be further trained. The model training data set is used to perform model training on the model to be trained to obtain model training data. The model training data set is used to train the model to be trained using methods such as deep learning or machine learning to obtain the final model training data. The quality prediction model is optimized according to the quality prediction model and the model training data to obtain quality prediction model optimization data to realize the Chinese medicine data traceability operation. The quality prediction model and model training data obtained above are optimized and adjusted to make them better suitable for the quality prediction of traditional Chinese medicine, forming optimized quality prediction model data to support the traceability application of traditional Chinese medicine data.

[0189] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0190] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A modeling method for a traditional Chinese medicine data traceability model, characterized in that: The following steps are involved: Step S1: Collect Chinese medicine in the target Chinese medicine area to obtain on-site record data of Chinese medicine collection; Perform Chinese medicine identification processing according to the on-site record data of Chinese medicine collection to obtain Chinese medicine identification data; Step S2: Performing quality inspection of the traditional Chinese medicine according to the identification data of the traditional Chinese medicine to obtain quality inspection data; Step S3: Integrate and clean the data according to the quality inspection data to obtain a cleaned data set of traditional Chinese medicine data; Extract key climate indicators from the cleaned dataset of traditional Chinese medicine data to obtain a climate indicator dataset; Perform quality indicator correlation analysis on the climate indicator dataset to obtain a correlation analysis dataset; Conduct influencing factor analysis based on the climate indicator dataset and the correlation analysis dataset to obtain influencing factor analysis data; A quality prediction model is constructed based on the influencing factor analysis data to obtain a quality prediction model; timeliness impact analysis is performed using the quality prediction model to obtain timeliness impact data; Step S4: Perform quality and environment correlation analysis based on the timeliness impact data to obtain quality and environment correlation data; perform regional characteristic analysis based on the quality and environment correlation data to obtain a regional characteristic analysis report; Step S5: constructing a traceability database based on the TCM identification data, quality inspection data, timeliness impact data, and regional feature analysis report using a preset traceability system architecture to obtain a traceability database; Step S6: Optimize the quality prediction model according to the traceability database and the quality prediction model to obtain quality prediction model optimization data to realize the traditional Chinese medicine data traceability operation.

2. The modeling method of the traditional Chinese medicine data traceability model according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Analyze the Chinese medicine collection plan for the target Chinese medicine area to obtain Chinese medicine collection plan data; Step S12: Record the on-site collection of Chinese medicine according to the Chinese medicine collection plan data to obtain on-site record data of Chinese medicine collection; Step S13: performing Chinese medicine identification coding according to the data recorded at the Chinese medicine collection site to obtain Chinese medicine coding data; Step S14: Entering Chinese medicine information into the Chinese medicine coding data and the Chinese medicine collection site record data to obtain Chinese medicine information data; Step S15: Encrypt the Chinese medicine identification data of the Chinese medicine information data and associate the data with the label to obtain the Chinese medicine identification data.

3. The modeling method of the traditional Chinese medicine data traceability model according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Formulate a quality inspection plan based on the TCM identification data to obtain quality inspection plan data; Step S22: pre-process each batch of Chinese medicine samples according to the quality inspection plan data to obtain sample processing data; Step S23 performs quality inspection on the sample processing data according to the quality inspection plan data to obtain quality inspection result data; Step S24: performing test result analysis on the quality test result data and performing result analysis verification to obtain test result analysis data; Step S25: generate quality inspection data based on the inspection result analysis data and archive the quality inspection data to obtain quality inspection data.

4. The modeling method of the traditional Chinese medicine data traceability model according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: collecting historical climate data of the target Chinese medicine region to obtain historical climate data; collecting Chinese medicine market data according to the Chinese medicine identification data to obtain Chinese medicine market data; Step S32: integrating and preprocessing the traditional Chinese medicine market data, historical climate data, and quality inspection data to obtain a preprocessed comprehensive data set; performing outlier detection and data cleaning on the preprocessed comprehensive data set to obtain a traditional Chinese medicine data cleaning data set; Step S33: extract key climate indicators from the cleaned data set of traditional Chinese medicine data to obtain a climate indicator data set; perform quality indicator correlation analysis on the climate indicator data set to obtain a correlation analysis data set; Step S34: performing influencing factor analysis based on the climate indicator data set and the correlation analysis data set to obtain influencing factor analysis data; Step S35: constructing a quality prediction model based on the influencing factor analysis data to obtain a quality prediction model; Step S36: Use the quality prediction model to predict the quality of Chinese medicines in different seasons on the Chinese medicine data cleaning data set to obtain the Chinese medicine quality change trend data; perform timeliness impact analysis based on the Chinese medicine quality change trend data to obtain timeliness impact data.

5. The modeling method of the traditional Chinese medicine data traceability model according to claim 4 is characterized in that: Step S33 includes the following steps: Step S331: classify the Chinese medicine types on the Chinese medicine data cleansing data set to obtain a Chinese medicine classification data set; divide the Chinese medicine classification data set into groups according to key seasons to obtain a Chinese medicine seasonal grouping data set; Step S332: dividing the Chinese medicine seasonal grouping data set into growth cycles to obtain a cycle-divided data set; Step S333: extract key climate indicators from the period-divided data set to obtain a climate indicator data set; Step S334: performing climate indicator statistical analysis on the climate indicator data set to obtain a climate statistical data set; Step S335: Perform quality indicator correlation analysis on the climate statistical data set and the traditional Chinese medicine data cleaning data set to obtain a correlation analysis data set.

6. The modeling method of the traditional Chinese medicine data traceability model according to claim 4 is characterized in that: Step S34 includes the following steps: Step S341: performing a significance test on the association analysis data set to obtain a significance test data set; Step S342: sorting the influencing factors of the climate indicator dataset according to the significance test dataset to obtain an influencing factor sorting dataset; Step S343: performing year difference analysis on the data set sorted according to the influencing factors to obtain a year difference analysis data set; Step S344: quantifying the impact degree according to the year difference analysis data set and the climate indicator data set to obtain a quantitative data set of impact factors; Step S345: Perform influencing factor analysis based on the climate indicator data set, the year difference analysis data set, and the influencing factor quantification data set to obtain influencing factor analysis data.

7. The modeling method of the traditional Chinese medicine data traceability model according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing quality difference analysis of Chinese medicines in different regions according to the timeliness impact data to obtain quality difference analysis data; Step S42: performing quality and environment correlation analysis based on the quality difference analysis data to obtain quality and environment correlation data; Step S43: performing regional comparison analysis based on the quality and environment correlation data to obtain regional comparison data; Step S44: extracting regional features according to the regional comparison data to obtain regional feature data; Step S45: performing relevant data association analysis on the regional characteristic data to obtain a regional characteristic analysis report.

8. The modeling method of the traditional Chinese medicine data traceability model according to claim 7, characterized in that: Step S42 includes the following steps: Step S421: performing regional characteristic demand analysis based on the quality difference analysis data to obtain regional characteristic demand data; Step S422: collecting regional characteristic data according to the regional characteristic demand data to obtain regional characteristic collection data; Step S423: integrating the regional characteristic collection data and the timeliness impact data with the quality and environment relationship to obtain the quality and environment relationship integrated data; Step S424: Use geographic information system to perform spatial analysis on the integrated data of the relationship between quality and environment to obtain spatial distribution data; Step S425: Perform quality and environment correlation analysis based on the spatial distribution data and the quality and environment relationship integration data to obtain quality and environment correlation data.

9. The modeling method of the traditional Chinese medicine data traceability model according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Designing a traceability system according to the TCM identification data, quality inspection data, timeliness impact data, and regional characteristic analysis report using a preset traceability system architecture to obtain traceability system design data; Step S52: integrating the traceability system design data using blockchain technology to obtain blockchain integrated data; Step S53: Generate a QR code and an RFID tag according to the Chinese medicine identification data to obtain identification tag data; Step S5: embed the identification tag data into the blockchain integrated data to test the traceability system and obtain the traceability system test data; Step S55: Generate a traceability database according to the traceability system test data to obtain a traceability database.

10. The modeling method of the traditional Chinese medicine data traceability model according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: performing decision support demand analysis according to the traceability database to obtain decision support demand data; Step S62: Select a model according to the decision support demand data to obtain an initial model; extract model training data from the traceability database to obtain a model training data set; Step S63: inputting the cleaned data set of traditional Chinese medicine data into the initial model for feature engineering processing to obtain a model to be trained; Step S64: Perform model training on the model to be trained using the model training data set to obtain model training data; Step S65: Optimize the quality prediction model according to the quality prediction model and the model training data to obtain quality prediction model optimization data to realize the traditional Chinese medicine data traceability operation.

Images