Traditional Chinese medicine and Tibetan medicine crossed medicinal material chemical component informatization platform

Abstract

The invention relates to the field of information processing of traditional Chinese medicine and Tibetan medicine, and discloses a traditional Chinese medicine and Tibetan medicine crossed medicinal material chemical component informatization platform which comprises a data acquisition module used for acquiring traditional Chinese medicine and Tibetan medicine chemical component information from data sources related to the traditional Chinese medicine and Tibetan medicine and preprocessing the information; the standardization module is used for carrying out standardization processing on the collected chemical component information of the medicinal materials; the storage management module is used for storing and managing a large amount of Chinese and Tibetan medicinal material component data; and the component cross analysis module is used for carrying out cross comparison and analysis on different chemical components of the ZhongTibetan medicinal materials, and meanwhile, presenting the components and efficacy information of the ZhongTibetan medicinal materials in an intuitive and visual manner. Through cooperative operation of the data acquisition module, the standardization module, the storage management module, the component cross analysis module, the evaluation prediction module, the query recommendation module and the update maintenance module, the data quality and the management efficiency are improved, and a standard, accurate and easy-to-obtain and easy-to-analyze data basis is provided for research of the traditional Chinese medicine and the Tibetan medicine.

Description

Technical Field

[0001] The present invention relates to the technical field of Chinese and Tibetan medicine information processing, and in particular to an information platform for chemical components of cross-medicinal materials of Chinese and Tibetan medicine. Background Art

[0002] Information processing of Chinese and Tibetan medicine refers to the process of collecting, organizing, analyzing and applying data, knowledge, research results, etc. related to Chinese and Tibetan medicine using modern information technology. It involves various fields of Chinese and Tibetan medicine, including information on medicinal materials, prescriptions, clinical applications, pharmacological research, etc. The purpose is to improve the research, production, management and promotion efficiency of Chinese and Tibetan medicine through information technology.

[0003] Chinese and Tibetan medicine have rich medicinal resources, including a large number of cross-medicinal materials with complex chemical compositions and high research value. Most of the existing Chinese and Tibetan medicinal material component data are scattered in the literature, lacking a standardized and systematic information platform, and the cross-information analysis and comparison of medicinal material components are not in-depth enough, resulting in low research efficiency and incomplete information. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an information platform for the chemical composition of cross-Chinese and Tibetan medicines, which solves the problems that most of the existing Chinese and Tibetan medicinal material component data are scattered in the literature, lack of a standardized and systematic information platform, and the cross-information analysis and comparison of medicinal material components are not in-depth enough, resulting in low research efficiency and incomplete information.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cross-medicinal chemical composition information platform of Chinese and Tibetan medicines, comprising:

[0006] Data collection module: used to collect chemical composition information of Chinese and Tibetan medicinal materials from data sources related to Chinese and Tibetan medicine, and perform preprocessing;

[0007] Standardization module: used to standardize the collected chemical composition information of medicinal materials;

[0008] Storage management module: used to store and manage a large amount of Chinese and Tibetan medicinal material ingredient data;

[0009] Component cross-analysis module: used to cross-compare and analyze the chemical components of different Chinese and Tibetan medicinal materials, and present the components and efficacy information of Chinese and Tibetan medicinal materials in an intuitive and visual way;

[0010] Evaluation and prediction module: used to evaluate and predict the efficacy of Chinese and Tibetan medicinal materials based on pharmacological data and chemical composition information using machine learning and artificial intelligence algorithms;

[0011] Query and recommendation module: used to provide human-computer interaction, assist in quick query, ingredient comparison and efficacy analysis, and recommend relevant medicinal materials according to needs;

[0012] Update and maintenance module: used to ensure the continuous and stable operation of the platform and the timeliness of data.

[0013] Preferably, the data acquisition module includes a crawler unit and a preprocessing unit. The crawler unit is used to capture chemical component related information from multiple data sources, the multiple data sources include literature databases of Chinese and Tibetan medicines, experimental data, existing pharmacopoeias, scientific research results and data submitted by users. The preprocessing unit is used to preprocess the collected data, and the preprocessing includes removing noise and invalid information in the data.

[0014] The preprocessing unit includes a data cleaning subunit, a feature encoding subunit, and a feature selection subunit. The data cleaning subunit is used to clean the input pharmacological data and chemical component information. The cleaning includes checking the integrity of the data. For missing values, the corresponding filling method is used to fill in the missing values ​​according to the data distribution characteristics. At the same time, outliers are identified and processed, and the range of outliers is determined by statistical methods. The outliers are processed, and the processing includes deletion, correction or special processing according to domain knowledge. The corresponding filling methods include mean filling, median filling or model-based filling methods. The feature encoding subunit is used to convert the classification features into numerical forms using unique hot encoding or label encoding methods. At the same time, for continuous chemical component features, standardization processing is performed. The classification features include medicinal material categories and efficacy categories. The feature selection subunit is used to use feature selection algorithms to screen out features with high correlation with efficacy from chemical component features, and evaluate the importance of features by calculating indicators between features and efficacy, and remove redundant and irrelevant features. The indicators include information gain and Gini coefficient.

[0015] Preferably, the standardization module includes a unit unification unit, a name normalization unit, a synonym normalization unit, and a classification and filing unit. The unit unification unit is used to unify the units of component content in data from different sources, the name normalization unit is used to standardize the component names and unify the naming according to international standards or industry general standards, the synonym normalization unit is used to normalize different names of the same component, and the classification and filing unit is used to classify and file the chemical components according to their characteristics, and the characteristics include properties and structures.

[0016] Preferably, the storage management module includes a distributed storage unit, a backup and recovery unit, and a permission management unit. The distributed storage unit is used to implement large-scale data storage and query using a distributed architecture. The backup and recovery unit is used to back up and perform disaster recovery on various data. The permission management unit is used to assign different levels of data access rights.

[0017] Preferably, the component cross-analysis module includes a chemical component comparison unit, a synergy analysis unit, and a visualization interaction unit. The chemical component comparison algorithm unit is used to calculate the similarities and differences of components between different medicinal materials through corresponding algorithms. The corresponding algorithms include multi-spectral data fusion algorithms, chromatography-mass spectrometry data fusion algorithms, machine learning algorithms, and data mining algorithms. The synergy analysis unit is used to analyze the synergy between components and mine the relationship between components and efficacy. The visualization interaction unit is used to display the comparison and analysis results in intuitive interactive charts and customize the analysis dimensions.

[0018] Preferably, the chemical component comparison unit includes a feature extraction subunit and a similarity calculation subunit. The feature extraction subunit is used to extract key features from the collected and standardized Chinese and Tibetan medicinal material chemical component data. The similarity calculation subunit is used to calculate the similarity of chemical components of different medicinal materials based on the extracted features using a similarity algorithm, and at the same time, a weight adjustment mechanism is set to adjust the weights of different features in the similarity calculation according to the research focus. The similarity algorithm includes a cosine similarity algorithm, a Euclidean distance algorithm, and a Tanimoto coefficient. The research focus includes structural similarity and property similarity.

[0019] The synergistic effect analysis unit includes a component association mining subunit and a drug effect association analysis subunit. The component association mining subunit is used to collect the Chinese and Tibetan medicine compound data of each chemical component and the corresponding drug effect data, and use the association rule mining algorithm to mine the frequent item sets of different chemical components that appear simultaneously in the compound, and establish the association relationship model between the components. The drug effect association analysis subunit is used to combine pharmacological knowledge and the established component association model, analyze the correlation between the component combination and the specific drug effect by a statistical analysis method, and for the component combination with significant correlation, further construct a network-based analysis model, and use a graph theory algorithm to mine the potential mechanism of the effect of component synergy on drug effect;

[0020] The visualization interaction unit includes a graphics generation subunit and an interactive operation subunit. The graphics generation subunit is used to generate interactive charts based on chemical component comparison results and synergistic analysis results. The interactive charts include Venn diagrams, similarity matrix heat maps, and component-pharmacodynamic network graphs. The interactive operation subunit is used for interactive operation functions of visualization graphics.

[0021] Preferably, the evaluation and prediction module includes a model training unit and an evaluation and prediction unit. The model training unit is used to adopt corresponding algorithms and combine preprocessed data to train efficacy evaluation and prediction models. The corresponding algorithms include support vector machines and neural networks. The evaluation and prediction unit is used to provide quantitative basis for efficacy evaluation and predict potential efficacy based on the characteristics of the chemical composition of the medicinal materials using the trained efficacy evaluation and prediction models.

[0022] Preferably, the model training unit includes a model initialization subunit and a cross-validation subunit. The model initialization subunit is used to initialize the model structure according to the selected machine learning or artificial intelligence algorithm, and determine the kernel function type and related parameters. For the neural network, the number of network layers, the number of neurons in each layer, the activation function type and the optimization algorithm are designed. The cross-validation subunit is used to divide the data set into a training set and a validation set according to the cross-validation method, and divide the data into K subsets, each time selecting K-1 subsets as training sets and the remaining 1 subset as a validation set, training and validating the model in turn, evaluating the generalization ability and stability of the model, and adjusting the model parameters according to the cross-validation results to avoid overfitting;

[0023] The evaluation prediction unit includes an evaluation index calculation subunit and a prediction subunit. The evaluation index calculation subunit is used to evaluate model performance using test set data on a trained model, calculate evaluation indicators, and select appropriate indicators according to a specific drug efficacy evaluation task. At the same time, the classification performance of the model is evaluated by drawing a receiver operating characteristic curve and calculating the area under the curve. The evaluation indicators include accuracy, recall rate, F1 value, mean square error, and mean absolute error. The prediction subunit is used to input new Chinese and Tibetan medicinal material chemical component data into the trained model after data preprocessing, obtain drug efficacy evaluation results and potential drug efficacy predictions, and provide prediction confidence or probability information while outputting the prediction results.

[0024] Preferably, the query recommendation module includes an intelligent query unit and a medicinal material recommendation unit. The intelligent query unit is used to perform accurate and personalized queries through natural language processing technology and intelligent search algorithms. The medicinal material recommendation unit is used to recommend medicinal materials based on the similarity of ingredients and efficacy, and to update the recommended content in real time according to the latest scientific research results.

[0025] Preferably, the update and maintenance module includes a data update unit, a system monitoring unit, and a fault repair unit. The data update unit is used to regularly check and update the data source connection. The system monitoring unit is used to monitor the performance indicators of each module of the platform in real time and issue warnings when an abnormality is found. The performance indicators include operating speed and resource occupancy. The fault repair unit is used to store solutions to common faults and repair system faults at the same time.

[0026] The present invention provides an information platform for chemical composition of Chinese and Tibetan medicine cross-dressing materials. It has the following beneficial effects:

[0027] 1. The present invention improves data quality and management efficiency through the coordinated operation of a data acquisition module, a standardization module, a storage management module, a component cross-analysis module, an evaluation and prediction module, a query recommendation module, and an update and maintenance module, thereby providing a standardized, accurate, and easy-to-acquire and analyze data basis for the research of Chinese and Tibetan medicines, and deepening the research and development of Chinese and Tibetan medicinal materials. Through functions such as component analysis and efficacy evaluation and prediction, it helps the development of new drugs and the optimal utilization of medicinal materials, and promotes interdisciplinary cooperation and the development of the field of Chinese and Tibetan medicines.

[0028] 2. The present invention collects the chemical composition information of Chinese and Tibetan medicinal materials through the data acquisition module, and standardizes the collected information through the standardization module, thereby realizing the standardized processing of the collected chemical composition information of medicinal materials, ensuring data consistency and standardization, and providing high-quality data for researchers. At the same time, the distributed storage and efficient management system improve the efficiency of data storage and query.

[0029] 3. The present invention can deeply explore the composition relationship between different medicinal materials through the component cross-analysis module, and the evaluation and prediction module can help more accurate efficacy evaluation based on data-driven. The combination of the two provides strong support for new drug research and development and optimal utilization of medicinal materials, accelerates the modernization process of Chinese and Tibetan medicine, and solves the problems of insufficient analysis and comparison of cross-information of medicinal material components, insufficient intuitive display of analysis results, and insufficient accuracy and lack of scientific quantitative basis in the efficacy evaluation of Chinese and Tibetan medicinal materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of the chemical composition information platform for cross-drug materials of Chinese and Tibetan medicine proposed by the present invention;

[0031] Figure 2 This is a data acquisition module architecture diagram of a cross-medicine chemical composition information platform for Chinese and Tibetan medicine proposed in the present invention;

[0032] Figure 3 This is a standardized module architecture diagram of a cross-medicine chemical composition information platform for Chinese and Tibetan medicine proposed in the present invention;

[0033] Figure 4 This is a storage management module architecture diagram of a cross-medicine chemical composition information platform for Chinese and Tibetan medicine proposed by the present invention;

[0034] Figure 5 This is a component cross-analysis module architecture diagram of a Chinese and Tibetan medicine cross-medicine chemical component information platform proposed by the present invention;

[0035] Figure 6This is a diagram of the evaluation and prediction module architecture of a cross-medicine chemical composition information platform for Chinese and Tibetan medicine proposed in the present invention;

[0036] Figure 7 This is a query recommendation module architecture diagram of a cross-medicine chemical composition information platform for Chinese and Tibetan medicine proposed by the present invention;

[0037] Figure 8 This is an update and maintenance module architecture diagram of the cross-medicine chemical composition information platform for Chinese and Tibetan medicine proposed in the present invention. DETAILED DESCRIPTION

[0038] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example:

[0040] Please see attached Figure 1 The embodiment of the present invention provides an information platform for chemical composition of Chinese and Tibetan medicine cross-drugs, including:

[0041] Data collection module: used to collect chemical composition information of Chinese and Tibetan medicinal materials from data sources related to Chinese and Tibetan medicine, and perform preprocessing;

[0042] Standardization module: used to standardize the collected chemical composition information of medicinal materials;

[0043] Storage management module: used to store and manage a large amount of Chinese and Tibetan medicinal material ingredient data;

[0044] Component cross-analysis module: used to cross-compare and analyze the chemical components of different Chinese and Tibetan medicinal materials, and present the components and efficacy information of Chinese and Tibetan medicinal materials in an intuitive and visual way;

[0045] Evaluation and prediction module: used to evaluate and predict the efficacy of Chinese and Tibetan medicinal materials based on pharmacological data and chemical composition information using machine learning and artificial intelligence algorithms;

[0046] Query and recommendation module: used to provide human-computer interaction, assist in quick query, ingredient comparison and efficacy analysis, and recommend relevant medicinal materials according to needs;

[0047] Update and maintenance module: used to ensure the continuous and stable operation of the platform and the timeliness of data.

[0048] Specifically, the data collection module collects the chemical composition information of Chinese and Tibetan medicinal materials from the data sources related to Chinese and Tibetan medicine, and performs preprocessing, so that the chemical composition information of Chinese and Tibetan medicinal materials can be collected comprehensively and targetedly, and the chemical composition information is preliminarily processed to make it more suitable for subsequent analysis, thereby solving the problem of obtaining chemical composition information from many scattered data sources and the uneven data quality that needs preprocessing;

[0049] The collected chemical composition information of medicinal materials is standardized through the standardization module, thereby realizing the standardized processing of the collected chemical composition information of medicinal materials, ensuring data consistency and standardization, and thus solving the problem of non-standard and inconsistent expression of chemical composition information caused by different data sources;

[0050] The storage management module is used to store and manage a large amount of Chinese and Tibetan medicinal material component data, thus providing an efficient, safe and reliable database management system to ensure the efficiency of data storage and query as well as the security of data, thus solving the problems of efficiency and security in the storage and management of a large amount of Chinese and Tibetan medicinal material component data;

[0051] The component cross-analysis module is used to cross-compare and analyze the chemical components of different Chinese and Tibetan medicinal materials, and the components and efficacy information of Chinese and Tibetan medicinal materials are presented in an intuitive and visual way, so that the chemical components of different Chinese and Tibetan medicinal materials can be cross-compared and analyzed in depth, and the results can be presented in an intuitive and visual way to explore the relationship between components and efficacy, thus solving the problem of insufficient cross-information analysis and comparison of medicinal material components and insufficient intuitive display of analysis results;

[0052] Through the evaluation and prediction module, the efficacy of Chinese and Tibetan medicinal materials is evaluated and predicted based on pharmacological data and chemical composition information, using machine learning and artificial intelligence algorithms, thus accurately evaluating and predicting the efficacy of Chinese and Tibetan medicinal materials based on pharmacological data and chemical composition information, thereby solving the problem of inaccurate efficacy evaluation of Chinese and Tibetan medicinal materials and lack of scientific quantitative basis;

[0053] The query and recommendation module provides human-computer interaction, assists in quick query, ingredient comparison and efficacy analysis, and recommends relevant medicinal materials according to needs, thus providing users with convenient human-computer interaction functions, supporting quick query, ingredient comparison and efficacy analysis, and accurately recommending relevant medicinal materials, thus solving the problem that users are not convenient and accurate when querying information related to Tibetan medicinal materials and obtaining relevant recommendations;

[0054] Ensure the continuous and stable operation of the platform and the timeliness of data by updating and maintaining modules, thereby ensuring the continuous and stable operation of the platform and the timeliness of data;

[0055] Through the coordinated operation of various modules, the data quality and management efficiency are improved, providing a standardized, accurate, easy-to-acquire and easy-to-analyze data foundation for the research on Chinese and Tibetan medicine, and deepening the research and development of Chinese and Tibetan medicinal materials. Through functions such as component analysis and efficacy evaluation prediction, it helps the development of new drugs and the optimal utilization of medicinal materials. At the same time, it optimizes the user experience, making it convenient for different users (such as scientific researchers, pharmaceutical company staff, etc.) to easily query, analyze and use information related to Chinese and Tibetan medicine, promoting interdisciplinary cooperation and the development of the field of Chinese and Tibetan medicine, thus solving the problem that most of the existing Chinese and Tibetan medicinal material component data are scattered in the literature, lacking a standardized and systematic information platform, and the cross-information analysis and comparison of medicinal material components are not in-depth enough, resulting in low research efficiency and incomplete information.

[0056] The data acquisition module includes a crawler unit and a preprocessing unit. The crawler unit is used to capture chemical component related information from multiple data sources, including literature databases of Chinese and Tibetan medicines, experimental data, existing pharmacopoeias, scientific research results, and data submitted by users. The preprocessing unit is used to preprocess the collected data, including removing noise and invalid information from the data.

[0057] The preprocessing unit includes a data cleaning subunit, a feature encoding subunit, and a feature selection subunit. The data cleaning subunit is used to clean the input pharmacological data and chemical component information. Cleaning includes checking the integrity of the data. For missing values, the corresponding filling method is used to fill in the missing values ​​according to the data distribution characteristics. At the same time, outliers are identified and processed, and the range of outliers is determined by statistical methods. The outliers are processed, and the processing includes deletion, correction or special processing according to domain knowledge. The corresponding filling methods include mean filling, median filling or model-based filling methods. The feature encoding subunit is used to convert the classification features into numerical forms using unique hot encoding or label encoding methods. At the same time, for continuous chemical component features, standardization is performed. The classification features include medicinal material categories and efficacy categories. The feature selection subunit is used to use feature selection algorithms to screen out features with high correlation with efficacy from chemical component features, and evaluate the importance of features by calculating indicators between features and efficacy, while removing redundant and irrelevant features. The indicators include information gain and Gini coefficient.

[0058] Specifically, the crawler unit uses web crawler technology to capture chemical composition information from multiple data sources, thereby broadening the data acquisition channels and providing rich data for the follow-up. The data cleaning sub-unit checks the completeness, uses a variety of filling methods to handle missing values, and determines the range of abnormal values ​​based on statistics and processes them, thereby improving data quality, reducing interference, and facilitating subsequent processing. The feature encoding sub-unit transforms and standardizes categorical and continuous features according to corresponding methods, thereby facilitating subsequent analysis and processing of data. The feature selection sub-unit uses algorithms to select features related to efficacy, calculates indicators for evaluation, and removes redundant and irrelevant features, thereby streamlining features and improving efficiency and accuracy.

[0059] The standardization module includes a unit unification unit, a name normalization unit, a synonym normalization unit, and a classification and filing unit. The unit unification unit is used to unify the units of component content in data from different sources. The name normalization unit is used to standardize the names of components and unify the naming according to international standards or industry common standards. The synonym normalization unit is used to normalize different names of the same component. The classification and filing unit is used to classify and file chemical components according to their characteristics, which include properties and structures.

[0060] Specifically, the unit unification unit is used to unify the units of ingredient content according to the standard, so that the data can be comparable in terms of content and is easy to analyze and compare. The name normalization unit is used to standardize the ingredient names according to the standard to avoid name confusion and improve the accuracy of data operations. The synonym normalization unit is used to normalize different names of the same ingredient, thereby enhancing data consistency and facilitating accurate identification and processing. The classification and archiving unit is used to classify and archive according to ingredient characteristics, so that the data is organized and easy to find, manage and use.

[0061] The storage management module includes a distributed storage unit, a backup and recovery unit, and a permission management unit. The distributed storage unit is used to implement large-scale data storage and query using a distributed architecture. The backup and recovery unit is used to back up and recover various data in a disaster. The permission management unit is used to assign different levels of data access rights.

[0062] The component cross-analysis module includes a chemical component comparison unit, a synergy analysis unit, and a visualization interaction unit. The chemical component comparison algorithm unit is used to calculate the similarities and differences of components between different medicinal materials through corresponding algorithms. The corresponding algorithms include multi-spectral data fusion algorithm, chromatography-mass spectrometry data fusion algorithm, machine learning algorithm, and data mining algorithm. The synergy analysis unit is used to analyze the synergy between components and mine the relationship between components and efficacy. The visualization interaction unit is used to display the comparison and analysis results in intuitive interactive charts and customize the analysis dimensions.

[0063] The chemical component comparison unit includes a feature extraction subunit and a similarity calculation subunit. The feature extraction subunit is used to extract key features from the collected and standardized chemical component data of Chinese and Tibetan medicinal materials. The similarity calculation subunit is used to calculate the similarity of the chemical components of different medicinal materials based on the extracted features using a similarity algorithm. At the same time, a weight adjustment mechanism is set to adjust the weights of different features in the similarity calculation according to the research focus. The similarity algorithms include cosine similarity algorithm, Euclidean distance algorithm, and Tanimoto coefficient. The research focuses include structural similarity and property similarity.

[0064] The synergistic effect analysis unit includes a component association mining subunit and a drug efficacy association analysis subunit. The component association mining subunit is used to collect the data of the Chinese and Tibetan medicine compound prescriptions of each chemical component and the corresponding drug efficacy data, and use the association rule mining algorithm to mine the frequent item sets of different chemical components that appear simultaneously in the compound prescriptions, and establish the association relationship model between the components. The drug efficacy association analysis subunit is used to combine pharmacological knowledge and the established component association model, and analyze the correlation between the component combination and the specific drug efficacy through statistical analysis methods. For the component combination with significant correlation, a network-based analysis model is further constructed, and the graph theory algorithm is used to mine the potential mechanism of the effect of component synergy on drug efficacy.

[0065] The visualization interaction unit includes a graphics generation subunit and an interactive operation subunit. The graphics generation subunit is used to generate interactive charts based on the chemical composition comparison results and the synergistic effect analysis results. The interactive charts include Venn diagrams, similarity matrix heat maps, and component-pharmacodynamic network graphs. The interactive operation subunit is used for the interactive operation functions of the visualization graphics.

[0066] Specifically, the feature extraction subunit extracts key features from the standardized data, thereby providing a targeted data basis for similarity calculation. The similarity calculation subunit uses a similarity algorithm to calculate component similarity, and sets a weight adjustment mechanism to quantify the degree of similarity, which can flexibly adapt to research needs.

[0067] The component association mining subunit collects relevant data and uses algorithms to mine component association relationship models, thereby revealing component associations and helping to understand synergies and internal connections. The drug efficacy association analysis subunit combines knowledge and models to analyze the correlation between components and drug efficacy, builds a network model to mine potential mechanisms, and thus deeply analyzes relationships and provides references for drug efficacy evaluation.

[0068] The graphic generation subunit generates interactive charts based on the analysis results, thereby presenting the results intuitively and facilitating understanding and analysis. The interactive operation subunit provides chart interactive operation functions, thereby facilitating flexible operation and in-depth analysis by users.

[0069] The evaluation and prediction module includes a model training unit and an evaluation and prediction unit. The model training unit is used to adopt corresponding algorithms and combine preprocessed data to train efficacy evaluation and prediction models. The corresponding algorithms include support vector machines and neural networks. The evaluation and prediction unit is used to provide quantitative basis for efficacy evaluation and predict potential efficacy based on the characteristics of the chemical composition of medicinal materials using the trained efficacy evaluation and prediction models.

[0070] The model training unit includes a model initialization subunit and a cross-validation subunit. The model initialization subunit is used to initialize the model structure according to the selected machine learning or artificial intelligence algorithm, and determine the kernel function type and related parameters. For the neural network, the number of network layers, the number of neurons in each layer, the activation function type and the optimization algorithm are designed. The cross-validation subunit is used to divide the data set into a training set and a validation set according to the cross-validation method, and divide the data into K subsets. K-1 subsets are selected as training sets each time, and the remaining 1 subset is selected as a validation set. The model is trained and validated in turn to evaluate the generalization ability and stability of the model, and the model parameters are adjusted according to the cross-validation results to avoid overfitting.

[0071] The evaluation prediction unit includes an evaluation index calculation subunit and a prediction subunit. The evaluation index calculation subunit is used to evaluate the model performance using the test set data on the trained model, calculate the evaluation index, and select the appropriate index according to the specific drug efficacy evaluation task. At the same time, the classification performance of the model is evaluated by drawing the receiver operating characteristic curve and calculating the area under the curve. The evaluation indicators include accuracy, recall rate, F1 value, mean square error, and mean absolute error. The prediction subunit is used to input the new Chinese and Tibetan medicinal material chemical composition data into the trained model after data preprocessing, obtain the drug efficacy evaluation results and potential drug efficacy predictions, and give the prediction confidence or probability information while outputting the prediction results.

[0072] Specifically, the model initialization subunit initializes the model structure and determines the parameters according to the algorithm, thereby building the model framework and laying the foundation for training. The cross-validation subunit divides the data set for training and verification, adjusts the parameters to prevent overfitting, and thus evaluates the model performance and improves generalization and stability.

[0073] The evaluation index calculation subunit uses the test set to calculate the evaluation index and evaluate the classification performance, thereby comprehensively and accurately evaluating the model and providing a basis for optimization. The prediction subunit inputs new data to obtain the efficacy evaluation and prediction results and confidence, thereby realizing the efficacy evaluation prediction and informing the reliability of the results.

[0074] The query and recommendation module includes an intelligent query unit and a medicinal material recommendation unit. The intelligent query unit is used to perform accurate and personalized queries through natural language processing technology and intelligent search algorithms. The medicinal material recommendation unit is used to recommend medicinal materials based on the similarity of ingredients and efficacy, and to update the recommended content in real time based on the latest scientific research results.

[0075] The update and maintenance module includes a data update unit, a system monitoring unit, and a fault repair unit. The data update unit is used to regularly check and update the data source connection. The system monitoring unit is used to monitor the performance indicators of each module of the platform in real time and issue warnings when an abnormality is found. The performance indicators include operating speed and resource usage. The fault repair unit is used to store solutions to common faults and repair system faults at the same time.

[0076] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cross-medicine chemical composition information platform for Chinese and Tibetan medicines, characterized in that: include: Data collection module: used to collect chemical composition information of Chinese and Tibetan medicinal materials from data sources related to Chinese and Tibetan medicine, and perform preprocessing; Standardization module: used to standardize the collected chemical composition information of medicinal materials; Storage management module: used to store and manage a large amount of Chinese and Tibetan medicinal material ingredient data; Component cross-analysis module: used to cross-compare and analyze the chemical components of different Chinese and Tibetan medicinal materials, and present the components and efficacy information of Chinese and Tibetan medicinal materials in an intuitive and visual way; Evaluation and prediction module: used to evaluate and predict the efficacy of Chinese and Tibetan medicinal materials based on pharmacological data and chemical composition information using machine learning and artificial intelligence algorithms; Query and recommendation module: used to provide human-computer interaction, assist in quick query, ingredient comparison and efficacy analysis, and recommend relevant medicinal materials according to needs; Update and maintenance module: used to ensure the continuous and stable operation of the platform and the timeliness of data.

2. The cross-medicine chemical composition information platform of Chinese and Tibetan medicine according to claim 1 is characterized by: The data acquisition module includes a crawler unit and a preprocessing unit. The crawler unit is used to capture chemical component related information from multiple data sources, including literature databases of Chinese and Tibetan medicines, experimental data, existing pharmacopoeias, scientific research results, and data submitted by users. The preprocessing unit is used to preprocess the collected data, and the preprocessing includes removing noise and invalid information from the data. The preprocessing unit includes a data cleaning subunit, a feature encoding subunit, and a feature selection subunit. The data cleaning subunit is used to clean the input pharmacological data and chemical component information. The cleaning includes checking the integrity of the data. For missing values, the corresponding filling method is used to fill in the missing values ​​according to the data distribution characteristics. At the same time, outliers are identified and processed, and the range of outliers is determined by statistical methods. The outliers are processed, and the processing includes deletion, correction or special processing according to domain knowledge. The corresponding filling methods include mean filling, median filling or model-based filling methods. The feature encoding subunit is used to convert the classification features into numerical forms using unique hot encoding or label encoding methods. At the same time, for continuous chemical component features, standardization processing is performed. The classification features include medicinal material categories and efficacy categories. The feature selection subunit is used to use feature selection algorithms to screen out features with high correlation with efficacy from chemical component features, and evaluate the importance of features by calculating indicators between features and efficacy, and remove redundant and irrelevant features. The indicators include information gain and Gini coefficient.

3. The cross-medicine chemical composition information platform of Chinese and Tibetan medicine according to claim 1 is characterized by: The standardization module includes a unit unification unit, a name normalization unit, a synonym normalization unit, and a classification and filing unit. The unit unification unit is used to unify the units of component content in data from different sources. The name normalization unit is used to standardize the component names and unify the naming according to international standards or industry general standards. The synonym normalization unit is used to normalize different names of the same component. The classification and filing unit is used to classify and file the chemical components according to their characteristics, and the characteristics include properties and structures.

4. The Chinese and Tibetan medicine cross-drug chemical composition information platform according to claim 1 is characterized by: The storage management module includes a distributed storage unit, a backup and recovery unit, and a permission management unit. The distributed storage unit is used to implement large-scale data storage and query using a distributed architecture. The backup and recovery unit is used to back up and perform disaster recovery on various data. The permission management unit is used to assign different levels of data access rights.

5. The Chinese and Tibetan medicine cross-drug chemical composition information platform according to claim 1 is characterized by: The component cross-analysis module includes a chemical component comparison unit, a synergy analysis unit, and a visualization interaction unit. The chemical component comparison algorithm unit is used to calculate the similarities and differences of components between different medicinal materials through corresponding algorithms. The corresponding algorithms include multi-spectral data fusion algorithms, chromatography-mass spectrometry data fusion algorithms, machine learning algorithms, and data mining algorithms. The synergy analysis unit is used to analyze the synergy between components and mine the relationship between components and efficacy. The visualization interaction unit is used to display the comparison and analysis results in intuitive interactive charts and customize the analysis dimensions.

6. The Chinese and Tibetan medicine cross-drug chemical composition information platform according to claim 5 is characterized by: The chemical component comparison unit includes a feature extraction subunit and a similarity calculation subunit. The feature extraction subunit is used to extract key features from the collected and standardized Chinese and Tibetan medicinal material chemical component data. The similarity calculation subunit is used to calculate the similarity of chemical components of different medicinal materials based on the extracted features using a similarity algorithm, and at the same time, a weight adjustment mechanism is set to adjust the weights of different features in the similarity calculation according to the research focus. The similarity algorithm includes a cosine similarity algorithm, a Euclidean distance algorithm, and a Tanimoto coefficient. The research focus includes structural similarity and property similarity. The synergistic effect analysis unit includes a component association mining subunit and a drug effect association analysis subunit. The component association mining subunit is used to collect the Chinese and Tibetan medicine compound data of each chemical component and the corresponding drug effect data, and use the association rule mining algorithm to mine the frequent item sets of different chemical components that appear simultaneously in the compound, and establish the association relationship model between the components. The drug effect association analysis subunit is used to combine pharmacological knowledge and the established component association model, analyze the correlation between the component combination and the specific drug effect by a statistical analysis method, and for the component combination with significant correlation, further construct a network-based analysis model, and use a graph theory algorithm to mine the potential mechanism of the effect of component synergy on drug effect; The visualization interaction unit includes a graphics generation subunit and an interactive operation subunit. The graphics generation subunit is used to generate interactive charts based on chemical component comparison results and synergistic analysis results. The interactive charts include Venn diagrams, similarity matrix heat maps, and component-pharmacodynamic network graphs. The interactive operation subunit is used for interactive operation functions of visualization graphics.

7. The Chinese and Tibetan medicine cross-drug chemical composition information platform according to claim 1 is characterized by: The evaluation and prediction module includes a model training unit and an evaluation and prediction unit. The model training unit is used to adopt corresponding algorithms and combine preprocessed data to train efficacy evaluation and prediction models. The corresponding algorithms include support vector machines and neural networks. The evaluation and prediction unit is used to provide quantitative basis for efficacy evaluation and predict potential efficacy based on the characteristics of the chemical components of medicinal materials using the trained efficacy evaluation and prediction models.

8. The cross-medicine chemical composition information platform of Chinese and Tibetan medicine according to claim 7 is characterized by: The model training unit includes a model initialization subunit and a cross-validation subunit. The model initialization subunit is used to initialize the model structure according to the selected machine learning or artificial intelligence algorithm, and determine the kernel function type and related parameters. For the neural network, the number of network layers, the number of neurons in each layer, the activation function type and the optimization algorithm are designed. The cross-validation subunit is used to divide the data set into a training set and a validation set according to the cross-validation method, and divide the data into K subsets, each time selecting K-1 subsets as the training set, and the remaining 1 subset as the validation set, training and validating the model in turn, evaluating the generalization ability and stability of the model, and adjusting the model parameters according to the cross-validation results to avoid overfitting; The evaluation prediction unit includes an evaluation index calculation subunit and a prediction subunit. The evaluation index calculation subunit is used to evaluate model performance using test set data on a trained model, calculate evaluation indicators, and select appropriate indicators according to a specific drug efficacy evaluation task. At the same time, the classification performance of the model is evaluated by drawing a receiver operating characteristic curve and calculating the area under the curve. The evaluation indicators include accuracy, recall rate, F1 value, mean square error, and mean absolute error. The prediction subunit is used to input new Chinese and Tibetan medicinal material chemical component data into the trained model after data preprocessing, obtain drug efficacy evaluation results and potential drug efficacy predictions, and provide prediction confidence or probability information while outputting the prediction results.

9. The cross-medicine chemical composition information platform of Chinese and Tibetan medicine according to claim 1 is characterized by: The query recommendation module includes an intelligent query unit and a medicinal material recommendation unit. The intelligent query unit is used to perform accurate and personalized queries through natural language processing technology and intelligent search algorithms. The medicinal material recommendation unit is used to recommend medicinal materials based on the similarity of ingredients and efficacy, and to update the recommended content in real time according to the latest scientific research results.

10. The Chinese and Tibetan medicine cross-drug chemical composition information platform according to claim 1, characterized in that: The update and maintenance module includes a data update unit, a system monitoring unit, and a fault repair unit. The data update unit is used to regularly check and update the data source connection. The system monitoring unit is used to monitor the performance indicators of each module of the platform in real time and issue warnings when an abnormality is found. The performance indicators include operating speed and resource occupancy. The fault repair unit is used to store common fault solutions and repair system faults at the same time.

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