289 results about "Knowledge question" patented technology

The invention discloses an electric power field knowledge question-answering system construction method based on a large language model, and relates to the field of electric power field knowledge question-answering, and the method comprises the steps: judging the data type of electric power field knowledge, and carrying out the processing of the electric power field knowledge according to the judgment result through matching with a processing technology, and generating an entity relation triple; constructing a power field knowledge graph; optimizing the power field knowledge graph based on the attention network, outputting an answer causal path of the fault problem by using the optimized power field knowledge graph, and marking a confidence score of the answer causal path; and inputting the solution causal path and the confidence score into a language model to obtain a fault question answering result, and optimizing the question answering result according to the consistency of the fault question answering result and the power field knowledge graph. According to the method, on the premise that the fault diagnosis logic is rigorous and the result is traceable, knowledge in large-scale unstructured literatures in the power industry is activated, so that accurate question and answer services can be provided for operation and maintenance personnel in real time.

The invention discloses a knowledge graph-driven water conservancy intelligent question and answer interaction platform and method, and relates to the related field of data processing, and the platform comprises an entity disambiguation module which is used for constructing a water conservancy knowledge graph to carry out standardized modeling, generating structured knowledge nodes to carry out entity disambiguation on water conservancy objects, and generating entity identifiers; the full-text retrieval module is used for performing semantic analysis on a natural language query text of the user according to the entity identifier, identifying a query intention type, performing full-text retrieval and generating a multi-modal answer; and the iterative interaction module is used for performing dynamic questioning based on the multi-modal answers, generating user semantic feedback information, optimizing a query path according to the user semantic feedback information, and realizing iterative interaction of water conservancy knowledge questions and answers. The technical problem that existing water conservancy intelligent question and answer interaction is insufficient in question and answer accuracy and richness is solved, and the technical effect of improving the question and answer accuracy and richness is achieved.

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 legal knowledge question-answering system constructed based on a large language model and a method thereof, and relates to the technical field of large language models and the field of legal application. The system comprises a law and regulation knowledge base construction module, an RAG retrieval module, an LLM enhancement module and a user interaction interface. Based on an RAG retrieval enhancement generation related technology, the invention aims to perform efficient knowledge management on massive laws and regulations and recall related legal knowledge through accurate knowledge retrieval so as to enhance the effect and accuracy of an LLM large-scale language model in providing legal consultation service; by combining the RAG technology and the LLM technology, efficient knowledge management and accurate retrieval of massive laws and regulations are achieved, and an innovative and efficient solution is provided for the field of legal consultation services; according to the technology, developers do not need to re-train the whole LLM for each specific task, and only need to provide additional input for the LLM by connecting a related knowledge base, so that the accuracy of answers is improved.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The invention discloses a knowledge question and answer processing method fusing a large model and a knowledge graph, and relates to the technical field of artificial intelligence and natural language processing. Aiming at the defects of a traditional retrieval enhancement generation technology in the aspects of complex semantic association, context consistency and dynamic knowledge updating, the scheme adopted by the invention comprises the following two stages: knowledge graph construction and mixed index generation: collecting and processing internal and external multi-source data of an enterprise, and performing cleaning preprocessing such as coding normalization and de-duplication to obtain a mixed index; entities and relations are extracted through a pre-training model to generate a triple, a knowledge graph is constructed and stored in Neo4j, then a mixed index is generated through text and graph embedding fusion, and two types of retrieval are supported; retrieval and answer generation: obtaining user query, preprocessing, vectorizing, obtaining a candidate list through low-level semantic retrieval and high-level reasoning retrieval, fusing multi-dimensional indexes, rearranging and screening top-M candidates through Cross-encoder, constructing a JSON evidence list, and generating traceable answers through small model draft, large model fine calibration and consistency verification.

The invention relates to the technical field of large model knowledge questions and answers, and discloses an enterprise knowledge question and answer system based on a large language model, which comprises a heterogeneous semantic embedding module, a permission mask calculation module, an enterprise term alignment module, a candidate knowledge query module and a question and answer generation output module, in the prior art, an enterprise question and answer mode is realized only depending on keyword matching or a fixed FAQ rule, and particularly under the conditions of enterprise internal knowledge distribution fragmentation, authority differentiation and term ambiguity, accurate, compliant and context-consistent semantic question and answer cannot be realized. Due to the fact that multi-mode unified embedding, the semantic level authority control mechanism, the term graph alignment model and the RAG enhanced question and answer mechanism are introduced, cross-format and multi-department enterprise intelligent question and answer is achieved, the problems of semantic drift and unauthorized leakage are avoided, and the accuracy and safety of the question and answer system are improved.

The invention discloses a dialogue type enterprise knowledge question answering method and device, a storage medium and electronic equipment, and relates to the technical field of data analysis, and the method comprises the steps: obtaining user request information; obtaining a corresponding channel; reconstructing the question content to obtain the reconstructed question content; performing vectorization processing on the reconstructed question content to obtain vectorization information of the reconstructed question content; obtaining a plurality of first FAQ knowledge and second FAQ knowledge; obtaining a plurality of pieces of first document block knowledge and second document block knowledge, and obtaining documents corresponding to a plurality of document blocks; obtaining a corresponding prompt word template based on the dialogue intention and the channel; and constructing a large language model, and sorting to obtain corresponding answer information. Based on the question content of the user, the dialogue intention is obtained through analysis, the knowledge base is matched, the answer information is obtained through arrangement, the user question is automatically answered, and the method has the advantages of being simple, efficient and high in real-time performance.

The invention provides a method, system and device for achieving industry knowledge questions and answers based on a large model and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that standard answers of industry experts are collected for questions with the occurrence frequency higher than a threshold value in a target industry, and a standard answer library is constructed; collecting professional knowledge related to the target industry, and constructing an industry knowledge base; responding to a target question, input by a user, of a target industry on a front-end interface, matching a standard answer library and a retrieval industry knowledge library according to the target question, when a return result exists, constructing a cue word according to the return result, and when the return result does not exist, constructing the cue word according to the retrieval industry knowledge library; according to historical multi-round dialogue information of the user on the front-end interface, cue words are constructed and provided for the general large model; and returning a knowledge question and answer result output by the general large model to the user through a front-end interface. By constructing the standard answer library and the industry knowledge library and combining the general large model, intelligent answering of the industry questions is achieved, and the accuracy and specialty of question answering are improved.

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 household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

The invention provides a question and answer model training method and device and an intelligent question and answer method and device for a specific field, and relates to technologies such as large models, model training and deep learning in the field of artificial intelligence and the technical fields such as intelligent search and intelligent question and answer. According to the specific implementation scheme, corpus data of a specific field are obtained; meta-knowledge question and answer data are constructed based on the corpus data through a first model, and the meta-knowledge question and answer data comprise meta-questions and answers corresponding to the meta-questions; based on the meta-knowledge question-answer data, domain knowledge training is carried out on the to-be-trained question-answer model; on the basis of the meta-knowledge question and answer data, a semantic-related question set is constructed for the meta-questions, and structured reasoning training data is generated through a second model on the basis of the question set of the meta-questions; and based on the structured reasoning training data, performing reasoning ability enhancement training on the question and answer model to be trained after domain knowledge training. The question and answer performance of the model in the vertical field can be remarkably improved.

The invention discloses a knowledge question-answering method and system based on topic knowledge graph retrieval enhancement, and the method comprises the steps: firstly extracting a local topic represented in a triple form based on an original document through employing a large language model, carrying out the clustering, and generating a global topic triple set representing the global perspective of the whole document; secondly, on the basis of the global topic triple set, topic-guided entity and relation extraction is adopted, and a mixed knowledge graph is constructed; secondly, providing a semantic perception personalized PageRank algorithm, matching query semantics with semantics of edges in the mixed knowledge graph, and dynamically adjusting the weight of score propagation between nodes; and finally, designing a three-level progressive retrieval mechanism, retrieving multi-level information related to user query from the mixed knowledge graph, and inputting the multi-level information into the large language model to generate a final answer. According to the method, the semantic integrity and retrieval precision of the knowledge graph are remarkably improved, and the accuracy, comprehensiveness and enabling performance of generated answers are ensured.

The invention relates to the field of power question answering, in particular to a power knowledge question-answer matching method and system based on an improved retrieval enhancement generation technology, and the method comprises the steps: employing a training set to carry out the decomposition of a pre-constructed Embedding model, and carrying out the training of the Embedding model, and obtaining an Embedding fine tuning model; the method comprises the following steps: vectorizing a power field file by adopting an Embedding fine tuning model to obtain a power knowledge base, and constructing a regular expression rule base; the user consultation content is converted into a query vector, the query vector is matched with the regular expression rule base to obtain an accurate matching result, and meanwhile the query vector is matched with the power knowledge base to obtain a fuzzy matching result; semantic correlation sorting is carried out on the accurate matching result and the fuzzy matching result, and then resorting is carried out by adopting a resorter in combination with a service rule; and inputting the reordering result into a large language model to obtain a result corresponding to the user consultation content. The method can effectively solve the problem of inaccuracy during retrieval of a specific ID, and improves the retrieval precision of a retrieval enhancement generation system.

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention discloses a large model industry knowledge question-answering method and system supporting multi-modal input, and the method comprises the steps: obtaining a multi-modal query, and converting the multi-modal query into a corresponding feature set; importing the feature set into a preset semantic alignment and enhancement network to obtain enhanced semantic representation; the preset semantic alignment and enhancement network performs semantic alignment and fusion on features of different modals in a shared semantic space through a cross-modal attention mechanism, a multi-modal consistency constraint and a modal complementarity constraint; matching corresponding multi-modal knowledge fragments from the multi-modal industry knowledge base on the basis of enhanced semantic representation; and importing the multi-modal query, the enhanced semantic representation and the multi-modal knowledge fragment into a pre-training large model to generate a question and answer result. According to the method, semantic alignment and deep fusion are performed on the multi-modal query by establishing the semantic alignment and enhancement network, and the professional knowledge range is expanded by introducing the multi-modal industry knowledge base, so that the retrieval efficiency can be improved, and the question and answer generation quality can be improved.

The invention provides an intelligent knowledge question-answering method, device and equipment and a storage medium, and the method comprises the steps: carrying out the information recognition of a financial question uploaded by a user, extracting a terminology, a financial entity, a question type and a visual demand, and converting the financial question into a structured query intention expression; retrieving text content and visual information in a financial knowledge base according to the query intention, and performing multi-modal fusion to form a financial knowledge packet; applying a double attention mechanism to the visual information, extracting key area features and key attribute features through a space attention network and a channel attention network, and integrating to obtain fusion features; and finally, according to the fusion features and the text content, establishing a corresponding relationship, extracting high-correlation information fragments, performing knowledge reasoning and compliance processing, and generating a multi-modal financial answer. According to the method, complex multi-modal information in the financial field can be effectively processed, professional and accurate answers meeting supervision requirements are provided, and the method is suitable for various financial consultation and decision support scenes.

The invention discloses a system and a method for enhancing generation of hydrofracture questions and answers based on cross-language retrieval. The knowledge question-answering system aiming at the hydraulic fracturing field is developed by utilizing a cross-language retrieval enhancement generation technology, so that a user can obtain an optimal answer and a corresponding source from a multi-language knowledge base by matching only by using Chinese retrieval; the complex process that a user needs to find useful information from numerous and jumbled papers and reports is changed. According to the system, a dynamic text vector library framework is adopted, multi-language and complex document formats are supported, the analysis function of tables and formulas is integrated, documents in the hydraulic fracturing field are stored in the same knowledge base, the knowledge base is continuously updated along with document expansion in the professional field, a user can inquire related problems in the field in the system, and the user experience is improved. By integrating and updating a large amount of document information, required professional knowledge in the field can be efficiently obtained.

The embodiment of the invention provides an enterprise-oriented document question and answer model training method and system, and the method comprises the steps: obtaining general supervision fine tuning data, general document question and answer data and enterprise internal data; mixing the general supervision fine tuning data, the general document question and answer data and the enterprise internal data according to a predetermined proportion to obtain training data; and on the basis of the training data, performing supervised fine tuning training on a basic large language model according to a unified document question and answer prompt word template to obtain a document question and answer model. According to the embodiment of the invention, the accuracy, controllability and rejection capability of the document question and answer model are remarkably improved, the defects of an existing RAG system in an enterprise application scene are overcome, and the method is more suitable for enterprise-level knowledge question and answer tasks.

The invention belongs to the technical field of medical treatment and public health, and particularly relates to a hypertension group knowledge recommendation method and system based on a large language model multi-agent. Hypertension knowledge question-answer pair information, medical short video information and user personal health portraits are finely extracted by constructing a hypertension knowledge question-answer pair library; according to the method, video content quality evaluation and matching degree evaluation are performed, and user platform interaction data adjustment is combined, so that personalized and accurate knowledge recommendation services can be provided for hypertension users, the requirements of hypertension patients for health knowledge are met, the accuracy and effectiveness of knowledge recommendation are improved, and self-health management of the patients is facilitated.

The invention discloses a pediatric nursing knowledge question answering method and system based on multi-modal large model enhancement, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal pediatric nursing related data, extracting knowledge entities, obtaining description text data of each knowledge entity, and forming text representation of the entities; establishing triple information according to the knowledge entities to generate a pediatric nursing knowledge basic database, and extracting triads to construct a knowledge graph; extracting multi-order graph data of each entity, and learning to generate knowledge graph representation of the entity; the text representation and the knowledge graph representation are fused, comprehensive knowledge information representation of the entity is obtained, the comprehensive knowledge information representation is used for finely adjusting a preset multi-modal large model, and pediatric nursing knowledge questions and answers are conducted through the finely-adjusted multi-modal large model. According to the method, more accurate, more reliable and more intelligent knowledge questions and answers are realized in a pediatric nursing scene through deep fusion of the structured constraint of the knowledge graph and the generalization ability of the multi-modal large model.

The invention discloses a knowledge question-answering method based on multiple agents and heterogeneous data sources, which comprises the following steps of: collecting an original document, identifying data, processing the format of the data, and respectively realizing the construction of a vector database and a graph database; obtaining a standard question and answer data set, and generating expansion questions by utilizing partial sub-graphs of the knowledge graph to complete construction of a basic question pool; obtaining user questions, and performing preliminary retrieval in the basic question pool; for problems needing deep retrieval, according to the types and characteristics of the problems, the routing agent flexibly calls required tools or distributes tasks to different retrieval agents; and the answer agent integrates the obtained retrieval results to generate a final answer. According to the method, a multi-agent cooperation mechanism is utilized, the intention of the user can be accurately recognized, dynamic information retrieval is carried out in different types of data sources, and meanwhile efficient and rapid intelligent question and answer services are provided by constructing the basic question pool.

The invention provides a guarantee knowledge intelligent question answering method and system based on LLM and RAG technologies. The method comprises the following steps: S1, receiving a guarantee knowledge question input by a user; s2, carrying out word segmentation and vectorization on the guarantee knowledge question; s3, adopting a multi-dimensional query recall strategy to query and recall knowledge blocks conforming to semantics from a pre-constructed guarantee knowledge think tank; s4, according to an optimized rearrangement strategy, rearranging the knowledge blocks recalled by query and the guarantee knowledge problem; and S5, submitting the rearranged guarantee knowledge questions and knowledge blocks to a large language model LLM by using a prompt word template of the large language model LLM, generating answers by the LLM, and returning the answers to the user. Intelligent questioning and answering can be performed on guarantee knowledge.

The invention discloses a personalized scientific and technological knowledge question answering method and system, and the method comprises the steps: firstly obtaining and processing scientific and technological literature data, constructing a field basic model training corpus, and carrying out the continuous pre-training, and forming a field basic model; then, on the basis of the field basic model, parameters of a sampling model are initialized, initial training is conducted through seed data, the sampling model is trained through multi-round iterative reinforcement learning, and sample data are generated; and then, on the basis of the field basic model, initializing parameters of a question and answer model, constructing a distillation training data set by using sample data generated by the sampling model, and performing supervised fine tuning training until the model converges. And finally, packaging the trained question and answer model into an online reasoning interface service, and constructing a science and technology knowledge question and answer system. According to the method, scientific and technological knowledge question-answering service aiming at personalized requirements of the user is realized, and the question answering efficiency and accuracy are remarkably improved.

The invention relates to a multi-class archive knowledge question and answer agent based on a large language model, and belongs to the technical field of artificial intelligence and archive information management. The intelligent agent comprises a data preparation module, a semantic knowledge base construction module, a perception and analysis module, a decision and matching module, an execution and retrieval module, a cognition and reasoning module and an interaction and generation module. According to the method, the complex query intention of the user can be accurately understood, automatic association and semantic reasoning across archive categories are achieved, and the semantic splitting problem of a traditional retrieval mode in cross-category and multi-level query is effectively solved.

The invention discloses a knowledge question-answering method and system based on a large language model and a semantic abstract. The system comprises a tree structure abstract generation subsystem which comprises a knowledge base configuration module, a file management module, a document analysis module and a knowledge block management module and is used for automatically generating tree structure hierarchical knowledge blocks for large manual documents or multi-chapter manual documents; the dual-channel retrieval engine subsystem comprises a question rewriting module, a semantic abstract retrieval module, a detail knowledge block retrieval module, a father-son retrieval module and a prompt word construction module, and all the modules work cooperatively to ensure that semantic abstract knowledge blocks and detail knowledge blocks related to the question of the user are retrieved. According to the method, by introducing the semantic abstract based on the tree structure and the two-channel retrieval, perception of global knowledge and accurate extraction of local fine-grained knowledge can be achieved at the same time, and then the ability of a knowledge base question-answering system in processing the general and inductive problems is improved.

The invention discloses an electric power knowledge question-answering method and system based on multi-agent and retrieval enhancement generation. The method comprises the following steps: according to an electric power knowledge query problem, generating an answer to the electric power knowledge query problem based on a predictor agent, and generating a document-query-answer triple; evaluating and quantifying the document-question-answer triple by using a judger agent to obtain a correlation score; noise documents are filtered and sorted, and a document sequence is obtained; and finally, the predictor agent completes power knowledge questions and answers according to the document sequence. According to the method, through specific technical means of Doc-Q-A triple generation, logit difference correlation scoring, self-adaptive threshold filtering, traceability output and the like, the multi-agent question and answer method which is light in weight, explainable and free of training is constructed. According to the technical route, the defects that an existing method depends on large-scale training and lacks traceability are overcome, and the method has obvious engineering application value in the aspects of question and answer accuracy and credibility.

The invention relates to a knowledge question answering method, system and device, electronic equipment and a computer program product, relates to the technical field of natural language processing, and is applied to scenes for providing matched answers for user questions in different fields. The method comprises the steps that a pre-constructed knowledge question-answering system is adopted to conduct knowledge retrieval processing on a target question, a target answer corresponding to the target question is obtained, and the knowledge question-answering system is obtained by training recommended question-answering pairs of a specified field, the recommended question and answer pair in the specified field is a feedback result of a to-be-processed document in the specified field after knowledge retrieval enhancement processing, and the to-be-processed document in the specified field comprises table data. Through the knowledge retrieval enhancement scheme, when the problem of insufficient training corpora is faced, knowledge query in a specified field can still be effectively solved; and knowledge graph storage is carried out on table data, so that the model has the capability of processing non-standard natural language data.

The invention relates to the technical field of knowledge questions and answers, in particular to a large language model knowledge question and answer method and system fused with a multi-modal knowledge graph. The method comprises the following steps: acquiring structured and semi-structured data and unstructured multi-modal data in an enterprise business scene; capturing data change through a real-time synchronization technology and executing targeted preprocessing to ensure the consistency of data quality and modal characteristics; constructing a dynamically updated multi-modal knowledge graph based on the preprocessed data, and integrating a multi-modal embedded model, a reordering model and a fine-tuned generative large model to obtain a knowledge storage-feature matching-semantic generation integrated architecture; according to the method, the defect of knowledge lag of a traditional system is overcome, and seamless fusion of the AI capability and the enterprise core business process is realized.