Traditional Chinese medicine analysis and identification system, method and equipment based on large model and medium

Abstract

The invention discloses a traditional Chinese medicine analysis and identification system, method, equipment and medium based on a large model, and the system comprises a data collection module which is used for collecting multi-dimensional data of traditional Chinese medicines, a data preprocessing module which is used for carrying out the cleaning, standardization processing and feature extraction of the collected data, and a data analysis module. The data processing module is used for analyzing and processing received data by utilizing a pre-trained deep large model, the result output module is used for outputting an analysis result to a user in a visual manner, and the analysis result output module comprises a visual chart, a detailed report form and an intelligent feedback module according to the feedback of the user on the analysis result and the actual application condition. Through multi-dimensional data acquisition, advanced preprocessing, deep large model analysis and the like, the practicability is improved by modules of accurate identification, intelligent feedback, collaborative analysis and the like of the traditional Chinese medicine, data updating and the like ensure that the system advances with the times, powerful technical support is provided for industrial development of the traditional Chinese medicine, and the quality and safety of the traditional Chinese medicine are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of drug analysis and identification systems, and in particular to a large-model-based Chinese medicine analysis and identification system, method, equipment and medium. Background Art

[0002] As a treasure of traditional Chinese medicine, Chinese medicine plays an important role in the field of health care. However, the analysis and identification of Chinese medicines have always faced many challenges.

[0003] Traditional identification methods of traditional Chinese medicine mainly rely on manual experience, and make judgments by observing the appearance, smell, taste and other characteristics of the medicine. This method is highly subjective, and its accuracy and reliability are difficult to guarantee. It also requires extremely high professional knowledge and experience of the identification personnel. At the same time, with the continuous expansion and internationalization of the traditional Chinese medicine market, traditional identification methods are difficult to meet the needs of large-scale, fast and accurate identification.

[0004] Driven by modern science and technology, some instrumental analysis methods such as chromatography and spectroscopy have been applied to the identification of traditional Chinese medicine. Although these methods can provide certain objective data, they can only analyze a single or a few indicators and cannot fully reflect the overall characteristics of traditional Chinese medicine. Moreover, these methods usually require complex sample pretreatment and professional operation skills, which are costly and not convenient for widespread application.

[0005] With the development of artificial intelligence technology, especially the emergence of big models, new opportunities have been brought to the analysis and identification of traditional Chinese medicine. Big models have powerful data analysis and processing capabilities, can integrate multi-source data, learn complex patterns and relationships, and provide more comprehensive and accurate solutions for the identification of traditional Chinese medicine.

[0006] In addition, with the development of the Chinese medicine industry, the requirements for the quality of Chinese medicine are getting higher and higher, and more scientific and accurate analysis and identification methods are needed to ensure the safety and effectiveness of Chinese medicine. At the same time, the protection and sustainable use of Chinese medicine resources also require accurate identification technology to prevent the circulation of counterfeit and inferior products.

[0007] In this context, the development of a large-scale model-based TCM drug analysis and identification system has important practical significance. The system can make full use of modern scientific and technological means, integrate multi-dimensional data, realize rapid and accurate identification of TCM drugs, and provide strong support for the development of the TCM industry. Summary of the invention

[0008] The present invention proposes a large-model-based Chinese medicine analysis and identification system, method, equipment and medium to solve the problems mentioned in the above-mentioned prior art.

[0009] In order to achieve the above object, the present invention adopts the following technical scheme: a Chinese medicine analysis and identification system based on a large model, comprising:

[0010] The data acquisition module is used to collect multi-dimensional data of traditional Chinese medicine, including high-resolution images, odor characteristics, chemical composition content, and physical properties. The data can be expressed as a vector D = (I, S, C, P, ...), where I represents image data, and the appearance, morphology, color and texture information of traditional Chinese medicine are obtained through high-resolution image acquisition equipment; S represents odor data, and advanced odor sensors are used to accurately capture the unique odor of traditional Chinese medicine; C represents chemical composition data, and the content of various active ingredients is determined with the help of high-precision chemical analysis instruments; P represents physical property data, such as hardness and density. The module is also equipped with an automatic adjustment function, which can automatically optimize the acquisition parameters according to the characteristics of traditional Chinese medicine and environmental factors to ensure the accuracy and comprehensiveness of the data.

[0011] The data preprocessing module cleans, standardizes and extracts features from the collected data. First, an advanced data cleaning algorithm is used to remove outliers and noise interference to ensure the purity of the data. Suppose the original data is X = {x1, x2, ..., x n}, the denoising function is h(X) = {x′1, x′2, …, x′ n},in (The sliding average method is used for denoising, and m is the window size). Here, h(X) represents the denoising function, and X is the original data set, x1, x2, …, x n is the data point in the original data, and is the data point after denoising.

[0012] Then perform standardization, and assume that the standardized data is Y = {y1, y2, ..., y n},in μ is the mean of the denoised data, and σ is the standard deviation of the denoised data. Here, Y is the standardized data set, y i is the normalized data point, x′ i is the denoised data point.

[0013] Finally, feature extraction is performed, and the extracted feature vector is assumed to be F = (f1, f2, ..., f k ),in α ij is the feature extraction coefficient, satisfying Here, F is the eigenvector, f1, f2, …, f k is the feature in the feature vector, y i is the normalized data point, α ij is the feature extraction coefficient, n is the total number of data points, and k is the number of features. preprocessed .

[0014] The data analysis module uses a pre-trained deep model to analyze and process the received data. The model innovates on the traditional deep learning architecture and introduces a multimodal fusion layer, which can efficiently integrate data of different modalities such as images, odors, and chemical components. Assume that the data vectors of different modalities are A = (a1, a2, ..., a l ), B=(b1,b2,…,b m ), C=(c1,c2,…,c n ), the fused data vector is Z = (z1, z2, ..., z p ), where z i =ω1a i +ω2b i +ω3c i , ω1, ω2, ω3 are the fusion weight coefficients of the corresponding modes, satisfying ω1+ω2+ω3=1. Here, A, B, C are the data vectors of different modes, a i , b i , c i are the elements in the corresponding modal data vector, Z is the fused data vector, and z i is the element in the fused data vector, .

[0015] At the same time, a dynamic weight adjustment mechanism is used to dynamically assign weights according to the importance of different data types. Assume that the data type is T = {t1, t2, ..., t q}, the corresponding weight vector is W = (w1, w2, ..., w q ),in β j Is the same as the data type t j Related parameters. Here, T is a data type set, W is a weight vector, w j is the element in the weight vector, and q is the total number of data types. In addition, the large model also incorporates knowledge graph technology to integrate professional knowledge in the field of traditional Chinese medicine into the model to enhance the understanding and identification of traditional Chinese medicine. preprocessed The model’s training data covers a wide range of multi-dimensional data and corresponding labels of different types of Chinese medicines. Through the training of a large amount of data, the model has powerful analytical and identification capabilities.

[0016] The result output module outputs the analysis results to the user in an intuitive way, including visual charts and detailed reports. At the same time, this module can also provide personalized analysis result display and interpretation according to user needs.

[0017] The intelligent feedback module continuously adjusts and optimizes the system parameters and models according to the user's feedback on the analysis results and the actual application situation, so as to improve the accuracy and practicality of the system. Assume that the user feedback is R, and the adjustment function is g(R) = {g1(R), g2(R), ..., g k (R)}, where g1(R), g2(R), …, g k (R) represents the adjustment amount of 1-k parameters according to user feedback. Here, g(R) is the adjustment function.

[0018] Collaborative analysis module, which can interact with other TCM analysis systems or databases, share data and analysis results, and achieve more extensive TCM drug analysis and identification. Suppose the data of the collaborative system is S c =(s1, s2, ..., s r ), the data of this system is S = (s′1, s′2, …, s′ t ), the fused data is S f =(f1, f2, ..., f u ), where f i =θ1s′ i +θ2s i％r+1 , θ1, θ2 are fusion weight coefficients, satisfying θ1+θ2=1. Here, S c is the data set of the collaborative system, S is the data set of this system, S f is the fused data set, s1, s2, …, s r ,s′1,s′2,…,s′ t ,f1,f2,…,f u They are the elements in the corresponding data sets, r is the total number of collaborative system data, t is the total number of system data, and u is the total number of fused data.

[0019] The security protection module sets up strict security protection measures to ensure the security and confidentiality of traditional Chinese medicine data, including data encryption, access control, and security audit functions to prevent data leakage and malicious attacks, and ensure the stable operation of the system and the security of data.

[0020] Furthermore, the data acquisition module includes a high-resolution image acquisition device, an odor sensor and a chemical analysis instrument.

[0021] Furthermore, the large model is a neural network model trained based on a large amount of traditional Chinese medicine data, and the training data of the model includes images, smells, chemical components and corresponding labels of different types of traditional Chinese medicines.

[0022] Furthermore, a Chinese medicine analysis and identification system based on a large model also includes a data update module:

[0023] This module continuously monitors new research results, new drug varieties and data changes in the field of traditional Chinese medicine. Let the time variable be t and the new data set be N t ={n1(t), n2(t), ..., n k (t)}. As time passes, the module determines whether the system data needs to be updated according to certain rules. The update function is Here, t represents the time variable, N t represents the new data set at time, n1(t), n2(t), ..., n k (t) represents the data element in the new data set at time. The update condition can be set according to the importance of the data and the change factor of the quantity.

[0024] Furthermore, a large model-based Chinese medicine analysis and identification system includes a visualization module:

[0025] This module displays the analysis results to the user in an intuitive and easy-to-understand graphical interface. Let the analysis result be R and the visualization function be g vis (R)={V1(R), V2(R),…,V m (R)}, where V1(R), V2(R), …, V m (R) indicates the visualization of the analysis results, such as bar charts, pie charts, and heat maps. Through the visualization module, users can understand the analysis and identification results of traditional Chinese medicines more quickly, improving user experience.

[0026] Furthermore, a Chinese medicine analysis and identification system based on a large model has a self-diagnosis module:

[0027] The self-diagnosis module regularly detects and evaluates each module of the system. Assume that the system module set is M = {M1, M2, ..., M n}, the diagnostic function is h diag (M)={d1(M1), d2(M2),…,d n (M n )}, where d1(M1), d2(M2), …, d n (M n ) represents the diagnostic results of 1-n modules. If the diagnostic results show that there is a problem with the module, the self-diagnosis module will issue an alarm and provide possible solution suggestions. Here, M is the set of system modules, h diag (M) is the diagnostic function.

[0028] Furthermore, a method for analyzing and identifying traditional Chinese medicine based on a large model comprises the following steps:

[0029] Collect multi-dimensional data of traditional Chinese medicine through the data collection module;

[0030] Use the data preprocessing module to clean, standardize and extract features from the collected data;

[0031] The data analysis module uses a pre-trained large model to analyze and process the data;

[0032] Output the analysis results to the user through the result output module;

[0033] Based on user feedback, the intelligent feedback module adjusts system parameters and models;

[0034] The collaborative analysis module interacts with external systems to obtain more reference information;

[0035] The security protection module ensures the security and confidentiality of data;

[0036] The data update module updates the system data according to the situation;

[0037] The visualization module displays the analysis results;

[0038] The self-diagnosis module detects and evaluates the system.

[0039] Furthermore, a storage medium stores a computer program, and when the computer program is executed by a processor, a method for analyzing and identifying traditional Chinese medicine based on a large model is implemented.

[0040] Furthermore, a device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it realizes the analysis and identification of traditional Chinese medicine based on a large model.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention uses a data acquisition module to comprehensively collect multi-dimensional data of traditional Chinese medicines, including high-resolution images, odor characteristics, chemical composition content, and physical properties. This enables the system to conduct a comprehensive analysis of traditional Chinese medicines from multiple perspectives, overcoming the limitations of single-index analysis in traditional methods and providing a rich data foundation for accurate identification.

[0043] The data preprocessing module uses advanced cleaning, standardization and feature extraction technology to remove outliers and noise interference and improve data quality. The processed data is purer and more standardized, which is conducive to subsequent analysis and processing, and improves the accuracy and reliability of identification results.

[0044] The data analysis module uses a pre-trained deep model and introduces a multimodal fusion layer, a dynamic weight adjustment mechanism, and knowledge graph technology. The multimodal fusion layer can efficiently integrate data of different modalities and fully reflect the characteristics of traditional Chinese medicine; the dynamic weight adjustment mechanism dynamically assigns weights according to the importance of data types to make the analysis more accurate; and the knowledge graph technology enhances the understanding and identification of traditional Chinese medicines.

[0045] The result output module outputs the analysis results to the user in an intuitive way, which is convenient for the user to quickly understand and apply. At the same time, the intelligent feedback module continuously adjusts the system parameters and models according to user feedback, which improves the practicality and adaptability of the system.

[0046] The collaborative analysis module interacts with other TCM analysis systems or databases to share data and analysis results, achieve a wider range of TCM drug analysis and identification, and provide more reference information for TCM research and application.

[0047] The security protection module ensures the security and confidentiality of traditional Chinese medicine data, prevents data leakage and malicious attacks, and ensures the stable operation of the system.

[0048] In addition, the data update module continuously monitors new achievements and data changes in the field of traditional Chinese medicine, so that the system always keeps pace with the times; the visualization module displays the analysis results in a graphical interface, which improves the user experience; the self-diagnosis module regularly detects and evaluates the system to ensure the stable and reliable operation of the system. In short, the traditional Chinese medicine drug analysis and identification system of the present invention provides strong technical support for the development of the traditional Chinese medicine industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic block diagram of a large model-based Chinese medicine analysis and identification system proposed by the present invention;

[0050] Figure 2 This is a schematic diagram of the large model-based traditional Chinese medicine analysis and identification method proposed in the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise" and "counterclockwise" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0053] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0054] Reference Figure 1-2 :A Chinese medicine analysis and identification system based on a large model, comprising:

[0055] The data acquisition module is used to collect multi-dimensional data of traditional Chinese medicine, including high-resolution images, odor characteristics, chemical composition content, and physical properties. The data is represented by a vector D = (I, S, C, P, ...), where I represents image data, and the appearance, morphology, color and texture information of traditional Chinese medicine are obtained through image acquisition equipment. S represents odor data, and the odor sensor is used to accurately capture the odor of traditional Chinese medicine. C represents chemical composition data, and the content of various active ingredients is determined by chemical analysis instruments. P represents physical property data, including hardness and density. It is also equipped with an automatic adjustment function to automatically optimize the acquisition parameters according to the characteristics of traditional Chinese medicine and environmental factors.

[0056] The data preprocessing module cleans, standardizes and extracts features from the collected data, and uses data cleaning algorithms to remove outliers and noise interference. Suppose the original data is X = {x1, x2, ..., x n}, the denoising function is h(X) = {x′1, x′2, …, x′ n},in The sliding average method is used for denoising, m is the window size, h(X) represents the denoising function, X is the original data set, x1, x2, ..., x n is the data point in the original data, x′ i is the data point after denoising; then standardization is performed, and the standardized data is assumed to be Y = {y1, y2, ..., y n},in μ is the mean of the denoised data, σ is the standard deviation of the denoised data, Y is the standardized data set, y i is the normalized data point;

[0057] Perform feature extraction and set the extracted feature vector to be F = (f1, f2, ..., f k ),in α ij is the feature extraction coefficient, satisfying F is the eigenvector, f1, f2, …, f k is the feature in the feature vector, y i is the normalized data point, α ij is the feature extraction coefficient, n is the total number of data points, k is the number of features, and the preprocessed data D is obtained. preprocessed ;

[0058] The data analysis module uses a pre-trained deep model to analyze and process the received data. It introduces a multimodal fusion layer to integrate data of different modalities such as image, odor, and chemical composition. The data vectors of different modalities are A=(a1, a2, ..., a l ), B=(b1,b2,…,b m ), C=(c1,c2,…,c n ), the fused data vector is Z = (z1, z2, ..., z p ), where z i =ω1a i +ω2b i +ω3c i , ω1, ω2, ω3 are the fusion weight coefficients of the corresponding modes, satisfying ω1+ω2+ω3=1, A, B, C are the data vectors of different modes, a i , b i , c i are the elements in the corresponding modal data vector, Z is the fused data vector, and z i is the element in the fused data vector;

[0059] A dynamic weight adjustment mechanism is used to dynamically assign weights according to the importance of different data types. Suppose the data type is T = {t1, t2, ..., t q}, the corresponding weight vector is W = {w1, w2, ..., w q},in β j Is the same as the data type t j Related parameters, T is a data type set, W is a weight vector, w j is the element in the weight vector, q is the total number of data types, and the knowledge graph technology is also integrated to integrate the professional knowledge in the field of traditional Chinese medicine into the model. preprocessed Processing, determine the type, quality, and origin of traditional Chinese medicine;

[0060] The result output module outputs the analysis results to the user in an intuitive way, including visual charts and detailed reports, and provides personalized analysis result display and interpretation according to user needs;

[0061] The intelligent feedback module adjusts and optimizes the system parameters and models according to the user's feedback on the analysis results and the actual application situation. Let the user feedback be R, and the adjustment function be g(R) = {g1(R), g2(R), …, g k (R)}, where g1(R), g2(R), …, g k (R) represents the adjustment amount of 1-k parameters according to user feedback, and g(R) is the adjustment function;

[0062] The collaborative analysis module interacts with other TCM analysis systems or databases to share data and analysis results. The data of the collaborative system is S c =(s1, s2, ..., s r ), the data of this system is S = (s′1, s′2, …, s′ t ), the fused data is S f =(f1, f2, ..., f u ), where f i =θ1s′ i +θ2s i％r+1 , θ1, θ2 are fusion weight coefficients, satisfying θ1+θ2=1, S c is the data set of the collaborative system, S is the data set of this system, S f is the fused data set, s1, s2, …, s r ,s′1,s′2,…,s′ r ,f1,f2,…,f u are the elements in the corresponding data set, r is the total number of collaborative system data, t is the total number of system data, and u is the total number of fused data;

[0063] In the present invention, the security protection module plays a vital role. In order to ensure the security and confidentiality of traditional Chinese medicine data, a series of strict security protection measures are carefully set up.

[0064] In terms of data encryption, advanced encryption algorithms are used to encrypt data in storage and transmission with high strength. For example, the Advanced Encryption Standard (AES) algorithm is used to encrypt data block by block to ensure that even if the data is illegally obtained, it is difficult to crack and understand. At the same time, for key data, multi-layer encryption technology will be used to further enhance data security.

[0065] In terms of access control, a sophisticated permission management system has been established. Different levels of access rights are assigned according to the user's role and responsibilities. For example, administrators have the highest level of authority and can fully manage and configure the system; ordinary users can only view analysis results and data within a specific range. At the same time, identity authentication technologies such as username and password, fingerprint recognition, and two-factor authentication are used to ensure that only legitimate users can access the system. For sensitive operations, additional authorization verification will be performed to prevent unauthorized access and operation.

[0066] The security audit function comprehensively records and monitors all operations and access behaviors of the system. Through the security audit log, the user's operation trajectory, data access status and system operation status can be tracked in real time. Once abnormal behavior is found, such as illegal access, data tampering, etc., the system will immediately issue an alarm and activate the corresponding emergency response mechanism. Security audit can also provide important basis for subsequent security analysis and investigation, helping to find security vulnerabilities and improve security strategies.

[0067] In the present invention, the data acquisition module is carefully designed and covers a variety of advanced acquisition equipment, providing a comprehensive and accurate data source for the analysis and identification of traditional Chinese medicines.

[0068] The high-resolution image acquisition device in the data acquisition module uses advanced imaging technology. For example, a high-pixel digital camera can capture the subtle texture, color distribution and morphological characteristics of Chinese medicine. At the same time, the device also has functions such as autofocus and optical image stabilization to ensure clear and stable images in different shooting environments. For some special shapes or tiny Chinese medicine samples, macro shooting mode can also be used to show more detailed details. In addition, the image acquisition device can also be used in conjunction with the lighting system to highlight the key features of Chinese medicine and improve image quality by adjusting the lighting angle and intensity.

[0069] Odor sensors use sensitive chemical sensing technology. These sensors can detect various volatile organic compounds emitted by traditional Chinese medicines, thereby identifying the unique smells of different traditional Chinese medicines. The sensors are highly sensitive and selective, and can accurately distinguish different odor components. In order to improve the accuracy of detection, array odor sensors are also used to perform multi-dimensional analysis of odors through the collaborative work of multiple sensors. At the same time, odor sensors are also regularly calibrated and maintained to ensure the stability and reliability of their performance.

[0070] Chemical analysis instruments include high-precision chromatographs, mass spectrometers, etc. Chromatographs can separate and analyze various chemical components in traditional Chinese medicines, and determine their content and composition by measuring the retention time and peak area of ​​different components. Mass spectrometers can further identify the structure of chemical components and provide more detailed chemical information. These chemical analysis instruments have high resolution, high sensitivity and accuracy, and can detect trace amounts of chemical components. At the same time, in order to improve analysis efficiency, automated sample processing and injection systems will be used to reduce human errors and operation time.

[0071] In the present invention, the large model is a neural network model carefully trained based on a large amount of traditional Chinese medicine data, which provides powerful intelligent support for the analysis and identification of traditional Chinese medicines.

[0072] The training data of the model is extremely rich, covering the images, smells, chemical components and corresponding labels of different types of Chinese medicines. For image data, after preprocessing and feature extraction, it is input into the neural network for learning. For example, a convolutional neural network (CNN) is used to extract features from images and learn the texture, shape, color and other features of Chinese medicines. The odor data is converted into a format suitable for neural network input through signal processing and feature engineering. After standardization, the chemical composition data is fused with the image and odor data and used as the input of the model. The label is an accurate annotation of the type, quality, origin and other information of Chinese medicines, which is used to guide the learning and training of the model.

[0073] During the training process, advanced optimization algorithms and loss functions are used to continuously adjust the model's parameters to minimize the prediction error. For example, optimization algorithms such as stochastic gradient descent (SGD) are used to update the model's weights and biases. At the same time, regularization techniques are also used to prevent the model from overfitting and improve the model's generalization ability. After training and optimization with a large amount of data, the large model can accurately identify and distinguish different types of traditional Chinese medicines, providing reliable analysis results for the traditional Chinese medicine analysis and identification system.

[0074] The present invention also includes a data update module to continuously monitor new research results, new drug varieties and data changes in the field of traditional Chinese medicine. Assume that the time variable is t and the new data set is N.t ={n1(t), n2(t), ..., n k (t)}, when time passes, it is determined whether the system data needs to be updated according to the rules, and the update function is t represents the time variable, N t represents the new data set at time, n1(t), n2(t), ..., n k (t) represents the data element in the new data set at time, and the update condition is set according to the importance and quantity change factors of the data.

[0075] The present invention includes a visualization module:

[0076] The analysis results are displayed to the user in a graphical interface. Let R be the analysis result and g be the visualization function. vis (R)={V1(R), V2(R),…,V m (R)}, where V1(R), V2(R), ..., V m (R) indicates the visualization methods of analysis results, including bar charts, pie charts, and heat maps.

[0077] The present invention is provided with a self-diagnosis module:

[0078] The self-diagnosis module regularly detects and evaluates each module of the system. Suppose the system module set is M = {M1, M2, ..., M n}, the diagnostic function is h diag (M)={d1(M1), d2(M2),…,d n (M n )}, where d1(M1), d2(M2), …, d n (M n ) represents the diagnostic results of 1-n modules. If the diagnostic results show that there is a problem with the module, the self-diagnosis module will issue an alarm and provide possible solution suggestions. M is the set of system modules, h diag (M) is the diagnostic function.

[0079] The present invention discloses a method for a Chinese medicine analysis and identification system based on a large model, comprising the following steps:

[0080] S1. Collect multi-dimensional data of traditional Chinese medicine through the data collection module;

[0081] S2, using the data preprocessing module to clean, standardize and extract features from the collected data;

[0082] S3, the data analysis module calls the pre-trained large model to analyze and process the data;

[0083] S4, outputting the analysis results to the user through the result output module;

[0084] S5. According to user feedback, the intelligent feedback module adjusts system parameters and models;

[0085] S6, the collaborative analysis module interacts with external systems to obtain more reference information;

[0086] S7, security protection module ensures data security and confidentiality;

[0087] S8, the data update module updates the system data according to the situation;

[0088] S9, visualization module displays the analysis results;

[0089] S10. The self-diagnosis module performs detection and evaluation on the system.

[0090] The invention discloses a storage medium which has high reliability and stability under the support of modern information technology.

[0091] The storage medium can be in various forms such as a hard disk, a solid state drive, a USB flash drive, and an optical disk. Among them, a solid state drive has the advantages of fast read and write speed and good shock resistance, and can quickly store and read computer programs and related data. A hard disk has a large storage capacity and is suitable for storing a large amount of Chinese medicine analysis and identification data and program files. USB flash drives and optical disks are portable and convenient for data transmission and program installation between different devices.

[0092] The computer programs stored in the storage medium are carefully designed and optimized, and use efficient data storage structures and algorithms to reduce storage space usage and improve program execution efficiency. For example, for image data of traditional Chinese medicines, compression algorithms can be used for storage to reduce storage capacity while ensuring image quality. For numerical data such as chemical composition data, binary format can be used for storage to improve data reading and writing speeds.

[0093] When a computer program is executed by a processor, the storage medium can respond quickly and transfer the program and data to the processor for processing. The data transmission channel between the storage medium and the processor is optimized to ensure the stability and efficiency of data transmission. For example, high-speed bus interfaces such as SATA and PCIe are used to increase the data transmission speed. At the same time, the storage medium also has error detection and correction functions, which can automatically detect and repair errors in the data storage process to ensure the integrity and accuracy of the data.

[0094] The present invention also discloses a device, which includes a memory and a processor, which work together to provide powerful hardware support for implementing a large model-based traditional Chinese medicine analysis and identification method.

[0095] The memory can be random access memory (RAM), read-only memory (ROM), etc. RAM is used to temporarily store computer programs and data, providing fast data reading and writing capabilities during program operation. ROM is used to store permanent programs and data, such as the device's startup program and basic configuration information. The capacity and speed of the memory are selected according to the needs of the device to meet the requirements of the traditional Chinese medicine analysis and identification system for data storage and processing.

[0096] The processor is the core component of the device and is responsible for executing computer programs stored in the memory. The processor can be a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), etc. Different types of processors have different characteristics and advantages, and they are selected according to the needs of the TCM drug analysis and identification system. For example, the CPU has strong general computing capabilities and is suitable for processing complex logical operations and control tasks; the GPU has strong parallel computing capabilities and is suitable for processing large-scale data and image computing tasks; the DSP is specifically used for digital signal processing and is suitable for processing signal processing tasks in TCM drug analysis and identification.

[0097] During the operation of the device, the memory and processor are connected through a high-speed data bus to ensure fast data transmission and processing. At the same time, the device is also equipped with a cooling system to ensure the stability and reliability of the processor when running under high load. The cooling system can use fans, heat sinks, etc. to effectively reduce the temperature of the processor and prevent performance degradation or equipment damage caused by overheating.

[0098] In summary, the storage medium and device disclosed in the present invention provide reliable technical support for the implementation of the large-model-based traditional Chinese medicine analysis and identification method, and bring new development opportunities to the field of traditional Chinese medicine analysis and identification.

[0099] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can replace or change the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A Chinese medicine drug analysis and identification system based on a large model, characterized in that: include The data acquisition module is used to collect multi-dimensional data of traditional Chinese medicine, including high-resolution images, odor characteristics, chemical composition content, and physical properties. The data is represented by a vector D = (I, S, C, P, ...), where I represents image data, and the appearance, morphology, color and texture information of traditional Chinese medicine are obtained through image acquisition equipment. S represents odor data, and the odor sensor is used to accurately capture the odor of traditional Chinese medicine. C represents chemical composition data, and the content of various active ingredients is determined by chemical analysis instruments. P represents physical property data, including hardness and density. It is also equipped with an automatic adjustment function to automatically optimize the acquisition parameters according to the characteristics of traditional Chinese medicine and environmental factors. The data preprocessing module cleans, standardizes and extracts features from the collected data, and uses data cleaning algorithms to remove outliers and noise interference. Suppose the original data is X = {x1, x2, ..., x n }, the denoising function is h(X) = {x′1, x′2, …, x′ n },in The sliding average method is used for denoising, m is the window size, h(X) represents the denoising function, X is the original data set, x1, x2, ..., x n is the data point in the original data, x′ i is the data point after denoising; then standardization is performed, and the standardized data is assumed to be Y = {y1, y2, ..., y n },in μ is the mean of the denoised data, σ is the standard deviation of the denoised data, Y is the standardized data set, y i is the normalized data point; Perform feature extraction and set the extracted feature vector to be F = (f1, f2, ..., f k ),in α ij is the feature extraction coefficient, satisfying F is the eigenvector, f1, f2, …, f k is the feature in the feature vector, y i is the normalized data point, α ij is the feature extraction coefficient, n is the total number of data points, k is the number of features, and the preprocessed data D is obtained. preprocessed ; The data analysis module uses a pre-trained deep model to analyze and process the received data. It introduces a multimodal fusion layer to integrate data of different modalities such as image, odor, and chemical composition. The data vectors of different modalities are A=(a1, a2, ..., a l ), R=(b1,b2,…,b m ), C=(c1,c2,…,c n ), the fused data vector is Z = (z1, z2, ..., z p ), where z i =ω1a i +ω2b i +ω3c i , ω1, ω2, ω3 are the fusion weight coefficients of the corresponding modes, satisfying ω1+ω2+ω3=1, A, B, C are the data vectors of different modes, a i , b i , c i are the elements in the corresponding modal data vector, Z is the fused data vector, and z i is the element in the fused data vector; A dynamic weight adjustment mechanism is used to dynamically assign weights according to the importance of different data types. Suppose the data type is T = {t1, t2, ..., t q }, the corresponding weight vector is W = (w1, w2, ..., w q ),in β j Is the same as the data type t j Related parameters, T is a data type set, W is a weight vector, w j is the element in the weight vector, q is the total number of data types, and the knowledge graph technology is also integrated to integrate the professional knowledge in the field of traditional Chinese medicine into the model. preprocessed Processing, determine the type, quality, and origin of traditional Chinese medicine; The result output module outputs the analysis results to the user in an intuitive way, including visual charts and detailed reports, and provides personalized analysis result display and interpretation according to user needs; The intelligent feedback module adjusts and optimizes the system parameters and models according to the user's feedback on the analysis results and the actual application situation. Let the user feedback be R, and the adjustment function be g(R) = {g1(R), g2(R), …, g k (R)}, where g1(R), g2(R), …, g k (R) represents the adjustment amount of 1-k parameters according to user feedback, and g(R) is the adjustment function; The collaborative analysis module interacts with other TCM analysis systems or databases to share data and analysis results. The data of the collaborative system is S c =(s1, s2, ..., s r ), the data of this system is S = (s′1, s′2, …, s′ t ), the fused data is S f =(f1, f2, ..., f u ), where f i =θ1s′ i +θ2s i％r+1 , θ1, θ2 are fusion weight coefficients, satisfying θ1+θ2=1, S c is the data set of the collaborative system, S is the data set of this system, S f is the fused data set, s1, s2, …, s r ,s′1,s′2,…,s′ t ,f1,f2,…,f u are the elements in the corresponding data set, r is the total number of collaborative system data, t is the total number of system data, and u is the total number of fused data; The security protection module has set up security protection measures, including data encryption, access control, and security audit functions.

2. A Chinese medicine drug analysis and identification system based on a large model according to claim 1, characterized in that: The data acquisition module includes a high-resolution image acquisition device, an odor sensor, and a chemical analysis instrument.

3. A Chinese medicine drug analysis and identification system based on a large model according to claim 1, characterized in that: The large model is a neural network model trained based on traditional Chinese medicine data, and the training data of the model includes images, smells, chemical components and corresponding labels of different types of traditional Chinese medicines.

4. A Chinese medicine drug analysis and identification system based on a large model according to claim 1, characterized in that: It also includes a data update module to continuously monitor new research results, new drug varieties and data changes in the field of traditional Chinese medicine. Let the time variable be t and the new data set be N. t ={n1(t), n2(t), ..., n k (t)}, when time passes, it is determined whether the system data needs to be updated according to the rules, and the update function is t represents the time variable, N t represents the new data set at time, n1(t), n2(t), ..., n k (t) represents the data element in the new data set at time, and the update condition is set according to the importance and quantity change factors of the data.

5. A Chinese medicine drug analysis and identification system based on a large model according to claim 1, characterized in that: Includes visualization modules: The analysis results are displayed to the user in a graphical interface. Let R be the analysis result and g be the visualization function. vis (R)={V1(R), V2(R),…,V m (R)}, where V1(R), V2(R), …, V m (R) indicates the visualization methods of analysis results, including bar charts, pie charts, and heat maps.

6. A Chinese medicine drug analysis and identification system based on a large model according to claim 1, characterized in that: Equipped with self-diagnosis module: The self-diagnosis module regularly detects and evaluates each module of the system. Suppose the system module set is M = {M1, M2, ..., M n }, the diagnostic function is h diag (M)={d1(M1), d2(M2),…,d n (M n )}, where d1(M1), d2(M2), …, d n (M n ) represents the diagnostic results of 1-n modules. If the diagnostic results show that there is a problem with the module, the self-diagnosis module will issue an alarm and provide possible solution suggestions. M is the set of system modules, h diag (M) is the diagnostic function.

7. A method for applying the large model-based traditional Chinese medicine analysis and identification system according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Collect multi-dimensional data of traditional Chinese medicine through the data collection module; S2, using the data preprocessing module to clean, standardize and extract features from the collected data; S3, the data analysis module calls the pre-trained large model to analyze and process the data; S4, outputting the analysis results to the user through the result output module; S5. According to user feedback, the intelligent feedback module adjusts system parameters and models; S6, the collaborative analysis module interacts with external systems to obtain more reference information; S7, security protection module ensures data security and confidentiality; S8, the data update module updates the system data according to the situation; S9, visualization module displays the analysis results; S10. The self-diagnosis module performs detection and evaluation on the system.

8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, it implements the large model-based traditional Chinese medicine analysis and identification method as described in claim 7.

9. A device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the large model-based traditional Chinese medicine analysis and identification method as claimed in claim 9 is implemented.

Images