Digital human knowledge graph construction method for famous and old traditional Chinese medicine experience active inheritance

Abstract

The invention relates to the technical field of knowledge maps, in particular to a digital human knowledge map construction method for famous and old traditional Chinese medicine experience live inheritance, which comprises the following steps of: acquiring traditional Chinese medicine literature data, disassembling literatures sentence by sentence, cutting continuous statements into independent vocabulary units, and marking the grammatical effect of each vocabulary in the sentence; by analyzing a combination rule between adjacent vocabularies, multiple entities in a text are identified, and a literature entity set is generated. According to the method, through grammar labeling and context mode analysis, the accuracy and coverage of entity extraction are improved, and through combination of co-occurrence frequency and modification relation derivation, entity association interpretability and semantic network dimension optimization, dynamic weight calculation and attribute fusion, the self-adaptive optimization capability of the knowledge graph is achieved; according to the method, a matching degree calculation and closed-loop verification mechanism is adopted, the accuracy of clinical taboo recognition is improved, a term system and a dynamic evolution mechanism are structured, and a traditional Chinese medicine knowledge system considering deep semantic expression and persistent evolution is constructed.

Description

Technical Field

[0001] The present invention relates to the field of knowledge graph technology, and in particular to a method for constructing a digital human knowledge graph for the living inheritance of the experience of famous traditional Chinese medicine practitioners. Background Art

[0002] The field of knowledge graph technology includes multiple fields such as computer science, artificial intelligence and data management. It involves representing and processing complex knowledge systems in a graphical way. The core content includes knowledge acquisition, modeling, storage, query, reasoning and visualization. The knowledge graph is composed of nodes and edges, that is, the relationships between entities. It effectively organizes and manages large amounts of data and reveals the intrinsic connections between information through semantic relationships. Application scenarios include intelligent question and answer, recommendation systems and semantic search. In the process of constructing the knowledge graph, various technical means are involved, such as data extraction, knowledge fusion, graph optimization and updating, to support more intelligent and accurate information processing.

[0003] Among them, the method for constructing a digital human knowledge graph for the living inheritance of the experience of famous old Chinese medicine practitioners refers to modeling the medical knowledge and experience of famous old Chinese medicine practitioners through digital means to build a proprietary knowledge graph. The technical matters targeted by the patent subject include extracting knowledge from multiple aspects of the clinical experience, treatment methods, and drug use of famous old Chinese medicine practitioners, and organizing and storing them in the form of a graph. Specifically, natural language processing technology is used to analyze the medical records, literature materials, and various text data of famous old Chinese medicine practitioners to identify the entities and relationships therein, and knowledge extraction and graph construction algorithms are used to present knowledge points in a structured manner, and comprehensive modeling is carried out in combination with the multi-dimensional information in the process of Chinese medicine diagnosis and treatment, including diseases, symptoms, and treatment plans, to construct a knowledge graph, and combined with the update and maintenance of the graph to ensure continuous updating and improvement in the process of living inheritance, providing a more systematic knowledge management and experience inheritance platform for the field of Chinese medicine.

[0004] Existing knowledge graph construction methods have the problem of insufficient integration of multi-source heterogeneous data in the application of traditional Chinese medicine. The split processing mode of text analysis and speech recognition makes it difficult to form an effective knowledge complementation mechanism. There is a lack of in-depth mining of grammatical structure in the document parsing process. Simply relying on word frequency statistics tends to ignore contextual semantic associations, resulting in deviations in entity relationship extraction. Traditional entity recognition methods have not established a cross-document co-occurrence frequency analysis mechanism and cannot effectively capture the complex network relationships between symptoms, drugs, and treatment methods in the process of traditional Chinese medicine diagnosis and treatment. The static graph construction model does not provide sufficient support for the attribute fusion of synonymous entities and is difficult to adapt to the terminology in the traditional Chinese medicine knowledge system. The diverse characteristics of oral experience lead to knowledge redundancy and fragmentation, insufficient consideration of the dynamic characteristics of physician experience inheritance, and no closed-loop feedback mechanism has been formed. Knowledge updating relies on manual intervention and has poor timeliness. Simply relying on literature data can easily miss implicit knowledge in oral experience. The lack of cross-validation of voice entities and text entities may lead to the loss of key treatment plans. The derivation of entity relationships does not quantify the correlation strength index, which affects the accuracy of diagnosis and treatment decisions. It is impossible to effectively identify key clinical information such as drug compatibility contraindications. These defects restrict the actual application value of knowledge graphs in the inheritance of TCM experience and make it difficult to support a dynamic and systematic knowledge management system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and propose a method for constructing a digital human knowledge graph for the living inheritance of the experience of famous old Chinese medicine practitioners.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a method for constructing a digital human knowledge graph for the living inheritance of the experience of famous traditional Chinese medicine practitioners, comprising the following steps: S1: Obtain TCM literature data, disassemble the literature sentence by sentence, cut continuous sentences into independent vocabulary units, mark the grammatical role of each word in the sentence, identify multiple entities in the text by analyzing the combination rules between adjacent words, and generate a literature entity set; S2: Based on the document entity set, the oral speech of the famous traditional Chinese medicine practitioner is obtained, the speech signal is divided into multiple segments and converted into a text symbol sequence, and multiple entities in the speech are extracted by comparing the collocation rules of adjacent words in the text sequence and comparing multiple entities in the document, and a speech recognition entity list is output; S3: calling the speech recognition entity list, performing spatial mapping on the entity set of the text and speech sources, analyzing the co-occurrence frequency of entities across documents, identifying the mutual relationship between multiple entities by analyzing the grammatical structure at the sentence level, and generating an entity relationship derivation graph; S4: Using the entity relationship derivation graph, entities are converted into nodes and relationships are converted into edges. The association strength is calculated based on the connection frequency between nodes, and the attributes of synonymous entity nodes are fused. Combined with physician feedback, the connection relationship is updated in real time to output a traditional Chinese medicine knowledge graph.

[0007] As a further solution of the present invention, the document entity set specifically includes sentence decomposition results, vocabulary unit extraction results, and medical entity data sets; the speech recognition entity list specifically includes oral speech collection results, text conversion records, and speech entity sets; the entity relationship derivation graph specifically includes entity co-occurrence relationships, before and after modification relationships, and relationships between entities; the traditional Chinese medicine knowledge graph includes entity node conversion results, relationship edge conversion records, and synonymous entity attribute fusion.

[0008] As a further solution of the present invention, the process of obtaining the document entity set is specifically as follows: S111: Obtain TCM literature data, disassemble the literature content sentence by sentence, analyze the sentence structure, extract vocabulary units, perform part-of-speech tagging and syntactic role assignment for each vocabulary, identify various types of vocabulary, including nouns, verbs, and adjectives, and establish syntactic structure analysis results; S112: Based on the syntactic structure analysis result, the collocation pattern between adjacent words is analyzed, and the stability of the word combination in the sentence is calculated using the formula: ; Calculate the association strength of word pairs, and obtain a set of high-frequency word combinations by counting the co-occurrence frequencies between multiple words; in, represents the association strength of the word combination, Representative words The co-occurrence frequency of Representative words The weight in the syntactic structure, Represents the number of words involved in the calculation, An index of vocabulary; S113: calling the high-frequency word combination set, comparing with the existing medical terminology library, identifying medical entities in the text, including symptoms, drugs, treatment methods, causes, counting the distribution of multiple types of entities in the document, and establishing a document entity set.

[0009] As a further solution of the present invention, the process of obtaining the speech recognition entity list is specifically as follows: S211: Based on the document entity set, the oral speech of the famous traditional Chinese medicine practitioner is obtained, and the audio signal is preprocessed, including sampling rate adjustment, noise removal and speech enhancement, and the processed audio signal is divided into multiple segments. The speech is converted into time and frequency using short-time Fourier transform, and time domain and frequency domain features are extracted. The speech is denoised to obtain a speech segmentation result; S212: Based on the speech segmentation result, convert the audio data into a text symbol sequence to obtain speech text data; S213: calling the speech text data, combining multiple entities of the document, comparing the collocation rules of adjacent words in the text, and calculating the similarity between the candidate words and the known medical entities, using the formula: ; Calculate the matching credibility of each word in the speech text, evaluate the meaning consistency of multiple medical entity words in the speech text of famous traditional Chinese medicine practitioners and literature, detect and extract medical terms in the speech text, and obtain the speech recognition entity list; in, Represents the matching confidence of the text vocabulary, Representing text words The context weight of Represents the semantic matching degree between the word and the medical entity. represents the phoneme matching probability of the vocabulary, represents the contextual deformation error of the word, Represents the number of valid words in the text, The index of the vocabulary in the speech text.

[0010] As a further solution of the present invention, the process of obtaining the entity relationship derivation graph is specifically as follows: S311: calling the speech recognition entity list, comparing the entity sets of the text and speech sources, calculating the distribution of the same entity in multiple sources, analyzing the occurrence frequency of multiple entities in the speech text and the literature text, and obtaining a cross-text entity distribution matrix; S312: Based on the cross-text entity distribution matrix, the co-occurrence frequencies of entities from multiple text sources are calculated, the co-occurrence times of the target entity in multiple documents are analyzed, and the association strength of entity pairs is calculated using the formula: ; Calculate the relevance of entities in multiple documents by calculating the association strength of each entity pair, using rows and columns to represent the two entities in the entity pair, and generate a cross-document entity co-occurrence matrix; in, Represents the entity association strength, Representing Entity Normalized frequency of occurrence in the spoken text, Representing Entity The normalized frequency of occurrence in the document text, and are the entity means of the two text sources, Represents the number of entities counted, is the index of the entity; S313: calling the cross-document entity co-occurrence matrix, parsing the grammatical structure of the sentence, analyzing the modification relationship and semantic dependency path between entities, evaluating the interaction between multiple entities in the sentence structure, and constructing an entity relationship derivation graph.

[0011] As a further solution of the present invention, the acquisition process of the TCM knowledge graph is specifically as follows: S411: calling the entity relationship derivation graph, converting multiple medical entities into nodes of the graph, establishing edge connections according to the semantics, co-occurrence frequency, and dependency relationship between entities, calculating the connection strength of each node, and obtaining an entity network topology structure; S412: Based on the entity network topology, the connection strength between multiple nodes is calculated, and synonymous entities are merged, and the formula is set as follows: ; Calculate the fusion adjustment factors between entities and generate a synonymous entity fusion matrix; in, represents the fusion adjustment factor, Representing Entity The structural weights in the network, Representing Entity Frequency in the knowledge base, Represents the number of entities counted, is the index of the entity; S413: Call the synonymous entity fusion matrix, and update the connection relationship between nodes in real time based on physician feedback to generate a traditional Chinese medicine knowledge graph.

[0012] As a further embodiment of the present invention, the method further comprises: S5: calling the TCM knowledge graph, extracting a set of drug entities associated with multiple disease entities, calculating the matching degree between multiple drugs and diseases by comparing the drug entity attribute data with the disease entity feature data, identifying contraindicated drugs associated with the target disease, and generating a set of drug and disease relationship; The drug and symptom relationship set includes a set of drug entities associated with symptoms, a drug and symptom matching degree, and a contraindicated drug identification result.

[0013] As a further solution of the present invention, the process of obtaining the drug and disease relationship set is specifically as follows: S511: calling the TCM knowledge graph, extracting a set of drug entities related to multiple diseases, and obtaining a set of disease-related drugs; S512: Based on the set of disease-associated drugs, the attribute data of each drug entity, including efficacy, meridians, and contraindications, and the characteristic data of the disease entity, including cause, symptoms, and physiological effects, are matched and analyzed using the formula: ; Calculate and obtain the drug matching value; in, Represents drug matching, is the attribute vector of the disease characteristics, is the mean vector corresponding to the drug, is the covariance matrix of the attributes, is the inverse of the covariance matrix, represents the transpose operation; S513: Call the drug matching degree value, identify and record contraindicated drugs related to multiple diseases, and form a drug and disease relationship set.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by analyzing the combination rules between adjacent words, multiple entities in the text are identified and a document entity set is generated. In the present invention, the accuracy and coverage of entity extraction are improved through grammatical annotation and context pattern analysis, and the co-occurrence frequency and modification relationship derivation are combined to optimize the interpretability of entity association and semantic network dimension, dynamic weight calculation and attribute fusion, realize the adaptive optimization ability of knowledge graph, matching degree calculation and closed-loop verification mechanism, improve the accuracy of clinical contraindication identification, structure the terminology system and dynamic evolution mechanism, and build a Chinese medicine knowledge system that takes into account deep semantic expression and continuous evolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the main steps of the present invention; Figure 2 Obtaining a flow chart for the document entity set of the present invention; Figure 3 A flow chart for obtaining a speech recognition entity list of the present invention; Figure 4 Obtaining a flow chart for the entity relationship derivation diagram of the present invention; Figure 5 Obtaining a flow chart for the TCM knowledge graph of the present invention; Figure 6 A flow chart is obtained for the drug and condition relationship set of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: a method for constructing a digital human knowledge graph for the living inheritance of the experience of famous old Chinese medicine practitioners, comprising the following steps: S1: Obtain TCM literature data, disassemble the literature sentence by sentence, cut continuous sentences into independent vocabulary units, mark the grammatical role of each word in the sentence, and identify multiple entities in the text by analyzing the combination rules between adjacent words, including symptoms, drugs, treatment methods, and causes of disease. According to the frequency of repeated appearance of words in the paragraph and the contextual matching pattern, a corresponding relationship table for each category of medical terms is established to generate a document entity set; S2: Based on the document entity set, the oral speech of famous traditional Chinese medicine practitioners is obtained, the speech signal is divided into multiple segments and converted into a text symbol sequence. By comparing the collocation rules of adjacent words in the text sequence and comparing multiple entities in the document, multiple entities in the speech are extracted and a speech recognition entity list is output; S3: Call the speech recognition entity list, perform spatial mapping on the entity sets of text and speech sources, analyze the co-occurrence frequency of entities across documents, extract the pre- and post-modification relationships between entities in the sentence by analyzing the grammatical structure at the sentence level, identify the relationships between multiple entities, and generate an entity relationship derivation graph; S4: Using the entity relationship derivation graph, entities are converted into nodes and relationships into edges. The association strength is calculated based on the connection frequency between nodes. The attributes of synonymous entity nodes are fused. Combined with physician feedback, the connection relationship is updated in real time to output the TCM knowledge graph. S5: Call the TCM knowledge graph to extract a set of drug entities associated with multiple disease entities. By comparing the drug entity attribute data with the disease entity feature data, calculate the matching degree between multiple drugs and diseases, identify the contraindicated drugs associated with the target disease, and generate a set of drug and disease relationship.

[0019] The document entity set specifically includes sentence decomposition results, vocabulary unit extraction results, and medical entity data sets. The speech recognition entity list specifically includes oral speech collection results, text conversion records, and speech entity sets. The entity relationship derivation graph specifically includes entity co-occurrence relationships, before and after modification relationships, and relationships between entities. The traditional Chinese medicine knowledge graph includes entity node conversion results, relationship edge conversion records, and synonymous entity attribute fusion. The drug and disease relationship set includes the drug entity set associated with the disease, the drug and disease matching degree, and the contraindicated drug identification results.

[0020] See also Figure 2 , the specific steps for obtaining the document entity set are: S111: Obtain TCM literature data, disassemble the literature content sentence by sentence, analyze the sentence structure, extract vocabulary units, perform part-of-speech tagging and syntactic role assignment for each vocabulary, identify various types of vocabulary, including nouns, verbs, and adjectives, and establish syntactic structure analysis results; Obtain TCM literature data, disassemble it based on the content of the literature, extract sentence-level text, and parse it according to lexical and syntactic rules. First, convert TCM literature into standardized text data, and split the content of the literature at the sentence level so that each sentence becomes an independent analysis unit. Subsequently, perform lexical analysis on each sentence to decompose the basic vocabulary units, such as nouns, verbs, adjectives, etc., and mark the specific roles of these words in the sentence, including subject, predicate, object, etc. Through such analysis, the internal structure of the text can be understood more accurately. For example, in the sentence "cold and wind cold can be relieved by ginger soup", "cold and wind cold" can be extracted as a disease entity, "ginger soup" as a drug entity, and "relieve" as a related descriptive word for the treatment method.

[0021] After completing the basic vocabulary analysis, the next step is to calculate the frequency of occurrence of each word and normalize it to measure the importance of each word in the overall document. In order to calculate the normalized frequency of a word, use the following formula: ; in, For vocabulary The normalized word frequency of For vocabulary The number of occurrences in the literature, The total number of words counted. Suppose that the number of times "cold" appears in a medical document The occurrence frequency of "ginger decoction" The occurrence frequency of "relieve" The occurrence frequency of "etiology" 。

[0022] Calculate the normalized word frequency of "wind-cold common cold" as follows: ; The normalized word frequency of "ginger decoction" is: ; Among them, It shows that "wind-cold common cold" is more important in the medical literature dataset and should be given priority in keyword screening.

[0023] S112: Based on the syntactic structure analysis results, analyze the collocation patterns between adjacent words, calculate the stability of word combinations in the sentence, and use the formula: ; Calculate the association strength of word pairs, and obtain a set of high-frequency word combinations by statistically analyzing the co-occurrence frequencies of multiple words; Among them, represents the association strength of the word combination, represents the word co-occurrence frequency, represents the word weight in the syntactic structure, represents the number of words participating in the calculation, is the index of the word; Based on the syntactic structure analysis results, further calculate the collocation patterns between adjacent words to determine which words have strong semantic associations. First, count the co-occurrence frequencies of word pairs, that is, the frequencies of occurrence in the same document paragraph or adjacent sentences. Then, calculate the semantic weights of word pairs and use a normalization method to adjust the importance of different words. Suppose "wind-cold common cold" and "ginger decoction" appear simultaneously in multiple sentences, which indicates that they have strong collocability, while "relieve" may be collocated with different drugs in different contexts, so it is necessary to further calculate the collocation stability of each word.

[0024] To more accurately evaluate the strong association of word combinations, use the following formula to calculate the association strength of word pairs: ; Among them, is the association strength of the word combination, is the word co-occurrence frequency, is the word weight in the syntactic structure, The number of words involved in the calculation. , , , , , , , , the association strength is calculated as follows: ; in, This indicates that the vocabulary combination has a strong correlation in the literature and can be used for medical term extraction and text mining later.

[0025] S113: calling a high-frequency word combination set, comparing it with an existing medical terminology library, identifying medical entities in the text, including symptoms, drugs, treatment methods, and causes, counting the distribution of multiple types of entities in the literature, and establishing a literature entity set; Call the combination set of high-frequency words, combine with the existing medical terminology library, match the medical entities that may be involved in the text, and classify and organize them. In the processing process, the candidate words are first screened, and the categories such as symptoms, drugs, treatment methods, and causes are classified according to the existing medical vocabulary. Then, the distribution of these entities in different documents is calculated to measure the importance of a certain medical entity. For example, in the sentence "Ginger soup is suitable for colds and flu", "colds and flu" is identified as a symptom and "ginger soup" is a drug, and they are classified. The distribution of symptoms, drugs, treatment methods, and causes in the literature is counted, and an index list of medical entities is formed.

[0026] In order to measure the importance of a medical entity in the entire literature, the Inverse Document Frequency (IDF) is used for calculation: ; in, For Entity The inverse document frequency of is the total number of documents, The number of documents containing this entity, plus 1 to prevent the denominator from being zero. , , , , , the IDF value of "cold" is calculated as follows: ; Calculate the IDF value of "ginger soup": ; Calculate the IDF value of "cause": ; in, This indicates that “ginger soup” appears less frequently in the literature and is therefore more discriminatory, whereas “wind-cold cold” and “cause of disease” are relatively common and have lower weights.

[0027] See also Figure 3 , the specific process of obtaining the speech recognition entity list is as follows: S211: Based on the document entity set, the oral speech of famous traditional Chinese medicine practitioners is obtained, and the audio signal is preprocessed, including sampling rate adjustment, noise removal and speech enhancement. The processed audio signal is divided into multiple segments, and the short-time Fourier transform is used to perform time-frequency conversion on the speech, and the time domain and frequency domain features are extracted. The speech is denoised to obtain the speech segmentation result; After obtaining the oral speech data of famous traditional Chinese medicine practitioners, the sampling rate of the speech signal is first adjusted to ensure the stability of data processing. In general, the sampling rate of the speech signal is set to 16kHz or 44.1kHz to meet different speech analysis needs. Secondly, the adaptive filtering method is used to remove the background noise, and the bandpass filter is used to retain the speech components between 500Hz and 4kHz to improve the clarity of the speech signal. After noise removal, the short-time Fourier transform (STFT) is applied to convert the speech signal to the frequency domain to extract its features. In order to further optimize the processing, the energy threshold detection method is used to identify the effective part of the speech and remove the silent section in the audio. In this process, the background noise threshold is set to 30dB, and the speech signal strength should be kept above 60dB. In order to evaluate the signal quality, the signal-to-noise ratio (SNR) is calculated using the following formula: ; in, is the signal-to-noise ratio, is the voice signal power, is the noise power. Assume that the signal power of a speech segment is , the noise power is , then calculate: ; This result shows that the speech signal quality is good and can be used for subsequent speech-to-text processing to finally obtain the speech segmentation result.

[0028] S212: Based on the speech segmentation result, convert the audio data into a text symbol sequence to obtain speech text data; Based on the speech segmentation results, the automatic speech recognition (ASR) technology is used to convert the audio data into a sequence of text symbols. In this process, the Mel-frequency cepstral coefficient (MFCC) features of the speech are first extracted, and its feature vector is calculated to match the existing acoustic model. Then, the time series characteristics of the speech are modeled using the hidden Markov model (HMM), and the optimal path search is performed in combination with the language model to predict the most likely text output. Since mistranslation may occur during the speech conversion process, the dynamic time warping (DTW) algorithm is used to optimize the match between the text output and the speech features. During the processing, the text error correction threshold is set to 5%, that is, the error rate of the converted text is allowed to not exceed 5%. In order to measure the accuracy of speech recognition, the word error rate (WER) is calculated using the following formula: ; in, is the word error rate, To replace the error number, To delete the error number, is the number of insertion errors, is the total number of words. Suppose the number of words with incorrect recognition in a test text is as follows: , , , the total number of words is , then calculate: ; That is, the WER is 10%, indicating that the speech recognition accuracy is high, and the final output is a text symbol sequence.

[0029] S213: Call the speech text data, combine multiple entities in the literature, compare the collocation rules of adjacent words in the text, calculate the similarity between the candidate words and the known medical entities, and use the formula: ; Calculate the matching credibility of each word in the speech text, evaluate the meaning consistency of multiple medical entity words in the speech text of famous traditional Chinese medicine practitioners and literature, detect and extract medical terms in the speech text, and obtain the speech recognition entity list; in, Represents the matching confidence of the text vocabulary, Representing text words The context weight of Represents the semantic matching degree between the word and the medical entity. represents the phoneme matching probability of the vocabulary, represents the contextual deformation error of the word, Represents the number of valid words in the text, An index of words in the spoken text; The text symbol sequence is called, and based on the document entity set, the collocation rules of adjacent words in the text are compared to calculate the similarity between the candidate words and the known medical entities. During the processing, the context weight of the vocabulary is first calculated, and high-frequency medical vocabulary is given a higher weight value to enhance the matching accuracy of professional terms. Then, the semantic matching algorithm is used to calculate the similarity score between each vocabulary and the medical entity, and the pronunciation similarity of the converted text is evaluated in combination with the phoneme matching probability. In addition, considering the context error in speech recognition, the context deformation error of each vocabulary is calculated to correct the recognition bias caused by accent or connected reading. The following formula is used to calculate the matching credibility: ; in, Represents the matching confidence of the text vocabulary, Representing text words The context weight of Represents the semantic matching degree between the word and the medical entity. represents the phoneme matching probability of the vocabulary, represents the contextual deformation error of the word, Represents the number of valid words in the text. Assuming the sample data, the context weight of "cold" is 0.85, the semantic matching degree is 0.92, the phoneme matching probability is 0.90, and the context deformation error is 0.05. The matching reliability of the word is calculated as follows: ; Calculated: ; The result shows that the matching credibility of medical-related vocabulary reaches 0.649, which has a high text matching accuracy in the medical context. Finally, medical terms are screened out and the speech recognition entity list is obtained.

[0030] See also Figure 4 , the specific process of obtaining the entity relationship derivation diagram is as follows: S311: calling the speech recognition entity list, comparing the entity sets of the text and speech sources, calculating the distribution of the same entity in multiple sources, analyzing the occurrence frequency of multiple entities in the speech text and the literature text, and obtaining a cross-text entity distribution matrix; Call the speech recognition entity list, compare the entity sets of the text and speech sources, and use the text matching algorithm to calculate the distribution of the same entity in different sources. First, perform entity extraction on the speech text and document text respectively, count the number of occurrences of each entity in different sources, and perform normalization to ensure that the probability of entity occurrence in different text sources is comparable. During the normalization process, set the standardized frequency of each entity, and the calculation method is as follows: ; wherein, represents the normalized entity occurrence frequency, represents the number of times the entity appears in the text, represents the total number of words in the text. For example, in a speech text, "wind-cold cold" appears 85 times, and the total number of words in the speech text is 1000, then its normalized frequency is calculated as follows: ; Similarly, in a literature text, the entity may appear 92 times, and the total number of words in the literature is 1200, then its normalized frequency is calculated as follows: ; Subsequently, a cross-text entity distribution matrix is established, and the occurrence frequency of each entity can be used for subsequent matching calculations. In this process, in order to exclude interference information, stop word filtering is first performed to remove common but semantically uncontributing words such as "of", "is", "can", etc., and then a lemmatization method is applied to merge different variants (such as "ginger decoction" and "decoction of ginger") to ensure that the same entity has a unified representation in different texts. Finally, the TF-IDF (term frequency-inverse document frequency) method is used to calculate the importance weight of the entity and store it in the database for subsequent calculation of the cross-text entity distribution matrix.

[0031] S312: Based on the cross-text entity distribution matrix, calculate the co-occurrence frequency of entities from multiple text sources, analyze the co-occurrence times of the target entity in multiple documents, calculate the association strength of entity pairs, using the formula: ; Calculate the correlation of entities in multiple documents. By calculating the association strength of each entity pair, using rows and columns to represent the two entities in the entity pair, generate a cross-document entity co-occurrence matrix; wherein, represents the entity association strength, represents the entity in the normalized occurrence frequency in the speech text, represents the entity in the normalized occurrence frequency in the literature text, and are the entity means of the two text sources respectively, represents the number of entities counted, is the index of the entity; Based on the cross-text entity distribution matrix, calculate the entity co-occurrence frequency of different text sources, count the number of co-occurrences of the same entity in different documents, and calculate the association strength of entity pairs. First, conduct co-occurrence statistics for each entity pair (such as "wind-cold cold - ginger soup"), record the number of times they appear simultaneously in the same text passage or adjacent paragraphs to ensure the accuracy of co-occurrence calculation. The formula is set as follows: ; Among them, represents the entity association strength, represents the entity 's standardized occurrence frequency in the speech text, represents the entity 's standardized occurrence frequency in the literature text, and are the entity means of the two text sources respectively, represents the number of entities counted. Taking "wind-cold cold - ginger soup" as an example, in the speech text, its standardized frequency is 0.085, while in the literature text, the standardized frequency of this entity is 0.0767. The calculation process is as follows: ; Calculated: ; ; To ensure the accuracy of entity co-occurrence data and exclude the influence of random co-occurrence on the calculation results, set the minimum threshold for co-occurrence frequency. For example, the number of co-occurrences of an entity pair in 1000 documents should not be less than 5 times, otherwise the entity pair will be excluded. The final calculation results are stored in the database and output to the cross-document entity co-occurrence matrix.

[0032] S313: Invoke the cross-document entity co-occurrence matrix, parse the syntactic structure of the statement, analyze the modification relationship and semantic dependency path between entities, evaluate the interaction of multiple entities in the sentence structure, and construct an entity relationship derivation graph; Invoke the cross-document entity co-occurrence matrix, parse the syntactic structure of the statement, analyze the modification relationship and semantic dependency path between entities, and determine the interaction of different entities in the sentence structure. First, apply the dependency syntax analysis method to decompose the statement and determine the functional roles of entities in the sentence, such as subject, object, attributive, etc. For example, after parsing the sentence "Wind-cold cold can be relieved by ginger soup", "wind-cold cold" is the subject, "ginger soup" is the object, and "relieved" is the main verb, and the dependency path is "wind-cold cold, relieved, ginger soup".

[0033] Next, calculate the syntactic dependency degree of entity pairs to evaluate the degree of association tightness of entities in the syntactic structure. The following formula is adopted: ; Among them, represents the syntactic dependency degree of the entity pair, represents the number of times the entity pair appears in the dependency relationship, represents the total co-occurrence times of the entity. For example, in 100 co-occurrences, the syntactic dependency relationship between "wind-cold cold - ginger soup" is 82 times, then its syntactic dependency degree is calculated as follows: ; In addition, considering that there may be indirect relationships between some entities (such as "wind-cold cold" and "ginger soup" may be indirectly related through "relieve"), a path length parameter is introduced to calculate the influence of the syntactic dependency path. Set the path length as the shortest syntactic distance between entities, then the syntactic corrected dependency degree is calculated as: ; Set that "wind-cold cold" and "ginger soup" are connected through "relieve", and the path length , then calculate: ; That is, after path correction, the syntactic dependency degree decreases, indicating that this entity pair may be indirectly related. Finally, map all calculation results to a directed graph to form an entity relationship derivation graph.

[0034] Please refer to Figure 5 , the acquisition process of the traditional Chinese medicine knowledge graph is specifically as follows: S411: Call the entity relationship derivation graph, convert multiple medical entities into nodes of the graph, and establish edge connections according to the semantics, co-occurrence frequency, and dependency relationship between entities, calculate the connection strength of each node, and obtain the entity network topology structure; Call the entity relationship derivation graph, convert each medical entity into a node of the graph, and establish edge connections based on the semantics, co-occurrence frequency, and dependency relationship between entities. Each edge represents the semantic association relationship between two entities. In this process, first, all extracted medical entities are de-duplicated and standardized to ensure that different expressions of the same concept (such as "ginger soup" and "ginger decoction") can be uniformly processed. Then, calculate the connection frequency of each entity in the corpus, and use association analysis methods to screen out entity pairs with high-frequency associations. For example, if "wind-cold cold" and "ginger soup" co-occur 200 times in 1000 traditional Chinese medicine literatures, while "wind-cold cold" and "relieve" co-occur 150 times, then the connection strengths of the two are different. Subsequently, use graph theory methods to calculate the connection strength of each entity node and conduct a preliminary classification of the nodes, such as classifying nodes belonging to the same traditional Chinese medicine treatment category. To calculate the node influence of the entity, set the following formula: ; Among them, Represents the influence index of the entity node, Representing Entity The connection frequency, Represents the number of connections of the node in the network. Represents the number of entities counted. If the connection frequency of "cold" is set to 200 and the number of nodes is 10, the calculation is as follows: ; ; This calculation is used to measure the importance of physical nodes in the network and is applied to subsequent network optimization to ultimately form a physical network topology.

[0035] S412: Based on the entity network topology, the connection strength between multiple nodes is calculated, and synonymous entities are merged. The formula is set as follows: ; Calculate the fusion adjustment factors between entities and generate a synonymous entity fusion matrix; in, represents the fusion adjustment factor, Representing Entity The structural weights in the network, Representing Entity Frequency in the knowledge base, Represents the number of entities counted, is the index of the entity; Based on the entity network topology, the connection strength between different nodes is calculated, and synonymous entities are fused to reduce redundant information and enhance the overall stability of the network. To measure the degree of fusion of two entities, the calculation formula is as follows: ; in, represents the fusion adjustment factor, For Entity The structural weights in the network, For Entity Frequency in the knowledge base, is the number of entities counted. Assuming that the structural weight of a certain entity for "cold-ginger soup" is 0.85 and the knowledge base frequency is 0.90, and the structural weight of another entity for "cold-relief" is 0.78 and the knowledge base frequency is 0.82, the calculation is as follows: ; ; ; This value is used to determine the rationality of the fusion between entities. If the value exceeds the set threshold (such as 0.8), the entity pair is considered to be merged. The calculation results are stored in the synonymous entity fusion matrix.

[0036] S413: calling the synonymous entity fusion matrix, and updating the connection relationship between nodes in real time in combination with physician feedback to generate a TCM knowledge graph; The synonymous entity fusion matrix is ​​called, and the connection relationship between nodes is updated in real time in combination with physician feedback, the correlation between entities is optimized, and the category attribution and relationship direction of the entities are adjusted. In this process, the number of times different physicians confirm the same entity relationship during the diagnosis and treatment process is first counted, and the physician feedback correction coefficient is set. For example, among the feedback of 50 physicians, 40 believe that "cold-ginger soup" has a direct treatment relationship, while 10 believe that they are indirectly related. The correction coefficient of the relationship is calculated as follows: ; in, represents the physician feedback correction factor, represents the number of physicians who confirmed the relationship, Represents the total number of participating physicians. Assuming that a certain relationship is confirmed by 40 physicians and 50 physicians participate, the calculation is as follows: ; Then, the correction coefficient is combined with the fusion adjustment factor to adjust the connection weight of the entity pair. Finally, based on the updated entity network structure, a TCM knowledge graph is formed, which contains the final relationship status of each entity pair and the physician feedback correction value.

[0037] See also Figure 6 , the specific process of obtaining the drug and disease relationship set is: S511: Calling the TCM knowledge graph, extracting a set of drug entities related to multiple diseases, and obtaining a set of disease-related drugs; The TCM knowledge graph is called to extract a set of drug entities related to a specific disease from the database. First, all drugs that may be related to the target disease are identified, and a subset of drugs that are directly used to treat the disease is screened out. During the extraction process, it is necessary to classify the disease based on the TCM syndrome differentiation, compare the characteristics of the disease entity, such as symptoms, causes, physical tendencies, etc., and find the corresponding drug entity. Preliminary screening is performed based on the relationship connection strength of the knowledge graph. To ensure the accuracy of the screening, it is necessary to traverse the connection path of each disease node in the knowledge graph to determine whether there is a drug node directly connected to the target disease. If a connection path exists, further analyze the indications, efficacy, and ingredient ratio of the drug, and screen out drugs that may be applicable to multiple diseases but have no obvious effect on the target disease. Finally, a set of candidate drugs is obtained. During the screening process, an entity relationship matching threshold needs to be set to measure the direct relationship strength between the disease and the drug. If the connection strength of a drug entity is lower than the threshold, it is removed from the candidate list.

[0038] S512: Based on the set of disease-related drugs, the attribute data of each drug entity, including efficacy, meridians, and contraindications, and the characteristic data of the disease entity, including cause, symptoms, and physiological effects, are matched and analyzed using the formula: ; Calculate and obtain the drug matching value; in, Represents drug matching, is the attribute vector of the disease characteristics, is the mean vector corresponding to the drug, is the covariance matrix of the attributes, is the inverse of the covariance matrix, represents the transpose operation; Based on the set of disease-associated drugs, the attribute data of each drug entity (such as efficacy, meridians, and contraindications) and the characteristic data of the disease entity (such as cause, symptoms, and physiological effects) are matched and analyzed. In this process, the importance of different drug attributes needs to be weighted to ensure that the degree of influence of different attributes in the calculation process meets the clinical use standards of traditional Chinese medicine. The Mahalanobis distance is used to evaluate the matching degree between a single drug and a single disease. This distance is used to measure the normalized distance between a sample point and the overall mean. The calculation formula is as follows: ; in, Indicates the matching degree between the disease and the drug. is the attribute vector of the drug entity, is the mean vector of the disease entity, is the attribute covariance matrix, is the inverse matrix of the covariance matrix. This formula can fully consider the correlation between different variables and avoid matching based on absolute differences alone, which affects the matching accuracy.

[0039] Calculation Example Suppose the characteristic vector of a disease , the mean vector of a drug , the covariance matrix , then the matching degree is calculated as follows: calculate : ; calculate : ; calculate : ; After matrix calculation, we get: ; The matching degree is used to measure the degree of suitability of the drug to the disease. The lower the value, the higher the matching degree.

[0040] S513: Calling the drug matching value, identifying and recording contraindicated drugs related to multiple diseases, and forming a drug and disease relationship set; Call the drug matching value, set the contraindication threshold, screen the drugs with matching degrees greater than the set value or with contraindications, and mark these drugs as contraindications to ensure the safety of treatment. The contraindication screening is based on the Chinese medicine pharmacopoeia and clinical feedback data. By querying the contraindication records of the drugs and comparing the properties of the symptoms, the drugs that may cause adverse reactions to the patients are excluded. In the contraindication screening process, the drug interactions that may be caused by the symptoms are statistically analyzed, and drug combinations that have had adverse reactions in historical case data are extracted. Combined with the main chemical components in the drug ingredients, it is detected whether there are specific interaction risks. For example, some drugs may enhance or inhibit the efficacy of other drugs, resulting in unstable treatment effects. If a drug is found to have a potential contraindication relationship with the target disease, the drug is excluded from the final matching set, and finally a set of drug and disease relationships is formed.

[0041] Table 1 Example of drug matching calculation As shown in Table 1 , drug B and drug D were marked as contraindicated drugs because their matching degree was higher than the set threshold (0.4) or they had contraindications, and finally drug A and drug C were screened out as available drugs.

[0042] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for constructing a digital human knowledge graph for the living inheritance of the experience of famous traditional Chinese medicine practitioners, characterized in that: The following steps are involved: S1: Obtain TCM literature data, disassemble the literature sentence by sentence, cut continuous sentences into independent vocabulary units, mark the grammatical role of each word in the sentence, identify multiple entities in the text by analyzing the combination rules between adjacent words, and generate a literature entity set; S2: Based on the document entity set, the oral speech of the famous traditional Chinese medicine practitioner is obtained, the speech signal is divided into multiple segments and converted into a text symbol sequence, and multiple entities in the speech are extracted by comparing the collocation rules of adjacent words in the text sequence and comparing multiple entities in the document, and a speech recognition entity list is output; S3: calling the speech recognition entity list, performing spatial mapping on the entity set of the text and speech sources, analyzing the co-occurrence frequency of entities across documents, identifying the mutual relationship between multiple entities by analyzing the grammatical structure at the sentence level, and generating an entity relationship derivation graph; S4: Using the entity relationship derivation graph, entities are converted into nodes and relationships are converted into edges. The association strength is calculated based on the connection frequency between nodes, and the attributes of synonymous entity nodes are fused. Combined with physician feedback, the connection relationship is updated in real time to output a traditional Chinese medicine knowledge graph.

2. The method for constructing a digital human knowledge graph for the living inheritance of the experience of famous traditional Chinese medicine practitioners according to claim 1 is characterized in that: The document entity set specifically includes sentence decomposition results, vocabulary unit extraction results, and medical entity data sets; the speech recognition entity list specifically includes oral speech collection results, text conversion records, and speech entity sets; the entity relationship derivation graph specifically includes entity co-occurrence relationships, front and back modification relationships, and relationships between entities; the traditional Chinese medicine knowledge graph includes entity node conversion results, relationship edge conversion records, and synonymous entity attribute fusion.

3. The method for constructing a digital human knowledge graph for the living inheritance of the experience of famous veteran Chinese medicine practitioners according to claim 1 is characterized in that: The specific process of obtaining the document entity set is as follows: S111: Obtain TCM literature data, disassemble the literature content sentence by sentence, analyze the sentence structure, extract vocabulary units, perform part-of-speech tagging and syntactic role assignment for each vocabulary, identify various types of vocabulary, including nouns, verbs, and adjectives, and establish syntactic structure analysis results; S112: Based on the syntactic structure analysis result, the collocation pattern between adjacent words is analyzed, and the stability of the word combination in the sentence is calculated using the formula: ； Calculate the association strength of word pairs, and obtain a set of high-frequency word combinations by counting the co-occurrence frequencies between multiple words; in, represents the association strength of the word combination, Representative words The co-occurrence frequency of Representative words The weight in the syntactic structure, Represents the number of words involved in the calculation, An index of vocabulary; S113: calling the high-frequency word combination set, comparing with the existing medical terminology library, identifying medical entities in the text, including symptoms, drugs, treatment methods, causes, counting the distribution of multiple types of entities in the document, and establishing a document entity set.

4. The method for constructing a digital human knowledge graph for the living inheritance of the experience of famous veteran Chinese medicine practitioners according to claim 1 is characterized in that: The specific process of obtaining the speech recognition entity list is as follows: S211: Based on the document entity set, the oral speech of the famous traditional Chinese medicine practitioner is obtained, and the audio signal is preprocessed, including sampling rate adjustment, noise removal and speech enhancement, and the processed audio signal is divided into multiple segments. The speech is converted into time and frequency using short-time Fourier transform, and time domain and frequency domain features are extracted. The speech is denoised to obtain a speech segmentation result; S212: Based on the speech segmentation result, convert the audio data into a text symbol sequence to obtain speech text data; S213: calling the speech text data, combining multiple entities of the document, comparing the collocation rules of adjacent words in the text, and calculating the similarity between the candidate words and the known medical entities, using the formula: ； Calculate the matching credibility of each word in the speech text, evaluate the meaning consistency of multiple medical entity words in the speech text of famous traditional Chinese medicine practitioners and literature, detect and extract medical terms in the speech text, and obtain the speech recognition entity list; in, Represents the matching confidence of text words, Representing text words The context weight of Represents the semantic matching degree between the word and the medical entity. represents the phoneme matching probability of the vocabulary, represents the contextual deformation error of the word, Represents the number of valid words in the text, The index of the vocabulary in the speech text.

5. The method for constructing a digital human knowledge graph for the living inheritance of the experience of famous veteran Chinese medicine practitioners according to claim 1 is characterized in that: The specific process of obtaining the entity relationship derivation diagram is as follows: S311: calling the speech recognition entity list, comparing the entity sets of the text and speech sources, calculating the distribution of the same entity in multiple sources, analyzing the occurrence frequency of multiple entities in the speech text and the literature text, and obtaining a cross-text entity distribution matrix; S312: Based on the cross-text entity distribution matrix, the co-occurrence frequencies of entities from multiple text sources are calculated, the co-occurrence times of the target entity in multiple documents are analyzed, and the association strength of entity pairs is calculated using the formula: ； Calculate the relevance of entities in multiple documents by calculating the association strength of each entity pair, using rows and columns to represent the two entities in the entity pair, and generate a cross-document entity co-occurrence matrix; in, Represents the entity association strength, Representing Entity Normalized frequency of occurrence in the spoken text, Representing Entity The normalized frequency of occurrence in the document text, and are the entity means of the two text sources, Represents the number of entities counted, is the index of the entity; S313: calling the cross-document entity co-occurrence matrix, parsing the grammatical structure of the sentence, analyzing the modification relationship and semantic dependency path between entities, evaluating the interaction between multiple entities in the sentence structure, and constructing an entity relationship derivation graph.

6. The method for constructing a digital human knowledge graph for the living inheritance of the experience of famous veteran Chinese medicine practitioners according to claim 1 is characterized in that: The specific process of obtaining the TCM knowledge graph is as follows: S411: calling the entity relationship derivation graph, converting multiple medical entities into nodes of the graph, establishing edge connections according to the semantics, co-occurrence frequency, and dependency relationship between entities, calculating the connection strength of each node, and obtaining an entity network topology structure; S412: Based on the entity network topology, the connection strength between multiple nodes is calculated, and synonymous entities are merged, and the formula is set as follows: ； Calculate the fusion adjustment factors between entities and generate a synonymous entity fusion matrix; in, represents the fusion adjustment factor, Representing Entity The structural weights in the network, Representing Entity Frequency in the knowledge base, Represents the number of entities counted, is the index of the entity; S413: Call the synonymous entity fusion matrix, and update the connection relationship between nodes in real time based on physician feedback to generate a traditional Chinese medicine knowledge graph.

7. The method for constructing a digital human knowledge graph for the living inheritance of the experience of famous veteran Chinese medicine practitioners according to claim 1 is characterized in that: The method further comprises: Calling the TCM knowledge graph, extracting a set of drug entities associated with multiple disease entities, calculating the matching degree between multiple drugs and diseases by comparing the drug entity attribute data with the disease entity feature data, identifying contraindicated drugs associated with the target disease, and generating a set of drug and disease relationship; The drug and symptom relationship set includes a set of drug entities associated with symptoms, a drug and symptom matching degree, and a contraindicated drug identification result.

8. The method for constructing a digital human knowledge graph for the living inheritance of the experience of famous veteran Chinese medicine practitioners according to claim 7 is characterized in that: The specific process of obtaining the drug and disease relationship set is as follows: S511: calling the TCM knowledge graph, extracting a set of drug entities related to multiple diseases, and obtaining a set of disease-related drugs; S512: Based on the set of disease-associated drugs, the attribute data of each drug entity, including efficacy, meridians, and contraindications, and the characteristic data of the disease entity, including cause, symptoms, and physiological effects, are matched and analyzed using the formula: ； Calculate and obtain the drug matching value; in, Represents drug matching, is the attribute vector of the disease characteristics, is the mean vector corresponding to the drug, is the covariance matrix of the attributes, is the inverse matrix of the covariance matrix, represents the transpose operation; S513: Call the drug matching degree value, identify and record contraindicated drugs related to multiple diseases, and form a drug and disease relationship set.

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