169 results about "Knowledge sources" patented technology

The invention provides a multi-agent collaborative question and answer enhancement method and system based on heterogeneous data knowledge, and belongs to the technical field of information management. The planning optimization intelligent agent carries out structured processing on the input complex natural language problem; a multi-modal retrieval mechanism on the heterogeneous knowledge source is constructed based on the problem disassembly and entity recognition result, and a multi-path recall agent passes through a semantic matching model of a double-tower structure; the abstract extraction agent performs semantic fusion and information extraction on the text segments and the knowledge graph sub-graphs output by the multi-path recall module; logic verification and quality evaluation are carried out on the answers generated by the abstract extraction module by the reflection iteration agent; and by setting a threshold mechanism and combining importance weights of the sub-questions, performing scoring and reflection optimization on the generated answers by utilizing a large language model LLM (Language Language Model). The method can effectively cope with cross-domain and multi-level complex question and answer tasks, and has good expandability and intelligent level.

The invention discloses a question answering method for graph-driven fusion retrieval in the field of computer networks, and relates to technologies such as intention recognition, multi-channel fusion retrieval, hierarchical semantic matching and answer traceable generation. Aiming at the problems of professional term matching, incomplete knowledge, single retrieval and low answer credibility of an existing question-answering system, a dual-channel fusion retrieval and hierarchical semantic matching mechanism based on a knowledge graph is provided, a retrieval strategy is dynamically controlled through intention recognition, accurate matching of structured knowledge is realized by adopting phrase preliminary screening and triple fine matching, and the accuracy of the structured knowledge is improved. In combination with graph subgraph dynamic construction and Prompt generation, a large language model is guided to output high-quality answers, traceable knowledge sources are attached, and the accuracy and credibility of the answers are improved. The method is widely applicable to computer network professional question and answer scenes such as network protocols, configuration management and troubleshooting.

The invention relates to the technical field of system engineering modeling, and particularly discloses an MBSE optimization method based on a large language model, which realizes MBSE whole process automation and intelligentization by constructing a'demand-knowledge-model 'dynamic closed-loop framework and fusing RAG and LLM. The method specifically comprises the following steps: constructing a domain knowledge enhancement library, and integrating LLM to construct a demand analysis engine and a dynamic modeling optimization system; a domain expert inputs a demand through a natural language interaction interface, and the demand is analyzed into structured data through LLM; generating a parameterized model conforming to the MBSE specification by combining the RAG technology with the knowledge in the library; after the model runs through a simulation tool chain, the LLM adjusts parameters according to a simulation result to generate an iteration scheme; and after the modeler passes verification, storing the model and data into a database to form a knowledge source. According to the method, domain knowledge dual-drive modeling and cross-role collaboration are achieved, the knowledge base self-evolution capacity is achieved, the problems that traditional MBSE is high in manual dependence and insufficient in semantic fault and knowledge fusion are effectively solved, and the modeling efficiency and reliability are remarkably improved.

The invention discloses a multi-source knowledge-enhanced big language model question and answer method, device and equipment and a medium, and the method comprises the steps: inputting a user question into a big language model, and obtaining an initial answer and a cooperation strategy deduced by the big language model for the user question; after taking the initial answer as a current candidate answer, extracting a target knowledge source type in a collaborative strategy and generating a corresponding calling expression; according to the calling expressions, target knowledge sources matched with the target knowledge source types are called respectively, and an enhanced retrieval document set is obtained; re-inputting the user question and the enhanced retrieval document set into the large language model to obtain a new answer and a collaboration strategy; and taking the new answer as the current candidate answer, repeating the operation of extracting the target knowledge source type, and determining a target answer from all the candidate answers as a feedback result when an iteration ending condition is met. According to the technical scheme, the question and answer accuracy can be optimized through iterative retrieval and knowledge enhancement.

The invention discloses a milk industry knowledge question-answering method and device based on a large model and RAG and a medium, and the method comprises the steps: collecting multi-source heterogeneous data of the milk industry, carrying out the term protection word segmentation processing of the multi-source heterogeneous data, and generating a labeled field corpus; based on a pre-training language model base, through a domain corpus injection and adversarial learning mechanism in a domain corpus, generating an enhanced language model adapted to the dairy industry terminology; receiving a milk industry problem of a user, executing semantic vector retrieval and term extension retrieval in parallel, and screening a multi-modal retrieval result through a dynamic sorting algorithm; and splicing the multi-modal retrieval result and the milk industry question into an enhanced prompt, inputting the enhanced prompt into an enhanced language model, generating a final answer corresponding to the milk industry question, and associating the final answer with a knowledge source.

The invention discloses a multi-modal fused intelligent question answering and knowledge retrieval method and system, and the method comprises the steps: building a multi-modal data index model oriented to a heterogeneous knowledge source, carrying out the feature mapping of text, image, table, chart, audio and video contents through a unified semantic embedding space, and generating a cross-modal index set; after a query request is received, performing semantic matching and structure matching on the cross-modal index set by using a multi-channel retriever to obtain candidate evidence fragments; and based on an evidence granularity decomposition strategy, performing minimum evidence unit division on text statements, table units, chart data points and multimedia frame contents in the candidate evidence fragments, and establishing a semantic consistency graph among the units. According to the method, high-credibility traceable generation of question and answer results is realized through multi-modal fusion and space-time consistency constraint, and the retrieval precision and interpretation transparency in a complex knowledge scene are remarkably improved.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The invention relates to an enhanced LLM-RAG multi-hop question and answer method based on logic tree reasoning, and belongs to the technical field of new-generation information, and the method comprises the following steps: inputting a multi-hop question and answer question into a computer system; the computer system calls a pre-training large language model LLM, the multi-hop question-answer question is decomposed into a hierarchical logic tree in a recursive mode, and each node of the logic tree comprises a sub-question and a corresponding hypothesis answer; performing image retrieval from a structured knowledge source Wikidata and performing text retrieval from an unstructured knowledge source Wikipedia on the basis of each node sub-question and the hypothesis answer to obtain corresponding evidence; traversing the logic tree, verifying the consistency between the hypothetical answer of each node and the evidence through LLM, if the contradiction exists, reconstructing the corresponding sub-tree, and dynamically correcting the reasoning path; and integrating the verified logic tree node information, and outputting an accurate answer to the multi-hop question and answer question.

The invention provides an agent collaborative question-answering method and system based on context awareness and authority control, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring user query, and triggering a user context manager to generate a session state user context object containing a permission label and preference; the central scheduler performs task decomposition based on the query intention and the context, dynamically selects an intelligent agent through a multi-dimensional scoring model and binds a knowledge source; the authority-aware agent execution engine executes triple authority verification and intermediate result desensitization processing in the whole process, and supports agent fault tolerance and audit log recording. The method realizes unification of knowledge security and access flexibility, supports multi-scene adaptive question and answer, and has good expandability and fault-tolerant capability.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention provides a private deployment-oriented multi-modal AI knowledge base construction system. The private deployment-oriented multi-modal AI knowledge base construction system comprises a knowledge storage module, an intelligent document loading module, a document partitioning engine module, a data enhancement engine module, a multi-language semantic vector alignment module and a private deployment module, the knowledge storage module comprises a knowledge authority management sub-module and a knowledge source management sub-module, the knowledge authority management sub-module is used for managing and storing knowledge from different sources, and the knowledge source management sub-module is used for managing electronic documents and multimedia documents; according to the method, intelligent identification, partitioning, vectorization and source file storage can be carried out on different types of electronic files, knowledge graph construction is carried out for specific fields, semantic relevance between texts and topics and key entities is fully considered, the method has wider applicability, higher robustness and controllability, the data leakage risk is effectively reduced, and the method is suitable for popularization and application. The method is suitable for enterprise sensitive data protection and personal user elastic computing power requirements.

The invention is suitable for the technical field of intelligent questioning and answering, and particularly relates to an intelligent service method and system based on deep reasoning and knowledge enhancement, and the method comprises the steps: obtaining the identity information of a questioning student and target questioning information; the question and answer feature parameters are constructed by using the target question information, and the question understanding depth and precision are improved; screening candidate question and answer units from a preset question and answer knowledge base on the basis of the question and answer feature parameters, quickly positioning historical similar questions and corresponding answers, obtaining associated document fragments, and providing rich and authoritative knowledge sources for answer generation; when a candidate answer scheme is generated based on the candidate question and answer unit, content expansion and optimization are carried out by taking the candidate question and answer unit as a core carrier; according to student identity information, candidate answer schemes are screened by using a pre-trained deep reasoning screening model, the professionality and expression style of answers are dynamically adjusted, and a target answer scheme is displayed, so that pertinence and authority from question understanding, knowledge retrieval to answer generation are realized.

The invention discloses an intelligent translation calibration method based on an RAG knowledge base, and belongs to the technical field of artificial intelligence and natural language processing. In order to solve the problems that an existing large language model (LLM) is insufficient in professional term translation accuracy in text translation, low in RAG system retrieval efficiency and the like, an intelligent calibration triggering mechanism is constructed, so that the LLM can autonomously judge whether translation content needs knowledge base enhancement or not in the modes of field detection, term density analysis, user-defined triggering and the like; a full-amount retrieval strategy is changed, and the system efficiency is improved; an RAG knowledge base retrieval framework based on function calling is constructed, multiple types of knowledge sources such as a translation memory library, a term table and a professional corpus are integrated, and accurate domain knowledge is provided for translation calibration; flexible multi-knowledge-base configuration and priority management are supported, and a user can select knowledge base types and set retrieval strategies and fusion weights according to needs; through a progressive translation optimization process of initial translation, intelligent judgment, knowledge base retrieval and calibration optimization, accurate calibration of different granularities such as term level, sentence level and paragraph level is realized, and a standardized knowledge base interface specification is provided. According to the method, the defects of LLM in the aspects of translation accuracy and knowledge base utilization efficiency in the professional field are effectively overcome, and the accuracy and efficiency of knowledge-intensive translation tasks are remarkably improved.

The invention relates to the technical field of large language models, and discloses a zero-illusion large language model output method and system, and the method comprises the steps: sequentially carrying out the deterministic lexical analysis and standardized mapping of a received original query, and generating a standard term set; locking to-be-reasoned items through hierarchical matching to form a deterministic retrieval result set; executing five rounds of progressive verification processing on the result set to obtain an integrated result set with an LLM verification mark; and outputting a layered zero illusion result through multiple verification and auditing of structure compliance, semantic consistency and knowledge base double recheck. According to the method, probabilistic reasoning is replaced by full-link deterministic operation, so that each code can be traced back to a credible knowledge source, illusion is thoroughly eliminated, absolute reliability and complete traceability of an output result in a professional field are realized by a verifiable deterministic path, and a foundation is laid for zero-illusion application.

The invention relates to the technical field of text data processing, and particularly discloses a structured text generation and quality control method based on a knowledge-driven large model, which comprises the steps of knowledge ontology integration, entity extraction and mapping, initial structured generation, difference extraction, enhanced rewriting prompt and multi-round iterative optimization. According to the method, the knowledge graph, the ontology structure and the large language model are deeply fused, and the method has accurate control capability on entity, relation and context semantic levels, so that semantic-driven text construction with controllable generation, credible content and standard structure is realized; by means of a retrieval enhancement generation mechanism, each generated or corrected content of the model in the method can be mapped to a clear knowledge source. By means of a knowledge support mode, the'illusion 'phenomenon of the model is remarkably reduced, the structured text generation has a clear interpretable path, the reliability and the reviewable property of a generation result are effectively enhanced, and the method is particularly suitable for high-credibility scenes such as medical treatment, law and scientific research.

The invention provides a large model retrieval enhancement generation method fused with a multi-source knowledge base, and relates to the technical field of large model retrieval enhancement generation. The method comprises the steps that firstly, user query is analyzed, multiple implied independent retrieval intentions are recognized and separated, and a knowledge source identifier is distributed to each retrieval intention subtask based on a mapping rule; according to the method, related evidence fragments are acquired in parallel from a heterogeneous knowledge source library in a targeted manner, multi-level relation analysis is further performed on the original evidence fragments by introducing a cross validation engine, so that a structured evidence conflict graph is constructed, and then the evidence conflict graph is intelligently processed by applying a game theory excitation algorithm. The method comprises the following steps: obtaining an evidence set with internal consistency, forming a guide signal through structured analysis, and finally, inputting original user query, the synthesized evidence set and the guide signal into a large language model in combination with a pre-constructed cue word template to generate a final answer under constraint, so as to realize retrieval enhancement generation fused with multi-source knowledge base retrieval.

The invention belongs to the technical field of knowledge Token generation and traceability, and discloses a knowledge Token generation and traceability method based on structured metadata, which is characterized in that a current knowledge scene is quickly matched and a core field is dynamically adjusted through few-sample learning in combination with various technical models and a preset industry field model library; a mixed semantic segmentation mode is adopted, knowledge fragments are completely segmented according to business logic, then a refined element extraction strategy is matched, three types of key elements including entities, relationships and clauses are accurately extracted, and errors are reduced through multi-dimensional verification; originally dispersed PDF, audio and video and other unstructured knowledge can be converted into standardized assets with anchoring information. Meanwhile, a multi-dimensional verification process is designed, comprehensive verification is carried out from a data source, access permission and logic consistency to a health state, it is ensured that a knowledge source can be checked, authenticity can be distinguished, and the information distortion risk during large model calling is reduced.

The invention discloses a retrieval enhancement generation optimization method and device based on adaptive classification and autonomous reasoning. The method comprises the following steps: constructing a domain knowledge vector database; inputting a proprietary domain problem, and finding a plurality of matched knowledge documents with the highest score in the vector database from two dimensions of semantics and word frequency; inputting the matched knowledge document into an adaptive classification module to obtain a semantic attribute category of the document; the document and the corresponding semantic attribute category are input into an autonomous reasoning module, uncertain items and error items are eliminated, and enhanced knowledge is obtained; the enhanced knowledge is sent to the intelligent question and answer module to generate a response answer; the response answers are sent to the self-adaptive classification module again, and semantic attribute categories of the response answers are checked; if the answer is not judged to be correct, the autonomous reasoning module and the subsequent steps are executed again on the response answer; and if the answer is judged to be correct, displaying the answer to the user. The problem that in the prior art, an external knowledge source has negative effects on a generation result can be solved.

The invention discloses an automatic generation method for health science popularization articles, and belongs to the crossing field of computer technology and health science popularization. The method comprises the following steps that S1, health authority information is collected through a multi-source crawler, and an original material pool is obtained through Sension-BERT vectorization duplicate removal and BioBERT medical entity labeling; s2, classifying nine types of health themes by using a RoBERTa fine tuning model; s3, multiple AI model supplementary materials are made into a structured package; s4, predetermining optimal selection questions in combination with AI three-dimensional scoring and editing; s5, calling the multi-dimensional knowledge base to generate a professional knowledge packet; s6, generating an outline and performing three-dimensional five-score system auditing of knowledge point fullness and the like; s7, the LLM expands and writes the text and marks knowledge sources; s8, performing double-stage auditing; s9, anthropomorphic draft moistening; s10, intelligently illustrating the picture; s11, when the matching degree is smaller than a preset threshold value, automatically updating the knowledge base; and S12, integrating and outputting. According to the method, the health science popularization article is generated, the manual participation time is shortened to be within 30 minutes, the medical error rate is reduced to be below 5%, the daily output of a 10-person team is improved by 3-5 times, the annual human cost is reduced by 60% or above, and the large-scale and high-quality science popularization requirements are met.

The invention relates to the technical field of agents, and discloses a double-layer credible guarantee multi-agent cooperation system, which comprises a knowledge source credible layer for dynamically evaluating the authenticity and integrity of the source of data transmitted by each agent, and comprises a knowledge source credible layer for dynamically evaluating the authenticity and integrity of the source of data transmitted by each agent based on message quality evaluation and a dynamic reputation updating mode, screening credible intelligent body co-authors; carrying out identity verification and data integrity verification on an intelligent agent transmission message; according to a constraint criterion based on decision consensus and uncertainty change, verifying the credibility of each collaborative message content, identifying malicious messages, and tracing and marking the identity of an intelligent agent; the knowledge transmission credible layer is used for guaranteeing privacy security and distortion compensation, and comprises the following steps: blocking a sensitive information leakage channel through noise injection and privacy budget control of intelligent agent observation data or coding characteristics based on a differential privacy mechanism; and carrying out local prediction and error correction on the cooperation message after noise disturbance by adopting a teammate modeling mode.

The invention provides a large model output content illusion detection method and system oriented to a zero knowledge scene, and belongs to the field of large model security application, and the method comprises the following steps: S1, extracting internal state features and output probability features from a large language model LLMs, the internal state features including full context average embedding ACE and end word embedding FTE; the output probability features comprise word probability TP entropy En; s2, inputting the features ACE, FTE, TP and En into a classification detection module for training; a trained classification detection module is obtained; and S3, when the LLMs receives an input prompt and starts to generate a text, extracting features ACE, FTE, TP and En in real time, inputting the features into the trained classification detection module, and judging whether the content of the text is illusion or not. According to the method, external knowledge sources are not needed, and illusion detection is efficiently and accurately carried out by combining internal and external features.

The invention relates to the field of deep learning, and particularly discloses an intelligent management method and system for equipment operation and maintenance based on artificial intelligence, and the method comprises the steps: carrying out the parallel execution of sparse and dense retrieval to guarantee the comprehensiveness of recall, carrying out the interaction analysis of deep semantic and structured entity features of user query through a self-adaptive weight network, and carrying out the calculation of the deep semantic and structured entity features. An optimal and dynamic fusion weight is generated in real time for each query. According to the weight, the dependency degree of current query on keyword matching and the dependency degree of current query on semantic understanding can be accurately judged, and intelligent dynamic weighting and rearrangement are carried out on two retrieval results according to the dependency degree and the dependency degree, so that accurate candidate document screening with one query and one strategy is realized. The dynamic fusion mechanism fundamentally solves the problem that the static weight cannot adapt to query diversity, and ensures that the knowledge source provided for the downstream generation model is always highly related to the current query intention, so that the accuracy and reliability of the finally generated answer are remarkably improved.

A system and method for automated domain advertising utilizing artificial intelligence is provided. The system includes a processor and memory in communication with the processor. The memory includes a user interface module, a domain advertising (DA) module, an artificial intelligence (AI) module, a posting module, and a domain parking module. The system receives the domain name from a user. The AI module generates the domain advertising suggestion based on the domain name. The domain advertising suggestion is provided to the user and may be posted to social media or a domain parking website. The AI module includes a large language model (LLM) and a retrieval-augmented generation (RAG) module. The RAG module includes a knowledge base to retrieve a knowledge-based response from an external knowledge source, an expert module to retrieve an expert response from a specialized domain model, and a ranker to rank the knowledge-based response and the expert response.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention discloses an aspect-level sentiment analysis optimization method based on multivariate external knowledge fusion, which relates to the technical field of sentiment analysis optimization, and comprises the following steps: constructing a multivariate external knowledge source comprising a Chinese sentiment dictionary, a domain knowledge graph and a user comment prior mode library; the sentiment module is used for providing vocabulary-level sentiment polarity, entity attribute relations and high-frequency evaluation semantic modes; semantic coding is performed on the input text and the specified aspect words to generate context semantic representation, and global semantic features and local position features are extracted in combination with aspect word position information; based on a semantic coding result, converting the multivariate external knowledge sources into structured knowledge representations, and dynamically adjusting contribution weights of various types of knowledge through a gating fusion mechanism to generate fused knowledge representations; performing dependency syntactic analysis on the input text, constructing an original syntactic structure, and calculating the correlation strength of each grammatical component and aspect words in combination with context semantic representation; and pruning the original syntactic structure according to the correlation intensity.

According to the abnormal information monitoring and processing method based on the large model and the intelligent agent, system operation data and log information are collected in real time, abnormity is detected based on threshold rules, automatic discovery of abnormal events is achieved, and manual intervention is not needed; after the exception is detected, the exception event object is constructed and the context semantic analysis is performed, so that the exception category, the trigger module and the operation environment parameters can be accurately extracted, and the accurate positioning of the complex exception is realized. Cue words are generated based on the semantic enhancement model, and a scheme retrieval request is constructed, so that the retrieval process better conforms to semantic logic, and the accuracy and correlation of scheme matching are improved. Through semantic similarity calculation and an external knowledge source extension mechanism, the system can efficiently retrieve candidate solutions inside and outside a knowledge base, and determines an optimal solution based on credibility scores, so that the intelligence and reliability of decision processing are remarkably improved.

The invention provides an industry knowledge base dynamic construction method and system based on multi-source heterogeneous data, and relates to the technical field of data processing.The method comprises the steps that target industry multi-source heterogeneous data, user historical behavior data, a natural language question text and a processing text are collected to generate a query data stream; mapping the user question into a query semantic vector, converting the user interaction behavior into a behavior feature vector, and generating a joint query vector; industry knowledge units are automatically extracted from multi-source heterogeneous data and external knowledge sources, and a dynamic knowledge topological graph is constructed; establishing a dynamic knowledge routing table, taking the joint query vector as a routing request, matching a target knowledge node, recording a query path and caching an association rule; and counting and querying path node transition probability to identify a hotspot path, mining a deep association rule, and reversely adjusting a knowledge topological graph structure, so that user intentions can be accurately matched, an industry knowledge base can be dynamically optimized, and knowledge retrieval and association efficiency can be improved.

Embodiments described herein provide systems and methods for retrieval augmented generation. A neural network based language model may be provided a question as a user input. Based on the user input, semantically diverse queries may be generated for retrieval from diverse data sources. For example, a structured data source (e.g., database or knowledge base) and unstructured data (e.g., text articles) may be used to retrieve information relevant to the user input. The retrieve information may be ranked so that the most relevant information is used by the language model in generating an answer to the question in the user input. A non-retrieval based answer generated by the language model may be utilized in some embodiments in generating the final answer.

The invention discloses a typhoid theory teaching method based on a knowledge graph, and belongs to the technical field of typhoid theory teaching. The method comprises the steps of multi-version data acquisition and standardization, ancient Chinese context semantic unit construction, term semantic disambiguation based on comparative learning, semantic conflict detection based on consistency constraint, multi-source information fusion and map optimization and teaching path generation. Through ancient Chinese context semantic modeling and comparative learning, precise disambiguation of traditional Chinese medicine ancient book terms is realized, and knowledge errors caused by polysemy of one word are reduced; through an automatic detection mechanism of logic consistency constraint, the quality and reliability of the knowledge graph are improved; a version traceability and belief propagation optimization strategy is adopted, so that the finally constructed knowledge graph not only can integrate multi-version essence, but also can clearly trace knowledge sources and evaluate knowledge credibility; the teaching path chain generated based on the atlas can dynamically simulate the whole process of traditional Chinese medicine syndrome differentiation treatment, and the clinical thinking training effect is remarkably improved.

The invention relates to the technical field of digital data processing, in particular to an automatic session quality detection method applied to a customer service scene. The detection method comprises the following steps: model training: selecting an LLaMA pre-training large language model as a core engine, and guiding training by means of cue words with session key element extraction and semantic consistency comparison functions; judging the format of the target session data to be subjected to quality detection, and converting the voice format into a text format; performing key element extraction and comparative analysis on the session data in the text format by means of the trained model; consistency comparison is carried out on the robot reply and a corresponding standard reply in a knowledge base; verifying whether business data contained in the robot reply is consistent with real data of a back-end business system; and generating a detailed report of the quality inspection result. Defects are accurately recognized, understood and expressed by means of the large model reasoning ability, and robot reply and a semantic verification closed loop of a knowledge source are achieved.